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fohy24 commited on
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1 Parent(s): d79e14c

gitignore inference.ipynb

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  1. .gitignore +2 -1
  2. inference.ipynb +0 -1012
.gitignore CHANGED
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- *.pt
 
 
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+ *.pt
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inference.ipynb DELETED
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- {
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- "cells": [
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- {
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- "cell_type": "code",
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- "execution_count": 1,
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- "metadata": {
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- "metadata": {}
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- },
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- "outputs": [],
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- "source": [
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- "import pandas as pd\n",
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- "import numpy as np\n",
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- "\n",
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- "import torch\n",
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- "# from torch.utils.data import Dataset, DataLoader, Subset\n",
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- "from torch import nn\n",
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- "from torchvision import models\n",
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- "import torch.nn.functional as F\n",
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- "from torchvision.transforms import v2\n",
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- "\n",
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- "from PIL import Image\n",
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- "\n",
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- "# from PIL import ImageFile\n",
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- "# ImageFile.LOAD_TRUNCATED_IMAGES = True"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 2,
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- "metadata": {
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- "metadata": {}
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- },
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- "outputs": [],
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- "source": [
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- "labels = ['Pastel',\n",
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- " 'Yellow Belly',\n",
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- " 'Enchi',\n",
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- " 'Clown',\n",
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- " 'Leopard',\n",
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- " 'Piebald',\n",
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- " 'Orange Dream',\n",
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- " 'Fire',\n",
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- " 'Mojave',\n",
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- " 'Pinstripe',\n",
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- " 'Banana',\n",
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- " 'Normal',\n",
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- " 'Black Pastel',\n",
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- " 'Lesser',\n",
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- " 'Spotnose',\n",
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- " 'Cinnamon',\n",
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- " 'GHI',\n",
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- " 'Hypo',\n",
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- " 'Spider',\n",
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- " 'Super Pastel']\n",
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- "num_labels = len(labels)"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 3,
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- "metadata": {
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- "metadata": {}
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- },
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- "outputs": [],
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- "source": [
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- "new_layers = nn.Sequential(\n",
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- " nn.Linear(1920, 1000), # Reduce dimension from 1024 to 500\n",
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- " nn.BatchNorm1d(1000), # Normalize the activations from the previous layer\n",
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- " nn.ReLU(), # Non-linear activation function\n",
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- " nn.Dropout(0.5), # Dropout for regularization (50% probability)\n",
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- " nn.Linear(1000, num_labels) # Final layer for class predictions\n",
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- ")\n",
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- "\n",
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- "IMAGE_SIZE = 512\n",
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- "transform = v2.Compose([\n",
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- " v2.ToImage(),\n",
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- " v2.Resize((IMAGE_SIZE, IMAGE_SIZE)),\n",
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- " v2.ToDtype(torch.float32, scale=True),\n",
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- " v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
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- " ])\n"
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- ]
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- },
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- {
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- "cell_type": "markdown",
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- "metadata": {},
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- "source": [
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- "## Predictions"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 4,
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- "metadata": {
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- "metadata": {}
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- },
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- "outputs": [
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- {
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- "data": {
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- "text/plain": [
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- "DenseNet(\n",
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- " (features): Sequential(\n",
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- " (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
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- " (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu0): ReLU(inplace=True)\n",
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- " (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
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- " (denseblock1): _DenseBlock(\n",
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- " (denselayer1): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer2): _DenseLayer(\n",
116
- " (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer3): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer4): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer5): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer6): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " )\n",
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- " (transition1): _Transition(\n",
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- " (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu): ReLU(inplace=True)\n",
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- " (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
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- " )\n",
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- " (denseblock2): _DenseBlock(\n",
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- " (denselayer1): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer2): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer3): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
181
- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
183
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer4): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer5): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer6): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer7): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer8): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer9): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer10): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer11): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer12): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " )\n",
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- " (transition2): _Transition(\n",
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- " (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
262
- " (relu): ReLU(inplace=True)\n",
263
- " (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
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- " )\n",
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- " (denseblock3): _DenseBlock(\n",
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- " (denselayer1): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer2): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer3): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer4): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer5): _DenseLayer(\n",
300
- " (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
301
- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer6): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu1): ReLU(inplace=True)\n",
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- " (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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- " (relu2): ReLU(inplace=True)\n",
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- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
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- " )\n",
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- " (denselayer7): _DenseLayer(\n",
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- " (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
317
- " (relu1): ReLU(inplace=True)\n",
318
- " (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
319
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
320
- " (relu2): ReLU(inplace=True)\n",
321
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
322
- " )\n",
323
- " (denselayer8): _DenseLayer(\n",
324
- " (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
325
- " (relu1): ReLU(inplace=True)\n",
326
- " (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
327
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
328
- " (relu2): ReLU(inplace=True)\n",
329
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
330
- " )\n",
331
- " (denselayer9): _DenseLayer(\n",
332
- " (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
333
- " (relu1): ReLU(inplace=True)\n",
334
- " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
335
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
336
- " (relu2): ReLU(inplace=True)\n",
337
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
338
- " )\n",
339
- " (denselayer10): _DenseLayer(\n",
340
- " (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
341
- " (relu1): ReLU(inplace=True)\n",
342
- " (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
343
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
344
- " (relu2): ReLU(inplace=True)\n",
345
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
346
- " )\n",
347
- " (denselayer11): _DenseLayer(\n",
348
- " (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
349
- " (relu1): ReLU(inplace=True)\n",
350
- " (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
351
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
352
- " (relu2): ReLU(inplace=True)\n",
353
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
354
- " )\n",
355
- " (denselayer12): _DenseLayer(\n",
356
- " (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
357
- " (relu1): ReLU(inplace=True)\n",
358
- " (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
359
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
360
- " (relu2): ReLU(inplace=True)\n",
361
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
362
- " )\n",
363
- " (denselayer13): _DenseLayer(\n",
364
- " (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
365
- " (relu1): ReLU(inplace=True)\n",
366
- " (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
367
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
368
- " (relu2): ReLU(inplace=True)\n",
369
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
370
- " )\n",
371
- " (denselayer14): _DenseLayer(\n",
372
- " (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
373
- " (relu1): ReLU(inplace=True)\n",
374
- " (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
375
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
376
- " (relu2): ReLU(inplace=True)\n",
377
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
378
- " )\n",
379
- " (denselayer15): _DenseLayer(\n",
380
- " (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
381
- " (relu1): ReLU(inplace=True)\n",
382
- " (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
383
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
384
- " (relu2): ReLU(inplace=True)\n",
385
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
386
- " )\n",
387
- " (denselayer16): _DenseLayer(\n",
388
- " (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
389
- " (relu1): ReLU(inplace=True)\n",
390
- " (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
391
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
392
- " (relu2): ReLU(inplace=True)\n",
393
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
394
- " )\n",
395
- " (denselayer17): _DenseLayer(\n",
396
- " (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
397
- " (relu1): ReLU(inplace=True)\n",
398
- " (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
399
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
400
- " (relu2): ReLU(inplace=True)\n",
401
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
402
- " )\n",
403
- " (denselayer18): _DenseLayer(\n",
404
- " (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
405
- " (relu1): ReLU(inplace=True)\n",
406
- " (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
407
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
408
- " (relu2): ReLU(inplace=True)\n",
409
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
410
- " )\n",
411
- " (denselayer19): _DenseLayer(\n",
412
- " (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
413
- " (relu1): ReLU(inplace=True)\n",
414
- " (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
415
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
416
- " (relu2): ReLU(inplace=True)\n",
417
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
418
- " )\n",
419
- " (denselayer20): _DenseLayer(\n",
420
- " (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
421
- " (relu1): ReLU(inplace=True)\n",
422
- " (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
423
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
424
- " (relu2): ReLU(inplace=True)\n",
425
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
426
- " )\n",
427
- " (denselayer21): _DenseLayer(\n",
428
- " (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
429
- " (relu1): ReLU(inplace=True)\n",
430
- " (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
431
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
432
- " (relu2): ReLU(inplace=True)\n",
433
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
434
- " )\n",
435
- " (denselayer22): _DenseLayer(\n",
436
- " (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
437
- " (relu1): ReLU(inplace=True)\n",
438
- " (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
439
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
440
- " (relu2): ReLU(inplace=True)\n",
441
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
442
- " )\n",
443
- " (denselayer23): _DenseLayer(\n",
444
- " (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
445
- " (relu1): ReLU(inplace=True)\n",
446
- " (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
447
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
448
- " (relu2): ReLU(inplace=True)\n",
449
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
450
- " )\n",
451
- " (denselayer24): _DenseLayer(\n",
452
- " (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
453
- " (relu1): ReLU(inplace=True)\n",
454
- " (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
455
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
456
- " (relu2): ReLU(inplace=True)\n",
457
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
458
- " )\n",
459
- " (denselayer25): _DenseLayer(\n",
460
- " (norm1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
461
- " (relu1): ReLU(inplace=True)\n",
462
- " (conv1): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
463
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
464
- " (relu2): ReLU(inplace=True)\n",
465
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
466
- " )\n",
467
- " (denselayer26): _DenseLayer(\n",
468
- " (norm1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
469
- " (relu1): ReLU(inplace=True)\n",
470
- " (conv1): Conv2d(1056, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
471
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
472
- " (relu2): ReLU(inplace=True)\n",
473
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
474
- " )\n",
475
- " (denselayer27): _DenseLayer(\n",
476
- " (norm1): BatchNorm2d(1088, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
477
- " (relu1): ReLU(inplace=True)\n",
478
- " (conv1): Conv2d(1088, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
479
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
480
- " (relu2): ReLU(inplace=True)\n",
481
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
482
- " )\n",
483
- " (denselayer28): _DenseLayer(\n",
484
- " (norm1): BatchNorm2d(1120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
485
- " (relu1): ReLU(inplace=True)\n",
486
- " (conv1): Conv2d(1120, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
487
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
488
- " (relu2): ReLU(inplace=True)\n",
489
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
490
- " )\n",
491
- " (denselayer29): _DenseLayer(\n",
492
- " (norm1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
493
- " (relu1): ReLU(inplace=True)\n",
494
- " (conv1): Conv2d(1152, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
495
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
496
- " (relu2): ReLU(inplace=True)\n",
497
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
498
- " )\n",
499
- " (denselayer30): _DenseLayer(\n",
500
- " (norm1): BatchNorm2d(1184, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
501
- " (relu1): ReLU(inplace=True)\n",
502
- " (conv1): Conv2d(1184, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
503
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
504
- " (relu2): ReLU(inplace=True)\n",
505
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
506
- " )\n",
507
- " (denselayer31): _DenseLayer(\n",
508
- " (norm1): BatchNorm2d(1216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
509
- " (relu1): ReLU(inplace=True)\n",
510
- " (conv1): Conv2d(1216, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
511
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
512
- " (relu2): ReLU(inplace=True)\n",
513
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
514
- " )\n",
515
- " (denselayer32): _DenseLayer(\n",
516
- " (norm1): BatchNorm2d(1248, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
517
- " (relu1): ReLU(inplace=True)\n",
518
- " (conv1): Conv2d(1248, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
519
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
520
- " (relu2): ReLU(inplace=True)\n",
521
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
522
- " )\n",
523
- " (denselayer33): _DenseLayer(\n",
524
- " (norm1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
525
- " (relu1): ReLU(inplace=True)\n",
526
- " (conv1): Conv2d(1280, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
527
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
528
- " (relu2): ReLU(inplace=True)\n",
529
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
530
- " )\n",
531
- " (denselayer34): _DenseLayer(\n",
532
- " (norm1): BatchNorm2d(1312, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
533
- " (relu1): ReLU(inplace=True)\n",
534
- " (conv1): Conv2d(1312, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
535
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
536
- " (relu2): ReLU(inplace=True)\n",
537
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
538
- " )\n",
539
- " (denselayer35): _DenseLayer(\n",
540
- " (norm1): BatchNorm2d(1344, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
541
- " (relu1): ReLU(inplace=True)\n",
542
- " (conv1): Conv2d(1344, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
543
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
544
- " (relu2): ReLU(inplace=True)\n",
545
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
546
- " )\n",
547
- " (denselayer36): _DenseLayer(\n",
548
- " (norm1): BatchNorm2d(1376, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
549
- " (relu1): ReLU(inplace=True)\n",
550
- " (conv1): Conv2d(1376, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
551
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
552
- " (relu2): ReLU(inplace=True)\n",
553
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
554
- " )\n",
555
- " (denselayer37): _DenseLayer(\n",
556
- " (norm1): BatchNorm2d(1408, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
557
- " (relu1): ReLU(inplace=True)\n",
558
- " (conv1): Conv2d(1408, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
559
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
560
- " (relu2): ReLU(inplace=True)\n",
561
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
562
- " )\n",
563
- " (denselayer38): _DenseLayer(\n",
564
- " (norm1): BatchNorm2d(1440, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
565
- " (relu1): ReLU(inplace=True)\n",
566
- " (conv1): Conv2d(1440, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
567
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
568
- " (relu2): ReLU(inplace=True)\n",
569
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
570
- " )\n",
571
- " (denselayer39): _DenseLayer(\n",
572
- " (norm1): BatchNorm2d(1472, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
573
- " (relu1): ReLU(inplace=True)\n",
574
- " (conv1): Conv2d(1472, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
575
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
576
- " (relu2): ReLU(inplace=True)\n",
577
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
578
- " )\n",
579
- " (denselayer40): _DenseLayer(\n",
580
- " (norm1): BatchNorm2d(1504, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
581
- " (relu1): ReLU(inplace=True)\n",
582
- " (conv1): Conv2d(1504, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
583
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
584
- " (relu2): ReLU(inplace=True)\n",
585
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
586
- " )\n",
587
- " (denselayer41): _DenseLayer(\n",
588
- " (norm1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
589
- " (relu1): ReLU(inplace=True)\n",
590
- " (conv1): Conv2d(1536, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
591
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
592
- " (relu2): ReLU(inplace=True)\n",
593
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
594
- " )\n",
595
- " (denselayer42): _DenseLayer(\n",
596
- " (norm1): BatchNorm2d(1568, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
597
- " (relu1): ReLU(inplace=True)\n",
598
- " (conv1): Conv2d(1568, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
599
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
600
- " (relu2): ReLU(inplace=True)\n",
601
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
602
- " )\n",
603
- " (denselayer43): _DenseLayer(\n",
604
- " (norm1): BatchNorm2d(1600, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
605
- " (relu1): ReLU(inplace=True)\n",
606
- " (conv1): Conv2d(1600, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
607
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
608
- " (relu2): ReLU(inplace=True)\n",
609
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
610
- " )\n",
611
- " (denselayer44): _DenseLayer(\n",
612
- " (norm1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
613
- " (relu1): ReLU(inplace=True)\n",
614
- " (conv1): Conv2d(1632, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
615
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
616
- " (relu2): ReLU(inplace=True)\n",
617
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
618
- " )\n",
619
- " (denselayer45): _DenseLayer(\n",
620
- " (norm1): BatchNorm2d(1664, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
621
- " (relu1): ReLU(inplace=True)\n",
622
- " (conv1): Conv2d(1664, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
623
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
624
- " (relu2): ReLU(inplace=True)\n",
625
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
626
- " )\n",
627
- " (denselayer46): _DenseLayer(\n",
628
- " (norm1): BatchNorm2d(1696, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
629
- " (relu1): ReLU(inplace=True)\n",
630
- " (conv1): Conv2d(1696, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
631
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
632
- " (relu2): ReLU(inplace=True)\n",
633
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
634
- " )\n",
635
- " (denselayer47): _DenseLayer(\n",
636
- " (norm1): BatchNorm2d(1728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
637
- " (relu1): ReLU(inplace=True)\n",
638
- " (conv1): Conv2d(1728, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
639
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
640
- " (relu2): ReLU(inplace=True)\n",
641
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
642
- " )\n",
643
- " (denselayer48): _DenseLayer(\n",
644
- " (norm1): BatchNorm2d(1760, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
645
- " (relu1): ReLU(inplace=True)\n",
646
- " (conv1): Conv2d(1760, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
647
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
648
- " (relu2): ReLU(inplace=True)\n",
649
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
650
- " )\n",
651
- " )\n",
652
- " (transition3): _Transition(\n",
653
- " (norm): BatchNorm2d(1792, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
654
- " (relu): ReLU(inplace=True)\n",
655
- " (conv): Conv2d(1792, 896, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
656
- " (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
657
- " )\n",
658
- " (denseblock4): _DenseBlock(\n",
659
- " (denselayer1): _DenseLayer(\n",
660
- " (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
661
- " (relu1): ReLU(inplace=True)\n",
662
- " (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
663
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
664
- " (relu2): ReLU(inplace=True)\n",
665
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
666
- " )\n",
667
- " (denselayer2): _DenseLayer(\n",
668
- " (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
669
- " (relu1): ReLU(inplace=True)\n",
670
- " (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
671
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
672
- " (relu2): ReLU(inplace=True)\n",
673
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
674
- " )\n",
675
- " (denselayer3): _DenseLayer(\n",
676
- " (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
677
- " (relu1): ReLU(inplace=True)\n",
678
- " (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
679
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
680
- " (relu2): ReLU(inplace=True)\n",
681
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
682
- " )\n",
683
- " (denselayer4): _DenseLayer(\n",
684
- " (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
685
- " (relu1): ReLU(inplace=True)\n",
686
- " (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
687
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
688
- " (relu2): ReLU(inplace=True)\n",
689
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
690
- " )\n",
691
- " (denselayer5): _DenseLayer(\n",
692
- " (norm1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
693
- " (relu1): ReLU(inplace=True)\n",
694
- " (conv1): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
695
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
696
- " (relu2): ReLU(inplace=True)\n",
697
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
698
- " )\n",
699
- " (denselayer6): _DenseLayer(\n",
700
- " (norm1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
701
- " (relu1): ReLU(inplace=True)\n",
702
- " (conv1): Conv2d(1056, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
703
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
704
- " (relu2): ReLU(inplace=True)\n",
705
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
706
- " )\n",
707
- " (denselayer7): _DenseLayer(\n",
708
- " (norm1): BatchNorm2d(1088, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
709
- " (relu1): ReLU(inplace=True)\n",
710
- " (conv1): Conv2d(1088, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
711
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
712
- " (relu2): ReLU(inplace=True)\n",
713
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
714
- " )\n",
715
- " (denselayer8): _DenseLayer(\n",
716
- " (norm1): BatchNorm2d(1120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
717
- " (relu1): ReLU(inplace=True)\n",
718
- " (conv1): Conv2d(1120, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
719
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
720
- " (relu2): ReLU(inplace=True)\n",
721
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
722
- " )\n",
723
- " (denselayer9): _DenseLayer(\n",
724
- " (norm1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
725
- " (relu1): ReLU(inplace=True)\n",
726
- " (conv1): Conv2d(1152, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
727
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
728
- " (relu2): ReLU(inplace=True)\n",
729
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
730
- " )\n",
731
- " (denselayer10): _DenseLayer(\n",
732
- " (norm1): BatchNorm2d(1184, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
733
- " (relu1): ReLU(inplace=True)\n",
734
- " (conv1): Conv2d(1184, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
735
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
736
- " (relu2): ReLU(inplace=True)\n",
737
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
738
- " )\n",
739
- " (denselayer11): _DenseLayer(\n",
740
- " (norm1): BatchNorm2d(1216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
741
- " (relu1): ReLU(inplace=True)\n",
742
- " (conv1): Conv2d(1216, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
743
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
744
- " (relu2): ReLU(inplace=True)\n",
745
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
746
- " )\n",
747
- " (denselayer12): _DenseLayer(\n",
748
- " (norm1): BatchNorm2d(1248, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
749
- " (relu1): ReLU(inplace=True)\n",
750
- " (conv1): Conv2d(1248, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
751
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
752
- " (relu2): ReLU(inplace=True)\n",
753
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
754
- " )\n",
755
- " (denselayer13): _DenseLayer(\n",
756
- " (norm1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
757
- " (relu1): ReLU(inplace=True)\n",
758
- " (conv1): Conv2d(1280, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
759
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
760
- " (relu2): ReLU(inplace=True)\n",
761
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
762
- " )\n",
763
- " (denselayer14): _DenseLayer(\n",
764
- " (norm1): BatchNorm2d(1312, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
765
- " (relu1): ReLU(inplace=True)\n",
766
- " (conv1): Conv2d(1312, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
767
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
768
- " (relu2): ReLU(inplace=True)\n",
769
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
770
- " )\n",
771
- " (denselayer15): _DenseLayer(\n",
772
- " (norm1): BatchNorm2d(1344, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
773
- " (relu1): ReLU(inplace=True)\n",
774
- " (conv1): Conv2d(1344, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
775
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
776
- " (relu2): ReLU(inplace=True)\n",
777
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
778
- " )\n",
779
- " (denselayer16): _DenseLayer(\n",
780
- " (norm1): BatchNorm2d(1376, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
781
- " (relu1): ReLU(inplace=True)\n",
782
- " (conv1): Conv2d(1376, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
783
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
784
- " (relu2): ReLU(inplace=True)\n",
785
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
786
- " )\n",
787
- " (denselayer17): _DenseLayer(\n",
788
- " (norm1): BatchNorm2d(1408, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
789
- " (relu1): ReLU(inplace=True)\n",
790
- " (conv1): Conv2d(1408, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
791
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
792
- " (relu2): ReLU(inplace=True)\n",
793
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
794
- " )\n",
795
- " (denselayer18): _DenseLayer(\n",
796
- " (norm1): BatchNorm2d(1440, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
797
- " (relu1): ReLU(inplace=True)\n",
798
- " (conv1): Conv2d(1440, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
799
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
800
- " (relu2): ReLU(inplace=True)\n",
801
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
802
- " )\n",
803
- " (denselayer19): _DenseLayer(\n",
804
- " (norm1): BatchNorm2d(1472, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
805
- " (relu1): ReLU(inplace=True)\n",
806
- " (conv1): Conv2d(1472, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
807
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
808
- " (relu2): ReLU(inplace=True)\n",
809
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
810
- " )\n",
811
- " (denselayer20): _DenseLayer(\n",
812
- " (norm1): BatchNorm2d(1504, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
813
- " (relu1): ReLU(inplace=True)\n",
814
- " (conv1): Conv2d(1504, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
815
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
816
- " (relu2): ReLU(inplace=True)\n",
817
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
818
- " )\n",
819
- " (denselayer21): _DenseLayer(\n",
820
- " (norm1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
821
- " (relu1): ReLU(inplace=True)\n",
822
- " (conv1): Conv2d(1536, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
823
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
824
- " (relu2): ReLU(inplace=True)\n",
825
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
826
- " )\n",
827
- " (denselayer22): _DenseLayer(\n",
828
- " (norm1): BatchNorm2d(1568, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
829
- " (relu1): ReLU(inplace=True)\n",
830
- " (conv1): Conv2d(1568, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
831
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
832
- " (relu2): ReLU(inplace=True)\n",
833
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
834
- " )\n",
835
- " (denselayer23): _DenseLayer(\n",
836
- " (norm1): BatchNorm2d(1600, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
837
- " (relu1): ReLU(inplace=True)\n",
838
- " (conv1): Conv2d(1600, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
839
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
840
- " (relu2): ReLU(inplace=True)\n",
841
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
842
- " )\n",
843
- " (denselayer24): _DenseLayer(\n",
844
- " (norm1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
845
- " (relu1): ReLU(inplace=True)\n",
846
- " (conv1): Conv2d(1632, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
847
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
848
- " (relu2): ReLU(inplace=True)\n",
849
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
850
- " )\n",
851
- " (denselayer25): _DenseLayer(\n",
852
- " (norm1): BatchNorm2d(1664, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
853
- " (relu1): ReLU(inplace=True)\n",
854
- " (conv1): Conv2d(1664, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
855
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
856
- " (relu2): ReLU(inplace=True)\n",
857
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
858
- " )\n",
859
- " (denselayer26): _DenseLayer(\n",
860
- " (norm1): BatchNorm2d(1696, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
861
- " (relu1): ReLU(inplace=True)\n",
862
- " (conv1): Conv2d(1696, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
863
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
864
- " (relu2): ReLU(inplace=True)\n",
865
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
866
- " )\n",
867
- " (denselayer27): _DenseLayer(\n",
868
- " (norm1): BatchNorm2d(1728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
869
- " (relu1): ReLU(inplace=True)\n",
870
- " (conv1): Conv2d(1728, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
871
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
872
- " (relu2): ReLU(inplace=True)\n",
873
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
874
- " )\n",
875
- " (denselayer28): _DenseLayer(\n",
876
- " (norm1): BatchNorm2d(1760, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
877
- " (relu1): ReLU(inplace=True)\n",
878
- " (conv1): Conv2d(1760, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
879
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
880
- " (relu2): ReLU(inplace=True)\n",
881
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
882
- " )\n",
883
- " (denselayer29): _DenseLayer(\n",
884
- " (norm1): BatchNorm2d(1792, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
885
- " (relu1): ReLU(inplace=True)\n",
886
- " (conv1): Conv2d(1792, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
887
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
888
- " (relu2): ReLU(inplace=True)\n",
889
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
890
- " )\n",
891
- " (denselayer30): _DenseLayer(\n",
892
- " (norm1): BatchNorm2d(1824, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
893
- " (relu1): ReLU(inplace=True)\n",
894
- " (conv1): Conv2d(1824, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
895
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
896
- " (relu2): ReLU(inplace=True)\n",
897
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
898
- " )\n",
899
- " (denselayer31): _DenseLayer(\n",
900
- " (norm1): BatchNorm2d(1856, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
901
- " (relu1): ReLU(inplace=True)\n",
902
- " (conv1): Conv2d(1856, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
903
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
904
- " (relu2): ReLU(inplace=True)\n",
905
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
906
- " )\n",
907
- " (denselayer32): _DenseLayer(\n",
908
- " (norm1): BatchNorm2d(1888, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
909
- " (relu1): ReLU(inplace=True)\n",
910
- " (conv1): Conv2d(1888, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
911
- " (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
912
- " (relu2): ReLU(inplace=True)\n",
913
- " (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
914
- " )\n",
915
- " )\n",
916
- " (norm5): BatchNorm2d(1920, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
917
- " )\n",
918
- " (classifier): Sequential(\n",
919
- " (0): Linear(in_features=1920, out_features=1000, bias=True)\n",
920
- " (1): BatchNorm1d(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
921
- " (2): ReLU()\n",
922
- " (3): Dropout(p=0.5, inplace=False)\n",
923
- " (4): Linear(in_features=1000, out_features=20, bias=True)\n",
924
- " )\n",
925
- ")"
926
- ]
927
- },
928
- "execution_count": 4,
929
- "metadata": {},
930
- "output_type": "execute_result"
931
- }
932
- ],
933
- "source": [
934
- "densenet = models.densenet201(weights='DenseNet201_Weights.DEFAULT')\n",
935
- "densenet.classifier = new_layers\n",
936
- "\n",
937
- "# If using GPU\n",
938
- "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
939
- "\n",
940
- "checkpoint = torch.load(f'model/model_v8_epoch9.pt', map_location=device)\n",
941
- "densenet.load_state_dict(checkpoint['model_state_dict'])\n",
942
- "\n",
943
- "densenet.eval()"
944
- ]
945
- },
946
- {
947
- "cell_type": "code",
948
- "execution_count": 10,
949
- "metadata": {
950
- "metadata": {}
951
- },
952
- "outputs": [
953
- {
954
- "data": {
955
- "text/plain": [
956
- "[0.6777233481407166, 0.581626296043396, 0.5196343660354614]"
957
- ]
958
- },
959
- "execution_count": 10,
960
- "metadata": {},
961
- "output_type": "execute_result"
962
- }
963
- ],
964
- "source": [
965
- "test_img_path = 'test_img/test3_leopard_fire.png'\n",
966
- "img = Image.open(test_img_path)\n",
967
- "input_img = transform(img)\n",
968
- "input_img = input_img.unsqueeze(0)\n",
969
- "\n",
970
- "\n",
971
- "with torch.no_grad():\n",
972
- " output = densenet(input_img)\n",
973
- "\n",
974
- "predicted_probs = torch.sigmoid(output).to('cpu')\n",
975
- "prediction = pd.DataFrame(predicted_probs, index=['predictions'],\n",
976
- " columns=labels).T.sort_values(by=['predictions'], ascending=False)\n",
977
- "\n",
978
- "\n",
979
- "prediction_probs = prediction.query('predictions > 0.5').reset_index(names='morphs')['morphs'].to_list()\n",
980
- "prediction_confidence = prediction.query('predictions > 0.5')['predictions'].to_list()\n",
981
- "\n",
982
- "prediction_confidence\n",
983
- "\n",
984
- "# predicted_probs_list = predicted_probs.flatten().tolist()\n",
985
- "# idx = [i for i, x in enumerate(predicted_probs_list) if x > 0.5]\n",
986
- "# prediction = [labels[idx] for idx in idx]\n",
987
- "# prediction"
988
- ]
989
- }
990
- ],
991
- "metadata": {
992
- "kernelspec": {
993
- "display_name": "deep-learning",
994
- "language": "python",
995
- "name": "python3"
996
- },
997
- "language_info": {
998
- "codemirror_mode": {
999
- "name": "ipython",
1000
- "version": 3
1001
- },
1002
- "file_extension": ".py",
1003
- "mimetype": "text/x-python",
1004
- "name": "python",
1005
- "nbconvert_exporter": "python",
1006
- "pygments_lexer": "ipython3",
1007
- "version": "3.11.6"
1008
- }
1009
- },
1010
- "nbformat": 4,
1011
- "nbformat_minor": 2
1012
- }