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- import torch
- from torch import nn
- from torch.nn import init
- import torch.nn.functional as F
- import math
- from torch.autograd import Variable
- import numpy as np
- from resnet import resnet50
- from vgg import vgg16
- config_vgg = {'convert': [[128,256,512,512,512],[64,128,256,512,512]], 'merge1': [[128, 256, 128, 3,1], [256, 512, 256, 3, 1], [512, 0, 512, 5, 2], [512, 0, 512, 5, 2],[512, 0, 512, 7, 3]], 'merge2': [[128], [256, 512, 512, 512]]} # no convert layer, no conv6
- config_resnet = {'convert': [[64,256,512,1024,2048],[128,256,512,512,512]], 'deep_pool': [[512, 512, 256, 256, 128], [512, 256, 256, 128, 128], [False, True, True, True, False], [True, True, True, True, False]], 'score': 256, 'edgeinfo':[[16, 16, 16, 16], 128, [16,8,4,2]],'edgeinfoc':[64,128], 'block': [[512, [16]], [256, [16]], [256, [16]], [128, [16]]], 'fuse': [[16, 16, 16, 16], True], 'fuse_ratio': [[16,1], [8,1], [4,1], [2,1]], 'merge1': [[128, 256, 128, 3,1], [256, 512, 256, 3, 1], [512, 0, 512, 5, 2], [512, 0, 512, 5, 2],[512, 0, 512, 7, 3]], 'merge2': [[128], [256, 512, 512, 512]]}
- class ConvertLayer(nn.Module):
- def __init__(self, list_k):
- super(ConvertLayer, self).__init__()
- up0, up1, up2 = [], [], []
- for i in range(len(list_k[0])):
-
- up0.append(nn.Sequential(nn.Conv2d(list_k[0][i], list_k[1][i], 1, 1, bias=False), nn.ReLU(inplace=True)))
- self.convert0 = nn.ModuleList(up0)
- def forward(self, list_x):
- resl = []
- for i in range(len(list_x)):
- resl.append(self.convert0[i](list_x[i]))
- return resl
-
- class MergeLayer1(nn.Module): # list_k: [[64, 512, 64], [128, 512, 128], [256, 0, 256] ... ]
- def __init__(self, list_k):
- super(MergeLayer1, self).__init__()
- self.list_k = list_k
- trans, up, score = [], [], []
- for ik in list_k:
- if ik[1] > 0:
- trans.append(nn.Sequential(nn.Conv2d(ik[1], ik[0], 1, 1, bias=False), nn.ReLU(inplace=True)))
-
- up.append(nn.Sequential(nn.Conv2d(ik[0], ik[2], ik[3], 1, ik[4]), nn.ReLU(inplace=True), nn.Conv2d(ik[2], ik[2], ik[3], 1, ik[4]), nn.ReLU(inplace=True), nn.Conv2d(ik[2], ik[2], ik[3], 1, ik[4]), nn.ReLU(inplace=True)))
- score.append(nn.Conv2d(ik[2], 1, 3, 1, 1))
- trans.append(nn.Sequential(nn.Conv2d(512, 128, 1, 1, bias=False), nn.ReLU(inplace=True)))
- self.trans, self.up, self.score = nn.ModuleList(trans), nn.ModuleList(up), nn.ModuleList(score)
- self.relu =nn.ReLU()
- def forward(self, list_x, x_size):
- up_edge, up_sal, edge_feature, sal_feature = [], [], [], []
-
-
- num_f = len(list_x)
- tmp = self.up[num_f - 1](list_x[num_f-1])
- sal_feature.append(tmp)
- U_tmp = tmp
- up_sal.append(F.interpolate(self.score[num_f - 1](tmp), x_size, mode='bilinear', align_corners=True))
-
- for j in range(2, num_f ):
- i = num_f - j
-
- if list_x[i].size()[1] < U_tmp.size()[1]:
- U_tmp = list_x[i] + F.interpolate((self.trans[i](U_tmp)), list_x[i].size()[2:], mode='bilinear', align_corners=True)
- else:
- U_tmp = list_x[i] + F.interpolate((U_tmp), list_x[i].size()[2:], mode='bilinear', align_corners=True)
-
-
-
-
-
- tmp = self.up[i](U_tmp)
- U_tmp = tmp
- sal_feature.append(tmp)
- up_sal.append(F.interpolate(self.score[i](tmp), x_size, mode='bilinear', align_corners=True))
- U_tmp = list_x[0] + F.interpolate((self.trans[-1](sal_feature[0])), list_x[0].size()[2:], mode='bilinear', align_corners=True)
- tmp = self.up[0](U_tmp)
- edge_feature.append(tmp)
-
- up_edge.append(F.interpolate(self.score[0](tmp), x_size, mode='bilinear', align_corners=True))
- return up_edge, edge_feature, up_sal, sal_feature
-
- class MergeLayer2(nn.Module):
- def __init__(self, list_k):
- super(MergeLayer2, self).__init__()
- self.list_k = list_k
- trans, up, score = [], [], []
- for i in list_k[0]:
- tmp = []
- tmp_up = []
- tmp_score = []
- feature_k = [[3,1],[5,2], [5,2], [7,3]]
- for idx, j in enumerate(list_k[1]):
- tmp.append(nn.Sequential(nn.Conv2d(j, i, 1, 1, bias=False), nn.ReLU(inplace=True)))
- tmp_up.append(nn.Sequential(nn.Conv2d(i , i, feature_k[idx][0], 1, feature_k[idx][1]), nn.ReLU(inplace=True), nn.Conv2d(i, i, feature_k[idx][0],1 , feature_k[idx][1]), nn.ReLU(inplace=True), nn.Conv2d(i, i, feature_k[idx][0], 1, feature_k[idx][1]), nn.ReLU(inplace=True)))
- tmp_score.append(nn.Conv2d(i, 1, 3, 1, 1))
- trans.append(nn.ModuleList(tmp))
- up.append(nn.ModuleList(tmp_up))
- score.append(nn.ModuleList(tmp_score))
-
- self.trans, self.up, self.score = nn.ModuleList(trans), nn.ModuleList(up), nn.ModuleList(score)
- self.final_score = nn.Sequential(nn.Conv2d(list_k[0][0], list_k[0][0], 5, 1, 2), nn.ReLU(inplace=True), nn.Conv2d(list_k[0][0], 1, 3, 1, 1))
- self.relu =nn.ReLU()
- def forward(self, list_x, list_y, x_size):
- up_score, tmp_feature = [], []
- list_y = list_y[::-1]
-
- for i, i_x in enumerate(list_x):
- for j, j_x in enumerate(list_y):
- tmp = F.interpolate(self.trans[i][j](j_x), i_x.size()[2:], mode='bilinear', align_corners=True) + i_x
- tmp_f = self.up[i][j](tmp)
- up_score.append(F.interpolate(self.score[i][j](tmp_f), x_size, mode='bilinear', align_corners=True))
- tmp_feature.append(tmp_f)
-
- tmp_fea = tmp_feature[0]
- for i_fea in range(len(tmp_feature) - 1):
- tmp_fea = self.relu(torch.add(tmp_fea, F.interpolate((tmp_feature[i_fea+1]), tmp_feature[0].size()[2:], mode='bilinear', align_corners=True)))
- up_score.append(F.interpolate(self.final_score(tmp_fea), x_size, mode='bilinear', align_corners=True))
-
- return up_score
-
- # extra part
- def extra_layer(base_model_cfg, vgg):
- if base_model_cfg == 'vgg':
- config = config_vgg
- elif base_model_cfg == 'resnet':
- config = config_resnet
- merge1_layers = MergeLayer1(config['merge1'])
- merge2_layers = MergeLayer2(config['merge2'])
- return vgg, merge1_layers, merge2_layers
- # TUN network
- class TUN_bone(nn.Module):
- def __init__(self, base_model_cfg, base, merge1_layers, merge2_layers):
- super(TUN_bone, self).__init__()
- self.base_model_cfg = base_model_cfg
- if self.base_model_cfg == 'vgg':
- self.base = base
- # self.base_ex = nn.ModuleList(base_ex)
- self.merge1 = merge1_layers
- self.merge2 = merge2_layers
- elif self.base_model_cfg == 'resnet':
- self.convert = ConvertLayer(config_resnet['convert'])
- self.base = base
- self.merge1 = merge1_layers
- self.merge2 = merge2_layers
- def forward(self, x):
- x_size = x.size()[2:]
- conv2merge = self.base(x)
- if self.base_model_cfg == 'resnet':
- conv2merge = self.convert(conv2merge)
- up_edge, edge_feature, up_sal, sal_feature = self.merge1(conv2merge, x_size)
- up_sal_final = self.merge2(edge_feature, sal_feature, x_size)
- return up_edge, up_sal, up_sal_final
- # build the whole network
- def build_model(base_model_cfg='vgg'):
- if base_model_cfg == 'vgg':
- return TUN_bone(base_model_cfg, *extra_layer(base_model_cfg, vgg16()))
- elif base_model_cfg == 'resnet':
- return TUN_bone(base_model_cfg, *extra_layer(base_model_cfg, resnet50()))
- # weight init
- def xavier(param):
- # init.xavier_uniform(param)
- init.xavier_uniform_(param)
- def weights_init(m):
- if isinstance(m, nn.Conv2d):
- # xavier(m.weight.data)
- m.weight.data.normal_(0, 0.01)
- if m.bias is not None:
- m.bias.data.zero_()
- if __name__ == '__main__':
- from torch.autograd import Variable
- net = TUN(*extra_layer(vgg(base['tun'], 3), vgg(base['tun_ex'], 512), config['merge_block'], config['fuse'])).cuda()
- img = Variable(torch.randn((1, 3, 256, 256))).cuda()
- out = net(img, mode = 2)
- print(len(out))
- print(len(out[0]))
- print(out[0].shape)
- print(len(out[1]))
- # print(net)
- input('Press Any to Continue...')
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