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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"import torch\n",
"# from torch.utils.data import Dataset, DataLoader, Subset\n",
"from torch import nn\n",
"from torchvision import models\n",
"import torch.nn.functional as F\n",
"from torchvision.transforms import v2\n",
"\n",
"from PIL import Image\n",
"\n",
"# from PIL import ImageFile\n",
"# ImageFile.LOAD_TRUNCATED_IMAGES = True"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"labels = ['Pastel',\n",
" 'Yellow Belly',\n",
" 'Enchi',\n",
" 'Clown',\n",
" 'Leopard',\n",
" 'Piebald',\n",
" 'Orange Dream',\n",
" 'Fire',\n",
" 'Mojave',\n",
" 'Pinstripe',\n",
" 'Banana',\n",
" 'Normal',\n",
" 'Black Pastel',\n",
" 'Lesser',\n",
" 'Spotnose',\n",
" 'Cinnamon',\n",
" 'GHI',\n",
" 'Hypo',\n",
" 'Spider',\n",
" 'Super Pastel']\n",
"num_labels = len(labels)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"metadata": {}
},
"outputs": [],
"source": [
"new_layers = nn.Sequential(\n",
" nn.Linear(1920, 1000), # Reduce dimension from 1024 to 500\n",
" nn.BatchNorm1d(1000), # Normalize the activations from the previous layer\n",
" nn.ReLU(), # Non-linear activation function\n",
" nn.Dropout(0.5), # Dropout for regularization (50% probability)\n",
" nn.Linear(1000, num_labels) # Final layer for class predictions\n",
")\n",
"\n",
"IMAGE_SIZE = 512\n",
"transform = v2.Compose([\n",
" v2.ToImage(),\n",
" v2.Resize((IMAGE_SIZE, IMAGE_SIZE)),\n",
" v2.ToDtype(torch.float32, scale=True),\n",
" v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
" ])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Predictions"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"metadata": {}
},
"outputs": [
{
"data": {
"text/plain": [
"DenseNet(\n",
" (features): Sequential(\n",
" (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
" (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu0): ReLU(inplace=True)\n",
" (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
" (denseblock1): _DenseBlock(\n",
" (denselayer1): _DenseLayer(\n",
" (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer2): _DenseLayer(\n",
" (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer3): _DenseLayer(\n",
" (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer4): _DenseLayer(\n",
" (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer5): _DenseLayer(\n",
" (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer6): _DenseLayer(\n",
" (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" )\n",
" (transition1): _Transition(\n",
" (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
" )\n",
" (denseblock2): _DenseBlock(\n",
" (denselayer1): _DenseLayer(\n",
" (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer2): _DenseLayer(\n",
" (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer3): _DenseLayer(\n",
" (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer4): _DenseLayer(\n",
" (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer5): _DenseLayer(\n",
" (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer6): _DenseLayer(\n",
" (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer7): _DenseLayer(\n",
" (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer8): _DenseLayer(\n",
" (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer9): _DenseLayer(\n",
" (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer10): _DenseLayer(\n",
" (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer11): _DenseLayer(\n",
" (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer12): _DenseLayer(\n",
" (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" )\n",
" (transition2): _Transition(\n",
" (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
" )\n",
" (denseblock3): _DenseBlock(\n",
" (denselayer1): _DenseLayer(\n",
" (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer2): _DenseLayer(\n",
" (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer3): _DenseLayer(\n",
" (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer4): _DenseLayer(\n",
" (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer5): _DenseLayer(\n",
" (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer6): _DenseLayer(\n",
" (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer7): _DenseLayer(\n",
" (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer8): _DenseLayer(\n",
" (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer9): _DenseLayer(\n",
" (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer10): _DenseLayer(\n",
" (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer11): _DenseLayer(\n",
" (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer12): _DenseLayer(\n",
" (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer13): _DenseLayer(\n",
" (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer14): _DenseLayer(\n",
" (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer15): _DenseLayer(\n",
" (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer16): _DenseLayer(\n",
" (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer17): _DenseLayer(\n",
" (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer18): _DenseLayer(\n",
" (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer19): _DenseLayer(\n",
" (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer20): _DenseLayer(\n",
" (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer21): _DenseLayer(\n",
" (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer22): _DenseLayer(\n",
" (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer23): _DenseLayer(\n",
" (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer24): _DenseLayer(\n",
" (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer25): _DenseLayer(\n",
" (norm1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer26): _DenseLayer(\n",
" (norm1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1056, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer27): _DenseLayer(\n",
" (norm1): BatchNorm2d(1088, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1088, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer28): _DenseLayer(\n",
" (norm1): BatchNorm2d(1120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1120, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer29): _DenseLayer(\n",
" (norm1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1152, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer30): _DenseLayer(\n",
" (norm1): BatchNorm2d(1184, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1184, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer31): _DenseLayer(\n",
" (norm1): BatchNorm2d(1216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1216, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer32): _DenseLayer(\n",
" (norm1): BatchNorm2d(1248, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1248, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer33): _DenseLayer(\n",
" (norm1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1280, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer34): _DenseLayer(\n",
" (norm1): BatchNorm2d(1312, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1312, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer35): _DenseLayer(\n",
" (norm1): BatchNorm2d(1344, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1344, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer36): _DenseLayer(\n",
" (norm1): BatchNorm2d(1376, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1376, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer37): _DenseLayer(\n",
" (norm1): BatchNorm2d(1408, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1408, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer38): _DenseLayer(\n",
" (norm1): BatchNorm2d(1440, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1440, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer39): _DenseLayer(\n",
" (norm1): BatchNorm2d(1472, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1472, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer40): _DenseLayer(\n",
" (norm1): BatchNorm2d(1504, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1504, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer41): _DenseLayer(\n",
" (norm1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1536, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer42): _DenseLayer(\n",
" (norm1): BatchNorm2d(1568, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1568, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer43): _DenseLayer(\n",
" (norm1): BatchNorm2d(1600, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1600, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer44): _DenseLayer(\n",
" (norm1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1632, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer45): _DenseLayer(\n",
" (norm1): BatchNorm2d(1664, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1664, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer46): _DenseLayer(\n",
" (norm1): BatchNorm2d(1696, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1696, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer47): _DenseLayer(\n",
" (norm1): BatchNorm2d(1728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1728, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer48): _DenseLayer(\n",
" (norm1): BatchNorm2d(1760, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1760, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" )\n",
" (transition3): _Transition(\n",
" (norm): BatchNorm2d(1792, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (conv): Conv2d(1792, 896, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
" )\n",
" (denseblock4): _DenseBlock(\n",
" (denselayer1): _DenseLayer(\n",
" (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer2): _DenseLayer(\n",
" (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer3): _DenseLayer(\n",
" (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer4): _DenseLayer(\n",
" (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer5): _DenseLayer(\n",
" (norm1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer6): _DenseLayer(\n",
" (norm1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1056, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer7): _DenseLayer(\n",
" (norm1): BatchNorm2d(1088, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1088, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer8): _DenseLayer(\n",
" (norm1): BatchNorm2d(1120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1120, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer9): _DenseLayer(\n",
" (norm1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1152, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer10): _DenseLayer(\n",
" (norm1): BatchNorm2d(1184, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1184, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer11): _DenseLayer(\n",
" (norm1): BatchNorm2d(1216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1216, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer12): _DenseLayer(\n",
" (norm1): BatchNorm2d(1248, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1248, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer13): _DenseLayer(\n",
" (norm1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1280, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer14): _DenseLayer(\n",
" (norm1): BatchNorm2d(1312, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1312, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer15): _DenseLayer(\n",
" (norm1): BatchNorm2d(1344, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1344, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer16): _DenseLayer(\n",
" (norm1): BatchNorm2d(1376, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1376, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer17): _DenseLayer(\n",
" (norm1): BatchNorm2d(1408, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1408, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer18): _DenseLayer(\n",
" (norm1): BatchNorm2d(1440, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1440, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer19): _DenseLayer(\n",
" (norm1): BatchNorm2d(1472, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1472, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer20): _DenseLayer(\n",
" (norm1): BatchNorm2d(1504, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1504, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer21): _DenseLayer(\n",
" (norm1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1536, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer22): _DenseLayer(\n",
" (norm1): BatchNorm2d(1568, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1568, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer23): _DenseLayer(\n",
" (norm1): BatchNorm2d(1600, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1600, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer24): _DenseLayer(\n",
" (norm1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1632, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer25): _DenseLayer(\n",
" (norm1): BatchNorm2d(1664, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1664, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer26): _DenseLayer(\n",
" (norm1): BatchNorm2d(1696, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1696, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer27): _DenseLayer(\n",
" (norm1): BatchNorm2d(1728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1728, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer28): _DenseLayer(\n",
" (norm1): BatchNorm2d(1760, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1760, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer29): _DenseLayer(\n",
" (norm1): BatchNorm2d(1792, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1792, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer30): _DenseLayer(\n",
" (norm1): BatchNorm2d(1824, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1824, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer31): _DenseLayer(\n",
" (norm1): BatchNorm2d(1856, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1856, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" (denselayer32): _DenseLayer(\n",
" (norm1): BatchNorm2d(1888, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu1): ReLU(inplace=True)\n",
" (conv1): Conv2d(1888, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu2): ReLU(inplace=True)\n",
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" )\n",
" )\n",
" (norm5): BatchNorm2d(1920, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (classifier): Sequential(\n",
" (0): Linear(in_features=1920, out_features=1000, bias=True)\n",
" (1): BatchNorm1d(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" (3): Dropout(p=0.5, inplace=False)\n",
" (4): Linear(in_features=1000, out_features=20, bias=True)\n",
" )\n",
")"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"densenet = models.densenet201(weights='DenseNet201_Weights.DEFAULT')\n",
"densenet.classifier = new_layers\n",
"\n",
"# If using GPU\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"checkpoint = torch.load(f'model/model_v8_epoch9.pt', map_location=device)\n",
"densenet.load_state_dict(checkpoint['model_state_dict'])\n",
"\n",
"densenet.eval()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"metadata": {}
},
"outputs": [
{
"data": {
"text/plain": [
"[0.6777233481407166, 0.581626296043396, 0.5196343660354614]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_img_path = 'test_img/test3_leopard_fire.png'\n",
"img = Image.open(test_img_path)\n",
"input_img = transform(img)\n",
"input_img = input_img.unsqueeze(0)\n",
"\n",
"\n",
"with torch.no_grad():\n",
" output = densenet(input_img)\n",
"\n",
"predicted_probs = torch.sigmoid(output).to('cpu')\n",
"prediction = pd.DataFrame(predicted_probs, index=['predictions'],\n",
" columns=labels).T.sort_values(by=['predictions'], ascending=False)\n",
"\n",
"\n",
"prediction_probs = prediction.query('predictions > 0.5').reset_index(names='morphs')['morphs'].to_list()\n",
"prediction_confidence = prediction.query('predictions > 0.5')['predictions'].to_list()\n",
"\n",
"prediction_confidence\n",
"\n",
"# predicted_probs_list = predicted_probs.flatten().tolist()\n",
"# idx = [i for i, x in enumerate(predicted_probs_list) if x > 0.5]\n",
"# prediction = [labels[idx] for idx in idx]\n",
"# prediction"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "deep-learning",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|