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"height": 0 + }, + "id": "y1pz_G13mSq_", + "outputId": "d3f58294-4042-4f33-b4a5-913af51a172f", + "collapsed": true + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Wed May 21 02:30:42 2025 \n", + "+-----------------------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 550.54.15 Driver Version: 550.54.15 CUDA Version: 12.4 |\n", + "|-----------------------------------------+------------------------+----------------------+\n", + "| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|=========================================+========================+======================|\n", + "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", + "| N/A 48C P8 10W / 70W | 0MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-----------------------------------------+------------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=========================================================================================|\n", + "| No running processes found |\n", + "+-----------------------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "!pip install anomalib[full]" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# New Section" + ], + "metadata": { + "id": "i7sLiK7OnSRY" + } + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "id": "je0Q6FOLoT4M", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "outputId": "eb166ff1-f855-414c-8d74-2b3afa64dbc5" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from anomalib.data import Folder, MVTecAD\n", + "from anomalib.deploy import ExportType, OpenVINOInferencer\n", + "from anomalib.engine import Engine\n", + "from anomalib.models import Cflow, EfficientAd, Patchcore\n", + "from anomalib.utils.visualization import ImageResult\n", + "from torchvision.transforms import v2\n", + "import os" + ], + "metadata": { + "id": "Yw9kHNfEqFdV" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "augmentations = v2.Compose(\n", + " [\n", + " v2.RandomHorizontalFlip(\n", + " p=0.5\n", + " ), # Randomly flip images horizontally with 50% probability\n", + " v2.RandomVerticalFlip(\n", + " p=0.2\n", + " ), # Randomly flip images vertically with 20% probability\n", + " v2.RandomRotation(\n", + " degrees=30\n", + " ), # Randomly rotate images within a range of ±30 degrees\n", + " v2.RandomResizedCrop(\n", + " size=(256, 256), scale=(0.8, 1.0)\n", + " ), # Randomly crop and resize images\n", + " v2.ColorJitter(\n", + " brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1\n", + " ), # Randomly adjust colors\n", + " v2.RandomGrayscale(\n", + " p=0.1\n", + " ), # Convert images to grayscale with 10% probability\n", + " ]\n", + ")\n", + "datamodule = Folder(\n", + " name=\"custom_folder\",\n", + " root=\"/content/drive/MyDrive/processed\",\n", + " normal_dir=\"train/good\",\n", + " abnormal_dir=\"test/quexian\",\n", + " mask_dir=\"ground_truth/quexian\",\n", + " train_batch_size=1,\n", + ")\n", + "datamodule.setup()\n", + "model = EfficientAd(imagenet_dir=\"data/imagenette2\")\n", + "# self.model = Cflow(\n", + "# anomaly_source_path=os.path.join(\n", + "# self.data_dir, \"images\", \"processed\", \"test\"\n", + "# ), # 异常样本路径\n", + "# task=\"segmentation\",\n", + "# )\n", + "# TODO\n", + "engine = Engine(max_epochs=300, accelerator=\"cuda\")\n", + "engine.fit(datamodule=datamodule, model=model)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 5396, + "referenced_widgets": [ + "d21a5fe96bc740adb3aa49aa4f7b51f6", + "48f1f66f47e54d3481872c35840793d7", + "ac1d8666ec9242e1902830655a03574f", + "92f8b0727e504957b3affbb2421ae9e0", + "8505528627df460ba1dd146a2de8b1ea", + "c0e2df0c47334d39a7a63fdfbec7d7f7", + "b5174d36979e47ecaa3d6f5a5008c47f", + "c7803595fbe0412fab228d8003dc9225", + "4b41c4e002624e9c818113f8d54f43af", + "015ac1a67eb440fcb20ba62d3eaf4f4f", + "dbe8378414dc4ba2998a7ac1fe79d494", + 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0 TPU cores\n", + "INFO:lightning_fabric.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", + "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:624: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", + " warnings.warn(\n", + "INFO: \n", + " | Name | Type | Params | Mode \n", + "------------------------------------------------------------\n", + "0 | pre_processor | PreProcessor | 0 | train\n", + "1 | post_processor | PostProcessor | 0 | train\n", + "2 | evaluator | Evaluator | 0 | train\n", + "3 | model | EfficientAdModel | 8.1 M | train\n", + "------------------------------------------------------------\n", + "8.1 M Trainable params\n", + "0 Non-trainable params\n", + "8.1 M Total params\n", + "32.235 Total estimated model params size (MB)\n", + "48 Modules in train mode\n", + "7 Modules in eval mode\n", + "INFO:lightning.pytorch.callbacks.model_summary:\n", + " | Name | Type | Params | Mode \n", + "------------------------------------------------------------\n", + "0 | pre_processor | PreProcessor | 0 | train\n", + "1 | post_processor | PostProcessor | 0 | train\n", + "2 | evaluator | Evaluator | 0 | train\n", + "3 | model | EfficientAdModel | 8.1 M | train\n", + "------------------------------------------------------------\n", + "8.1 M Trainable params\n", + "0 Non-trainable params\n", + "8.1 M Total params\n", + "32.235 Total estimated model params size (MB)\n", + "48 Modules in train mode\n", + "7 Modules in eval mode\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Training: | | 0/? 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