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import torch |
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from torch import nn |
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from torchvision.ops import roi_align |
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def convert_to_roi_format(lines_box): |
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concat_boxes = torch.cat(lines_box, dim=0) |
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device, dtype = concat_boxes.device, concat_boxes.dtype |
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ids = torch.cat( |
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[ |
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torch.full((lines_box_pi.shape[0], 1), i, dtype=dtype, device=device) |
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for i, lines_box_pi in enumerate(lines_box) |
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], |
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dim=0 |
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) |
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rois = torch.cat([ids, concat_boxes], dim=1) |
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return rois |
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class RoiFeatExtraxtor(nn.Module): |
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def __init__(self, scale, pool_size, input_dim, output_dim): |
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super().__init__() |
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self.scale = scale |
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self.pool_size = pool_size |
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self.output_dim = output_dim |
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input_dim = input_dim * self.pool_size[0] * self.pool_size[1] |
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self.fc = nn.Sequential( |
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nn.Linear(input_dim, self.output_dim), |
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nn.ReLU(), |
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nn.Linear(self.output_dim, self.output_dim) |
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) |
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def forward(self, feats, lines_box): |
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rois = convert_to_roi_format(lines_box) |
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lines_feat = roi_align( |
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input=feats, |
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boxes=rois, |
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output_size=self.pool_size, |
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spatial_scale=self.scale, |
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sampling_ratio=2 |
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) |
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lines_feat = lines_feat.reshape(lines_feat.shape[0], -1) |
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lines_feat = self.fc(lines_feat) |
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lines_feat = torch.split(lines_feat, [item.shape[0] for item in lines_box]) |
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return list(lines_feat) |
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class RoiPosFeatExtraxtor(nn.Module): |
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def __init__(self, scale, pool_size, input_dim, output_dim): |
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super().__init__() |
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self.scale = scale |
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self.pool_size = pool_size |
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self.output_dim = output_dim |
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input_dim = input_dim * self.pool_size[0] * self.pool_size[1] |
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self.fc = nn.Sequential( |
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nn.Linear(input_dim, self.output_dim), |
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nn.ReLU(), |
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nn.Linear(self.output_dim, self.output_dim) |
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) |
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self.bbox_ln = nn.LayerNorm(self.output_dim) |
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self.bbox_tranform = nn.Linear(4, self.output_dim) |
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self.add_ln = nn.LayerNorm(self.output_dim) |
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def forward(self, feats, lines_box, img_sizes): |
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rois = convert_to_roi_format(lines_box) |
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lines_feat = roi_align( |
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input=feats, |
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boxes=rois, |
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output_size=self.pool_size, |
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spatial_scale=self.scale, |
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sampling_ratio=2 |
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) |
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lines_feat = lines_feat.reshape(lines_feat.shape[0], -1) |
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lines_feat = self.fc(lines_feat) |
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lines_feat = list(torch.split(lines_feat, [item.shape[0] for item in lines_box])) |
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feats_H, feats_W = feats.shape[-2:] |
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for idx, (line_box, img_size) in enumerate(zip(lines_box, img_sizes)): |
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line_box[:, 0] = line_box[:, 0] * self.scale / feats_W |
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line_box[:, 1] = line_box[:, 1] * self.scale / feats_H |
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line_box[:, 2] = line_box[:, 2] * self.scale / feats_W |
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line_box[:, 3] = line_box[:, 3] * self.scale / feats_H |
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lines_feat[idx] = self.add_ln(lines_feat[idx] + self.bbox_ln(self.bbox_tranform(line_box))) |
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return list(lines_feat) |
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