Unet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from Unet.model import Unet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.rand(64, 1, 500, 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "unet = Unet(1, 21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unet(\n",
      "  (in_layer): DoubleConv(\n",
      "    (double_conv): Sequential(\n",
      "      (0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (2): ReLU(inplace=True)\n",
      "      (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (5): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (down1): Down(\n",
      "    (maxpool_conv): Sequential(\n",
      "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "      (1): DoubleConv(\n",
      "        (double_conv): Sequential(\n",
      "          (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU(inplace=True)\n",
      "          (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (5): ReLU(inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (down2): Down(\n",
      "    (maxpool_conv): Sequential(\n",
      "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "      (1): DoubleConv(\n",
      "        (double_conv): Sequential(\n",
      "          (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU(inplace=True)\n",
      "          (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (5): ReLU(inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (down3): Down(\n",
      "    (maxpool_conv): Sequential(\n",
      "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "      (1): DoubleConv(\n",
      "        (double_conv): Sequential(\n",
      "          (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU(inplace=True)\n",
      "          (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (5): ReLU(inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (down4): Down(\n",
      "    (maxpool_conv): Sequential(\n",
      "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "      (1): DoubleConv(\n",
      "        (double_conv): Sequential(\n",
      "          (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU(inplace=True)\n",
      "          (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "          (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (5): ReLU(inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (up1): Up(\n",
      "    (up): Upsample(scale_factor=2.0, mode=bilinear)\n",
      "    (conv): DoubleConv(\n",
      "      (double_conv): Sequential(\n",
      "        (0): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (2): ReLU(inplace=True)\n",
      "        (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "        (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (5): ReLU(inplace=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (up2): Up(\n",
      "    (up): Upsample(scale_factor=2.0, mode=bilinear)\n",
      "    (conv): DoubleConv(\n",
      "      (double_conv): Sequential(\n",
      "        (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (2): ReLU(inplace=True)\n",
      "        (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (5): ReLU(inplace=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (up3): Up(\n",
      "    (up): Upsample(scale_factor=2.0, mode=bilinear)\n",
      "    (conv): DoubleConv(\n",
      "      (double_conv): Sequential(\n",
      "        (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (2): ReLU(inplace=True)\n",
      "        (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (5): ReLU(inplace=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (up4): Up(\n",
      "    (up): Upsample(scale_factor=2.0, mode=bilinear)\n",
      "    (conv): DoubleConv(\n",
      "      (double_conv): Sequential(\n",
      "        (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (2): ReLU(inplace=True)\n",
      "        (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "        (5): ReLU(inplace=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (out_layer): OutConv(\n",
      "    (conv): Conv2d(64, 21, kernel_size=(1, 1), stride=(1, 1))\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(unet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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