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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import os\n",
"from tqdm import tqdm\n",
"from S1_CNN_Model import CNN_Model\n",
"from S2_TimberDataset import TimberDataset, compile_image_df\n",
"from S3_intermediateDataset import build_intermediate_dataset_if_not_exists, intermediate_dataset\n",
"\n",
"from torch.utils.data import DataLoader\n",
"from torchvision import transforms"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"test_df, _ = compile_image_df(\"data/image/test\", split_at=1.0)\n",
"\n",
"def listdir_full(path: str) -> list[str]:\n",
" return [f\"{path}/{p}\" for p in os.listdir(path)]\n",
"\n",
"transform = transforms.Compose([\n",
" transforms.Resize((320,320)),\n",
" transforms.ToTensor(),\n",
"])\n",
"\n",
"test_loader = DataLoader(TimberDataset(test_df, is_train=True,transform=transform),\n",
" shuffle=True,\n",
" batch_size=12)\n",
"\n",
"build_intermediate_dataset_if_not_exists(lambda x:x, \"test\", test_loader)\n",
"test_loader = intermediate_dataset(\"test\") "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 17/17 [09:00<00:00, 31.82s/it]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('ckpt/model_37.pt', 92.36111111111111), ('ckpt/model_36.pt', 91.39957264957265), ('ckpt/model.pt', 90.11752136752136), ('ckpt/model_23.pt', 90.11752136752136), ('ckpt/model_27.pt', 90.03739316239316), ('ckpt/model_19.pt', 89.95726495726495), ('ckpt/model_17.pt', 88.56837606837607), ('ckpt/model_16.pt', 87.79380341880342), ('ckpt/model_15.pt', 85.79059829059828), ('ckpt/model_13.pt', 85.04273504273505), ('ckpt/model_11.pt', 85.01602564102564), ('ckpt/model_14.pt', 84.58867521367522), ('ckpt/model_7.pt', 79.8076923076923), ('ckpt/model_6.pt', 79.27350427350427), ('ckpt/model_5.pt', 75.58760683760684), ('ckpt/model_4.pt', 70.86004273504274), ('ckpt/model_3.pt', 63.67521367521367)]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"def evaluate_model(model: CNN_Model):\n",
" n_correct = 0\n",
" n_samples = 0\n",
"\n",
" with torch.no_grad(): \n",
" with tqdm(test_loader, position=1, leave=False) as pb:\n",
" for images, labels in pb:\n",
" images = images.reshape(images.shape[1:])\n",
" labels = labels.reshape(labels.shape[1:])\n",
"\n",
" x = model.forward(images)\n",
" _, predictions = torch.max(x,1)\n",
" \n",
" n_samples += labels.shape[0]\n",
" n_correct += (predictions == labels).sum().item()\n",
"\n",
" pb.set_description(f\"{n_correct}/{n_samples} correct predictions ({n_correct/n_samples*100 :.2f}%)\")\n",
"\n",
" return n_correct/n_samples*100\n",
"\n",
"accuracies = [(model_path, evaluate_model(torch.load(model_path))) for model_path in tqdm(listdir_full(\"ckpt\"), position=0)]\n",
"accuracies.sort(key = lambda x : x[1], reverse=True)\n",
"accuracies"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"test_loader = DataLoader(TimberDataset(test_df, is_train=True,transform=transform),\n",
" batch_size=12)\n",
"\n",
"build_intermediate_dataset_if_not_exists(lambda x:x, \"test_full\", test_loader)\n",
"test_loader = intermediate_dataset(\"test_full\") "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 17/17 [08:32<00:00, 30.14s/it]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('ckpt/model_37.pt', 97.11538461538461), ('ckpt/model_23.pt', 96.15384615384616), ('ckpt/model_36.pt', 96.15384615384616), ('ckpt/model_27.pt', 95.51282051282051), ('ckpt/model_19.pt', 95.1923076923077), ('ckpt/model.pt', 94.23076923076923), ('ckpt/model_17.pt', 94.23076923076923), ('ckpt/model_16.pt', 93.26923076923077), ('ckpt/model_15.pt', 92.94871794871796), ('ckpt/model_11.pt', 91.98717948717949), ('ckpt/model_13.pt', 91.34615384615384), ('ckpt/model_14.pt', 91.02564102564102), ('ckpt/model_7.pt', 88.78205128205127), ('ckpt/model_6.pt', 86.85897435897436), ('ckpt/model_5.pt', 82.05128205128204), ('ckpt/model_4.pt', 78.84615384615384), ('ckpt/model_3.pt', 70.83333333333334)]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"def full_images_evaluation(model: CNN_Model):\n",
" model.image_size = (320,320)\n",
" n_correct = 0\n",
" n_samples = 0\n",
"\n",
" with torch.no_grad(): \n",
" with tqdm(test_loader, position=1, leave = False) as pb:\n",
" for images, labels in pb:\n",
" images = images.reshape(images.shape[1:])\n",
" labels = labels.reshape(labels.shape[1:])\n",
"\n",
" assert torch.all(labels == labels[0]).item()\n",
" label = labels[0]\n",
"\n",
" x = model.forward(images)\n",
" _, preds = torch.max(x,1)\n",
" pred = torch.mode(preds,0).values\n",
" \n",
" n_samples += 1\n",
" n_correct += (pred == label).item()\n",
"\n",
" pb.set_description(f\"{n_correct}/{n_samples} correct predictions ({n_correct/n_samples*100 :.2f}%)\")\n",
"\n",
" return n_correct/n_samples*100\n",
"\n",
"full_accuracies = [(model_path, full_images_evaluation(torch.load(model_path)))\n",
" for model_path in tqdm(listdir_full(\"ckpt\"), position=0)]\n",
"full_accuracies.sort(key = lambda x : x[1], reverse=True)\n",
"print(full_accuracies)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<zip at 0x286ec807c00>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "fyp",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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"nbconvert_exporter": "python",
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