当前位置: 首页>>代码示例>>Python>>正文


Python layers.interpolate方法代码示例

本文整理汇总了Python中maskrcnn_benchmark.layers.interpolate方法的典型用法代码示例。如果您正苦于以下问题:Python layers.interpolate方法的具体用法?Python layers.interpolate怎么用?Python layers.interpolate使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在maskrcnn_benchmark.layers的用法示例。


在下文中一共展示了layers.interpolate方法的4个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: forward

# 需要导入模块: from maskrcnn_benchmark import layers [as 别名]
# 或者: from maskrcnn_benchmark.layers import interpolate [as 别名]
def forward(self, x):
        x = self.kps_score_lowres(x)
        x = layers.interpolate(
            x, scale_factor=self.up_scale, mode="bilinear", align_corners=False
        )
        return x 
开发者ID:Res2Net,项目名称:Res2Net-maskrcnn,代码行数:8,代码来源:roi_keypoint_predictors.py

示例2: create_mask_montage

# 需要导入模块: from maskrcnn_benchmark import layers [as 别名]
# 或者: from maskrcnn_benchmark.layers import interpolate [as 别名]
def create_mask_montage(self, image, predictions):
        """
        Create a montage showing the probability heatmaps for each one one of the
        detected objects

        Arguments:
            image (np.ndarray): an image as returned by OpenCV
            predictions (BoxList): the result of the computation by the model.
                It should contain the field `mask`.
        """
        masks = predictions.get_field("mask")
        masks_per_dim = self.masks_per_dim
        masks = L.interpolate(
            masks.float(), scale_factor=1 / masks_per_dim
        ).byte()
        height, width = masks.shape[-2:]
        max_masks = masks_per_dim ** 2
        masks = masks[:max_masks]
        # handle case where we have less detections than max_masks
        if len(masks) < max_masks:
            masks_padded = torch.zeros(max_masks, 1, height, width, dtype=torch.uint8)
            masks_padded[: len(masks)] = masks
            masks = masks_padded
        masks = masks.reshape(masks_per_dim, masks_per_dim, height, width)
        result = torch.zeros(
            (masks_per_dim * height, masks_per_dim * width), dtype=torch.uint8
        )
        for y in range(masks_per_dim):
            start_y = y * height
            end_y = (y + 1) * height
            for x in range(masks_per_dim):
                start_x = x * width
                end_x = (x + 1) * width
                result[start_y:end_y, start_x:end_x] = masks[y, x]
        return cv2.applyColorMap(result.numpy(), cv2.COLORMAP_JET) 
开发者ID:Res2Net,项目名称:Res2Net-maskrcnn,代码行数:37,代码来源:predictor.py

示例3: create_mask_montage

# 需要导入模块: from maskrcnn_benchmark import layers [as 别名]
# 或者: from maskrcnn_benchmark.layers import interpolate [as 别名]
def create_mask_montage(self, image, predictions):
        """
        Create a montage showing the probability heatmaps for each one one of the
        detected objects

        Arguments:
            image (np.ndarray): an image as returned by OpenCV
            predictions (BoxList): the result of the computation by the model.
                It should contain the field `mask`.
        """
        masks = predictions.get_field("mask")
        masks_per_dim = self.masks_per_dim
        masks = L.interpolate(
            masks.float(), scale_factor=1 / masks_per_dim
        ).byte()
        height, width = masks.shape[-2:]
        max_masks = masks_per_dim ** 2
        masks = masks[:max_masks]
        # handle case where we have less detections than max_masks
        if len(masks) < max_masks:
            masks_padded = torch.zeros(max_masks, 1, height, width, dtype=torch.bool)
            masks_padded[: len(masks)] = masks
            masks = masks_padded
        masks = masks.reshape(masks_per_dim, masks_per_dim, height, width)
        result = torch.zeros(
            (masks_per_dim * height, masks_per_dim * width), dtype=torch.bool
        )
        for y in range(masks_per_dim):
            start_y = y * height
            end_y = (y + 1) * height
            for x in range(masks_per_dim):
                start_x = x * width
                end_x = (x + 1) * width
                result[start_y:end_y, start_x:end_x] = masks[y, x]
        return cv2.applyColorMap(result.numpy(), cv2.COLORMAP_JET) 
开发者ID:Xiangyu-CAS,项目名称:R2CNN.pytorch,代码行数:37,代码来源:predictor.py

示例4: create_mask_montage

# 需要导入模块: from maskrcnn_benchmark import layers [as 别名]
# 或者: from maskrcnn_benchmark.layers import interpolate [as 别名]
def create_mask_montage(self, image, predictions):
        """
        Create a montage showing the probability heatmaps for each one one of the
        detected objects

        Arguments:
            image (np.ndarray): an image as returned by OpenCV
            predictions (BoxList): the result of the computation by the model.
                It should contain the field `mask`.
        """
        masks = predictions.get_field("mask")
        masks_per_dim = self.masks_per_dim
        masks = L.interpolate(
            masks.float(), scale_factor=1 / masks_per_dim
        ).byte()
        height, width = masks.shape[-2:]
        max_masks = masks_per_dim ** 2
        masks = masks[:max_masks]
        # handle case where we have less detections than max_masks
        if len(masks) < max_masks:
            masks_padded = torch.zeros(
                max_masks, 1, height, width, dtype=torch.uint8)
            masks_padded[: len(masks)] = masks
            masks = masks_padded
        masks = masks.reshape(masks_per_dim, masks_per_dim, height, width)
        result = torch.zeros(
            (masks_per_dim * height, masks_per_dim * width), dtype=torch.uint8
        )
        for y in range(masks_per_dim):
            start_y = y * height
            end_y = (y + 1) * height
            for x in range(masks_per_dim):
                start_x = x * width
                end_x = (x + 1) * width
                result[start_y:end_y, start_x:end_x] = masks[y, x]
        return cv2.applyColorMap(result.numpy(), cv2.COLORMAP_JET) 
开发者ID:HuangQinJian,项目名称:DF-Traffic-Sign-Identification,代码行数:38,代码来源:predictor.py


注:本文中的maskrcnn_benchmark.layers.interpolate方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。