79 lines
3.0 KiB
Python
79 lines
3.0 KiB
Python
from torch import nn
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import torch
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import torch.nn.functional as F
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from .util import Hourglass, make_coordinate_grid, AntiAliasInterpolation2d
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class SegmentationModule(nn.Module):
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"""
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Computing a segmentation map and affine transformations.
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"""
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def __init__(self, block_expansion, num_segments, num_channels, max_features,
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num_blocks, temperature, estimate_affine_part=False, scale_factor=1):
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super(SegmentationModule, self).__init__()
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self.predictor = Hourglass(block_expansion, in_features=num_channels,
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max_features=max_features, num_blocks=num_blocks)
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self.num_segments = num_segments
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self.shift = nn.Conv2d(in_channels=self.predictor.out_filters, out_channels=num_segments, kernel_size=(7, 7),
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padding=(3, 3))
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if estimate_affine_part:
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self.affine = nn.Conv2d(in_channels=self.predictor.out_filters,
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out_channels=4 * num_segments, kernel_size=(7, 7), padding=(3, 3))
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self.affine.weight.data.zero_()
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self.affine.bias.data.copy_(torch.tensor([1, 0, 0, 1] * num_segments, dtype=torch.float))
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else:
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self.affine = None
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self.segmentation = nn.Conv2d(in_channels=self.predictor.out_filters,
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out_channels=(1 + num_segments), kernel_size=(7, 7), padding=(3, 3))
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self.temperature = temperature
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self.scale_factor = scale_factor
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if self.scale_factor != 1:
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self.down = AntiAliasInterpolation2d(num_channels, self.scale_factor)
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def gaussian2kp(self, heatmap):
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"""
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Extract the mean and the variance from a heatmap
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"""
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shape = heatmap.shape
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heatmap = heatmap.unsqueeze(-1)
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grid = make_coordinate_grid(shape[2:], heatmap.type()).unsqueeze_(0).unsqueeze_(0)
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result = (heatmap * grid).sum(dim=(2, 3))
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return result
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def forward(self, x):
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if self.scale_factor != 1:
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x = self.down(x)
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out = {}
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feature_map = self.predictor(x)
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out['segmentation'] = F.softmax(self.segmentation(feature_map), dim=1)
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prediction = self.shift(feature_map)
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final_shape = prediction.shape
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heatmap = prediction.view(final_shape[0], final_shape[1], -1)
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heatmap = F.softmax(heatmap / self.temperature, dim=2)
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heatmap = heatmap.view(*final_shape)
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out['shift'] = self.gaussian2kp(heatmap)
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if self.affine is not None:
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affine_map = self.affine(feature_map)
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affine_map = affine_map.reshape(final_shape[0], self.num_segments, 4, final_shape[2],
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final_shape[3])
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heatmap = heatmap.unsqueeze(2)
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affine = heatmap * affine_map
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affine = affine.view(final_shape[0], final_shape[1], 4, -1)
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affine = affine.sum(dim=-1)
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affine = affine.view(affine.shape[0], affine.shape[1], 2, 2)
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out['affine'] = affine
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return out
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