darkliang
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
import torch
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from ..base_model import BaseModel
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from ..modules import SetBlockWrapper, HorizontalPoolingPyramid, PackSequenceWrapper, SeparateFCs, SeparateBNNecks
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class Baseline(BaseModel):
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def build_network(self, model_cfg):
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self.Backbone = self.get_backbone(model_cfg['backbone_cfg'])
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self.Backbone = SetBlockWrapper(self.Backbone)
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self.FCs = SeparateFCs(**model_cfg['SeparateFCs'])
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self.BNNecks = SeparateBNNecks(**model_cfg['SeparateBNNecks'])
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self.TP = PackSequenceWrapper(torch.max)
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self.HPP = HorizontalPoolingPyramid(bin_num=model_cfg['bin_num'])
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def forward(self, inputs):
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ipts, labs, _, _, seqL = inputs
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sils = ipts[0]
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if len(sils.size()) == 4:
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sils = sils.unsqueeze(2)
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del ipts
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outs = self.Backbone(sils) # [n, s, c, h, w]
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# Temporal Pooling, TP
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outs = self.TP(outs, seqL, dim=1)[0] # [n, c, h, w]
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# Horizontal Pooling Matching, HPM
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feat = self.HPP(outs) # [n, c, p]
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feat = feat.permute(2, 0, 1).contiguous() # [p, n, c]
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embed_1 = self.FCs(feat) # [p, n, c]
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embed_2, logits = self.BNNecks(embed_1) # [p, n, c]
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embed_1 = embed_1.permute(1, 0, 2).contiguous() # [n, p, c]
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embed_2 = embed_2.permute(1, 0, 2).contiguous() # [n, p, c]
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logits = logits.permute(1, 0, 2).contiguous() # [n, p, c]
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embed = embed_1
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n, s, _, h, w = sils.size()
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retval = {
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'training_feat': {
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'triplet': {'embeddings': embed_1, 'labels': labs},
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'softmax': {'logits': logits, 'labels': labs}
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},
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'visual_summary': {
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'image/sils': sils.view(n*s, 1, h, w)
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},
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'inference_feat': {
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'embeddings': embed
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}
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}
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return retval
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