{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discriminator(\n",
      "  (conv_pipe): Sequential(\n",
      "    (0): Conv2d(3, 100, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
      "    (1): ReLU()\n",
      "    (2): Conv2d(100, 200, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
      "    (3): BatchNorm2d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (4): ReLU()\n",
      "    (5): Conv2d(200, 400, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
      "    (6): BatchNorm2d(400, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (7): ReLU()\n",
      "    (8): Conv2d(400, 800, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
      "    (9): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (10): ReLU()\n",
      "    (11): Conv2d(800, 1600, kernel_size=(8, 8), stride=(2, 2), padding=(1, 1))\n",
      "    (12): BatchNorm2d(1600, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (13): ReLU()\n",
      "    (14): Conv2d(1600, 1, kernel_size=(4, 4), stride=(1, 1))\n",
      "    (15): Sigmoid()\n",
      "  )\n",
      ")\n",
      "Generator(\n",
      "  (pipe): Sequential(\n",
      "    (0): ConvTranspose2d(200, 800, kernel_size=(6, 6), stride=(1, 1))\n",
      "    (1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU()\n",
      "    (3): ConvTranspose2d(800, 400, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
      "    (4): BatchNorm2d(400, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (5): ReLU()\n",
      "    (6): ConvTranspose2d(400, 200, kernel_size=(6, 6), stride=(1, 1), padding=(1, 1))\n",
      "    (7): BatchNorm2d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (8): ReLU()\n",
      "    (9): ConvTranspose2d(200, 100, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
      "    (10): BatchNorm2d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (11): ReLU()\n",
      "    (12): ConvTranspose2d(100, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
      "    (13): Tanh()\n",
      "  )\n",
      ")\n",
      "Generator output\n",
      "torch.Size([16, 3, 60, 60])\n",
      "Traceback (most recent call last):\n",
      "  File \"ganSynthesisImage_100.py\", line 249, in <module>\n",
      "    dis_output_true_v = net_discr(batch_v)\n",
      "  File \"/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py\", line 489, in __call__\n",
      "    result = self.forward(*input, **kwargs)\n",
      "  File \"ganSynthesisImage_100.py\", line 75, in forward\n",
      "    conv_out = self.conv_pipe(x)\n",
      "  File \"/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py\", line 489, in __call__\n",
      "    result = self.forward(*input, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.6/site-packages/torch/nn/modules/container.py\", line 92, in forward\n",
      "    input = module(input)\n",
      "  File \"/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py\", line 489, in __call__\n",
      "    result = self.forward(*input, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.6/site-packages/torch/nn/modules/conv.py\", line 320, in forward\n",
      "    self.padding, self.dilation, self.groups)\n",
      "RuntimeError: std::exception\n"
     ]
    }
   ],
   "source": [
    "!python ganSynthesisImage_100.py --folder gan_synthesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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