29 lines
1.1 KiB
Python
29 lines
1.1 KiB
Python
import torch
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import torchvision
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from collections import OrderedDict
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# featmap_names (List[str]): the names of the feature maps that will be used
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# for the pooling.
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# output_size (List[Tuple[int, int]] or List[int]): output size for the pooled region
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# sampling_ratio (int): sampling ratio for ROIAlign
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# canonical_scale (int, optional): canonical_scale for LevelMapper
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# canonical_level (int, optional): canonical_level for LevelMapper
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# 依次是要处理的特征图名字、输出尺寸、采样系数
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roi = torchvision.ops.MultiScaleRoIAlign(['feat1', 'feat3'], 5, 2)
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i = OrderedDict()
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# 构建仿真的特征
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i['feat1'] = torch.rand(1, 5, 64, 64)
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# this feature won't be used in the pooling
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i['feat2'] = torch.rand(1, 5, 32, 32)
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i['feat3'] = torch.rand(1, 5, 16, 16)
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# 创建随机的矩形框
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boxes = torch.rand(6, 4) * 256; boxes[:, 2:] += boxes[:, :2]
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# original image size, before computing the feature maps
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image_sizes = [(512, 512)]
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output = roi(i, [boxes], image_sizes)
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print(output.shape)
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#print(output)
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# 6个矩形框、5个通道、3x3是怎么来的？
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# torch.Size([6, 5, 3, 3]) |