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lukas committed 2024-09-12 13:58:25 +02:00
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notebooks/caml.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "8dbc4931-506b-4eb7-9049-11fda71fa2fd",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/q315433/micromamba/envs/pmf/lib/python3.12/site-packages/torch/__init__.py:749: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:431.)\n",
" _C._set_default_tensor_type(t)\n"
]
}
],
"source": [
"import sys\n",
"import torch\n",
"from pyprojroot import here as project_root\n",
"import numpy as np\n",
"\n",
"sys.path.insert(0, str(project_root()))\n",
"\n",
"from src.evaluation.utils import get_test_path, get_model\n",
"from src.evaluation.eval import meta_test\n",
"\n",
"from src.train_utils.trainer import train_parser\n",
"from src.models.feature_extractors.pretrained_fe import get_fe_metadata\n",
"import torchvision.transforms as transforms\n",
"from PIL import Image\n",
"\n",
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a90ff098-ad85-45db-8576-54ffe4c8a7cc",
"metadata": {},
"outputs": [],
"source": [
"def test_transform():\n",
" def _convert_image_to_rgb(im):\n",
" return im.convert('RGB')\n",
"\n",
" return transforms.Compose([\n",
" #transforms.Resize(224),\n",
" transforms.Resize(224),\n",
" #transforms.CenterCrop(224),\n",
" _convert_image_to_rgb,\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=torch.tensor([0.4815, 0.4578, 0.4082]), std=torch.tensor([0.2686, 0.2613, 0.2758])),\n",
" ])\n",
"\n",
"preprocess = test_transform()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3a400d41-cc0b-4af2-aafe-fb1c82bf21a2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Defaulting to float32 dtype\n",
"Loaded pretrained timm model vit_base_patch16_clip_224.openai\n",
"../caml_pretrained_models/CAML_CLIP/model.pth\n"
]
}
],
"source": [
"import enum\n",
"\n",
"class T:\n",
" fe_type = \"timm:vit_base_patch16_clip_224.openai:768\"\n",
" #fe_type = \"timm:vit_huge_patch14_clip_224.laion2b:1280\"\n",
" fe_dim = 768\n",
" fe_dtype = \"float32\"\n",
" model = \"CAML\"\n",
" dropout = 0.0\n",
" encoder_size = \"large\"\n",
"\n",
"fe_metadata = get_fe_metadata(T())\n",
"#test_path = get_test_path(args, data_path)\n",
"#device = torch.device(f'cuda:{args.gpu}')\n",
"\n",
"# Get the model and load its weights.\n",
"model, model_path = get_model(T(), fe_metadata, device)\n",
"print(model_path)\n",
"#print(model)\n",
"if model_path:\n",
" model.load_state_dict(torch.load(model_path, map_location=f'cuda:0'), strict=False)\n",
"model.to(device)\n",
"_= model.eval()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "534d1750-0433-403c-9d51-c0522747a97f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)\n",
"torch.Size([65, 3, 224, 224])\n",
"3\n",
"62\n",
"torch.Size([62, 4, 768])\n",
"tensor([0, 1, 2], device='cuda:0')\n",
"tensor([0, 1, 2], device='cuda:0')\n",
"torch.Size([3])\n",
"torch.Size([62, 4, 768])\n",
"herre\n",
"torch.Size([62])\n",
"(62,)\n",
"(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)\n",
"torch.Size([65, 3, 224, 224])\n",
"9\n",
"56\n",
"torch.Size([56, 10, 768])\n",
"tensor([0, 0, 0, 1, 1, 1, 2, 2, 2], device='cuda:0')\n",
"tensor([0, 0, 0, 1, 1, 1, 2, 2, 2], device='cuda:0')\n",
"torch.Size([9])\n",
"torch.Size([56, 10, 768])\n",
"herre\n",
"torch.Size([56])\n",
"(56,)\n",
"(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)\n",
"torch.Size([65, 3, 224, 224])\n",
"15\n",
"50\n",
"torch.Size([50, 16, 768])\n",
"tensor([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2], device='cuda:0')\n",
"tensor([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2], device='cuda:0')\n",
"torch.Size([15])\n",
"torch.Size([50, 16, 768])\n",
"herre\n",
"torch.Size([50])\n",
"(50,)\n",
"[0.58064516 0.51785714 0.52 ]\n"
]
}
],
"source": [
"import os\n",
"\n",
"img_path = \"../pmf_cvpr22/data_custom\"\n",
"\n",
"def filecnt_in_dir(dirr, typ):\n",
" _, _, files = next(os.walk(f\"{img_path}/{dirr}/test/{typ}/\"))\n",
" return len(files)\n",
"\n",
"def evaluate(shot, way, folder):\n",
" ts = [\"good\", \"broken_small\", \"broken_large\", \"contamination\"]\n",
" tss = [\"good\", \"cable_swap\", \"combined\", \"cut_inner_insulation\", \"cut_outer_insulation\", \"missing_cable\", \"missing_wire\", \"poke_insulation\"]\n",
" tss = ts\n",
" cat = [\"bottle\", \"cable\"]\n",
"\n",
" #goodnr = (len(tss)-1) * shot\n",
" \n",
" with torch.no_grad():\n",
" #img_supp = [preprocess(Image.open(f\"{img_path}/{folder}/train/good/{i:03d}.png\")).unsqueeze(0).to(device) for i in range(shot)]\n",
" img_supp = [preprocess(Image.open(f\"{img_path}/{folder}/test/{n}/{i:03d}.png\")).unsqueeze(0).to(device) for n in tss[1:4] for i in range(shot)]\n",
" \n",
" tmp = [(preprocess(Image.open(f\"{img_path}/{folder}/test/{n}/{i:03d}.png\")).unsqueeze(0).to(device), tss.index(n)-1) for n in tss[1:4] for i in range(shot, filecnt_in_dir(folder, n))]\n",
" img_query, query_labels = zip(*tmp)\n",
" #print(tmp)\n",
" print(query_labels)\n",
" \n",
" img_concat = img_supp + list(img_query)\n",
" img_concat = torch.cat(img_concat, 0)\n",
" print(img_concat.shape)\n",
" print(len(img_supp))\n",
" print(len(img_query))\n",
" #shot = (len(tss)-1) * shot\n",
" \n",
" #logits = model.meta_test(img_concat, way=4, shot=shot, query_shot=1)\n",
" #print(logits)\n",
" #\n",
" feature_vector = model.get_feature_vector(img_concat)\n",
" support_features = feature_vector[:way * shot]\n",
" query_features = feature_vector[way * shot:]\n",
" b, d = query_features.shape\n",
" \n",
" # Reshape query and support to a sequence.\n",
" support = support_features.reshape(1, way * shot, d).repeat(b, 1, 1)\n",
" query = query_features.reshape(-1, 1, d)\n",
" feature_sequences = torch.cat([query, support], dim=1)\n",
" print(feature_sequences.shape)\n",
" \n",
" #labels = torch.LongTensor([i // shot for i in range(shot * way)]).to(device)\n",
" labels = torch.arange(way).repeat(shot, 1).T.flatten().to(model.device)\n",
" print(labels)\n",
" #labels = torch.from_numpy(np.ones(shape=shot, dtype=int)).to(device)\n",
" #labels = torch.cat([torch.from_numpy(np.zeros(shape=shot, dtype=int)).to(device), labels])\n",
" print(labels)\n",
" \n",
" #labels = torch.LongTensor([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ]).to(device)\n",
" print(labels.shape)\n",
" print(feature_sequences.shape)\n",
" logits = model.transformer_encoder.forward_imagenet_v2(feature_sequences, labels, way, shot)\n",
" #print(logits)\n",
" _, max_index = torch.max(logits[:, :way], 1)\n",
" #print(max_index.cpu().numpy())\n",
" #bbb = np.ones(shape=(14*4))\n",
" #bbb[:14] = 0\n",
" #print(np.mean(max_index.cpu().numpy() == bbb))\n",
" print(\"herre\")\n",
" print(max_index.shape)\n",
" print(np.array(query_labels).shape)\n",
"\n",
" return np.mean(max_index.cpu().numpy() == np.array(query_labels))\n",
"\n",
"scores = [evaluate(shot, 3, \"bottle\") for shot in [1,3,5]]\n",
"print(np.array(scores))"
]
},
{
"cell_type": "markdown",
"id": "22d68c6b-0df6-4953-8447-7843932fa974",
"metadata": {},
"source": [
"CAML:\n",
"Resulsts:\n",
"\n",
"bottle:\n",
"jeweils 1,3,5 shots normal\n",
"[0.40740741 0.39726027 0.30769231]\n",
"\n",
"inbalanced - mehr good shots 5,10,15,30 -> alle anderen nur 5\n",
"- not possible\n",
"1q\n",
"2 ways nur detektieren ob fehlerhaft oder nicht 3,6,9 shots -> wegen model restrictions\n",
"[0.79012346 0.84415584 0.87671233]\n",
"\n",
"inbalance 2 way 5,10,15,30 -> rest 5\n",
"- not possible\n",
"\n",
"nur fehlerklasse erkennen 1,3,5\n",
"[0.58064516 0.51785714 0.52 ]\n",
"\n",
"\n",
"cable:\n",
"jeweils 1,3,5 shots normal\n",
"[0.24031008 0.19834711 0.15929204]\n",
"\n",
"inbalanced - mehr good shots 5,10,15,30 -> alle anderen nur 5\n",
"- not possible\n",
"\n",
"2 ways nur detektieren ob fehlerhaft oder nicht 1,3,5 shots\n",
"[0.57364341 0.54545455 0.59292035]\n",
"\n",
"inbalance 2 way 5,10,15,30 -> rest 5\n",
"- not possible\n",
"\n",
"nur fehlerklasse erkennen 1,3,5\n",
"[0.12962963 0.36363636 0.58823529]\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
notebooks/plots.ipynb
+168
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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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truncated
"text/plain": [
"<Figure size 1400x1000 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Define data\n",
"bottle_data = {\n",
" \"ResNet50\": {\n",
" \"1,3,5 shots normal\": [0.5892857142857143, 0.7321428571428571, 0.75],\n",
" \"inbalanced - more good shots\": [0.75, 0.732, 0.696, 0.696],\n",
" \"2 ways only detect if faulty or not\": [0.8395, 0.8315, 0.8031],\n",
" \"inbalance 2 way\": [0.8031, 0.81893, 0.8336, 0.8031],\n",
" \"only faulty class detect\": [0.7638, 0.7428, 0.787]\n",
" },\n",
" \"P>M>F\": {\n",
" \"1,3,5 shots normal\": [0.67910401, 0.71710526, 0.78860294],\n",
" \"inbalanced - more good shots\": [0.78768382, 0.78860294, 0.75827206, 0.74356618],\n",
" \"2 ways only detect if faulty or not\": [0.86422306, 0.93201754, 0.93933824],\n",
" \"inbalance 2 way\": [0.92371324, 0.87867647, 0.86397059, 0.87775735],\n",
" \"only faulty class detect\": [0.57380952, 0.76705653, 0.84191176]\n",
" },\n",
" \"CAML\": {\n",
" \"1,3,5 shots normal\": [0.40740741, 0.39726027, 0.30769231],\n",
" \"2 ways only detect if faulty or not\": [0.79012346, 0.84415584, 0.87671233],\n",
" \"only faulty class detect\": [0.58064516, 0.51785714, 0.52]\n",
" }\n",
"}\n",
"\n",
"cable_data = {\n",
" \"ResNet50\": {\n",
" \"1,3,5 shots normal\": [0.21808, 0.43815, 0.4321478],\n",
" \"inbalanced - more good shots\": [0.4321478, 0.432986, 0.42340, 0.4464635],\n",
" \"2 ways only detect if faulty or not\": [0.8592, 0.8772, 0.8495],\n",
" \"inbalance 2 way\": [0.8495, 0.8180, 0.7460, 0.6846],\n",
" \"only faulty class detect\": [0.240, 0.4740, 0.4805]\n",
" },\n",
" \"P>M>F\": {\n",
" \"1,3,5 shots normal\": [0.25199021, 0.44388328, 0.46975059],\n",
" \"inbalanced - more good shots\": [0.50425859, 0.48023277, 0.43118282, 0.41842534],\n",
" \"2 ways only detect if faulty or not\": [0.79263485, 0.8707712, 0.86756514],\n",
" \"inbalance 2 way\": [0.86966158, 0.80142425, 0.80961366, 0.66028834],\n",
" \"only faulty class detect\": [0.24383256, 0.43800505, 0.51304563]\n",
" },\n",
" \"CAML\": {\n",
" \"1,3,5 shots normal\": [0.24031008, 0.19834711, 0.15929204],\n",
" \"2 ways only detect if faulty or not\": [0.57364341, 0.54545455, 0.59292035],\n",
" \"only faulty class detect\": [0.12962963, 0.36363636, 0.58823529]\n",
" }\n",
"}\n",
"# Prepare the data\n",
"measurement_types = [\n",
" \"1,3,5 shots normal\",\n",
" \"inbalanced - more good shots\",\n",
" \"2 ways only detect if faulty or not\",\n",
" \"inbalance 2 way\",\n",
" \"only faulty class detect\"\n",
"]\n",
"\n",
"models = [\"ResNet50\", \"P>M>F\", \"CAML\"]\n",
"\n",
"# Create subplots\n",
"fig, axes = plt.subplots(3, 2, figsize=(14, 10))\n",
"axes = axes.flatten()\n",
"\n",
"# Loop through each measurement type\n",
"for i, measurement in enumerate(measurement_types):\n",
" ax = axes[i]\n",
" for model in models:\n",
" # Get the bottle and cable data for the current measurement and model\n",
" bottle_accuracy = bottle_data[model].get(measurement, [])\n",
" cable_accuracy = cable_data[model].get(measurement, [])\n",
" \n",
" # Plot both bottle and cable data\n",
" ax.plot(bottle_accuracy, marker='o', label=f'{model} - Bottle', linestyle='-')\n",
" \n",
" ax.set_title(measurement)\n",
" ax.set_xlabel(\"Shots / Samples\")\n",
" ax.set_ylabel(\"Accuracy\")\n",
" ax.legend()\n",
" ax.grid(True)\n",
"\n",
"# Adjust layout\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABW0AAAPdCAYAAADxjUr8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3gUVd/H4c/uplcICUloSegl9Bp670h5UZoCAoIiIAJSFIQAgoBUJYhKEUFFRVTKAwQhdAQEFAi9l9AhBRKy2Z33jyEry24aqSS/22su2bMzZ86cbDaz3z1zRqMoioIQQgghhBBCCCGEEEKIHEGb3Q0QQgghhBBCCCGEEEII8R8JbYUQQgghhBBCCCGEECIHkdBWCCGEEEIIIYQQQgghchAJbYUQQgghhBBCCCGEECIHkdBWCCGEEEIIIYQQQgghchAJbYUQQgghhBBCCCGEECIHkdBWCCGEEEIIIYQQQgghchAJbYUQQgghhBBCCCGEECIHkdBWCCGEEEIIIYQQQgghchAJbYUQL4Xo6GhGjx5Ny5Yt8fLyQqPRMGnSpFRvf/ToUdq1a0exYsVwdHTEw8ODoKAgVq5cmartw8LC0Gg0Vpf9+/e/4FGZ8/f3p3379hlSF8CNGzeYNGkSR48ezbA6c6vGjRvTuHHj7G6GEEIIIZ6zfPlyNBoNly5dSvO2iedvv/zyS4a1J7HOsLCwDKszu0yaNAmNRpPdzRApyMjXXOPGjQkMDEx/o556/PgxkyZNyhW/D0LkRDbZ3QAhhEiNe/fu8dVXX1G5cmU6derEN998k6btHz58SNGiRenRoweFCxfm0aNHrFq1ijfeeINLly4xfvz4VNUzbdo0mjRpYlaWkSc+GenGjRsEBwfj7+9PlSpVsrs5QgghhBBp1q5dO/bt24evr292N0UI8ZzHjx8THBwMIAMghMgEEtoKIV4Kfn5+PHjwAI1Gw927d9Mc2lobSdm+fXsuXrzIV199lerQtlSpUtSpUydN+xZpoygKcXFxODo6ZndThBBCCJHNvLy88PLyyu5miJeAnEMKIXIbmR5BCPFSSJyKIKN5enpiY5P5319duHCB7t27U6hQIezt7fH29qZZs2ZWpy7YtGkT1apVw9HRkbJly7J06VKLdY4fP07Hjh3Jnz8/Dg4OVKlShW+//db0fFhYGDVr1gTgzTffNPVf4pQSaWnPs/r27YuLiwvnzp2jbdu2uLi4ULRoUUaOHMmTJ0/M1r1//z6DBw+mcOHC2NnZUbx4cT766COL9TQaDUOGDOHLL7+kXLly2Nvb8+2335ouh9y2bRtvvfUWBQoUwM3Njd69e/Po0SNu3rzJa6+9Rr58+fD19WXUqFHo9XqzuoODg6lduzYeHh64ublRrVo1lixZgqIoyR6nEEIIIXIGa9MjJF7iffDgQRo0aICTkxPFixfn008/xWg0WtQRFxfHiBEj8PHxwdHRkUaNGnHkyBGzdQ4dOkT37t3x9/fH0dERf39/evToweXLl1NsY2q3TTyW7du388477+Dp6UmBAgXo0qULN27csKj3+++/JygoCBcXF1xcXKhSpQpLliwxW2fr1q00a9YMNzc3nJycqFevHn/++adFXRs2bKBKlSrY29sTEBDAZ599luJxvYiMOH9L7zkkwNmzZ+nZsycFCxbE3t6ecuXKsXDhwlQdw8OHD+nfvz8eHh64uLjQrl07Lly4YHV6tt27d9OsWTNcXV1xcnKibt26bNiwwaLOlM7dE506dYrWrVvj5OSEp6cnb7/9NtHR0alq9507dxg4cCBFixbF3t4eLy8v6tWrx9atWy3WTc3vzpUrV3j99dfN+nD27Nmm9S5dumT6QiU4ONj0eaNv375pbo8QwjoZaSuEyFOMRiNGo5EHDx7w888/s3nzZr744otUb//uu+/SvXt3nJycCAoKYsKECdSvXz/F7dq2bYvBYGDmzJkUK1aMu3fvsnfvXh4+fGi23j///MPIkSMZO3Ys3t7efPPNN/Tv35+SJUvSsGFDAE6fPk3dunUpWLAgCxYsoECBAqxcuZK+ffty69YtRo8eTbVq1Vi2bBlvvvkm48ePp127dgAUKVIkTe2xRq/X88orr9C/f39GjhzJzp07mTJlCu7u7nz88ceA+uGoSZMmnD9/nuDgYCpVqsSuXbuYPn06R48etTiZ/e2339i1axcff/wxPj4+FCxYkIMHDwIwYMAAunTpwo8//siRI0f48MMPSUhI4PTp03Tp0oWBAweydetWZsyYQaFChRgxYoSp3kuXLjFo0CCKFSsGwP79+xk6dCjXr183tVUIIYQQL5+bN2/Sq1cvRo4cycSJE1m7di3jxo2jUKFC9O7d22zdDz/8kGrVqvHNN98QGRnJpEmTaNy4MUeOHKF48eKAes5QpkwZunfvjoeHBxERESxatIiaNWsSHh6Op6dnkm1J67YDBgygXbt2fP/991y9epUPPviA119/nW3btpnW+fjjj5kyZQpdunRh5MiRuLu7c/z4cbMgeOXKlfTu3ZuOHTvy7bffYmtry+LFi2nVqhWbN2+mWbNmAPz555907NiRoKAgfvzxR9M54K1bt9L9c0jKi56/ZcQ5ZHh4OHXr1qVYsWLMnj0bHx8fNm/ezLBhw7h79y4TJ05Mst1Go5EOHTpw6NAhJk2aRLVq1di3bx+tW7e2WHfHjh20aNGCSpUqsWTJEuzt7QkJCaFDhw788MMPdOvWDUjduTvArVu3aNSoEba2toSEhODt7c2qVasYMmRIqvr8jTfe4PDhw3zyySeULl2ahw8fcvjwYe7du2e2Xmp+d+7cuUPdunWJj49nypQp+Pv7s379ekaNGsX58+cJCQnB19eXTZs20bp1a/r378+AAQMATEFuatsjhEiGIoQQL5k7d+4ogDJx4sQ0bzto0CAFUADFzs5OCQkJSdV2hw8fVt577z1l7dq1ys6dO5WlS5cq5cqVU3Q6nbJp06Zkt717964CKPPmzUt2PT8/P8XBwUG5fPmyqSw2Nlbx8PBQBg0aZCrr3r27Ym9vr1y5csVs+zZt2ihOTk7Kw4cPFUVRlIMHDyqAsmzZshdqjzV9+vRRAOWnn34yK2/btq1SpkwZ0+Mvv/zS6nozZsxQAGXLli2mMkBxd3dX7t+/b7busmXLFEAZOnSoWXmnTp0UQJkzZ45ZeZUqVZRq1aol2XaDwaDo9Xpl8uTJSoECBRSj0Wh6rlGjRkqjRo2SP3ghhBBCZLnE84GLFy+ayho1aqQAyl9//WW2bvny5ZVWrVqZHm/fvl0BlGrVqpn93b906ZJia2urDBgwIMn9JiQkKDExMYqzs7Myf/58izq3b9+e5m0Tj2Xw4MFm68+cOVMBlIiICEVRFOXChQuKTqdTevXqleQ+Hj16pHh4eCgdOnQwKzcYDErlypWVWrVqmcpq166tFCpUSImNjTWVRUVFKR4eHkpGRwLpPX/LiHPIVq1aKUWKFFEiIyPNyocMGaI4ODhYrP+sDRs2KICyaNEis/Lp06dbfP6oU6eOUrBgQSU6OtpUlpCQoAQGBipFihQxveZSe+4+ZswYRaPRKEePHjVbr0WLFim+5hRFUVxcXJThw4cnu05qf3fGjh1rdb133nlH0Wg0yunTpxVFSf5zWWraI4RInkyPIITIUz788EMOHjzIhg0b6NevH0OGDEnV5WFVq1Zl3rx5dOrUiQYNGvDmm2+yd+9efH19Td+OJ8XDw4MSJUowa9Ys5syZw5EjR6xeugdQpUoV06hQAAcHB0qXLm02qmLbtm00a9aMokWLmm3bt29fHj9+zL59+zKsPdZoNBo6dOhgVlapUiWLNjo7O9O1a1eLNgIWl+01bdqU/PnzW91f+/btzR6XK1cOwDR6+Nny5y9D3LZtG82bN8fd3R2dToetrS0ff/wx9+7d4/bt2ykcqRBCCCFyKh8fH2rVqmVW9vz5SKKePXuaTbPl5+dH3bp12b59u6ksJiaGMWPGULJkSWxsbLCxscHFxYVHjx5x8uTJZNuS1m1feeUVi3YDpraHhoZiMBh49913k9zn3r17uX//Pn369CEhIcG0GI1GWrduzcGDB3n06BGPHj3i4MGDdOnSBQcHB9P2rq6uFudz1iiKYlZ/QkJCitvAi5+/pfccMi4ujj///JPOnTvj5ORk1u62bdsSFxfH/v37k2z3jh07AHjttdfMynv06GH2+NGjR/z111907doVFxcXU7lOp+ONN97g2rVrnD592nRMqTl33759OxUqVKBy5cpm6/Xs2TPJ9j6rVq1aLF++nKlTp7J//36LaScSpeZ3Z9u2bZQvX95ivb59+6Ioitmo8PS2RwiRNAlthRB5SrFixahRowZt27Zl0aJFDBw4kHHjxnHnzp0015UvXz7at2/Pv//+S2xsbJLraTQa/vzzT1q1asXMmTOpVq0aXl5eDBs2zGKOqgIFClhsb29vb1b/vXv3rN5BuVChQqbnk5OW9ljj5ORkdtKf2Ma4uDizNvr4+FjMQ1ywYEFsbGws2pjcHaE9PDzMHtvZ2SVZ/mwbDhw4QMuWLQH4+uuv2bNnDwcPHuSjjz4CSPZnJoQQQoicLTXnTIl8fHyslj17PtKzZ0+++OILBgwYwObNmzlw4AAHDx7Ey8srxXOGtG77fNvt7e2B/85NEs9LE6e1siZxaoOuXbtia2trtsyYMQNFUbh//z4PHjzAaDQm2QcpSZx24dklNV70/C2955D37t0jISGBzz//3KLdbdu2BeDu3btJtvvevXvY2NhYtNPb29vs8YMHD1AUJVXn5Kk9d0889uel5ucEsHr1avr06cM333xDUFAQHh4e9O7dm5s3b5qtlxWfN9LSHiFE0mROWyFEnlarVi2+/PJLLly48EJ3Jlae3tAqpZuk+fn5mW4ccebMGX766ScmTZpEfHw8X375ZZr2WaBAASIiIizKE29gkdyca5nRnqTa+Ndff6Eoilnf3L5Line truncated
"text/plain": [
"<Figure size 1400x1000 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create subplots\n",
"fig, axes = plt.subplots(3, 2, figsize=(14, 10))\n",
"axes = axes.flatten()\n",
"\n",
"# Loop through each measurement type\n",
"for i, measurement in enumerate(measurement_types):\n",
" ax = axes[i]\n",
" for model in models:\n",
" # Get the bottle and cable data for the current measurement and model\n",
" bottle_accuracy = bottle_data[model].get(measurement, [])\n",
" cable_accuracy = cable_data[model].get(measurement, [])\n",
" \n",
" # Plot both bottle and cable data\n",
" ax.plot(cable_accuracy, marker='o', label=f'{model} - Cable', linestyle='-')\n",
" \n",
" ax.set_title(measurement)\n",
" ax.set_xlabel(\"Shots / Samples\")\n",
" ax.set_ylabel(\"Accuracy\")\n",
" ax.legend()\n",
" ax.grid(True)\n",
"\n",
"# Adjust layout\n",
"plt.tight_layout()\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
notebooks/pmf.ipynb
+895
View File
@@ -0,0 +1,895 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import numpy as np\n",
"import time\n",
"import random\n",
"import torch\n",
"import torchvision.transforms as transforms\n",
"#import gradio as gr\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from models import get_model\n",
"from dotmap import DotMap\n",
"from PIL import Image"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pretrained weights found at dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth\n"
]
}
],
"source": [
"args = DotMap()\n",
"args.deploy = 'finetune'\n",
"args.arch = 'dino_base_patch16'\n",
"args.no_pretrain = True\n",
"small = \"https://huggingface.co/hushell/pmf_metadataset_dino/resolve/main/md_full_128x128_dinosmall_fp16_lr5e-5/best.pth?download=true\"\n",
"full = 'https://huggingface.co/hushell/pmf_metadataset_dino/resolve/main/md_full_128x128_dinobase_fp16_lr5e-5/best.pth?download=true'\n",
"args.resume = full\n",
"args.api_key = 'AIzaSyAFkOGnXhy-2ZB0imDvNNqf2rHb98vR_qY'\n",
"args.cx = '06d75168141bc47f1'\n",
"\n",
"args.ada_steps = 100\n",
"#args.ada_lr= 0.0001\n",
"#args.aug_prob = .95\n",
"args.ada_lr= 0.0001\n",
"args.aug_prob = .9\n",
"args.aug_types = [\"color\", \"translation\"]\n",
"\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"model = get_model(args)\n",
"model.to(device)\n",
"checkpoint = torch.hub.load_state_dict_from_url(args.resume, map_location='cpu')\n",
"model.load_state_dict(checkpoint['model'], strict=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# image transforms\n",
"def test_transform():\n",
" def _convert_image_to_rgb(im):\n",
" return im.convert('RGB')\n",
"\n",
" return transforms.Compose([\n",
" transforms.Resize(224),\n",
" #transforms.CenterCrop(224),\n",
" _convert_image_to_rgb,\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
" std=[0.229, 0.224, 0.225]),\n",
" ])\n",
"\n",
"preprocess = test_transform()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"def build_sup_set(shotnr, type_name, binary, good_sample_nr):\n",
" classes = next(os.walk(f'data_custom/{type_name}/test'))[1]\n",
" classes.remove(\"good\")\n",
"\n",
" supp_x = []\n",
" supp_y = []\n",
" mapping = {\n",
" \"good\" : 0\n",
" }\n",
"\n",
" # add good manually\n",
" x_good = [Image.open(f\"data_custom/{type_name}/train/good/{x:03d}.png\") for x in range(0, good_sample_nr)]\n",
" supp_x.extend([preprocess(x) for x in x_good]) # (3, H, W))\n",
" supp_y.extend([0] * good_sample_nr)\n",
" \n",
" for i,c in enumerate(classes):\n",
" #i-=1\n",
" x_im = [Image.open(f\"data_custom/{type_name}/test/{c}/{x:03d}.png\") for x in range(0, shotnr)]\n",
" supp_x.extend([preprocess(x) for x in x_im]) # (3, H, W))\n",
" if binary:\n",
" supp_y.extend([1] * shotnr)\n",
" mapping[\"anomaly\"] = 1\n",
" else:\n",
" supp_y.extend([i+1] * shotnr)\n",
" mapping[c] = i+1\n",
" \n",
" supp_x = torch.stack(supp_x, dim=0).unsqueeze(0).to(device) # (1, n_supp*n_labels, 3, H, W)\n",
" supp_y = torch.tensor(supp_y).long().unsqueeze(0).to(device) # (1, n_supp*n_labels)\n",
" return supp_x, supp_y, mapping\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"def build_test_set(shotnr, keyy, type):\n",
" _, _, files = next(os.walk(f\"data_custom/cable/test/{type}/\"))\n",
" file_count = len(files)\n",
" print(file_count)\n",
"\n",
" queries = [preprocess(Image.open(f\"data_custom/cable/test/{type}/{i:03d}.png\")).unsqueeze(0).unsqueeze(0).to(device) for i in range(shotnr,file_count)]\n",
" labels = [keyy for x in range(shotnr,file_count)]\n",
" return queries, labels\n",
"\n",
"def test(type, keyy, shotnr, folder):\n",
" predictions = []\n",
" _, _, files = next(os.walk(f\"data_custom/{folder}/test/{type}/\"))\n",
" file_count = len(files)\n",
" print(file_count)\n",
"\n",
" queries = [preprocess(Image.open(f\"data_custom/{folder}/test/{type}/{i:03d}.png\")).unsqueeze(0).unsqueeze(0).to(device) for i in range(shotnr,file_count)]\n",
" queries = torch.cat(queries)\n",
" with torch.cuda.amp.autocast(True):\n",
" output = model(supp_x, supp_y, queries) # (1, 1, n_labels)\n",
"\n",
" probs = output.softmax(dim=-1).detach().cpu().numpy()\n",
" predictions = np.argmax(probs, axis=2)\n",
" print()\n",
" return np.mean([x == keyy for x in predictions])\n",
" pass\n",
" \n",
"#def test2(folder):\n",
"# accs = []\n",
"# queries = []\n",
"# labels = []\n",
"# for t in next(os.walk(f'data_custom/cable/test'))[1]:\n",
"# q, l = build_test_set(shots, types.get(t, 1), t)\n",
"# queries+=q\n",
"# labels+=l\n",
"#\n",
"# queries = torch.cat(queries)\n",
"# labels = np.array(labels)\n",
"#\n",
"# with torch.cuda.amp.autocast(True):\n",
"# output = model(supp_x, supp_y, queries) # (1, 1, n_labels)\n",
"#\n",
"# probs = output.softmax(dim=-1).detach().cpu().numpy()\n",
"# predictions = np.argmax(probs, axis=2)\n",
"# print()\n",
"# return np.mean([predictions == labels])\n",
"# pass\n",
"\n",
"#print(f\"overall accuracy: {test(\"cable\")}\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"14\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp45, nQry9: loss = 0.1475423127412796: 100%|██| 100/100 [00:29<00:00, 3.40it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for cut_inner_insulation = 1.0\n",
"10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp45, nQry5: loss = 0.20609889924526215: 100%|█| 100/100 [00:29<00:00, 3.37it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for poke_insulation = 1.0\n",
"12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp45, nQry7: loss = 0.12025140225887299: 100%|█| 100/100 [00:29<00:00, 3.34it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for cable_swap = 0.8571428571428571\n",
"10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp45, nQry5: loss = 0.2130972295999527: 100%|██| 100/100 [00:30<00:00, 3.30it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for cut_outer_insulation = 1.0\n",
"58\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp45, nQry53: loss = 0.13926956057548523: 100%|█| 100/100 [00:30<00:00, 3.30it/s\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for good = 0.16981132075471697\n",
"12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp45, nQry7: loss = 0.16337624192237854: 100%|█| 100/100 [00:30<00:00, 3.28it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for missing_cable = 1.0\n",
"11\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp45, nQry6: loss = 0.16593313217163086: 100%|█| 100/100 [00:30<00:00, 3.27it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for combined = 1.0\n",
"13\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp45, nQry8: loss = 0.16560573875904083: 100%|█| 100/100 [00:30<00:00, 3.27it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for bent_wire = 1.0\n",
"10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp45, nQry5: loss = 0.18611018359661102: 100%|█| 100/100 [00:30<00:00, 3.28it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for missing_wire = 0.8\n",
"overall accuracy: 0.8696615753219527\n",
"14\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp50, nQry9: loss = 0.3357824385166168: 100%|██| 100/100 [00:33<00:00, 2.96it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for cut_inner_insulation = 0.7777777777777778\n",
"10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp50, nQry5: loss = 0.3290153741836548: 100%|██| 100/100 [00:33<00:00, 2.96it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for poke_insulation = 0.6\n",
"12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp50, nQry7: loss = 0.22177687287330627: 100%|█| 100/100 [00:33<00:00, 2.96it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for cable_swap = 0.8571428571428571\n",
"10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp50, nQry5: loss = 0.299775630235672: 100%|███| 100/100 [00:33<00:00, 2.96it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for cut_outer_insulation = 1.0\n",
"58\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp50, nQry53: loss = 0.31954386830329895: 100%|█| 100/100 [00:33<00:00, 2.98it/s\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for good = 0.32075471698113206\n",
"12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp50, nQry7: loss = 0.336273193359375: 100%|███| 100/100 [00:33<00:00, 2.98it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for missing_cable = 0.8571428571428571\n",
"11\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp50, nQry6: loss = 0.3643767237663269: 100%|██| 100/100 [00:33<00:00, 2.98it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for combined = 1.0\n",
"13\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp50, nQry8: loss = 0.3085792660713196: 100%|██| 100/100 [00:33<00:00, 2.98it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for bent_wire = 1.0\n",
"10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp50, nQry5: loss = 0.34715649485588074: 100%|█| 100/100 [00:33<00:00, 2.98it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for missing_wire = 0.8\n",
"overall accuracy: 0.8014242454494026\n",
"14\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp55, nQry9: loss = 0.375447154045105: 100%|███| 100/100 [00:36<00:00, 2.76it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for cut_inner_insulation = 0.6666666666666666\n",
"10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp55, nQry5: loss = 0.42370423674583435: 100%|█| 100/100 [00:36<00:00, 2.75it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for poke_insulation = 1.0\n",
"12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp55, nQry7: loss = 0.3982161581516266: 100%|██| 100/100 [00:36<00:00, 2.74it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for cable_swap = 0.8571428571428571\n",
"10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp55, nQry5: loss = 0.3903641104698181: 100%|██| 100/100 [00:36<00:00, 2.75it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for cut_outer_insulation = 1.0\n",
"58\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp55, nQry53: loss = 0.4019339382648468: 100%|█| 100/100 [00:36<00:00, 2.75it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for good = 0.41509433962264153\n",
"12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp55, nQry7: loss = 0.4283098876476288: 100%|██| 100/100 [00:36<00:00, 2.75it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for missing_cable = 0.7142857142857143\n",
"11\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp55, nQry6: loss = 0.3741377890110016: 100%|██| 100/100 [00:36<00:00, 2.74it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for combined = 0.8333333333333334\n",
"13\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp55, nQry8: loss = 0.3858358860015869: 100%|██| 100/100 [00:36<00:00, 2.75it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for bent_wire = 1.0\n",
"10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp55, nQry5: loss = 0.3570959270000458: 100%|██| 100/100 [00:36<00:00, 2.74it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for missing_wire = 0.8\n",
"overall accuracy: 0.8096136567834681\n",
"14\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp70, nQry9: loss = 0.5021733045578003: 100%|██| 100/100 [00:45<00:00, 2.21it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for cut_inner_insulation = 0.5555555555555556\n",
"10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp70, nQry5: loss = 0.5203520059585571: 100%|██| 100/100 [00:45<00:00, 2.20it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for poke_insulation = 0.4\n",
"12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp70, nQry7: loss = 0.524366021156311: 100%|███| 100/100 [00:45<00:00, 2.21it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for cable_swap = 0.42857142857142855\n",
"10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp70, nQry5: loss = 0.5256413221359253: 100%|██| 100/100 [00:45<00:00, 2.21it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for cut_outer_insulation = 1.0\n",
"58\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp70, nQry53: loss = 0.5186663866043091: 100%|█| 100/100 [00:45<00:00, 2.21it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for good = 0.7358490566037735\n",
"12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp70, nQry7: loss = 0.5123675465583801: 100%|██| 100/100 [00:45<00:00, 2.21it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for missing_cable = 0.7142857142857143\n",
"11\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp70, nQry6: loss = 0.5076506733894348: 100%|██| 100/100 [00:45<00:00, 2.21it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for combined = 0.8333333333333334\n",
"13\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp70, nQry8: loss = 0.490247517824173: 100%|███| 100/100 [00:45<00:00, 2.21it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for bent_wire = 0.875\n",
"10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"lr0.0001, nSupp70, nQry5: loss = 0.3723257780075073: 100%|██| 100/100 [00:45<00:00, 2.21it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"accuracy for missing_wire = 0.4\n",
"overall accuracy: 0.6602883431499785\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"#bottle_accs = []\n",
"cable_accs = []\n",
"\n",
"for nr in [5, 10, 15, 30]:\n",
" folder = \"cable\"\n",
" shot = 5\n",
" supp_x, supp_y, types = build_sup_set(shot, folder, True, nr)\n",
" accs = []\n",
" for t in next(os.walk(f'data_custom/{folder}/test'))[1]:\n",
" #if t == \"good\":\n",
" # continue\n",
" accuracy = test(t, types.get(t, 1), shot, folder)\n",
" print(f\"accuracy for {t} = {accuracy}\")\n",
" accs.append(accuracy)\n",
" print(f\"overall accuracy: {np.mean(accs)}\")\n",
" cable_accs.append(np.mean(accs))\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.57380952 0.76705653 0.84191176]\n"
]
}
],
"source": [
"print(np.array(bottle_accs))"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.86966158 0.80142425 0.80961366 0.66028834]\n"
]
}
],
"source": [
"print(np.array(cable_accs))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"P>M>F:\n",
"Resulsts:\n",
"\n",
"bottle:\n",
"jeweils 1,3,5 shots normal\n",
"[0.67910401 0.71710526 0.78860294]\n",
"\n",
"inbalanced - mehr good shots 5,10,15,30 -> alle anderen nur 5\n",
"[0.78768382 0.78860294 0.75827206 0.74356618]\n",
"\n",
"2 ways nur detektieren ob fehlerhaft oder nicht 1,3,5 shots\n",
"[0.86422306 0.93201754 0.93933824]\n",
"\n",
"inbalance 2 way 5,10,15,30 -> rest 5\n",
"[0.92371324 0.87867647 0.86397059 0.87775735]\n",
"\n",
"nur fehlerklasse erkennen 1,3,5\n",
"[0.57380952 0.76705653 0.84191176]\n",
"\n",
"\n",
"cable:\n",
"jeweils 1,3,5 shots normal\n",
"[0.25199021 0.44388328 0.46975059]\n",
"\n",
"inbalanced - mehr good shots 5,10,15,30 -> alle anderen nur 5\n",
"[0.50425859 0.48023277 0.43118282 0.41842534]\n",
"\n",
"2 ways nur detektieren ob fehlerhaft oder nicht 1,3,5 shots\n",
"[0.79263485 0.8707712 0.86756514]\n",
"\n",
"inbalance 2 way 5,10,15,30 -> rest 5\n",
"[0.86966158 0.80142425 0.80961366 0.66028834]\n",
"\n",
"nur fehlerklasse erkennen 1,3,5\n",
"[0.24383256 0.43800505 0.51304563]\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
notebooks/resnet50.ipynb
+495
View File
@@ -0,0 +1,495 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"imports imported\n"
]
}
],
"source": [
"#import numpy as np\n",
"import numpy as np\n",
"import torch\n",
"import cv2\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from torch import optim, nn\n",
"import torchvision\n",
"from torchvision import datasets, models, transforms\n",
"import albumentations as A\n",
"from albumentations.pytorch import ToTensorV2\n",
"\n",
"\n",
"print(\"imports imported\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"class Identity(nn.Module):\n",
" def __init__(self):\n",
" super(Identity, self).__init__()\n",
" \n",
" def forward(self, x):\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"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",
"\n"
]
}
],
"source": [
"resnet50 = models.resnet50(weights=models.ResNetshotnr0_Weights.DEFAULT)\n",
"\n",
"print(resnetshotnr0)\n",
"# Step 2: Modify the model to output features from the layer before the fully connected layer\n",
"class ResNetshotnr0Embeddings(nn.Module):\n",
" def __init__(self, original_model, layernr):\n",
" super(ResNetshotnr0Embeddings, self).__init__()\n",
" #print(list(original_model.children())[4 + layernr])\n",
" #print(nn.Sequential(*list(original_model.children())[:4 + shotnr]))\n",
" self.features = nn.Sequential(*list(original_model.children())[:4+layernr])\n",
" #self.features = nn.Sequential(*list(original_model.children())[:-1]) # Exclude the fully connected layer\n",
"\n",
" def forward(self, x):\n",
" x = self.features(x)\n",
" x = torch.flatten(x, 1) # Flatten the tensor to (batch_size, 2048)\n",
" return x\n",
"\n",
"# Instantiate the modified model\n",
"model = ResNetshotnr0Embeddings(resnetshotnr0, shotnr) # 3 = layer before fully connected one\n",
"model.eval() # Set the model to evaluation mode\n",
"print()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"...............\n",
"accuracy for broken_large = 0.6666666666666666\n",
".................\n",
"accuracy for broken_small = 0.8823529411764706\n",
"................\n",
"accuracy for contamination = 0.8125\n",
"overall accuracy: 0.7871732026143791\n"
]
}
],
"source": [
"from sklearn.metrics.pairwise import cosine_similarity,euclidean_distances\n",
"from metric_learn import LMNN,NCA\n",
"import math\n",
"\n",
"pipe = A.Compose([A.Resize(256,256), A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), ToTensorV2()])\n",
"#pipe = A.Compose([A.Resize(256,256), ToTensorV2()])\n",
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"m = ResNet50Embeddings(resnet50, 5) # 5 = all without fully ocnnected\n",
"m.eval()\n",
"m.to(device)\n",
"\n",
"def read_img(path):\n",
" img = cv2.imread(path, cv2.IMREAD_COLOR)\n",
" img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
" #plt.imshow(img)\n",
"\n",
" imgpiped = pipe(image=img)[\"image\"].unsqueeze(0)\n",
" return imgpiped\n",
"\n",
"def compare_embeddings(emb1, emb2, distance_metric):\n",
" #cosi = torch.nn.CosineSimilarity(dim=0) \n",
" #output = cosine_similarity([emb1.flatten(), emb2.flatten()])\n",
" #output = euclidean_distances([emb1.flatten(), emb2.flatten()], [emb1.flatten(), emb2.flatten()])\n",
" output = distance_metric(emb1, emb2)\n",
" return output\n",
"\n",
"def merge_embeddings(embeddings):\n",
" # todo calc cluster center or similar\n",
" return np.average(embeddings, axis=0)\n",
"\n",
"import os\n",
"#embedding_good_1 = m(read_img(f\"./data/bottle/test/good/001.png\")).detach().numpy()\n",
"#embedding_good_2 = m(read_img(f\"./data/bottle/test/good/002.png\")).detach().numpy()\n",
"#embedding_good = merge_embeddings([embedding_good_1, embedding_good_2])\n",
"#embedding_contermination_1 = m(read_img(f\"./data/bottle/test/contamination/001.png\")).detach().numpy()\n",
"#embedding_contermination_2 = m(read_img(f\"./data/bottle/test/contamination/002.png\")).detach().numpy()\n",
"#embedding_contermination = merge_embeddings([embedding_contermination_1, embedding_contermination_2])\n",
"#embedding_broken_small_1 = m(read_img(f\"./data/bottle/test/broken_small/001.png\")).detach().numpy()\n",
"\n",
"#embeddings_test = m(read_img(f\"./data/bottle/test/contamination/004.png\")).detach().numpy()\n",
"\n",
"#score = compare_embeddings(embedding_good_1, embeddings_test)\n",
"\n",
"#def calc_base_emb(t):\n",
"# base_emb_1 = m(read_img(f\"./data/bottle/test/{t}/000.png\")).detach().numpy()\n",
"# base_emb_2 = m(read_img(f\"./data/bottle/test/{t}/001.png\")).detach().numpy()\n",
"# base_emb_3 = m(read_img(f\"./data/bottle/test/{t}/002.png\")).detach().numpy()\n",
"# base_emb_4 = m(read_img(f\"./data/bottle/test/{t}/003.png\")).detach().numpy()\n",
"# base_emb_5 = m(read_img(f\"./data/bottle/test/{t}/004.png\")).detach().numpy()\n",
"# base_emb = merge_embeddings([base_emb_1, base_emb_2, base_emb_3, base_emb_4, base_emb_5])\n",
"# return base_emb\n",
"\n",
"MAIN_TYPE=\"bottle\"\n",
"\n",
"def calc_base_emb(t, nr):\n",
" embs = []\n",
" for i in range(nr):\n",
" if t == \"good\":\n",
" emb = m(read_img(f\"./data/{MAIN_TYPE}/train/{t}/{i:03d}.png\")).detach().numpy()\n",
" else:\n",
" emb = m(read_img(f\"./data/{MAIN_TYPE}/test/{t}/{i:03d}.png\")).detach().numpy()\n",
" embs.append(emb)\n",
" base_emb = merge_embeddings(embs)\n",
" return base_emb\n",
"\n",
"shotnr=5\n",
"goodnr=5\n",
"\n",
"types = {#\"good\": calc_base_emb(\"good\", goodnr), \n",
" #\"bad\": merge_embeddings([calc_base_emb(\"broken_large\", shotnr), calc_base_emb(\"broken_small\", shotnr), calc_base_emb(\"contamination\", shotnr)]),\n",
" \"broken_large\": calc_base_emb(\"broken_large\", shotnr), \n",
" \"broken_small\": calc_base_emb(\"broken_small\", shotnr), \n",
" \"contamination\": calc_base_emb(\"contamination\", shotnr)\n",
" }\n",
"\n",
"\n",
"#types = {#\"good\": calc_base_emb(\"good\", goodnr), \n",
"# #\"bad\": merge_embeddings([calc_base_emb(\"bent_wire\", shotnr), calc_base_emb(\"cable_swap\", shotnr), calc_base_emb(\"combined\", shotnr), \n",
"# # calc_base_emb(\"cut_inner_insulation\", shotnr), calc_base_emb(\"cut_outer_insulation\", shotnr), calc_base_emb(\"missing_cable\", shotnr), \n",
"# # calc_base_emb(\"missing_wire\", shotnr), calc_base_emb(\"poke_insulation\", shotnr)]),\n",
"# \"bent_wire\": calc_base_emb(\"bent_wire\", shotnr),\n",
"# \"cable_swap\": calc_base_emb(\"cable_swap\", shotnr), \n",
"# \"combined\": calc_base_emb(\"combined\", shotnr), \n",
"# \"cut_inner_insulation\": calc_base_emb(\"cut_inner_insulation\", shotnr), \n",
"# \"cut_outer_insulation\": calc_base_emb(\"cut_outer_insulation\", shotnr), \n",
"# \"missing_cable\": calc_base_emb(\"missing_cable\", shotnr), \n",
"# \"missing_wire\": calc_base_emb(\"missing_wire\", shotnr), \n",
"# \"poke_insulation\": calc_base_emb(\"poke_insulation\", shotnr), \n",
"# }\n",
"\n",
"# euclidean distance\n",
"euclidean_distance_metric = lambda emb1,emb2 : math.pow(euclidean_distances([emb1.flatten(), emb2.flatten()], [emb1.flatten(), emb2.flatten()])[0][1], 2)\n",
"# cosine metric\n",
"cosine_similarity_metric = lambda emb1,emb2 : cosine_similarity([emb1.flatten(), emb2.flatten()])[0][1]\n",
"\n",
"lmnn = LMNN(n_neighbors=2, learn_rate=1e-3, verbose=True)\n",
"#lmnn.fit(data, [0,0,0,1,1,1,2,2,2,3,3,3])\n",
"\n",
"lmnn_similarity_metric = lambda emb1,emb2 : lmnn.get_metric()(emb1.flatten(), emb2.flatten())\n",
"\n",
"Smaller_Better_Metric = False\n",
"\n",
"def test(type):\n",
" predictions = []\n",
"\n",
" _, _, files = next(os.walk(f\"./data/{MAIN_TYPE}/test/{type}/\"))\n",
" file_count = len(files)\n",
" for i in range(5,file_count):\n",
" print(\".\", end=\"\")\n",
"\n",
" emb = m(read_img(f\"./data/{MAIN_TYPE}/test/{type}/{i:03d}.png\")).detach().numpy()\n",
" curr_score = .0 if Smaller_Better_Metric else 999999.0\n",
" max_type = \"\"\n",
" for t in types.keys():\n",
"# for t in [\"good\", \"bad\"]:\n",
" score = compare_embeddings(emb, types[t], euclidean_distance_metric)\n",
" \n",
" if Smaller_Better_Metric:\n",
" if score > curr_score:\n",
" curr_score = score\n",
" max_type = t\n",
" else:\n",
" if score < curr_score:\n",
" curr_score = score\n",
" max_type = t\n",
" pass\n",
" predictions.append(max_type)\n",
" pass\n",
" print()\n",
" return np.mean([x == type for x in predictions])\n",
"# return np.mean([x == (\"good\" if type == \"good\" else \"bad\") for x in predictions])\n",
" pass\n",
"\n",
"accs = []\n",
"for t in types.keys():\n",
" if t == \"bad\":\n",
" continue\n",
" accuracy = test(t)\n",
" print(f\"accuracy for {t} = {accuracy}\")\n",
" accs.append(accuracy)\n",
"print(f\"overall accuracy: {np.mean(accs)}\")\n",
"#print(m)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"RESNET 50:\n",
"Resulsts:\n",
"\n",
"bottle:\n",
"jeweils 1,3,5 shots normal\n",
"[0.5892857142857143 0.7321428571428571 0.75]\n",
"\n",
"inbalanced - mehr good shots 5,10,15,30 -> alle anderen nur 5\n",
"[0.75 0.732 0.696 0.696]\n",
"\n",
"2 ways nur detektieren ob fehlerhaft oder nicht 1,3,5 shots\n",
"[0.8395 0.8315 0.8031]\n",
"\n",
"inbalance 2 way 5,10,15,30 -> rest 5\n",
"[0.8031 0.81893 0.8336 0.8031]\n",
"\n",
"nur fehlerklasse erkennen 1,3,5\n",
"[0.7638 0.7428 0.787]\n",
"\n",
"\n",
"cable:\n",
"jeweils 1,3,5 shots normal\n",
"[0.21808 0.43815 0.4321478]\n",
"\n",
"inbalanced - mehr good shots 5,10,15,30 -> alle anderen nur 5\n",
"[0.4321478 0.432986 0.42340 0.4464635]\n",
"\n",
"2 ways nur detektieren ob fehlerhaft oder nicht 1,3,5 shots\n",
"[0.8592 0.8772 0.8495]\n",
"\n",
"inbalance 2 way 5,10,15,30 -> rest 5\n",
"[0.8495 0.8180 0.7460 0.6846]\n",
"\n",
"nur fehlerklasse erkennen 1,3,5\n",
"[0.240 0.4740 0.4805]\n"
]
}
],
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src/introduction.tex
+29 -1
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@@ -1 +1,29 @@
Test intro
\section{Introduction}\label{sec:introduction}
\subsection{Motivation}\label{subsec:motivation}
For most supervised learning tasks lots of training samples are essential.
With too less training data the model will not generalize well and not fit a real world task.
Labeling datasets is commonly seen as an expensive task and wants to be avoided as much as possible.\cite{generalAI}
That's why there is a machine-learning field called active learning.
The general approach is to train a model that predicts within every iteration a ranking metric or Pseudo-Labels which then can be used to rank the importance of samples to be labeled by an oracle.
These labeled samples are then used to train the model.\cite{activelearning}
The goal of this practical work is to test active learning within a simple classification task and evaluate its performance.
\subsection{Research Questions}\label{subsec:research-questions}
\subsubsection{Is Few-Shot learning a suitable fit for anomaly detection?}
Should Few-Shot learning be used for anomaly detection tasks?
How does it compare to well established algorithms such as Patchcore or EfficientAD?
\subsubsection{How does disbalancing the Shot number affect performance?}
Does giving the Few-Shot learner more good than bad samples improve the model performance?
\subsubsection{How does the 3 methods perform in only detecting the anomaly class?}
How much does the performance improve if only detecting an anomaly or not?
How does it compare to PatchCore and EfficientAD?
\subsubsection{Extra: How does Euclidean distance compare to Cosine-similarity when using ResNet as a feature-extractor?}
I've tried different distance measures -> but results are pretty much the same.
\subsection{Outline}\label{subsec:outline}