summaryrefslogtreecommitdiff
diff options
context:
space:
mode:
authoraktersnurra <gustaf.rydholm@gmail.com>2020-10-22 22:45:58 +0200
committeraktersnurra <gustaf.rydholm@gmail.com>2020-10-22 22:45:58 +0200
commit4d7713746eb936832e84852e90292936b933e87d (patch)
tree2b2519d1d2ce53d4e1390590f52018d55dadbc7c
parent1b3b8073a19f939d18a0bb85247eb0d99284f7cc (diff)
Transfomer added, many other changes.
Diffstat
-rw-r--r--src/notebooks/00-testing-stuff-out.ipynb1437
-rw-r--r--src/notebooks/01-look-at-emnist.ipynb21
-rw-r--r--src/notebooks/04a-look-at-iam-lines.ipynb1370
-rw-r--r--src/notebooks/04b-look-at-iam-paragraphs.ipynb2
-rw-r--r--src/notebooks/05-sanity-check-multihead-attention.ipynb169
-rw-r--r--src/notebooks/Untitled.ipynb310
-rwxr-xr-xsrc/tasks/train.sh67
-rwxr-xr-xsrc/tasks/train_crnn_line_ctc_model.sh5
-rwxr-xr-xsrc/tasks/train_embedding_model.sh5
-rw-r--r--src/text_recognizer/datasets/__init__.py4
-rw-r--r--src/text_recognizer/datasets/dataset.py22
-rw-r--r--src/text_recognizer/datasets/emnist_dataset.py3
-rw-r--r--src/text_recognizer/datasets/emnist_lines_dataset.py9
-rw-r--r--src/text_recognizer/datasets/iam_lines_dataset.py6
-rw-r--r--src/text_recognizer/datasets/transforms.py33
-rw-r--r--src/text_recognizer/datasets/util.py29
-rw-r--r--src/text_recognizer/models/__init__.py11
-rw-r--r--src/text_recognizer/models/base.py55
-rw-r--r--src/text_recognizer/models/character_model.py1
-rw-r--r--src/text_recognizer/models/line_ctc_model.py8
-rw-r--r--src/text_recognizer/models/vision_transformer_model.py117
-rw-r--r--src/text_recognizer/networks/__init__.py18
-rw-r--r--src/text_recognizer/networks/cnn_transformer.py111
-rw-r--r--src/text_recognizer/networks/crnn.py (renamed from src/text_recognizer/networks/line_lstm_ctc.py)58
-rw-r--r--src/text_recognizer/networks/densenet.py225
-rw-r--r--src/text_recognizer/networks/lenet.py6
-rw-r--r--src/text_recognizer/networks/loss.py (renamed from src/text_recognizer/networks/losses.py)3
-rw-r--r--src/text_recognizer/networks/mlp.py6
-rw-r--r--src/text_recognizer/networks/residual_network.py6
-rw-r--r--src/text_recognizer/networks/sparse_mlp.py78
-rw-r--r--src/text_recognizer/networks/transformer.py5
-rw-r--r--src/text_recognizer/networks/transformer/__init__.py3
-rw-r--r--src/text_recognizer/networks/transformer/attention.py93
-rw-r--r--src/text_recognizer/networks/transformer/positional_encoding.py31
-rw-r--r--src/text_recognizer/networks/transformer/sparse_transformer.py1
-rw-r--r--src/text_recognizer/networks/transformer/transformer.py241
-rw-r--r--src/text_recognizer/networks/util.py (renamed from src/text_recognizer/networks/misc.py)40
-rw-r--r--src/text_recognizer/networks/vision_transformer.py158
-rw-r--r--src/text_recognizer/networks/wide_resnet.py6
-rw-r--r--src/text_recognizer/weights/CharacterModel_EmnistDataset_DenseNet_weights.ptbin0 -> 1273881 bytes
-rw-r--r--src/text_recognizer/weights/LineCTCModel_IamLinesDataset_LineRecurrentNetwork_weights.ptbin5701134 -> 3457858 bytes
-rw-r--r--src/training/experiments/embedding_experiment.yml22
-rw-r--r--src/training/experiments/line_ctc_experiment.yml92
-rw-r--r--src/training/run_experiment.py150
-rw-r--r--src/training/trainer/callbacks/base.py18
-rw-r--r--src/training/trainer/callbacks/checkpoint.py6
-rw-r--r--src/training/trainer/callbacks/wandb_callbacks.py34
-rw-r--r--src/training/trainer/train.py33
48 files changed, 4313 insertions, 815 deletions
diff --git a/src/notebooks/00-testing-stuff-out.ipynb b/src/notebooks/00-testing-stuff-out.ipynb
index 0294394..3b74c84 100644
--- a/src/notebooks/00-testing-stuff-out.ipynb
+++ b/src/notebooks/00-testing-stuff-out.ipynb
@@ -22,7 +22,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -31,85 +31,27 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "IdentityBlock(\n",
- " (blocks): Identity()\n",
- " (activation_fn): ReLU(inplace=True)\n",
- " (shortcut): Identity()\n",
- ")"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"IdentityBlock(32, 64)"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "ResidualBlock(\n",
- " (blocks): Identity()\n",
- " (activation_fn): ReLU(inplace=True)\n",
- " (shortcut): Sequential(\n",
- " (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
- " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- ")"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"ResidualBlock(32, 64)"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "BasicBlock(\n",
- " (blocks): Sequential(\n",
- " (0): Sequential(\n",
- " (0): Conv2dAuto(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " (1): ReLU(inplace=True)\n",
- " (2): Sequential(\n",
- " (0): Conv2dAuto(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " )\n",
- " (activation_fn): ReLU(inplace=True)\n",
- " (shortcut): Sequential(\n",
- " (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
- " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- ")\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"dummy = torch.ones((1, 32, 224, 224))\n",
"\n",
@@ -120,39 +62,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "BottleNeckBlock(\n",
- " (blocks): Sequential(\n",
- " (0): Sequential(\n",
- " (0): Conv2dAuto(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
- " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " (1): ReLU(inplace=True)\n",
- " (2): Sequential(\n",
- " (0): Conv2dAuto(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " (3): ReLU(inplace=True)\n",
- " (4): Sequential(\n",
- " (0): Conv2dAuto(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
- " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " )\n",
- " (activation_fn): ReLU(inplace=True)\n",
- " (shortcut): Sequential(\n",
- " (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
- " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- ")\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"dummy = torch.ones((1, 32, 10, 10))\n",
"\n",
@@ -185,7 +97,7 @@
},
{
"cell_type": "code",
- "execution_count": 69,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -194,92 +106,16 @@
},
{
"cell_type": "code",
- "execution_count": 75,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Encoder(\n",
- " (gate): Sequential(\n",
- " (0): Conv2d(1, 96, kernel_size=(3, 3), stride=(2, 2), padding=(3, 3), bias=False)\n",
- " (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (2): ReLU(inplace=True)\n",
- " (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
- " )\n",
- " (blocks): Sequential(\n",
- " (0): ResidualLayer(\n",
- " (blocks): Sequential(\n",
- " (0): BasicBlock(\n",
- " (blocks): Sequential(\n",
- " (0): Sequential(\n",
- " (0): Conv2dAuto(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " (1): ReLU(inplace=True)\n",
- " (2): Sequential(\n",
- " (0): Conv2dAuto(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " )\n",
- " (activation_fn): ReLU(inplace=True)\n",
- " (shortcut): None\n",
- " )\n",
- " (1): BasicBlock(\n",
- " (blocks): Sequential(\n",
- " (0): Sequential(\n",
- " (0): Conv2dAuto(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " (1): ReLU(inplace=True)\n",
- " (2): Sequential(\n",
- " (0): Conv2dAuto(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " )\n",
- " (activation_fn): ReLU(inplace=True)\n",
- " (shortcut): None\n",
- " )\n",
- " )\n",
- " )\n",
- " (1): ResidualLayer(\n",
- " (blocks): Sequential(\n",
- " (0): BasicBlock(\n",
- " (blocks): Sequential(\n",
- " (0): Sequential(\n",
- " (0): Conv2dAuto(96, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
- " (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " (1): ReLU(inplace=True)\n",
- " (2): Sequential(\n",
- " (0): Conv2dAuto(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
- " (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " )\n",
- " (activation_fn): ReLU(inplace=True)\n",
- " (shortcut): Sequential(\n",
- " (0): Conv2d(96, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
- " (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- " )\n",
- ")"
- ]
- },
- "execution_count": 75,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"Encoder(**{\"depths\": [2, 1], \"block_sizes\": [96, 128]})"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -288,7 +124,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -306,7 +142,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -315,291 +151,971 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "summary(wr, (1, 28, 14), device=\"cpu\", depth=10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "np.inf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from text_recognizer.networks.transformer.positional_encoding import PositionalEncoding"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 1080x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(15, 5))\n",
+ "pe = PositionalEncoding(20, 0)\n",
+ "y = pe.forward(torch.zeros(1, 100, 20))\n",
+ "plt.plot(np.arange(100), y[0, :, 4:8].data.numpy())\n",
+ "plt.legend([\"dim %d\"%p for p in [4,5,6,7]])\n",
+ "None"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from text_recognizer.networks.densenet import DenseNet,_DenseLayer,_DenseBlock"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dl = _DenseLayer(64, 4, 4, 0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "db = _DenseBlock(2, 64, 32, 4, 0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = torch.randn(2, 64, 28, 28)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dl(x).shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "db(x).shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "IndentationError",
+ "evalue": "unexpected indent (<ipython-input-18-9316fb6caa59>, line 2)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-18-9316fb6caa59>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m num_init_features=24, bn_size=4, drop_rate=0, avgpool_size=8,\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
+ ]
+ }
+ ],
+ "source": [
+ "growth_rate=4, block_config=(6, 6, 6), compression=0.5,\n",
+ " num_init_features=24, bn_size=4, drop_rate=0, avgpool_size=8,\n",
+ " num_classes=10"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dnet = DenseNet(8, (6, 6, 6), 1, 24, 80, 4, 0, True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "===============================================================================================\n",
- "Layer (type:depth-idx) Output Shape Param #\n",
- "===============================================================================================\n",
- "├─Sequential: 1-1 [-1, 256, 4, 2] --\n",
- "| └─Conv2d: 2-1 [-1, 16, 28, 14] 144\n",
- "| └─Sequential: 2-2 [-1, 32, 28, 14] --\n",
- "| | └─WideBlock: 3-1 [-1, 32, 28, 14] --\n",
- "| | | └─Sequential: 4-1 [-1, 32, 28, 14] --\n",
- "| | | | └─Conv2d: 5-1 [-1, 32, 28, 14] 512\n",
- "| | | └─Sequential: 4-2 [-1, 32, 28, 14] --\n",
- "| | | | └─BatchNorm2d: 5-2 [-1, 16, 28, 14] 32\n",
- "| | | └─SELU: 4-3 [-1, 16, 28, 14] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-3 [-1, 16, 28, 14] --\n",
- "| | | | └─Conv2d: 5-4 [-1, 32, 28, 14] 4,608\n",
- "| | | | └─Dropout: 5-5 [-1, 32, 28, 14] --\n",
- "| | | | └─BatchNorm2d: 5-6 [-1, 32, 28, 14] 64\n",
- "| | | └─SELU: 4-4 [-1, 32, 28, 14] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-7 [-1, 32, 28, 14] --\n",
- "| | | | └─Conv2d: 5-8 [-1, 32, 28, 14] 9,216\n",
- "| | └─WideBlock: 3-2 [-1, 32, 28, 14] --\n",
- "| | | └─Sequential: 4-5 [-1, 32, 28, 14] --\n",
- "| | | | └─BatchNorm2d: 5-9 [-1, 32, 28, 14] 64\n",
- "| | | └─SELU: 4-6 [-1, 32, 28, 14] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-10 [-1, 32, 28, 14] --\n",
- "| | | | └─Conv2d: 5-11 [-1, 32, 28, 14] 9,216\n",
- "| | | | └─Dropout: 5-12 [-1, 32, 28, 14] --\n",
- "| | | | └─BatchNorm2d: 5-13 [-1, 32, 28, 14] 64\n",
- "| | | └─SELU: 4-7 [-1, 32, 28, 14] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-14 [-1, 32, 28, 14] --\n",
- "| | | | └─Conv2d: 5-15 [-1, 32, 28, 14] 9,216\n",
- "| └─Sequential: 2-3 [-1, 64, 14, 7] --\n",
- "| | └─WideBlock: 3-3 [-1, 64, 14, 7] --\n",
- "| | | └─Sequential: 4-8 [-1, 64, 14, 7] --\n",
- "| | | | └─Conv2d: 5-16 [-1, 64, 14, 7] 2,048\n",
- "| | | └─Sequential: 4-9 [-1, 64, 14, 7] --\n",
- "| | | | └─BatchNorm2d: 5-17 [-1, 32, 28, 14] 64\n",
- "| | | └─SELU: 4-10 [-1, 32, 28, 14] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-18 [-1, 32, 28, 14] --\n",
- "| | | | └─Conv2d: 5-19 [-1, 64, 28, 14] 18,432\n",
- "| | | | └─Dropout: 5-20 [-1, 64, 28, 14] --\n",
- "| | | | └─BatchNorm2d: 5-21 [-1, 64, 28, 14] 128\n",
- "| | | └─SELU: 4-11 [-1, 64, 28, 14] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-22 [-1, 64, 28, 14] --\n",
- "| | | | └─Conv2d: 5-23 [-1, 64, 14, 7] 36,864\n",
- "| | └─WideBlock: 3-4 [-1, 64, 14, 7] --\n",
- "| | | └─Sequential: 4-12 [-1, 64, 14, 7] --\n",
- "| | | | └─BatchNorm2d: 5-24 [-1, 64, 14, 7] 128\n",
- "| | | └─SELU: 4-13 [-1, 64, 14, 7] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-25 [-1, 64, 14, 7] --\n",
- "| | | | └─Conv2d: 5-26 [-1, 64, 14, 7] 36,864\n",
- "| | | | └─Dropout: 5-27 [-1, 64, 14, 7] --\n",
- "| | | | └─BatchNorm2d: 5-28 [-1, 64, 14, 7] 128\n",
- "| | | └─SELU: 4-14 [-1, 64, 14, 7] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-29 [-1, 64, 14, 7] --\n",
- "| | | | └─Conv2d: 5-30 [-1, 64, 14, 7] 36,864\n",
- "| └─Sequential: 2-4 [-1, 128, 7, 4] --\n",
- "| | └─WideBlock: 3-5 [-1, 128, 7, 4] --\n",
- "| | | └─Sequential: 4-15 [-1, 128, 7, 4] --\n",
- "| | | | └─Conv2d: 5-31 [-1, 128, 7, 4] 8,192\n",
- "| | | └─Sequential: 4-16 [-1, 128, 7, 4] --\n",
- "| | | | └─BatchNorm2d: 5-32 [-1, 64, 14, 7] 128\n",
- "| | | └─SELU: 4-17 [-1, 64, 14, 7] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-33 [-1, 64, 14, 7] --\n",
- "| | | | └─Conv2d: 5-34 [-1, 128, 14, 7] 73,728\n",
- "| | | | └─Dropout: 5-35 [-1, 128, 14, 7] --\n",
- "| | | | └─BatchNorm2d: 5-36 [-1, 128, 14, 7] 256\n",
- "| | | └─SELU: 4-18 [-1, 128, 14, 7] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-37 [-1, 128, 14, 7] --\n",
- "| | | | └─Conv2d: 5-38 [-1, 128, 7, 4] 147,456\n",
- "| | └─WideBlock: 3-6 [-1, 128, 7, 4] --\n",
- "| | | └─Sequential: 4-19 [-1, 128, 7, 4] --\n",
- "| | | | └─BatchNorm2d: 5-39 [-1, 128, 7, 4] 256\n",
- "| | | └─SELU: 4-20 [-1, 128, 7, 4] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-40 [-1, 128, 7, 4] --\n",
- "| | | | └─Conv2d: 5-41 [-1, 128, 7, 4] 147,456\n",
- "| | | | └─Dropout: 5-42 [-1, 128, 7, 4] --\n",
- "| | | | └─BatchNorm2d: 5-43 [-1, 128, 7, 4] 256\n",
- "| | | └─SELU: 4-21 [-1, 128, 7, 4] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-44 [-1, 128, 7, 4] --\n",
- "| | | | └─Conv2d: 5-45 [-1, 128, 7, 4] 147,456\n",
- "| └─Sequential: 2-5 [-1, 256, 4, 2] --\n",
- "| | └─WideBlock: 3-7 [-1, 256, 4, 2] --\n",
- "| | | └─Sequential: 4-22 [-1, 256, 4, 2] --\n",
- "| | | | └─Conv2d: 5-46 [-1, 256, 4, 2] 32,768\n",
- "| | | └─Sequential: 4-23 [-1, 256, 4, 2] --\n",
- "| | | | └─BatchNorm2d: 5-47 [-1, 128, 7, 4] 256\n",
- "| | | └─SELU: 4-24 [-1, 128, 7, 4] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-48 [-1, 128, 7, 4] --\n",
- "| | | | └─Conv2d: 5-49 [-1, 256, 7, 4] 294,912\n",
- "| | | | └─Dropout: 5-50 [-1, 256, 7, 4] --\n",
- "| | | | └─BatchNorm2d: 5-51 [-1, 256, 7, 4] 512\n",
- "| | | └─SELU: 4-25 [-1, 256, 7, 4] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-52 [-1, 256, 7, 4] --\n",
- "| | | | └─Conv2d: 5-53 [-1, 256, 4, 2] 589,824\n",
- "| | └─WideBlock: 3-8 [-1, 256, 4, 2] --\n",
- "| | | └─Sequential: 4-26 [-1, 256, 4, 2] --\n",
- "| | | | └─BatchNorm2d: 5-54 [-1, 256, 4, 2] 512\n",
- "| | | └─SELU: 4-27 [-1, 256, 4, 2] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-55 [-1, 256, 4, 2] --\n",
- "| | | | └─Conv2d: 5-56 [-1, 256, 4, 2] 589,824\n",
- "| | | | └─Dropout: 5-57 [-1, 256, 4, 2] --\n",
- "| | | | └─BatchNorm2d: 5-58 [-1, 256, 4, 2] 512\n",
- "| | | └─SELU: 4-28 [-1, 256, 4, 2] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-59 [-1, 256, 4, 2] --\n",
- "| | | | └─Conv2d: 5-60 [-1, 256, 4, 2] 589,824\n",
- "===============================================================================================\n",
- "Total params: 2,788,784\n",
- "Trainable params: 2,788,784\n",
+ "==========================================================================================\n",
+ "Layer (type:depth-idx) Output Shape Param #\n",
+ "==========================================================================================\n",
+ "├─Sequential: 1-1 [-1, 80] --\n",
+ "| └─Conv2d: 2-1 [-1, 24, 28, 28] 216\n",
+ "| └─BatchNorm2d: 2-2 [-1, 24, 28, 28] 48\n",
+ "| └─ReLU: 2-3 [-1, 24, 28, 28] --\n",
+ "| └─_DenseBlock: 2-4 [-1, 72, 28, 28] 23,184\n",
+ "| └─_Transition: 2-5 [-1, 36, 14, 14] 2,736\n",
+ "| └─_DenseBlock: 2-6 [-1, 84, 14, 14] 25,632\n",
+ "| └─_Transition: 2-7 [-1, 42, 7, 7] 3,696\n",
+ "| └─_DenseBlock: 2-8 [-1, 90, 7, 7] 26,856\n",
+ "| └─ReLU: 2-9 [-1, 90, 7, 7] --\n",
+ "| └─AdaptiveAvgPool2d: 2-10 [-1, 90, 1, 1] --\n",
+ "| └─Rearrange: 2-11 [-1, 90] --\n",
+ "| └─Linear: 2-12 [-1, 80] 7,280\n",
+ "==========================================================================================\n",
+ "Total params: 89,648\n",
+ "Trainable params: 89,648\n",
"Non-trainable params: 0\n",
- "Total mult-adds (M): 84.83\n",
- "===============================================================================================\n",
+ "Total mult-adds (M): 0.35\n",
+ "==========================================================================================\n",
"Input size (MB): 0.00\n",
- "Forward/backward pass size (MB): 2.26\n",
- "Params size (MB): 10.64\n",
- "Estimated Total Size (MB): 12.90\n",
- "===============================================================================================\n"
+ "Forward/backward pass size (MB): 0.29\n",
+ "Params size (MB): 0.34\n",
+ "Estimated Total Size (MB): 0.63\n",
+ "==========================================================================================\n"
]
},
{
"data": {
"text/plain": [
- "===============================================================================================\n",
- "Layer (type:depth-idx) Output Shape Param #\n",
- "===============================================================================================\n",
- "├─Sequential: 1-1 [-1, 256, 4, 2] --\n",
- "| └─Conv2d: 2-1 [-1, 16, 28, 14] 144\n",
- "| └─Sequential: 2-2 [-1, 32, 28, 14] --\n",
- "| | └─WideBlock: 3-1 [-1, 32, 28, 14] --\n",
- "| | | └─Sequential: 4-1 [-1, 32, 28, 14] --\n",
- "| | | | └─Conv2d: 5-1 [-1, 32, 28, 14] 512\n",
- "| | | └─Sequential: 4-2 [-1, 32, 28, 14] --\n",
- "| | | | └─BatchNorm2d: 5-2 [-1, 16, 28, 14] 32\n",
- "| | | └─SELU: 4-3 [-1, 16, 28, 14] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-3 [-1, 16, 28, 14] --\n",
- "| | | | └─Conv2d: 5-4 [-1, 32, 28, 14] 4,608\n",
- "| | | | └─Dropout: 5-5 [-1, 32, 28, 14] --\n",
- "| | | | └─BatchNorm2d: 5-6 [-1, 32, 28, 14] 64\n",
- "| | | └─SELU: 4-4 [-1, 32, 28, 14] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-7 [-1, 32, 28, 14] --\n",
- "| | | | └─Conv2d: 5-8 [-1, 32, 28, 14] 9,216\n",
- "| | └─WideBlock: 3-2 [-1, 32, 28, 14] --\n",
- "| | | └─Sequential: 4-5 [-1, 32, 28, 14] --\n",
- "| | | | └─BatchNorm2d: 5-9 [-1, 32, 28, 14] 64\n",
- "| | | └─SELU: 4-6 [-1, 32, 28, 14] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-10 [-1, 32, 28, 14] --\n",
- "| | | | └─Conv2d: 5-11 [-1, 32, 28, 14] 9,216\n",
- "| | | | └─Dropout: 5-12 [-1, 32, 28, 14] --\n",
- "| | | | └─BatchNorm2d: 5-13 [-1, 32, 28, 14] 64\n",
- "| | | └─SELU: 4-7 [-1, 32, 28, 14] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-14 [-1, 32, 28, 14] --\n",
- "| | | | └─Conv2d: 5-15 [-1, 32, 28, 14] 9,216\n",
- "| └─Sequential: 2-3 [-1, 64, 14, 7] --\n",
- "| | └─WideBlock: 3-3 [-1, 64, 14, 7] --\n",
- "| | | └─Sequential: 4-8 [-1, 64, 14, 7] --\n",
- "| | | | └─Conv2d: 5-16 [-1, 64, 14, 7] 2,048\n",
- "| | | └─Sequential: 4-9 [-1, 64, 14, 7] --\n",
- "| | | | └─BatchNorm2d: 5-17 [-1, 32, 28, 14] 64\n",
- "| | | └─SELU: 4-10 [-1, 32, 28, 14] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-18 [-1, 32, 28, 14] --\n",
- "| | | | └─Conv2d: 5-19 [-1, 64, 28, 14] 18,432\n",
- "| | | | └─Dropout: 5-20 [-1, 64, 28, 14] --\n",
- "| | | | └─BatchNorm2d: 5-21 [-1, 64, 28, 14] 128\n",
- "| | | └─SELU: 4-11 [-1, 64, 28, 14] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-22 [-1, 64, 28, 14] --\n",
- "| | | | └─Conv2d: 5-23 [-1, 64, 14, 7] 36,864\n",
- "| | └─WideBlock: 3-4 [-1, 64, 14, 7] --\n",
- "| | | └─Sequential: 4-12 [-1, 64, 14, 7] --\n",
- "| | | | └─BatchNorm2d: 5-24 [-1, 64, 14, 7] 128\n",
- "| | | └─SELU: 4-13 [-1, 64, 14, 7] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-25 [-1, 64, 14, 7] --\n",
- "| | | | └─Conv2d: 5-26 [-1, 64, 14, 7] 36,864\n",
- "| | | | └─Dropout: 5-27 [-1, 64, 14, 7] --\n",
- "| | | | └─BatchNorm2d: 5-28 [-1, 64, 14, 7] 128\n",
- "| | | └─SELU: 4-14 [-1, 64, 14, 7] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-29 [-1, 64, 14, 7] --\n",
- "| | | | └─Conv2d: 5-30 [-1, 64, 14, 7] 36,864\n",
- "| └─Sequential: 2-4 [-1, 128, 7, 4] --\n",
- "| | └─WideBlock: 3-5 [-1, 128, 7, 4] --\n",
- "| | | └─Sequential: 4-15 [-1, 128, 7, 4] --\n",
- "| | | | └─Conv2d: 5-31 [-1, 128, 7, 4] 8,192\n",
- "| | | └─Sequential: 4-16 [-1, 128, 7, 4] --\n",
- "| | | | └─BatchNorm2d: 5-32 [-1, 64, 14, 7] 128\n",
- "| | | └─SELU: 4-17 [-1, 64, 14, 7] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-33 [-1, 64, 14, 7] --\n",
- "| | | | └─Conv2d: 5-34 [-1, 128, 14, 7] 73,728\n",
- "| | | | └─Dropout: 5-35 [-1, 128, 14, 7] --\n",
- "| | | | └─BatchNorm2d: 5-36 [-1, 128, 14, 7] 256\n",
- "| | | └─SELU: 4-18 [-1, 128, 14, 7] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-37 [-1, 128, 14, 7] --\n",
- "| | | | └─Conv2d: 5-38 [-1, 128, 7, 4] 147,456\n",
- "| | └─WideBlock: 3-6 [-1, 128, 7, 4] --\n",
- "| | | └─Sequential: 4-19 [-1, 128, 7, 4] --\n",
- "| | | | └─BatchNorm2d: 5-39 [-1, 128, 7, 4] 256\n",
- "| | | └─SELU: 4-20 [-1, 128, 7, 4] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-40 [-1, 128, 7, 4] --\n",
- "| | | | └─Conv2d: 5-41 [-1, 128, 7, 4] 147,456\n",
- "| | | | └─Dropout: 5-42 [-1, 128, 7, 4] --\n",
- "| | | | └─BatchNorm2d: 5-43 [-1, 128, 7, 4] 256\n",
- "| | | └─SELU: 4-21 [-1, 128, 7, 4] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-44 [-1, 128, 7, 4] --\n",
- "| | | | └─Conv2d: 5-45 [-1, 128, 7, 4] 147,456\n",
- "| └─Sequential: 2-5 [-1, 256, 4, 2] --\n",
- "| | └─WideBlock: 3-7 [-1, 256, 4, 2] --\n",
- "| | | └─Sequential: 4-22 [-1, 256, 4, 2] --\n",
- "| | | | └─Conv2d: 5-46 [-1, 256, 4, 2] 32,768\n",
- "| | | └─Sequential: 4-23 [-1, 256, 4, 2] --\n",
- "| | | | └─BatchNorm2d: 5-47 [-1, 128, 7, 4] 256\n",
- "| | | └─SELU: 4-24 [-1, 128, 7, 4] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-48 [-1, 128, 7, 4] --\n",
- "| | | | └─Conv2d: 5-49 [-1, 256, 7, 4] 294,912\n",
- "| | | | └─Dropout: 5-50 [-1, 256, 7, 4] --\n",
- "| | | | └─BatchNorm2d: 5-51 [-1, 256, 7, 4] 512\n",
- "| | | └─SELU: 4-25 [-1, 256, 7, 4] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-52 [-1, 256, 7, 4] --\n",
- "| | | | └─Conv2d: 5-53 [-1, 256, 4, 2] 589,824\n",
- "| | └─WideBlock: 3-8 [-1, 256, 4, 2] --\n",
- "| | | └─Sequential: 4-26 [-1, 256, 4, 2] --\n",
- "| | | | └─BatchNorm2d: 5-54 [-1, 256, 4, 2] 512\n",
- "| | | └─SELU: 4-27 [-1, 256, 4, 2] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-55 [-1, 256, 4, 2] --\n",
- "| | | | └─Conv2d: 5-56 [-1, 256, 4, 2] 589,824\n",
- "| | | | └─Dropout: 5-57 [-1, 256, 4, 2] --\n",
- "| | | | └─BatchNorm2d: 5-58 [-1, 256, 4, 2] 512\n",
- "| | | └─SELU: 4-28 [-1, 256, 4, 2] --\n",
- "| | | └─Sequential: 4 [] --\n",
- "| | | | └─SELU: 5-59 [-1, 256, 4, 2] --\n",
- "| | | | └─Conv2d: 5-60 [-1, 256, 4, 2] 589,824\n",
- "===============================================================================================\n",
- "Total params: 2,788,784\n",
- "Trainable params: 2,788,784\n",
+ "==========================================================================================\n",
+ "Layer (type:depth-idx) Output Shape Param #\n",
+ "==========================================================================================\n",
+ "├─Sequential: 1-1 [-1, 80] --\n",
+ "| └─Conv2d: 2-1 [-1, 24, 28, 28] 216\n",
+ "| └─BatchNorm2d: 2-2 [-1, 24, 28, 28] 48\n",
+ "| └─ReLU: 2-3 [-1, 24, 28, 28] --\n",
+ "| └─_DenseBlock: 2-4 [-1, 72, 28, 28] 23,184\n",
+ "| └─_Transition: 2-5 [-1, 36, 14, 14] 2,736\n",
+ "| └─_DenseBlock: 2-6 [-1, 84, 14, 14] 25,632\n",
+ "| └─_Transition: 2-7 [-1, 42, 7, 7] 3,696\n",
+ "| └─_DenseBlock: 2-8 [-1, 90, 7, 7] 26,856\n",
+ "| └─ReLU: 2-9 [-1, 90, 7, 7] --\n",
+ "| └─AdaptiveAvgPool2d: 2-10 [-1, 90, 1, 1] --\n",
+ "| └─Rearrange: 2-11 [-1, 90] --\n",
+ "| └─Linear: 2-12 [-1, 80] 7,280\n",
+ "==========================================================================================\n",
+ "Total params: 89,648\n",
+ "Trainable params: 89,648\n",
"Non-trainable params: 0\n",
- "Total mult-adds (M): 84.83\n",
- "===============================================================================================\n",
+ "Total mult-adds (M): 0.35\n",
+ "==========================================================================================\n",
"Input size (MB): 0.00\n",
- "Forward/backward pass size (MB): 2.26\n",
- "Params size (MB): 10.64\n",
- "Estimated Total Size (MB): 12.90\n",
- "==============================================================================================="
+ "Forward/backward pass size (MB): 0.29\n",
+ "Params size (MB): 0.34\n",
+ "Estimated Total Size (MB): 0.63\n",
+ "=========================================================================================="
]
},
- "execution_count": 14,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "summary(wr, (1, 28, 14), device=\"cpu\", depth=10)"
+ "summary(dnet, (1, 28, 28), device=\"cpu\", depth=2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Sequential(\n",
+ " (0): Conv2d(1, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (2): ReLU(inplace=True)\n",
+ " (3): _DenseBlock(\n",
+ " (dense_block): ModuleList(\n",
+ " (0): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(24, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (1): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (2): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(40, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (3): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(48, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (4): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(56, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (5): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (4): _Transition(\n",
+ " (transition): Sequential(\n",
+ " (0): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(72, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
+ " )\n",
+ " )\n",
+ " (5): _DenseBlock(\n",
+ " (dense_block): ModuleList(\n",
+ " (0): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(36, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (1): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(44, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(44, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (2): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(52, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (3): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(60, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(60, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (4): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(68, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(68, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (5): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(76, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(76, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (6): _Transition(\n",
+ " (transition): Sequential(\n",
+ " (0): BatchNorm2d(84, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(84, 42, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
+ " )\n",
+ " )\n",
+ " (7): _DenseBlock(\n",
+ " (dense_block): ModuleList(\n",
+ " (0): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(42, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(42, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (1): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(50, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (2): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(58, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (3): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(66, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(66, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (4): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(74, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(74, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (5): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(82, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(82, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (8): ReLU(inplace=True)\n",
+ " (9): AdaptiveAvgPool2d(output_size=(1, 1))\n",
+ ")"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "list(dnet.children())[0][:-2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Sequential(\n",
+ " (0): Conv2d(1, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (2): ReLU(inplace=True)\n",
+ " (3): _DenseBlock(\n",
+ " (dense_block): ModuleList(\n",
+ " (0): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(24, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (1): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (2): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(40, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (3): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(48, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (4): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(56, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (5): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (4): _Transition(\n",
+ " (transition): Sequential(\n",
+ " (0): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(72, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
+ " )\n",
+ " )\n",
+ " (5): _DenseBlock(\n",
+ " (dense_block): ModuleList(\n",
+ " (0): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(36, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (1): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(44, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(44, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (2): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(52, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (3): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(60, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(60, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (4): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(68, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(68, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (5): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(76, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(76, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (6): _Transition(\n",
+ " (transition): Sequential(\n",
+ " (0): BatchNorm2d(84, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(84, 42, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
+ " )\n",
+ " )\n",
+ " (7): _DenseBlock(\n",
+ " (dense_block): ModuleList(\n",
+ " (0): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(42, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(42, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (1): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(50, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (2): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(58, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (3): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(66, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(66, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (4): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(74, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(74, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (5): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(82, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(82, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (8): ReLU(inplace=True)\n",
+ " (9): AdaptiveAvgPool2d(output_size=(1, 1))\n",
+ ")"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "torch.nn.Sequential(*list(dnet.children())[0][:-2])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "DenseNet(\n",
+ " (densenet): Sequential(\n",
+ " (0): Conv2d(1, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (2): ReLU(inplace=True)\n",
+ " (3): _DenseBlock(\n",
+ " (dense_block): ModuleList(\n",
+ " (0): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(24, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (1): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (2): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(40, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (3): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(48, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (4): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(56, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (5): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (4): _Transition(\n",
+ " (transition): Sequential(\n",
+ " (0): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(72, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
+ " )\n",
+ " )\n",
+ " (5): _DenseBlock(\n",
+ " (dense_block): ModuleList(\n",
+ " (0): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(36, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (1): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(44, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(44, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (2): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(52, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(52, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (3): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(60, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(60, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (4): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(68, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(68, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (5): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(76, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(76, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (6): _Transition(\n",
+ " (transition): Sequential(\n",
+ " (0): BatchNorm2d(84, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(84, 42, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
+ " )\n",
+ " )\n",
+ " (7): _DenseBlock(\n",
+ " (dense_block): ModuleList(\n",
+ " (0): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(42, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(42, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (1): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(50, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (2): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(58, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (3): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(66, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(66, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (4): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(74, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(74, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " (5): _DenseLayer(\n",
+ " (dense_layer): Sequential(\n",
+ " (0): BatchNorm2d(82, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (1): ReLU(inplace=True)\n",
+ " (2): Conv2d(82, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (4): ReLU(inplace=True)\n",
+ " (5): Conv2d(32, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (8): ReLU(inplace=True)\n",
+ " (9): AdaptiveAvgPool2d(output_size=(1, 1))\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dnet.eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([1, 80])"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dnet(torch.randn(1, 28,28)).shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "img = torch.randn(28, 28)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([1, 1, 28, 28])"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "img[(None,)*2].shape"
]
},
{
@@ -608,6 +1124,41 @@
"metadata": {},
"outputs": [],
"source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -626,7 +1177,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.2"
+ "version": "3.7.4"
}
},
"nbformat": 4,
diff --git a/src/notebooks/01-look-at-emnist.ipynb b/src/notebooks/01-look-at-emnist.ipynb
index 564d14e..d727cb4 100644
--- a/src/notebooks/01-look-at-emnist.ipynb
+++ b/src/notebooks/01-look-at-emnist.ipynb
@@ -2,18 +2,9 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The autoreload extension is already loaded. To reload it, use:\n",
- " %reload_ext autoreload\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
@@ -31,7 +22,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -40,7 +31,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -49,7 +40,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -336,7 +327,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.2"
+ "version": "3.7.4"
}
},
"nbformat": 4,
diff --git a/src/notebooks/04a-look-at-iam-lines.ipynb b/src/notebooks/04a-look-at-iam-lines.ipynb
index d64b391..eb0ec33 100644
--- a/src/notebooks/04a-look-at-iam-lines.ipynb
+++ b/src/notebooks/04a-look-at-iam-lines.ipynb
@@ -2,9 +2,18 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 16,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ }
+ ],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
@@ -15,6 +24,7 @@
"from PIL import Image\n",
"import torch\n",
"from torch import nn\n",
+ "\n",
"from importlib.util import find_spec\n",
"if find_spec(\"text_recognizer\") is None:\n",
" import sys\n",
@@ -23,16 +33,16 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
- "from text_recognizer.datasets import IamLinesDataset"
+ "from text_recognizer.datasets import IamLinesDataset, AddTokens"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -56,7 +66,27 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(28, 952)"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dataset.input_shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -65,7 +95,7 @@
"(97, 80)"
]
},
- "execution_count": 4,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -76,174 +106,156 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'A MOVE to stop Mr. Gaitskell from'"
+ "'A MOVE to stop Mr. Gaitskell from________________________________________________________________'"
]
},
- "execution_count": 5,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def convert_y_label_to_string(y, dataset=dataset):\n",
- " return ''.join([dataset.mapper(int(i)) for i in y]).rstrip(\"_\")\n",
+ " return ''.join([dataset.mapper(int(i)) for i in y])\n",
"\n",
"convert_y_label_to_string(dataset.targets[0])"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Griffiths resolution. Mr. Foot's line will\n",
- "be that as Labour M Ps opposed the\n",
- "Government Bill which brought life peers\n",
- "into existence, they should not now put\n",
- "forward nominees. He believes that the\n",
- "House of Lords should be abolished and\n",
- "that Labour should not take any steps\n",
- "which would appear to \"prop up\" an out-\n",
- "Since 1958, 13 Labour life Peers and\n",
- "Peeresses have been created. Most Labour\n"
+ "Griffiths resolution. Mr. Foot's line will_______________________________________________________\n",
+ "be that as Labour M Ps opposed the_______________________________________________________________\n",
+ "Government Bill which brought life peers_________________________________________________________\n",
+ "into existence, they should not now put__________________________________________________________\n",
+ "forward nominees. He believes that the___________________________________________________________\n",
+ "House of Lords should be abolished and___________________________________________________________\n",
+ "that Labour should not take any steps____________________________________________________________\n",
+ "which would appear to \"prop up\" an out-__________________________________________________________\n",
+ "Since 1958, 13 Labour life Peers and_____________________________________________________________\n",
+ "Peeresses have been created. Most Labour_________________________________________________________\n"
]
},
{
"data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAABH4AAABQCAYAAABvXLJMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAABZ0klEQVR4nO29d3xcx3nv/Z1tAHbReydIECDYO8VeJFES1a1iOS6SnThOnMSJbxz7jZO896bcFCd+b5qvk8glrrLsWI4tS7LVSFESKVKg2ECKIDoBgugdWOwusJj3j90ZHxzugmCnqPl+PvwQe8qcOXPmnDPP7zzPM0JKicFgMBgMBoPBYDAYDAaD4ebDcb0rYDAYDAaDwWAwGAwGg8FguDoY4cdgMBgMBoPBYDAYDAaD4SbFCD8Gg8FgMBgMBoPBYDAYDDcpRvgxGAwGg8FgMBgMBoPBYLhJMcKPwWAwGAwGg8FgMBgMBsNNihF+DAaDwWAwGAwGg8FgMBhuUozwYzAYDAbDDYYQ4t+FEP+v5fenhRBdQohRIUSWEGKTEKI++vtBIcQvhBBPzFDet4QQ//va1P7yEUJIIcT8S9x3ixDi9JWukwGEEH8uhPhe9O/SaP9zXsf6jAoh5kX/1n1cCLFdCHH2etXLYDAYDIYbDSP8GAwGg8FwFRFCfEgIcVAIMSaE6I7+/TtCCBFvHynlb0sp/yq6vxv4P8AdUspkKWUf8JfAV6K/fyql3CWl/HZ0+48LId68Fud2I2AXiaSUb0gpF1yD4348eux/tC1/ILr8W5dY7reEEKGoqKH+PXaZdX1NCPFJ2zJ5OWVKKVuj/S98OeVcZh2SpZRN1+v4BoPBYDC8VzDCj8FgMBgMVwkhxOeAfwb+AcgH8oDfBjYBnjj72D0o8oBE4KRl2Rzb72uOiPB+H0c0Ah8UQrgsy54A6uLtYNs2Hn8fFTXUvx9ebkUNBoPBYDC8f3m/D9gMBoPBYLgqCCHSiHjm/I6U8sdSyhEZ4YiU8iNSymB0u28JIf5NCPGCEGIM2KHCVoQQlYAKWxoUQuwWQjQC84CfR71BEpRHhxBiIfDvwIboukFLlTKEEM8LIUaiXkfl0eMLIcQ/Rr2RhoUQNUKIJXHO6TUhxF8LIfYBfmCeEKJKCPGyEKJfCHFaCPFBy/Z3CyHejR6zXQjxR5Z1vymEaIju96wQonCGY37S8lt7NAkhXo8uPqY8Y+xhPkKIhdEyBoUQJ4UQ91vWfUsI8X9jtcss6QRqgDuj5WUCG4FnLccoi3oA/YYQohXYfRHlT2OmNhNCbBRCVAshhqL/b4wu/2tgC/CVaBt9JUa5HxdCNEXboFkI8ZFZ1EWdlyv6+zUhxF8JIfZFy3lJCJFt2X69EGJ/9DocE0Jsj1PuJ4QQP7f8rhdC/Jfld5sQYkX070sOCTQYDAaD4f2EEX4MBoPBYLg6bAASgJ/NYtsPA38NpAA6TEtKWQcsjv5Ml1LeKqUsB1qB+6LeIEHL9qeIeBS9FV2XbjnGh4C/ADKAhujxAO4AtgKVQBrwQaBvhrp+DPhUtK49wMvAU0Bu9BhfFUIsim77DeC3pJQpwBKioocQ4lbgb6PHKgDOAE9fsJVsSCm3Rv9cHsszRkTC5H4OvBSt32eA7wshrKFg8dpltnwHeNxS1s+AYIzttgELiYpEF8tMbRYVnJ4H/gXIIhIa+LwQIktK+afAG8DvRdvo9wCklCK6ry+6367oddoIHL2UOhLpx58g0tYe4I+ixyiK1u9/A5nR5c8IIXJilLEX2CKEcESFLQ+RewkRyeeTDBy/xPoZDAaDwfC+xAg/BoPBYDBcHbKBXinlpFpg8XgYF0JstWz7MynlPinllJQycJXq899Syrej9fk+sCK6fIKIiFMFCCnlKSllxwzlfEtKeTJazl1Ai5TyP6WUk1LKI8AzwKOWshcJIVKllANSysPR5R8BvimlPBwVrr5IxEup7AqeL8B6IkLB30kpQ1LK3cBzwK9ZtonXLrPlv4HtIuLh9TgRISgWfy6lHJNSjs+izD+K9pNBIURvdNlMbXYPUC+l/G70OvwAqAXum+U5TAFLhBBJUsoOKeWlhhH+p5SyLnqOP+JXbflR4AUp5QvRPv4ycAi4215ANGfPSHTfrcCLwDkhRBUR8ewNKeXUJdbPYDAYDIb3JUb4MRgMBoPh6tAHZAtLThcp5caoF04f09/BbdegPp2Wv/1EBBGiYshXgP8LdAshnhRCpM5QjrWuc4BbLCLFIBGBIj+6/mEixv0ZIcReIcSG6PJCIh4rROswSqRNii715OJQCLTZhIIztuPEbJfZEhU5ngf+DMiSUu6Ls+nFXOMvSynTo/9UuNRMbTZtXRT7ecar/xjwGBFPsY5o2FvVRdTVSry2nAM8ausnm4l4LsViL7CdiPCzF3iNiOizLfrbYDAYDAbDRWCEH4PBYDAYrg5vEQn5eWAW217WDEuXW5aU8l+klKuBRURCvj4/y/LbgL0WkSI9Gk706Wi51VLKB4iE/vyUiBcIwDkiYgCgw42ygPYYxxsDvJbf+TG2icc5oERMT0JdGuc4l8N3gM8B35thm8u9xjO12bR1UaznOeOxpZQvSil3EhFiaoGvXWZd7bQB37X1E5+U8u/ibK+Eny3Rv/dihB+DwWAwGC4ZI/wYDAaDwXAVkFIOEskd81UhxCNCiJRo3pIVgO8qHroLKBZCxJw1zI4QYq0Q4pZoPpwxIEAk9Gc2PAdUCiE+JoRwR/+tjSZU9gghPiKESJNSTgDDlnJ/AHxCCLFCCJEA/A1wUErZEuMYR4GHhBDeaCLf34hxvvPi1O8gEc+TL0Trtp1I+NNF5xO6AHuBncC/XuFyrczUZi8QuQ4fFkK4RGT690VErg/M0EZCiDwRmYLeR0SoHGX213+2fA+4TwhxpxDCKYRIFJEk3MVxtt8L7ACSpJRnieQououI0HXkCtfNYDAYDIabHiP8GAwGg8FwlZBS/j3wh8AXiBjfXcB/AP8PsP8qHXY3kaneOy35YWYilYiHxwCR8KA+ItPPXxAp5QiR5NAfIuJ10gl8iUhSa4gkgm4RQgwTCSX6SHS/V4D/l0g+oA6gPFpGLP4RCBFpu28TycNj5c+Bb0dDiD5oXSGlDBERenYBvcBXgcellLWzOT8RmQXsgjNcRWdre1VK2T+LMkujs2uVzqYOlmPEbTMpZR9wLxGvoz4i/e1eKaW6/v8MPCKEGBBC/IutaAeRPnoO6CfiVfPpi6nbLOreRsTz7U+IJARvI+JVFnMcGk1qPkpE8EFKOQw0AfuklOErWTeDwWAwGN4PCCmvpHe5wWAwGAwGg8FgMBgMBoPhRsF4/BgMBoPBYDAYDAaDwWAw3KQY4cdgMBgMBoPBYDAYDAaD4SblsoQfIcRdQojTQogGIcQfX6lKGQwGg8FgMBgMBoPBYDAYLp9LzvEjhHACdURmsTgLVAO/JqV898pVz2AwGAwGg8FgMBgMBoPBcKlcjsfPOqBBStkUnTXjaSIzNhgMBoPBYDAYDAaDwWAwGG4AXJexbxGR6TgVZ4FbZtpBCHFZU4gJIQAwM5EZDBGu1T0hhDD3neGaca37mxAClyvyOpycnDR93WAwGAwGg8HwXqRXSpkTa8XlCD+zQgjxKeBT+oCuq37Ii0YIgcPhIBwOX++qGN4nXAvD9lJFoVj7GeHn/cnNfN2FEAghcDqdfPzjH+e2227j1KlTfP/736exsfE9f94OhwOXy8XExMR7/lyuBVeir6s+NTU1dUn7T05OXtbxDQaDwWAwvO85E2/F5agw7UCJ5XdxdNk0pJRPAk/C5Xv8XC7xBnZSykseqF1LrrZ3x8UMfG9mg/BaYBdV7MuuBKrciy073j1ieH/gdrvx+XwkJibicDjw+/2EQiEmJyf1c1Ld/zOJ5e+VPpOQkMCGDRtYsGABo6Oj5OXl0dTU9J6pvx0hBB6Ph49+9KPk5OTw9NNP09raesXecTfrs/9KnNPN2C4Gg8FgMBhuDi5H+KkGKoQQc4kIPh8CPnxFanUdeD8N2GYauN+Ig/pLra993Y0eKnihc4HLF3BuxOtruDFwOBwUFBRQWVlJUVERGRkZeDwehoaG6O/v154jExMThEIhgsEgw8PDTE5OMjExwdjYGMFgUK+bmppCSnnD9jcppfb2TEtLIzExEY/HQ1JS0jTR9L2Gw+EgIyODRx99lISEBF555RXa29vfEx83DAaDwWAwGAxXh0sWfqSUk0KI3wNeBJzAN6WUJ69YzS6SS/VueK9xJVzR1f/2spQhNBtx4L3axjdiiJXabrZtP1veq9fIcO0RQpCamsqGDRtYv349ZWVlZGVl4fV6GRsbo7+/X/fP8fFxxsbGGBsbY2BggFAopLcZHh5mZGSE3t5eRkdHGRoaYmho6IYWHaamphgbG2NyclKf43sZl8tFbm4uK1asoKGhAafTeUXPyTxXZsa0j8FgMBgMhhuRy0q4I6V8AXjhCtXlsnA4HDq2Pp6nw8Ua1ReK17+SRvpshCslzNjXq2WzERzi7Wtfb11+Ke12qe0ykyB1obrbieXtM1sD1G4ozeZ8LienzkyeEbMtdzYeTtZreil1NdycuN1uli9fzoMPPsjw8DC9vb2MjY3h9Xq1KJSSkkJycvK0fqbyyLhcLqSUBINBhoaGaGtr4/Tp0+zbt4+9e/cSCAQuqj5X+tnqdDpxu91MTk4SDoenhayFw2F6e3sJhUIAN7SX0mxwOBwkJyfT0dHBf//3f9Pe3n5D5465Ub0QhRC43W6Ai8qTZJ6tBoPBYDAYbkRuvEzLl4DVcI5n2F/KYP5C+1yJgd2FjP7ZHFMtk1LicDhirnM4HDGPM5O443A4YgppFyO4XCyXG8p0Jba9lO3t+1xJD7RL7RtXu+8abg6EECQmJrJr1y5aWlr4zne+Q1dXl87f43A4SExMxOv1kp6eTlZWFqmpqTpx8Jw5c8jKyiIjI4O0tDQyMzMpKyujsrKSwcFBDhw4QDAYvKr364XOb/Xq1WzevJmjR49y8uRJuru79fsiHA7T3t6O3+8nMTGRtLS097TXTzAY5OjRo9x33310dXXNepay6yXA3IjPIiEEXq+Xbdu2kZqays9//nP8fj8Qv77qQ5HL5WJqauqGFtsMBoPBYDC8/7jphB/7ssstF2YemMY7jsodEU84USijSkqpE6heTn3iiUhq+UxlXIqXy7XiRqjLxfSpSzFyL3R9r6UHmuH9g+p3x48fp6Ojg56eHsbGxqZ52o2PjzM0NERHR4feR+3ncDhwOp16e7fbTWJiIm63W3sOXa9+qerz0EMPsXTpUhISEujr66O7u1uvD4fDNDQ0MDo6qgWu9zJTU1P4/X7Gx8evmdim3nU3Aw6Hg4SEBObOncuXvvQlxsbGqK6uprW1Ne77WQhBeno65eXlrF27lvr6evbs2WNmCjUYDAaDwXDDcFMIP7EGnFfC0LiQQW4NsbJuY/c+ihVOpfapqKjgscceY2hoiN27d3PixIkZB9BWY2ymUJ5453Kh9TOd74WOYd0/XnjWbI5hF8bC4fCMAtvVMCrt19YqqMVr/3heArNtrwu1xWw80GK1fawQsEtpMxPCcPMyPj7OK6+8wvj4OH6/f9qzC9DCtLofrH3I5XJRVlZGSkoK3d3ddHV1MTIyAlxciIydK3VvOxwOMjMzSU5OJjs7m5SUlGllSyl555132L9/P9nZ2YTD4evax1XY8kwerLPhWp7De1n0ifV+Vh47gUCA5ORk0tLSdKhgvDJyc3NZs2YNW7Zswel08uabbzI1NYUQkVnWEhISmJqaYnR01DxDDQaDwWAwXHNuCuEnHpeam0YZNgkJCXi9XjweD729vdMMAvsX7+TkZCYmJggGgzpJKMzsveFyuVi8eDGrVq2ir6+PhoYG3n333RlFGhWyZV8/m+NZxYp4+9vLmo2IE48LbT8bYeliDIrZ1vFitlP1iSXczVTeTDmF7NdqJkHrYtrebpBb62s/tvo/ISGBoqIiwuEwbW1tF5ye2y5yGt77SCmZnJykr69vmoeiXRyx/4bI88jj8bB161aysrKorq5mYGCAQCAQN9+anZnunyuBqqPb7cbpdOJ0Oqc9RwF6e3t59tln8fl8uh2uBz6fj+XLl+Pz+ejq6qKmpsbcZ9eBcDjMwMAAr7zyCosXL9YevLFQz0S3201ycjI+n4+UlBTdz3JycliwYAGVlZV0d3fz3HPPxXzOvpfDCw0Gg8FgMNz43NTCjxWVx6KkpISOjg7GxsbiDu4dDod22y4vL8flcnHq1Cnq6uoYHx/XHihqsLdw4ULmzZvH6Ogozc3NnDt37rx8APFy6KSnp5OSkkI4HCYjIwOn06kNpngG/0xG0sUINBdrcM227Cu1jXW72YSmXQ9iiWkXE04322PMBruocyGBJiUlhfnz53PPPffQ2dnJ9773PQKBwKy8it4vKG+RxMRExsbGGBwcvOkMcXU+1ueaXeiM5VWotk1JSWHNmjUkJCRQX1+vt7uRBMKpqSlcLpcWf+x9OBQKceLEievet7Oysli7di0FBQXU1tZSW1urk04brg6xhPJwOMzw8DDPPfccDQ0N9PX1XTBnjxJQATweD0JEkorn5eWxYsUK1q1bR2NjIy+88MKsRVGDwWAwGAyGK8VNLfxYB1aJiYmUlpby6KOP8uyzz8YcUFtzVCxcuJC77rqLjRs3Eg6HOXr0KD/84Q9pbGxkdHRUGxIZGRk8+uijrF69mv7+ft544w3efPNNTp8+TSgUOs9IshtQ/f39TExMaO8iq5u/GjhOTU1pkcouVl1suNGFvDYuFBJ0NY25iyk7XnvOhqu1ndVL51LC5OJ5GM2GWG1nFYCcTicpKSmMjY3pkJ3c3Fxuu+02Pv/5z7N//35+/OMfEwwGL1jX95PB4vF4WLp0KaWlpZw5c4Z9+/YxMTFxvat1VYn1DLGHflnJzc1l3rx59Pb2EgwGCQQCF3UvX+n+FEsI9/v92ovT7XZPe36ocJwrEQ55uWRnZ1NcXMz8+fORUpKUlHTVhJ/rdY7vBaSUBAIB3nrrLd56660LPtOljMxoNzY2hhCCpKQk7SWUnp5Obm4uWVlZDA0NTXunX8r7y2AwGAwGg+FSuKmFH4UQgry8PD7wgQ/wqU99ir6+Ppqbm6cNqK2D4MzMTO68807Wr1+PEIKJiQmWLVtGRkYGX//616mtrWV8fJy0tDQ2btzIrbfeit/vJzs7m9tvv53U1FT6+vro6uqaZlRY82Mot/Hm5mZGRkbIy8sjPz8ft9utpxtOSkrSA3+VFyBWyFasc7AuU8QTfWYTBhbrmJfiXTTTPnbxaiZvKWsY1KVi33cmrwbrsnj1t4py9ra0hpYocc868L8UocfeXrHyskBEvCgoKOCee+7htddeo6mpiVAohM/nIycnh3A4THZ2tp66+ELHi/X7ZkQIQWZmJmvWrGH79u10d3dTU1NDf3//VRErrkV7zuY4szFGrc+xRYsWUVhYqJ9RdiFlpmPO5NUYL3RxpnOLhcPhYHh4mMnJSR2Kq7ZXdbWKttcT5XHn8Xjwer14vV6GhoauyrEu5Zmjrrn1uXUjtNvFEO89Z8f6jpnJk1OtDwQCjI2N4XK5SE1NJSEhgXA4rK9lcnIy6enp+h1/qc9+g8FgMBgMhkvhhhd+lECiZo1RhsXFDpYCgQDt7e2Ew2Gqqqp06IbVWFez0yxevJiCggI6Ozuprq6mpqaGD3/4wxQWFnL33XczMTFBXV0d2dnZ3HXXXQQCAf7pn/6JDRs2UF5eTmVlJXfccQc/+MEPdH0V1kFnOBymtraWH//4x2RkZFBXV6en3vV4PHz0ox9l3rx5HDlyhOeff16Hj01NTcWdReVCOV7Ub7txZw8Psi63D1CtA+CLDf+60Lbx6n+xYW8X8qyJZ6zEEqhmqns8IyKecJaQkEBJSQlz587lzJkznD59Om49rfvGqrsywGYS6ZR48eCDD/LAAw+QlJTE97//ffr6+ujo6OCtt97iE5/4BJmZmaSkpDA4OKg9WuKFsFnP72YUgOz9w+v1kpmZiRCCkpISBgcHYz6DYl2P2aL2vdq5ZWYr+sTKZ+J0OvU2SgTweDwUFRXh8XgIhUKEw2F9LtbE7DN5pKljxbuX7Z5wsQSeWPVW24bDYQKBAE6nk7lz51JQUEBiYiLhcJiJiQkttF/v0DQhBAMDA4TDYZxOJ263G5/Pd13qYkflqlm1ahV5eXk4nU6Gh4fp6OigpaWFjo6O98QMVkp0T0pK0sma1axzsZ5x1j440wcLh8NBIBBgcHAQiHhueb1eJicnSUlJISsri9zcXILBIF6vl1AoZMK9DAaDwWAwXFNuKOFHDdpVMs7MzExWrFjB4sWLyc7OJhgM0tHRwalTpzh06BDDw8OzLnt4eJhTp04xPDxMeXk5Xq93Wr4O9SXT4XCQmpqK2+0mEAjQ09PDyZMn+eY3v8lv/dZvMX/+fObPn8/AwABJSUlkZGQwNTVFc3MzLS0t7Nixg+XLl7N06VJeeuklOjs79cBQYQ3dmpyc5LnnntNG0sTEhDaoKisrmT9/PkNDQ+Tl5XHmzJlpA1BVpjJurF9jYwk7dqNLffG2b2M11Kzb2oUftZ91u4vxCpnJW8d+jJlEl1j1shqT9jaximbxRKZYdbOWE89IiCWOqToIIVi0aBG33norS5Ys0SGBsc5rprIU8cL+1L7qmA6HA7fbrb0zfD4f/f39jIyM0NjYSE1NDceOHdOChmo7ZZhY+5n1WLGMeOu696pRY+0nfr+fUCiEw+EgKSmJwsJCTpw4AfzqnL1eL+Xl5ezYsYNXX32V+vr6aWGeqiy7AKyuTWZmJn6/n2AweJ6XwdVkJs86632gxPAdO3YQCoVobW2lsbGR9vZ2HA4HXq8XKSUJCQl4PJ5pIpbL5Yr5jFF9Mjs7m3nz5pGXl0dCQgKBQICamho9dXasPu52u0lKSiIhIQGI5OcJBoNMTU2RkpKCw+FgfHxce8+43W5KSkpwuVzk5OSwbds2SkpKyM7Oprm5meeff56enh6CwaD2xLhaApzL5YqbK0ZKqUUflRg4OTn5vOeXEpCzsrJ0eF0sEhISdBteTl9yOp1kZmbye7/3e+Tn5zM6OorH4yE3N5fc3FxGRkbYv38///Ef/8Hg4OANKwAJIfB6vXziE59g/fr1ZGRk0NnZyVe/+lWOHTsWdwa6C92LatwwNTXFxMSEvm4pKSmMj4+TlZVFTk4OCQkJpKSk4Ha7Yz7bDQaDwWAwGK4m11X4sRvXTqeTnJwcli5dytq1aykvLyczM1Mn5QyFQlRUVLBgwQLmzJnD008/fcFktOoYoVCItrY2vve979HY2KiN3Fjbq/ISExPx+XxMTEzQ1NTEsWPHWLp0Kbm5ueTk5CClZGJiAo/HQ3JyMq2trbS1tVFaWkpqairFxcV0dXWdZzjbfwcCAf3bOshXX6MzMjKYM2cOZ8+e1XW2G5IOhwOfz8fSpUsJBAK0trbS29s77TgzeWzEE2FiGfHxvrzHEmDs21xIFIhVrlWkitVnYs22ZhdI7F4HFxrgz1awsp+zfbYg+8A+LS2NnJwcMjMzKSwsPM/t335s5elmDeOyChPx6qnOVwhBIBCgsbFRe6xkZmbS1dVFKBTi3LlzfOlLX6K9vR2/3x9THFQCktPpJCkpidTUVFJSUpicnMTv99Pd3c3k5KTeTnmt2ev7XkPKSN6OkZERRkdHcblcFBUVAdPPKysri82bN7Nt2zb6+vo4c+aMDiONdy8IIUhPT+eWW27hQx/6EP39/bzwwgvU1NTQ29t7njB5saLqhYhncNrvTyXC33LLLaxbtw6fz0c4HGZsbIyOjg7OnDlDRUUFTqeT1NRUMjMzSU1N1X1pcnJSP8cUKhRm+/bt7Nq1i9LSUrxerxawe3t72bNnD6+99hpnz55lfHxc38OFhYWsWbOG5cuXay+s8fFxent76e3tZcGCBQghqK6u5sCBA/T19VFaWkpRUZEWUxYtWqTF/5UrV7Js2TLq6upoa2ujq6uLoaEh/H4//f399Pf3X9Hp3WM9nxROp5PJyUktNKamppKdnX3eM7q0tJR169axcuVKvv3tb9PQ0KDbV4lCZWVlPPTQQ+zevZuTJ0/OOIW48oApLCyktLSUN954Q9/DymNw+/btlJaW8vWvf52RkRHtPbVu3TrWrl3LbbfdxtGjR3njjTcYHR0FbkwxQ0pJZ2cnfX195ObmsmTJEj71qU/xR3/0R+d5filcLhe5ubl4PB6GhoYYGBiY9tFEoaZ/h8jYISUlhYGBAf28V8/PtLQ0PUuoIt7HBoPBYDAYDIYrxXX3+FGDneTkZJYvX86KFStYsGAB+fn5hMNhzpw5Q1tbGy6XC4/HQ1paGhkZGaxatUp7Klwo2aoapA0NDfGLX/yC3t5ebZjE8mwZHBwkEAiQk5NDTk4OTqeTsbExjh07RkpKit43GAzS29vL3LlzmTdvHu3t7fpfWloa2dnZMYWAeIKJXUQ4e/YsxcXFZGdnM3/+fN5++23tEWQv0+FwMG/ePG6//XadMFrlIpnJW+ZSB5uxXOBnY4zGq0s8jyT7l26rwWr1SFCCSlpaGi6Xi6GhIc6dO8fIyMi0nB7WOsQSmGKdh/2YMwll8fZXXg5qWmkl5sxGJLOf+0znEOtLcjAYpLW1FSmlFiTb29vp7+9nfHyc6upqxsfHmZycjCmseb1e5s+fz9y5c8nLyyMtLY2kpCSkjOS1OHnyJL29vSQlJZGYmEh9fT11dXVx22c23CiCUTgcxu/3Mz4+TmZmJnPnzp0WdqqM5vz8fPLy8rRnyUyCD0SMycLCQh566CG2bNlCMBgkPT2dffv2cfjwYZqamhgZGYkZJnq57XIx5SgvlLq6OpKTk5k/fz5z5syhuLiYxYsX09vbS3Z2NsnJyZSUlHDXXXdRWFjIwMAAAJ2dnbz00ktaDHC73eTk5LBr1y62bdtGeno6/f39dHV1kZaWxvz58yktLSUtLY3y8nKqq6s5duwYvb29LF68mHXr1pGbm0tKSgoJCQkkJiaSlJTEwoULGR8fJy8vj6mpKXw+H263m5qaGnbs2IHP56OlpYWsrCy8Xi8pKSm4XC7S09NJT0+npKSErq4uBgcHGRsbY3x8nP7+fk6fPk1TUxM9PT34/f7L9p650P6hUIihoSFCoRApKSkUFBRokd/hcOByuSgtLWXLli2Ul5eTkpIy7dngcDhISUlhw4YNbNq0if7+flpbW3U4UyyUUHTXXXeRmZnJ0aNHGRoa0s8nr9dLaWkp2dnZFBYWcujQIYaGhujr62N8fJyMjAxuueUWSktLSUhImFFkutp4PB5SU1MZGho6T4BWXrU1NTX4/X6Gh4e56667WLFiBbm5uQQCgWnvCiklLpeLbdu2sXHjRvx+PwcOHODgwYPaw9b67g6Hw4RCISYmJvB6vaSlpeF2u3WuJofDQWJiIoWFhbS0tMT1MDIYDAaDwWC4Glw34cdqtKekpLBq1Spuu+025s2bRzgcprGxkfr6ek6fPk1zczOJiYkkJydrg2PRokWsWbOGd999d9oAb6bjhUIhamtrp02Xbq+PEILu7m78fj+JiYlkZ2eTkJCA3+/n9OnTZGRkMDQ0pJOFtrW1sW7dOpYsWcKRI0fo7e2lubmZrKwsgsHgeQZ7LM8T+wBSbVNfX8+iRYsoKipi7ty5JCcn60Sf1n1VOxYVFXHLLbfQ09NDfX09DQ0NOgzgQq7l1vaI53VjrWO8vByxyptpmbXdrWWpwTJAX1/fece0eqMUFxezaNEi5s2bR05ODm63m97eXk6ePEl1dfW08AOruBNLxIklLtnrH0+8sy6LJfhZQ6iUJ0S88iEihipxxVp/e+jMhcS7yclJent7GR0dJSUlhaqqKsbGxujr62NiYoK2trbzEkMLEQmpKSgoYO7cuaxYsYKqqioyMzPx+Xykp6fjckUeH1VVVbS3t5OcnExCQgKHDh3SHkR2ccvez250VPsHg0ESExO1sa3uQ2v7u91u0tLSzpsuXHlvAIyOjhIOh0lMTKSoqIiNGzciZSRMavv27eTn51NUVMSbb77J4cOHp93v9npd7nnNdtnExARvvfUW7e3tLFiwgKqqKubPn09xcTEpKSl4vV48Ho8Oo1q2bBnd3d0MDQ3x1ltvsXfvXvx+P06nk+zsbDZs2MDDDz+sy62trWVsbIzCwkJuu+02KioqWLhwIVVVVcyZM4fMzEwaGhrYtWsXhYWF1NbWUldXpw3swsJCKisrmTdvHg6Hg4mJCRYuXEhCQgK5ubls2LCB1tZWDh8+rAWKxMREfW0GBwfx+/2kpKSQnp4+LTSyvr6empoaGhoaaG5u5ty5cxf0Mp2JmcKglIeU+vCQlpZGcXGxDiVyu90UFRWxaNEiKioq8Hq9JCYm6meN8rqqqKhg48aN5OTksGjRIvbv38/AwIAOJbS/ewsKCrTXztTUFJWVlZw4cUJ7r0xOTmoh76677sLj8dDU1MTQ0BCjo6MMDw/rGa3g+om2TqeTiooK5syZw+HDh/UHECU2pqSk0NraqnMSCSF0f8jNzeXcuXPnCa0pKSk8/PDDbNu2jXPnztHX18eRI0cYHx8/7+NJOBwmGAxqkTgjIwO32z0tBFKFcFdXV8cN0TMYDAaDwWC4GlwX4UcNPF0uF8nJyaxcuZLf/d3fxel08s4777Bv3z6OHz/O8PDweUZtS0sLZ8+eJTMzk9LS0vO+riusBqbVOLN/cbV+KVXr+/v7GR0d1QNjJbj09/dz/PhxQqEQfr8fj8dDY2Mj4XCYJUuWkJOTQ1NTEw0NDfj9fp0LxC5YuN1ubRzajXqroFNfX09vby9lZWXk5eVRVFTE8PBwTNHF5XIRDodJS0sjMTFRh/R0dnbq81Jtbs0xZBeQ7Mus7WcNQ1PL7OXMdM1VW8QyYq0CgRJzqqqqcDqd/PKXv2RychKv10swGGRiYkJ/AU9NTeUDH/gAGzdu1FPoqsH2jh07+Nu//VuOHj2qE2PH85SJV99YYo9KvKquVzgc1t4yykhTbaO+IKtlY2NjjI2N6bwZarn9Gng8HhYvXszU1BT19fUMDw/H7LfWutnDwawGyfDwMK2trRQWFrJ27Vqqqqp0LpTvfe971NTU6GTnQggSExMpLi7m/vvvZ8OGDQwODtLR0UFdXR3p6els2rRJ51NRoZeTk5NMTk6SmprKvn37puUvsrZdUlISExMTBAKBuB4Q11MUsrbt1NSUFn6SkpKYO3cupaWl1NbWasFZJYh1OBwkJCToJMiAzoGzdu1aHA4H+/fvx+/3k5qaSn5+Pg6Hg1OnTtHf36/bMS8vT0+PfuLEifPEYVW32baRXWibjUhuP87ExAQtLS20tbXx+uuvk5KSout57733cs899yClpL+/n97eXnp6eqiurubpp5/WbZORkcHq1av58Ic/jNPp5Mtf/jLHjh3TAkNqaipdXV1s27aNhx9+GI/Ho2dT7OzsZN68eTz55JM8//zz2oNIeftt3ryZP/iDP8Dj8RAOh0lKSmLp0qUsWLCA5uZmvv3tb3P69Gnt7ZObm6s9aV577TXefvttPetSamoqpaWlLFiwgKVLl7Jx40a6u7vZvXs3zzzzDE1NTVetf4bDYUZHR/H7/eTn52vBf2JigszMTG699VY2bdpERkYGUkqKi4s5deqUzm+0cOFCduzYoe/vRYsWsXTpUvx+P729vUxOTurnY3JyMosXL2bZsmVUVlbqkOpHHnkEv99PR0cH4+PjjIyMcODAARYuXMjSpUv59V//derr6+no6MDr9bJ+/Xq6urr0tbS+S68V6pn1yCOPUFFRwfj4OMeOHWNkZISsrCzuvfdeqqqq+Lu/+zv90WZkZITe3l5KS0t1XiVr6LBKXL5p0yY8Hg+Tk5P6+aUSmVuPr+4T1QY5OTn4fD4SExP1jIlOp5Nly5bxox/9aFoY73tBBDcYDAaDwfDe5poLP1bjPjc3l9tvv53HH3+cxsZGvvGNb9DQ0DAtxMFqMKqv7+3t7bz88sskJiZO85qwG/XxPAvieXiobf1+P319fYRCIZ18VOXX6ejo0IbY1NQUtbW1+P1+cnJyWL58OQMDA7S2tnLmzBltGCrPCIh4BajQgtLSUl555RVGRkam5ZFwOBz6K2ttbS0lJSXk5eWxefNm6urqzhMKrEl81eB93bp19Pf38+KLL+pEsx6Ph+LiYu3tEW/QaU2ybfW0cbvdeDweAG14xRNQYnmi2I1VVW+r27wawC9fvpy77rqLiYkJamtrCYfD7Nq1iyNHjvDuu+8yNDSE1+vlYx/7GLt27eJb3/oWp06dIhQKUVJSwq5du1i5ciWPPfYYbW1tOk+IlXiGsFUgs14TIYROOl5WVkZKSgoTExP09PTQ3t6OEJEcOj6fj5GRETo7O6flkpJScu7cOTo7O9mwYQNz5szROX6soprT6aS0tJTPfvazDAwM8G//9m+cOnVq2gxJSvjyer34fD4cDgc9PT26LPt1DYfDHD16lPXr17N06VIdciaEYMuWLfzxH/8xr776KsFgELfbzdKlS3niiSdYuHAhP/rRj/jJT37CyMgIbrebqqoqvF4vt9xyCy6XC6fTSU1NDVJKHRJyzz336GnjVZsmJyezatUqHn74YQ4ePMjPf/5znS9DhWHcaDgcDp3Xw+Vy4fP52LFjB01NTfrZY70/ysvL8fl8OtTE6/WyZMkSvvCFL3D27FndR7Ozs5kzZw7hcJhjx47xla98hdLSUpYuXUpCQgJNTU10d3fHFGYvltkIstbtrPe8+m0tQyVOHhgYoLGxkby8PG677Tbq6+v57ne/y0svvaTFMvXsEUKwePFi7rjjDrKzs/nmN785LWQGIrnOent7qaqqYnR0FCEEPp+P8vJysrKyeOmll3j22We1QKn6eV9fH6+//jqPPvoomZmZvPPOO+Tk5FBQUEBrayt/8zd/o/Mm7d27l/T0dLKysrSR73a7OX36NH19fboPqhw2H/rQh7j//vtZtmwZXq+XiYkJ/vmf//mqeLWo58TJkydZvnw5q1atYtmyZdxxxx1IKdm5cyepqalIKeno6KC4uJiPf/zjJCcnk5yczIIFCygtLcXtdlNbW0tZWRmZmZn89m//Nlu2bOHkyZMMDg5SWlrK/PnzKSwsxOFwUFdXx9GjR0lISGDnzp1s376defPm8c4773D8+HE6OjrweDy8/vrrJCYmsmrVKm655RYtcAYCAU6fPk1KSor2bFPerqrvXE1hQ92D6t2alpamZ00MBoPk5eXx8MMPa09FNcNbZmYmxcXF+P1+/ey05tBLSkpi8+bNeDwezp07x9NPP011dfU0YddaB/UuGx8fx+VykZmZSVZWFunp6SQmJur7atOmTWRlZWnPMatobzAYDAaDwXC1uC7Cj8PhoKysjB07dvDII4/wyiuv8J//+Z9aALEaU/ArF3g1uPL7/Rw/flwLP2pAZfV+sM/cZDWgrLPOqGXKA0f96+3tZWxsjIKCAsrLy9m3b9953kGhUIiuri727NnD3XffzbZt2wgGg6SmpmpvpvT0dDIzM8nPzyc3N5eCggIdIgHQ29vL0aNHGRkZmTZAVnVTiT6LiorYvHkzTz31lB6gWo17t9vNsmXL9N8LFy5kaGiI5uZmamtrSUpK4oMf/CAbNmzg0KFDvPTSS5w9exZgmvFlRbVrUlISK1euZOfOnaxatYrm5mY+//nPT5v222oIqSSlsUKl7Ntbt3G73UxNTREOh+nu7qa7u5vKyko+8IEPMDExwdatW8nNzUUIwdGjR8nLy2PXrl20t7dTXV1NW1sbUkrOnDnDiRMn+OxnP0tJSYnOr2A1PuKFjnk8HpYsWcK2bds4dOgQp06dYmBgQHvgfOpTn2LFihU6x4gQgmAwSE9PD6FQiIKCAtxuN36/n7q6Or75zW/y8ssv68H92NgYIyMjOBwOsrKyWLx48Xl5qlTbZGRkkJeXx9y5c+no6NCz0ClhKjMzk4997GMsXryYlpYW/umf/uk80dTq6dbf36/7zOjoKOPj4wDMmTOH//W//hdNTU06OfmGDRsoLS3lBz/4AT/4wQ+0d04wGOTkyZN897vf1V4Fp06dYs+ePQQCAZYsWcL999/P7bffzg9/+EN6enqYmpqivLycu+66iyeeeEKLqQcPHtRf3+1eb/FE22uBvW+o/5WotnPnTp555hkt/OTl5bFmzRqcTifFxcXMmzdPh4O6XK5pIUSZmZkMDg6Slpam89G0t7czOjpKTU2N9vCxepFdajvMRpyI5UWmno325dZ+pYQ65XEppdQJ+NVz3JovRYUhpaenMzIyQnV19TTPQ5/Px/z587n77rupq6vjJz/5CR/5yEeYO3cuUkq6urp46qmndJ9VRrY6rtfrxe12c/jwYV566SU2b96Mz+ebFvIDcOrUKfLz88nKymLRokXTZvyynrcK8/qHf/gHurq6ePjhh0lMTNTXUXnGXQ3xp7m5mcbGRkZHR8nIyODzn/88Y2NjvPHGGzz11FN0dXVRVVXF5z//ecrKyvjMZz7D2NgYJ06c4Nlnn+XYsWM0NzezaNEivvjFL1JYWMj27dvZtm2b9kppbGzk5z//OQcOHKCtrQ2/309SUhLvvvsujz/+OEVFRZSUlPDAAw/o57Q658HBQRobG3E6neTm5pKVlcXdd9/N7bffztmzZ3n33Xepq6ujpaWFU6dO6ZBS6/vqSrabqptKOg6Qnp6uQ+FSUlLw+Xw6sfLQ0BCrVq1i586dJCUl8eyzz9Le3j5NWFd9XIVo9fT00NDQQGtr63kz9lnroDyJwuEw2dnZOnF5Y2MjTU1N3HnnnTpHoQrpu9rCmMFgMBgMBgNcY+FHGRDJycmsXr2aDRs20NDQwFNPPcXw8PA0F+ucnByWLVtGOBzm7bffZnR0dJqQMzExoV2vVchPaWkpCxcupKCggJycHG2YDQ4O0tLSQk1NDWfOnNHHsYfJWF2vu7q66OvrY/78+VRVVelpeMPhsDYAvV4vBQUFbNq0SU9tO3fuXPx+vzZk1eDR5/MhpdQhN/Pnz9eu+x0dHTohpvL28Xg8OJ1OysrK9CxhS5Ys4U//9E/Zu3cvdXV1Om+Dx+Nh0aJF3HLLLZw+fVq7mC9btozExES+9a1v4fP52LlzJwUFBQghGBoa0lMY2z2glGeDSuhbVVXF2rVrmTNnjp6SNjk5Wed9sApqypiLJyTFWm4PpVLGz4kTJ1i+fDlr165lbGyM9PR0li9fTk9PDz09Pfh8Pv0VXglNavCtcrIog88+O5Y9lE3NNHTrrbfy+OOPk56eTmlpKc8++yz19fWsXbuWT3ziE3g8Hvbu3YvT6cTj8ZCSkkJ5eTn5+fm6XDWDVkVFBZ/5zGc4c+YMjY2N2mNteHiY8fFxcnNz2b59u/Zys04lPTw8zOjoKMXFxSxZsoTW1ladcFXVOS0tjbKyMubNm0coFCIrK2tang0rU1NTOql0IBCgs7OTgwcPUltby+c+9zny8vL4zGc+w9e+9jWWLVvGqlWr6Onp4dVXX50WkqXEsbS0NKSUnDp1iu9973ucPXtWG/j333+/Prc333yTBQsWsHXrVtatW6cNnPLycioqKuju7p7Wj66GYXgpWA370dFRhoaG9L2iwuX2799Peno6a9as0fllnE4ny5cvp7m5mdHRUZKTk6moqNBJZ6uqqujv7ycpKQmv14vf76e5uVk/V9SxY7WFmo68oKCAkydP6hCpeMQT0Ox9I1abxxJnreK69V7zeDyEQiFqampob2/Xz0n7c0CFFyUlJVFcXKwTjkNERG9ubua//uu/KC0t5aGHHtLJ8dX93NfXd57gDWgBorm5mZdeeomlS5dSVlbGuXPnOHDggJ5VTIhIaO3x48dJSUmhsLCQrKwsPZOW8ljbuHEjmzdvpqGhge985zv6vIeHh2lra5v2zrjSKCG5pqaGF154gWXLllFdXc3+/ftpamrS+coGBwf58pe/zJw5cxgaGqKlpWVawnbl4feFL3yB8vJy0tLSABgZGaG/v5+2tjadyFo9O0OhkH63VFVVkZ+fT2pqKh6Ph4mJCXp7e7W3pTU8TwnFixcvprCwUAtNExMT+P1+2traaG1tpaGhQYcBtrW1aY+2K4UqKzk5GZ/Ph8fj0c9or9eLy+Xi8ccfZ2JigoqKClJTU6murua//uu/tEhprU8oFOL48eNAZCa1wsJC6uvr9ccYa/+29mP1LMzLy6OyshKv10tTUxOvvfYaCxYsYO7cuTz88MOcPn2akZER7RV5I3o8GgwGg8FguHm4Lh4/ZWVllJeXA/D666/T09OjRRo1xe5tt93GggULAFizZg3PP/88LS0tjI+Pk5KSwrp160hPT+eFF14gKSmJTZs2sWbNGgoKCkhPT6ewsJBwOIzT6SQQCNDf3099fT3f+c53OHHixHmeBbpBop46KjzL4/FQWFhISkqKFqeUEaRm6VBTtarwGafTSUdHB11dXYyMjDA4OKiNR/X1eceOHfzar/0amzdv5tChQ/T19WmPDquAojwHpJQkJSWxYcMGSkpKdLJelbAzNTWVvr4+nn/+eTIyMtiyZQtVVVUsXbqUj3/84/q83G43c+bMYevWrbhcLk6fPo3b7cbn85GQkKDd4bOzs8nPz9feK9XV1XR1dbFy5UrtraC8lOzTyscyiOzijn3AbP16LqVkaGiI7u5uJiYm9Mw7CQkJ2tAGdF6agoIC5s+fz+joKCMjI/h8PhYvXsycOXOora3VRp1V7FPt7Ha7yc/PZ8GCBaxdu5a1a9cSCATIyMhg5cqVnDlzhoSEBG6//XacTic//OEPqampAdDXXIUMfOxjHyMjI4Pm5maGh4dZuHAheXl5rFq1ipaWFsLhsDagmpubKSoqYu3atfzsZz/ToWjKg0Ilf1ahFcqwGxgY0AaCyteRlJREbm4u69at09O0W1Hi45w5c4BfearV19dz9OhRnn32WT75yU9yyy23cPToUUpLS8nIyKCnp0fvD+j2nz9/Phs3bmR0dJRXX32VlpYWnby3s7OThoYG1qxZw+23386SJUvIzMxkcnKSPXv2MDAwwB/+4R+SkJDAypUraWxsZGhoaJrQYe0L19vzRwk/g4ODOgzO6/Xy4IMPkp6eTl5eHsuXL9eCbmVlJbfccguHDh1icHCQ3NxcVq9erdtx2bJlNDQ0kJSURFJSEuPj4/T09EzzqrCer9Ujcfv27dxxxx3k5+fz13/91zqHUqy2ieVpF+/3hfazLrN7k01MTPDmm29q4ae+vn5ayIwyjqempnRIZEFBAR/84AfJycnRedxSUlLIyckhNzeX1NRUQqEQP/3pT1m3bp0On1u9ejWvv/76tOndFSr/2pIlS6ioqKCrq4sjR47ohL3quRQOh+np6eHQoUMkJCSwYcMGDhw4oJ+jRUVFrF+/no0bN7Jo0SKdbDszM5P6+nq6u7vjCtv2drzUPjs5OUl9fT2jo6Ps2bOHzs5OOjs7p+Xg6u/v17nwgsEgfr8fv98/LbQuFArR0NBAV1eXzjEzOTnJ+Pg4fr//vFmvICIMqXxAycnJ+gOElJEQ6IGBAUKhkL6+LpeLc+fO0dTURF5eng71zM3N1V5DixYtorKykjVr1jA+Ps7Y2BhNTU38y7/8ixZ9LxV7f1UhwPPmzdMTLJSVlQGR5/WKFSsYHByku7ubN998k7fffpuzZ8/qPmttj4mJCRoaGujo6CAnJ4f777+fuXPncvLkSY4dO6YFHtXeSlRvaWlBSklmZiYVFRW4XC66uro4ffo0L7zwAr/zO7/D0qVL2blzJ8FgkPr6+ln1KYPBYDAYDIbL4ZoLP2ra8ZycHAYGBjh27Ni0L11paWkUFRXpxKcLFiygsLAQgL1799LY2Kinq1UzvBQXF7N+/XqKi4vp6emhr69PGxRqph01gG9qaqK2tlaHU9g9f3w+H1u2bGHJkiXMnTsXn89HSUkJ99xzD83NzVrMUQPnsbExzp07p0O31CxJe/bs4cyZM1rwUeJPIBDQM8Y8+OCDlJaWcueddxIKhTh48CAjIyPaUJqamqKhoYGmpibKy8v1l/WMjAyys7MJh8OMjIzQ3d1NW1sbdXV1vPPOO3g8HqampvD7/RQWFlJaWkpfXx8HDhwgPz+fkpISKisryc7OpqGhAUAn6lV1C4VC9Pf3k52dTWJiIl1dXXr2ltTU1GmJie1ijn0wPBPxBruhUIiBgQHOnTunE1b39fXphJtutxu3283o6CiFhYVs2rQJIQQDAwMUFRWxevVqJiYmOHTokE7sbM2FlJiYSGlpKWVlZVRVVVFRUUF2djZdXV1UV1eTnZ1NTk4OCxYswO12M3fuXOrr69mzZw9dXV3TDGA1LfTWrVtxu92cPXuWxsZGhoeH2bVrF/n5+TovhPImO3HiBJs3b6akpIQVK1boPiJlJCdUVlYWCQkJOtG18sCprq5mdHQUh8Ohr43b7SY7O5tNmzZRX1/PqVOnpoXZeDwe8vLyqKio0NPcHz9+XE9TvXv3bnbt2kVxcTGrV68mISGBhIQE8vLyuPXWW6mvr0cIoQXBnJwcMjIyOHjwIMeOHdMeblJKBgYGOHz4MOvWraOqqoqsrCxaW1s5evQohw8fZmxsjI997GMUFRVRUVFBVlaWDhuareFztcUguwgZDAYJBAKEQiECgQBJSUksX75cJxMPhUK89tpr9Pf3a1F7yZIlTE1N6Vxeg4OD+Hw+KisrtQdGUlISgUBAX3erp4/9nnK5XKxYsYJdu3bhdrtJTEycVXvFE4Xsyy82LEz9PTU1xalTp+js7KS/v/88kdUq6J09e5aTJ0+ycOFCVqxYgc/n09OMq7CcQCDAuXPnOHLkiH42qamx77zzTrxeL/39/brPOZ1OEhMTSUtL0/2ytbWVU6dOcfLkSYaHh6eJUA6Hg0AgwNmzZ9mzZw/9/f2cPHlSl6e8Q0tLSykoKGDLli2Mjo7S3t7O8ePHtVF/NZFSMjg4yNDQ0DTPIutxg8Eg3d3dWpy1emFZQxVVPiZ7+ao97MvUrGi9vb309/fr9fZ7Qv2vkhn39PRQW1tLRkaGfj8VFBRQVlam89yonGL2xMhXAtVO3d3deva9FStWUFJSwsKFC3X/HBwc5Ny5c9TV1VFfX09rayvj4+O6n1rbOBwO09fXx8svv8ydd95JZWUlGRkZJCcn09zcrMOHrc8j1beCwaAOPxweHqarq4vOzk7eeOMN7r33XubOncvmzZs5efKk9vgzwo/BYDAYDIaryXURfgoKCkhKSqKlpYXOzk49IAdITEykv7+fN998k6mpKR588EHKysq47777tBfO6Ogo+fn5lJWVsX79ehYsWEBeXh5nzpzhl7/8Jb29vdq1Ww0CN27cyKZNm9i2bRtPP/00fr9/2hd2JWCkp6fz6KOPsmjRIp2rJzExkU9+8pPU1tZSW1tLW1sb9fX11NfXMzAwoBOGKs+GlpYW9uzZQ2Njoy7f6ik0OTnJyZMnqa+vZ968eWzatImOjg5qa2t1yJfap66ujsOHD+vcMWr6+uTkZJxOJwMDA9TX11NdXT3Ndf6VV16hsbGRJUuWkJWVRWNjI0eOHNG5glavXk1BQQHZ2dn6mH6/n6GhITo6Ojh9+jTV1dXce++9bN26leLiYpKSkrTBqb4gq3OC2B4CduPPut46YI4Veqe+4peXlxMKhaiuriYvL4/CwkItSqjZ1bZt20ZGRgaBQIDS0lJycnJ4/fXX2b9/P6FQiKSkJG0kquSu69evZ/369VRWViKl5ODBgzz99NN0dnZy6623agNGedZ0dXXpcCtrMm5lIBw/fpzR0VEaGxt10u9NmzbpWY1UW/T393PixAna29spKyvj1ltvpa+vj4aGBn1tV6xYQUJCAl1dXfo3wNjYGK2trbhcLtasWYPP52N8fBy3282qVau0Z4BKZqs8klatWkVGRgZHjhyhurqaxsZGOjo68Pv9nDp1irfffpuioiKWLl3K8PCwni3pkUceobm5GYgY4EII+vr6OHbsGHv37tXHgYghOTAwQHV1NY899hihUIiTJ0+ye/duTpw4wcDAAD6fj/r6ekpLSykuLtbilbofrf3I/jdEQksyMjIIBoMMDQ3pnC9XC2v4oTK2VVhdfn4+DQ0NHDhwgL179xIMBrnzzjspLy9nw4YNFBUVkZSUBMCRI0dYsmSJ9gDzeDx4vV4GBga0+BEv1E0Z8i6XS3tj9Pb2XlCsuZC4E0tAi+dpZfWSU8vVOuVBotbb72vVfh0dHbzzzjs6fDYrK4ucnBxCoRDj4+P09vZy5MgR9u/fT2trq/YESkxMZNOmTaxdu5bKykp6enro7+8nGAzqmdRUKO6RI0d46623aGtrY2xsbJpBbfWq8vv9epYylScOoLa2lqGhIc6cOcOaNWvIy8vTSfYPHz6sQzYvxKWKQ3bBzP639TyUYOp0Os8Th4QQ2ovF+uyxPmdjEevaqX1ihTZZ/1ZeXb29vTQ2NuoPCSkpKaSlpWlPWuWBOzIyckltFK/dwuEwDQ0N+v7csGGDzs+2b98+jhw5Qnt7O11dXdNC3GKJLup8JyYm+NGPfkRSUhIlJSVA5KOENdzL2l7qGdHV1UVJSQlut5v29nb9Eaiuro7du3dz77334vP59Ht8pmtiMBgMBoPBcCW45sKPy+XC4XAQDAZ1Ylc1GHe5XJw5c4a2tjadu+fEiRN87nOfY+nSpWzYsIHe3l5+8YtfcOLECcrKyrjzzjtJTk7WX5PffvttAD0gdjgctLa2kpiYyPbt26mqqmLRokUcO3Zs2hd2Nbj1+Xzk5ubi9XqR8ldJpXNzc0lKSqKyspLe3l5eeukl6urq8Pv9/PSnP6Wqqop58+aRlJRESkoK8+bNo7a2FvhVzg7r4Lunp4fvfve7fPCDH6SlpYV33nlHzypjNTaHhoZ45ZVXqK6uBtCzjyiUCGGdzQlgYGCA4eFhampqpn0NVvkV9u3bR1lZmRZY+vr66OnpYXh4WHszqXacM2cO2dnZehac7u7uabOb2YUdu4Fg/9u6DKZP8W4dAPf393Pw4EG2b9/OiRMn+OEPf0hWVhYrV65k7dq1LFiwgNzcXD1Dy86dO3VoTktLCwcPHiQtLY3U1FTcbjd5eXksXryYFStW6OmLVa6V4eFhHA4H5eXlbNu2jYqKCkZHR3VSZYfDwcaNG3nxxRdpamrSeTRUnaWUPPPMMzppckFBAXl5eVoQsnrgBINBWlpa+P73v8/nPvc5Vq1ahc/n0x4KRUVFVFVV0dLSQnV1Nbfeeivz589n8+bNzJs3j7feeouEhATWrFmj2ygpKYlVq1bxwAMPkJeXx4svvkgwGCQ3N5eKigpWrFjBqVOnePLJJ6eFaygPiBdeeIGdO3dqsaK9vZ2WlhZSUlLIz8+ns7OTmpoa3n33XU6fPq1zudinkB8fH6e5uZmnn36anp4e9u3bNy1/VzAY5MCBA2zbto05c+awdOlS6uvraWxsnOaZEC/kaPPmzTz44IM0NTXxi1/8gmPHjp33jLlSHkGqnMTERC1OKeN/+/btnDx5kkOHDtHc3Kyv789//nN+8zd/kxUrVrBo0SKGhoY4ceIE//7v/85DDz3Ejh072Lp1K06nE7/fz9mzZ6fl97L2KetzIxQK8fLLL3PixAk6Ojq0YG4nnoePdV2sbezLYv22ep9Yl1tnpIsltCjD1u/3c+zYMVpaWpg/fz7p6elA5D7v7+9neHiYwcHBac+IAwcOcObMGd59913uu+8+8vPzycnJ0R6WKr9NW1sbr7zyCh0dHfp5aE0eHUv4kDKS18ba1wKBgH4H/eQnP9F5sVQesZlymF0p4gmf9r/jzeBnf6fNJCrasbeZ2t76TlLEu8+sAlsoFMLv99PV1XXe+iuBtW4qr9Hbb79NRUUFwWCQ9vZ2jhw5wr59+3Rosr2OscQfaz85c+YMf//3f6/FxWAwqO936z2q7o+RkRFef/11PvCBDzA2NkZ1dTU1NTU619RTTz1FX18fZ8+e5fDhw9MmSTAYDAaDwWC4WogLDcCEECXAd4A8QAJPSin/WQjx58BvAj3RTf9ESvnCTGU5HA6ZkZHBr//6r7NkyRLa2tr4xje+MS1vgsqdo0JjHA4H+fn5/OVf/iV5eXns3r2bb3zjG+Tk5PA//+f/1LM8vfjii7z44oucPn1af6lTYTjJycncfvvt/MVf/AXJycm8+OKLfPGLX9S5NdSAS+Xo+fCHP8wHPvAB5s2bh9PpJBgMcu7cOb7+9a/T3d3N2bNn6evr0y7iQgjy8/O54447yMnJ0UlF6+vrmZycnDaLmDon9U+FZVkToqrBvPq6bvWEUYNUmO4po7aze87YBRXrtupLsdrHmpsDIoPapKQktmzZwhNPPEFWVpaewemXv/zlNAPLbhzEMtzt3gT2ga71y7XqCx6Ph4yMDMbGxvTMMG63m9zcXNasWcPChQspLCxkcnKSkpISsrOz9exgKmRNiY3K20olLd6zZw8rV67kgQceYPXq1bp9RkdHaWpq4utf/zrHjx+ntLSUT3/605SVldHd3c0LL7ygZxFTyZQnJyfx+Xzk5+ezZMkSVq5cSUFBAdXV1Xzta19jfHx8mteEEp0effRRHnvsMTIyMpBS6hlzdu/eze7duwkEApSVlfHQQw9x5513kpaWxuDgILW1tTQ1NbFnzx6am5vx+Xxs27aNJ554QnsBKRFMJRb92c9+Ns0DzVoXj8fDn/3Zn7FlyxZefPFFfvzjH9Pc3IzL5SIhIUGHdFj7qZrVSfVpq0GnZkmyexhIKcnPz+drX/salZWVhEIh9uzZw5NPPsk777wzrZ/Y8Xg8vPDCC6xevZpnn32Wr371qxw8ePC87a6k8COEYOnSpdxxxx2sX7+eH/3oRzz33HO6Da3bShmZie0v//IvWbhwIefOneO1117j+eefp6+vD5/Pxx/+4R+yfv16UlJSaGho4Ktf/SpvvfXWtKTFdi8bu1CquJTzm01Il9puNsdR96mayc++v6q71VPI+nxX29rP3zrLluqjbreb5ORkAP1cDgaD00QZ6zlaRQzrOcTqH3ZPJmvbqxw3iquR1HkmLtSfY13TC13n2fSD2Xh9qeXXuk3ioTx03W43k5OTul9Y33F2kWymyQgALf7Fui+t26nnhZq9b82aNZw9e5auri49OYXa1t431d/2/GwGg8FgMBgMF8k7Uso1sVbMRvgpAAqklIeFECnAO8CDwAeBUSnll2dbC4fDIb1eLx/+8IfZunUrHo+HV155hWeeeUYbktYBmBCChIQEFi9ezKc//WkGBgbYvXs3r776Kk6nk/vuu4+PfvSjdHZ28swzz/DGG2/osInJyUlt8DudTsrLy7nnnntIS0vjySef5Ny5c+cZbi6Xi6mpKebOncvv//7vs3nzZrxer55l6jd+4zcYHh7WAoQQYtpxEhMTp81Co0SaWING+3T11npY3eqtgphabw2tUQNaZZyor8B2w81+PNUu1gG7fVCs2sTpdJKcnIwQYpoAoOqg/rbmPLDXUR3X7ulj/1udr12gsp6XqrvH49EGotPpxOfzUVpaSmVlJZmZmWRmZuLxeAgEAjQ1NVFXV6c9LNQ5JCQkUFJSwvLly7UYeeTIEZqbm3WoiNfrpby8nI9//OM6+bNKUjo+Po7D4aC7u5vs7GySk5Pp7++ntraW6upqneQ3lvGmjJTi4mI9tbfKweP3+wkEAtroVQm1s7Ky6O/v13kklOeOEEKHNW7evJmpqSna2tpobm6mvb2dsbExgsHgtGtr7Q8ul4uMjAwSExP1jGLK6FbX1Sr0Wa+bXdxRZVqvpbpu6u/S0lLuvvtuMjIyqK2t5ZVXXpkWqhjL6BRCsG7dOlauXEl9fT1vv/02w8PDWLlSoo/C6XSybds27rzzTkpLS3nyySd1GKr9WaW2Ly4u1vlqhoaGdBiU0+mkqKiIbdu2EQ6HOXToEKdPn9bXJZbxPpNReqXPdTbEui5WYdsuMNvvZ6sQZPcMsqKexVYxR3kO2cOX7H3SWqZVxInHTM9f+3q1zbVq84sR6i4k9MCl9xX7RwRrWeo9obx3ZzrW1e6z6l1s7TvW+lv/V9g9puz3tfWDjbVf27H2feVtDNP7t9WjT5VjvUesz2iDwWAwGAyGS+DShZ/zdhDiZ8BXgE1cgvCTmJjI2rVr2blzJ6tXryYYDPL222/zxhtv6HwhKg9Lfn4+q1evZs6cOYyOjuqQJ5XfIjs7m1tvvZWWlhaampr0dL/KQLUaC2r2LZfLRXt7+3lf1qwGTHJyMg899BD33HMPS5cuxel00tPTw2OPPUZPT4/e1zoYtk4XbjWYVT2soordS0ctU7/tX1DtQojVK0jtZxVO7MaV9Yu6VUCyhlgprMaS8q5RQpH1q7y1LmowazXMYnn92I9vXafOIZ53g9rPek7qb6s4lpCQoPM7eTwe3fZKrFFiibUsj8ej8y34/X5GR0e1W77azuv1UlZWxrZt28jPz8fn8+np5D0ej555p76+XueuUgm9rSEZsQxilUxZCWvqn3Ubl8ul/6kcE7EMVjV9sfJ6CwQCBIPBuF5YVmFHtaH1/lHbxPJIsXtx2L1T4vVh+NUMYU6nk/HxcT1d+oVITk7G6/USCAS0MHc5XMhgdrlc3HfffWzdupVgMMi//uu/ag9FO6otVVLucDh8nieKmtZdhYSopMKx+jxc2EC2e19cTS8gta39OPZrbxd+7Pup9arfWJ9ddrFxJtFF/W3v2/Z91Xp7HRXW9fbrYBc77MuvFTNdI/s6+zWKJUBcrHg1G2HpcvrQlcD6jryQ0Bevj9qvt7Xf2HMlWZ97alu13urRZj+uXWiyrldhXwaDwWAwGAyXSFzh56Jy/AghyoCVwEEiws/vCSEeBw4Bn5NSDlyoDCklzc3NHDx4UE+nu3HjRgoLC3XYjJqiOiUlRSeBfvvttzl+/Dh9fX3aw6Snp4c333yTwcFBAoHAed41VqPA7/frRLCxBl7W/BqBQIAjR46Qn5+vEyB7PB7S0tL0TCfKWLMKK+p41uPOZAzbDZRYxlssI8V6HGu72ge8VoMonoEY7yuuOoY9rMxqTMXb337OsQw4K/bBcCxjTy23CnrqOljXhUIhRkdHzztn+9d/6/EmJyd1wlF72JraXyVtDgQCepYx9c/pdDIyMsLAwIDOCRQMBqfNGhdrBjnVjmrGKCv2tlZCjnVdLANnYmJC30f2PhfP2LALOtZ1an/7+lh9/EJ92VrvQCAQN0/NTIyNjenzm0kUmA3xxBYrUkra2trYv38/fX19uo9Y11uPrURGe9mqDdV6a7vPph7xzu1KGM8zCQqzOcbF1itWn4Dz+1msPme9d+x9ThGvv9ufSfHqFut6zPY63ShY+0u852q87S+mbMXF9MOr3Zb2PgOx+3KsOsd6Z9vLnqnvqP9jiUnWY9ife++lvmUwGAwGg+G9yayFHyFEMvAM8Fkp5bAQ4t+AvyKS9+evgP8P+PUY+30K+JT6LaWkr6+PmpoaHA4HGRkZ5Ofns3btWqamprRHRigUYnBwkGPHjvHWW29x6tQp7T2hBlLhcJizZ8+eN+CKZRDYDXnr4MsqwKgyVGLdnJwcVqxYwdjY2LQvevArYcWem0YdQ4Ug2AedM309jldn+/bWwWmsr9XxhB3rMns4mf249gFsrDpfqO7W6xDPuLCfg91As5dlF8PsbWKvbyxPlViGt7VPWPuGIhAI0NDQMO346n/VL2MZr/bZX6xlWsUTaxtZhRVr28YS6OIZ3lbhy3qdrW1ov/ZWLyO7x471uGpbe/6qeHWy1/dCBlY8rPteLrMpR4V5tre3MzIyoj2trPW3188aimRvW/uzZrYCyYW2udKG40zlzWT0z0ZssT+LZhJ8ZjLiYx1npvsh1u94gsDlihtXmovpJ/HeBbMt72KPfylcjbaM9f64mONd6B16oTLszzZ7OfHqNtv6GQwGg8FgMFwOsxJ+hBBuIqLP96WUPwGQUnZZ1n8NeC7WvlLKJ4EnIRLqpQZH/f39HDp0iL6+PjZs2MDKlSuZnJyko6NDz/JSW1vL6dOntaeOGjSp/A/2AZk9J0QsA9y6vdrOWrZaNz4+ztGjRxkeHqanp4eRkRF6e3v1LDAXEjasniX2UAhrfa1lWF3JrWFRdiPFfm7xzsF2HWIORtX/KhxJedJIKXWeApWg2t529rJUXezG1Ez7WuttvybWfe3bKVHNGmJnbQ9raJVdlFBeQzMN1K3tpULYrLOnWes5kyEfq3/YZwMDpp2LdQYw6/JY/dluWMcTji7kWWMPHbQfK5YHgSo3npF5JUUaxUzlXexxZuNtpJ5VsY5t7T/23/HEVLUuXn+ZSeyNxYUM/EslVlmzvZbWNrGL5fbyrfentb/MFMJn9zSzH3Om+sQ7pwsZ6xfLle731nLhwtf6Yj3pLqauN7JIEesaXmi7mZbPVJ79Po8nZsfqq9blBoPBYDAYDNeC2SR3FsC3gX4p5WctywuklB3Rv/8HcIuU8kMzleVwOKTb7Z42q5XT6dQzAzmdTkKh0LTcKmoWIbshYTVUY+W3UYavKsdS72k5aKwJG+3CjUpsLITQM4TEGxha881Y66Cmg7cbQ8pLKFYeFPt5qXXqtyrTPri07ms/H5V8M574Zf1nNUrjiUyqDOt2ap3VwyWeoWI3mO3eKVYxTIlQVkHE2paxsF7XWGF5SlCx1s1qdCpx0d5HrImsrf3M2s52ISjeAH8mI85+LazLJiYmdP+JlUQ0ViiSSpCt2tRatmrbWMdUx7ULQ2q5Opa1Le2eVPa/7edvr+tMxMv9ci2JVYdYz5DZ7Gs3Em8kQzDedZ/tvhD7/Ox9IdZ6u6gZzzsxXjn2dfHqOJvzirXdjWS0299bMPMz5WJFIUME+71rfwfZRSJ7f7S2v7WPW9/3ZlYvg8FgMBgMl8llzeq1GXgDqAHUiPFPgF8DVgASaAF+SwlBM5TVA4wBvbOvu8FguA5kY+5Tg+FGx9ynBsN7A3OvGgw3PuY+NdwMzJFS5sRacdGzel0uQohD8VQog8FwY2DuU4PhxsfcpwbDewNzrxoMNz7mPjXc7DguvInBYDAYDAaDwWAwGAwGg+G9iBF+DAaDwWAwGAwGg8FgMBhuUq6H8PPkdTimwWC4OMx9ajDc+Jj71GB4b2DuVYPhxsfcp4abmmue48dgMBgMBoPBYDAYDAaDwXBtMKFeBoPBYDAYDAaDwWAwGAw3KddM+BFC3CWEOC2EaBBC/PG1Oq7BYJiOEKJECLFHCPGuEOKkEOIPosszhRAvCyHqo/9nRJcLIcS/RO/d40KIVdf3DAyG9xdCCKcQ4ogQ4rno77lCiIPRe/KHQghPdHlC9HdDdH3Zda24wfA+QQiRLoT4sRCiVghxSgixwbxTDYYbDyHE/4iOfU8IIX4ghEg071TD+4VrIvwIIZzA/wV2AYuAXxNCLLoWxzYYDOcxCXxOSrkIWA/8bvR+/GPgVSllBfBq9DdE7tuK6L9PAf927atsMLyv+QPglOX3l4B/lFLOBwaA34gu/w1gILr8H6PbGQyGq88/A7+UUlYBy4ncr+adajDcQAghioDfB9ZIKZcATuBDmHeq4X3CtfL4WQc0SCmbpJQh4GnggWt0bIPBYEFK2SGlPBz9e4TIALWIyD357ehm3wYejP79APAdGeEAkC6EKLi2tTYY3p8IIYqBe4CvR38L4Fbgx9FN7Pequod/DNwW3d5gMFwlhBBpwFbgGwBSypCUchDzTjUYbkRcQJIQwgV4gQ7MO9XwPuFaCT9FQJvl99noMoPBcB2Juq2uBA4CeVLKjuiqTiAv+re5fw2G68c/AV8ApqK/s4BBKeVk9Lf1ftT3anT9UHR7g8Fw9ZgL9AD/GQ3J/LoQwod5pxoMNxRSynbgy0ArEcFnCHgH8041vE8wyZ0NhvcpQohk4Bngs1LKYes6GZnuz0z5ZzBcR4QQ9wLdUsp3rnddDAZDXFzAKuDfpJQrgTF+FdYFmHeqwXAjEM2z9QARsbYQ8AF3XddKGQzXkGsl/LQDJZbfxdFlBoPhOiCEcBMRfb4vpfxJdHGXcjeP/t8dXW7uX4Ph+rAJuF8I0UIkRPpWIrlE0qNu6jD9ftT3anR9GtB3LStsMLwPOQuclVIejP7+MREhyLxTDYYbi9uBZillj5RyAvgJkfeseaca3hdcK+GnGqiIZk33EEmk9ew1OrbBYLAQjU/+BnBKSvl/LKueBZ6I/v0E8DPL8sejM5GsB4Ys7usGg+EqIaX8opSyWEpZRuS9uVtK+RFgD/BIdDP7varu4Uei2xsvA4PhKiKl7ATahBALootuA97FvFMNhhuNVmC9EMIbHQure9W8Uw3vC8S16r9CiLuJ5CpwAt+UUv71NTmwwWCYhhBiM/AGUMOv8ob8CZE8Pz8CSoEzwAellP3Rl+NXiLjD+oFPSCkPXfOKGwzvY4QQ24E/klLeK4SYR8QDKBM4AnxUShkUQiQC3yWSt6sf+JCUsuk6VdlgeN8ghFhBJAG7B2gCPkHk46p5pxoMNxBCiL8AHiMyw+0R4JNEcvmYd6rhpueaCT8Gg8FgMBgMBoPBYDAYDIZri0nubDAYDAaDwWAwGAwGg8Fwk2KEH4PBYDAYDAaDwWAwGAyGmxQj/BgMBoPBYDAYDAaDwWAw3KQY4cdgMBgMBoPBYDAYDAaD4SbFCD8Gg8FgMBgMBoPBYDAYDDcpRvgxGAwGg8FgMBgMBoPBYLhJMcKPwWAwGAwGg8FgMBgMBsNNihF+DAaDwWAwGAwGg8FgMBhuUv5/hZlg8l8oYm4AAAAASUVORK5CYII=\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 1440x1440 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 1440x1440 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 1440x1440 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 1440x1440 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 1440x1440 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 1440x1440 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 1440x1440 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 1440x1440 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAABG0AAABCCAYAAADt2ys3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABNcUlEQVR4nO29eXRb13m3+2wABAiSAElwnmeKIimJEjXZmizLsmzLQxTbdewkdtyMbb9+bfM16b3t7crQ9N407U1667RfpiZ13Lpx7AyeBzmSNVETqVkixXmeRxAgAILAuX+AZweEqFmyaHs/a3GRxAHO2WfE3r/9vr9XaJqGQqFQKBQKhUKhUCgUCoViYWG41Q1QKBQKhUKhUCgUCoVCoVBciBJtFAqFQqFQKBQKhUKhUCgWIEq0USgUCoVCoVAoFAqFQqFYgCjRRqFQKBQKhUKhUCgUCoViAaJEG4VCoVAoFAqFQqFQKBSKBYgSbRQKhUKhUCgUCoVCoVAoFiBKtFEoFAqFQqFQKBQKhUKhWIAo0UahULwvCCE+KYR451a3QxFCCHGHEKL7Fm6/XQhx1+zffy2E+EnYsh1CiC4hhEsIsfxWtXEhIoT4uhDiP291OxQKhUKhUCgU7w9KtFEoFDcMIcR6IUSNEGJCCDEqhDgghFgFoGnaf2madvf73J7NQojds+1pn2f57UKII0KISSHEKSHE+rBldwghgrPCgf7zVNjyfCHEG0KIMSFEvxDi+0II0xW2q1II8bYQYlgIoc2z/D+FEH1CCKcQolEI8blLrOszQoj9V7LdhYqmaf+3pmnh+/hPwP/QNC1O07TjV7MuIcR/CCGmZ8/XqBBipxCi7Ma2WKFQKBQKhUKheH9Qoo1CobghCCHswGvAM4ADyAK+AfhuYbPcwE+Br0QuEEI4gFeBfwQSgO8ArwohEsPe1jsrHOg/z4Yt+zdgEMgAqoBNwB9fYbv8wC+Bz15k+f8D5GuaZgceBL4lhKi+wnUvKESIq/2uyQPOXsdmv6NpWhyQTegc/cd1rOsCrlScUygUCoVCoVAorhcl2igUihtFKYCmaf+taVpA0zSPpmnvaJp2Ci6MCBFCaEKILwkhmoQQ40KIfxVCiLDlnxdC1M9GwZwTQqyYfT1TCPErIcSQEKJNCPE/L9YgTdOOaJr2HNA6z+LbgX5N016cbe9/AkPAx69wfwuAX2qa5tU0rR94C6i4kg9qmnZe07R/5yLChKZpZzVN08Uubfan6ArbJRFCPB12DFuFEF+c5z1/PRvx0y6E+GTY6/FCiJ/PHucOIcT/pYsvkSk6s1FHmi5mCCHeE0L8vRDiADAFFF6mnV+fjS6yCCFcgBE4KYRomV1+xec8HE3TpoDngcrLrUcIYRBC/B9CiBYhxIgQ4pezwl74/n1WCNEJ7BJCRM+2eWT2+j0qhEi7yP7p69Wv5R1hyz4jhNgvhPin2aitNiHEvWHLC4QQe2Y/uxNIvpJ9VygUCoVCoVB8OFCijUKhuFE0AgEhxLNCiHsjIlYuxv3AKmAp8AfANgAhxKPA14EnAT3aZGRWNHgVOEkokmcL8OdCiG3X2GYxz/+VYf+nCiEGZgfS3xNCxIYt+2fgE0KIGCFEFnAvIeHmhiCE+DchxBTQAPQBb1zDagYJHWM78DTwPV38miWdkAiQBTwF/EgIsWh22TNAPCHBZROhc/H0VWz708AXABvQcSUf0DTNNxshA7BM07Si6znnQog44JPA8StYz58CHyO0r5nAGPCvEavcBCwmdJ0+Rej45ABJwJcAz0Wa0gJsmH3/N4D/FEJkhC1fA5wndC6+A/x7mID5PFA3u+zvZrerUCgUCoVCofiIoEQbhUJxQ9A0zQmsJxQV8mNgSAjxysWiD2b5tqZp45qmdQK7CaUZAXyOUIrLUS1Es6ZpHYQEnhRN076padq0pmmts9v6xDU0+SCQKYR4XAgRJUJ+NUVAzOzyhtn2ZAB3AtXAd8M+v5dQZI0T6AZqgd9eQzvmRdO0PyYkeGwAfs01pJlpmva6pmkts8dwD/DO7PrC+dtZsWQP8DrwB0III6Fj+n9qmjapaVo78P8SEmKulP+YjRia0TTNf7VtD+NazvlfCiHGgWYgDvjMFaznS8DfaJrWPRvl9HXgkYhUqK9rmubWNM1DKMUtCSiejdSqm70HLmA2mqtX07SgpmkvAE3A6rC3dGia9mNN0wLAs4SuuTQhRO5su/VztJeQ8KRQKBQKhUKh+IigRBuFQnHD0DStXtO0z2ialk0oYiWTUETKxegP+3uK0AAbQtELLfO8P4+Q0DKu/wB/DVxKGLpYW0eAh4AvAwPAPcC7hAQYNE3r1zTt3OxAuw34KvAwhFJpCEXV/BqIJRQFkQj8w9W24zJtDGiatp+QN8sfXe3nZyOeDomQIe84cB9z02vGNE1zh/3fQeicJQNRzI2Q6SAUoXKldF1tey/CtZzzf9I0LUHTtHRN0x7UNK3lCtaTB/wmbFk9EIjYTvg+PQe8DfxCCNErhPiOECJqvsYIIZ4UQpwIW3clc8+DvA9mU7ogdC9kMv85UigUCoVCoVB8RFCijUKhuClomtZAyAC28jJvnY8u5vdw6QLaZgfk+o9N07T7rrGNezRNW6VpmoNQFEkZcORib+f3z0wHkAt8fzYCYgT4GSFR5GZg4io9bYQQFuBXhCoxpWmalkAoxSo8JSwxIuUrF+gFhglFkuRFLOuZ/dvN7yOSIJRmFckFVbGukRt1zi+3ni7g3ojl0Zqm9YStQ+6Tpml+TdO+oWlaOSF/pPsJpZDNQQiRRyii538ASbPn4QwXpubNRx/znyOFQqFQKBQKxUcEJdooFIobghCiTAjxv4QQ2bP/5wCPA4euYXU/IZTiUi1CFM8Ofo8Ak0KIvxJCWIUQRhEqn73qIm0yCCGiCUWNiFnzWHPY8uWzqVF2QuJGl6Zpb88u2yyEyJvdfg7wbeBlAE3ThoE24I+EECYhRAIhr5FTYetuF0J85iLtErPtMs/+Hz0rsiCESBVCfEIIETe7f9tmj+PvLnG89H2TP7PrthAyV56ZNbedr+T6N4QQZiHEBkLCw4uzaTq/BP5eCGGbPfZfBnTz4RPARiFErhAiHvg/L9G26+Wqzvl1rOcHhPY3D0AIkSKEeOhiK5u9PpbMppI5CYlcwXneGktI7Bma/dzTXKGQOZsSWMvvz9F64IEr2luFQqFQKBQKxYcCJdooFIobxSQhQ9XDQgg3IbHmDPC/rnZFmqa9CPw9IRPWSUJeMY5ZMeF+Ql4zbYQiQn5CyOB1PjYSMod9g1CEgoeQr4vOV2fX0UXIR2RH2LLlQA2hqJIa4DQQXrXo44RSqoYIeaf4gb8AmBWGkri4YJU32xa9epSHkBEthAb4f0QoTWuMkJj055qmvXKRdUEo0sMzz8//JCS+jAFPAJHr6J9d1gv8F/Cl2QgpCBnzuglV3tpP6Fz8FEDTtJ3AC4REqjpCpd5vCtdwzq91Pf8foePzjhBiktC5W3OJVaYDLxESbOqBPYRSpiK3e46QH9BBQml4S4ADV9H0J2bbMQp8Dfj5VXxWoVAoFAqFQvEBR2jajYpgVygUCgXAbETEn2ia9vitbotCoVAoFAqFQqH44KJEG4VCoVAoFAqFQqFQKBSKBYjp8m9RKBQKhUKxkBBCfBL44TyLhoAU9bp6Xb2uXlevL7jXOzRNq5jndYVCobgkKtJGoVAoFAqFQqFQKBQKhWIBclWRNkIIpfAoFAqF4oYghEAIgclkIhAIEAwGURMJCoVCoVAoFIqPKMOapl0QqXfV6VEmk8qoUigUCsX1k5WVxcaNG3n44Yd5++23ef7555mamrpAuBFCEBMTg8FgwO/3Mz09TTA4X3VtheLDSzAYVNe9QqFQKBQfbjrme1EpMAqFQqG4JURFRZGYmEh+fj633XYbr776Kl6vl0AgAITEGovFwqpVq6iqqiI6Opquri7OnTtHU1MTHo/nFu+BQqFQKBQKhUJxc1GijWJBIoSY879KmVAoPnxMTk7S1NTEyZMnGR8fJyYmBqPRSCAQkIJNZWUl27Zto7CwEJPJREFBARkZGSQlJXH48GE8Ho96Pig+MAgh1PWqUCgUCoXiqlCijWJBIIQgOjqamJgYrFYrZrOZYDCI0WhE0zRmZmbweDy4XC48Ho/0wYiKisJgMAAQCATw+/0EAgHVKVYoPgCMjY1RW1uLyWRCCMHMzIxcZjQacTgcbN26laVLl9Lb2ytfW7t2Lenp6YyNjVFfX8/09PSCvud17x6DwSB/ICRG6ykv4WkvC3lfFNePEm4UCoVCoVBcDVdVPUoIoSlPG8XNIDY2lpKSEiorK1m0aBGJiYl4vV6sVivBYJDx8XEaGxs5evQo58+fx2KxkJqaSnp6OhaLBU3T8Hg89Pf3Mz4+jsfjIRgMIoTAaDRiNpsRQszrl6H48KIPlOH3A2TF+4suWBiNRvx+/yXfF35v2u12li1bxl//9V/T39/PM888Q1JSEitWrKCiogKr1UpnZyfPPPMM3d3dcwSfhUL48yc6Ohqr1Up0dDTR0dGYTCa8Xi9erxePx8P09DQzMzNKfFZcFOVpo1AoFArFh546TdNWRr6oFBjFLUcIwac+9Sk+9rGPUVxcjNFoRAghzUYnJiaYnJzEbrczPDxMT08Pd9xxB5/+9KdxOBwYjUYAZmZmmJmZ4eDBg/z0pz9lYGAAIQSlpaXs2LEDi8XCN7/5Tfx+vxoMfUQwm81kZ2djtVrp7+9nZGREnfv3GavVSmpqKsXFxezateuig87w8yKEICUlhbVr12KxWKitraW1tZWzZ89y9uxZqqqqeOihhygsLOSJJ57gxz/+MSMjIwtqQKtHDy5evJg777yToqIiEhISMJvNmEwm4uLi8Hg8eDwefD4fY2NjDA0N0d3dzcmTJ2lvb8fpdMqBurpuFQqFQqFQKD6a3FDRRvcgiImJwWKxMD09jdvtxuv13sjNKG4gQghsNhsGg0EOHq7ms1FRUcTFxREdHc3g4OA1zXbHxMSQlJTE2bNn2bdvH729vYyMjDA2NobL5cLlcsnZaKvVyuc//3keeOABmpubOXLkCD6fD4fDQWVlJStXrqSyshKbzcbPfvYzzGYz27dv58knn8TlcvHd736X8fFxNQB6nxFCsGjRIioqKqitraWvr4/p6embur0lS5awY8cOqqqqSEpKoqenh29961ucPXv2pm1XMReTycTy5ct5+OGHSU5Opru7m8bGxsuKKwaDAZvNRl5eHkajkf7+fjRNQ9M0RkZGOHr0KJOTkzz11FMsX76c9evXU1NTw+Dg4IK4t00mE5mZmWzZsoW1a9cyMzNDf38/PT09MvJmenoag8FAbm4udrudhIQEioqKCAaDPPDAA4yNjUmRqr6+/orvGZV6cyFGo3HBCF/hfm0Wi4Xs7GwAOjs7ZSTaQminQqFQKBSKhcMNE23MZjOVlZWsXbuW4uJiYmNj8Xq9DA0N8cYbb3D+/Hk8Hg/R0dEkJyeTmZlJbW3tggxp/yhRVFTEXXfdRWxsLMeOHeO99967bIdRCEF8fDyVlZVUV1dTXl5OVFQUb7zxBq+//vpVVXTRBxgvvfQS09PT+Hw+WdI3EAjIWeZAIIDZbCYnJ4cNGzbg8Xj4yU9+Qk9PDzMzM1gsFjIyMrj//vv59Kc/zbp166irqyM2NpalS5cyMzPDm2+++ZFIjwofFCyEfRVCEBsbyyc/+Umys7Px+Xx4vV4GBgZuyvYMBgOFhYV88YtfJDMzk2AwyNTUFEVFRXzxi1/kb/7mb3C5XAvi2HwUMJvN2Gw24uLiyMnJoaWl5YpEG30CIBAIMDU1JZ8HmqYxMTFBQ0MDb731Fn/wB3/A6tWr6erqwul0LoiKUvHx8eTl5ZGRkcEbb7xBU1MTbrebmZkZmbKnH4PY2FhiY2NJTEwkLS2NrKwsaba8fv16li9fTltbGydOnODQoUNSwLqSiKUrRX9m6M/jD+K9YTAYZLv13/r+XK9go6f4XU0kV+QxjXzdbrdTXV3N9u3bGR4eltFimqYp4U2hUCgUCsUcbohoYzQa2bx5M3fccQc5OTmYzWYCgQAOh0N6juzevZvGxkZyc3O59957cTgc+P1+Tp8+rdJVbiG5ubksWrSI9PR0zGYzNTU1l4y2MRgMFBcXs3nzZiorK0lPT8dqtZKYmEhUVBQnTpygq6vrshE7epROYmIigUCA7u5umQ4FFw48NE3DarVis9lIS0uju7ubjo4ORkZGCAQCGAwGxsbGANiyZQspKSk89thjQGjW+/XXX+fXv/71gjcsnQ/9WF3uPjEYDGRmZpKZmUliYiJCCIaHh+nr62NoaOiW3mfBYJDo6GiSkpLIyMjAZrPdFNFGj/bbvn07hYWF7N27l7a2NjIzM7nnnntYuXIlVVVVHD58+KZG+ihC6INd3Tg8Ojr6gspw86ELB+EeHuEDcD1tsra2luXLl2Oz2cjIyKCnpwev13vLB74zMzOMjIxw7NgxTp48yejo6EU9agwGA1FRUVitVuLi4khISCA5OZmCggIqKirIy8ujrKyMhIQEMjIyqK2t5eTJk/IZazQasVqtOBwOYmNjcbvdOJ1OJicnmZmZueQxMJlM2Gw2srKyiIuLY3h4mOHhYVwulyy7/kF7XgLS7FkIIfcDrn1frvZzBoMBs9lMfHw8WVlZxMTE4HQ6aW9vx+v1EgwGiY2NpbCwkGXLljE2NkZiYiKTk5PyOR25Tf0+UigUCoVC8dHjukUbg8FAUlISW7dupbS0lLa2Nrq6upiamsJms5GTk0N5eTlerxefz0dGRgbV1dVkZGQwODjI2NgYPT09V5WWo7gx6BEQdrud9PR0XC4XVqv1oudCCEFqaip33303t912G5qm0d3dzdTUFHfddRdLly5l8eLFjI6OXpFoY7FYyMvLY2BgAKfTeVnjTV280A09dVNs/TMGg0EaexoMBjIyMujo6KCuro733nuPU6dOLSjPiytBFzoKCwvp6emhvb193n0wGAyUlZVRXV1NYWEhSUlJmEwmBgYG6OjooKGhgcbGRiYmJuYMYt4vZmZmGB0dRdM0bDYbFovlpmzHYDBgt9tZt24dXq+XQ4cOUV9fT15eHjk5Odx///2sW7eO48ePfyDFYiEEdrsdm80GICureb1eZmZm8Pl8F1QhupX7GF41yWQySVPoKxFUgsGgjMSc73qZmZmhr6+P2tpasrOz8Xg8GI1GLBaLTNvUo/b04xMZiXGj0ffL6/XS39/P2NgYAwMDl9yeHkk4PT2Ny+WS92hMTAxjY2MMDg6SnZ1NUlIS69evJykpCYD6+npMJhPp6enk5eWRn59PbGwsLpeLwcFB6urq6Ovru+BZrJ+P+Ph4cnJyKC4uprCwUAqpnZ2ddHV1yTTVyCiVcCHuSiJY9HMON/56DI9msVqtJCQkyKgui8WCyWSiu7ub3t5efD7fVW078hq9kghUCEVO5eXlkZaWJs9NbGwso6OjnD17lvPnzzMyMoLJZMJsNuNwOKToODQ0JCPKFAqFQqFQKHSuW7QxmUwUFxdTVVVFT08Pr732GidOnGBmZoaYmBhWrFghDWbPnDnDxMQEzc3NZGVl8dBDD9He3s67777L4ODgJSuLKG48utmvEIKYmBji4uKwWq2Mj4/P+16LxcLatWvZsWMHPT097N69m7q6OqKjo9m0aZP0lTl16hRjY2NXJMCUlpZiMBgYHx8nEAhcVFDQZ0y9Xi+BQIDMzEwWLVrEzMwMfr9f+l+sXLmSqKgoWltbOXLkCPv37+fkyZPX7Ldzq3E4HKxatYrt27dTW1vLs88+O+/gIyoqiq1bt7JmzRoZzRAbG0tZWRmBQID6+npeeOEFeQ++n+jREWNjY/j9foxGozSPvhKuJnXDZDKRlpZGTk4OBw4coKuri/HxcYxGI0eOHOHBBx+kurpaipPhER3zrXchpSkYDAYSEhJYvnw5hYWFAFKoGRkZweVyMTIyIkVLPa1oeHj4lu6DHkliNBrnCLP66xaLBY/Hc8HzXxdtjEbjHJEqHJ/Px5EjR6ivr8fr9WIymcjPzycpKYns7GympqZwOp0MDAwwOTmJ1+vF7XZf9QD+avH7/UxOTuJyuWQVu4tdXzqapmEwGGSZ86ysLL7//e9z+vRpFi9eTHV1NUuWLGHTpk3YbDZeeuklbDYb5eXllJeXS28U/XgKIdi3b98FPj9RUVE4HA7Ky8tZs2YNZWVlxMTEYDabMRgMTE5O0tTUxMGDB6mpqZmTSqibKyclJUkfM7fbfdH7JyoqCrvdLkU3r9eLy+W6Id4tunCkR2xmZ2dTXFxMTk4OGRkZxMfHYzab2b9/P++++y79/f0yyuVqxZuLtTV8mf4duWjRIu6++27y8/Pl5IKe7ldRUcFbb73F8ePHmZ6eZnBwELPZTExMDCUlJXR0dFw0hfdWC7AKhUKhUChuHdcs2ugdJrPZzNKlS4mOjqahoYGuri45U+h0Otm5cye5ubkUFRUxNTXF6dOnGRkZISYmhurqar7whS/g9XrZu3evzNVXvD8Eg0E5s6eXobVYLPMOMKKioigqKuKP//iPMRgMvPjiixw+fBin00l2djajo6OkpqbK2d7Loc+MrlmzhjvuuIN/+qd/orOzE5/PN69wo2kaPp+P4eFhuru7qaio4M/+7M94/fXXMZlMVFZWsmLFCsxmM6+//jovvvgi7e3tuN3uD2zpXD3dae3atZSWluJwOHj55ZflOdPRB1Jms5kDBw7IqjNpaWmsXLmSNWvWsHLlSiYmJvD5fNTW1t6yfdJNWMNn3y+GHhEQFRVFVFQUQgj8fv8F0SThWCwWCgoKMJvNnDhxgsnJSYLBIG63m+7ubrxeL7GxsdhsNqxWK0IIfD6fHNDrx1XfttFonJOSc6uuI11Y3bFjB3/+539OXFycTP/Q7w2/38/g4CDj4+Ny8F1XV8czzzxzywVL/fsi/LqNjo6mpKSE8vJyampq6OjokMuCwSA+nw+Xy4XdbiczM1MOfvXzoJ+L3t5ejEYjxcXFbNq0iXXr1pGSkoLNZpPnbmBggJ6eHpqbm9mzZw8NDQ035ZjobdKvl8uls+iDff1HH7w//fTTuFwufvSjHzEwMMDQ0BDt7e2Mjo5y3333sX79eoqKirBarbS1tXHy5EneeecdYmJiuOuuu8jMzGTp0qV0d3czPDwsj5nBYCA1NVUadHs8Hl566SWGhoYoLi5m0aJFFBYWsmLFCjIyMnA6nRw5ckTui8FgoLS0lCeffJLy8nJ++tOf8rvf/Y7JyUn5nA2/RzIzM2XlLIPBQEdHB/v376ejo0Peb9fqwaMLIbm5uTzyyCOsWrWK6elppqampHhrNBrZsWMHdrud3bt309LSgtPplNdFuA9O+PnQtxF+Ti91vvX3V1RU8IUvfIG4uDhOnz7N0aNH6evrIzk5mW3btlFcXMyWLVswGAwcPHiQ1tZWJicnSU5OZsOGDTIKRxder+QaUigUCoVC8eHnmkWbyNlSk8k0J/Rc7xQJIdi9ezenTp2io6OD8fFxxsfH+frXv87f//3fs3TpUv70T/8Uu93OK6+8ctPMSRXz09PTg8vlIioqioSEBIqLi+ns7JzTGdVT4B5//HGSkpL43ve+R11dnYzIcblcHD16lNLSUgoKCnA4HJjN5jlh+ZFCkN7hTk5OJjExkU996lOcP3+ec+fOUV9fj8fjuaCzHAgEGBwc5Nlnn+U73/kOq1evZsOGDfh8Ps6fP88vfvEL3nzzTdrb25mZmfnAijXh6CJFXFwcDoeDvLw8Jicn58zGapqG0+nkxz/+sRS99MHHO++8w7Zt2/jsZz9LWVkZJ06c4NSpU++7n4seJWIymeaNqghHF6EyMzNZvXo1t99+u/TGamxs5O233+bdd98FLhw0xcTEUFFRAYSu7ampKWZmZpiensbj8TA9PU1cXBxf/epXWbRoEXa7HafTSVNTEy+//DK7du2Sxzs3N5eCggJSUlKYmJjg8OHDDAwMXNYnZD6io6MJBALX9FkIiV2pqal8/vOfJzo6ml/84hcMDQ0BIaPf2NhYKioqiI2NJTo6Wka1xMTEYDKZbploEwwGZXRLMBjEbDZLAW7NmjXcfffdFBcXI4Sgp6dnzrXrdrtpb28nOjqaqqoq7Ha7vHYiz3t8fDyPPvooq1atore3l6997WtkZ2cTGxtLQUEBlZWVVFVVUVVVxZYtW/je977HwYMHb5pvS2QExpWsXxey09PTSU5OZmZmRgqcgUCA3t5edu3aRUpKCvfffz95eXm8/fbbvPHGGzL1UzdrT0pKktd7OCaTie3bt1NVVUVzczO/+tWv6OrqQgjBqVOnpAC+YcMGqqureeyxxzh16hRut1t+1+tpUSUlJXz729/mxRdf5LnnnqO1tRW/3y8FK4PBQEVFBYWFhSxatIjU1FQ2bNjAk08+ycmTJ/nRj35ES0sLbrdbHqvLRWfqx9NkMpGTk8PmzZt5+OGHGRkZ4be//S01NTUMDw9jNBpJTEykurqap556irvvvpucnBxeffVV3nvvPdm+yPVHR0fjcDgwmUz09PTM67EWKfToApLdbufzn/88cXFxPPvss5w8eZLJyUkA2tvbOX/+PI8//jiVlZWUlZUxNDQkn2ef/exn2bhxI01NTUxMTNDU1DRHAIwUkhQKhUKhUHy0uO5Im0AgwOTkJCaTSZoZwu87OTMzMzQ3N8uBtD54GBwc5Bvf+Ab33HOPLHOalJTE8PDwDfXcEEJgNBoxmUwy/eZSnUN9cJmRkSEHwyMjI1IE+LDkmuudv5SUFJKSkrBardjtdjZu3Mi+ffvw+XzyPVarldzcXFauXEljYyP79++Xxpq6h0R8fDxGo5GMjAxWrVrF0NCQ7HjGxMSwevVq0tLSqKmpkYMEk8mE3W4nNTWVBx54gG3bttHU1MTOnTt5/vnnL/BiiIqKIi0tje3btxMfH4/BYMDv9+P3+3E6nbS1tck0qJtxnvTO+vslBGmahsvlYmhoCIvFIqNU5muDpmky7Um/N/U0JD0tSR/Evd9Clh4Root5brcbr9c773v19JYNGzawfft27HY7Q0ND8jorKChg/fr1jIyMUFhYyM6dO2U0jY7uEQJzIwLtdrt8FlitVhoaGkhOTiY9PZ3q6moWL15MWVkZdXV1bNq0iYqKChwOB1FRUUxPT/Pggw/y3HPPceLECdxuNykpKVRWVtLZ2cm5c+fmveZMJhMbN25k7dq1nDp1iiNHjjA4ODjvvuuV9QoKCjh8+PAF4oQefQJQV1dHbW0tY2Nj8l4ym80yGkX3upmamloQhsv6syI9PV36zVRWVrJkyRJsNhtr167ltddew+PxyEGqnvqoV6rLzc3F7XbLqI5AICCvc6vVSkxMDDExMVgsFmZmZnjvvfeA0HHVz9Vtt91GQUEBK1asoLa29qYJu1fjhQK/H/ibTCaMRiM+n096wBUVFZGZmUlOTg65ubnk5uZKr55z587R09ODyWQiMTGRu+66i4qKCk6ePCkjS8KrVeXm5rJkyRKcTqcsI65fK5qm4ff7aWxsJCkpidLSUux2O/Hx8dLceWZmhqamJn74wx/S2dnJn/3Zn7Fjxw7MZjO/+c1vqKurk98dQghqamo4c+YMycnJlJaWsmnTJqqqqli8eDFPPPEEL7zwAmfPnr0ifyldsDEYDBQUFLBlyxbuvfdeWltb+ed//mfGx8fnRMt5vV7279/PAw88QHZ2NllZWSQnJ8vUTH1iCX7vhVVZWckf/uEf4vP5+NrXvsb4+Lg8NuGCkT4poUcvWSwWSktLSU1Npaamhra2NpxO5xzRZ3Jykj179pCQkIDVaiUlJYWzZ89SU1PD008/jcVi4c4772R8fJzBwUH5HRt+DX3QJyEUCoVCoVBcG9ftaRMMBunq6iIYDFJWVkZeXh5tbW2Mjo7KmTG/33/BDLNuIvnKK68QFxeHy+VifHz8hg229eiQoqIiVqxYQXJyMh6Ph/b2do4dO0ZnZ+cFs/1Wq5WKigrWrFnD0qVL0TQNj8dDb28v4+PjNDQ00NDQIMtyfpA7UJqmYTKZWLRoEYmJiUBoxj4/Px+TyTRncG+32ykoKMBqtXLq1Cmmpqbk63l5eaxfv56qqip8Ph8Gg4GtW7ficrlwu914PB6eeOIJ7rnnHn7729/OMbCcmpri/PnznDp1ivz8fLKzs6U4tHPnTnp6euZ4KcTHx1NWVsaKFSsYHx/ntddeY9myZWRnZ1NQUMCnP/1p8vPzeemll26KR9KtOOc+nw+3243ZbMZoNBIdHX3R1CL9nCYnJ8tjUl5eTmFhIVNTU+zdu5f6+vr3PepCH9hER0dL4WG+NpjNZtatW8eGDRsoKSmhp6eH//qv/6K3txez2UxRURHR0dEA/OEf/iFpaWm4XC4OHDggZ7T17QkhyM/Pp76+nunpaSnMCCF44YUXOHToEB6PB6vVSmFhIRs2bOC2225j69atVFZWYrVaaW9v57333sPhcLBkyRIcDgf33XefvHaXL1/O6tWr6ezs5B//8R9xu90XzMCnpaXx8MMPk5OTg9FoZGhoiKGhoQuuI4PBQH5+Po8++ig5OTkMDg7S0tIij5P+HJ2YmCArKwu73S7bEV5dKTxlSk+JuZVCs6ZpBAIB/H4/QgiSk5NJSUlh9erVrFixgtTUVKKioliyZAmrVq3i8OHDUpjy+XzS8yM1NZWnnnqKPXv2UF9fT39/v/SmAWQJebfbTX5+Pp/61Kf4j//4D2mKPzAwQGxsLOnp6WRmZjI1NbWgohbMZjOlpaWsXLmSdevWYTQaSUhI4Mtf/jIWi4XR0VGGhobo6uri0KFDPPDAA5hMJqampkhJSWHZsmWymt/+/fupq6uTKUj6+TcajZSUlGC322lra5vjIxZ+jUxNTTE2NsbIyAjx8fGyqp9+X/l8Pvr7+3nzzTdJTU3l0UcfZf369fLabmtrk0Kh2+1menqaiYkJ+vv7aWtr4+677+auu+6iuLiYzMxMKXCEi+KX863S0+qmp6d5++23GR4enhPlEz6hVF9fT1paGnFxccTHx0uzZl30C0/DjI+PJz8/H03TWL16NYcOHZLp3iaTiaSkJB555BGio6N54YUX6O/vB0ITCnrlTL/fP0c01vcnEAjQ0dHBu+++i9lsZmRkhKioKEZGRhgfH8fhcMjoIbfbzS9/+UspQH+Q+xoKhUKhUCiun+tOj5qZmaGjo4Ph4WGSk5NZvXo1o6Oj1NXVyZB4YM7AQjcPNBqN9PX1yfWFd9b02avp6ek5HSCj0Yjdbr+kwKPn3S9fvpxly5aRk5NDQkIC09PTlJSUyKib9vZ2+RkhBEVFRWzYsIHS0lJZ/SM1NZXy8nKioqIoKSkhKyuL/fv309vb+4HuSBkMBmJiYli2bBmJiYkEg0EsFgvZ2dkkJCTMSU+yWCwkJCQQHR1Neno6a9asISoqiqSkJJKTk7HZbBw8eBC/38+WLVvIysrijjvuIBgM0tzczLZt2ygoKJCzoLpPyOTkJG+++SYej4fs7Gy2bNlCaWkpSUlJxMfH09PTM6fN4bPqgPRCysvLo7CwkOLiYulTcuDAARobG+fMdl4vt+J8e71eJicn5f0SGxs7Z2AeSWZmJmvWrGHZsmVkZmaSlZVFb28v7777LocOHbpsJZubhS44BYNBXC6XnI0PF+Wqqqq46667yMjIoKuri927d3P8+HGZvtfV1UV8fDxFRUWUlZURHx/PypUrOXHiBC6XCwilk+keHmVlZezbtw+DwcDixYtZvnw5DQ0N/O53v6OtrU2aIvf09DAxMYHFYmHNmjXYbDYOHTrEgQMHaGhowGq10tzczJo1a7Db7RQWFhIMBikvL6e4uBiHw0FycrL0ldHRjUlzc3NxOBykpKRgt9vnPXe6cXReXh4lJSUsXbqUnp4eKQTpUQ4DAwOUlJRgs9kwmUxzvHYiPWMWApqmyWvYbDZTXl7OI488QnZ2NsFgkI6ODjIzM0lKSuKee+6hr6+P7u5uKfLo+2Oz2di4cSPp6el0dXVJ756JiQkpImdmZmI2m0lISGDVqlX4/X6amprks003Wz937hxnz569pEHw+4kQgvLycjZv3kx1dTVFRUXSJF6vQNXR0UFPTw/j4+OMjo6yadMmUlNTWbt2LU6nE4vFgqZp1NXVUVdXR2dnJx6P5wLvq8TERKKiouQzBH7/3au/ppceNxqN0l8lXODSU956enp4/fXXWbZsGSUlJVRXV8t2hvsO6ZM2Pp8Pj8eD2Wzmtttuk+fDbDZf1mQ8/DyZTCYcDgcOh0PeF1FRUXOeJXrJ7eTkZAB5/pcsWcLQ0JCMMtIFKd2cOSMjQwrDd955J16vl8bGRsbGxoiKimLRokVs374dv9/Pzp075bNGF5wMBgNZWVnYbDYpJOnHOBgM4vF4aG5uxmg0yudPUlKSjOq1Wq0sWrQIn89HV1cXBw4cmCO8LiShUaFQKBQKxfvHdUXa6LNHg4ODnD59mk2bNrFixQrZETp//jyjo6NzZtWNRqMceBkMBo4dO3ZBaLTBYCA7O5vs7Gza29sZGRnB7/djsVhYvHgxeXl57Nq164K0CB2bzcaaNWvYtGkTycnJtLe3MzExQVJSEvn5+VRUVNDZ2XmBaFNcXExFRQUxMTHs3r2bmpoaacpYVlbG0qVLcTgcctbwg5wqJYQgPT2dRYsWATAxMYHdbiclJYXS0lKGhobkeZuZmZEiTnFxMffdd58ME5+cnOTcuXMcPnwYt9tNYmIiGzZsYMmSJcTExHDixAmKi4uZmZkhKyuLxYsXk5CQgNPplOH9evrJ8uXL8Xq9c4S6cPSop/r6eiorKykuLmbfvn04nU6cTidlZWXk5uZK40mAhoYGnE7n+3dgbzC6yKmnvSQnJxMVFXXR9yckJMiqMDabDSEE586do7u7W6aVvN8DVV2kjY2NlWJdeHqU7iWxZcsWli5dSnNzMzt37uTgwYMykmJ6ehq3243NZsPhcACh6ISMjAxMJpOM3vJ6vdJbo7S0lJycHDRNo7KykqSkJF566SVaWlpkJNnMzAz9/f0yYuH222/HYrFw5swZTpw4QV9fH4FAgNbWVrxeL4sWLULTNOx2O7GxsVgsFtLT08nOzmZ4eFhGFOqDq+npaVkBKT4+XqaIzZcepg/OrFYra9eupampifb2dlm9JxAI0N3dzdTU1LxmzrdafJgPPW2vo6MDt9tNUlISlZWVjIyMcPz4cQKBAFVVVaxfv57169fT3t5OXV0dLpeL+Ph4CgsLmZmZQQhBVlYW8fHxlJeXMzU1JaMz9eOjiwDBYJDo6Gi2b98uTbstFgtOp5OGhgZqampobGyUok3kQPhWHMfExERsNpssU61fly+//DJtbW0MDQ3hdrulb5PP58NqtbJu3Tp6enpoaGjg+PHjHDlyhPHx8TmV0cLR7yeHwyErQOnHV08hLCgokN/PeqRapMCjV/Y6e/Yse/fulWl969ato7Gxkf7+flkuPJyZmRl6enpkVGbkd3/4vTMfkecrJiaGqqoq+R2lpz/qkX3p6ekkJCTI76by8nJiYmLIzMykubkZl8vF9PQ0VquV7OxsioqKmJycxGKxsHLlSvx+P+np6fT29gKwZs0aiouLmZiYIDk5mc7OThnNNDAwgN/vp7CwkIqKClkdKjxlS3/+6RMQCQkJLF26FKfTSWtrKwUFBWRnZ7N8+XI+/vGP09fXR0tLyxWljykUCoVCofjwct3pURCa3X711VcpKysjJyeHrVu3UlhYyGuvvca+ffuk6BIMBomLi2PJkiX80R/9EV6vl7/4i79gYmJiTufOZDKxbds2Nm/ezCuvvEJNTQ0DAwOkpqby5S9/mcWLF9PR0SENa8PR0yJWrlxJWloaZ86c4Uc/+hGBQIA777yTLVu2zJlVDO8I6TOQZrMZm83GyMgI7e3t1NbWct9993HHHXeQlZXFbbfdxq5du255RZbrRRehWlpaMJlMLFmyBIvFwpYtWzhx4oRMvdD9Dzo6OsjIyCAvL49z585x/Phx6uvr6e3tlYLAr3/9a7KysqiqqmLJkiXk5OQQCASwWq08/fTT3HHHHTQ2NtLS0iJLsOrRC8XFxfh8PrkskqmpKRoaGvjJT37CV77yFTIyMtiyZQstLS10dHQwMDDAww8/TEJCAvfccw8+n4+pqSnOnDlzC47u9aNXQCktLSUqKopAIEBJSYmMJpqvEz80NERra6sUPWNiYli1ahVpaWm888471NbW0tnZKUsRvx/oA02Hw8H09LSMtAn3qEhLS2PDhg1omsbJkyc5cuTIBWWZ9dns6elpJicnSU9Pv6C0vNfrlUJdbm4ut99+O9HR0WRnZ9Pc3Mwrr7wi/Tl09HX29PRIMcXpdMoIDk3TcLvd7Nq1i+bmZkwmE8XFxeTl5UmvndLSUrq7uwkGgzI1RI/om5ycJCsri6ysLEpLS8nMzKSnp0e+T496i42NlZ4musH222+/LSPGIHR+JyYmPlADuIGBAY4cOUJGRgZCCE6ePElzc7M0jG1vb2fJkiXY7XaefvppKisrGR8fx263U1RUxPDwsDTA1SspTU1NYTAYmJ6enlP1zu12Mzg4yNjYmIza0z/r9XqZmppifHxcijt6ql54Ckr478h0t5tx3IPBIAcPHuT06dOsWbOGxx9/nKVLl9LZ2Ultba30VQGk4b/X68VoNCKEoK2tjXfeeUeWPQ/39NLbq/u46KlLOTk5LF68mObmZrq6umTqz+LFi1m2bBkJCQl0dHSwb9++OVEz4fesHkXz2muvUVVVxYoVK1izZg0mk4n9+/dz8OBB+ZzRU7CsViv5+flYLBZaW1vp7e2VUXKRkU+RQpH+v55a3dvbS2ZmJvfffz+33347brdbnmtd5HS73fT397N//37Ky8vJyMigvLyc0tJSxsfH5bNETzudmpri1KlTZGRkUFBQwIYNG1i3bh1Op5PJyUkyMjLw+XzExMRQUFBAe3s7vb29MuK4q6uLxYsXc//99+NwODh69Cjd3d3SQFuvSGc0GomLi6OoqIjbbruN/fv386tf/YpNmzZx3333UVFRwbZt2xgZGeFf/uVfmJiY+MD3NxQKhUKhUFw71yXa6J0ov99PXV0dP//5z3nssccoKyuTg/b777+f7373u5w/f57p6Wmys7Olf0RfXx92u13OlIZ3MIuLi0lJSeHBBx8kEAiwZ88ekpKSWL16NVarlaqqKvr6+i4YgBmNRioqKkhJScHlctHc3CzDtd966y2mp6cZGhqio6PjgoFbU1MTq1evpqSkhK1bt8pyqJOTk/zud78jLS2NjRs3YrPZZMfrVpYBvlb0tI21a9fi9XqpqanB6XTi8XjYunUrd999t6zCFB4R89WvfpX8/Hy6u7uZmJjAaDSSnJxMeXk5J06cwOPxcOLECZ577jn8fj8bN24kNTUVl8vF2NgYNpuN3Nxc8vLyuPvuu6UnhRACl8vFmTNnOHDgAAcPHpx3YBoMBpmcnGT//v0YDAY++clPkpKSwpo1a9i4cSNRUVGYzWbZ+U9LSyMxMVGacH6QMBgMlJSU8LGPfYxPfOITUmgsLS0lOjr6orPR/f39vPjii+zevZvFixdzxx13sGnTJvLy8njqqafYtGkT+/fv5ze/+Q2jo6Pvy77ofg95eXk0NTXJWePwGfFly5Zht9vlNQfICki6MJKcnEx+fj4rVqwgJSVFphTpkTYQukYmJiaoqanh4Ycf5jOf+Qxer5dDhw7x3HPPXSDy6PdCZmYmjz/+OLGxsfj9fiorK+nr65sjDPt8Prq7u4mNjSUpKQmXyyWjiL74xS9SVlbGzp07ZUqOXu44Pz8fq9VKbGws27ZtIz09nV/84hccO3aMQCBAcnIyy5cvZ9OmTRQUFNDd3U16ejp33nmnrPh15MgRWltbZfSUbtgLzBnkLrRnkS54nTlzhsbGRgApVunXdG1tLf/yL//CZz/7WbKysli3bh0DAwO0tbXx7rvv0tvby9/93d9hMBjYs2cPr7/+OqdPnwaQUTR6+mAgEMDr9eLxeEhPT+e+++5j7dq1FBYWUlRURGFhIQ8++CAdHR3U1NRw+vRpOjo6GBoakj4n+oA/8vjerGOrp0LpEWh6tM3o6CjT09MyvQiQ5ev/+7//W6anLl26lKioKN566y2OHj0qozh08SbcmLy3t5fOzk4SExNZu3at9MDJzc1l+fLluN1uTpw4wdtvv8358+elGK9Pcujfd/prgUCA0dFRJicn0TSNjIwMHnjgAe6++26ampo4e/YsAwMDOJ1OaSq9ceNGDAYDr732mowiCRdsLnUd6204fPgww8PD9PX1UVZWRkxMjIwIGh4epqWlhfr6enp6eqQHXVFREUuXLmXx4sXY7XYZXed2uzl9+jQNDQ00NTXJVO+HHnqIoqIi0tPTpcn12bNnMZvNrFmzhq1bt9LT00N/f79My/zf//t/8+Uvf5mcnBweeeQR7rrrLs6fP09PT4+M7NWfHxkZGaSmprJ7925effVVnE4n/f39DAwM8Nhjj1FVVcVDDz3Eu+++y5kzZ25oqq9CoVAoFIoPFtck2uhVbKKiomSY9MzMDDt37qS+vp5169axbds2aer7/e9/n2effZa9e/fK8GDdKDg1NZXR0VFZ9lPvCO7evZvVq1dTWFjIxo0bmZ6elt45GRkZ7Nixg87OTtxu9wVRA3oeucPhID8/n+joaFwuFwMDA/ziF7+YN2xcCEFLSwu7d+/GbDazatUqOcg9ePAgCQkJVFVVIYSgs7PzWo/3gsBgMJCTk0NpaSmnTp2irq6OwcFBNE1j8+bNOBwO/u3f/o3R0VF27tzJG2+8wZkzZ+jp6aGvr0+aAn/iE5/g0UcfJSsri6amJl544QUOHjzI8ePH8fv9jI+Ps2PHDuLi4ti3bx8nTpwgNzeXxMRE2THXBxFHjhyR5qLhKSaRAyZ99nT37t00NDSwePFiWVY2KSkJi8VCU1MTb7zxBo2NjQwNDV1RR9dgMJCRkcHQ0NAtD0U3GAwsXbqUT3/606xYsYKenh727NnDww8/TFVVFcXFxYyMjOB0Oi8wndVn4fXBxMGDB3nxxRdZtWoVd9xxB/n5+aSmppKdnc13vvOdCyLVbjS6qJGdnY3FYsFut3PbbbdRXl5Odna2NMJOS0uT0V7x8fGsWrWK5uZmDAYDeXl55ObmYrPZ8Pl8jI2NcerUKdauXcuaNWtkWt3Q0JAUb5qbm7FYLBgMBsbGxujq6qK3t1cuNxgMsmLM0qVLefTRR6moqGB4eJj4+HgefvhhNm7cKNMo9QiccCFwamqKXbt2UV1dTXJyMvfddx+bN2+W0Q6AfOYEAgHWrFlDeXk5a9eupbKyko6ODgKBAKmpqRgMBkZGRnjjjTf49a9/TWFhITt27KCgoIDNmzezYcMGmf7hdDqpra2Vfho6C02w0dEH+fOlhAWDQcbHx3n55ZfZt28f2dnZaJomhQC/3y+fxxs2bKCiooLz589z5swZWdUvnHBvFLfbzQ9+8AOef/55cnJyWLRoEUuWLJG+MeXl5TJFp7e3l8bGRoaHh5menqa5uZnOzk75/aYL/TdDpA9Pc4s0/I3cnu6LcvToUZ555hkeffRRSkpKWLVqFcuWLaOjo4Pz589LoUI/JiaTCZfLRWlpKZWVlaSmpmK1WsnMzGTjxo14PB727t3Lb3/7W5mSp3+P6pMTRqNRRtjoIpDu9VZQUMDU1BSNjY1MT0+zbNkyysvLKSkpkddoIBBgbGyMY8eO8YMf/ED67oRH0YSXt54P3cMpGAxSX19PS0uLjK7RPZB0wU1Pf9Q9Z86cOcO5c+fkfujRR3rqkv5Zg8FAV1cXP/rRj+RzApgjVpWUlJCQkEBzc/OcMvVdXV1861vfYt26dVRWVpKXl0dVVRVlZWVywigQCDAwMCAjVbu7u2U7dWPlxsZGPv7xj1NUVCTT5T6IE0QKhUKhUChuDOJqOgFCCM1sNlNYWMjWrVtZt24dmqbR2NjIM888w/T0NCaTCZvNRlZWFqtXr+aJJ57A4XAwMjLCK6+8Ql1dHcnJyXzrW9/CbDbzta99jbfeemtOqW+9yshf/dVfsXLlSgwGA0NDQzidTtauXUtiYiITExOcOnWKd955h927d8tqUAaDgYqKCv7kT/6EqqoqRkZG+N3vfsebb74pvS4iUy4g5I8RGxtLRkYGS5YsYcOGDaxcuVIaHOqpUyMjI9TV1fHOO+/Q1NTEyMiI7OCGh5DDwh1ERUVFUV5eTnl5OadPn6arqwu/309mZiY7duxgyZIlHD16lP7+furr62U6TXgkVEJCAo899hiPPPIIixYtkgaznZ2dnD9/nvr6evx+P3fddRcbN27E5XLx85//nP3799PX1yeP1/T0NB6PR5ajDhcfLoVe7UMv9Wu1WomKipI+O2NjY3g8nguqlkViNBpxOBx87nOfY/369Xz729/m5MmTt8wHRwhBdXU1n/vc52RJ2F27duH1evn3f/937HY7zz//PG+99RaaprFixQqMRiMvvvii9F8JH/jpXhU2m43KykruvfdeVq5cyejoKF/96ldpbW296ddpXFwc9957L//wD/+ApmlSZNUH7HV1dZw6dYotW7ZQXFyMxWLB7XYzPDxMa2ur9NZob2+XHhEJCQl89atfpbi4mNbWVg4fPiyrTKWmprJ8+XJ8Ph+5ubnExsZKP5MDBw5QX1+PzWYjOTmZsrIyCgsLMRqN7Nq1i66uLrZu3SqrRenll/1+P6dPn6a9vZ3Ozk76+vqkX1dSUhJVVVXSaNjv90tD3ebmZkZHRzEYDGRmZrJo0SKWLl1KSUkJk5OTjI+Py6iSlpYWent7pelpamoqZWVl0pvHarUyOjpKTU0NNTU1sqrNB5nwctRRUVHSUFe/jnVxYP369fzlX/4lCQkJ7N+/n5dffpkjR45Ib6LwgX/4/3oKUVRUFNHR0cTGxmK328nIyKC4uFiW1M7OzsZut8uoHb3ktn6NHjlyhG9+85vSE+ZmHAOTycTtt9/Ol770JVatWsXPfvYz/vVf/1XeL/p9qgsOetqfntJUUVFBQkKCLJWuHwv9XnO5XHKCZGJiAqvVSkFBASUlJWiaxtTUFN3d3fT29jIwMMDg4CADAwMyPSs6OlpO1lgsFhwOhzSBt9ls1NbWcvDgQcbHx0lOTiY9PZ3k5GTsdjsej4fBwUH6+vro7OyUJr16pSV938JFyHDxJlyAChe5wj1uIqtPhQt4+vsiU6/018KFmcg0sPBlevv0/kB4FFS4/5jVasVmsxEXF0dMTAwzMzPyuy4QCDAzM4PX65UebnpklL49/ZltsVhkOuB8lb4UCoVCoVB86KjTNG1l5ItXFWmjd2KqqqrYuHEjq1atIhgMUlBQQFNTE7W1tTICQC9VPDU1xfbt2yksLGT16tUyZam3t5f8/Hy2bdvG4OAgZ8+eZXJyUhqO6rOvAGVlZSQkJMg8+Pj4eNLT0ykrKyM2NpaysjJOnjxJT08Px44do6+vjxMnTsjSx1u2bKGgoICzZ8/KUtD6LKEQgri4ONm5tNlsJCYmkpKSQiAQoL+/H03TiIuLIyEhgYSEBKqrq8nOzmZ8fFyGNA8PD0sPDL3Dr5fzbGtro6ura8GIOIFAgK6uLoaGhqRpJYRSa1599VXpIeR2u5mcnJSzoeEda7fbzZ49exgcHGTRokVUV1fLGcWsrCwqKytxuVzExcXJ43fnnXfS1tZGa2srg4ODcyrfRP5cDl3w8fv9uFwuGb4fPtt6JeuyWCzk5eXx0EMPER8fj9lsvv4DfI0YDAZSU1N55JFHSEpK4tChQ+zbt4/z588THR3NiRMn2Lx5M5s2bSI3N1dGTBmNRhYvXsy5c+fo7++X16PT6ZQDBIvFgt/vl6aYJpMJi8XyvuxXuAeFyWTC7XbT09Mj0yd0P6KOjg5ycnKIjY0lGAwyPDxMf38/gUBA7o9+LY6OjvLb3/6Wxx9/nNTUVDZu3IjT6ZQRN++88w6tra3k5OSwcuVKioqKZKWntWvXEh0dLa/vY8eOcf78eRobG3G73YyNjbFnzx4SEhKIiYmZUy1ndHSU8fFxmdqnaRrDw8MMDQ0RExMj0/NcLhcul2uO+bPut1JfX09SUhLT09MyLWZ8fFymx+gz7l1dXTidThobG7HZbERFReHxeOjr6/vQpEroA2EhhPSW0V8PH2ifPXuW9vZ2iouLSUpKIicnhyNHjsz7zAg/LrrJrj5IdjqdDA0N0dfXR1NTE7GxsdKUNykpibS0NBISEmR1LpfLRX9/P6dPn75pxztcWNEje3QPnvDnWPh+6ilyHR0djI+P09TUxP79+8nOziYuLk4+A3VhxOv14nK5mJiYwOfzyQkW/VhmZGSQlZWFw+Fg0aJFFBUV4fP58Hq9+Hy+Cyo46n5CXq+Xo0ePyojJ/v5+fD4ffX19NDc3z7kn9KhYPbUvXGAOF93Cj0u4IBNZiVJvj/47Mp0tfHlkBI++TV2QifxseLpW5DUZGdmob0dfn35fu1wuTCaT9LYKFyIj16H/HX4v6L5Nl5t4UCgUCoVC8eHnmtKjPB4PY2NjTE5OkpqaSkFBAQ8//DD5+fk0NjbS2dnJ0NAQw8PD7N27l8TERNLT08nMzCQ9PZ3jx49TV1dHTk4OlZWVbN++ncWLF9Pb20tLSwtHjx7F7/dz6tQpYmJi6OzslDnoExMTsjpUcnKyTFsymUykpKRw5swZJiYmOHr0qKzCopdBzs3NlT4BumgDyIoT+myX2+2moaGB8fFxurq6mJmZkbOzmZmZOBwOUlNTyc/PJyoqSnaG9dKouphgNBppbm7mnXfeoaur64aeuOshGAzO6+0xNTVFS0sLLS0tF4gp4Wiahs/no7GxkZ6eHs6ePUtzczNtbW2ykk5GRob0B9H9f8rLy1m8eLEstxo+w6qv90o7p+HRTOEd68jll8NisZCTk0N2drachb5Vho96dajs7GyOHz/Ovn37qK+vZ3JyEp/Px1tvvUVmZqb0AdJLpycnJ7NlyxbKysoYGRmR957uI6Gfg7i4ODIzMxkeHqa+vv59M7SdmZmhvb2dF154Yc5AuK2tjc7OTil2joyM0NDQIKMtdBEmfFADSGGupqZGmlgbjUa8Xi9jY2O0trZy7NgxBgcHaWpqor+/n9LSUhwOBzExMWiahsfjobu7m/b2dlpbW6VBK8C5c+dobGyUkQW6QKinPUVG0wUCAaampi4YFEaW6dVF7P7+fnnu5hsA6kxPTzM8PMzIyIhcT/hnPixEpsdEvq6nuB0/fpzo6GhpkK7/hL8/cr2R/+tigZ6+qWmaTPfVzbLj4uKw2+2YTCampqYYGRm5IBXtZhwDXdgYGRkhEAjI3/OJ2roQqEdrDA0N0dzcLK9xQEaxREZ56PusR/c0NDSQlpYmzbITExOx2+3ExcVJfx29wp/X68Xv9+Pz+RgaGpKVI0dGRuakc+lprDDXUDiyPH3k8/9KjpPOxbxvrua18Pv4cu8L34/I915sXYFAYE4EKXBBVM9867rYvX6p1DGFQqFQKBQfXq5atNE0jbq6OjRNY3x8nJUrV5KVlcX69euprq6moaGBkydPcvbsWVk1QZ8t18vUTkxM8O6777Js2TLS0tJYv349lZWV0qiytrZWms7u27ePuro6TCaTrMBgsVhobGykpKSErKwsLBaLrESkD1LPnTuH1+tldHSU6upqMjMzZSqM3nHX6e/vZ3BwEKfTyfDwMAMDAzJEXA9Nj46OxuFwkJ2dTWFhIQUFBaSlpUlxIjY2lsTERJlOZTabZRh6TEzMgjMJnW9AczWDQU0L+QU4nU5cLhdtbW3s3buX3NxcqqurWblypRwo6+HiLpdLGjpebsB1PftyNVgsFjIyMmR51rGxMTl4vxUYjUZOnz7Nq6++SkdHB1NTU2hayKfmzTffJD4+HpfLJSvqFBcXs2zZMjIzM4mJiSEvL4+ioqI5lVb0QZYepVFXV8fOnTvnrdB1M9DL/P7whz+cY2Yaec0FAoE56ScXG0jpn+nu7ub5558nMzNTeszoVV70AaE+oD106JBMpdOjqfQyyuEz/jqRbYlsUziXu2fm24/wlIpLXb8L6ZlxMwkXuOY73zMzM9TU1MgohLa2tgsG/VezrfBzEgwG8fv9MiVPT3EJj4B5P57fwWCQoaEhjh8/Lk2750sZDT9W+o8eodPb2zvHNFh/HyDTb/SIkHAxcWBggPr6eiwWC3FxcdLkOC4uDqfTKUUbl8vF1NQUbreb0dFR+Z0b3hZdrNC3pR/n8P0MT2UKb2M44a9d6h6bbx2Rwke4ybG+fD5hNfz94fsTnsYXvk/hxz9y8iA8detSExL6svC0r/BzHd5uhUKhUCgUH02uytPGYDBoetSE0WjEarWSkZHBPffcwyc+8QmysrLkgMjv98uZuLi4ONLT02lpaeHll1/ml7/8JdPT06xYsYInn3wSi8XCkSNHOHjwIE1NTYyNjcmOUHjHR0f3KdCjYwA5oxhuYqtXnYmNjSUlJUXmievvCwQCeDweent75ewtXHwGLLyjq4c96zOz8fHxMqzeYDCQkJBAfX29LG07nwnnB5X5OuPh3kD6cSkoKGD58uXcfvvtpKens3fvXnbv3s2ZM2fmGATfKkFLCEFeXh5f/OIXefTRR3nrrbf49re/TW9v7y2LZDAYDMTFxc1bklu/puH34fsmk4mYmBhSU1MpLi4mPz+fpKQkEhISiI+PB5CRLa2trZw/f15WaPowRGtEDozCf4e/R389fLA4XzqG4tYS+SwI9zDRvT706IX5BsKR18LlXr+VXGxfzWYziYmJjI2NyeiVq0GfOIi8zvXonPDtRXrChBMudujL9ddMptB8jy5shgsY4ca84cLHfKJT5Pau9nhdy+fCPxvez4hc13xCSaSAM9/+hYtIkRFkF3teRZ4XnchzHznZoVAoFAqF4kPHvJ42V21ErJfX1X90U0S9HHZeXh6VlZXSmNPr9TI4OMju3bt5/fXXOXnypEzLCO/8RVZ9mN3enN/zzXDpRAoAetvCZxb1NBGdq40uucgxuaCDGpkL/1EgstOpH3t9EBIVFSVnZeeLbLgV6CW0v/KVr7Bu3Tr+9m//lrfffluWr13oRN4X4b4+4fcncIEBt+r4KxYClxoszzfQvpLIhSvd7kK8x8P370raOF+qTbiori/T162LFJGfizTcDf8ODQQCcypH6ejRYpHPH10cihT0w5/5JpPpgu+AS+3rpYSXazmP13v+w4WuSKEqsn9yMXEm8pqP7D+ETyKFC2/q2a1QKBQKxYea6zci1omcNdO0UOnm3/zmN0RFRWGz2aTni8/no76+nrGxMaampuTgUZ+lCjf5my8sPvz3fG242GydLpqEGyiGCyk3qsMevr2FOAh4v4jc9/DOpZ5uFB5GvhAwm82kpKRQWlqKpoXS/nSj2w8Cke28lPfBB2WfFB8tLvZsv9h7b/Rz+0ZzvWLAlYoYOleamqf/HRn5Eb5M/04OX6Y/r/XKjOGEizqRkx/zCcTzCT5XytVcJ+Htu5bPzpdmFfmZyKih+fou8312vj6LzpWkgCkUCoVCofhock2iTTjhQkgwGMTj8TA1NcXY2BgdHR3SpFP3kZmvM3et+dqXm2m7VOiy4sq4ltnMix3rhTa7raftORwOmpubGRsb+9BERy2k46xQfFS4mWLQla7/Us9fmFsa+2Kfjfw//Nk9XxRJ5LP9cmlG87Vrvu1fKfNFYV3ruq5UDJrv/ZERUpcSaSLXsdC+HxUKhUKhUCwcrrrk93wdi/BZJyEEfr9flqwMX36pDsnN6mBFdh5Vp+jquJHHa6Ede120MRqN1NTUfKCibBQKxUeHGymAhwsakQLDpbY53/b1VKv5okQiJ0wutp7LvTbf569U6IkUm673+X6x9l6v6BKZCg4L7/tSoVAoFArFreOaIm0u1jmJnHHSNO2Ss3rzfe5quBrBRqGIRNM03G43ra2t7N+//wLfF4VCofiwc6konnBBIpLI7/XrMQa+koiUKxFFLidu3QjhJlyguZFii5pUUigUCoVCcTGuSrS5ks7Plfhs3KqOieoQKXSEEAwODnLgwAEAjh079qFJjVro3OrngELxQedaJ0HCKyXp752vctHlRJXw7/Vw37jwz15NKlfk5y4nJIX/fan058j+x6VSqS+2vfnaeDERaz6uJoLpYm251hRyhUKhUCgUHw6uqXqUQvFhQs1wKhQKxZVxK6JKIqtqzbfta3mOvx/P/hu5DVXyW6FQKBSKDz03pHrU8MzMTMcNapBCoVAoFAqFQqFQKBQKhQLy5nvxqiJtFAqFQqFQKBQKhUKhUCgU7w+GW90AhUKhUCgUCoVCoVAoFArFhSjRRqFQKBQKhUKhUCgUCoViAaJEG4VCoVAoFAqFQqFQKBSKBYgSbRQKhUKhUCgUCoVCoVAoFiBKtFEoFAqFQqFQKBQKhUKhWIAo0UahUCgUCoVCoVAoFAqFYgGiRBuFQqFQKBQKhUKhUCgUigWIEm0UCoVCoVAoFAqFQqFQKBYgSrRRKBQKhUKhUCgUCoVCoViA/P+Gqnt7BmFwCAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1440x1440 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 1440x1440 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i in range(10, 20):\n",
" plt.figure(figsize=(20, 20))\n",
+ " plt.xticks([])\n",
+ " plt.yticks([])\n",
" data, target = dataset[i]\n",
- " sentence = convert_y_label_to_string(target) \n",
+ " sentence = convert_y_label_to_string(target, dataset) \n",
" print(sentence)\n",
" plt.title(sentence)\n",
" plt.imshow(data.squeeze(0).numpy(), cmap='gray')"
@@ -251,26 +263,46 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
- "data, target = dataset[0]\n",
+ "data, target = dataset[10]\n",
"sentence = convert_y_label_to_string(target) "
]
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 74,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([97])"
+ ]
+ },
+ "execution_count": 74,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "target.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
- "h, w, s = 28, 18, 4"
+ "h, w, s = 28, 6, 6"
]
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
@@ -280,7 +312,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
@@ -289,21 +321,21 @@
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 78,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "A MOVE to stop Mr. Gaitskell from\n"
+ "Griffiths resolution. Mr. Foot's line will_______________________________________________________\n"
]
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
- "<Figure size 1440x1440 with 60 Axes>"
+ "<Figure size 1440x1440 with 50 Axes>"
]
},
"metadata": {},
@@ -312,7 +344,7 @@
],
"source": [
"# remove batch size\n",
- "n = 60\n",
+ "n = 50\n",
"patches = patches.squeeze(0)\n",
"fig = plt.figure(figsize=(20, 20))\n",
"print(sentence)\n",
@@ -325,16 +357,16 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "torch.Size([234, 1, 28, 18])"
+ "torch.Size([158, 1, 28, 6])"
]
},
- "execution_count": 44,
+ "execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
@@ -345,22 +377,1196 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 120,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x7f8797c56cd0>"
+ ]
+ },
+ "execution_count": 120,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 1440x1440 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(20, 20))\n",
+ "plt.imshow(data.squeeze(0).numpy(), cmap='gray')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from text_recognizer.datasets.transforms import Compose, AddTokens"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "target_transform = Compose([torch.tensor, AddTokens(init_token=\"<sos>\", eos_token=\"<eos>\")])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "IAM Lines Dataset\n",
+ "Number classes: 82\n",
+ "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F', 16: 'G', 17: 'H', 18: 'I', 19: 'J', 20: 'K', 21: 'L', 22: 'M', 23: 'N', 24: 'O', 25: 'P', 26: 'Q', 27: 'R', 28: 'S', 29: 'T', 30: 'U', 31: 'V', 32: 'W', 33: 'X', 34: 'Y', 35: 'Z', 36: 'a', 37: 'b', 38: 'c', 39: 'd', 40: 'e', 41: 'f', 42: 'g', 43: 'h', 44: 'i', 45: 'j', 46: 'k', 47: 'l', 48: 'm', 49: 'n', 50: 'o', 51: 'p', 52: 'q', 53: 'r', 54: 's', 55: 't', 56: 'u', 57: 'v', 58: 'w', 59: 'x', 60: 'y', 61: 'z', 62: ' ', 63: '!', 64: '\"', 65: '#', 66: '&', 67: \"'\", 68: '(', 69: ')', 70: '*', 71: '+', 72: ',', 73: '-', 74: '.', 75: '/', 76: ':', 77: ';', 78: '?', 79: '_', 80: '<sos>', 81: '<eos>'}\n",
+ "Data: (7101, 28, 952)\n",
+ "Targets: (7101, 97)\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "dataset = IamLinesDataset(train=True, init_token=\"<sos>\", pad_token=\"_\", eos_token=\"<eos>\", target_transform=target_transform)\n",
+ "dataset.load_or_generate_data()\n",
+ "print(dataset)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data, target = dataset[0]\n",
+ "sentence = convert_y_label_to_string(target, dataset) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "([], [])"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 1440x1440 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(20, 20))\n",
+ "plt.title(sentence)\n",
+ "plt.imshow(data.squeeze(0).numpy(), cmap='gray')\n",
+ "plt.xticks([])\n",
+ "plt.yticks([])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 176,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from text_recognizer.networks import VisionTransformer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 191,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tt = Transformer(3, 3, 512, 8, 2048, 0.1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 193,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vt = VisionTransformer(6, 6, 256, 82, 8, 118, 512, 256, 0.1, 79, (28, 16), (1, 8), \"gelu\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 169,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from torchsummary import summary"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 194,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "VisionTransformer(\n",
+ " (slidning_window): Sequential(\n",
+ " (0): Unfold(kernel_size=(28, 16), dilation=1, padding=0, stride=(1, 8))\n",
+ " (1): Rearrange('b (c h w) t -> b t (c h w)', h=28, w=16, c=1)\n",
+ " )\n",
+ " (character_embedding): Embedding(82, 256)\n",
+ " (position_encoding): PositionalEncoding(\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (linear_projection): Linear(in_features=448, out_features=256, bias=True)\n",
+ " (transformer): Transformer(\n",
+ " (encoder): Encoder(\n",
+ " (layers): ModuleList(\n",
+ " (0): EncoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (1): EncoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (2): EncoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (3): EncoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (4): EncoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (5): EncoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (decoder): Decoder(\n",
+ " (layers): ModuleList(\n",
+ " (0): DecoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (multihead_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block3): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (1): DecoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (multihead_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block3): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (2): DecoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (multihead_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block3): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (3): DecoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (multihead_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block3): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (4): DecoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (multihead_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block3): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (5): DecoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (multihead_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block3): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (head): Sequential(\n",
+ " (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (1): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (2): GELU()\n",
+ " (3): Dropout(p=0.1, inplace=False)\n",
+ " (4): Linear(in_features=256, out_features=82, bias=True)\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "execution_count": 194,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "vt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 214,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "==========================================================================================\n",
+ "Layer (type:depth-idx) Output Shape Param #\n",
+ "==========================================================================================\n",
+ "├─Sequential: 1-1 [-1, 118, 448] --\n",
+ "| └─Unfold: 2-1 [-1, 448, 118] --\n",
+ "| └─Rearrange: 2-2 [-1, 118, 448] --\n",
+ "├─Linear: 1-2 [-1, 118, 256] 114,944\n",
+ "├─PositionalEncoding: 1-3 [-1, 118, 256] --\n",
+ "| └─Dropout: 2-3 [-1, 118, 256] --\n",
+ "├─Embedding: 1-4 [-1, 97, 256] 20,992\n",
+ "├─PositionalEncoding: 1-5 [-1, 97, 256] --\n",
+ "| └─Dropout: 2-4 [-1, 97, 256] --\n",
+ "├─Transformer: 1-6 [-1, 97, 256] --\n",
+ "| └─Encoder: 2-5 [-1, 118, 256] --\n",
+ "| └─Decoder: 2-6 [-1, 97, 256] --\n",
+ "├─Sequential: 1-7 [-1, 97, 82] --\n",
+ "| └─LayerNorm: 2-7 [-1, 97, 256] 512\n",
+ "| └─Linear: 2-8 [-1, 97, 256] 65,792\n",
+ "| └─GELU: 2-9 [-1, 97, 256] --\n",
+ "| └─Dropout: 2-10 [-1, 97, 256] --\n",
+ "| └─Linear: 2-11 [-1, 97, 82] 21,074\n",
+ "==========================================================================================\n",
+ "Total params: 223,314\n",
+ "Trainable params: 223,314\n",
+ "Non-trainable params: 0\n",
+ "Total mult-adds (M): 23.93\n",
+ "==========================================================================================\n",
+ "Input size (MB): 0.10\n",
+ "Forward/backward pass size (MB): 0.86\n",
+ "Params size (MB): 0.85\n",
+ "Estimated Total Size (MB): 1.81\n",
+ "==========================================================================================\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "==========================================================================================\n",
+ "Layer (type:depth-idx) Output Shape Param #\n",
+ "==========================================================================================\n",
+ "├─Sequential: 1-1 [-1, 118, 448] --\n",
+ "| └─Unfold: 2-1 [-1, 448, 118] --\n",
+ "| └─Rearrange: 2-2 [-1, 118, 448] --\n",
+ "├─Linear: 1-2 [-1, 118, 256] 114,944\n",
+ "├─PositionalEncoding: 1-3 [-1, 118, 256] --\n",
+ "| └─Dropout: 2-3 [-1, 118, 256] --\n",
+ "├─Embedding: 1-4 [-1, 97, 256] 20,992\n",
+ "├─PositionalEncoding: 1-5 [-1, 97, 256] --\n",
+ "| └─Dropout: 2-4 [-1, 97, 256] --\n",
+ "├─Transformer: 1-6 [-1, 97, 256] --\n",
+ "| └─Encoder: 2-5 [-1, 118, 256] --\n",
+ "| └─Decoder: 2-6 [-1, 97, 256] --\n",
+ "├─Sequential: 1-7 [-1, 97, 82] --\n",
+ "| └─LayerNorm: 2-7 [-1, 97, 256] 512\n",
+ "| └─Linear: 2-8 [-1, 97, 256] 65,792\n",
+ "| └─GELU: 2-9 [-1, 97, 256] --\n",
+ "| └─Dropout: 2-10 [-1, 97, 256] --\n",
+ "| └─Linear: 2-11 [-1, 97, 82] 21,074\n",
+ "==========================================================================================\n",
+ "Total params: 223,314\n",
+ "Trainable params: 223,314\n",
+ "Non-trainable params: 0\n",
+ "Total mult-adds (M): 23.93\n",
+ "==========================================================================================\n",
+ "Input size (MB): 0.10\n",
+ "Forward/backward pass size (MB): 0.86\n",
+ "Params size (MB): 0.85\n",
+ "Estimated Total Size (MB): 1.81\n",
+ "=========================================================================================="
+ ]
+ },
+ "execution_count": 214,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "summary(vt, [(1, 28, 952), (97,)], device=\"cpu\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 195,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = vt.preprocess_input(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 196,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([1, 118, 256])"
+ ]
+ },
+ "execution_count": 196,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 197,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = vt.encoder(x)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 201,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "trg = torch.tensor([10, 62, 22, 24, 31, 14, 62, 55, 50, 62, 54, 55, 50, 51, 62, 22, 53, 74,\n",
+ " 62, 16, 36, 44, 55, 54, 46, 40, 47, 47, 62, 41, 53, 50, 48, 79, 79, 79,\n",
+ " 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,\n",
+ " 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,\n",
+ " 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79,\n",
+ " 79, 79, 79, 79, 79, 79, 79])[None, :]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 204,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "t, tm = vt.preprocess_target(trg)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 209,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "VisionTransformer(\n",
+ " (slidning_window): Sequential(\n",
+ " (0): Unfold(kernel_size=(28, 16), dilation=1, padding=0, stride=(1, 8))\n",
+ " (1): Rearrange('b (c h w) t -> b t (c h w)', h=28, w=16, c=1)\n",
+ " )\n",
+ " (character_embedding): Embedding(82, 256)\n",
+ " (position_encoding): PositionalEncoding(\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (linear_projection): Linear(in_features=448, out_features=256, bias=True)\n",
+ " (transformer): Transformer(\n",
+ " (encoder): Encoder(\n",
+ " (layers): ModuleList(\n",
+ " (0): EncoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (1): EncoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (2): EncoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (3): EncoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (4): EncoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (5): EncoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (decoder): Decoder(\n",
+ " (layers): ModuleList(\n",
+ " (0): DecoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (multihead_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block3): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (1): DecoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (multihead_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block3): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (2): DecoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (multihead_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block3): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (3): DecoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (multihead_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block3): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (4): DecoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (multihead_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block3): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (5): DecoderLayer(\n",
+ " (self_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (multihead_attention): MultiHeadAttention(\n",
+ " (fc_q): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_k): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_v): Linear(in_features=256, out_features=256, bias=False)\n",
+ " (fc_out): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (cnn): _ConvolutionalLayer(\n",
+ " (layer): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): GELU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=256, bias=True)\n",
+ " )\n",
+ " )\n",
+ " (block1): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block2): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (block3): _IntraLayerConnection(\n",
+ " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (head): Sequential(\n",
+ " (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+ " (1): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (2): GELU()\n",
+ " (3): Dropout(p=0.1, inplace=False)\n",
+ " (4): Linear(in_features=256, out_features=82, bias=True)\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "execution_count": 209,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "vt.eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "t"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 211,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[[-0.4344, 0.4939, 0.0382, ..., -0.0808, -0.0290, 0.4399],\n",
+ " [-0.4350, 0.5146, 0.0356, ..., -0.0634, -0.0289, 0.4271],\n",
+ " [-0.4303, 0.5245, 0.0435, ..., -0.0755, -0.0267, 0.4240],\n",
+ " ...,\n",
+ " [-0.4477, 0.5377, 0.0596, ..., -0.0866, -0.0283, 0.4457],\n",
+ " [-0.4475, 0.5435, 0.0606, ..., -0.0900, -0.0293, 0.4440],\n",
+ " [-0.4488, 0.5476, 0.0689, ..., -0.0914, -0.0276, 0.4411]]],\n",
+ " grad_fn=<AddBackward0>)"
+ ]
+ },
+ "execution_count": 211,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "vt.decoder(t, x, tm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 213,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "24.0"
+ "tensor([[[-0.4179, 0.4755, 0.0407, ..., -0.0609, -0.0870, 0.4562],\n",
+ " [-0.4262, 0.4845, 0.0361, ..., -0.0497, -0.0847, 0.4459],\n",
+ " [-0.4237, 0.4900, 0.0409, ..., -0.0573, -0.0812, 0.4434],\n",
+ " ...,\n",
+ " [-0.4477, 0.5053, 0.0394, ..., -0.0489, -0.0815, 0.4589],\n",
+ " [-0.4469, 0.5069, 0.0407, ..., -0.0500, -0.0808, 0.4573],\n",
+ " [-0.4464, 0.5079, 0.0416, ..., -0.0510, -0.0801, 0.4570]]],\n",
+ " grad_fn=<AddBackward0>)"
]
},
- "execution_count": 13,
+ "execution_count": 213,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "32 * 0.75"
+ "vt(data, trg)"
]
},
{
@@ -387,7 +1593,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.2"
+ "version": "3.7.4"
}
},
"nbformat": 4,
diff --git a/src/notebooks/04b-look-at-iam-paragraphs.ipynb b/src/notebooks/04b-look-at-iam-paragraphs.ipynb
index a442420..9c5ade4 100644
--- a/src/notebooks/04b-look-at-iam-paragraphs.ipynb
+++ b/src/notebooks/04b-look-at-iam-paragraphs.ipynb
@@ -245,7 +245,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.2"
+ "version": "3.7.4"
}
},
"nbformat": 4,
diff --git a/src/notebooks/05-sanity-check-multihead-attention.ipynb b/src/notebooks/05-sanity-check-multihead-attention.ipynb
new file mode 100644
index 0000000..54f0432
--- /dev/null
+++ b/src/notebooks/05-sanity-check-multihead-attention.ipynb
@@ -0,0 +1,169 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "import cv2\n",
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch import nn\n",
+ "from importlib.util import find_spec\n",
+ "if find_spec(\"text_recognizer\") is None:\n",
+ " import sys\n",
+ " sys.path.append('..')\n",
+ "\n",
+ "from text_recognizer.networks.transformer.attention import MultiHeadAttention"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "temp_mha = MultiHeadAttention(hidden_dim=512, num_heads=8)\n",
+ "def print_out(Q, K, V):\n",
+ " temp_out, temp_attn = temp_mha.scaled_dot_product_attention(Q, K, V)\n",
+ " print('Attention weights are:', temp_attn.squeeze())\n",
+ " print('Output is:', temp_out.squeeze())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test_K = torch.tensor(\n",
+ " [[10, 0, 0],\n",
+ " [ 0,10, 0],\n",
+ " [ 0, 0,10],\n",
+ " [ 0, 0,10]]\n",
+ ").float()[None,None]\n",
+ "\n",
+ "test_V = torch.tensor(\n",
+ " [[ 1,0,0],\n",
+ " [ 10,0,0],\n",
+ " [ 100,5,0],\n",
+ " [1000,6,0]]\n",
+ ").float()[None,None]\n",
+ "\n",
+ "test_Q = torch.tensor(\n",
+ " [[0, 10, 0]]\n",
+ ").float()[None,None]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Attention weights are: tensor([8.4333e-26, 1.0000e+00, 8.4333e-26, 8.4333e-26])\n",
+ "Output is: tensor([1.0000e+01, 9.2766e-25, 0.0000e+00])\n"
+ ]
+ }
+ ],
+ "source": [
+ "print_out(test_Q, test_K, test_V)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Attends to the second element, as it should!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Attention weights are: tensor([4.2166e-26, 4.2166e-26, 5.0000e-01, 5.0000e-01])\n",
+ "Output is: tensor([550.0000, 5.5000, 0.0000])\n"
+ ]
+ }
+ ],
+ "source": [
+ "test_Q = torch.tensor([[0, 0, 10]]).float()[None,None]\n",
+ "print_out(test_Q, test_K, test_V)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Focuses equally on the third and fourth key."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Attention weights are: tensor([[4.2166e-26, 4.2166e-26, 5.0000e-01, 5.0000e-01],\n",
+ " [8.4333e-26, 1.0000e+00, 8.4333e-26, 8.4333e-26],\n",
+ " [5.0000e-01, 5.0000e-01, 4.2166e-26, 4.2166e-26]])\n",
+ "Output is: tensor([[5.5000e+02, 5.5000e+00, 0.0000e+00],\n",
+ " [1.0000e+01, 9.2766e-25, 0.0000e+00],\n",
+ " [5.5000e+00, 4.6383e-25, 0.0000e+00]])\n"
+ ]
+ }
+ ],
+ "source": [
+ "test_Q = torch.tensor(\n",
+ " [[0, 0, 10], [0, 10, 0], [10, 10, 0]]\n",
+ ").float()[None,None]\n",
+ "print_out(test_Q, test_K, test_V)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/notebooks/Untitled.ipynb b/src/notebooks/Untitled.ipynb
new file mode 100644
index 0000000..76c4d28
--- /dev/null
+++ b/src/notebooks/Untitled.ipynb
@@ -0,0 +1,310 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ }
+ ],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "import importlib\n",
+ "import cv2\n",
+ "import yaml\n",
+ "\n",
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "from torch import nn\n",
+ "from importlib.util import find_spec\n",
+ "if find_spec(\"text_recognizer\") is None:\n",
+ " import sys\n",
+ " sys.path.append('..')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def convert_y_label_to_string(y, dataset=dataset):\n",
+ " return ''.join([dataset.mapper(int(i)) for i in y])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from text_recognizer.models import VisionTransformerModel\n",
+ "from text_recognizer.datasets import IamLinesDataset\n",
+ "from text_recognizer.datasets.transforms import Compose, AddTokens"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "target_transform = Compose([torch.tensor, AddTokens(init_token=\"<sos>\", eos_token=\"<eos>\")])\n",
+ "dataset = IamLinesDataset(train=True, init_token=\"<sos>\", pad_token=\"_\", eos_token=\"<eos>\", target_transform=target_transform)\n",
+ "dataset.load_or_generate_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "config_path = \"../training/experiments/VisionTransformerModel_IamLinesDataset_VisionTransformer/1021_083538/config.yml\"\n",
+ "with open(config_path, \"r\") as f:\n",
+ " experiment_config = yaml.safe_load(f)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dataset_args = experiment_config.get(\"dataset\", {})\n",
+ "datasets_module = importlib.import_module(\"text_recognizer.datasets\")\n",
+ "dataset_ = getattr(datasets_module, dataset_args[\"type\"])\n",
+ "\n",
+ "network_module = importlib.import_module(\"text_recognizer.networks\")\n",
+ "network_fn_ = getattr(network_module, experiment_config[\"network\"][\"type\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2020-10-21 23:27:40.719 | DEBUG | text_recognizer.models.base:load_weights:454 - Loading network with pretrained weights.\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = VisionTransformerModel(network_fn=network_fn_, dataset=dataset_, dataset_args=dataset_args)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2020-10-21 23:29:55.892 | DEBUG | text_recognizer.models.base:load_from_checkpoint:402 - Loading checkpoint...\n"
+ ]
+ }
+ ],
+ "source": [
+ "checkpoint_path = \"../training/experiments/VisionTransformerModel_IamLinesDataset_VisionTransformer/1021_083538/model/last.pt\"\n",
+ "model.load_from_checkpoint(checkpoint_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data, target = dataset[18]\n",
+ "sentence = convert_y_label_to_string(target, dataset) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "([], [])"
+ ]
+ },
+ "execution_count": 91,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 1440x1440 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(20, 20))\n",
+ "plt.title(sentence)\n",
+ "plt.imshow(data.squeeze(0).numpy(), cmap='gray')\n",
+ "plt.xticks([])\n",
+ "plt.yticks([])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "('Since 1958, 13 Labour life Peers and<eos>', 0.9999997615814209)"
+ ]
+ },
+ "execution_count": 92,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.predict_on_image(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[[1, 28, 952], [92]]"
+ ]
+ },
+ "execution_count": 95,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "experiment_config[\"train_args\"][\"input_shape\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 99,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "=========================================================================================================\n",
+ "Layer (type:depth-idx) Output Shape Param #\n",
+ "=========================================================================================================\n",
+ "├─Sequential: 1-1 [-1, 158, 1, 28, 6] --\n",
+ "| └─Unfold: 2-1 [-1, 168, 158] --\n",
+ "| └─Rearrange: 2-2 [-1, 158, 1, 28, 6] --\n",
+ "├─Linear: 1-2 [-1, 158, 512] 86,528\n",
+ "├─PositionalEncoding: 1-3 [-1, 158, 512] --\n",
+ "| └─Dropout: 2-3 [-1, 158, 512] --\n",
+ "├─Embedding: 1-4 [-1, 92, 512] 41,984\n",
+ "├─PositionalEncoding: 1-5 [-1, 92, 512] --\n",
+ "| └─Dropout: 2-4 [-1, 92, 512] --\n",
+ "├─Transformer: 1-6 [-1, 92, 512] --\n",
+ "| └─Encoder: 2-5 [-1, 158, 512] --\n",
+ "| | └─ModuleList: 3 [] --\n",
+ "| | | └─EncoderLayer: 4-1 [-1, 158, 512] 3,150,848\n",
+ "| | | └─EncoderLayer: 4-2 [-1, 158, 512] 3,150,848\n",
+ "| | | └─EncoderLayer: 4-3 [-1, 158, 512] 3,150,848\n",
+ "| | | └─EncoderLayer: 4-4 [-1, 158, 512] 3,150,848\n",
+ "| | └─LayerNorm: 3-1 [-1, 158, 512] 1,024\n",
+ "| └─Decoder: 2-6 [-1, 92, 512] --\n",
+ "| | └─ModuleList: 3 [] --\n",
+ "| | | └─DecoderLayer: 4-5 [-1, 92, 512] 4,200,960\n",
+ "| | | └─DecoderLayer: 4-6 [-1, 92, 512] 4,200,960\n",
+ "| | | └─DecoderLayer: 4-7 [-1, 92, 512] 4,200,960\n",
+ "| | | └─DecoderLayer: 4-8 [-1, 92, 512] 4,200,960\n",
+ "| | └─LayerNorm: 3-2 [-1, 92, 512] 1,024\n",
+ "├─Sequential: 1-7 [-1, 92, 82] --\n",
+ "| └─LayerNorm: 2-7 [-1, 92, 512] 1,024\n",
+ "| └─Linear: 2-8 [-1, 92, 512] 262,656\n",
+ "| └─GELU: 2-9 [-1, 92, 512] --\n",
+ "| └─Dropout: 2-10 [-1, 92, 512] --\n",
+ "| └─Linear: 2-11 [-1, 92, 82] 42,066\n",
+ "=========================================================================================================\n",
+ "Total params: 29,843,538\n",
+ "Trainable params: 29,843,538\n",
+ "Non-trainable params: 0\n",
+ "Total mult-adds (M): 118.22\n",
+ "=========================================================================================================\n",
+ "Input size (MB): 0.10\n",
+ "Forward/backward pass size (MB): 2.73\n",
+ "Params size (MB): 113.84\n",
+ "Estimated Total Size (MB): 116.68\n",
+ "=========================================================================================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "model.summary(experiment_config[\"train_args\"][\"input_shape\"], 4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/tasks/train.sh b/src/tasks/train.sh
new file mode 100755
index 0000000..1fbc8d7
--- /dev/null
+++ b/src/tasks/train.sh
@@ -0,0 +1,67 @@
+#!/bin/bash
+
+# Add checkpoint and resume experiment
+usage() {
+ cat << EOF
+ usage: ./tasks/train_crnn_line_ctc_model.sh
+ -f | --experiment_config Name of the experiment config.
+ -c | --checkpoint (Optional) The experiment name to continue from.
+ -p | --pretrained_weights (Optional) Path to pretrained weights.
+ -n | --notrain (Optional) Evaluates a trained model.
+ -t | --test (Optional) If set, evaluates the model on test set.
+ -v | --verbose (Optional) Sets the verbosity.
+ -h | --help Shows this message.
+EOF
+exit 1
+}
+
+experiment_config=""
+checkpoint=""
+pretrained_weights=""
+notrain=""
+test=""
+verbose=""
+train_command=""
+
+while getopts 'f:c:p:nthv' flag; do
+ case "${flag}" in
+ f) experiment_config="${OPTARG}" ;;
+ c) checkpoint="${OPTARG}" ;;
+ p) pretrained_weights="${OPTARG}" ;;
+ n) notrain="--notrain" ;;
+ t) test="--test" ;;
+ v) verbose="${verbose}v" ;;
+ h) usage ;;
+ *) error "Unexpected option ${flag}" ;;
+ esac
+done
+
+
+if [ -z ${experiment_config} ];
+then
+ echo "experiment_config not specified!"
+ usage
+ exit 1
+fi
+
+experiments_filename="training/experiments/${experiment_config}"
+train_command=$(./tasks/prepare_experiments.sh $experiments_filename)
+
+if [ ${checkpoint} ];
+then
+ train_command="${train_command} --checkpoint $checkpoint"
+fi
+
+if [ ${pretrained_weights} ];
+then
+ train_command="${train_command} --pretrained_weights $pretrained_weights"
+fi
+
+if [ ${verbose} ];
+then
+ train_command="${train_command} -$verbose"
+fi
+
+
+echo $train_command $notrain $test
+eval $train_command $notrain $test
diff --git a/src/tasks/train_crnn_line_ctc_model.sh b/src/tasks/train_crnn_line_ctc_model.sh
deleted file mode 100755
index 020c4a6..0000000
--- a/src/tasks/train_crnn_line_ctc_model.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/bin/bash
-experiments_filename=${1:-training/experiments/line_ctc_experiment.yml}
-OUTPUT=$(./tasks/prepare_experiments.sh $experiments_filename)
-echo $OUTPUT
-eval $OUTPUT
diff --git a/src/tasks/train_embedding_model.sh b/src/tasks/train_embedding_model.sh
deleted file mode 100755
index da59116..0000000
--- a/src/tasks/train_embedding_model.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/bin/bash
-experiments_filename=${1:-training/experiments/embedding_experiment.yml}
-OUTPUT=$(./tasks/prepare_experiments.sh $experiments_filename)
-echo $OUTPUT
-eval $OUTPUT
diff --git a/src/text_recognizer/datasets/__init__.py b/src/text_recognizer/datasets/__init__.py
index a3af9b1..d8372e3 100644
--- a/src/text_recognizer/datasets/__init__.py
+++ b/src/text_recognizer/datasets/__init__.py
@@ -1,5 +1,5 @@
"""Dataset modules."""
-from .emnist_dataset import EmnistDataset, Transpose
+from .emnist_dataset import EmnistDataset
from .emnist_lines_dataset import (
construct_image_from_string,
EmnistLinesDataset,
@@ -8,6 +8,7 @@ from .emnist_lines_dataset import (
from .iam_dataset import IamDataset
from .iam_lines_dataset import IamLinesDataset
from .iam_paragraphs_dataset import IamParagraphsDataset
+from .transforms import AddTokens, Transpose
from .util import (
_download_raw_dataset,
compute_sha256,
@@ -19,6 +20,7 @@ from .util import (
__all__ = [
"_download_raw_dataset",
+ "AddTokens",
"compute_sha256",
"construct_image_from_string",
"DATA_DIRNAME",
diff --git a/src/text_recognizer/datasets/dataset.py b/src/text_recognizer/datasets/dataset.py
index 05520e5..2de7f09 100644
--- a/src/text_recognizer/datasets/dataset.py
+++ b/src/text_recognizer/datasets/dataset.py
@@ -18,6 +18,9 @@ class Dataset(data.Dataset):
subsample_fraction: float = None,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
+ init_token: Optional[str] = None,
+ pad_token: Optional[str] = None,
+ eos_token: Optional[str] = None,
) -> None:
"""Initialization of Dataset class.
@@ -26,12 +29,14 @@ class Dataset(data.Dataset):
subsample_fraction (float): The fraction of the dataset to use for training. Defaults to None.
transform (Optional[Callable]): Transform(s) for input data. Defaults to None.
target_transform (Optional[Callable]): Transform(s) for output data. Defaults to None.
+ init_token (Optional[str]): String representing the start of sequence token. Defaults to None.
+ pad_token (Optional[str]): String representing the pad token. Defaults to None.
+ eos_token (Optional[str]): String representing the end of sequence token. Defaults to None.
Raises:
ValueError: If subsample_fraction is not None and outside the range (0, 1).
"""
-
self.train = train
self.split = "train" if self.train else "test"
@@ -40,19 +45,18 @@ class Dataset(data.Dataset):
raise ValueError("The subsample fraction must be in (0, 1).")
self.subsample_fraction = subsample_fraction
- self._mapper = EmnistMapper()
+ self._mapper = EmnistMapper(
+ init_token=init_token, eos_token=eos_token, pad_token=pad_token
+ )
self._input_shape = self._mapper.input_shape
self._output_shape = self._mapper._num_classes
self.num_classes = self.mapper.num_classes
# Set transforms.
- self.transform = transform
- if self.transform is None:
- self.transform = ToTensor()
-
- self.target_transform = target_transform
- if self.target_transform is None:
- self.target_transform = torch.tensor
+ self.transform = transform if transform is not None else ToTensor()
+ self.target_transform = (
+ target_transform if target_transform is not None else torch.tensor
+ )
self._data = None
self._targets = None
diff --git a/src/text_recognizer/datasets/emnist_dataset.py b/src/text_recognizer/datasets/emnist_dataset.py
index d01dcee..a8901d6 100644
--- a/src/text_recognizer/datasets/emnist_dataset.py
+++ b/src/text_recognizer/datasets/emnist_dataset.py
@@ -53,9 +53,6 @@ class EmnistDataset(Dataset):
if transform is None:
self.transform = Compose([Transpose(), ToTensor()])
- # The EMNIST dataset is already casted to tensors.
- self.target_transform = target_transform
-
self.seed = seed
def __getitem__(self, index: Union[int, Tensor]) -> Tuple[Tensor, Tensor]:
diff --git a/src/text_recognizer/datasets/emnist_lines_dataset.py b/src/text_recognizer/datasets/emnist_lines_dataset.py
index beb5343..6091da8 100644
--- a/src/text_recognizer/datasets/emnist_lines_dataset.py
+++ b/src/text_recognizer/datasets/emnist_lines_dataset.py
@@ -37,6 +37,9 @@ class EmnistLinesDataset(Dataset):
max_overlap: float = 0.33,
num_samples: int = 10000,
seed: int = 4711,
+ init_token: Optional[str] = None,
+ pad_token: Optional[str] = None,
+ eos_token: Optional[str] = None,
) -> None:
"""Set attributes and loads the dataset.
@@ -50,6 +53,9 @@ class EmnistLinesDataset(Dataset):
max_overlap (float): The maximum overlap between concatenated images. Defaults to 0.33.
num_samples (int): Number of samples to generate. Defaults to 10000.
seed (int): Seed number. Defaults to 4711.
+ init_token (Optional[str]): String representing the start of sequence token. Defaults to None.
+ pad_token (Optional[str]): String representing the pad token. Defaults to None.
+ eos_token (Optional[str]): String representing the end of sequence token. Defaults to None.
"""
super().__init__(
@@ -57,6 +63,9 @@ class EmnistLinesDataset(Dataset):
transform=transform,
target_transform=target_transform,
subsample_fraction=subsample_fraction,
+ init_token=init_token,
+ pad_token=pad_token,
+ eos_token=eos_token,
)
# Extract dataset information.
diff --git a/src/text_recognizer/datasets/iam_lines_dataset.py b/src/text_recognizer/datasets/iam_lines_dataset.py
index 4a74b2b..fdd2fe6 100644
--- a/src/text_recognizer/datasets/iam_lines_dataset.py
+++ b/src/text_recognizer/datasets/iam_lines_dataset.py
@@ -32,12 +32,18 @@ class IamLinesDataset(Dataset):
subsample_fraction: float = None,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
+ init_token: Optional[str] = None,
+ pad_token: Optional[str] = None,
+ eos_token: Optional[str] = None,
) -> None:
super().__init__(
train=train,
subsample_fraction=subsample_fraction,
transform=transform,
target_transform=target_transform,
+ init_token=init_token,
+ pad_token=pad_token,
+ eos_token=eos_token,
)
@property
diff --git a/src/text_recognizer/datasets/transforms.py b/src/text_recognizer/datasets/transforms.py
index 17231a8..c058972 100644
--- a/src/text_recognizer/datasets/transforms.py
+++ b/src/text_recognizer/datasets/transforms.py
@@ -3,6 +3,9 @@ import numpy as np
from PIL import Image
import torch
from torch import Tensor
+from torchvision.transforms import Compose, ToTensor
+
+from text_recognizer.datasets.util import EmnistMapper
class Transpose:
@@ -11,3 +14,33 @@ class Transpose:
def __call__(self, image: Image) -> np.ndarray:
"""Swaps axis."""
return np.array(image).swapaxes(0, 1)
+
+
+class AddTokens:
+ """Adds start of sequence and end of sequence tokens to target tensor."""
+
+ def __init__(self, init_token: str, pad_token: str, eos_token: str,) -> None:
+ self.init_token = init_token
+ self.pad_token = pad_token
+ self.eos_token = eos_token
+ self.emnist_mapper = EmnistMapper(
+ init_token=self.init_token,
+ pad_token=self.pad_token,
+ eos_token=self.eos_token,
+ )
+ self.pad_value = self.emnist_mapper(self.pad_token)
+ self.sos_value = self.emnist_mapper(self.init_token)
+ self.eos_value = self.emnist_mapper(self.eos_token)
+
+ def __call__(self, target: Tensor) -> Tensor:
+ """Adds a sos token to the begining and a eos token to the end of a target sequence."""
+ dtype, device = target.dtype, target.device
+ sos = torch.tensor([self.sos_value], dtype=dtype, device=device)
+
+ # Find the where padding starts.
+ pad_index = torch.nonzero(target == self.pad_value, as_tuple=False)[0].item()
+
+ target[pad_index] = self.eos_value
+
+ target = torch.cat([sos, target], dim=0)
+ return target
diff --git a/src/text_recognizer/datasets/util.py b/src/text_recognizer/datasets/util.py
index 125f05a..d2df8b5 100644
--- a/src/text_recognizer/datasets/util.py
+++ b/src/text_recognizer/datasets/util.py
@@ -4,6 +4,7 @@ import importlib
import json
import os
from pathlib import Path
+import string
from typing import Callable, Dict, List, Optional, Type, Union
from urllib.request import urlopen, urlretrieve
@@ -43,11 +44,21 @@ def download_emnist() -> None:
class EmnistMapper:
"""Mapper between network output to Emnist character."""
- def __init__(self) -> None:
+ def __init__(
+ self,
+ pad_token: str,
+ init_token: Optional[str] = None,
+ eos_token: Optional[str] = None,
+ ) -> None:
"""Loads the emnist essentials file with the mapping and input shape."""
+ self.init_token = init_token
+ self.pad_token = pad_token
+ self.eos_token = eos_token
+
self.essentials = self._load_emnist_essentials()
# Load dataset infromation.
- self._mapping = self._augment_emnist_mapping(dict(self.essentials["mapping"]))
+ self._mapping = dict(self.essentials["mapping"])
+ self._augment_emnist_mapping()
self._inverse_mapping = {v: k for k, v in self.mapping.items()}
self._num_classes = len(self.mapping)
self._input_shape = self.essentials["input_shape"]
@@ -103,7 +114,7 @@ class EmnistMapper:
essentials = json.load(f)
return essentials
- def _augment_emnist_mapping(self, mapping: Dict) -> Dict:
+ def _augment_emnist_mapping(self) -> None:
"""Augment the mapping with extra symbols."""
# Extra symbols in IAM dataset
extra_symbols = [
@@ -127,14 +138,20 @@ class EmnistMapper:
]
# padding symbol, and acts as blank symbol as well.
- extra_symbols.append("_")
+ extra_symbols.append(self.pad_token)
+
+ if self.init_token is not None:
+ extra_symbols.append(self.init_token)
+
+ if self.eos_token is not None:
+ extra_symbols.append(self.eos_token)
- max_key = max(mapping.keys())
+ max_key = max(self.mapping.keys())
extra_mapping = {}
for i, symbol in enumerate(extra_symbols):
extra_mapping[max_key + 1 + i] = symbol
- return {**mapping, **extra_mapping}
+ self._mapping = {**self.mapping, **extra_mapping}
def compute_sha256(filename: Union[Path, str]) -> str:
diff --git a/src/text_recognizer/models/__init__.py b/src/text_recognizer/models/__init__.py
index a3cfc15..0855079 100644
--- a/src/text_recognizer/models/__init__.py
+++ b/src/text_recognizer/models/__init__.py
@@ -3,5 +3,14 @@ from .base import Model
from .character_model import CharacterModel
from .line_ctc_model import LineCTCModel
from .metrics import accuracy, cer, wer
+from .vision_transformer_model import VisionTransformerModel
-__all__ = ["Model", "cer", "CharacterModel", "LineCTCModel", "accuracy", "wer"]
+__all__ = [
+ "Model",
+ "cer",
+ "CharacterModel",
+ "CNNTransfromerModel",
+ "LineCTCModel",
+ "accuracy",
+ "wer",
+]
diff --git a/src/text_recognizer/models/base.py b/src/text_recognizer/models/base.py
index e89b670..cbef787 100644
--- a/src/text_recognizer/models/base.py
+++ b/src/text_recognizer/models/base.py
@@ -6,7 +6,7 @@ import importlib
from pathlib import Path
import re
import shutil
-from typing import Callable, Dict, Optional, Tuple, Type
+from typing import Callable, Dict, List, Optional, Tuple, Type, Union
from loguru import logger
import torch
@@ -15,6 +15,7 @@ from torch import Tensor
from torch.optim.swa_utils import AveragedModel, SWALR
from torch.utils.data import DataLoader, Dataset, random_split
from torchsummary import summary
+from torchvision.transforms import Compose
from text_recognizer.datasets import EmnistMapper
@@ -128,16 +129,41 @@ class Model(ABC):
self._configure_criterion()
self._configure_optimizers()
- # Prints a summary of the network in terminal.
- self.summary()
-
# Set this flag to true to prevent the model from configuring again.
self.is_configured = True
+ def _configure_transforms(self) -> None:
+ # Load transforms.
+ transforms_module = importlib.import_module(
+ "text_recognizer.datasets.transforms"
+ )
+ if (
+ "transform" in self.dataset_args["args"]
+ and self.dataset_args["args"]["transform"] is not None
+ ):
+ transform_ = [
+ getattr(transforms_module, t["type"])()
+ for t in self.dataset_args["args"]["transform"]
+ ]
+ self.dataset_args["args"]["transform"] = Compose(transform_)
+ if (
+ "target_transform" in self.dataset_args["args"]
+ and self.dataset_args["args"]["target_transform"] is not None
+ ):
+ target_transform_ = [
+ torch.tensor,
+ ]
+ for t in self.dataset_args["args"]["target_transform"]:
+ args = t["args"] or {}
+ target_transform_.append(getattr(transforms_module, t["type"])(**args))
+ self.dataset_args["args"]["target_transform"] = Compose(target_transform_)
+
def prepare_data(self) -> None:
"""Prepare data for training."""
# TODO add downloading.
if not self.data_prepared:
+ self._configure_transforms()
+
# Load train dataset.
train_dataset = self.dataset(train=True, **self.dataset_args["args"])
train_dataset.load_or_generate_data()
@@ -327,20 +353,20 @@ class Model(ABC):
else:
return self.network(x)
- def loss_fn(self, output: Tensor, targets: Tensor) -> Tensor:
- """Compute the loss."""
- return self.criterion(output, targets)
-
def summary(
- self, input_shape: Optional[Tuple[int, int, int]] = None, depth: int = 3
+ self,
+ input_shape: Optional[Union[List, Tuple]] = None,
+ depth: int = 4,
+ device: Optional[str] = None,
) -> None:
"""Prints a summary of the network architecture."""
+ device = self.device if device is None else device
if input_shape is not None:
- summary(self.network, input_shape, depth=depth, device=self.device)
+ summary(self.network, input_shape, depth=depth, device=device)
elif self._input_shape is not None:
input_shape = (1,) + tuple(self._input_shape)
- summary(self.network, input_shape, depth=depth, device=self.device)
+ summary(self.network, input_shape, depth=depth, device=device)
else:
logger.warning("Could not print summary as input shape is not set.")
@@ -364,18 +390,21 @@ class Model(ABC):
return state
- def load_from_checkpoint(self, checkpoint_path: Path) -> None:
+ def load_from_checkpoint(self, checkpoint_path: Union[str, Path]) -> None:
"""Load a previously saved checkpoint.
Args:
checkpoint_path (Path): Path to the experiment with the checkpoint.
"""
+ checkpoint_path = Path(checkpoint_path)
+ self.prepare_data()
+ self.configure_model()
logger.debug("Loading checkpoint...")
if not checkpoint_path.exists():
logger.debug("File does not exist {str(checkpoint_path)}")
- checkpoint = torch.load(str(checkpoint_path))
+ checkpoint = torch.load(str(checkpoint_path), map_location=self.device)
self._network.load_state_dict(checkpoint["model_state"])
if self._optimizer is not None:
diff --git a/src/text_recognizer/models/character_model.py b/src/text_recognizer/models/character_model.py
index 50e94a2..3cf6695 100644
--- a/src/text_recognizer/models/character_model.py
+++ b/src/text_recognizer/models/character_model.py
@@ -65,6 +65,7 @@ class CharacterModel(Model):
Tuple[str, float]: The predicted character and the confidence in the prediction.
"""
+ self.eval()
if image.dtype == np.uint8:
# Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1].
diff --git a/src/text_recognizer/models/line_ctc_model.py b/src/text_recognizer/models/line_ctc_model.py
index 16eaed3..cdc2d8b 100644
--- a/src/text_recognizer/models/line_ctc_model.py
+++ b/src/text_recognizer/models/line_ctc_model.py
@@ -51,7 +51,7 @@ class LineCTCModel(Model):
self._mapper = EmnistMapper()
self.tensor_transform = ToTensor()
- def loss_fn(self, output: Tensor, targets: Tensor) -> Tensor:
+ def criterion(self, output: Tensor, targets: Tensor) -> Tensor:
"""Computes the CTC loss.
Args:
@@ -82,11 +82,13 @@ class LineCTCModel(Model):
torch.Tensor(target_lengths).type(dtype=torch.long).to(self.device)
)
- return self.criterion(output, targets, input_lengths, target_lengths)
+ return self._criterion(output, targets, input_lengths, target_lengths)
@torch.no_grad()
def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tuple[str, float]:
"""Predict on a single input."""
+ self.eval()
+
if image.dtype == np.uint8:
# Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1].
image = self.tensor_transform(image)
@@ -110,6 +112,6 @@ class LineCTCModel(Model):
log_probs, _ = log_probs.max(dim=2)
predicted_characters = "".join(raw_pred[0])
- confidence_of_prediction = torch.exp(log_probs.sum()).item()
+ confidence_of_prediction = torch.exp(-log_probs.sum()).item()
return predicted_characters, confidence_of_prediction
diff --git a/src/text_recognizer/models/vision_transformer_model.py b/src/text_recognizer/models/vision_transformer_model.py
new file mode 100644
index 0000000..20bd4ca
--- /dev/null
+++ b/src/text_recognizer/models/vision_transformer_model.py
@@ -0,0 +1,117 @@
+"""Defines the CNN-Transformer class."""
+from typing import Callable, Dict, List, Optional, Tuple, Type, Union
+
+import numpy as np
+import torch
+from torch import nn
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchvision.transforms import ToTensor
+
+from text_recognizer.datasets import EmnistMapper
+from text_recognizer.models.base import Model
+from text_recognizer.networks import greedy_decoder
+
+
+class VisionTransformerModel(Model):
+ """Model for predicting a sequence of characters from an image of a text line with a cnn-transformer."""
+
+ def __init__(
+ self,
+ network_fn: Type[nn.Module],
+ dataset: Type[Dataset],
+ network_args: Optional[Dict] = None,
+ dataset_args: Optional[Dict] = None,
+ metrics: Optional[Dict] = None,
+ criterion: Optional[Callable] = None,
+ criterion_args: Optional[Dict] = None,
+ optimizer: Optional[Callable] = None,
+ optimizer_args: Optional[Dict] = None,
+ lr_scheduler: Optional[Callable] = None,
+ lr_scheduler_args: Optional[Dict] = None,
+ swa_args: Optional[Dict] = None,
+ device: Optional[str] = None,
+ ) -> None:
+ super().__init__(
+ network_fn,
+ dataset,
+ network_args,
+ dataset_args,
+ metrics,
+ criterion,
+ criterion_args,
+ optimizer,
+ optimizer_args,
+ lr_scheduler,
+ lr_scheduler_args,
+ swa_args,
+ device,
+ )
+ self.init_token = dataset_args["args"]["init_token"]
+ self.pad_token = dataset_args["args"]["pad_token"]
+ self.eos_token = dataset_args["args"]["eos_token"]
+ if network_args is not None:
+ self.max_len = network_args["max_len"]
+ else:
+ self.max_len = 128
+
+ if self._mapper is None:
+ self._mapper = EmnistMapper(
+ init_token=self.init_token,
+ pad_token=self.pad_token,
+ eos_token=self.eos_token,
+ )
+ self.tensor_transform = ToTensor()
+
+ self.softmax = nn.Softmax(dim=2)
+
+ @torch.no_grad()
+ def _generate_sentence(self, image: Tensor) -> Tuple[List, float]:
+ src = self.network.preprocess_input(image)
+ memory = self.network.encoder(src)
+
+ confidence_of_predictions = []
+ trg_indices = [self.mapper(self.init_token)]
+
+ for _ in range(self.max_len):
+ trg = torch.tensor(trg_indices, device=self.device)[None, :].long()
+ trg, trg_mask = self.network.preprocess_target(trg)
+ logits = self.network.decoder(trg=trg, memory=memory, trg_mask=trg_mask)
+
+ # Convert logits to probabilities.
+ probs = self.softmax(logits)
+
+ pred_token = probs.argmax(2)[:, -1].item()
+ confidence = probs.max(2).values[:, -1].item()
+
+ trg_indices.append(pred_token)
+ confidence_of_predictions.append(confidence)
+
+ if pred_token == self.mapper(self.eos_token):
+ break
+
+ confidence = np.min(confidence_of_predictions)
+ predicted_characters = "".join([self.mapper(x) for x in trg_indices[1:]])
+
+ return predicted_characters, confidence
+
+ @torch.no_grad()
+ def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tuple[str, float]:
+ """Predict on a single input."""
+ self.eval()
+
+ if image.dtype == np.uint8:
+ # Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1].
+ image = self.tensor_transform(image)
+
+ # Rescale image between 0 and 1.
+ if image.dtype == torch.uint8:
+ # If the image is an unscaled tensor.
+ image = image.type("torch.FloatTensor") / 255
+
+ # Put the image tensor on the device the model weights are on.
+ image = image.to(self.device)
+
+ predicted_characters, confidence_of_prediction = self._generate_sentence(image)
+
+ return predicted_characters, confidence_of_prediction
diff --git a/src/text_recognizer/networks/__init__.py b/src/text_recognizer/networks/__init__.py
index a39975f..8b87797 100644
--- a/src/text_recognizer/networks/__init__.py
+++ b/src/text_recognizer/networks/__init__.py
@@ -1,21 +1,31 @@
"""Network modules."""
+from .cnn_transformer import CNNTransformer
+from .crnn import ConvolutionalRecurrentNetwork
from .ctc import greedy_decoder
+from .densenet import DenseNet
from .lenet import LeNet
-from .line_lstm_ctc import LineRecurrentNetwork
-from .losses import EmbeddingLoss
-from .misc import sliding_window
+from .loss import EmbeddingLoss
from .mlp import MLP
from .residual_network import ResidualNetwork, ResidualNetworkEncoder
+from .sparse_mlp import SparseMLP
+from .transformer import Transformer
+from .util import sliding_window
+from .vision_transformer import VisionTransformer
from .wide_resnet import WideResidualNetwork
__all__ = [
+ "CNNTransformer",
+ "ConvolutionalRecurrentNetwork",
+ "DenseNet",
"EmbeddingLoss",
"greedy_decoder",
"MLP",
"LeNet",
- "LineRecurrentNetwork",
"ResidualNetwork",
"ResidualNetworkEncoder",
"sliding_window",
+ "Transformer",
+ "SparseMLP",
+ "VisionTransformer",
"WideResidualNetwork",
]
diff --git a/src/text_recognizer/networks/cnn_transformer.py b/src/text_recognizer/networks/cnn_transformer.py
new file mode 100644
index 0000000..8666f11
--- /dev/null
+++ b/src/text_recognizer/networks/cnn_transformer.py
@@ -0,0 +1,111 @@
+"""A DETR style transfomers but for text recognition."""
+from typing import Dict, Optional, Tuple, Type
+
+from einops.layers.torch import Rearrange
+from loguru import logger
+import torch
+from torch import nn
+from torch import Tensor
+
+from text_recognizer.networks.transformer import PositionalEncoding, Transformer
+from text_recognizer.networks.util import configure_backbone
+
+
+class CNNTransformer(nn.Module):
+ """CNN+Transfomer for image to sequence prediction, sort of based on the ideas from DETR."""
+
+ def __init__(
+ self,
+ num_encoder_layers: int,
+ num_decoder_layers: int,
+ hidden_dim: int,
+ vocab_size: int,
+ num_heads: int,
+ max_len: int,
+ expansion_dim: int,
+ dropout_rate: float,
+ trg_pad_index: int,
+ backbone: str,
+ backbone_args: Optional[Dict] = None,
+ activation: str = "gelu",
+ ) -> None:
+ super().__init__()
+ self.trg_pad_index = trg_pad_index
+ self.backbone_args = backbone_args
+ self.backbone = configure_backbone(backbone, backbone_args)
+ self.character_embedding = nn.Embedding(vocab_size, hidden_dim)
+ self.position_encoding = PositionalEncoding(hidden_dim, dropout_rate, max_len)
+ self.collapse_spatial_dim = nn.Sequential(
+ Rearrange("b t h w -> b t (h w)"), nn.AdaptiveAvgPool2d((None, hidden_dim))
+ )
+ self.transformer = Transformer(
+ num_encoder_layers,
+ num_decoder_layers,
+ hidden_dim,
+ num_heads,
+ expansion_dim,
+ dropout_rate,
+ activation,
+ )
+ self.head = nn.Linear(hidden_dim, vocab_size)
+
+ def _create_trg_mask(self, trg: Tensor) -> Tensor:
+ # Move this outside the transformer.
+ trg_pad_mask = (trg != self.trg_pad_index)[:, None, None]
+ trg_len = trg.shape[1]
+ trg_sub_mask = torch.tril(
+ torch.ones((trg_len, trg_len), device=trg.device)
+ ).bool()
+ trg_mask = trg_pad_mask & trg_sub_mask
+ return trg_mask
+
+ def encoder(self, src: Tensor) -> Tensor:
+ """Forward pass with the encoder of the transformer."""
+ return self.transformer.encoder(src)
+
+ def decoder(self, trg: Tensor, memory: Tensor, trg_mask: Tensor) -> Tensor:
+ """Forward pass with the decoder of the transformer + classification head."""
+ return self.head(
+ self.transformer.decoder(trg=trg, memory=memory, trg_mask=trg_mask)
+ )
+
+ def preprocess_input(self, src: Tensor) -> Tensor:
+ """Encodes src with a backbone network and a positional encoding.
+
+ Args:
+ src (Tensor): Input tensor.
+
+ Returns:
+ Tensor: A input src to the transformer.
+
+ """
+ # If batch dimenstion is missing, it needs to be added.
+ if len(src.shape) < 4:
+ src = src[(None,) * (4 - len(src.shape))]
+ src = self.backbone(src)
+ src = self.collapse_spatial_dim(src)
+ src = self.position_encoding(src)
+ return src
+
+ def preprocess_target(self, trg: Tensor) -> Tuple[Tensor, Tensor]:
+ """Encodes target tensor with embedding and postion.
+
+ Args:
+ trg (Tensor): Target tensor.
+
+ Returns:
+ Tuple[Tensor, Tensor]: Encoded target tensor and target mask.
+
+ """
+ trg_mask = self._create_trg_mask(trg)
+ trg = self.character_embedding(trg.long())
+ trg = self.position_encoding(trg)
+ return trg, trg_mask
+
+ def forward(self, x: Tensor, trg: Tensor) -> Tensor:
+ """Forward pass with CNN transfomer."""
+ src = self.preprocess_input(x)
+ trg, trg_mask = self.preprocess_target(trg)
+ out = self.transformer(src, trg, trg_mask=trg_mask)
+ logits = self.head(out)
+ return logits
diff --git a/src/text_recognizer/networks/line_lstm_ctc.py b/src/text_recognizer/networks/crnn.py
index 9009f94..3e605e2 100644
--- a/src/text_recognizer/networks/line_lstm_ctc.py
+++ b/src/text_recognizer/networks/crnn.py
@@ -10,15 +10,16 @@ import torch
from torch import nn
from torch import Tensor
+from text_recognizer.networks.util import configure_backbone
-class LineRecurrentNetwork(nn.Module):
+
+class ConvolutionalRecurrentNetwork(nn.Module):
"""Network that takes a image of a text line and predicts tokens that are in the image."""
def __init__(
self,
backbone: str,
backbone_args: Dict = None,
- flatten: bool = True,
input_size: int = 128,
hidden_size: int = 128,
bidirectional: bool = False,
@@ -26,6 +27,7 @@ class LineRecurrentNetwork(nn.Module):
num_classes: int = 80,
patch_size: Tuple[int, int] = (28, 28),
stride: Tuple[int, int] = (1, 14),
+ recurrent_cell: str = "lstm",
) -> None:
super().__init__()
self.backbone_args = backbone_args or {}
@@ -34,17 +36,19 @@ class LineRecurrentNetwork(nn.Module):
self.sliding_window = self._configure_sliding_window()
self.input_size = input_size
self.hidden_size = hidden_size
- self.backbone = self._configure_backbone(backbone)
+ self.backbone = configure_backbone(backbone, backbone_args)
self.bidirectional = bidirectional
- self.flatten = flatten
- if self.flatten:
- self.fc = nn.Linear(
- in_features=self.input_size, out_features=self.hidden_size
+ if recurrent_cell.upper() in ["LSTM", "GRU"]:
+ recurrent_cell = getattr(nn, recurrent_cell)
+ else:
+ logger.warning(
+ f"Option {recurrent_cell} not valid, defaulting to LSTM cell."
)
+ recurrent_cell = nn.LSTM
- self.rnn = nn.LSTM(
- input_size=self.hidden_size,
+ self.rnn = recurrent_cell(
+ input_size=self.input_size,
hidden_size=self.hidden_size,
bidirectional=bidirectional,
num_layers=num_layers,
@@ -57,32 +61,6 @@ class LineRecurrentNetwork(nn.Module):
nn.LogSoftmax(dim=2),
)
- def _configure_backbone(self, backbone: str) -> Type[nn.Module]:
- network_module = importlib.import_module("text_recognizer.networks")
- backbone_ = getattr(network_module, backbone)
-
- if "pretrained" in self.backbone_args:
- logger.info("Loading pretrained backbone.")
- checkpoint_file = Path(__file__).resolve().parents[
- 2
- ] / self.backbone_args.pop("pretrained")
-
- # Loading state directory.
- state_dict = torch.load(checkpoint_file)
- network_args = state_dict["network_args"]
- weights = state_dict["model_state"]
-
- # Initializes the network with trained weights.
- backbone = backbone_(**network_args)
- backbone.load_state_dict(weights)
- if "freeze" in self.backbone_args and self.backbone_args["freeze"] is True:
- for params in backbone.parameters():
- params.requires_grad = False
-
- return backbone
- else:
- return backbone_(**self.backbone_args)
-
def _configure_sliding_window(self) -> nn.Sequential:
return nn.Sequential(
nn.Unfold(kernel_size=self.patch_size, stride=self.stride),
@@ -96,8 +74,8 @@ class LineRecurrentNetwork(nn.Module):
def forward(self, x: Tensor) -> Tensor:
"""Converts images to sequence of patches, feeds them to a CNN, then predictions are made with an LSTM."""
- if len(x.shape) == 3:
- x = x.unsqueeze(0)
+ if len(x.shape) < 4:
+ x = x[(None,) * (4 - len(x.shape))]
x = self.sliding_window(x)
# Rearrange from a sequence of patches for feedforward network.
@@ -106,11 +84,7 @@ class LineRecurrentNetwork(nn.Module):
x = self.backbone(x)
# Avgerage pooling.
- x = (
- self.fc(reduce(x, "(b t) c h w -> t b c", "mean", b=b, t=t))
- if self.flatten
- else rearrange(x, "(b t) h -> t b h", b=b, t=t)
- )
+ x = reduce(x, "(b t) c h w -> t b c", "mean", b=b, t=t)
# Sequence predictions.
x, _ = self.rnn(x)
diff --git a/src/text_recognizer/networks/densenet.py b/src/text_recognizer/networks/densenet.py
new file mode 100644
index 0000000..d2aad60
--- /dev/null
+++ b/src/text_recognizer/networks/densenet.py
@@ -0,0 +1,225 @@
+"""Defines a Densely Connected Convolutional Networks in PyTorch.
+
+Sources:
+https://arxiv.org/abs/1608.06993
+https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py
+
+"""
+from typing import List, Optional, Union
+
+from einops.layers.torch import Rearrange
+import torch
+from torch import nn
+from torch import Tensor
+
+from text_recognizer.networks.util import activation_function
+
+
+class _DenseLayer(nn.Module):
+ """A dense layer with pre-batch norm -> activation function -> Conv-layer x 2."""
+
+ def __init__(
+ self,
+ in_channels: int,
+ growth_rate: int,
+ bn_size: int,
+ dropout_rate: float,
+ activation: str = "relu",
+ ) -> None:
+ super().__init__()
+ activation_fn = activation_function(activation)
+ self.dense_layer = [
+ nn.BatchNorm2d(in_channels),
+ activation_fn,
+ nn.Conv2d(
+ in_channels=in_channels,
+ out_channels=bn_size * growth_rate,
+ kernel_size=1,
+ stride=1,
+ bias=False,
+ ),
+ nn.BatchNorm2d(bn_size * growth_rate),
+ activation_fn,
+ nn.Conv2d(
+ in_channels=bn_size * growth_rate,
+ out_channels=growth_rate,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ bias=False,
+ ),
+ ]
+ if dropout_rate:
+ self.dense_layer.append(nn.Dropout(p=dropout_rate))
+
+ self.dense_layer = nn.Sequential(*self.dense_layer)
+
+ def forward(self, x: Union[Tensor, List[Tensor]]) -> Tensor:
+ if isinstance(x, list):
+ x = torch.cat(x, 1)
+ return self.dense_layer(x)
+
+
+class _DenseBlock(nn.Module):
+ def __init__(
+ self,
+ num_layers: int,
+ in_channels: int,
+ bn_size: int,
+ growth_rate: int,
+ dropout_rate: float,
+ activation: str = "relu",
+ ) -> None:
+ super().__init__()
+ self.dense_block = self._build_dense_blocks(
+ num_layers, in_channels, bn_size, growth_rate, dropout_rate, activation
+ )
+
+ def _build_dense_blocks(
+ self,
+ num_layers: int,
+ in_channels: int,
+ bn_size: int,
+ growth_rate: int,
+ dropout_rate: float,
+ activation: str = "relu",
+ ) -> nn.ModuleList:
+ dense_block = []
+ for i in range(num_layers):
+ dense_block.append(
+ _DenseLayer(
+ in_channels=in_channels + i * growth_rate,
+ growth_rate=growth_rate,
+ bn_size=bn_size,
+ dropout_rate=dropout_rate,
+ activation=activation,
+ )
+ )
+ return nn.ModuleList(dense_block)
+
+ def forward(self, x: Tensor) -> Tensor:
+ feature_maps = [x]
+ for layer in self.dense_block:
+ x = layer(feature_maps)
+ feature_maps.append(x)
+ return torch.cat(feature_maps, 1)
+
+
+class _Transition(nn.Module):
+ def __init__(
+ self, in_channels: int, out_channels: int, activation: str = "relu",
+ ) -> None:
+ super().__init__()
+ activation_fn = activation_function(activation)
+ self.transition = nn.Sequential(
+ nn.BatchNorm2d(in_channels),
+ activation_fn,
+ nn.Conv2d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ stride=1,
+ bias=False,
+ ),
+ nn.AvgPool2d(kernel_size=2, stride=2),
+ )
+
+ def forward(self, x: Tensor) -> Tensor:
+ return self.transition(x)
+
+
+class DenseNet(nn.Module):
+ """Implementation of Densenet, a network archtecture that concats previous layers for maximum infomation flow."""
+
+ def __init__(
+ self,
+ growth_rate: int = 32,
+ block_config: List[int] = (6, 12, 24, 16),
+ in_channels: int = 1,
+ base_channels: int = 64,
+ num_classes: int = 80,
+ bn_size: int = 4,
+ dropout_rate: float = 0,
+ classifier: bool = True,
+ activation: str = "relu",
+ ) -> None:
+ super().__init__()
+ self.densenet = self._configure_densenet(
+ in_channels,
+ base_channels,
+ num_classes,
+ growth_rate,
+ block_config,
+ bn_size,
+ dropout_rate,
+ classifier,
+ activation,
+ )
+
+ def _configure_densenet(
+ self,
+ in_channels: int,
+ base_channels: int,
+ num_classes: int,
+ growth_rate: int,
+ block_config: List[int],
+ bn_size: int,
+ dropout_rate: float,
+ classifier: bool,
+ activation: str,
+ ) -> nn.Sequential:
+ activation_fn = activation_function(activation)
+ densenet = [
+ nn.Conv2d(
+ in_channels=in_channels,
+ out_channels=base_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ bias=False,
+ ),
+ nn.BatchNorm2d(base_channels),
+ activation_fn,
+ ]
+
+ num_features = base_channels
+
+ for i, num_layers in enumerate(block_config):
+ densenet.append(
+ _DenseBlock(
+ num_layers=num_layers,
+ in_channels=num_features,
+ bn_size=bn_size,
+ growth_rate=growth_rate,
+ dropout_rate=dropout_rate,
+ activation=activation,
+ )
+ )
+ num_features = num_features + num_layers * growth_rate
+ if i != len(block_config) - 1:
+ densenet.append(
+ _Transition(
+ in_channels=num_features,
+ out_channels=num_features // 2,
+ activation=activation,
+ )
+ )
+ num_features = num_features // 2
+
+ densenet.append(activation_fn)
+
+ if classifier:
+ densenet.append(nn.AdaptiveAvgPool2d((1, 1)))
+ densenet.append(Rearrange("b c h w -> b (c h w)"))
+ densenet.append(
+ nn.Linear(in_features=num_features, out_features=num_classes)
+ )
+
+ return nn.Sequential(*densenet)
+
+ def forward(self, x: Tensor) -> Tensor:
+ """Forward pass of Densenet."""
+ # If batch dimenstion is missing, it needs to be added.
+ if len(x.shape) < 4:
+ x = x[(None,) * (4 - len(x.shape))]
+ return self.densenet(x)
diff --git a/src/text_recognizer/networks/lenet.py b/src/text_recognizer/networks/lenet.py
index 53c575e..527e1a0 100644
--- a/src/text_recognizer/networks/lenet.py
+++ b/src/text_recognizer/networks/lenet.py
@@ -5,7 +5,7 @@ from einops.layers.torch import Rearrange
import torch
from torch import nn
-from text_recognizer.networks.misc import activation_function
+from text_recognizer.networks.util import activation_function
class LeNet(nn.Module):
@@ -63,6 +63,6 @@ class LeNet(nn.Module):
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""The feedforward pass."""
# If batch dimenstion is missing, it needs to be added.
- if len(x.shape) == 3:
- x = x.unsqueeze(0)
+ if len(x.shape) < 4:
+ x = x[(None,) * (4 - len(x.shape))]
return self.layers(x)
diff --git a/src/text_recognizer/networks/losses.py b/src/text_recognizer/networks/loss.py
index 73e0641..ff843cf 100644
--- a/src/text_recognizer/networks/losses.py
+++ b/src/text_recognizer/networks/loss.py
@@ -4,6 +4,9 @@ from torch import nn
from torch import Tensor
+__all__ = ["EmbeddingLoss"]
+
+
class EmbeddingLoss:
"""Metric loss for training encoders to produce information-rich latent embeddings."""
diff --git a/src/text_recognizer/networks/mlp.py b/src/text_recognizer/networks/mlp.py
index d66af28..1101912 100644
--- a/src/text_recognizer/networks/mlp.py
+++ b/src/text_recognizer/networks/mlp.py
@@ -5,7 +5,7 @@ from einops.layers.torch import Rearrange
import torch
from torch import nn
-from text_recognizer.networks.misc import activation_function
+from text_recognizer.networks.util import activation_function
class MLP(nn.Module):
@@ -63,8 +63,8 @@ class MLP(nn.Module):
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""The feedforward pass."""
# If batch dimenstion is missing, it needs to be added.
- if len(x.shape) == 3:
- x = x.unsqueeze(0)
+ if len(x.shape) < 4:
+ x = x[(None,) * (4 - len(x.shape))]
return self.layers(x)
@property
diff --git a/src/text_recognizer/networks/residual_network.py b/src/text_recognizer/networks/residual_network.py
index 046600d..6405192 100644
--- a/src/text_recognizer/networks/residual_network.py
+++ b/src/text_recognizer/networks/residual_network.py
@@ -7,8 +7,8 @@ import torch
from torch import nn
from torch import Tensor
-from text_recognizer.networks.misc import activation_function
from text_recognizer.networks.stn import SpatialTransformerNetwork
+from text_recognizer.networks.util import activation_function
class Conv2dAuto(nn.Conv2d):
@@ -225,8 +225,8 @@ class ResidualNetworkEncoder(nn.Module):
in_channels=in_channels,
out_channels=self.block_sizes[0],
kernel_size=3,
- stride=2,
- padding=3,
+ stride=1,
+ padding=1,
bias=False,
),
nn.BatchNorm2d(self.block_sizes[0]),
diff --git a/src/text_recognizer/networks/sparse_mlp.py b/src/text_recognizer/networks/sparse_mlp.py
new file mode 100644
index 0000000..53cf166
--- /dev/null
+++ b/src/text_recognizer/networks/sparse_mlp.py
@@ -0,0 +1,78 @@
+"""Defines the Sparse MLP network."""
+from typing import Callable, Dict, List, Optional, Union
+import warnings
+
+from einops.layers.torch import Rearrange
+from pytorch_block_sparse import BlockSparseLinear
+import torch
+from torch import nn
+
+from text_recognizer.networks.util import activation_function
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+
+class SparseMLP(nn.Module):
+ """Sparse multi layered perceptron network."""
+
+ def __init__(
+ self,
+ input_size: int = 784,
+ num_classes: int = 10,
+ hidden_size: Union[int, List] = 128,
+ num_layers: int = 3,
+ density: float = 0.1,
+ activation_fn: str = "relu",
+ ) -> None:
+ """Initialization of the MLP network.
+
+ Args:
+ input_size (int): The input shape of the network. Defaults to 784.
+ num_classes (int): Number of classes in the dataset. Defaults to 10.
+ hidden_size (Union[int, List]): The number of `neurons` in each hidden layer. Defaults to 128.
+ num_layers (int): The number of hidden layers. Defaults to 3.
+ density (float): The density of activation at each layer. Default to 0.1.
+ activation_fn (str): Name of the activation function in the hidden layers. Defaults to
+ relu.
+
+ """
+ super().__init__()
+
+ activation_fn = activation_function(activation_fn)
+
+ if isinstance(hidden_size, int):
+ hidden_size = [hidden_size] * num_layers
+
+ self.layers = [
+ Rearrange("b c h w -> b (c h w)"),
+ nn.Linear(in_features=input_size, out_features=hidden_size[0]),
+ activation_fn,
+ ]
+
+ for i in range(num_layers - 1):
+ self.layers += [
+ BlockSparseLinear(
+ in_features=hidden_size[i],
+ out_features=hidden_size[i + 1],
+ density=density,
+ ),
+ activation_fn,
+ ]
+
+ self.layers.append(
+ nn.Linear(in_features=hidden_size[-1], out_features=num_classes)
+ )
+
+ self.layers = nn.Sequential(*self.layers)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """The feedforward pass."""
+ # If batch dimenstion is missing, it needs to be added.
+ if len(x.shape) < 4:
+ x = x[(None,) * (4 - len(x.shape))]
+ return self.layers(x)
+
+ @property
+ def __name__(self) -> str:
+ """Returns the name of the network."""
+ return "mlp"
diff --git a/src/text_recognizer/networks/transformer.py b/src/text_recognizer/networks/transformer.py
deleted file mode 100644
index c091ba0..0000000
--- a/src/text_recognizer/networks/transformer.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""TBC."""
-from typing import Dict
-
-import torch
-from torch import Tensor
diff --git a/src/text_recognizer/networks/transformer/__init__.py b/src/text_recognizer/networks/transformer/__init__.py
new file mode 100644
index 0000000..020a917
--- /dev/null
+++ b/src/text_recognizer/networks/transformer/__init__.py
@@ -0,0 +1,3 @@
+"""Transformer modules."""
+from .positional_encoding import PositionalEncoding
+from .transformer import Decoder, Encoder, Transformer
diff --git a/src/text_recognizer/networks/transformer/attention.py b/src/text_recognizer/networks/transformer/attention.py
new file mode 100644
index 0000000..cce1ecc
--- /dev/null
+++ b/src/text_recognizer/networks/transformer/attention.py
@@ -0,0 +1,93 @@
+"""Implementes the attention module for the transformer."""
+from typing import Optional, Tuple
+
+from einops import rearrange
+import numpy as np
+import torch
+from torch import nn
+from torch import Tensor
+
+
+class MultiHeadAttention(nn.Module):
+ """Implementation of multihead attention."""
+
+ def __init__(
+ self, hidden_dim: int, num_heads: int = 8, dropout_rate: float = 0.0
+ ) -> None:
+ super().__init__()
+ self.hidden_dim = hidden_dim
+ self.num_heads = num_heads
+ self.fc_q = nn.Linear(
+ in_features=hidden_dim, out_features=hidden_dim, bias=False
+ )
+ self.fc_k = nn.Linear(
+ in_features=hidden_dim, out_features=hidden_dim, bias=False
+ )
+ self.fc_v = nn.Linear(
+ in_features=hidden_dim, out_features=hidden_dim, bias=False
+ )
+ self.fc_out = nn.Linear(in_features=hidden_dim, out_features=hidden_dim)
+
+ self._init_weights()
+
+ self.dropout = nn.Dropout(p=dropout_rate)
+
+ def _init_weights(self) -> None:
+ nn.init.normal_(
+ self.fc_q.weight,
+ mean=0,
+ std=np.sqrt(self.hidden_dim + int(self.hidden_dim / self.num_heads)),
+ )
+ nn.init.normal_(
+ self.fc_k.weight,
+ mean=0,
+ std=np.sqrt(self.hidden_dim + int(self.hidden_dim / self.num_heads)),
+ )
+ nn.init.normal_(
+ self.fc_v.weight,
+ mean=0,
+ std=np.sqrt(self.hidden_dim + int(self.hidden_dim / self.num_heads)),
+ )
+ nn.init.xavier_normal_(self.fc_out.weight)
+
+ def scaled_dot_product_attention(
+ self, query: Tensor, key: Tensor, value: Tensor, mask: Optional[Tensor] = None
+ ) -> Tensor:
+ """Calculates the scaled dot product attention."""
+
+ # Compute the energy.
+ energy = torch.einsum("bhlk,bhtk->bhlt", [query, key]) / np.sqrt(
+ query.shape[-1]
+ )
+
+ # If we have a mask for padding some inputs.
+ if mask is not None:
+ energy = energy.masked_fill(mask == 0, -np.inf)
+
+ # Compute the attention from the energy.
+ attention = torch.softmax(energy, dim=3)
+
+ out = torch.einsum("bhlt,bhtv->bhlv", [attention, value])
+ out = rearrange(out, "b head l v -> b l (head v)")
+ return out, attention
+
+ def forward(
+ self, query: Tensor, key: Tensor, value: Tensor, mask: Optional[Tensor] = None
+ ) -> Tuple[Tensor, Tensor]:
+ """Forward pass for computing the multihead attention."""
+ # Get the query, key, and value tensor.
+ query = rearrange(
+ self.fc_q(query), "b l (head k) -> b head l k", head=self.num_heads
+ )
+ key = rearrange(
+ self.fc_k(key), "b t (head k) -> b head t k", head=self.num_heads
+ )
+ value = rearrange(
+ self.fc_v(value), "b t (head v) -> b head t v", head=self.num_heads
+ )
+
+ out, attention = self.scaled_dot_product_attention(query, key, value, mask)
+
+ out = self.fc_out(out)
+ out = self.dropout(out)
+ return out, attention
diff --git a/src/text_recognizer/networks/transformer/positional_encoding.py b/src/text_recognizer/networks/transformer/positional_encoding.py
new file mode 100644
index 0000000..a47141b
--- /dev/null
+++ b/src/text_recognizer/networks/transformer/positional_encoding.py
@@ -0,0 +1,31 @@
+"""A positional encoding for the image features, as the transformer has no notation of the order of the sequence."""
+import numpy as np
+import torch
+from torch import nn
+from torch import Tensor
+
+
+class PositionalEncoding(nn.Module):
+ """Encodes a sense of distance or time for transformer networks."""
+
+ def __init__(
+ self, hidden_dim: int, dropout_rate: float, max_len: int = 1000
+ ) -> None:
+ super().__init__()
+ self.dropout = nn.Dropout(p=dropout_rate)
+
+ pe = torch.zeros(max_len, hidden_dim)
+ position = torch.arange(0, max_len).unsqueeze(1)
+ div_term = torch.exp(
+ torch.arange(0, hidden_dim, 2) * -(np.log(10000.0) / hidden_dim)
+ )
+
+ pe[:, 0::2] = torch.sin(position * div_term)
+ pe[:, 1::2] = torch.cos(position * div_term)
+ pe = pe.unsqueeze(0)
+ self.register_buffer("pe", pe)
+
+ def forward(self, x: Tensor) -> Tensor:
+ """Encodes the tensor with a postional embedding."""
+ x = x + self.pe[:, : x.shape[1]]
+ return self.dropout(x)
diff --git a/src/text_recognizer/networks/transformer/sparse_transformer.py b/src/text_recognizer/networks/transformer/sparse_transformer.py
new file mode 100644
index 0000000..8c391c8
--- /dev/null
+++ b/src/text_recognizer/networks/transformer/sparse_transformer.py
@@ -0,0 +1 @@
+"""Encoder and Decoder modules using spares activations."""
diff --git a/src/text_recognizer/networks/transformer/transformer.py b/src/text_recognizer/networks/transformer/transformer.py
new file mode 100644
index 0000000..1c9c7dd
--- /dev/null
+++ b/src/text_recognizer/networks/transformer/transformer.py
@@ -0,0 +1,241 @@
+"""Transfomer module."""
+import copy
+from typing import Dict, Optional, Type, Union
+
+import numpy as np
+import torch
+from torch import nn
+from torch import Tensor
+
+from text_recognizer.networks.transformer.attention import MultiHeadAttention
+from text_recognizer.networks.util import activation_function
+
+
+def _get_clones(module: Type[nn.Module], num_layers: int) -> nn.ModuleList:
+ return nn.ModuleList([copy.deepcopy(module) for _ in range(num_layers)])
+
+
+class _IntraLayerConnection(nn.Module):
+ """Preforms the residual connection inside the transfomer blocks and applies layernorm."""
+
+ def __init__(self, dropout_rate: float, hidden_dim: int) -> None:
+ super().__init__()
+ self.norm = nn.LayerNorm(normalized_shape=hidden_dim)
+ self.dropout = nn.Dropout(p=dropout_rate)
+
+ def forward(self, src: Tensor, residual: Tensor) -> Tensor:
+ return self.norm(self.dropout(src) + residual)
+
+
+class _ConvolutionalLayer(nn.Module):
+ def __init__(
+ self,
+ hidden_dim: int,
+ expansion_dim: int,
+ dropout_rate: float,
+ activation: str = "relu",
+ ) -> None:
+ super().__init__()
+ self.layer = nn.Sequential(
+ nn.Linear(in_features=hidden_dim, out_features=expansion_dim),
+ activation_function(activation),
+ nn.Dropout(p=dropout_rate),
+ nn.Linear(in_features=expansion_dim, out_features=hidden_dim),
+ )
+
+ def forward(self, x: Tensor) -> Tensor:
+ return self.layer(x)
+
+
+class EncoderLayer(nn.Module):
+ """Transfomer encoding layer."""
+
+ def __init__(
+ self,
+ hidden_dim: int,
+ num_heads: int,
+ expansion_dim: int,
+ dropout_rate: float,
+ activation: str = "relu",
+ ) -> None:
+ super().__init__()
+ self.self_attention = MultiHeadAttention(hidden_dim, num_heads, dropout_rate)
+ self.cnn = _ConvolutionalLayer(
+ hidden_dim, expansion_dim, dropout_rate, activation
+ )
+ self.block1 = _IntraLayerConnection(dropout_rate, hidden_dim)
+ self.block2 = _IntraLayerConnection(dropout_rate, hidden_dim)
+
+ def forward(self, src: Tensor, mask: Optional[Tensor] = None) -> Tensor:
+ """Forward pass through the encoder."""
+ # First block.
+ # Multi head attention.
+ out, _ = self.self_attention(src, src, src, mask)
+
+ # Add & norm.
+ out = self.block1(out, src)
+
+ # Second block.
+ # Apply 1D-convolution.
+ cnn_out = self.cnn(out)
+
+ # Add & norm.
+ out = self.block2(cnn_out, out)
+
+ return out
+
+
+class Encoder(nn.Module):
+ """Transfomer encoder module."""
+
+ def __init__(
+ self,
+ num_layers: int,
+ encoder_layer: Type[nn.Module],
+ norm: Optional[Type[nn.Module]] = None,
+ ) -> None:
+ super().__init__()
+ self.layers = _get_clones(encoder_layer, num_layers)
+ self.norm = norm
+
+ def forward(self, src: Tensor, src_mask: Optional[Tensor] = None) -> Tensor:
+ """Forward pass through all encoder layers."""
+ for layer in self.layers:
+ src = layer(src, src_mask)
+
+ if self.norm is not None:
+ src = self.norm(src)
+
+ return src
+
+
+class DecoderLayer(nn.Module):
+ """Transfomer decoder layer."""
+
+ def __init__(
+ self,
+ hidden_dim: int,
+ num_heads: int,
+ expansion_dim: int,
+ dropout_rate: float = 0.0,
+ activation: str = "relu",
+ ) -> None:
+ super().__init__()
+ self.hidden_dim = hidden_dim
+ self.self_attention = MultiHeadAttention(hidden_dim, num_heads, dropout_rate)
+ self.multihead_attention = MultiHeadAttention(
+ hidden_dim, num_heads, dropout_rate
+ )
+ self.cnn = _ConvolutionalLayer(
+ hidden_dim, expansion_dim, dropout_rate, activation
+ )
+ self.block1 = _IntraLayerConnection(dropout_rate, hidden_dim)
+ self.block2 = _IntraLayerConnection(dropout_rate, hidden_dim)
+ self.block3 = _IntraLayerConnection(dropout_rate, hidden_dim)
+
+ def forward(
+ self,
+ trg: Tensor,
+ memory: Tensor,
+ trg_mask: Optional[Tensor] = None,
+ memory_mask: Optional[Tensor] = None,
+ ) -> Tensor:
+ """Forward pass of the layer."""
+ out, _ = self.self_attention(trg, trg, trg, trg_mask)
+ trg = self.block1(out, trg)
+
+ out, _ = self.multihead_attention(trg, memory, memory, memory_mask)
+ trg = self.block2(out, trg)
+
+ out = self.cnn(trg)
+ out = self.block3(out, trg)
+
+ return out
+
+
+class Decoder(nn.Module):
+ """Transfomer decoder module."""
+
+ def __init__(
+ self,
+ decoder_layer: Type[nn.Module],
+ num_layers: int,
+ norm: Optional[Type[nn.Module]] = None,
+ ) -> None:
+ super().__init__()
+ self.layers = _get_clones(decoder_layer, num_layers)
+ self.num_layers = num_layers
+ self.norm = norm
+
+ def forward(
+ self,
+ trg: Tensor,
+ memory: Tensor,
+ trg_mask: Optional[Tensor] = None,
+ memory_mask: Optional[Tensor] = None,
+ ) -> Tensor:
+ """Forward pass through the decoder."""
+ for layer in self.layers:
+ trg = layer(trg, memory, trg_mask, memory_mask)
+
+ if self.norm is not None:
+ trg = self.norm(trg)
+
+ return trg
+
+
+class Transformer(nn.Module):
+ """Transformer network."""
+
+ def __init__(
+ self,
+ num_encoder_layers: int,
+ num_decoder_layers: int,
+ hidden_dim: int,
+ num_heads: int,
+ expansion_dim: int,
+ dropout_rate: float,
+ activation: str = "relu",
+ ) -> None:
+ super().__init__()
+
+ # Configure encoder.
+ encoder_norm = nn.LayerNorm(hidden_dim)
+ encoder_layer = EncoderLayer(
+ hidden_dim, num_heads, expansion_dim, dropout_rate, activation
+ )
+ self.encoder = Encoder(num_encoder_layers, encoder_layer, encoder_norm)
+
+ # Configure decoder.
+ decoder_norm = nn.LayerNorm(hidden_dim)
+ decoder_layer = DecoderLayer(
+ hidden_dim, num_heads, expansion_dim, dropout_rate, activation
+ )
+ self.decoder = Decoder(decoder_layer, num_decoder_layers, decoder_norm)
+
+ self._reset_parameters()
+
+ def _reset_parameters(self) -> None:
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+ def forward(
+ self,
+ src: Tensor,
+ trg: Tensor,
+ src_mask: Optional[Tensor] = None,
+ trg_mask: Optional[Tensor] = None,
+ memory_mask: Optional[Tensor] = None,
+ ) -> Tensor:
+ """Forward pass through the transformer."""
+ if src.shape[0] != trg.shape[0]:
+ raise RuntimeError("The batch size of the src and trg must be the same.")
+ if src.shape[2] != trg.shape[2]:
+ raise RuntimeError(
+ "The number of features for the src and trg must be the same."
+ )
+
+ memory = self.encoder(src, src_mask)
+ output = self.decoder(trg, memory, trg_mask, memory_mask)
+ return output
diff --git a/src/text_recognizer/networks/misc.py b/src/text_recognizer/networks/util.py
index 1f853e9..0d08506 100644
--- a/src/text_recognizer/networks/misc.py
+++ b/src/text_recognizer/networks/util.py
@@ -1,7 +1,10 @@
"""Miscellaneous neural network functionality."""
-from typing import Tuple, Type
+import importlib
+from pathlib import Path
+from typing import Dict, Tuple, Type
from einops import rearrange
+from loguru import logger
import torch
from torch import nn
@@ -43,3 +46,38 @@ def activation_function(activation: str) -> Type[nn.Module]:
]
)
return activation_fns[activation.lower()]
+
+
+def configure_backbone(backbone: str, backbone_args: Dict) -> Type[nn.Module]:
+ """Loads a backbone network."""
+ network_module = importlib.import_module("text_recognizer.networks")
+ backbone_ = getattr(network_module, backbone)
+
+ if "pretrained" in backbone_args:
+ logger.info("Loading pretrained backbone.")
+ checkpoint_file = Path(__file__).resolve().parents[2] / backbone_args.pop(
+ "pretrained"
+ )
+
+ # Loading state directory.
+ state_dict = torch.load(checkpoint_file)
+ network_args = state_dict["network_args"]
+ weights = state_dict["model_state"]
+
+ # Initializes the network with trained weights.
+ backbone = backbone_(**network_args)
+ backbone.load_state_dict(weights)
+ if "freeze" in backbone_args and backbone_args["freeze"] is True:
+ for params in backbone.parameters():
+ params.requires_grad = False
+
+ else:
+ backbone_ = getattr(network_module, backbone)
+ backbone = backbone_(**backbone_args)
+
+ if "remove_layers" in backbone_args and backbone_args["remove_layers"] is not None:
+ backbone = nn.Sequential(
+ *list(backbone.children())[0][: -backbone_args["remove_layers"]]
+ )
+
+ return backbone
diff --git a/src/text_recognizer/networks/vision_transformer.py b/src/text_recognizer/networks/vision_transformer.py
new file mode 100644
index 0000000..4d204d3
--- /dev/null
+++ b/src/text_recognizer/networks/vision_transformer.py
@@ -0,0 +1,158 @@
+"""VisionTransformer module.
+
+Splits each image into patches and feeds them to a transformer.
+
+"""
+
+from typing import Dict, Optional, Tuple, Type
+
+from einops import rearrange, reduce
+from einops.layers.torch import Rearrange
+from loguru import logger
+import torch
+from torch import nn
+from torch import Tensor
+
+from text_recognizer.networks.transformer import PositionalEncoding, Transformer
+from text_recognizer.networks.util import configure_backbone
+
+
+class VisionTransformer(nn.Module):
+ """Linear projection+Transfomer for image to sequence prediction, sort of based on the ideas from ViT."""
+
+ def __init__(
+ self,
+ num_encoder_layers: int,
+ num_decoder_layers: int,
+ hidden_dim: int,
+ vocab_size: int,
+ num_heads: int,
+ max_len: int,
+ expansion_dim: int,
+ mlp_dim: int,
+ dropout_rate: float,
+ trg_pad_index: int,
+ patch_size: Tuple[int, int] = (28, 28),
+ stride: Tuple[int, int] = (1, 14),
+ activation: str = "gelu",
+ backbone: Optional[str] = None,
+ backbone_args: Optional[Dict] = None,
+ ) -> None:
+ super().__init__()
+
+ self.patch_size = patch_size
+ self.stride = stride
+ self.trg_pad_index = trg_pad_index
+ self.slidning_window = self._configure_sliding_window()
+ self.character_embedding = nn.Embedding(vocab_size, hidden_dim)
+ self.position_encoding = PositionalEncoding(hidden_dim, dropout_rate, max_len)
+
+ self.use_backbone = False
+ if backbone is None:
+ self.linear_projection = nn.Linear(
+ self.patch_size[0] * self.patch_size[1], hidden_dim
+ )
+ else:
+ self.backbone = configure_backbone(backbone, backbone_args)
+ self.use_backbone = True
+
+ self.transformer = Transformer(
+ num_encoder_layers,
+ num_decoder_layers,
+ hidden_dim,
+ num_heads,
+ expansion_dim,
+ dropout_rate,
+ activation,
+ )
+
+ self.head = nn.Sequential(
+ nn.LayerNorm(hidden_dim),
+ nn.Linear(hidden_dim, mlp_dim),
+ nn.GELU(),
+ nn.Dropout(p=dropout_rate),
+ nn.Linear(mlp_dim, vocab_size),
+ )
+
+ def _configure_sliding_window(self) -> nn.Sequential:
+ return nn.Sequential(
+ nn.Unfold(kernel_size=self.patch_size, stride=self.stride),
+ Rearrange(
+ "b (c h w) t -> b t c h w",
+ h=self.patch_size[0],
+ w=self.patch_size[1],
+ c=1,
+ ),
+ )
+
+ def _create_trg_mask(self, trg: Tensor) -> Tensor:
+ # Move this outside the transformer.
+ trg_pad_mask = (trg != self.trg_pad_index)[:, None, None]
+ trg_len = trg.shape[1]
+ trg_sub_mask = torch.tril(
+ torch.ones((trg_len, trg_len), device=trg.device)
+ ).bool()
+ trg_mask = trg_pad_mask & trg_sub_mask
+ return trg_mask
+
+ def encoder(self, src: Tensor) -> Tensor:
+ """Forward pass with the encoder of the transformer."""
+ return self.transformer.encoder(src)
+
+ def decoder(self, trg: Tensor, memory: Tensor, trg_mask: Tensor) -> Tensor:
+ """Forward pass with the decoder of the transformer + classification head."""
+ return self.head(
+ self.transformer.decoder(trg=trg, memory=memory, trg_mask=trg_mask)
+ )
+
+ def _backbone(self, x: Tensor) -> Tensor:
+ b, t = x.shape[:2]
+ if self.use_backbone:
+ x = rearrange(x, "b t c h w -> (b t) c h w", b=b, t=t)
+ x = self.backbone(x)
+ x = rearrange(x, "(b t) h -> b t h", b=b, t=t)
+ else:
+ x = rearrange(x, "b t c h w -> b t (c h w)", b=b, t=t)
+ x = self.linear_projection(x)
+ return x
+
+ def preprocess_input(self, src: Tensor) -> Tensor:
+ """Encodes src with a backbone network and a positional encoding.
+
+ Args:
+ src (Tensor): Input tensor.
+
+ Returns:
+ Tensor: A input src to the transformer.
+
+ """
+ # If batch dimenstion is missing, it needs to be added.
+ if len(src.shape) < 4:
+ src = src[(None,) * (4 - len(src.shape))]
+ src = self.slidning_window(src) # .squeeze(-2)
+ src = self._backbone(src)
+ src = self.position_encoding(src)
+ return src
+
+ def preprocess_target(self, trg: Tensor) -> Tuple[Tensor, Tensor]:
+ """Encodes target tensor with embedding and postion.
+
+ Args:
+ trg (Tensor): Target tensor.
+
+ Returns:
+ Tuple[Tensor, Tensor]: Encoded target tensor and target mask.
+
+ """
+ trg_mask = self._create_trg_mask(trg)
+ trg = self.character_embedding(trg.long())
+ trg = self.position_encoding(trg)
+ return trg, trg_mask
+
+ def forward(self, x: Tensor, trg: Tensor) -> Tensor:
+ """Forward pass with vision transfomer."""
+ src = self.preprocess_input(x)
+ trg, trg_mask = self.preprocess_target(trg)
+ out = self.transformer(src, trg, trg_mask=trg_mask)
+ logits = self.head(out)
+ return logits
diff --git a/src/text_recognizer/networks/wide_resnet.py b/src/text_recognizer/networks/wide_resnet.py
index 618f414..aa79c12 100644
--- a/src/text_recognizer/networks/wide_resnet.py
+++ b/src/text_recognizer/networks/wide_resnet.py
@@ -8,7 +8,7 @@ import torch
from torch import nn
from torch import Tensor
-from text_recognizer.networks.misc import activation_function
+from text_recognizer.networks.util import activation_function
def conv3x3(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
@@ -206,8 +206,8 @@ class WideResidualNetwork(nn.Module):
def forward(self, x: Tensor) -> Tensor:
"""Feedforward pass."""
- if len(x.shape) == 3:
- x = x.unsqueeze(0)
+ if len(x.shape) < 4:
+ x = x[(None,) * int(4 - len(x.shape))]
x = self.encoder(x)
if self.decoder is not None:
x = self.decoder(x)
diff --git a/src/text_recognizer/weights/CharacterModel_EmnistDataset_DenseNet_weights.pt b/src/text_recognizer/weights/CharacterModel_EmnistDataset_DenseNet_weights.pt
new file mode 100644
index 0000000..6a9a915
--- /dev/null
+++ b/src/text_recognizer/weights/CharacterModel_EmnistDataset_DenseNet_weights.pt
Binary files differ
diff --git a/src/text_recognizer/weights/LineCTCModel_IamLinesDataset_LineRecurrentNetwork_weights.pt b/src/text_recognizer/weights/LineCTCModel_IamLinesDataset_LineRecurrentNetwork_weights.pt
index c001528..7fe1fa3 100644
--- a/src/text_recognizer/weights/LineCTCModel_IamLinesDataset_LineRecurrentNetwork_weights.pt
+++ b/src/text_recognizer/weights/LineCTCModel_IamLinesDataset_LineRecurrentNetwork_weights.pt
Binary files differ
diff --git a/src/training/experiments/embedding_experiment.yml b/src/training/experiments/embedding_experiment.yml
index e674c26..1e5f941 100644
--- a/src/training/experiments/embedding_experiment.yml
+++ b/src/training/experiments/embedding_experiment.yml
@@ -1,8 +1,10 @@
experiment_group: Embedding Experiments
experiments:
- train_args:
- batch_size: 256
- max_epochs: &max_epochs 8
+ transformer_model: false
+ batch_size: &batch_size 256
+ max_epochs: &max_epochs 32
+ input_shape: [[1, 28, 28]]
dataset:
type: EmnistDataset
args:
@@ -14,17 +16,21 @@ experiments:
train_args:
num_workers: 8
train_fraction: 0.85
+ batch_size: *batch_size
model: CharacterModel
metrics: []
network:
- type: ResidualNetwork
+ type: DenseNet
args:
+ growth_rate: 4
+ block_config: [4, 4]
in_channels: 1
- num_classes: 64 # Embedding
- depths: [2,2]
- block_sizes: [32, 64]
- activation: selu
- stn: false
+ base_channels: 24
+ num_classes: 128
+ bn_size: 4
+ dropout_rate: 0.1
+ classifier: true
+ activation: elu
criterion:
type: EmbeddingLoss
args:
diff --git a/src/training/experiments/line_ctc_experiment.yml b/src/training/experiments/line_ctc_experiment.yml
deleted file mode 100644
index ef97527..0000000
--- a/src/training/experiments/line_ctc_experiment.yml
+++ /dev/null
@@ -1,92 +0,0 @@
-experiment_group: Lines Experiments
-experiments:
- - train_args:
- batch_size: 64
- max_epochs: &max_epochs 64
- dataset:
- type: IamLinesDataset
- args:
- subsample_fraction: null
- transform: null
- target_transform: null
- train_args:
- num_workers: 8
- train_fraction: 0.85
- model: LineCTCModel
- metrics: [cer, wer]
- network:
- type: LineRecurrentNetwork
- args:
- # backbone: ResidualNetwork
- # backbone_args:
- # in_channels: 1
- # num_classes: 64 # Embedding
- # depths: [2,2]
- # block_sizes: [32, 64]
- # activation: selu
- # stn: false
- backbone: ResidualNetwork
- backbone_args:
- pretrained: training/experiments/CharacterModel_EmnistDataset_ResidualNetwork/0920_025816/model/best.pt
- freeze: false
- flatten: false
- input_size: 64
- hidden_size: 64
- bidirectional: true
- num_layers: 2
- num_classes: 80
- patch_size: [28, 18]
- stride: [1, 4]
- criterion:
- type: CTCLoss
- args:
- blank: 79
- optimizer:
- type: AdamW
- args:
- lr: 1.e-02
- betas: [0.9, 0.999]
- eps: 1.e-08
- weight_decay: 5.e-4
- amsgrad: false
- lr_scheduler:
- type: OneCycleLR
- args:
- max_lr: 1.e-02
- epochs: *max_epochs
- anneal_strategy: cos
- pct_start: 0.475
- cycle_momentum: true
- base_momentum: 0.85
- max_momentum: 0.9
- div_factor: 10
- final_div_factor: 10000
- interval: step
- # lr_scheduler:
- # type: CosineAnnealingLR
- # args:
- # T_max: *max_epochs
- swa_args:
- start: 48
- lr: 5.e-2
- callbacks: [Checkpoint, ProgressBar, WandbCallback, WandbImageLogger, EarlyStopping]
- callback_args:
- Checkpoint:
- monitor: val_loss
- mode: min
- ProgressBar:
- epochs: *max_epochs
- EarlyStopping:
- monitor: val_loss
- min_delta: 0.0
- patience: 10
- mode: min
- WandbCallback:
- log_batch_frequency: 10
- WandbImageLogger:
- num_examples: 6
- verbosity: 1 # 0, 1, 2
- resume_experiment: null
- train: true
- test: true
- test_metric: test_cer
diff --git a/src/training/run_experiment.py b/src/training/run_experiment.py
index 9d45841..c0f969d 100644
--- a/src/training/run_experiment.py
+++ b/src/training/run_experiment.py
@@ -6,12 +6,15 @@ import json
import os
from pathlib import Path
import re
-from typing import Callable, Dict, List, Tuple, Type
+from typing import Callable, Dict, List, Optional, Tuple, Type
+import warnings
+import adabelief_pytorch
import click
from loguru import logger
import numpy as np
import torch
+from torchsummary import summary
from tqdm import tqdm
from training.gpu_manager import GPUManager
from training.trainer.callbacks import Callback, CallbackList
@@ -21,26 +24,23 @@ import yaml
from text_recognizer.models import Model
-from text_recognizer.networks import losses
-
+from text_recognizer.networks import loss as custom_loss_module
EXPERIMENTS_DIRNAME = Path(__file__).parents[0].resolve() / "experiments"
-CUSTOM_LOSSES = ["EmbeddingLoss"]
DEFAULT_TRAIN_ARGS = {"batch_size": 64, "epochs": 16}
-def get_level(experiment_config: Dict) -> int:
+def _get_level(verbose: int) -> int:
"""Sets the logger level."""
- if experiment_config["verbosity"] == 0:
- return 40
- elif experiment_config["verbosity"] == 1:
- return 20
- else:
- return 10
+ levels = {0: 40, 1: 20, 2: 10}
+ verbose = verbose if verbose <= 2 else 2
+ return levels[verbose]
-def create_experiment_dir(experiment_config: Dict) -> Path:
+def _create_experiment_dir(
+ experiment_config: Dict, checkpoint: Optional[str] = None
+) -> Path:
"""Create new experiment."""
EXPERIMENTS_DIRNAME.mkdir(parents=True, exist_ok=True)
experiment_dir = EXPERIMENTS_DIRNAME / (
@@ -48,19 +48,21 @@ def create_experiment_dir(experiment_config: Dict) -> Path:
+ f"{experiment_config['dataset']['type']}_"
+ f"{experiment_config['network']['type']}"
)
- if experiment_config["resume_experiment"] is None:
+
+ if checkpoint is None:
experiment = datetime.now().strftime("%m%d_%H%M%S")
logger.debug(f"Creating a new experiment called {experiment}")
else:
available_experiments = glob(str(experiment_dir) + "/*")
available_experiments.sort()
- if experiment_config["resume_experiment"] == "last":
+ if checkpoint == "last":
experiment = available_experiments[-1]
logger.debug(f"Resuming the latest experiment {experiment}")
else:
- experiment = experiment_config["resume_experiment"]
+ experiment = checkpoint
if not str(experiment_dir / experiment) in available_experiments:
raise FileNotFoundError("Experiment does not exist.")
+ logger.debug(f"Resuming the from experiment {checkpoint}")
experiment_dir = experiment_dir / experiment
@@ -71,14 +73,10 @@ def create_experiment_dir(experiment_config: Dict) -> Path:
return experiment_dir, log_dir, model_dir
-def load_modules_and_arguments(experiment_config: Dict) -> Tuple[Callable, Dict]:
+def _load_modules_and_arguments(experiment_config: Dict) -> Tuple[Callable, Dict]:
"""Loads all modules and arguments."""
- # Import the data loader arguments.
- train_args = experiment_config.get("train_args", {})
-
# Load the dataset module.
dataset_args = experiment_config.get("dataset", {})
- dataset_args["train_args"]["batch_size"] = train_args["batch_size"]
datasets_module = importlib.import_module("text_recognizer.datasets")
dataset_ = getattr(datasets_module, dataset_args["type"])
@@ -102,15 +100,18 @@ def load_modules_and_arguments(experiment_config: Dict) -> Tuple[Callable, Dict]
network_args = experiment_config["network"].get("args", {})
# Criterion
- if experiment_config["criterion"]["type"] in CUSTOM_LOSSES:
- criterion_ = getattr(losses, experiment_config["criterion"]["type"])
- criterion_args = experiment_config["criterion"].get("args", {})
+ if experiment_config["criterion"]["type"] in custom_loss_module.__all__:
+ criterion_ = getattr(custom_loss_module, experiment_config["criterion"]["type"])
else:
criterion_ = getattr(torch.nn, experiment_config["criterion"]["type"])
- criterion_args = experiment_config["criterion"].get("args", {})
+ criterion_args = experiment_config["criterion"].get("args", {})
# Optimizers
- optimizer_ = getattr(torch.optim, experiment_config["optimizer"]["type"])
+ if experiment_config["optimizer"]["type"] == "AdaBelief":
+ warnings.filterwarnings("ignore", category=UserWarning)
+ optimizer_ = getattr(adabelief_pytorch, experiment_config["optimizer"]["type"])
+ else:
+ optimizer_ = getattr(torch.optim, experiment_config["optimizer"]["type"])
optimizer_args = experiment_config["optimizer"].get("args", {})
# Learning rate scheduler
@@ -146,10 +147,12 @@ def load_modules_and_arguments(experiment_config: Dict) -> Tuple[Callable, Dict]
return model_class_, model_args
-def configure_callbacks(experiment_config: Dict, model_dir: Dict) -> CallbackList:
+def _configure_callbacks(experiment_config: Dict, model_dir: Path) -> CallbackList:
"""Configure a callback list for trainer."""
if "Checkpoint" in experiment_config["callback_args"]:
- experiment_config["callback_args"]["Checkpoint"]["checkpoint_path"] = model_dir
+ experiment_config["callback_args"]["Checkpoint"]["checkpoint_path"] = str(
+ model_dir
+ )
# Initializes callbacks.
callback_modules = importlib.import_module("training.trainer.callbacks")
@@ -161,13 +164,13 @@ def configure_callbacks(experiment_config: Dict, model_dir: Dict) -> CallbackLis
return callbacks
-def configure_logger(experiment_config: Dict, log_dir: Path) -> None:
+def _configure_logger(log_dir: Path, verbose: int = 0) -> None:
"""Configure the loguru logger for output to terminal and disk."""
# Have to remove default logger to get tqdm to work properly.
logger.remove()
# Fetch verbosity level.
- level = get_level(experiment_config)
+ level = _get_level(verbose)
logger.add(lambda msg: tqdm.write(msg, end=""), colorize=True, level=level)
logger.add(
@@ -176,20 +179,27 @@ def configure_logger(experiment_config: Dict, log_dir: Path) -> None:
)
-def save_config(experiment_dir: Path, experiment_config: Dict) -> None:
+def _save_config(experiment_dir: Path, experiment_config: Dict) -> None:
"""Copy config to experiment directory."""
config_path = experiment_dir / "config.yml"
with open(str(config_path), "w") as f:
yaml.dump(experiment_config, f)
-def load_from_checkpoint(model: Type[Model], log_dir: Path, model_dir: Path) -> None:
+def _load_from_checkpoint(
+ model: Type[Model], log_dir: Path, model_dir: Path, pretrained_weights: str = None
+) -> None:
"""If checkpoint exists, load model weights and optimizers from checkpoint."""
# Get checkpoint path.
- checkpoint_path = model_dir / "last.pt"
+ if pretrained_weights is not None:
+ logger.info(f"Loading weights from {pretrained_weights}.")
+ checkpoint_path = Path(pretrained_weights) / "model" / "best.pt"
+ else:
+ logger.info(f"Loading weights from {model_dir}.")
+ checkpoint_path = model_dir / "last.pt"
if checkpoint_path.exists():
logger.info("Loading and resuming training from last checkpoint.")
- model.load_checkpoint(checkpoint_path)
+ model.load_from_checkpoint(checkpoint_path)
def evaluate_embedding(model: Type[Model]) -> Dict:
@@ -217,38 +227,50 @@ def evaluate_embedding(model: Type[Model]) -> Dict:
def run_experiment(
- experiment_config: Dict, save_weights: bool, device: str, use_wandb: bool = False
+ experiment_config: Dict,
+ save_weights: bool,
+ device: str,
+ use_wandb: bool = False,
+ train: bool = True,
+ test: bool = False,
+ verbose: int = 0,
+ checkpoint: Optional[str] = None,
+ pretrained_weights: Optional[str] = None,
) -> None:
"""Runs an experiment."""
logger.info(f"Experiment config: {json.dumps(experiment_config)}")
# Create new experiment.
- experiment_dir, log_dir, model_dir = create_experiment_dir(experiment_config)
+ experiment_dir, log_dir, model_dir = _create_experiment_dir(
+ experiment_config, checkpoint
+ )
# Make sure the log/model directory exists.
log_dir.mkdir(parents=True, exist_ok=True)
model_dir.mkdir(parents=True, exist_ok=True)
# Load the modules and model arguments.
- model_class_, model_args = load_modules_and_arguments(experiment_config)
+ model_class_, model_args = _load_modules_and_arguments(experiment_config)
# Initializes the model with experiment config.
model = model_class_(**model_args, device=device)
- callbacks = configure_callbacks(experiment_config, model_dir)
+ callbacks = _configure_callbacks(experiment_config, model_dir)
# Setup logger.
- configure_logger(experiment_config, log_dir)
+ _configure_logger(log_dir, verbose)
# Load from checkpoint if resuming an experiment.
- if experiment_config["resume_experiment"] is not None:
- load_from_checkpoint(model, log_dir, model_dir)
+ resume = False
+ if checkpoint is not None or pretrained_weights is not None:
+ resume = True
+ _load_from_checkpoint(model, log_dir, model_dir, pretrained_weights)
logger.info(f"The class mapping is {model.mapping}")
# Initializes Weights & Biases
if use_wandb:
- wandb.init(project="text-recognizer", config=experiment_config)
+ wandb.init(project="text-recognizer", config=experiment_config, resume=resume)
# Lets W&B save the model and track the gradients and optional parameters.
wandb.watch(model.network)
@@ -265,24 +287,29 @@ def run_experiment(
experiment_config["device"] = device
# Save the config used in the experiment folder.
- save_config(experiment_dir, experiment_config)
+ _save_config(experiment_dir, experiment_config)
+
+ # Prints a summary of the network in terminal.
+ model.summary(experiment_config["train_args"]["input_shape"])
# Load trainer.
trainer = Trainer(
- max_epochs=experiment_config["train_args"]["max_epochs"], callbacks=callbacks,
+ max_epochs=experiment_config["train_args"]["max_epochs"],
+ callbacks=callbacks,
+ transformer_model=experiment_config["train_args"]["transformer_model"],
)
# Train the model.
- if experiment_config["train"]:
+ if train:
trainer.fit(model)
# Run inference over test set.
- if experiment_config["test"]:
+ if test:
logger.info("Loading checkpoint with the best weights.")
model.load_from_checkpoint(model_dir / "best.pt")
logger.info("Running inference on test set.")
- if experiment_config["criterion"]["type"] in CUSTOM_LOSSES:
+ if experiment_config["criterion"]["type"] in custom_loss_module.__all__:
logger.info("Evaluating embedding.")
score = evaluate_embedding(model)
else:
@@ -314,7 +341,24 @@ def run_experiment(
@click.option(
"--nowandb", is_flag=False, help="If true, do not use wandb for this run."
)
-def run_cli(experiment_config: str, gpu: int, save: bool, nowandb: bool) -> None:
+@click.option("--notrain", is_flag=False, help="Do not train the model.")
+@click.option("--test", is_flag=True, help="If true, test the model.")
+@click.option("-v", "--verbose", count=True)
+@click.option("--checkpoint", type=str, help="Path to the experiment.")
+@click.option(
+ "--pretrained_weights", type=str, help="Path to pretrained model weights."
+)
+def run_cli(
+ experiment_config: str,
+ gpu: int,
+ save: bool,
+ nowandb: bool,
+ notrain: bool,
+ test: bool,
+ verbose: int,
+ checkpoint: Optional[str] = None,
+ pretrained_weights: Optional[str] = None,
+) -> None:
"""Run experiment."""
if gpu < 0:
gpu_manager = GPUManager(True)
@@ -323,7 +367,17 @@ def run_cli(experiment_config: str, gpu: int, save: bool, nowandb: bool) -> None
experiment_config = json.loads(experiment_config)
os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu}"
- run_experiment(experiment_config, save, device, use_wandb=not nowandb)
+ run_experiment(
+ experiment_config,
+ save,
+ device,
+ use_wandb=not nowandb,
+ train=not notrain,
+ test=test,
+ verbose=verbose,
+ checkpoint=checkpoint,
+ pretrained_weights=pretrained_weights,
+ )
if __name__ == "__main__":
diff --git a/src/training/trainer/callbacks/base.py b/src/training/trainer/callbacks/base.py
index f81fc1f..500b642 100644
--- a/src/training/trainer/callbacks/base.py
+++ b/src/training/trainer/callbacks/base.py
@@ -62,6 +62,14 @@ class Callback:
"""Called at the end of an epoch."""
pass
+ def on_test_begin(self) -> None:
+ """Called at the beginning of test."""
+ pass
+
+ def on_test_end(self) -> None:
+ """Called at the end of test."""
+ pass
+
class CallbackList:
"""Container for abstracting away callback calls."""
@@ -104,6 +112,16 @@ class CallbackList:
for callback in self._callbacks:
callback.on_fit_end()
+ def on_test_begin(self) -> None:
+ """Called when test begins."""
+ for callback in self._callbacks:
+ callback.on_test_begin()
+
+ def on_test_end(self) -> None:
+ """Called when test ends."""
+ for callback in self._callbacks:
+ callback.on_test_end()
+
def on_epoch_begin(self, epoch: int, logs: Optional[Dict] = None) -> None:
"""Called at the beginning of an epoch."""
for callback in self._callbacks:
diff --git a/src/training/trainer/callbacks/checkpoint.py b/src/training/trainer/callbacks/checkpoint.py
index 6fe06d3..a54e0a9 100644
--- a/src/training/trainer/callbacks/checkpoint.py
+++ b/src/training/trainer/callbacks/checkpoint.py
@@ -21,7 +21,7 @@ class Checkpoint(Callback):
def __init__(
self,
- checkpoint_path: Path,
+ checkpoint_path: Union[str, Path],
monitor: str = "accuracy",
mode: str = "auto",
min_delta: float = 0.0,
@@ -29,14 +29,14 @@ class Checkpoint(Callback):
"""Monitors a quantity that will allow us to determine the best model weights.
Args:
- checkpoint_path (Path): Path to the experiment with the checkpoint.
+ checkpoint_path (Union[str, Path]): Path to the experiment with the checkpoint.
monitor (str): Name of the quantity to monitor. Defaults to "accuracy".
mode (str): Description of parameter `mode`. Defaults to "auto".
min_delta (float): Description of parameter `min_delta`. Defaults to 0.0.
"""
super().__init__()
- self.checkpoint_path = checkpoint_path
+ self.checkpoint_path = Path(checkpoint_path)
self.monitor = monitor
self.mode = mode
self.min_delta = torch.tensor(min_delta)
diff --git a/src/training/trainer/callbacks/wandb_callbacks.py b/src/training/trainer/callbacks/wandb_callbacks.py
index d2df4d7..f24e5cc 100644
--- a/src/training/trainer/callbacks/wandb_callbacks.py
+++ b/src/training/trainer/callbacks/wandb_callbacks.py
@@ -64,37 +64,55 @@ class WandbImageLogger(Callback):
"""
super().__init__()
+ self.caption = None
self.example_indices = example_indices
+ self.test_sample_indices = None
self.num_examples = num_examples
self.transpose = Transpose() if use_transpose else None
def set_model(self, model: Type[Model]) -> None:
"""Sets the model and extracts validation images from the dataset."""
self.model = model
+ self.caption = "Validation Examples"
if self.example_indices is None:
self.example_indices = np.random.randint(
0, len(self.model.val_dataset), self.num_examples
)
- self.val_images = self.model.val_dataset.dataset.data[self.example_indices]
- self.val_targets = self.model.val_dataset.dataset.targets[self.example_indices]
- self.val_targets = self.val_targets.tolist()
+ self.images = self.model.val_dataset.dataset.data[self.example_indices]
+ self.targets = self.model.val_dataset.dataset.targets[self.example_indices]
+ self.targets = self.targets.tolist()
+
+ def on_test_begin(self) -> None:
+ """Get samples from test dataset."""
+ self.caption = "Test Examples"
+ if self.test_sample_indices is None:
+ self.test_sample_indices = np.random.randint(
+ 0, len(self.model.test_dataset), self.num_examples
+ )
+ self.images = self.model.test_dataset.data[self.test_sample_indices]
+ self.targets = self.model.test_dataset.targets[self.test_sample_indices]
+ self.targets = self.targets.tolist()
+
+ def on_test_end(self) -> None:
+ """Log test images."""
+ self.on_epoch_end(0, {})
def on_epoch_end(self, epoch: int, logs: Dict) -> None:
"""Get network predictions on validation images."""
images = []
- for i, image in enumerate(self.val_images):
+ for i, image in enumerate(self.images):
image = self.transpose(image) if self.transpose is not None else image
pred, conf = self.model.predict_on_image(image)
- if isinstance(self.val_targets[i], list):
+ if isinstance(self.targets[i], list):
ground_truth = "".join(
[
self.model.mapper(int(target_index))
- for target_index in self.val_targets[i]
+ for target_index in self.targets[i]
]
).rstrip("_")
else:
- ground_truth = self.val_targets[i]
+ ground_truth = self.targets[i]
caption = f"Prediction: {pred} Confidence: {conf:.3f} Ground Truth: {ground_truth}"
images.append(wandb.Image(image, caption=caption))
- wandb.log({"examples": images}, commit=False)
+ wandb.log({f"{self.caption}": images}, commit=False)
diff --git a/src/training/trainer/train.py b/src/training/trainer/train.py
index bd6a491..fb49103 100644
--- a/src/training/trainer/train.py
+++ b/src/training/trainer/train.py
@@ -4,6 +4,7 @@ from pathlib import Path
import time
from typing import Dict, List, Optional, Tuple, Type
+from einops import rearrange
from loguru import logger
import numpy as np
import torch
@@ -27,12 +28,18 @@ class Trainer:
# TODO: proper add teardown?
- def __init__(self, max_epochs: int, callbacks: List[Type[Callback]],) -> None:
+ def __init__(
+ self,
+ max_epochs: int,
+ callbacks: List[Type[Callback]],
+ transformer_model: bool = False,
+ ) -> None:
"""Initialization of the Trainer.
Args:
max_epochs (int): The maximum number of epochs in the training loop.
callbacks (CallbackList): List of callbacks to be called.
+ transformer_model (bool): Transformer model flag, modifies the input to the model. Default is False.
"""
# Training arguments.
@@ -43,6 +50,8 @@ class Trainer:
# Flag for setting callbacks.
self.callbacks_configured = False
+ self.transformer_model = transformer_model
+
# Model placeholders
self.model = None
@@ -97,10 +106,15 @@ class Trainer:
# Forward pass.
# Get the network prediction.
- output = self.model.forward(data)
+ if self.transformer_model:
+ output = self.model.network.forward(data, targets[:, :-1])
+ output = rearrange(output, "b t v -> (b t) v")
+ targets = rearrange(targets[:, 1:], "b t -> (b t)").long()
+ else:
+ output = self.model.forward(data)
# Compute the loss.
- loss = self.model.loss_fn(output, targets)
+ loss = self.model.criterion(output, targets)
# Backward pass.
# Clear the previous gradients.
@@ -148,10 +162,15 @@ class Trainer:
# Forward pass.
# Get the network prediction.
# Use SWA if available and using test dataset.
- output = self.model.forward(data)
+ if self.transformer_model:
+ output = self.model.network.forward(data, targets[:, :-1])
+ output = rearrange(output, "b t v -> (b t) v")
+ targets = rearrange(targets[:, 1:], "b t -> (b t)").long()
+ else:
+ output = self.model.forward(data)
# Compute the loss.
- loss = self.model.loss_fn(output, targets)
+ loss = self.model.criterion(output, targets)
# Compute metrics.
metrics = self.compute_metrics(output, targets, loss, loss_avg)
@@ -237,6 +256,8 @@ class Trainer:
# Configure callbacks.
self._configure_callbacks()
+ self.callbacks.on_test_begin()
+
self.model.eval()
# Check if SWA network is available.
@@ -252,6 +273,8 @@ class Trainer:
metrics = self.validation_step(batch, samples, loss_avg)
summary.append(metrics)
+ self.callbacks.on_test_end()
+
# Compute mean of all test metrics.
metrics_mean = {
"test_" + metric: np.mean([x[metric] for x in summary])
generated by cgit v1.2.3-70-g09d2 (git 2.46.0) at 2024-12-25 17:31:12 +0000