{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"\n\ntez_path = '../input/tez-lib/'\neffnet_path = '../input/efficientnet-pytorch/'\nimport sys\nsys.path.append(tez_path)\nsys.path.append(effnet_path)\n\n\n\nimport os\nimport albumentations\nimport pandas as pd\nimport numpy as np\n\nimport tez\nfrom tez.datasets import ImageDataset\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\n\nfrom efficientnet_pytorch import EfficientNet\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class LeafModel(tez.Model):\n    def __init__(self, num_classes):\n        super().__init__()\n\n        self.effnet = EfficientNet.from_name(\"efficientnet-b4\")\n        self.dropout = nn.Dropout(0.1)\n        self.out = nn.Linear(1792, num_classes)\n        self.step_scheduler_after = \"epoch\"\n\n    def forward(self, image, targets=None):\n        batch_size, _, _, _ = image.shape\n\n        x = self.effnet.extract_features(image)\n        x = F.adaptive_avg_pool2d(x, 1).reshape(batch_size, -1)\n        outputs = self.out(self.dropout(x))\n        return outputs, None, None","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\n\n# augmentations taken from: https://www.kaggle.com/khyeh0719/pytorch-efficientnet-baseline-inference-tta\ntest_aug = albumentations.Compose([\n    albumentations.RandomResizedCrop(256, 256),\n    albumentations.Transpose(p=0.5),\n    albumentations.HorizontalFlip(p=0.5),\n    albumentations.VerticalFlip(p=0.5),\n    albumentations.HueSaturationValue(\n        hue_shift_limit=0.2, \n        sat_shift_limit=0.2,\n        val_shift_limit=0.2, \n        p=0.5\n    ),\n    albumentations.RandomBrightnessContrast(\n        brightness_limit=(-0.1,0.1), \n        contrast_limit=(-0.1, 0.1), \n        p=0.5\n    ),\n    albumentations.Normalize(\n        mean=[0.485, 0.456, 0.406], \n        std=[0.229, 0.224, 0.225], \n        max_pixel_value=255.0, \n        p=1.0\n    )\n], p=1.)\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\n\ndfx = pd.read_csv(\"../input/cassava-leaf-disease-classification/sample_submission.csv\")\nimage_path = \"../input/cassava-leaf-disease-classification/test_images/\"\ntest_image_paths = [os.path.join(image_path, x) for x in dfx.image_id.values]\n# fake targets\n#df1.label.values\ntest_targets = dfx.label.values\ntest_dataset = ImageDataset(\n    image_paths=test_image_paths,\n    targets=test_targets,\n    augmentations=test_aug,\n)\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_targets","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\ntrain_dfx = pd.read_csv(\"../input/cassava-leaf-disease-classification/train.csv\")\nmodel = LeafModel(num_classes=train_dfx.label.nunique())\nmodel.load(\"../input/leafmodel/model.bin\")\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\n\n# run inference 15 times\nfinal_preds = None\nfor j in range(20):\n    preds = model.predict(test_dataset, batch_size=32, n_jobs=-1, device=\"cuda\")\n    temp_preds = None\n    for p in preds:\n        if temp_preds is None:\n            temp_preds = p\n        else:\n            temp_preds = np.vstack((temp_preds, p))\n    if final_preds is None:\n        final_preds = temp_preds\n    else:\n        final_preds += temp_preds\nfinal_preds /= 20\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"final_preds = final_preds.argmax(axis=1)\ndfx.label = final_preds\ndfx.to_csv(\"submission.csv\", index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}