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| } | |
| } | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/aleksejalex/6e2313bed2f9670daa33f84611b1367d/su2_cv2_dcv.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# **SU2 cv2 DCv**\n", | |
| "by Aleksej Gaj ([@aleksejalex](https://github.com/aleksejalex))" | |
| ], | |
| "metadata": { | |
| "id": "4AQyW1jvtq5v" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## imports" | |
| ], | |
| "metadata": { | |
| "id": "47m0OOWGt-6i" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "\"\"\"\n", | |
| "dobry dataloader, lze pouzit v kodu\n", | |
| "\"\"\"\n", | |
| "\n", | |
| "import requests\n", | |
| "import zipfile\n", | |
| "import os\n", | |
| "import errno\n", | |
| "\n", | |
| "filenames = ['rps.zip', 'rps-test-set.zip']\n", | |
| "for filename in filenames:\n", | |
| " try:\n", | |
| " os.mkdir(f'{filename[:-4]}')\n", | |
| "\n", | |
| " localfile = f'{filename}'\n", | |
| " url = 'https://huggingface.co/datasets/jerpint/rock-paper-scissors/resolve/main/{}'\n", | |
| " r = requests.get(url.format(filename), allow_redirects=True)\n", | |
| " open(localfile, 'wb').write(r.content)\n", | |
| " with zipfile.ZipFile(localfile, 'r') as zip_ref:\n", | |
| " zip_ref.extractall(\"\")\n", | |
| " except OSError as e:\n", | |
| " if e.errno != errno.EEXIST:\n", | |
| " raise\n", | |
| " else:\n", | |
| " print(f'{filename[:-4]} folder already exists!')\n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "id": "Y6V5tagPranB" | |
| }, | |
| "execution_count": 1, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "%%capture\n", | |
| "!pip install lightning\n", | |
| "#!pip install wandb" | |
| ], | |
| "metadata": { | |
| "id": "rS4Q4JaYodvp" | |
| }, | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Required Libraries\n", | |
| "# common libs\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import pandas as pd\n", | |
| "#import wandb\n", | |
| "from PIL import Image\n", | |
| "from copy import deepcopy # what's that?!\n", | |
| "\n", | |
| "import torch\n", | |
| "import torch.nn as nn\n", | |
| "from torch.nn import Linear, MSELoss\n", | |
| "from torch.optim import SGD\n", | |
| "#import torch.nn.functional as F\n", | |
| "#import torch.optim as optim\n", | |
| "\n", | |
| "import torchvision\n", | |
| "from torch.utils.data import DataLoader, TensorDataset, random_split\n", | |
| "from torchvision.datasets import ImageFolder\n", | |
| "from torchvision.transforms import Compose, ToTensor, Normalize, ToPILImage, Resize, CenterCrop\n", | |
| "\n", | |
| "import lightning.pytorch as pl\n", | |
| "from lightning.pytorch.loggers import CSVLogger, WandbLogger\n", | |
| "\n", | |
| "from torchvision.models import alexnet\n", | |
| "from torch.utils.model_zoo import load_url as load_state_dict_from_url\n", | |
| "\n", | |
| "#plt.style.use('fivethirtyeight')" | |
| ], | |
| "metadata": { | |
| "id": "-FOmACN6obpO" | |
| }, | |
| "execution_count": 3, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## AlexNET\n", | |
| "just recap of what was done on labs2" | |
| ], | |
| "metadata": { | |
| "id": "p_7akHGzth-A" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "alex = alexnet(pretrained=False)\n", | |
| "print(alex)\n", | |
| "\n", | |
| "\"\"\"\n", | |
| "The element in the middle, `AdaptiveAvgPool2d` (`F.adaptive_avg_pool2d` in its\n", | |
| "functional form), is a special kind of pooling: instead of requiring the kernel size\n", | |
| "(and stride), it requires the desired output size. In other words, whatever the\n", | |
| "image size it gets as input, it will return a tensor with the desired size.\n", | |
| "\"\"\"" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 639 | |
| }, | |
| "id": "zOkEgBaHkIl4", | |
| "outputId": "d809e7b3-227c-48ec-e43e-a3fb48c2530a" | |
| }, | |
| "execution_count": 4, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", | |
| " warnings.warn(\n", | |
| "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n", | |
| " warnings.warn(msg)\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "AlexNet(\n", | |
| " (features): Sequential(\n", | |
| " (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n", | |
| " (1): ReLU(inplace=True)\n", | |
| " (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n", | |
| " (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", | |
| " (4): ReLU(inplace=True)\n", | |
| " (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n", | |
| " (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", | |
| " (7): ReLU(inplace=True)\n", | |
| " (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", | |
| " (9): ReLU(inplace=True)\n", | |
| " (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", | |
| " (11): ReLU(inplace=True)\n", | |
| " (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n", | |
| " )\n", | |
| " (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n", | |
| " (classifier): Sequential(\n", | |
| " (0): Dropout(p=0.5, inplace=False)\n", | |
| " (1): Linear(in_features=9216, out_features=4096, bias=True)\n", | |
| " (2): ReLU(inplace=True)\n", | |
| " (3): Dropout(p=0.5, inplace=False)\n", | |
| " (4): Linear(in_features=4096, out_features=4096, bias=True)\n", | |
| " (5): ReLU(inplace=True)\n", | |
| " (6): Linear(in_features=4096, out_features=1000, bias=True)\n", | |
| " )\n", | |
| ")\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "'\\nThe element in the middle, `AdaptiveAvgPool2d` (`F.adaptive_avg_pool2d` in its\\nfunctional form), is a special kind of pooling: instead of requiring the kernel size\\n(and stride), it requires the desired output size. In other words, whatever the\\nimage size it gets as input, it will return a tensor with the desired size.\\n'" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 4 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "url = 'https://download.pytorch.org/models/alexnet-owt-7be5be79.pth'\n", | |
| "state_dict = load_state_dict_from_url(url, model_dir='./pretrained', progress=True)\n", | |
| "alex.load_state_dict(state_dict)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "1p1QtWT0kIi8", | |
| "outputId": "311ada52-e08f-4ac0-91d8-159bd4b3b073" | |
| }, | |
| "execution_count": 5, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Downloading: \"https://download.pytorch.org/models/alexnet-owt-7be5be79.pth\" to ./pretrained/alexnet-owt-7be5be79.pth\n", | |
| "100%|██████████| 233M/233M [00:01<00:00, 181MB/s]\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<All keys matched successfully>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 5 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def freeze_model(model):\n", | |
| " for parameter in model.parameters():\n", | |
| " parameter.requires_grad = False\n", | |
| "\n", | |
| "freeze_model(alex) # zmazime vse, co v alexovi bylo natrenovano (at uz nami vuse, nebo naimportovano od nekoho jineho)" | |
| ], | |
| "metadata": { | |
| "id": "CzwYglt0kIgG" | |
| }, | |
| "execution_count": 6, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(alex.classifier) # zde jsme vypsali vsechny vrstvy, ktere alex obsahuje.\n", | |
| "# Vidime, ze posledni klasifikuje do 1000 trid. My ale potrebujeme jen do 3 (kamen, nuzky, papir)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "K8IDPmIakIdl", | |
| "outputId": "04c6566c-abdc-4f43-be67-5ea8093d3c42" | |
| }, | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Sequential(\n", | |
| " (0): Dropout(p=0.5, inplace=False)\n", | |
| " (1): Linear(in_features=9216, out_features=4096, bias=True)\n", | |
| " (2): ReLU(inplace=True)\n", | |
| " (3): Dropout(p=0.5, inplace=False)\n", | |
| " (4): Linear(in_features=4096, out_features=4096, bias=True)\n", | |
| " (5): ReLU(inplace=True)\n", | |
| " (6): Linear(in_features=4096, out_features=1000, bias=True)\n", | |
| ")\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "torch.manual_seed(11)\n", | |
| "alex.classifier[6] = nn.Linear(4096, 3) # zde jsme zamenili posledni vrstvu alexe na vlastni (klasifikujici do 3 trid)" | |
| ], | |
| "metadata": { | |
| "id": "7cUmLIfvkIa-" | |
| }, | |
| "execution_count": 8, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "for name, param in alex.named_parameters():\n", | |
| " if param.requires_grad == True:\n", | |
| " print(name)\n", | |
| "# zde vidime, ze jen posledni, 6-ta vrstva ma povolene uceni parametru (\"vyzaduji gradienty\"). Ostatni jsou zmrazene, to je ok." | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "BTYFl9ZEkIYq", | |
| "outputId": "2879c6c8-09c0-4ac6-eb77-5975c92e4767" | |
| }, | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "classifier.6.weight\n", | |
| "classifier.6.bias\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def freeze_model(model):\n", | |
| " for parameter in model.parameters():\n", | |
| " parameter.requires_grad = False\n", | |
| "\n", | |
| "class AlexDataModule(pl.LightningDataModule):\n", | |
| " def __init__(self, batch_size=16):\n", | |
| " super().__init__()\n", | |
| " self.batch_size = batch_size\n", | |
| " self.train_dir = 'rps'\n", | |
| " self.test_dir = 'rps-test-set'\n", | |
| " self.composer = None\n", | |
| "\n", | |
| " def setup(self, stage=None):\n", | |
| " # Define the normalization values (based on the original Alexnet paper)\n", | |
| " norm_mean = [0.485, 0.456, 0.406]\n", | |
| " norm_std = [0.229, 0.224, 0.225]\n", | |
| "\n", | |
| " # Build the transformation and datasets\n", | |
| " self.composer = torchvision.transforms.Compose([\n", | |
| " torchvision.transforms.Resize(256),\n", | |
| " torchvision.transforms.CenterCrop(224),\n", | |
| " torchvision.transforms.ToTensor(),\n", | |
| " torchvision.transforms.Normalize(mean=norm_mean, std=norm_std)\n", | |
| " ])\n", | |
| "\n", | |
| " self.train_data = ImageFolder(root=self.train_dir, transform=self.composer)\n", | |
| " self.val_data = ImageFolder(root=self.test_dir, transform=self.composer)\n", | |
| "\n", | |
| " def train_dataloader(self):\n", | |
| " return DataLoader(self.train_data, batch_size=self.batch_size, shuffle=True)\n", | |
| "\n", | |
| " def val_dataloader(self):\n", | |
| " return DataLoader(self.val_data, batch_size=self.batch_size)\n" | |
| ], | |
| "metadata": { | |
| "id": "d0mW06nmkIVV" | |
| }, | |
| "execution_count": 10, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class AlexModel(pl.LightningModule):\n", | |
| " def __init__(self):\n", | |
| " super().__init__()\n", | |
| "\n", | |
| " # Load the pretrained AlexNet model and replace the classifier\n", | |
| " alex = alexnet(pretrained=False)\n", | |
| " url = 'https://download.pytorch.org/models/alexnet-owt-7be5be79.pth'\n", | |
| " state_dict = load_state_dict_from_url(url, model_dir='./pretrained', progress=True)\n", | |
| " alex.load_state_dict(state_dict)\n", | |
| "\n", | |
| " # Freeze the parameters of the pretrained model\n", | |
| " freeze_model(alex)\n", | |
| "\n", | |
| " # Replace the last fully connected layer for the new classification task\n", | |
| " alex.classifier[6] = nn.Linear(4096, 3)\n", | |
| "\n", | |
| " # Assign the modified AlexNet to self.model\n", | |
| " self.model = alex\n", | |
| "\n", | |
| " # Loss function for the new classifier\n", | |
| " self.loss_fn = nn.CrossEntropyLoss()\n", | |
| "\n", | |
| " def forward(self, x):\n", | |
| " return self.model(x)\n", | |
| "\n", | |
| " def training_step(self, batch, batch_idx):\n", | |
| " x, y = batch\n", | |
| " y_hat = self(x)\n", | |
| " loss = self.loss_fn(y_hat, y)\n", | |
| " self.log('train_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)\n", | |
| " return loss\n", | |
| "\n", | |
| " def validation_step(self, batch, batch_idx):\n", | |
| " x, y = batch\n", | |
| " y_hat = self(x)\n", | |
| " loss = self.loss_fn(y_hat, y)\n", | |
| " self.log('val_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)\n", | |
| " return loss\n", | |
| "\n", | |
| " def configure_optimizers(self):\n", | |
| " return torch.optim.SGD(self.parameters(), lr=1e-3, momentum=0.9, nesterov=True)" | |
| ], | |
| "metadata": { | |
| "id": "dX6Nk7c9qdP4" | |
| }, | |
| "execution_count": 11, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Initialize the model and data module\n", | |
| "alex_model = AlexModel()\n", | |
| "alex_data_module = AlexDataModule()\n", | |
| "\n", | |
| "# Get the number of available GPUs, else use CPU\n", | |
| "ngpus = torch.cuda.device_count() if torch.cuda.is_available() else 1\n", | |
| "\n", | |
| "logger = CSVLogger(\"logs\", name=\"Alex_RockPaperScissors\")\n", | |
| "\n", | |
| "# Train and Validate the model\n", | |
| "trainer = pl.Trainer(logger=CSVLogger(\"logs\", name=\"Alex_RockPaperScissors\"), devices=ngpus, accelerator=\"gpu\" if ngpus > 0 else None, max_epochs=1)\n", | |
| "trainer.fit(alex_model, alex_data_module)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 726, | |
| "referenced_widgets": [ | |
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| }, | |
| "id": "PC0Pl8YvqgP2", | |
| "outputId": "6de19aa9-b554-4f8d-ac20-6f1f655a0814" | |
| }, | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO: GPU available: True (cuda), used: True\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (cuda), used: True\n", | |
| "INFO: TPU available: False, using: 0 TPU cores\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n", | |
| "INFO: IPU available: False, using: 0 IPUs\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n", | |
| "INFO: HPU available: False, using: 0 HPUs\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n", | |
| "WARNING: Missing logger folder: logs/Alex_RockPaperScissors\n", | |
| "WARNING:lightning.fabric.loggers.csv_logs:Missing logger folder: logs/Alex_RockPaperScissors\n", | |
| "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", | |
| "INFO:lightning.pytorch.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", | |
| "INFO: \n", | |
| " | Name | Type | Params\n", | |
| "---------------------------------------------\n", | |
| "0 | model | AlexNet | 57.0 M\n", | |
| "1 | loss_fn | CrossEntropyLoss | 0 \n", | |
| "---------------------------------------------\n", | |
| "12.3 K Trainable params\n", | |
| "57.0 M Non-trainable params\n", | |
| "57.0 M Total params\n", | |
| "228.065 Total estimated model params size (MB)\n", | |
| "INFO:lightning.pytorch.callbacks.model_summary:\n", | |
| " | Name | Type | Params\n", | |
| "---------------------------------------------\n", | |
| "0 | model | AlexNet | 57.0 M\n", | |
| "1 | loss_fn | CrossEntropyLoss | 0 \n", | |
| "---------------------------------------------\n", | |
| "12.3 K Trainable params\n", | |
| "57.0 M Non-trainable params\n", | |
| "57.0 M Total params\n", | |
| "228.065 Total estimated model params size (MB)\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Sanity Checking: | | 0/? [00:00<?, ?it/s]" | |
| ], | |
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| "version_major": 2, | |
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| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=1` in the `DataLoader` to improve performance.\n", | |
| "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=1` in the `DataLoader` to improve performance.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Training: | | 0/? [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "ec842ae29f9f40f1ae1962d442b12e63" | |
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| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Validation: | | 0/? [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "29c20be1cf20480a93c8266ca2f0f340" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO: `Trainer.fit` stopped: `max_epochs=1` reached.\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=1` reached.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "log_dir = 'version_0'\n", | |
| "\n", | |
| "log_file = f'/content/logs/Alex_RockPaperScissors/{log_dir}/metrics.csv'\n", | |
| "df = pd.read_csv(log_file)\n", | |
| "df_grouped = df.groupby('epoch').first().reset_index()\n", | |
| "df_grouped" | |
| ], | |
| "metadata": { | |
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| "base_uri": "https://localhost:8080/", | |
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| "outputId": "0ab77c73-ab93-4163-eeff-eda8b4ec322a" | |
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| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " epoch train_loss_step step val_loss_step val_loss_epoch \\\n", | |
| "0 0.0 0.064842 49 NaN 0.190937 \n", | |
| "\n", | |
| " train_loss_epoch \n", | |
| "0 0.097383 " | |
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| " <th></th>\n", | |
| " <th>epoch</th>\n", | |
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| "\n", | |
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| "metadata": {}, | |
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| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def correct(model, loader):\n", | |
| " \"\"\"\n", | |
| " fction pre-built by T.Kerepecky\n", | |
| " \"\"\"\n", | |
| " model.eval() # Set the model to evaluation mode\n", | |
| " device = next(model.parameters()).device # Get the device\n", | |
| "\n", | |
| " results = []\n", | |
| " for batch in loader:\n", | |
| " x, y = batch\n", | |
| " x, y = x.to(device), y.to(device)\n", | |
| "\n", | |
| " y_hat = model(x)\n", | |
| "\n", | |
| " _, n_dims = y_hat.shape\n", | |
| " _, predicted = torch.max(y_hat, 1)\n", | |
| "\n", | |
| " for c in range(n_dims):\n", | |
| " n_class = (y == c).sum().item()\n", | |
| " n_correct = (predicted[y == c] == c).sum().item()\n", | |
| " results.append((n_correct, n_class))\n", | |
| "\n", | |
| " model.train() # Set the model back to training mode\n", | |
| "\n", | |
| " return torch.tensor(results)" | |
| ], | |
| "metadata": { | |
| "id": "K9K6V9-Ys6r2" | |
| }, | |
| "execution_count": 15, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "train_results = correct(alex_model, alex_data_module.train_dataloader())\n", | |
| "train_results = train_results.sum(axis=0)\n", | |
| "print(f'The training set: our model got {train_results[0]} out of {train_results[1]} images correctly classified, an accuracy of {train_results[0]/train_results[1]:.2f}%!')\n", | |
| "\n", | |
| "val_results = correct(alex_model, alex_data_module.val_dataloader())\n", | |
| "val_results = val_results.sum(axis=0)\n", | |
| "print(f'The validation set: our model got {val_results[0]} out of {val_results[1]} images correctly classified, an accuracy of {val_results[0]/val_results[1]:.2f}%!')\n" | |
| ], | |
| "metadata": { | |
| "id": "3nrfvyREkIOU" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## VGG\n", | |
| "aka" | |
| ], | |
| "metadata": { | |
| "id": "JhbzHnj4tdvO" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# import pretrained NN and print its structure\n", | |
| "from torchvision.models import vgg16\n", | |
| "\n", | |
| "#vgg = vgg16(pretrained=True)\n", | |
| "vgg = vgg16(weights='DEFAULT')" | |
| ], | |
| "metadata": { | |
| "id": "vFKdYzPikIL8" | |
| }, | |
| "execution_count": 55, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(vgg.classifier)\n", | |
| "vgg.classifier[6] = nn.Linear(in_features=4096, out_features=3)\n", | |
| "print(vgg.classifier)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "h4nJT-Uw1x1I", | |
| "outputId": "2e7ccd41-d1a5-471b-fdbd-aec075ef7055" | |
| }, | |
| "execution_count": 56, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Sequential(\n", | |
| " (0): Linear(in_features=25088, out_features=4096, bias=True)\n", | |
| " (1): ReLU(inplace=True)\n", | |
| " (2): Dropout(p=0.5, inplace=False)\n", | |
| " (3): Linear(in_features=4096, out_features=4096, bias=True)\n", | |
| " (4): ReLU(inplace=True)\n", | |
| " (5): Dropout(p=0.5, inplace=False)\n", | |
| " (6): Linear(in_features=4096, out_features=1000, bias=True)\n", | |
| ")\n", | |
| "Sequential(\n", | |
| " (0): Linear(in_features=25088, out_features=4096, bias=True)\n", | |
| " (1): ReLU(inplace=True)\n", | |
| " (2): Dropout(p=0.5, inplace=False)\n", | |
| " (3): Linear(in_features=4096, out_features=4096, bias=True)\n", | |
| " (4): ReLU(inplace=True)\n", | |
| " (5): Dropout(p=0.5, inplace=False)\n", | |
| " (6): Linear(in_features=4096, out_features=3, bias=True)\n", | |
| ")\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class vggDataModule(pl.LightningDataModule):\n", | |
| " def __init__(self, batch_size=16):\n", | |
| " super().__init__()\n", | |
| " self.batch_size = batch_size\n", | |
| " self.train_dir = 'rps'\n", | |
| " self.test_dir = 'rps-test-set'\n", | |
| " self.composer = None\n", | |
| "\n", | |
| " def setup(self, stage=None):\n", | |
| " # Define the normalization values (based on the original Alexnet paper)\n", | |
| " norm_mean = [0.485, 0.456, 0.406]\n", | |
| " norm_std = [0.229, 0.224, 0.225]\n", | |
| "\n", | |
| " # Build the transformation and datasets\n", | |
| " self.composer = torchvision.transforms.Compose([\n", | |
| " torchvision.transforms.Resize(256),\n", | |
| " torchvision.transforms.CenterCrop(224),\n", | |
| " torchvision.transforms.ToTensor(),\n", | |
| " torchvision.transforms.Normalize(mean=norm_mean, std=norm_std)\n", | |
| " ])\n", | |
| "\n", | |
| " self.train_data = ImageFolder(root=self.train_dir, transform=self.composer)\n", | |
| " self.val_data = ImageFolder(root=self.test_dir, transform=self.composer)\n", | |
| "\n", | |
| " def train_dataloader(self):\n", | |
| " return DataLoader(self.train_data, batch_size=self.batch_size, shuffle=True, num_workers=2)\n", | |
| "\n", | |
| " def val_dataloader(self):\n", | |
| " return DataLoader(self.val_data, batch_size=self.batch_size, num_workers=2)" | |
| ], | |
| "metadata": { | |
| "id": "0r3hOdMj1xsp" | |
| }, | |
| "execution_count": 46, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class vggModel(pl.LightningModule):\n", | |
| " def __init__(self):\n", | |
| " super().__init__()\n", | |
| "\n", | |
| " # Load the pretrained AlexNet model and replace the classifier\n", | |
| " vgg = vgg16(pretrained=True)\n", | |
| "\n", | |
| " # Freeze the parameters of the pretrained model\n", | |
| " freeze_model(vgg)\n", | |
| "\n", | |
| " # Replace the last fully connected layer for the new classification task\n", | |
| " vgg.classifier[6] = nn.Linear(in_features=4096, out_features=3)\n", | |
| "\n", | |
| " # Assign the modified AlexNet to self.model\n", | |
| " self.model = vgg\n", | |
| "\n", | |
| " # Loss function for the new classifier\n", | |
| " self.loss_fn = nn.CrossEntropyLoss()\n", | |
| "\n", | |
| " def forward(self, x):\n", | |
| " return self.model(x)\n", | |
| "\n", | |
| " def training_step(self, batch, batch_idx):\n", | |
| " x, y = batch\n", | |
| " y_hat = self(x)\n", | |
| " loss = self.loss_fn(y_hat, y)\n", | |
| " self.log('train_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)\n", | |
| " return loss\n", | |
| "\n", | |
| " def validation_step(self, batch, batch_idx):\n", | |
| " x, y = batch\n", | |
| " y_hat = self(x)\n", | |
| " loss = self.loss_fn(y_hat, y)\n", | |
| " self.log('val_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)\n", | |
| " return loss\n", | |
| "\n", | |
| " def configure_optimizers(self):\n", | |
| " return torch.optim.SGD(self.parameters(), lr=1e-3, momentum=0.9, nesterov=True)" | |
| ], | |
| "metadata": { | |
| "id": "A6ZPemgw2Yw9" | |
| }, | |
| "execution_count": 47, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Initialize the model and data module\n", | |
| "vgg_model = vggModel()\n", | |
| "vgg_data_module = vggDataModule()\n", | |
| "\n", | |
| "# Get the number of available GPUs, else use CPU\n", | |
| "ngpus = torch.cuda.device_count() if torch.cuda.is_available() else 1\n", | |
| "\n", | |
| "logger = CSVLogger(\"logs\", name=\"vgg_RockPaperScissors\")\n", | |
| "\n", | |
| "# Train and Validate the model\n", | |
| "trainer = pl.Trainer(logger=CSVLogger(\"logs\", name=\"vgg_RockPaperScissors\"), devices=ngpus, accelerator=\"gpu\" if ngpus > 0 else None, max_epochs=1)\n", | |
| "trainer.fit(vgg_model, vgg_data_module)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 726, | |
| "referenced_widgets": [ | |
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| }, | |
| "id": "D0xCISVT2Yn_", | |
| "outputId": "4202cbef-4544-47b3-ca5d-d9e4c2006b4d" | |
| }, | |
| "execution_count": 48, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", | |
| " warnings.warn(\n", | |
| "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n", | |
| " warnings.warn(msg)\n", | |
| "INFO: GPU available: True (cuda), used: True\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (cuda), used: True\n", | |
| "INFO: TPU available: False, using: 0 TPU cores\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n", | |
| "INFO: IPU available: False, using: 0 IPUs\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n", | |
| "INFO: HPU available: False, using: 0 HPUs\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n", | |
| "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", | |
| "INFO:lightning.pytorch.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", | |
| "INFO: \n", | |
| " | Name | Type | Params\n", | |
| "---------------------------------------------\n", | |
| "0 | model | VGG | 134 M \n", | |
| "1 | loss_fn | CrossEntropyLoss | 0 \n", | |
| "---------------------------------------------\n", | |
| "12.3 K Trainable params\n", | |
| "134 M Non-trainable params\n", | |
| "134 M Total params\n", | |
| "537.091 Total estimated model params size (MB)\n", | |
| "INFO:lightning.pytorch.callbacks.model_summary:\n", | |
| " | Name | Type | Params\n", | |
| "---------------------------------------------\n", | |
| "0 | model | VGG | 134 M \n", | |
| "1 | loss_fn | CrossEntropyLoss | 0 \n", | |
| "---------------------------------------------\n", | |
| "12.3 K Trainable params\n", | |
| "134 M Non-trainable params\n", | |
| "134 M Total params\n", | |
| "537.091 Total estimated model params size (MB)\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Sanity Checking: | | 0/? [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "ce2f83d22b154a8f9f96f32f4271a728" | |
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| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Training: | | 0/? [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "9d685cb152094a2982dd0fbd960e7b35" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Validation: | | 0/? [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "0cfac08c924146aeac1a9c9dbe96b3ae" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO: `Trainer.fit` stopped: `max_epochs=1` reached.\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=1` reached.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "log_dir = 'version_0'\n", | |
| "\n", | |
| "log_file = f'/content/logs/vgg_RockPaperScissors/{log_dir}/metrics.csv'\n", | |
| "df = pd.read_csv(log_file)\n", | |
| "df_grouped = df.groupby('epoch').first().reset_index()\n", | |
| "df_grouped" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 81 | |
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| "id": "Jwknqauv2Yfr", | |
| "outputId": "f328723c-6433-46c4-f52a-3efc8e203e45" | |
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| "execution_count": 49, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " epoch train_loss_step step val_loss_step val_loss_epoch \\\n", | |
| "0 0.0 0.21497 49 NaN 0.69801 \n", | |
| "\n", | |
| " train_loss_epoch \n", | |
| "0 0.247861 " | |
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| " <th></th>\n", | |
| " <th>epoch</th>\n", | |
| " <th>train_loss_step</th>\n", | |
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| " <th>val_loss_epoch</th>\n", | |
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| "metadata": {}, | |
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| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "train_results1 = correct(vgg_model, vgg_data_module.train_dataloader())\n", | |
| "train_results1 = train_results1.sum(axis=0)\n", | |
| "print(f'The training set: our model got {train_results1[0]} out of {train_results1[1]} images correctly classified, an accuracy of {train_results1[0]/train_results1[1]:.2f}%!')\n", | |
| "\n", | |
| "val_results1 = correct(vgg_model, vgg_data_module.val_dataloader())\n", | |
| "val_results1 = val_results1.sum(axis=0)\n", | |
| "print(f'The validation set: our model got {val_results1[0]} out of {val_results1[1]} images correctly classified, an accuracy of {val_results1[0]/val_results1[1]:.2f}%!')\n", | |
| "" | |
| ], | |
| "metadata": { | |
| "colab": { | |
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| "height": 405 | |
| }, | |
| "id": "0Rj0HfDN2YX0", | |
| "outputId": "3a3e6e33-3bc7-45ae-f503-e104355861cb" | |
| }, | |
| "execution_count": 50, | |
| "outputs": [ | |
| { | |
| "output_type": "error", | |
| "ename": "KeyboardInterrupt", | |
| "evalue": "ignored", | |
| "traceback": [ | |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", | |
| "\u001b[0;32m<ipython-input-50-ab3885af39cb>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_results1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcorrect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvgg_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvgg_data_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dataloader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mtrain_results1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_results1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'The training set: our model got {train_results1[0]} out of {train_results1[1]} images correctly classified, an accuracy of {train_results1[0]/train_results1[1]:.2f}%!'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mval_results1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcorrect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvgg_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvgg_data_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mval_dataloader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
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| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1525\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1526\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1529\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 213\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 216\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", | |
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| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1525\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1526\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1529\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 459\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 460\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 455\u001b[0m _pair(0), self.dilation, self.groups)\n\u001b[0;32m--> 456\u001b[0;31m return F.conv2d(input, weight, bias, self.stride,\n\u001b[0m\u001b[1;32m 457\u001b[0m self.padding, self.dilation, self.groups)\n\u001b[1;32m 458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;31mKeyboardInterrupt\u001b[0m: " | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "NodS2KqQ2YQT" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "RhZtOMS51xae" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## ResNET\n", | |
| "aka" | |
| ], | |
| "metadata": { | |
| "id": "hzcNPvpAvWyb" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# import pretrained NN and print its structure\n", | |
| "from torchvision.models import resnet50\n", | |
| "\n", | |
| "rnet = resnet50(pretrained=True)\n", | |
| "print(rnet)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "FCxhHn8mkIJL", | |
| "outputId": "17837222-1868-4050-bcd0-368bc4a34eb5" | |
| }, | |
| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", | |
| " warnings.warn(\n", | |
| "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n", | |
| " warnings.warn(msg)\n", | |
| "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n", | |
| "100%|██████████| 97.8M/97.8M [00:00<00:00, 158MB/s]\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "ResNet(\n", | |
| " (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n", | |
| " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (relu): ReLU(inplace=True)\n", | |
| " (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", | |
| " (layer1): Sequential(\n", | |
| " (0): Bottleneck(\n", | |
| " (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
| " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (relu): ReLU(inplace=True)\n", | |
| " (downsample): Sequential(\n", | |
| " (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " )\n", | |
| " )\n", | |
| " (1): Bottleneck(\n", | |
| " (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
| " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (relu): ReLU(inplace=True)\n", | |
| " )\n", | |
| " (2): Bottleneck(\n", | |
| " (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
| " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (relu): ReLU(inplace=True)\n", | |
| " )\n", | |
| " )\n", | |
| " (layer2): Sequential(\n", | |
| " (0): Bottleneck(\n", | |
| " (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", | |
| " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (relu): ReLU(inplace=True)\n", | |
| " (downsample): Sequential(\n", | |
| " (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", | |
| " (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " )\n", | |
| " )\n", | |
| " (1): Bottleneck(\n", | |
| " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
| " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (relu): ReLU(inplace=True)\n", | |
| " )\n", | |
| " (2): Bottleneck(\n", | |
| " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
| " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (relu): ReLU(inplace=True)\n", | |
| " )\n", | |
| " (3): Bottleneck(\n", | |
| " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
| " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (relu): ReLU(inplace=True)\n", | |
| " )\n", | |
| " )\n", | |
| " (layer3): Sequential(\n", | |
| " (0): Bottleneck(\n", | |
| " (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", | |
| " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (relu): ReLU(inplace=True)\n", | |
| " (downsample): Sequential(\n", | |
| " (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", | |
| " (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " )\n", | |
| " )\n", | |
| " (1): Bottleneck(\n", | |
| " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
| " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (relu): ReLU(inplace=True)\n", | |
| " )\n", | |
| " (2): Bottleneck(\n", | |
| " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
| " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (relu): ReLU(inplace=True)\n", | |
| " )\n", | |
| " (3): Bottleneck(\n", | |
| " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
| " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (relu): ReLU(inplace=True)\n", | |
| " )\n", | |
| " (4): Bottleneck(\n", | |
| " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
| " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (relu): ReLU(inplace=True)\n", | |
| " )\n", | |
| " (5): Bottleneck(\n", | |
| " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
| " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (relu): ReLU(inplace=True)\n", | |
| " )\n", | |
| " )\n", | |
| " (layer4): Sequential(\n", | |
| " (0): Bottleneck(\n", | |
| " (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", | |
| " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (relu): ReLU(inplace=True)\n", | |
| " (downsample): Sequential(\n", | |
| " (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", | |
| " (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " )\n", | |
| " )\n", | |
| " (1): Bottleneck(\n", | |
| " (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
| " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (relu): ReLU(inplace=True)\n", | |
| " )\n", | |
| " (2): Bottleneck(\n", | |
| " (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", | |
| " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", | |
| " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", | |
| " (relu): ReLU(inplace=True)\n", | |
| " )\n", | |
| " )\n", | |
| " (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n", | |
| " (fc): Linear(in_features=2048, out_features=1000, bias=True)\n", | |
| ")\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# freeze to stop learning where we dont want it\n", | |
| "freeze_model(rnet)" | |
| ], | |
| "metadata": { | |
| "id": "0XnPXafwwop2" | |
| }, | |
| "execution_count": 17, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(rnet.fc)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "_bBR8IRvw-zQ", | |
| "outputId": "1d66fb3b-44fc-4c49-d1d2-46baeacad05c" | |
| }, | |
| "execution_count": 19, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Linear(in_features=2048, out_features=1000, bias=True)\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "rnet.fc = nn.Linear(2048, 3)" | |
| ], | |
| "metadata": { | |
| "id": "F-LKceqZxTkE" | |
| }, | |
| "execution_count": 21, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(rnet.fc)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Pr_mUoAQxfQT", | |
| "outputId": "cd90c1aa-8976-4f62-9830-b1a5d3dc546f" | |
| }, | |
| "execution_count": 22, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Linear(in_features=4096, out_features=3, bias=True)\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "for name, param in rnet.named_parameters():\n", | |
| " if param.requires_grad == True:\n", | |
| " print(name)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "P4yOrcebxkmQ", | |
| "outputId": "8cfad9e3-6cc0-45e6-a28f-9dc88701411c" | |
| }, | |
| "execution_count": 24, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "fc.weight\n", | |
| "fc.bias\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class ResNETDataModule(pl.LightningDataModule):\n", | |
| " def __init__(self, batch_size=16):\n", | |
| " super().__init__()\n", | |
| " self.batch_size = batch_size\n", | |
| " self.train_dir = 'rps'\n", | |
| " self.test_dir = 'rps-test-set'\n", | |
| " self.composer = None\n", | |
| "\n", | |
| " def setup(self, stage=None):\n", | |
| " # Define the normalization values (based on the original Alexnet paper)\n", | |
| " norm_mean = [0.485, 0.456, 0.406]\n", | |
| " norm_std = [0.229, 0.224, 0.225]\n", | |
| "\n", | |
| " # Build the transformation and datasets\n", | |
| " self.composer = torchvision.transforms.Compose([\n", | |
| " torchvision.transforms.Resize(256),\n", | |
| " torchvision.transforms.CenterCrop(224),\n", | |
| " torchvision.transforms.ToTensor(),\n", | |
| " torchvision.transforms.Normalize(mean=norm_mean, std=norm_std)\n", | |
| " ])\n", | |
| "\n", | |
| " self.train_data = ImageFolder(root=self.train_dir, transform=self.composer)\n", | |
| " self.val_data = ImageFolder(root=self.test_dir, transform=self.composer)\n", | |
| "\n", | |
| " def train_dataloader(self):\n", | |
| " return DataLoader(self.train_data, batch_size=self.batch_size, shuffle=True)\n", | |
| "\n", | |
| " def val_dataloader(self):\n", | |
| " return DataLoader(self.val_data, batch_size=self.batch_size)" | |
| ], | |
| "metadata": { | |
| "id": "mL_vYF_exkh6" | |
| }, | |
| "execution_count": 25, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class ResNETModel(pl.LightningModule):\n", | |
| " def __init__(self):\n", | |
| " super().__init__()\n", | |
| "\n", | |
| " # Load the pretrained AlexNet model and replace the classifier\n", | |
| " rnet = resnet50(pretrained=True)\n", | |
| "\n", | |
| " # Freeze the parameters of the pretrained model\n", | |
| " freeze_model(rnet)\n", | |
| "\n", | |
| " # Replace the last fully connected layer for the new classification task\n", | |
| " rnet.fc = nn.Linear(2048, 3)\n", | |
| "\n", | |
| " # Assign the modified AlexNet to self.model\n", | |
| " self.model = rnet\n", | |
| "\n", | |
| " # Loss function for the new classifier\n", | |
| " self.loss_fn = nn.CrossEntropyLoss()\n", | |
| "\n", | |
| " def forward(self, x):\n", | |
| " return self.model(x)\n", | |
| "\n", | |
| " def training_step(self, batch, batch_idx):\n", | |
| " x, y = batch\n", | |
| " y_hat = self(x)\n", | |
| " loss = self.loss_fn(y_hat, y)\n", | |
| " self.log('train_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)\n", | |
| " return loss\n", | |
| "\n", | |
| " def validation_step(self, batch, batch_idx):\n", | |
| " x, y = batch\n", | |
| " y_hat = self(x)\n", | |
| " loss = self.loss_fn(y_hat, y)\n", | |
| " self.log('val_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)\n", | |
| " return loss\n", | |
| "\n", | |
| " def configure_optimizers(self):\n", | |
| " return torch.optim.SGD(self.parameters(), lr=1e-3, momentum=0.9, nesterov=True)" | |
| ], | |
| "metadata": { | |
| "id": "NrsD6pbUyaKZ" | |
| }, | |
| "execution_count": 26, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Initialize the model and data module\n", | |
| "rnet_model = ResNETModel()\n", | |
| "rnet_data_module = ResNETDataModule()\n", | |
| "\n", | |
| "# Get the number of available GPUs, else use CPU\n", | |
| "ngpus = torch.cuda.device_count() if torch.cuda.is_available() else 1\n", | |
| "\n", | |
| "logger = CSVLogger(\"logs\", name=\"ResNET_RockPaperScissors\")\n", | |
| "\n", | |
| "# Train and Validate the model\n", | |
| "trainer = pl.Trainer(logger=CSVLogger(\"logs\", name=\"ResNET_RockPaperScissors\"), devices=ngpus, accelerator=\"gpu\" if ngpus > 0 else None, max_epochs=1)\n", | |
| "trainer.fit(rnet_model, rnet_data_module)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 798, | |
| "referenced_widgets": [ | |
| "20b4ca77a8f34fd38231368b7cd578ca", | |
| "f6a12d422bdb4c6a8a183c1c7aa114d2", | |
| "7aed0db4ffae47d4a483160ab8c866fe", | |
| "39d7ab6854f441989d7ec8c1e9182051", | |
| "76c50aa0fdc8433c8c2212debd036ec3", | |
| "1b966e2c4c6948d9870a35f3093bca70", | |
| "34073e80e2c84f91b60870d3e15bd22f", | |
| "62c7130f7aa74459bc72160c2d40e4c9", | |
| "ba92f785c25c4c9ebb85fb0e0bbb2582", | |
| "50ea6961a36043fbaf3346c9c1501f6a", | |
| "16102d80d445473a90ecc77ecfe82e4d", | |
| "2952fa035c1a4699ba9d8c7dcb6ce710", | |
| "59b39417536c4de39a0932c7afcba4bd", | |
| "75218aec6e9947699089d49d7c9a139d", | |
| "612a86f927144dc98f38af351d100e62", | |
| "6e9a406f23d94f11badde14d27a6d77d", | |
| "981fce8101044e639816dee282003e67", | |
| "17e6c17527104e62af617f9b9fad5a1d", | |
| "11a926a8365245e58238be0d9fa475bf", | |
| "e779fb221e924587adb553a0a51f056b", | |
| "4297bbafa4ac491cafe2215af7df61c8", | |
| "2dd9ccdc72a1480f9108abfeaca12599", | |
| "6893cf3eec454b5185d83631226c029f", | |
| "32ce06372aa44ff0b04628ed008647d7", | |
| "8e5547535152493e9d160eef8ac7866a", | |
| "d24fca2fdab344458300f9e039117f9a", | |
| "dd9ed63e5bd547f1b8f11d674d48708a", | |
| "06d3c4c9529c4ca6b3a5e60efb6f96c9", | |
| "d2abe57b89b149d7badbda92505a1e90", | |
| "e11d060d378d492288621a43d53cfb1d", | |
| "c3dd6037f2034d82a808bb021e4fa7d9", | |
| "7d7e8bd09f224824bb10e008375b08ce", | |
| "f1a59fd2c9814610903b66c3498e9e17" | |
| ] | |
| }, | |
| "id": "2x8_8g6Lxkdj", | |
| "outputId": "1431ebbb-4d9a-4579-a8c1-4c3a2728e0c3" | |
| }, | |
| "execution_count": 27, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", | |
| " warnings.warn(\n", | |
| "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n", | |
| " warnings.warn(msg)\n", | |
| "INFO: GPU available: True (cuda), used: True\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (cuda), used: True\n", | |
| "INFO: TPU available: False, using: 0 TPU cores\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n", | |
| "INFO: IPU available: False, using: 0 IPUs\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n", | |
| "INFO: HPU available: False, using: 0 HPUs\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n", | |
| "WARNING: Missing logger folder: logs/ResNET_RockPaperScissors\n", | |
| "WARNING:lightning.fabric.loggers.csv_logs:Missing logger folder: logs/ResNET_RockPaperScissors\n", | |
| "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", | |
| "INFO:lightning.pytorch.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", | |
| "INFO: \n", | |
| " | Name | Type | Params\n", | |
| "---------------------------------------------\n", | |
| "0 | model | ResNet | 23.5 M\n", | |
| "1 | loss_fn | CrossEntropyLoss | 0 \n", | |
| "---------------------------------------------\n", | |
| "6.1 K Trainable params\n", | |
| "23.5 M Non-trainable params\n", | |
| "23.5 M Total params\n", | |
| "94.057 Total estimated model params size (MB)\n", | |
| "INFO:lightning.pytorch.callbacks.model_summary:\n", | |
| " | Name | Type | Params\n", | |
| "---------------------------------------------\n", | |
| "0 | model | ResNet | 23.5 M\n", | |
| "1 | loss_fn | CrossEntropyLoss | 0 \n", | |
| "---------------------------------------------\n", | |
| "6.1 K Trainable params\n", | |
| "23.5 M Non-trainable params\n", | |
| "23.5 M Total params\n", | |
| "94.057 Total estimated model params size (MB)\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Sanity Checking: | | 0/? [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
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| } | |
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| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=1` in the `DataLoader` to improve performance.\n", | |
| "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=1` in the `DataLoader` to improve performance.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Training: | | 0/? [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "2952fa035c1a4699ba9d8c7dcb6ce710" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Validation: | | 0/? [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "6893cf3eec454b5185d83631226c029f" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO: `Trainer.fit` stopped: `max_epochs=1` reached.\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=1` reached.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "log_dir = 'version_0'\n", | |
| "\n", | |
| "log_file = f'/content/logs/ResNET_RockPaperScissors/{log_dir}/metrics.csv'\n", | |
| "df = pd.read_csv(log_file)\n", | |
| "df_grouped = df.groupby('epoch').first().reset_index()\n", | |
| "df_grouped" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 81 | |
| }, | |
| "id": "gq1vs_82xkZo", | |
| "outputId": "1d9187b9-de48-407c-e7e8-9406af5c6764" | |
| }, | |
| "execution_count": 28, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " epoch train_loss_step step val_loss_step val_loss_epoch \\\n", | |
| "0 0.0 0.42707 49 NaN 0.508469 \n", | |
| "\n", | |
| " train_loss_epoch \n", | |
| "0 0.494132 " | |
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| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>epoch</th>\n", | |
| " <th>train_loss_step</th>\n", | |
| " <th>step</th>\n", | |
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| "metadata": {}, | |
| "execution_count": 28 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "train_results2 = correct(rnet_model, rnet_data_module.train_dataloader())\n", | |
| "train_results2 = train_results2.sum(axis=0)\n", | |
| "print(f'The training set: our model got {train_results2[0]} out of {train_results2[1]} images correctly classified, an accuracy of {train_results2[0]/train_results2[1]:.2f}%!')\n", | |
| "\n", | |
| "val_results2 = correct(rnet_model, rnet_data_module.val_dataloader())\n", | |
| "val_results2 = val_results2.sum(axis=0)\n", | |
| "print(f'The validation set: our model got {val_results2[0]} out of {val_results2[1]} images correctly classified, an accuracy of {val_results2[0]/val_results2[1]:.2f}%!')\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "N6jNYYkzxkO8", | |
| "outputId": "2561c69e-3c04-44e3-c9fd-cfe86f28993a" | |
| }, | |
| "execution_count": 29, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "The training set: our model got 2508 out of 2520 images correctly classified, an accuracy of 1.00%!\n", | |
| "The validation set: our model got 316 out of 372 images correctly classified, an accuracy of 0.85%!\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## DenseNET\n", | |
| "aka the Densely Connected Convolutional Network" | |
| ], | |
| "metadata": { | |
| "id": "BnOaMN1gvXeZ" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# import pretrained NN and print its structure\n", | |
| "from torchvision.models import densenet201\n", | |
| "\n", | |
| "# dn = densenet201(pretrained=True)\n", | |
| "dn = densenet201(weights='DEFAULT')" | |
| ], | |
| "metadata": { | |
| "id": "0lKFTINgkIG7" | |
| }, | |
| "execution_count": 53, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(dn)" | |
| ], | |
| "metadata": { | |
| "id": "NwATLv_8kIER", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "458bc625-094f-4ea4-c5a9-dc87f0584648" | |
| }, | |
| "execution_count": 54, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "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): Linear(in_features=1920, out_features=1000, bias=True)\n", | |
| ")\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(dn.classifier)\n", | |
| "dn.classifier = nn.Linear(in_features=1920, out_features=3)\n", | |
| "print(dn.classifier)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "ixP601UO-Bu0", | |
| "outputId": "21e36545-4a31-4b2b-83be-66d1aa246aca" | |
| }, | |
| "execution_count": 58, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Linear(in_features=1920, out_features=1000, bias=True)\n", | |
| "Linear(in_features=1920, out_features=3, bias=True)\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class dnDataModule(pl.LightningDataModule):\n", | |
| " def __init__(self, batch_size=16):\n", | |
| " super().__init__()\n", | |
| " self.batch_size = batch_size\n", | |
| " self.train_dir = 'rps'\n", | |
| " self.test_dir = 'rps-test-set'\n", | |
| " self.composer = None\n", | |
| "\n", | |
| " def setup(self, stage=None):\n", | |
| " # Define the normalization values (based on the original Alexnet paper)\n", | |
| " norm_mean = [0.485, 0.456, 0.406]\n", | |
| " norm_std = [0.229, 0.224, 0.225]\n", | |
| "\n", | |
| " # Build the transformation and datasets\n", | |
| " self.composer = torchvision.transforms.Compose([\n", | |
| " torchvision.transforms.Resize(256),\n", | |
| " torchvision.transforms.CenterCrop(224),\n", | |
| " torchvision.transforms.ToTensor(),\n", | |
| " torchvision.transforms.Normalize(mean=norm_mean, std=norm_std)\n", | |
| " ])\n", | |
| "\n", | |
| " self.train_data = ImageFolder(root=self.train_dir, transform=self.composer)\n", | |
| " self.val_data = ImageFolder(root=self.test_dir, transform=self.composer)\n", | |
| "\n", | |
| " def train_dataloader(self):\n", | |
| " return DataLoader(self.train_data, batch_size=self.batch_size, shuffle=True, num_workers=2)\n", | |
| "\n", | |
| " def val_dataloader(self):\n", | |
| " return DataLoader(self.val_data, batch_size=self.batch_size, num_workers=2)" | |
| ], | |
| "metadata": { | |
| "id": "ZGAliMzi-BOm" | |
| }, | |
| "execution_count": 59, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class dnModel(pl.LightningModule):\n", | |
| " def __init__(self):\n", | |
| " super().__init__()\n", | |
| "\n", | |
| " # Load the pretrained AlexNet model and replace the classifier\n", | |
| " dn = densenet201(weights='DEFAULT')\n", | |
| "\n", | |
| " # Freeze the parameters of the pretrained model\n", | |
| " freeze_model(dn)\n", | |
| "\n", | |
| " # Replace the last fully connected layer for the new classification task\n", | |
| " dn.classifier = nn.Linear(in_features=1920, out_features=3)\n", | |
| "\n", | |
| " # Assign the modified AlexNet to self.model\n", | |
| " self.model = dn\n", | |
| "\n", | |
| " # Loss function for the new classifier\n", | |
| " self.loss_fn = nn.CrossEntropyLoss()\n", | |
| "\n", | |
| " def forward(self, x):\n", | |
| " return self.model(x)\n", | |
| "\n", | |
| " def training_step(self, batch, batch_idx):\n", | |
| " x, y = batch\n", | |
| " y_hat = self(x)\n", | |
| " loss = self.loss_fn(y_hat, y)\n", | |
| " self.log('train_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)\n", | |
| " return loss\n", | |
| "\n", | |
| " def validation_step(self, batch, batch_idx):\n", | |
| " x, y = batch\n", | |
| " y_hat = self(x)\n", | |
| " loss = self.loss_fn(y_hat, y)\n", | |
| " self.log('val_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)\n", | |
| " return loss\n", | |
| "\n", | |
| " def configure_optimizers(self):\n", | |
| " return torch.optim.SGD(self.parameters(), lr=1e-3, momentum=0.9, nesterov=True)" | |
| ], | |
| "metadata": { | |
| "id": "VA4kOKBF-AvK" | |
| }, | |
| "execution_count": 60, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Initialize the model and data module\n", | |
| "dn_model = dnModel()\n", | |
| "dn_data_module = dnDataModule()\n", | |
| "\n", | |
| "# Get the number of available GPUs, else use CPU\n", | |
| "ngpus = torch.cuda.device_count() if torch.cuda.is_available() else 1\n", | |
| "\n", | |
| "logger = CSVLogger(\"logs\", name=\"DenseNET_RockPaperScissors\")\n", | |
| "\n", | |
| "# Train and Validate the model\n", | |
| "trainer = pl.Trainer(logger=CSVLogger(\"logs\", name=\"DenseNET_RockPaperScissors\"), devices=ngpus, accelerator=\"gpu\" if ngpus > 0 else None, max_epochs=1)\n", | |
| "trainer.fit(dn_model, dn_data_module)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 669, | |
| "referenced_widgets": [ | |
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| }, | |
| "id": "FBauwOUp-AQn", | |
| "outputId": "4494a258-e73a-4f3f-8092-1fdd8df27053" | |
| }, | |
| "execution_count": 61, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO: GPU available: True (cuda), used: True\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (cuda), used: True\n", | |
| "INFO: TPU available: False, using: 0 TPU cores\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n", | |
| "INFO: IPU available: False, using: 0 IPUs\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n", | |
| "INFO: HPU available: False, using: 0 HPUs\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n", | |
| "WARNING: Missing logger folder: logs/DenseNET_RockPaperScissors\n", | |
| "WARNING:lightning.fabric.loggers.csv_logs:Missing logger folder: logs/DenseNET_RockPaperScissors\n", | |
| "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", | |
| "INFO:lightning.pytorch.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", | |
| "INFO: \n", | |
| " | Name | Type | Params\n", | |
| "---------------------------------------------\n", | |
| "0 | model | DenseNet | 18.1 M\n", | |
| "1 | loss_fn | CrossEntropyLoss | 0 \n", | |
| "---------------------------------------------\n", | |
| "5.8 K Trainable params\n", | |
| "18.1 M Non-trainable params\n", | |
| "18.1 M Total params\n", | |
| "72.395 Total estimated model params size (MB)\n", | |
| "INFO:lightning.pytorch.callbacks.model_summary:\n", | |
| " | Name | Type | Params\n", | |
| "---------------------------------------------\n", | |
| "0 | model | DenseNet | 18.1 M\n", | |
| "1 | loss_fn | CrossEntropyLoss | 0 \n", | |
| "---------------------------------------------\n", | |
| "5.8 K Trainable params\n", | |
| "18.1 M Non-trainable params\n", | |
| "18.1 M Total params\n", | |
| "72.395 Total estimated model params size (MB)\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Sanity Checking: | | 0/? [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "b7024be89aa74d3d8e849a52471f22ee" | |
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| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Training: | | 0/? [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "9c6e12409612454789e46a358034e786" | |
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| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Validation: | | 0/? [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "8165d7a2172a4661b936d767be42bc41" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO: `Trainer.fit` stopped: `max_epochs=1` reached.\n", | |
| "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=1` reached.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "log_dir = 'version_0'\n", | |
| "\n", | |
| "log_file = f'/content/logs/DenseNET_RockPaperScissors/{log_dir}/metrics.csv'\n", | |
| "df = pd.read_csv(log_file)\n", | |
| "df_grouped = df.groupby('epoch').first().reset_index()\n", | |
| "df_grouped" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
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| }, | |
| "id": "-hADKUnK9_yj", | |
| "outputId": "a81a7440-ac5c-4851-950e-87877465e794" | |
| }, | |
| "execution_count": 62, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " epoch train_loss_step step val_loss_step val_loss_epoch \\\n", | |
| "0 0.0 0.399444 49 NaN 0.51433 \n", | |
| "\n", | |
| " train_loss_epoch \n", | |
| "0 0.404125 " | |
| ], | |
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| " <div>\n", | |
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| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>epoch</th>\n", | |
| " <th>train_loss_step</th>\n", | |
| " <th>step</th>\n", | |
| " <th>val_loss_step</th>\n", | |
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| " <td>0.0</td>\n", | |
| " <td>0.399444</td>\n", | |
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| " <td>0.404125</td>\n", | |
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| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f7a65b9-3088-4f2d-b4f0-23759e18b8f6')\"\n", | |
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| " .colab-df-container {\n", | |
| " display:flex;\n", | |
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| " buttonEl.style.display =\n", | |
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| " const element = document.querySelector('#df-3f7a65b9-3088-4f2d-b4f0-23759e18b8f6');\n", | |
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| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
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| "metadata": {}, | |
| "execution_count": 62 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "train_results3 = correct(dn_model, dn_data_module.train_dataloader())\n", | |
| "train_results3 = train_results3.sum(axis=0)\n", | |
| "print(f'The training set: our model got {train_results3[0]} out of {train_results3[1]} images correctly classified, an accuracy of {train_results3[0]/train_results3[1]:.2f}%!')\n", | |
| "\n", | |
| "val_results3 = correct(dn_model, dn_data_module.val_dataloader())\n", | |
| "val_results3 = val_results3.sum(axis=0)\n", | |
| "print(f'The validation set: our model got {val_results3[0]} out of {val_results3[1]} images correctly classified, an accuracy of {val_results3[0]/val_results3[1]:.2f}%!')\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "IM5ewMpx9_VR", | |
| "outputId": "299511b0-46ff-4eec-af9c-624201612f27" | |
| }, | |
| "execution_count": 63, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "The training set: our model got 2518 out of 2520 images correctly classified, an accuracy of 1.00%!\n", | |
| "The validation set: our model got 296 out of 372 images correctly classified, an accuracy of 0.80%!\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "XMxJ4Neb9-5I" | |
| }, | |
| "execution_count": 60, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
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| "id": "BXa4PAMJ95GH" | |
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| "execution_count": null, | |
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| "metadata": { | |
| "id": "5QwOaC3nkIBS" | |
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| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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