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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/wej36how/.conda/envs/vit/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"import torch\n",
"from torch.utils.data import DataLoader\n",
"from transformers import AdamW, ViTImageProcessor, ViTForImageClassification\n",
"from NWRD_dataset import NWRD\n",
"from tqdm import tqdm\n",
"import numpy as np\n",
"import torch.nn.functional as F\n",
"import os\n",
"import torch.optim as optim\n",
"from torchvision import transforms\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"seed = 42\n",
"torch.manual_seed(seed)\n",
"np.random.seed(seed)\n",
"# If you are using CUDA, set this for further deterministic behavior\n",
"if torch.cuda.is_available():\n",
" torch.cuda.manual_seed(seed)\n",
" torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.\n",
" # Below settings are recommended for deterministic behavior when using specific convolution operations,\n",
" # but may reduce performance\n",
" torch.backends.cudnn.deterministic = True\n",
" torch.backends.cudnn.benchmark = False"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cpu\n"
]
}
],
"source": [
"device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
"CUDA_LAUNCH_BLOCKING=1\n",
"TORCH_USE_CUDA_DSA=1\n",
"print(device)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"transformations = transforms.Compose([\n",
" transforms.Resize((224, 224)), # Resize the image to 224x224\n",
" transforms.ToTensor() # Convert the image to a PyTorch tensor\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'C:\\\\Users\\\\hasee\\\\Desktop\\\\Germany_2024\\\\Dataset\\\\NWRDprocessed\\\\train\\\\calssification/rust'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_ds \u001b[38;5;241m=\u001b[39m \u001b[43mNWRD\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroot_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mC:\u001b[39;49m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43mUsers\u001b[39;49m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43mhasee\u001b[39;49m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43mDesktop\u001b[39;49m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43mGermany_2024\u001b[39;49m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43mDataset\u001b[39;49m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43mNWRDprocessed\u001b[39;49m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43mtrain\u001b[39;49m\u001b[38;5;130;43;01m\\\\\u001b[39;49;00m\u001b[38;5;124;43mcalssification\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtransformations\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m val_ds \u001b[38;5;241m=\u001b[39m NWRD(root_dir\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mC:\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mUsers\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mhasee\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mDesktop\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mGermany_2024\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mNWRDprocessed\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mval\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mcalssification\u001b[39m\u001b[38;5;124m\"\u001b[39m, train\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, transform\u001b[38;5;241m=\u001b[39mtransformations)\n\u001b[1;32m 4\u001b[0m train_loader \u001b[38;5;241m=\u001b[39m DataLoader(train_ds, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m8\u001b[39m, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
"File \u001b[0;32m~/codes/crossvit/NWRD_dataset.py:12\u001b[0m, in \u001b[0;36mNWRD.__init__\u001b[0;34m(self, root_dir, transform, train)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mimages \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabels \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/codes/crossvit/NWRD_dataset.py:19\u001b[0m, in \u001b[0;36mNWRD.load_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 16\u001b[0m non_rust_dir \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mroot_dir, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon_rust\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# Load rust images\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m filename \u001b[38;5;129;01min\u001b[39;00m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlistdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrust_dir\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 20\u001b[0m filepath \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(rust_dir, filename)\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mimages\u001b[38;5;241m.\u001b[39mappend(filepath)\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:\\\\Users\\\\hasee\\\\Desktop\\\\Germany_2024\\\\Dataset\\\\NWRDprocessed\\\\train\\\\calssification/rust'"
]
}
],
"source": [
"train_ds = NWRD(root_dir=\"C:\\\\Users\\\\hasee\\\\Desktop\\\\Germany_2024\\\\Dataset\\\\NWRDprocessed\\\\train\\\\calssification\", train=True, transform=transformations)\n",
"val_ds = NWRD(root_dir=\"C:\\\\Users\\\\hasee\\\\Desktop\\\\Germany_2024\\\\Dataset\\\\NWRDprocessed\\\\val\\\\calssification\", train=False, transform=transformations)\n",
" \n",
"train_loader = DataLoader(train_ds, batch_size=8, shuffle=True)\n",
"val_loader = DataLoader(val_ds, batch_size=8, shuffle=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"mean = [0.485, 0.456, 0.406] # Mean values for RGB channels\n",
"std = [0.229, 0.224, 0.225] # Standard deviation values for RGB channels\n",
"#processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224',transform={'mean': mean, 'std': std})\n",
"processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')\n",
"model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')\n",
"# processor.image_mean=mean\n",
"# processor.image_std=std\n",
"#print(processor)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ViTForImageClassification(\n",
" (vit): ViTModel(\n",
" (embeddings): ViTEmbeddings(\n",
" (patch_embeddings): ViTPatchEmbeddings(\n",
" (projection): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n",
" )\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (encoder): ViTEncoder(\n",
" (layer): ModuleList(\n",
" (0-11): 12 x ViTLayer(\n",
" (attention): ViTSdpaAttention(\n",
" (attention): ViTSdpaSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): ViTSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): ViTIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" (intermediate_act_fn): GELUActivation()\n",
" )\n",
" (output): ViTOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" )\n",
" )\n",
" )\n",
" (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" )\n",
" (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
")"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.classifier = torch.nn.Linear(model.config.hidden_size, 2)\n",
"model.to(device)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finetuning of the model based on pretraining weights."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# model_weights = torch.load('/home/Hirra/coding_files/crossvit/weights/wandb_vit_base_final_med_val_NWRD_epoch_50_lr_0.000000001_wd_0.001_batch_size_8_unaugmented_unequlaized/49.pth')\n",
"# model.load_state_dict(model_weights.state_dict())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"optimizer = optim.SGD(model.parameters(), lr=0.00000003, weight_decay=0.001)\n",
"criterion = torch.nn.CrossEntropyLoss()\n",
"weights_directory = 'wandb_vit_base_final_for_time_NWRD_epoch_50_lr_0.000000003_wd_0.001_batch_size_8_unaugmented_training'\n",
"weight_loc = f\"weights/{weights_directory}\"\n",
"os.makedirs(weight_loc, exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mgptautomated\u001b[0m (\u001b[33mtukl_labwork\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: C:\\Users\\hasee\\.netrc\n"
]
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{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import wandb, os\n",
"#wandb.login()\n",
"wandb.login(key=\"4e8a21c26ae61cced8d70053c80bbe1b112fec12\")\n",
"#4e8a21c26ae61cced8d70053c80bbe1b112fec12"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"env: WANDB_PROJECT=crossvit_rust_classifier_new\n"
]
}
],
"source": [
"%env WANDB_PROJECT=crossvit_rust_classifier_new\n",
"os.environ[\"WANDB_PROJECT\"] = \"<crossvit>\"\n",
"os.environ[\"WANDB_REPORT_TO\"] = \"wandb\""
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
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"Changes to your `wandb` environment variables will be ignored because your `wandb` session has already started. For more information on how to modify your settings with `wandb.init()` arguments, please refer to <a href='https://wandb.me/wandb-init' target=\"_blank\">the W&B docs</a>."
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]
},
"metadata": {},
"output_type": "display_data"
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"data": {
"text/html": [
"wandb version 0.17.3 is available! To upgrade, please run:\n",
" $ pip install wandb --upgrade"
],
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"Run data is saved locally in <code>c:\\Users\\hasee\\Desktop\\Germany_2024\\codes\\crossvit\\wandb\\run-20240626_161631-bgtm3oyt</code>"
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"Syncing run <strong><a href='https://wandb.ai/tukl_labwork/uncategorized/runs/bgtm3oyt' target=\"_blank\">glamorous-wood-74</a></strong> to <a href='https://wandb.ai/tukl_labwork/uncategorized' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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" View project at <a href='https://wandb.ai/tukl_labwork/uncategorized' target=\"_blank\">https://wandb.ai/tukl_labwork/uncategorized</a>"
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" View run at <a href='https://wandb.ai/tukl_labwork/uncategorized/runs/bgtm3oyt' target=\"_blank\">https://wandb.ai/tukl_labwork/uncategorized/runs/bgtm3oyt</a>"
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"name": "stderr",
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"text": [
" 0%| | 0/241 [00:00<?, ?it/s]c:\\Users\\hasee\\miniconda3\\envs\\segformer\\Lib\\site-packages\\transformers\\models\\vit\\modeling_vit.py:253: UserWarning: 1Torch was not compiled with flash attention. (Triggered internally at C:\\cb\\pytorch_1000000000000\\work\\aten\\src\\ATen\\native\\transformers\\cuda\\sdp_utils.cpp:455.)\n",
" context_layer = torch.nn.functional.scaled_dot_product_attention(\n",
"Epoch 0 train Loss 0.6551: 21%|██ | 51/241 [00:27<01:42, 1.85it/s]\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[12], line 22\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[38;5;66;03m# print(\"logits\", logits)\u001b[39;00m\n\u001b[0;32m 18\u001b[0m \u001b[38;5;66;03m# print(\"prediction\", predication)\u001b[39;00m\n\u001b[0;32m 19\u001b[0m \u001b[38;5;66;03m# print(\"labels\", labels)\u001b[39;00m\n\u001b[0;32m 21\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(logits, labels)\n\u001b[1;32m---> 22\u001b[0m train_losses\u001b[38;5;241m.\u001b[39mappend(\u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m 23\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[0;32m 24\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"wandb.init()\n",
"\n",
"best_epoch = {}\n",
"train_losses = []\n",
"for epoch in range(50):\n",
" model.train\n",
" train_losses=[]\n",
" loop = tqdm(enumerate(train_loader), total=len(train_loader))\n",
" for batch_idx, (images, labels) in loop:\n",
" inputs = processor(images=images, return_tensors=\"pt\", do_rescale=False).to(device)\n",
" labels = labels.to(device)\n",
"\n",
" outputs = model(**inputs)\n",
" logits = outputs.logits\n",
" predication = logits.argmax(axis=1)\n",
" \n",
" # print(\"logits\", logits)\n",
" # print(\"prediction\", predication)\n",
" # print(\"labels\", labels)\n",
" \n",
" loss = criterion(logits, labels)\n",
" train_losses.append(loss.item())\n",
" loss.backward()\n",
" optimizer.step()\n",
" loop.set_description(f\"Epoch {epoch} train Loss {np.mean(train_losses):.4f}\")\n",
"\n",
"\n",
" print(\"Epoch \"+str(epoch)+\" Train Loss \"+str(np.mean(train_losses)))\n",
" torch.save(model, weight_loc+'/{}.pth'.format(epoch))\n",
" wandb.log({\"train_loss\": np.mean(train_losses), \"epoch\": epoch})\n",
"\n",
" #validation\n",
" optimizer.zero_grad()\n",
" model.eval\n",
" val_losses=[]\n",
"\n",
" loop = tqdm(enumerate(val_loader), total=len(val_loader))\n",
" with torch.no_grad():\n",
" for batch_idx, (images, labels) in loop:\n",
" inputs = processor(images=images, return_tensors=\"pt\", do_rescale=False).to(device)\n",
" labels = labels.to(device)\n",
"\n",
" outputs = model(**inputs)\n",
" logits = outputs.logits\n",
" \n",
" loss = criterion(logits, labels)\n",
" val_losses.append(loss.item())\n",
"\n",
" predication = logits.argmax(axis=1)\n",
"\n",
" loss = criterion(logits, labels)\n",
" val_losses.append(loss.item())\n",
" \n",
" loop.set_description(f\"Epoch {epoch} Val Loss {np.mean(val_losses):.4f}\")\n",
" wandb.log({\"val_loss\": np.mean(val_losses), \"epoch\": epoch})\n",
"torch.cuda.empty_cache()\n"
]
}
],
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|