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-rw-r--r--src/notebooks/00-testing-stuff-out.ipynb1652
1 files changed, 129 insertions, 1523 deletions
diff --git a/src/notebooks/00-testing-stuff-out.ipynb b/src/notebooks/00-testing-stuff-out.ipynb
index 3f008c3..ff9fb20 100644
--- a/src/notebooks/00-testing-stuff-out.ipynb
+++ b/src/notebooks/00-testing-stuff-out.ipynb
@@ -22,307 +22,11 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "True"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "torch.cuda.is_available()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "torch.nn.modules.activation.SELU"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "torch.nn.SELU"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "a = \"nNone\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "b = a or \"relu\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'nnone'"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "b.lower()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'nNone'"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "b"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "torch.optim.lr_scheduler.StepLR"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "getattr(torch.optim.lr_scheduler, \"StepLR\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "a = getattr(torch.nn, \"ReLU\")()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
+ "execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
- "a"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "loss = getattr(torch.nn, \"L1Loss\")()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "input = torch.randn(3, 5, requires_grad=True)\n",
- "target = torch.randn(3, 5)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "b = torch.randn(2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "b"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "a(b)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "output = loss(input, target)\n",
- "output.backward()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor(1.1283)"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "torch.tensor(output.item())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "s = 1.\n",
- "if s is not None:\n",
- " assert 0.0 < s < 1.0"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "class A:\n",
- " @property\n",
- " def __name__(self):\n",
- " return \"adafa\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "a = A()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "a.__name__"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "from training.gpu_manager import GPUManager"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "gpu_manager = GPUManager(True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2020-07-21 14:10:13.170 | DEBUG | training.gpu_manager:_get_free_gpu:57 - pid 11721 picking gpu 0\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "0"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "gpu_manager.get_free_gpu()"
+ "from text_recognizer.networks.residual_network import IdentityBlock, ResidualBlock, BasicBlock, BottleNeckBlock, ResidualLayer, Encoder, ResidualNetwork"
]
},
{
@@ -331,7 +35,7 @@
"metadata": {},
"outputs": [],
"source": [
- "from pathlib import Path"
+ "IdentityBlock(32, 64)"
]
},
{
@@ -340,7 +44,7 @@
"metadata": {},
"outputs": [],
"source": [
- "p = Path(\"/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training\")"
+ "ResidualBlock(32, 64)"
]
},
{
@@ -349,342 +53,11 @@
"metadata": {},
"outputs": [],
"source": [
+ "dummy = torch.ones((1, 32, 224, 224))\n",
"\n",
- "str(p).split(\"/\")[0] + \"/\" + str(p).split(\"/\")[1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "p.parents[0].resolve()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "p.exists()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "d = 'Experiment JSON, e.g. \\'{\"dataset\": \"EmnistDataset\", \"model\": \"CharacterModel\", \"network\": \"mlp\"}\\''"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(d)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "import yaml"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "path = \"/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/sample_experiment.yml\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "with open(path) as f:\n",
- " d = yaml.safe_load(f)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "experiment_config = d[\"experiments\"][0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'dataloader': 'EmnistDataLoader',\n",
- " 'data_loader_args': {'splits': ['train', 'val'],\n",
- " 'sample_to_balance': True,\n",
- " 'subsample_fraction': None,\n",
- " 'transform': None,\n",
- " 'target_transform': None,\n",
- " 'batch_size': 256,\n",
- " 'shuffle': True,\n",
- " 'num_workers': 0,\n",
- " 'cuda': True,\n",
- " 'seed': 4711},\n",
- " 'model': 'CharacterModel',\n",
- " 'metrics': ['accuracy'],\n",
- " 'network': 'MLP',\n",
- " 'network_args': {'input_size': 784, 'num_layers': 2},\n",
- " 'train_args': {'batch_size': 256, 'epochs': 16},\n",
- " 'criterion': 'CrossEntropyLoss',\n",
- " 'criterion_args': {'weight': None, 'ignore_index': -100, 'reduction': 'mean'},\n",
- " 'optimizer': 'AdamW',\n",
- " 'optimizer_args': {'lr': 0.0003,\n",
- " 'betas': [0.9, 0.999],\n",
- " 'eps': 1e-08,\n",
- " 'weight_decay': 0,\n",
- " 'amsgrad': False},\n",
- " 'lr_scheduler': 'OneCycleLR',\n",
- " 'lr_scheduler_args': {'max_lr': 3e-05, 'epochs': 16}}"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "experiment_config"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "import importlib"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [],
- "source": [
- "network_module = importlib.import_module(\"text_recognizer.networks\")\n",
- "network_fn_ = getattr(network_module, experiment_config[\"network\"])\n",
- "network_args = experiment_config.get(\"network_args\", {})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(1, 784)"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "(1,) + (network_args[\"input_size\"],)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "optimizer_ = getattr(torch.optim, experiment_config[\"optimizer\"])\n",
- "optimizer_args = experiment_config.get(\"optimizer_args\", {})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "optimizer_"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "optimizer_args"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "network_args"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "network_fn_"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "net = network_fn_(**network_args)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "optimizer_(net.parameters() , **optimizer_args)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "criterion_ = getattr(torch.nn, experiment_config[\"criterion\"])\n",
- "criterion_args = experiment_config.get(\"criterion_args\", {})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "criterion_(**criterion_args)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "models_module = importlib.import_module(\"text_recognizer.models\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "metrics = {metric: getattr(models_module, metric) for metric in experiment_config[\"metrics\"]}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "torch.randn(3, 10)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "torch.randn(3, 1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "metrics['accuracy'](torch.randn(3, 10), torch.randn(3, 1))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "metric_fn_ = getattr(models_module, experiment_config[\"metric\"])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "metric_fn_"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "2.e-3"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "lr_scheduler_ = getattr(\n",
- " torch.optim.lr_scheduler, experiment_config[\"lr_scheduler\"]\n",
- ")\n",
- "lr_scheduler_args = experiment_config.get(\"lr_scheduler_args\", {})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "\"OneCycleLR\" in str(lr_scheduler_)"
+ "block = BasicBlock(32, 64)\n",
+ "block(dummy).shape\n",
+ "print(block)"
]
},
{
@@ -693,9 +66,11 @@
"metadata": {},
"outputs": [],
"source": [
- "datasets_module = importlib.import_module(\"text_recognizer.datasets\")\n",
- "data_loader_ = getattr(datasets_module, experiment_config[\"dataloader\"])\n",
- "data_loader_args = experiment_config.get(\"data_loader_args\", {})"
+ "dummy = torch.ones((1, 32, 10, 10))\n",
+ "\n",
+ "block = BottleNeckBlock(32, 64)\n",
+ "block(dummy).shape\n",
+ "print(block)"
]
},
{
@@ -704,422 +79,10 @@
"metadata": {},
"outputs": [],
"source": [
- "data_loader_(**data_loader_args)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "cuda = \"cuda:0\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import re\n",
- "cleanString = re.sub('[^A-Za-z]+','', cuda )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "cleanString = re.sub('[^0-9]+','', cuda )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'0'"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "cleanString"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "([28, 28], 1)"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "([28, 28], ) + (1,)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "list(range(3-1))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(1,)"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "tuple([1])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "from glob import glob"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/weights/CharacterModel_Emnist_MLP_weights.pt']"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "glob(\"/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/weights/CharacterModel_*MLP_weights.pt\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "def test(a, b, c, d):\n",
- " print(a,b,c,d)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "f = {\"a\": 2, \"b\": 3, \"c\": 4}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "dict_items([('a', 2), ('b', 3), ('c', 4)])\n"
- ]
- }
- ],
- "source": [
- "print(f.items())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "2 3 4 1\n"
- ]
- }
- ],
- "source": [
- "test(**f, d=1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "path = \"/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/*\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "l = glob(path)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "l.sort()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "True"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "'/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_124928' in l"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_124928',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_141139',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_141213',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_141433',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_141702',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_145028',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_150212',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_150301',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_150317',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_151135',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_151408',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_153144',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_153207',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_153310',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_175150',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_180741',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_181933',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_183347',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_190044',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_190633',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_190738',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_191111',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_191310',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_191412',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_191504',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_191826',\n",
- " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0722_191559']"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "l"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "class ModeKeys:\n",
- " \"\"\"Mode keys for CallbackList.\"\"\"\n",
+ "dummy = torch.ones((1, 64, 48, 48))\n",
"\n",
- " TRAIN = \"train\"\n",
- " VALIDATION = \"validation\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "m = ModeKeys()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'train'"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "m.TRAIN"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([1.00000000e-05, 1.26485522e-05, 1.59985872e-05, 2.02358965e-05,\n",
- " 2.55954792e-05, 3.23745754e-05, 4.09491506e-05, 5.17947468e-05,\n",
- " 6.55128557e-05, 8.28642773e-05, 1.04811313e-04, 1.32571137e-04,\n",
- " 1.67683294e-04, 2.12095089e-04, 2.68269580e-04, 3.39322177e-04,\n",
- " 4.29193426e-04, 5.42867544e-04, 6.86648845e-04, 8.68511374e-04,\n",
- " 1.09854114e-03, 1.38949549e-03, 1.75751062e-03, 2.22299648e-03,\n",
- " 2.81176870e-03, 3.55648031e-03, 4.49843267e-03, 5.68986603e-03,\n",
- " 7.19685673e-03, 9.10298178e-03, 1.15139540e-02, 1.45634848e-02,\n",
- " 1.84206997e-02, 2.32995181e-02, 2.94705170e-02, 3.72759372e-02,\n",
- " 4.71486636e-02, 5.96362332e-02, 7.54312006e-02, 9.54095476e-02,\n",
- " 1.20679264e-01, 1.52641797e-01, 1.93069773e-01, 2.44205309e-01,\n",
- " 3.08884360e-01, 3.90693994e-01, 4.94171336e-01, 6.25055193e-01,\n",
- " 7.90604321e-01, 1.00000000e+00])"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.logspace(-5, 0, base=10)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0.018420699693267165"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.random.choice(np.logspace(-5, 0, base=10))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "metadata": {},
- "outputs": [],
- "source": [
- "import tqdm"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tqdm.notebook.tqdm_notebook"
- ]
- },
- "execution_count": 52,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "tqdm.auto.tqdm"
+ "layer = ResidualLayer(64, 128, block=BasicBlock, num_blocks=3)\n",
+ "layer(dummy).shape"
]
},
{
@@ -1128,380 +91,107 @@
"metadata": {},
"outputs": [],
"source": [
- "tqdm.auto.tqdm"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 54,
- "metadata": {},
- "outputs": [],
- "source": [
- "def test():\n",
- " for i in tqdm.auto.tqdm(range(9)):\n",
- " pass\n",
- " print(i)\n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 55,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "e1d3b25d4ee141e882e316ec54e79d60",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "HBox(children=(FloatProgress(value=0.0, max=9.0), HTML(value='')))"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "8\n"
- ]
- }
- ],
- "source": [
- "test()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 58,
- "metadata": {},
- "outputs": [],
- "source": [
- "from time import sleep"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 71,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "41b743273ce14236bcb65782dbcd2e75",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
- "source": [
- "pbar = tqdm.auto.tqdm([\"a\", \"b\", \"c\", \"d\"], leave=True)\n",
- "for char in pbar:\n",
- " pbar.set_description(\"Processing %s\" % char)\n",
- "# pbar.set_prefix()\n",
- " sleep(0.25)\n",
- "pbar.set_postfix({\"hej\": 0.32})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 83,
- "metadata": {},
- "outputs": [],
- "source": [
- "pbar.close()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 96,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "cb5ad8d6109f4b1495b8fc7422bafd01",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
- "source": [
- "with tqdm.auto.tqdm(total=10, bar_format=\"{postfix[0]} {postfix[1][value]:>8.2g}\",\n",
- " postfix=[\"Batch\", dict(value=0)]) as t:\n",
- " for i in range(10):\n",
- " sleep(0.1)\n",
- "# t.postfix[2][\"value\"] = 3 \n",
- " t.postfix[1][\"value\"] = i / 2\n",
- " t.update()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 99,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "0b341d49ad074823881e84a538bcad0c",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
- "source": [
- "with tqdm.auto.tqdm(total=100, leave=True) as pbar:\n",
- " for i in range(2):\n",
- " for i in range(10):\n",
- " sleep(0.1)\n",
- " pbar.update(10)\n",
- " pbar.set_postfix({\"adaf\": 23})\n",
- " pbar.set_postfix({\"hej\": 0.32})\n",
- " pbar.reset()"
+ "blocks_sizes=[64, 128, 256, 512]\n",
+ "list(zip(blocks_sizes, blocks_sizes[1:]))"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
- "from text_recognizer.networks.residual_network import IdentityBlock, ResidualBlock, BasicBlock, BottleNeckBlock, ResidualLayer, Encoder, ResidualNetwork"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "IdentityBlock(\n",
- " (blocks): Identity()\n",
- " (activation_fn): ReLU(inplace=True)\n",
- " (shortcut): Identity()\n",
- ")"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "IdentityBlock(32, 64)"
+ "e = Encoder(depths=[2, 1], block_sizes= [96, 128])"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 75,
"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",
+ "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": 12,
+ "execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "ResidualBlock(32, 64)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "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"
- ]
- }
- ],
- "source": [
- "dummy = torch.ones((1, 32, 224, 224))\n",
- "\n",
- "block = BasicBlock(32, 64)\n",
- "block(dummy).shape\n",
- "print(block)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "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"
- ]
- }
- ],
- "source": [
- "dummy = torch.ones((1, 32, 10, 10))\n",
- "\n",
- "block = BottleNeckBlock(32, 64)\n",
- "block(dummy).shape\n",
- "print(block)"
+ "Encoder(**{\"depths\": [2, 1], \"block_sizes\": [96, 128]})"
]
},
{
"cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "torch.Size([1, 128, 24, 24])"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "dummy = torch.ones((1, 64, 48, 48))\n",
- "\n",
- "layer = ResidualLayer(64, 128, block=BasicBlock, num_blocks=3)\n",
- "layer(dummy).shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[(64, 128), (128, 256), (256, 512)]"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "blocks_sizes=[64, 128, 256, 512]\n",
- "list(zip(blocks_sizes, blocks_sizes[1:]))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [],
- "source": [
- "e = Encoder(depths=[1, 1])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
+ "execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
@@ -1510,7 +200,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 70,
"metadata": {},
"outputs": [
{
@@ -1520,41 +210,50 @@
"----------------------------------------------------------------\n",
" Layer (type) Output Shape Param #\n",
"================================================================\n",
- " Conv2d-1 [-1, 32, 15, 15] 800\n",
- " BatchNorm2d-2 [-1, 32, 15, 15] 64\n",
- " ReLU-3 [-1, 32, 15, 15] 0\n",
- " MaxPool2d-4 [-1, 32, 8, 8] 0\n",
- " Conv2dAuto-5 [-1, 32, 8, 8] 9,216\n",
- " BatchNorm2d-6 [-1, 32, 8, 8] 64\n",
- " ReLU-7 [-1, 32, 8, 8] 0\n",
- " ReLU-8 [-1, 32, 8, 8] 0\n",
- " Conv2dAuto-9 [-1, 32, 8, 8] 9,216\n",
- " BatchNorm2d-10 [-1, 32, 8, 8] 64\n",
- " ReLU-11 [-1, 32, 8, 8] 0\n",
- " ReLU-12 [-1, 32, 8, 8] 0\n",
- " BasicBlock-13 [-1, 32, 8, 8] 0\n",
- " ResidualLayer-14 [-1, 32, 8, 8] 0\n",
- " Conv2d-15 [-1, 64, 4, 4] 2,048\n",
- " BatchNorm2d-16 [-1, 64, 4, 4] 128\n",
- " Conv2dAuto-17 [-1, 64, 4, 4] 18,432\n",
- " BatchNorm2d-18 [-1, 64, 4, 4] 128\n",
- " ReLU-19 [-1, 64, 4, 4] 0\n",
- " ReLU-20 [-1, 64, 4, 4] 0\n",
- " Conv2dAuto-21 [-1, 64, 4, 4] 36,864\n",
- " BatchNorm2d-22 [-1, 64, 4, 4] 128\n",
- " ReLU-23 [-1, 64, 4, 4] 0\n",
- " ReLU-24 [-1, 64, 4, 4] 0\n",
- " BasicBlock-25 [-1, 64, 4, 4] 0\n",
- " ResidualLayer-26 [-1, 64, 4, 4] 0\n",
+ " Conv2d-1 [-1, 96, 16, 16] 864\n",
+ " BatchNorm2d-2 [-1, 96, 16, 16] 192\n",
+ " ReLU-3 [-1, 96, 16, 16] 0\n",
+ " MaxPool2d-4 [-1, 96, 8, 8] 0\n",
+ " Conv2dAuto-5 [-1, 96, 8, 8] 82,944\n",
+ " BatchNorm2d-6 [-1, 96, 8, 8] 192\n",
+ " ReLU-7 [-1, 96, 8, 8] 0\n",
+ " ReLU-8 [-1, 96, 8, 8] 0\n",
+ " Conv2dAuto-9 [-1, 96, 8, 8] 82,944\n",
+ " BatchNorm2d-10 [-1, 96, 8, 8] 192\n",
+ " ReLU-11 [-1, 96, 8, 8] 0\n",
+ " ReLU-12 [-1, 96, 8, 8] 0\n",
+ " BasicBlock-13 [-1, 96, 8, 8] 0\n",
+ " Conv2dAuto-14 [-1, 96, 8, 8] 82,944\n",
+ " BatchNorm2d-15 [-1, 96, 8, 8] 192\n",
+ " ReLU-16 [-1, 96, 8, 8] 0\n",
+ " ReLU-17 [-1, 96, 8, 8] 0\n",
+ " Conv2dAuto-18 [-1, 96, 8, 8] 82,944\n",
+ " BatchNorm2d-19 [-1, 96, 8, 8] 192\n",
+ " ReLU-20 [-1, 96, 8, 8] 0\n",
+ " ReLU-21 [-1, 96, 8, 8] 0\n",
+ " BasicBlock-22 [-1, 96, 8, 8] 0\n",
+ " ResidualLayer-23 [-1, 96, 8, 8] 0\n",
+ " Conv2d-24 [-1, 128, 4, 4] 12,288\n",
+ " BatchNorm2d-25 [-1, 128, 4, 4] 256\n",
+ " Conv2dAuto-26 [-1, 128, 4, 4] 110,592\n",
+ " BatchNorm2d-27 [-1, 128, 4, 4] 256\n",
+ " ReLU-28 [-1, 128, 4, 4] 0\n",
+ " ReLU-29 [-1, 128, 4, 4] 0\n",
+ " Conv2dAuto-30 [-1, 128, 4, 4] 147,456\n",
+ " BatchNorm2d-31 [-1, 128, 4, 4] 256\n",
+ " ReLU-32 [-1, 128, 4, 4] 0\n",
+ " ReLU-33 [-1, 128, 4, 4] 0\n",
+ " BasicBlock-34 [-1, 128, 4, 4] 0\n",
+ " ResidualLayer-35 [-1, 128, 4, 4] 0\n",
"================================================================\n",
- "Total params: 77,152\n",
- "Trainable params: 77,152\n",
+ "Total params: 604,704\n",
+ "Trainable params: 604,704\n",
"Non-trainable params: 0\n",
"----------------------------------------------------------------\n",
"Input size (MB): 0.00\n",
- "Forward/backward pass size (MB): 0.43\n",
- "Params size (MB): 0.29\n",
- "Estimated Total Size (MB): 0.73\n",
+ "Forward/backward pass size (MB): 1.69\n",
+ "Params size (MB): 2.31\n",
+ "Estimated Total Size (MB): 4.00\n",
"----------------------------------------------------------------\n"
]
}
@@ -1562,99 +261,6 @@
"source": [
"summary(e, (1, 28, 28), device=\"cpu\")"
]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [],
- "source": [
- "resnet = ResidualNetwork(1, 80, activation=\"selu\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "----------------------------------------------------------------\n",
- " Layer (type) Output Shape Param #\n",
- "================================================================\n",
- " Conv2d-1 [-1, 32, 15, 15] 800\n",
- " BatchNorm2d-2 [-1, 32, 15, 15] 64\n",
- " SELU-3 [-1, 32, 15, 15] 0\n",
- " MaxPool2d-4 [-1, 32, 8, 8] 0\n",
- " Conv2dAuto-5 [-1, 32, 8, 8] 9,216\n",
- " BatchNorm2d-6 [-1, 32, 8, 8] 64\n",
- " SELU-7 [-1, 32, 8, 8] 0\n",
- " SELU-8 [-1, 32, 8, 8] 0\n",
- " Conv2dAuto-9 [-1, 32, 8, 8] 9,216\n",
- " BatchNorm2d-10 [-1, 32, 8, 8] 64\n",
- " SELU-11 [-1, 32, 8, 8] 0\n",
- " SELU-12 [-1, 32, 8, 8] 0\n",
- " BasicBlock-13 [-1, 32, 8, 8] 0\n",
- " Conv2dAuto-14 [-1, 32, 8, 8] 9,216\n",
- " BatchNorm2d-15 [-1, 32, 8, 8] 64\n",
- " SELU-16 [-1, 32, 8, 8] 0\n",
- " SELU-17 [-1, 32, 8, 8] 0\n",
- " Conv2dAuto-18 [-1, 32, 8, 8] 9,216\n",
- " BatchNorm2d-19 [-1, 32, 8, 8] 64\n",
- " SELU-20 [-1, 32, 8, 8] 0\n",
- " SELU-21 [-1, 32, 8, 8] 0\n",
- " BasicBlock-22 [-1, 32, 8, 8] 0\n",
- " ResidualLayer-23 [-1, 32, 8, 8] 0\n",
- " Conv2d-24 [-1, 64, 4, 4] 2,048\n",
- " BatchNorm2d-25 [-1, 64, 4, 4] 128\n",
- " Conv2dAuto-26 [-1, 64, 4, 4] 18,432\n",
- " BatchNorm2d-27 [-1, 64, 4, 4] 128\n",
- " SELU-28 [-1, 64, 4, 4] 0\n",
- " SELU-29 [-1, 64, 4, 4] 0\n",
- " Conv2dAuto-30 [-1, 64, 4, 4] 36,864\n",
- " BatchNorm2d-31 [-1, 64, 4, 4] 128\n",
- " SELU-32 [-1, 64, 4, 4] 0\n",
- " SELU-33 [-1, 64, 4, 4] 0\n",
- " BasicBlock-34 [-1, 64, 4, 4] 0\n",
- " Conv2dAuto-35 [-1, 64, 4, 4] 36,864\n",
- " BatchNorm2d-36 [-1, 64, 4, 4] 128\n",
- " SELU-37 [-1, 64, 4, 4] 0\n",
- " SELU-38 [-1, 64, 4, 4] 0\n",
- " Conv2dAuto-39 [-1, 64, 4, 4] 36,864\n",
- " BatchNorm2d-40 [-1, 64, 4, 4] 128\n",
- " SELU-41 [-1, 64, 4, 4] 0\n",
- " SELU-42 [-1, 64, 4, 4] 0\n",
- " BasicBlock-43 [-1, 64, 4, 4] 0\n",
- " ResidualLayer-44 [-1, 64, 4, 4] 0\n",
- " Encoder-45 [-1, 64, 4, 4] 0\n",
- " Reduce-46 [-1, 64] 0\n",
- " Linear-47 [-1, 80] 5,200\n",
- " Decoder-48 [-1, 80] 0\n",
- "================================================================\n",
- "Total params: 174,896\n",
- "Trainable params: 174,896\n",
- "Non-trainable params: 0\n",
- "----------------------------------------------------------------\n",
- "Input size (MB): 0.00\n",
- "Forward/backward pass size (MB): 0.65\n",
- "Params size (MB): 0.67\n",
- "Estimated Total Size (MB): 1.32\n",
- "----------------------------------------------------------------\n"
- ]
- }
- ],
- "source": [
- "summary(resnet, (1, 28, 28), device=\"cpu\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {