import torch import torch.nn as nn import torch.nn.functional as F import timm import math from .submodule import * class ResidualBlock(nn.Module): def __init__(self, in_planes, planes, norm_fn='group', stride=1): super(ResidualBlock, self).__init__() self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1) self.relu = nn.ReLU(inplace=True) num_groups = planes // 8 if norm_fn == 'group': self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) if not (stride == 1 and in_planes == planes): self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) elif norm_fn == 'batch': self.norm1 = nn.BatchNorm2d(planes) self.norm2 = nn.BatchNorm2d(planes) if not (stride == 1 and in_planes == planes): self.norm3 = nn.BatchNorm2d(planes) elif norm_fn == 'instance': self.norm1 = nn.InstanceNorm2d(planes) self.norm2 = nn.InstanceNorm2d(planes) if not (stride == 1 and in_planes == planes): self.norm3 = nn.InstanceNorm2d(planes) elif norm_fn == 'none': self.norm1 = nn.Sequential() self.norm2 = nn.Sequential() if not (stride == 1 and in_planes == planes): self.norm3 = nn.Sequential() if stride == 1 and in_planes == planes: self.downsample = None else: self.downsample = nn.Sequential( nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3) def forward(self, x): y = x y = self.conv1(y) y = self.norm1(y) y = self.relu(y) y = self.conv2(y) y = self.norm2(y) y = self.relu(y) if self.downsample is not None: x = self.downsample(x) return self.relu(x+y) class MultiBasicEncoder(nn.Module): def __init__(self, output_dim=[128], norm_fn='batch', dropout=0.0, downsample=3): super(MultiBasicEncoder, self).__init__() self.norm_fn = norm_fn self.downsample = downsample if self.norm_fn == 'group': self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64) elif self.norm_fn == 'batch': self.norm1 = nn.BatchNorm2d(64) elif self.norm_fn == 'instance': self.norm1 = nn.InstanceNorm2d(64) elif self.norm_fn == 'none': self.norm1 = nn.Sequential() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=1 + (downsample > 2), padding=3) self.relu1 = nn.ReLU(inplace=True) self.in_planes = 64 self.layer1 = self._make_layer(64, stride=1) self.layer2 = self._make_layer(96, stride=1 + (downsample > 1)) self.layer3 = self._make_layer(128, stride=1 + (downsample > 0)) self.layer4 = self._make_layer(128, stride=2) self.layer5 = self._make_layer(128, stride=2) output_list = [] for dim in output_dim: conv_out = nn.Sequential( ResidualBlock(128, 128, self.norm_fn, stride=1), nn.Conv2d(128, dim[2], 3, padding=1)) output_list.append(conv_out) self.outputs04 = nn.ModuleList(output_list) output_list = [] for dim in output_dim: conv_out = nn.Sequential( ResidualBlock(128, 128, self.norm_fn, stride=1), nn.Conv2d(128, dim[1], 3, padding=1)) output_list.append(conv_out) self.outputs08 = nn.ModuleList(output_list) output_list = [] for dim in output_dim: conv_out = nn.Conv2d(128, dim[0], 3, padding=1) output_list.append(conv_out) self.outputs16 = nn.ModuleList(output_list) if dropout > 0: self.dropout = nn.Dropout2d(p=dropout) else: self.dropout = None for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)): if m.weight is not None: nn.init.constant_(m.weight, 1) if m.bias is not None: nn.init.constant_(m.bias, 0) def _make_layer(self, dim, stride=1): layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride) layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1) layers = (layer1, layer2) self.in_planes = dim return nn.Sequential(*layers) def forward(self, x, dual_inp=False, num_layers=3): x = self.conv1(x) x = self.norm1(x) x = self.relu1(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) if dual_inp: v = x x = x[:(x.shape[0]//2)] outputs04 = [f(x) for f in self.outputs04] if num_layers == 1: return (outputs04, v) if dual_inp else (outputs04,) y = self.layer4(x) outputs08 = [f(y) for f in self.outputs08] if num_layers == 2: return (outputs04, outputs08, v) if dual_inp else (outputs04, outputs08) z = self.layer5(y) outputs16 = [f(z) for f in self.outputs16] return (outputs04, outputs08, outputs16, v) if dual_inp else (outputs04, outputs08, outputs16) class Feature(SubModule): def __init__(self): super(Feature, self).__init__() pretrained = True model = timm.create_model('mobilenetv2_100', pretrained=pretrained, features_only=True) layers = [1,2,3,5,6] chans = [16, 24, 32, 96, 160] self.conv_stem = model.conv_stem self.bn1 = model.bn1 self.block0 = torch.nn.Sequential(*model.blocks[0:layers[0]]) self.block1 = torch.nn.Sequential(*model.blocks[layers[0]:layers[1]]) self.block2 = torch.nn.Sequential(*model.blocks[layers[1]:layers[2]]) self.block3 = torch.nn.Sequential(*model.blocks[layers[2]:layers[3]]) self.block4 = torch.nn.Sequential(*model.blocks[layers[3]:layers[4]]) self.deconv32_16 = Conv2x_IN(chans[4], chans[3], deconv=True, concat=True) self.deconv16_8 = Conv2x_IN(chans[3]*2, chans[2], deconv=True, concat=True) self.deconv8_4 = Conv2x_IN(chans[2]*2, chans[1], deconv=True, concat=True) self.conv4 = BasicConv_IN(chans[1]*2, chans[1]*2, kernel_size=3, stride=1, padding=1) def forward(self, x): B, V, _, H, W = x.size() x = x.view(B * V, -1, H, W) #x = self.act1(self.bn1(self.conv_stem(x))) x = self.bn1(self.conv_stem(x)) x2 = self.block0(x) x4 = self.block1(x2) # return x4,x4,x4,x4 x8 = self.block2(x4) x16 = self.block3(x8) x32 = self.block4(x16) x16 = self.deconv32_16(x32, x16) x8 = self.deconv16_8(x16, x8) x4 = self.deconv8_4(x8, x4) x4 = self.conv4(x4) x4 = x4.view(B, V, -1, H // 4, W // 4) x8 = x8.view(B, V, -1, H // 8, W // 8) x16 = x16.view(B, V, -1, H // 16, W // 16) x32 = x32.view(B, V, -1, H // 32, W // 32) return [x4, x8, x16, x32]