{"cells":[{"metadata":{},"cell_type":"markdown","source":"Copy from https://www.kaggle.com/pestipeti/pytorch-starter-fasterrcnn-inference","execution_count":null},{"metadata":{"_uuid":"d5c68465-f175-4023-956e-88d4980c1446","_cell_guid":"cb41a40b-bb1e-4baa-8608-872bda36a6f0","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport cv2\n\nimport albumentations as A\nfrom albumentations.pytorch.transforms import ToTensorV2\n\nimport torch\nimport torchvision\n\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n\nfrom torch.utils.data import DataLoader, Dataset\nimport warnings\n\nwarnings.filterwarnings('ignore')\n\n\nDIR_INPUT = '/kaggle/input/global-wheat-detection'\nDIR_TRAIN = f'{DIR_INPUT}/train'\nDIR_TEST = f'{DIR_INPUT}/test'\n\n\nWEIGHTS_FILE = '../input/weights/16-resNetFpn-model-64.pth'\n\ntest_df = pd.read_csv(f'{DIR_INPUT}/sample_submission.csv')\n\n\nclass WheatTestDataset(Dataset):\n\n    def __init__(self, dataframe, image_dir, transforms=None):\n        super().__init__()\n\n        self.image_ids = dataframe['image_id'].unique()\n        self.df = dataframe\n        self.image_dir = image_dir\n        self.transforms = transforms\n\n    def __getitem__(self, index: int):\n\n        image_id = self.image_ids[index]\n        records = self.df[self.df['image_id'] == image_id]\n\n        image = cv2.imread(f'{self.image_dir}/{image_id}.jpg', cv2.IMREAD_COLOR)\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.float32)\n        image /= 255.0\n\n        if self.transforms:\n            sample = {\n                'image': image,\n            }\n            sample = self.transforms(**sample)\n            image = sample['image']\n\n        return image, image_id\n\n    def __len__(self) -> int:\n        return self.image_ids.shape[0]\n    \n\ndef get_test_transform():\n    return A.Compose([\n        # A.Resize(512, 512),\n        ToTensorV2(p=1.0)\n    ])\n\n\nmodel = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=False)\n\ndevice = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n\nnum_classes = 2  # 1 class (wheat) + background\n\n# get number of input features for the classifier\nin_features = model.roi_heads.box_predictor.cls_score.in_features\n\n# replace the pre-trained head with a new one\nmodel.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n\n# Load the trained weights\nmodel.load_state_dict(torch.load(WEIGHTS_FILE)['model'])\nmodel.eval()\n\nx = model.to(device)\n\n\ndef collate_fn(batch):\n    return tuple(zip(*batch))\n\n\ntest_dataset = WheatTestDataset(test_df, DIR_TEST, get_test_transform())\n\ntest_data_loader = DataLoader(\n    test_dataset,\n    batch_size=4,\n    shuffle=False,\n    num_workers=4,\n    drop_last=False,\n    collate_fn=collate_fn\n)\n\n\ndef format_prediction_string(boxes, scores):\n    pred_strings = []\n    for j in zip(scores, boxes):\n        pred_strings.append(\"{0:.4f} {1} {2} {3} {4}\".format(j[0], j[1][0], j[1][1], j[1][2], j[1][3]))\n\n    return \" \".join(pred_strings)\n\n\ndetection_threshold = 0.5\nresults = []\n\nfor images, image_ids in test_data_loader:\n\n    images = list(image.to(device) for image in images)\n    outputs = model(images)\n\n    for i, image in enumerate(images):\n\n        boxes = outputs[i]['boxes'].data.cpu().numpy()\n        scores = outputs[i]['scores'].data.cpu().numpy()\n        \n        boxes = boxes[scores >= detection_threshold].astype(np.int32)\n        scores = scores[scores >= detection_threshold]\n        image_id = image_ids[i]\n        \n        boxes[:, 2] = boxes[:, 2] - boxes[:, 0]\n        boxes[:, 3] = boxes[:, 3] - boxes[:, 1]\n        \n        result = {\n            'image_id': image_id,\n            'PredictionString': format_prediction_string(boxes, scores)\n        }\n        \n        results.append(result)\n\ntest_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])\n\ntest_df.to_csv('submission.csv', index=False)","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}