Jingzhe Ma
192 lines
7.1 KiB
Python
192 lines
7.1 KiB
Python
import torch
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import numpy as np
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import torch.nn as nn
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import torch.nn.functional as F
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from ..base_model import BaseModel
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from ..modules import HorizontalPoolingPyramid, PackSequenceWrapper, SeparateFCs, SeparateBNNecks, SetBlockWrapper, conv3x3, conv1x1, BasicBlock2D, BasicBlockP3D
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from einops import rearrange
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import copy
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class SkeletonGaitPP(BaseModel):
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def build_network(self, model_cfg):
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#B, C = [1, 4, 4, 1], 2
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in_C, B, C = model_cfg['Backbone']['in_channels'], model_cfg['Backbone']['blocks'], model_cfg['Backbone']['C']
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self.inference_use_emb = model_cfg['use_emb2'] if 'use_emb2' in model_cfg else False
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self.inplanes = 32 * C
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self.sil_layer0 = SetBlockWrapper(nn.Sequential(
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conv3x3(1, self.inplanes, 1),
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nn.BatchNorm2d(self.inplanes),
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nn.ReLU(inplace=True)
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))
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self.map_layer0 = SetBlockWrapper(nn.Sequential(
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conv3x3(2, self.inplanes, 1),
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nn.BatchNorm2d(self.inplanes),
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nn.ReLU(inplace=True)
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))
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self.sil_layer1 = SetBlockWrapper(self.make_layer(BasicBlock2D, 32 * C, stride=[1, 1], blocks_num=B[0], mode='2d'))
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self.map_layer1 = copy.deepcopy(self.sil_layer1)
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self.fusion = AttentionFusion(32 * C)
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self.layer2 = self.make_layer(BasicBlockP3D, 64 * C, stride=[2, 2], blocks_num=B[1], mode='p3d')
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self.layer3 = self.make_layer(BasicBlockP3D, 128 * C, stride=[2, 2], blocks_num=B[2], mode='p3d')
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self.layer4 = self.make_layer(BasicBlockP3D, 256 * C, stride=[1, 1], blocks_num=B[3], mode='p3d')
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self.FCs = SeparateFCs(16, 256*C, 128*C)
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self.BNNecks = SeparateBNNecks(16, 128*C, class_num=model_cfg['SeparateBNNecks']['class_num'])
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self.TP = PackSequenceWrapper(torch.max)
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self.HPP = HorizontalPoolingPyramid(bin_num=[16])
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def make_layer(self, block, planes, stride, blocks_num, mode='2d'):
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if max(stride) > 1 or self.inplanes != planes * block.expansion:
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if mode == '3d':
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downsample = nn.Sequential(nn.Conv3d(self.inplanes, planes * block.expansion, kernel_size=[1, 1, 1], stride=stride, padding=[0, 0, 0], bias=False), nn.BatchNorm3d(planes * block.expansion))
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elif mode == '2d':
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downsample = nn.Sequential(conv1x1(self.inplanes, planes * block.expansion, stride=stride), nn.BatchNorm2d(planes * block.expansion))
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elif mode == 'p3d':
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downsample = nn.Sequential(nn.Conv3d(self.inplanes, planes * block.expansion, kernel_size=[1, 1, 1], stride=[1, *stride], padding=[0, 0, 0], bias=False), nn.BatchNorm3d(planes * block.expansion))
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else:
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raise TypeError('xxx')
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else:
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downsample = lambda x: x
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layers = [block(self.inplanes, planes, stride=stride, downsample=downsample)]
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self.inplanes = planes * block.expansion
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s = [1, 1] if mode in ['2d', 'p3d'] else [1, 1, 1]
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for i in range(1, blocks_num):
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layers.append(
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block(self.inplanes, planes, stride=s)
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)
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return nn.Sequential(*layers)
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def inputs_pretreament(self, inputs):
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### Ensure the same data augmentation for heatmap and silhouette
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pose_sils = inputs[0]
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new_data_list = []
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for pose, sil in zip(pose_sils[0], pose_sils[1]):
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sil = sil[:, np.newaxis, ...] # [T, 1, H, W]
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pose_h, pose_w = pose.shape[-2], pose.shape[-1]
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sil_h, sil_w = sil.shape[-2], sil.shape[-1]
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if sil_h != sil_w and pose_h == pose_w:
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cutting = (sil_h - sil_w) // 2
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pose = pose[..., cutting:-cutting]
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cat_data = np.concatenate([pose, sil], axis=1) # [T, 3, H, W]
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new_data_list.append(cat_data)
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new_inputs = [[new_data_list], inputs[1], inputs[2], inputs[3], inputs[4]]
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return super().inputs_pretreament(new_inputs)
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def forward(self, inputs):
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ipts, labs, _, _, seqL = inputs
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pose = ipts[0]
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pose = pose.transpose(1, 2).contiguous()
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assert pose.size(-1) in [44, 48, 88, 96]
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maps = pose[:, :2, ...]
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sils = pose[:, -1, ...].unsqueeze(1)
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del ipts
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map0 = self.map_layer0(maps)
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map1 = self.map_layer1(map0)
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sil0 = self.sil_layer0(sils)
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sil1 = self.sil_layer1(sil0)
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out1 = self.fusion(sil1, map1)
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out2 = self.layer2(out1)
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out3 = self.layer3(out2)
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out4 = self.layer4(out3) # [n, c, s, h, w]
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# Temporal Pooling, TP
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outs = self.TP(out4, seqL, options={"dim": 2})[0] # [n, c, h, w]
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n, c, h, w = outs.size()
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# Horizontal Pooling Matching, HPM
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feat = self.HPP(outs) # [n, c, p]
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embed_1 = self.FCs(feat) # [n, c, p]
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embed_2, logits = self.BNNecks(embed_1) # [n, c, p]
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if self.inference_use_emb:
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embed = embed_2
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else:
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embed = embed_1
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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': rearrange(pose * 255., 'n c s h w -> (n s) c 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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class AttentionFusion(nn.Module):
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def __init__(self, in_channels=64, squeeze_ratio=16):
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super(AttentionFusion, self).__init__()
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hidden_dim = int(in_channels / squeeze_ratio)
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self.conv = SetBlockWrapper(
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nn.Sequential(
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conv1x1(in_channels * 2, hidden_dim),
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nn.BatchNorm2d(hidden_dim),
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nn.ReLU(inplace=True),
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conv3x3(hidden_dim, hidden_dim),
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nn.BatchNorm2d(hidden_dim),
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nn.ReLU(inplace=True),
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conv1x1(hidden_dim, in_channels * 2),
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)
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)
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def forward(self, sil_feat, map_feat):
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'''
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sil_feat: [n, c, s, h, w]
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map_feat: [n, c, s, h, w]
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'''
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c = sil_feat.size(1)
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feats = torch.cat([sil_feat, map_feat], dim=1)
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score = self.conv(feats) # [n, 2 * c, s, h, w]
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score = rearrange(score, 'n (d c) s h w -> n d c s h w', d=2)
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score = F.softmax(score, dim=1)
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retun = sil_feat * score[:, 0] + map_feat * score[:, 1]
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return retun
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class CatFusion(nn.Module):
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def __init__(self, in_channels=64):
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super(CatFusion, self).__init__()
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self.conv = SetBlockWrapper(
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nn.Sequential(
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conv1x1(in_channels * 2, in_channels),
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)
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)
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def forward(self, sil_feat, map_feat):
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'''
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sil_feat: [n, c, s, h, w]
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map_feat: [n, c, s, h, w]
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'''
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feats = torch.cat([sil_feat, map_feat])
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retun = self.conv(feats)
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return retun
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class PlusFusion(nn.Module):
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def __init__(self):
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super(PlusFusion, self).__init__()
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def forward(self, sil_feat, map_feat):
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'''
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sil_feat: [n, c, s, h, w]
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map_feat: [n, c, s, h, w]
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'''
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return sil_feat + map_feat
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