Efficient net and non working transformer model.

1 files changed, 147 insertions, 408 deletions

diff --git a/notebooks/00-testing-stuff-out.ipynb b/notebooks/00-testing-stuff-out.ipynb
index e6cf099..7c7b3a6 100644
--- a/notebooks/00-testing-stuff-out.ipynb
+++ b/notebooks/00-testing-stuff-out.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,110 +52,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "seed: 4711\n",
-      "network:\n",
-      "  desc: Configuration of the PyTorch neural network.\n",
-      "  type: VQVAE\n",
-      "  args:\n",
-      "    in_channels: 1\n",
-      "    channels:\n",
-      "    - 32\n",
-      "    - 64\n",
-      "    - 96\n",
-      "    - 96\n",
-      "    - 128\n",
-      "    kernel_sizes:\n",
-      "    - 4\n",
-      "    - 4\n",
-      "    - 4\n",
-      "    - 4\n",
-      "    - 4\n",
-      "    strides:\n",
-      "    - 2\n",
-      "    - 2\n",
-      "    - 2\n",
-      "    - 2\n",
-      "    - 2\n",
-      "    num_residual_layers: 2\n",
-      "    embedding_dim: 128\n",
-      "    num_embeddings: 1024\n",
-      "    upsampling: null\n",
-      "    beta: 0.25\n",
-      "    activation: leaky_relu\n",
-      "    dropout_rate: 0.1\n",
-      "model:\n",
-      "  desc: Configuration of the PyTorch Lightning model.\n",
-      "  type: LitVQVAEModel\n",
-      "  args:\n",
-      "    optimizer:\n",
-      "      type: MADGRAD\n",
-      "      args:\n",
-      "        lr: 0.001\n",
-      "        momentum: 0.9\n",
-      "        weight_decay: 0\n",
-      "        eps: 1.0e-06\n",
-      "    lr_scheduler:\n",
-      "      type: OneCycleLR\n",
-      "      args:\n",
-      "        interval: step\n",
-      "        max_lr: 0.001\n",
-      "        three_phase: true\n",
-      "        epochs: 1024\n",
-      "        steps_per_epoch: 317\n",
-      "    criterion:\n",
-      "      type: MSELoss\n",
-      "      args:\n",
-      "        reduction: mean\n",
-      "    monitor: val_loss\n",
-      "    mapping: sentence_piece\n",
-      "data:\n",
-      "  desc: Configuration of the training/test data.\n",
-      "  type: IAMExtendedParagraphs\n",
-      "  args:\n",
-      "    batch_size: 64\n",
-      "    num_workers: 12\n",
-      "    train_fraction: 0.8\n",
-      "    augment: true\n",
-      "callbacks:\n",
-      "- type: ModelCheckpoint\n",
-      "  args:\n",
-      "    monitor: val_loss\n",
-      "    mode: min\n",
-      "    save_last: true\n",
-      "- type: LearningRateMonitor\n",
-      "  args:\n",
-      "    logging_interval: step\n",
-      "trainer:\n",
-      "  desc: Configuration of the PyTorch Lightning Trainer.\n",
-      "  args:\n",
-      "    stochastic_weight_avg: false\n",
-      "    auto_scale_batch_size: binsearch\n",
-      "    gradient_clip_val: 0\n",
-      "    fast_dev_run: false\n",
-      "    gpus: 1\n",
-      "    precision: 16\n",
-      "    max_epochs: 1024\n",
-      "    terminate_on_nan: true\n",
-      "    weights_summary: full\n",
-      "load_checkpoint: null\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(OmegaConf.to_yaml(conf))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -164,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,167 +79,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "VQVAE(\n",
-       "  (encoder): Encoder(\n",
-       "    (encoder): Sequential(\n",
-       "      (0): Sequential(\n",
-       "        (0): Conv2d(1, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "        (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
-       "      )\n",
-       "      (1): Dropout(p=0.1, inplace=False)\n",
-       "      (2): Sequential(\n",
-       "        (0): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "        (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
-       "      )\n",
-       "      (3): Dropout(p=0.1, inplace=False)\n",
-       "      (4): Sequential(\n",
-       "        (0): Conv2d(64, 96, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "        (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
-       "      )\n",
-       "      (5): Dropout(p=0.1, inplace=False)\n",
-       "      (6): Sequential(\n",
-       "        (0): Conv2d(96, 96, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "        (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
-       "      )\n",
-       "      (7): Dropout(p=0.1, inplace=False)\n",
-       "      (8): Sequential(\n",
-       "        (0): Conv2d(96, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "        (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
-       "      )\n",
-       "      (9): Dropout(p=0.1, inplace=False)\n",
-       "      (10): _ResidualBlock(\n",
-       "        (block): Sequential(\n",
-       "          (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): Dropout(p=0.1, inplace=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (11): _ResidualBlock(\n",
-       "        (block): Sequential(\n",
-       "          (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): Dropout(p=0.1, inplace=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (12): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
-       "    )\n",
-       "    (vector_quantizer): VectorQuantizer(\n",
-       "      (embedding): Embedding(1024, 128)\n",
-       "    )\n",
-       "  )\n",
-       "  (decoder): Decoder(\n",
-       "    (res_block): Sequential(\n",
-       "      (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
-       "      (1): _ResidualBlock(\n",
-       "        (block): Sequential(\n",
-       "          (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): Dropout(p=0.1, inplace=False)\n",
-       "        )\n",
-       "      )\n",
-       "      (2): _ResidualBlock(\n",
-       "        (block): Sequential(\n",
-       "          (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "          (1): ReLU(inplace=True)\n",
-       "          (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "          (3): Dropout(p=0.1, inplace=False)\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (upsampling_block): Sequential(\n",
-       "      (0): Sequential(\n",
-       "        (0): ConvTranspose2d(128, 96, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "        (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
-       "      )\n",
-       "      (1): Dropout(p=0.1, inplace=False)\n",
-       "      (2): Sequential(\n",
-       "        (0): ConvTranspose2d(96, 96, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "        (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
-       "      )\n",
-       "      (3): Dropout(p=0.1, inplace=False)\n",
-       "      (4): Sequential(\n",
-       "        (0): ConvTranspose2d(96, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "        (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
-       "      )\n",
-       "      (5): Dropout(p=0.1, inplace=False)\n",
-       "      (6): Sequential(\n",
-       "        (0): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "        (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
-       "      )\n",
-       "      (7): Dropout(p=0.1, inplace=False)\n",
-       "      (8): ConvTranspose2d(32, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "      (9): Tanh()\n",
-       "    )\n",
-       "    (decoder): Sequential(\n",
-       "      (0): Sequential(\n",
-       "        (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
-       "        (1): _ResidualBlock(\n",
-       "          (block): Sequential(\n",
-       "            (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "        (2): _ResidualBlock(\n",
-       "          (block): Sequential(\n",
-       "            (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "            (1): ReLU(inplace=True)\n",
-       "            (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "            (3): Dropout(p=0.1, inplace=False)\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "      (1): Sequential(\n",
-       "        (0): Sequential(\n",
-       "          (0): ConvTranspose2d(128, 96, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "          (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
-       "        )\n",
-       "        (1): Dropout(p=0.1, inplace=False)\n",
-       "        (2): Sequential(\n",
-       "          (0): ConvTranspose2d(96, 96, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "          (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
-       "        )\n",
-       "        (3): Dropout(p=0.1, inplace=False)\n",
-       "        (4): Sequential(\n",
-       "          (0): ConvTranspose2d(96, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "          (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
-       "        )\n",
-       "        (5): Dropout(p=0.1, inplace=False)\n",
-       "        (6): Sequential(\n",
-       "          (0): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "          (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
-       "        )\n",
-       "        (7): Dropout(p=0.1, inplace=False)\n",
-       "        (8): ConvTranspose2d(32, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "        (9): Tanh()\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       ")"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "vae"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -342,275 +97,259 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "proj = nn.Conv2d(1, 32, kernel_size=16, stride=16)"
+    "vae.encoder(datum)[0].shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "x = proj(datum)"
+    "vae(datum)[0].shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 32, 36, 40])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "x.shape"
+    "from text_recognizer.networks.backbones.efficientnet import EfficientNet"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "xx = x.flatten(2)"
+    "en = EfficientNet()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 32, 1440])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "xx.shape"
+    "datum = torch.randn([2, 1, 576, 640])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "xxx = xx.transpose(1,2)"
+    "trg = torch.randint(0, 1000, [2, 682])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 1440, 32])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "xxx.shape"
+    "trg.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from einops import rearrange"
+    "datum = torch.randn([2, 1, 224, 224])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [],
    "source": [
-    "xxxx = rearrange(x, \"b c h w -> b ( h w ) c\")"
+    "en(datum).shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 1440, 32])"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "xxxx.shape"
+    "path = \"../training/configs/cnn_transformer.yaml\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "        B, N, C = x.shape\n",
-    "        H, W = size\n",
-    "        assert N == 1 + H * W\n",
-    "\n",
-    "        # Extract CLS token and image tokens.\n",
-    "        cls_token, img_tokens = x[:, :1], x[:, 1:]                                       # Shape: [B, 1, C], [B, H*W, C].\n",
-    "        \n",
-    "        # Depthwise convolution.\n",
-    "        feat = img_tokens.transpose(1, 2).view(B, C, H, W)"
+    "conf = OmegaConf.load(path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 32, 36, 40])"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seed: 4711\n",
+      "network:\n",
+      "  desc: Configuration of the PyTorch neural network.\n",
+      "  type: CNNTransformer\n",
+      "  args:\n",
+      "    encoder:\n",
+      "      type: EfficientNet\n",
+      "      args: null\n",
+      "    num_decoder_layers: 4\n",
+      "    hidden_dim: 256\n",
+      "    num_heads: 4\n",
+      "    expansion_dim: 1024\n",
+      "    dropout_rate: 0.1\n",
+      "    transformer_activation: glu\n",
+      "model:\n",
+      "  desc: Configuration of the PyTorch Lightning model.\n",
+      "  type: LitTransformerModel\n",
+      "  args:\n",
+      "    optimizer:\n",
+      "      type: MADGRAD\n",
+      "      args:\n",
+      "        lr: 0.001\n",
+      "        momentum: 0.9\n",
+      "        weight_decay: 0\n",
+      "        eps: 1.0e-06\n",
+      "    lr_scheduler:\n",
+      "      type: OneCycleLR\n",
+      "      args:\n",
+      "        interval: step\n",
+      "        max_lr: 0.001\n",
+      "        three_phase: true\n",
+      "        epochs: 512\n",
+      "        steps_per_epoch: 1246\n",
+      "    criterion:\n",
+      "      type: CrossEntropyLoss\n",
+      "      args:\n",
+      "        weight: None\n",
+      "        ignore_index: -100\n",
+      "        reduction: mean\n",
+      "    monitor: val_loss\n",
+      "    mapping: sentence_piece\n",
+      "data:\n",
+      "  desc: Configuration of the training/test data.\n",
+      "  type: IAMExtendedParagraphs\n",
+      "  args:\n",
+      "    batch_size: 16\n",
+      "    num_workers: 12\n",
+      "    train_fraction: 0.8\n",
+      "    augment: true\n",
+      "callbacks:\n",
+      "- type: ModelCheckpoint\n",
+      "  args:\n",
+      "    monitor: val_loss\n",
+      "    mode: min\n",
+      "    save_last: true\n",
+      "- type: StochasticWeightAveraging\n",
+      "  args:\n",
+      "    swa_epoch_start: 0.8\n",
+      "    swa_lrs: 0.05\n",
+      "    annealing_epochs: 10\n",
+      "    annealing_strategy: cos\n",
+      "    device: null\n",
+      "- type: LearningRateMonitor\n",
+      "  args:\n",
+      "    logging_interval: step\n",
+      "- type: EarlyStopping\n",
+      "  args:\n",
+      "    monitor: val_loss\n",
+      "    mode: min\n",
+      "    patience: 10\n",
+      "trainer:\n",
+      "  desc: Configuration of the PyTorch Lightning Trainer.\n",
+      "  args:\n",
+      "    stochastic_weight_avg: true\n",
+      "    auto_scale_batch_size: binsearch\n",
+      "    gradient_clip_val: 0\n",
+      "    fast_dev_run: false\n",
+      "    gpus: 1\n",
+      "    precision: 16\n",
+      "    max_epochs: 512\n",
+      "    terminate_on_nan: true\n",
+      "    weights_summary: true\n",
+      "load_checkpoint: null\n",
+      "\n"
+     ]
     }
    ],
    "source": [
-    "xxx.transpose(1, 2).view(2, 32, 36, 40).shape"
+    "print(OmegaConf.to_yaml(conf))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "72.0"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "576 / 8"
+    "from text_recognizer.networks.cnn_transformer import CNNTransformer"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "80.0"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "640 / 8"
+    "t = CNNTransformer(input_shape=(1, 576, 640), output_shape=(682, 1), **conf.network.args)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 1, 576, 640])"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "datum.shape"
+    "t.encode(datum).shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 128, 18, 20])"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "vae.encoder(datum)[0].shape"
+    "trg.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "torch.Size([2, 1, 576, 640])"
+       "torch.Size([2, 682, 1004])"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "vae(datum)[0].shape"
+    "t(datum, trg).shape"
    ]
   },
   {