Dongyang Jin
218 lines
7.7 KiB
Python
218 lines
7.7 KiB
Python
import torch
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import torch.nn as nn
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import os
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import numpy as np
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import os.path as osp
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import matplotlib.pyplot as plt
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from ..base_model import BaseModel
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from ..modules import SetBlockWrapper, HorizontalPoolingPyramid, PackSequenceWrapper, SeparateFCs, SeparateBNNecks, conv1x1, conv3x3, BasicBlock2D, BasicBlockP3D, BasicBlock3D
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import torch.nn.functional as F
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from einops import rearrange
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import copy
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import cv2
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from kornia import morphology as morph
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blocks_map = {
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'2d': BasicBlock2D,
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'p3d': BasicBlockP3D,
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'3d': BasicBlock3D
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}
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class MultiGaitpp(BaseModel):
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def build_network(self, model_cfg):
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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.part1 = model_cfg['Backbone']['part1_channel']
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self.part2 = model_cfg['Backbone']['part2_channel']
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self.inplanes = 32 * C
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self.part1_layer0 = SetBlockWrapper(nn.Sequential(
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conv3x3(self.part1, 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.part2_layer0 = SetBlockWrapper(nn.Sequential(
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conv3x3(self.part2, 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.part1_layer1 = SetBlockWrapper(self.make_layer(BasicBlock2D, 32 * C, stride=[1, 1], blocks_num=B[0], mode='2d'))
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self.part2_layer1 = copy.deepcopy(self.part1_layer1)
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self.fusion = CatFusion(256)
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self.part1_layer2 = self.make_layer(BasicBlockP3D, 64 * C, stride=[2, 2], blocks_num=B[1], mode='p3d')
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self.part2_layer2 = copy.deepcopy(self.part1_layer2)
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self.layer2 = copy.deepcopy(self.part1_layer2)
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self.part1_layer3 = self.make_layer(BasicBlockP3D, 128 * C, stride=[2, 2], blocks_num=B[2], mode='p3d')
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self.part2_layer3 = copy.deepcopy(self.part1_layer3)
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self.layer3 = copy.deepcopy(self.part1_layer3)
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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.csquare = CSquare(64)
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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 forward(self, inputs):
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ipts, labs, _, _, seqL = inputs
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if len(ipts[0].size()) == 4:
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ipts = ipts[0].unsqueeze(1)
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else:
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ipts = ipts[0].transpose(1, 2).contiguous()
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part1 = ipts[:, :self.part1, ...]
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part2 = ipts[:, self.part1:, ...]
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del ipts
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part2 = self.part2_layer0(part2)
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part2 = self.part2_layer1(part2)
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part1 = self.part1_layer0(part1)
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part1 = self.part1_layer1(part1)
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out, attn1, attn2, attn_co = self.csquare(part2,part1)
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part2 = self.part2_layer2(part2*attn1)
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part1 = self.part1_layer2(part1*attn2)
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out = self.layer2(out)
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part2 = self.part2_layer3(part2)
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part1 = self.part1_layer3(part1)
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out = self.layer3(out)
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out = self.fusion([part1, out, part2])
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out = self.layer4(out)
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outs = self.TP(out, seqL, options={"dim": 2})[0] # [n, c, h, w]
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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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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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},
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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 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 * 3, in_channels),
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)
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)
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def forward(self, feat_list):
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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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# print(feat_list.shape)
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feats = torch.cat(feat_list, dim=1)
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retun = self.conv(feats)
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return retun
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class CSquare(nn.Module):
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def __init__(self, in_channels=64, squeeze_ratio=16, h=32, w=22):
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super(CSquare, self).__init__()
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hidden_dim = int(in_channels / squeeze_ratio)
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self.TP_mean = PackSequenceWrapper(torch.mean)
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self.conv2 = SetBlockWrapper(nn.Sequential(
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conv1x1(in_channels, 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, 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),
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))
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self.conv1 = SetBlockWrapper(nn.Sequential(
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conv1x1(in_channels, 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, 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),
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))
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self.kernel = torch.ones((3,3))
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def channel_normalization(self, masked_attn):
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min_vals = masked_attn.min(dim=1, keepdim=True).values
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max_vals = masked_attn.max(dim=1, keepdim=True).values
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min_vals = min_vals.expand_as(masked_attn)
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max_vals = max_vals.expand_as(masked_attn)
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attn_norm = (masked_attn - min_vals) / (max_vals - min_vals + 1e-6)
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attn_norm = attn_norm.clamp(0, 1)
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return attn_norm
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def forward(self, x1, x2):
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'''
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x1 [n, c, s, h, w]
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x2 [n, c, s, h, w] shape
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'''
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t = x2.size(2)
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attn_x2 = self.conv2(x2)
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n, c, t, h, w = attn_x2.size()
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attn_x1 = self.conv1(x1) # [n, c, h, w]
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attn_x = torch.stack((attn_x1, attn_x2), dim=1)
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attn_x = F.softmax(attn_x, dim=1)
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attn_x1_softmax = attn_x[:, 0, ...]
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attn_x2_softmax = attn_x[:, 1, ...]
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attn_ = torch.min(attn_x1_softmax,attn_x2_softmax) #* mask
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attn = self.channel_normalization(attn_)
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attn_co = rearrange(attn, 'n c s h w -> (n s) c h w')
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return (x1+x2)/2 * attn, (1.-attn)*attn_x1_softmax, (1.-attn)*attn_x2_softmax, attn_co #87.2
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