Python inference.Masker方法代码示例

# 需要导入模块: from maskrcnn_benchmark.modeling.roi_heads.mask_head import inference [as 别名]
# 或者: from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker [as 别名]
def __init__(
        self,
        cfg,
        confidence_threshold=0.7,
        show_mask_heatmaps=False,
        masks_per_dim=2,
        min_image_size=224,
    ):
        self.cfg = cfg.clone()
        self.model = build_detection_model(cfg)
        self.model.eval()
        self.device = torch.device(cfg.MODEL.DEVICE)
        self.model.to(self.device)
        self.min_image_size = min_image_size

        save_dir = cfg.OUTPUT_DIR
        checkpointer = DetectronCheckpointer(cfg, self.model, save_dir=save_dir)
        _ = checkpointer.load(cfg.MODEL.WEIGHT)

        self.transforms = self.build_transform()

        mask_threshold = -1 if show_mask_heatmaps else 0.5
        self.masker = Masker(threshold=mask_threshold, padding=1)

        # used to make colors for each class
        self.palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])

        self.cpu_device = torch.device("cpu")
        self.confidence_threshold = confidence_threshold
        self.show_mask_heatmaps = show_mask_heatmaps
        self.masks_per_dim = masks_per_dim 

# 需要导入模块: from maskrcnn_benchmark.modeling.roi_heads.mask_head import inference [as 别名]
# 或者: from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker [as 别名]
def __init__(
        self,
        cfg,
        confidence_threshold=0.7,
        show_mask_heatmaps=False,
        masks_per_dim=2,
        min_image_size=224,
        weight_loading = None
    ):
        self.cfg = cfg.clone()
        self.model = build_detection_model(cfg)
        self.model.eval()
        self.device = torch.device(cfg.MODEL.DEVICE)
        self.model.to(self.device)
        self.min_image_size = min_image_size

        save_dir = cfg.OUTPUT_DIR
        checkpointer = DetectronCheckpointer(cfg, self.model, save_dir=save_dir)
        _ = checkpointer.load(cfg.MODEL.WEIGHT)
        
        if weight_loading:
            print('Loading weight from {}.'.format(weight_loading))
            _ = checkpointer._load_model(torch.load(weight_loading))
        
        self.transforms = self.build_transform()

        mask_threshold = -1 if show_mask_heatmaps else 0.5
        self.masker = Masker(threshold=mask_threshold, padding=1)

        # used to make colors for each class
        self.palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])

        self.cpu_device = torch.device("cpu")
        self.confidence_threshold = confidence_threshold
        self.show_mask_heatmaps = show_mask_heatmaps
        self.masks_per_dim = masks_per_dim 

# 需要导入模块: from maskrcnn_benchmark.modeling.roi_heads.mask_head import inference [as 别名]
# 或者: from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker [as 别名]
def __init__(
        self,
        cfg,
        confidence_threshold=0.7,
        show_mask_heatmaps=False,
        masks_per_dim=2,
        min_image_size=224,
    ):
        self.color_list = colormap()
        self.cfg = cfg.clone()
        self.model = build_detection_model(cfg)
        self.model.eval()
        self.device = torch.device(cfg.MODEL.DEVICE)
        self.model.to(self.device)
        self.min_image_size = min_image_size

        save_dir = cfg.OUTPUT_DIR
        checkpointer = DetectronCheckpointer(cfg, self.model, save_dir=save_dir)
        _ = checkpointer.load(cfg.MODEL.WEIGHT)

        self.transforms = self.build_transform()

        mask_threshold = -1 if show_mask_heatmaps else 0.5
        self.masker = Masker(threshold=mask_threshold, padding=1)

        # used to make colors for each class
        self.palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])

        self.cpu_device = torch.device("cpu")
        self.confidence_threshold = torch.tensor(confidence_threshold)
        self.show_mask_heatmaps = show_mask_heatmaps
        self.masks_per_dim = masks_per_dim 

# 需要导入模块: from maskrcnn_benchmark.modeling.roi_heads.mask_head import inference [as 别名]
# 或者: from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker [as 别名]
def __init__(
        self,
        cfg,
        confidence_threshold=0.7,
        show_mask_heatmaps=False,
        masks_per_dim=2,
        min_image_size=224,
    ):
        self.cfg = cfg.clone()
        self.model = build_detection_model(cfg)
        self.model.eval()
        self.device = torch.device(cfg.MODEL.DEVICE)
        self.model.to(self.device)
        self.min_image_size = min_image_size

        checkpointer = DetectronCheckpointer(cfg, self.model)
        _ = checkpointer.load(cfg.MODEL.WEIGHT)

        self.checkpointer = checkpointer
        self.transforms = self.build_transform()

        mask_threshold = -1 if show_mask_heatmaps else 0.5
        self.masker = Masker(threshold=mask_threshold, padding=1)

        # used to make colors for each class
        self.palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])

        self.cpu_device = torch.device("cpu")
        self.confidence_threshold = confidence_threshold
        self.show_mask_heatmaps = show_mask_heatmaps
        self.masks_per_dim = masks_per_dim

        self.CATEGORIES = COCO_CATEGORIES if cfg.DATASETS.TEST[0][:4] == 'coco' else VOC_CATEGORIES 

# 需要导入模块: from maskrcnn_benchmark.modeling.roi_heads.mask_head import inference [as 别名]
# 或者: from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker [as 别名]
def __init__(
        self,
        cfg,
        confidence_threshold=0.7,
        show_mask_heatmaps=False,
        masks_per_dim=2,
        min_image_size=224,
    ):
        self.cfg = cfg.clone()
        self.model = build_detection_model(cfg)
        self.model.eval()
        self.device = torch.device(cfg.MODEL.DEVICE)
        self.model.to(self.device)
        self.min_image_size = min_image_size

        checkpointer = DetectronCheckpointer(cfg, self.model)
        _ = checkpointer.load(cfg.MODEL.WEIGHT)

        self.transforms = self.build_transform()

        mask_threshold = -1 if show_mask_heatmaps else 0.5
        self.masker = Masker(threshold=mask_threshold, padding=1)

        # used to make colors for each class
        self.palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])

        self.cpu_device = torch.device("cpu")
        self.confidence_threshold = confidence_threshold
        self.show_mask_heatmaps = show_mask_heatmaps
        self.masks_per_dim = masks_per_dim 

# 需要导入模块: from maskrcnn_benchmark.modeling.roi_heads.mask_head import inference [as 别名]
# 或者: from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker [as 别名]
def prepare_for_coco_segmentation(predictions, dataset):
    import pycocotools.mask as mask_util
    import numpy as np

    masker = Masker(threshold=0.5, padding=1)
    # assert isinstance(dataset, COCODataset)
    coco_results = []
    for image_id, prediction in tqdm(enumerate(predictions)):
        original_id = dataset.id_to_img_map[image_id]
        if len(prediction) == 0:
            continue

        img_info = dataset.get_img_info(image_id)
        image_width = img_info["width"]
        image_height = img_info["height"]
        prediction = prediction.resize((image_width, image_height))
        masks = prediction.get_field("mask")
        # t = time.time()
        # Masker is necessary only if masks haven't been already resized.
        if list(masks.shape[-2:]) != [image_height, image_width]:
            masks = masker(masks.expand(1, -1, -1, -1, -1), prediction)
            masks = masks[0]
        # logger.info('Time mask: {}'.format(time.time() - t))
        # prediction = prediction.convert('xywh')

        # boxes = prediction.bbox.tolist()
        scores = prediction.get_field("scores").tolist()
        labels = prediction.get_field("labels").tolist()

        # rles = prediction.get_field('mask')

        rles = [
            mask_util.encode(np.array(mask[0, :, :, np.newaxis], order="F"))[0]
            for mask in masks
        ]
        for rle in rles:
            rle["counts"] = rle["counts"].decode("utf-8")

        mapped_labels = [dataset.contiguous_category_id_to_json_id[i] for i in labels]

        coco_results.extend(
            [
                {
                    "image_id": original_id,
                    "category_id": mapped_labels[k],
                    "segmentation": rle,
                    "score": scores[k],
                }
                for k, rle in enumerate(rles)
            ]
        )
    return coco_results 

# 需要导入模块: from maskrcnn_benchmark.modeling.roi_heads.mask_head import inference [as 别名]
# 或者: from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker [as 别名]
def __init__(
        self,
        model,
        CATEGORIES,
        dataset,
        confidence_threshold=0.5,
        show_mask_heatmaps=False,
        masks_per_dim=2,
        min_image_size=224,
    ):
        if model == 'faster':
            config_file = "faster-retina/configs/e2e_faster_rcnn_R_50_FPN_1x_{}_test.yaml".format(dataset)
        if model == 'retinanet':
            config_file = 'faster-retina/configs/retinanet_R-50-FPN_1x-{}.yaml'.format(dataset)
        
        if model == 'maskrcnn':
            config_file = 'faster-retina/configs/e2e_mask_rcnn_R_50_FPN_1x-{}.yaml'.format(dataset)
        cfg.merge_from_file(config_file)
        self.cfg = cfg.clone()
        self.CATEGORIES = CATEGORIES
        self.model = build_detection_model(cfg)
        self.model.eval()
        self.device = torch.device('cuda')
        self.model.to(self.device)
        self.min_image_size = min_image_size
        self.feat_extractor = FeatureExtractorFromBoxes(self.model)
        save_dir = cfg.OUTPUT_DIR
        checkpointer = DetectronCheckpointer(cfg, self.model, save_dir=save_dir)
        _ = checkpointer.load(cfg.MODEL.WEIGHT)

        self.transforms = self.build_transform()

        mask_threshold = -1 if show_mask_heatmaps else 0.5
        self.masker = Masker(threshold=mask_threshold, padding=1)

        # used to make colors for each class
        self.palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])

        self.cpu_device = torch.device("cpu")
        self.confidence_threshold = confidence_threshold
        self.show_mask_heatmaps = show_mask_heatmaps
        self.masks_per_dim = masks_per_dim 

# 需要导入模块: from maskrcnn_benchmark.modeling.roi_heads.mask_head import inference [as 别名]
# 或者: from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker [as 别名]
def prepare_for_coco_segmentation(predictions, dataset):
    import pycocotools.mask as mask_util
    import numpy as np

    masker = Masker(threshold=0.5, padding=1)
    # assert isinstance(dataset, COCODataset)
    coco_results = []
    for image_id, prediction in tqdm(enumerate(predictions)):
        original_id = dataset.id_to_img_map[image_id]
        if len(prediction) == 0:
            continue

        # TODO replace with get_img_info?
        image_width = dataset.coco.imgs[original_id]["width"]
        image_height = dataset.coco.imgs[original_id]["height"]
        prediction = prediction.resize((image_width, image_height))
        masks = prediction.get_field("mask")
        # t = time.time()
        # Masker is necessary only if masks haven't been already resized.
        if list(masks.shape[-2:]) != [image_height, image_width]:
            masks = masker(masks.expand(1, -1, -1, -1, -1), prediction)
            masks = masks[0]
        # logger.info('Time mask: {}'.format(time.time() - t))
        # prediction = prediction.convert('xywh')

        # boxes = prediction.bbox.tolist()
        scores = prediction.get_field("scores").tolist()
        labels = prediction.get_field("labels").tolist()

        # rles = prediction.get_field('mask')

        rles = [
            mask_util.encode(np.array(mask[0, :, :, np.newaxis], order="F"))[0]
            for mask in masks
        ]
        for rle in rles:
            rle["counts"] = rle["counts"].decode("utf-8")

        mapped_labels = [dataset.contiguous_category_id_to_json_id[i] for i in labels]

        coco_results.extend(
            [
                {
                    "image_id": original_id,
                    "category_id": mapped_labels[k],
                    "segmentation": rle,
                    "score": scores[k],
                }
                for k, rle in enumerate(rles)
            ]
        )
    return coco_results


# inspired from Detectron 

# 需要导入模块: from maskrcnn_benchmark.modeling.roi_heads.mask_head import inference [as 别名]
# 或者: from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker [as 别名]
def prepare_for_coco_segmentation(predictions, dataset):
    import pycocotools.mask as mask_util
    import numpy as np

    masker = Masker(threshold=0.5, padding=1)
    # assert isinstance(dataset, COCODataset)
    coco_results = []
    for image_id, prediction in tqdm(enumerate(predictions)):
        original_id = dataset.id_to_img_map[image_id]
        if len(prediction) == 0:
            continue

        # TODO replace with get_img_info?
        image_width = dataset.coco.imgs[original_id]["width"]
        image_height = dataset.coco.imgs[original_id]["height"]
        prediction = prediction.resize((image_width, image_height))
        masks = prediction.get_field("mask")
        # t = time.time()
        masks = masker(masks, prediction)
        # logger.info('Time mask: {}'.format(time.time() - t))
        # prediction = prediction.convert('xywh')

        # boxes = prediction.bbox.tolist()
        scores = prediction.get_field("scores").tolist()
        labels = prediction.get_field("labels").tolist()

        # rles = prediction.get_field('mask')

        rles = [
            mask_util.encode(np.array(mask[0, :, :, np.newaxis], order="F"))[0]
            for mask in masks
        ]
        for rle in rles:
            rle["counts"] = rle["counts"].decode("utf-8")

        mapped_labels = [dataset.contiguous_category_id_to_json_id[i] for i in labels]

        coco_results.extend(
            [
                {
                    "image_id": original_id,
                    "category_id": mapped_labels[k],
                    "segmentation": rle,
                    "score": scores[k],
                }
                for k, rle in enumerate(rles)
            ]
        )
    return coco_results


# inspired from Detectron 

# 需要导入模块: from maskrcnn_benchmark.modeling.roi_heads.mask_head import inference [as 别名]
# 或者: from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker [as 别名]
def prepare_for_coco_segmentation(predictions, dataset):
    import pycocotools.mask as mask_util
    import numpy as np

    masker = Masker(threshold=0.5, padding=1)
    # assert isinstance(dataset, COCODataset)
    coco_results = []
    for image_id, prediction in tqdm(enumerate(predictions)):
        original_id = dataset.id_to_img_map[image_id]
        if len(prediction) == 0:
            continue

        img_info = dataset.get_img_info(image_id)
        image_width = img_info["width"]
        image_height = img_info["height"]
        prediction = prediction.resize((image_width, image_height))
        masks = prediction.get_field("mask")
        # t = time.time()
        # Masker is necessary only if masks haven't been already resized.
        if list(masks.shape[-2:]) != [image_height, image_width]:
            masks = masker(masks.expand(1, -1, -1, -1, -1), prediction)
            masks = masks[0]
        # logger.info('Time mask: {}'.format(time.time() - t))
        # prediction = prediction.convert('xywh')

        # boxes = prediction.bbox.tolist()
        scores = prediction.get_field("scores").tolist()
        labels = prediction.get_field("labels").tolist()

        # rles = prediction.get_field('mask')

        rles = [
            mask_util.encode(np.array(mask[0, :, :, np.newaxis], order="F"))[0]
            for mask in masks
        ]
        for rle in rles:
            rle["counts"] = rle["counts"].decode("utf-8")

        mapped_labels = [dataset.contiguous_category_id_to_json_id[i] for i in labels]

        coco_results.extend(
            [
                {
                    "image_id": original_id,
                    "category_id": mapped_labels[k],
                    "segmentation": rle,
                    "score": scores[k],
                }
                for k, rle in enumerate(rles)
            ]
        )
    return coco_results


# inspired from Detectron