| from collections import OrderedDict |
|
|
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from torchvision.models import resnet50, vgg16, vgg16_bn |
|
|
| from engine.BiRefNet.config import Config |
| from engine.BiRefNet.dataset import class_labels_TR_sorted |
| from engine.BiRefNet.models.backbones.build_backbone import build_backbone |
| from engine.BiRefNet.models.modules.decoder_blocks import BasicDecBlk |
| from engine.BiRefNet.models.modules.lateral_blocks import BasicLatBlk |
| from engine.BiRefNet.models.refinement.stem_layer import StemLayer |
|
|
|
|
| class RefinerPVTInChannels4(nn.Module): |
| def __init__(self, in_channels=3 + 1): |
| super(RefinerPVTInChannels4, self).__init__() |
| self.config = Config() |
| self.epoch = 1 |
| self.bb = build_backbone(self.config.bb, params_settings="in_channels=4") |
|
|
| lateral_channels_in_collection = { |
| "vgg16": [512, 256, 128, 64], |
| "vgg16bn": [512, 256, 128, 64], |
| "resnet50": [1024, 512, 256, 64], |
| "pvt_v2_b2": [512, 320, 128, 64], |
| "pvt_v2_b5": [512, 320, 128, 64], |
| "swin_v1_b": [1024, 512, 256, 128], |
| "swin_v1_l": [1536, 768, 384, 192], |
| } |
| channels = lateral_channels_in_collection[self.config.bb] |
| self.squeeze_module = BasicDecBlk(channels[0], channels[0]) |
|
|
| self.decoder = Decoder(channels) |
|
|
| if 0: |
| for key, value in self.named_parameters(): |
| if "bb." in key: |
| value.requires_grad = False |
|
|
| def forward(self, x): |
| if isinstance(x, list): |
| x = torch.cat(x, dim=1) |
| |
| if self.config.bb in ["vgg16", "vgg16bn", "resnet50"]: |
| x1 = self.bb.conv1(x) |
| x2 = self.bb.conv2(x1) |
| x3 = self.bb.conv3(x2) |
| x4 = self.bb.conv4(x3) |
| else: |
| x1, x2, x3, x4 = self.bb(x) |
|
|
| x4 = self.squeeze_module(x4) |
|
|
| |
|
|
| features = [x, x1, x2, x3, x4] |
| scaled_preds = self.decoder(features) |
|
|
| return scaled_preds |
|
|
|
|
| class Refiner(nn.Module): |
| def __init__(self, in_channels=3 + 1): |
| super(Refiner, self).__init__() |
| self.config = Config() |
| self.epoch = 1 |
| self.stem_layer = StemLayer( |
| in_channels=in_channels, |
| inter_channels=48, |
| out_channels=3, |
| norm_layer="BN" if self.config.batch_size > 1 else "LN", |
| ) |
| self.bb = build_backbone(self.config.bb) |
|
|
| lateral_channels_in_collection = { |
| "vgg16": [512, 256, 128, 64], |
| "vgg16bn": [512, 256, 128, 64], |
| "resnet50": [1024, 512, 256, 64], |
| "pvt_v2_b2": [512, 320, 128, 64], |
| "pvt_v2_b5": [512, 320, 128, 64], |
| "swin_v1_b": [1024, 512, 256, 128], |
| "swin_v1_l": [1536, 768, 384, 192], |
| } |
| channels = lateral_channels_in_collection[self.config.bb] |
| self.squeeze_module = BasicDecBlk(channels[0], channels[0]) |
|
|
| self.decoder = Decoder(channels) |
|
|
| if 0: |
| for key, value in self.named_parameters(): |
| if "bb." in key: |
| value.requires_grad = False |
|
|
| def forward(self, x): |
| if isinstance(x, list): |
| x = torch.cat(x, dim=1) |
| x = self.stem_layer(x) |
| |
| if self.config.bb in ["vgg16", "vgg16bn", "resnet50"]: |
| x1 = self.bb.conv1(x) |
| x2 = self.bb.conv2(x1) |
| x3 = self.bb.conv3(x2) |
| x4 = self.bb.conv4(x3) |
| else: |
| x1, x2, x3, x4 = self.bb(x) |
|
|
| x4 = self.squeeze_module(x4) |
|
|
| |
|
|
| features = [x, x1, x2, x3, x4] |
| scaled_preds = self.decoder(features) |
|
|
| return scaled_preds |
|
|
|
|
| class Decoder(nn.Module): |
| def __init__(self, channels): |
| super(Decoder, self).__init__() |
| self.config = Config() |
| DecoderBlock = eval("BasicDecBlk") |
| LateralBlock = eval("BasicLatBlk") |
|
|
| self.decoder_block4 = DecoderBlock(channels[0], channels[1]) |
| self.decoder_block3 = DecoderBlock(channels[1], channels[2]) |
| self.decoder_block2 = DecoderBlock(channels[2], channels[3]) |
| self.decoder_block1 = DecoderBlock(channels[3], channels[3] // 2) |
|
|
| self.lateral_block4 = LateralBlock(channels[1], channels[1]) |
| self.lateral_block3 = LateralBlock(channels[2], channels[2]) |
| self.lateral_block2 = LateralBlock(channels[3], channels[3]) |
|
|
| if self.config.ms_supervision: |
| self.conv_ms_spvn_4 = nn.Conv2d(channels[1], 1, 1, 1, 0) |
| self.conv_ms_spvn_3 = nn.Conv2d(channels[2], 1, 1, 1, 0) |
| self.conv_ms_spvn_2 = nn.Conv2d(channels[3], 1, 1, 1, 0) |
| self.conv_out1 = nn.Sequential(nn.Conv2d(channels[3] // 2, 1, 1, 1, 0)) |
|
|
| def forward(self, features): |
| x, x1, x2, x3, x4 = features |
| outs = [] |
| p4 = self.decoder_block4(x4) |
| _p4 = F.interpolate(p4, size=x3.shape[2:], mode="bilinear", align_corners=True) |
| _p3 = _p4 + self.lateral_block4(x3) |
|
|
| p3 = self.decoder_block3(_p3) |
| _p3 = F.interpolate(p3, size=x2.shape[2:], mode="bilinear", align_corners=True) |
| _p2 = _p3 + self.lateral_block3(x2) |
|
|
| p2 = self.decoder_block2(_p2) |
| _p2 = F.interpolate(p2, size=x1.shape[2:], mode="bilinear", align_corners=True) |
| _p1 = _p2 + self.lateral_block2(x1) |
|
|
| _p1 = self.decoder_block1(_p1) |
| _p1 = F.interpolate(_p1, size=x.shape[2:], mode="bilinear", align_corners=True) |
| p1_out = self.conv_out1(_p1) |
|
|
| if self.config.ms_supervision: |
| outs.append(self.conv_ms_spvn_4(p4)) |
| outs.append(self.conv_ms_spvn_3(p3)) |
| outs.append(self.conv_ms_spvn_2(p2)) |
| outs.append(p1_out) |
| return outs |
|
|
|
|
| class RefUNet(nn.Module): |
| |
| def __init__(self, in_channels=3 + 1): |
| super(RefUNet, self).__init__() |
| self.encoder_1 = nn.Sequential( |
| nn.Conv2d(in_channels, 64, 3, 1, 1), |
| nn.Conv2d(64, 64, 3, 1, 1), |
| nn.BatchNorm2d(64), |
| nn.ReLU(inplace=True), |
| ) |
|
|
| self.encoder_2 = nn.Sequential( |
| nn.MaxPool2d(2, 2, ceil_mode=True), |
| nn.Conv2d(64, 64, 3, 1, 1), |
| nn.BatchNorm2d(64), |
| nn.ReLU(inplace=True), |
| ) |
|
|
| self.encoder_3 = nn.Sequential( |
| nn.MaxPool2d(2, 2, ceil_mode=True), |
| nn.Conv2d(64, 64, 3, 1, 1), |
| nn.BatchNorm2d(64), |
| nn.ReLU(inplace=True), |
| ) |
|
|
| self.encoder_4 = nn.Sequential( |
| nn.MaxPool2d(2, 2, ceil_mode=True), |
| nn.Conv2d(64, 64, 3, 1, 1), |
| nn.BatchNorm2d(64), |
| nn.ReLU(inplace=True), |
| ) |
|
|
| self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True) |
| |
| self.decoder_5 = nn.Sequential( |
| nn.Conv2d(64, 64, 3, 1, 1), nn.BatchNorm2d(64), nn.ReLU(inplace=True) |
| ) |
| |
| self.decoder_4 = nn.Sequential( |
| nn.Conv2d(128, 64, 3, 1, 1), nn.BatchNorm2d(64), nn.ReLU(inplace=True) |
| ) |
|
|
| self.decoder_3 = nn.Sequential( |
| nn.Conv2d(128, 64, 3, 1, 1), nn.BatchNorm2d(64), nn.ReLU(inplace=True) |
| ) |
|
|
| self.decoder_2 = nn.Sequential( |
| nn.Conv2d(128, 64, 3, 1, 1), nn.BatchNorm2d(64), nn.ReLU(inplace=True) |
| ) |
|
|
| self.decoder_1 = nn.Sequential( |
| nn.Conv2d(128, 64, 3, 1, 1), nn.BatchNorm2d(64), nn.ReLU(inplace=True) |
| ) |
|
|
| self.conv_d0 = nn.Conv2d(64, 1, 3, 1, 1) |
|
|
| self.upscore2 = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True) |
|
|
| def forward(self, x): |
| outs = [] |
| if isinstance(x, list): |
| x = torch.cat(x, dim=1) |
| hx = x |
|
|
| hx1 = self.encoder_1(hx) |
| hx2 = self.encoder_2(hx1) |
| hx3 = self.encoder_3(hx2) |
| hx4 = self.encoder_4(hx3) |
|
|
| hx = self.decoder_5(self.pool4(hx4)) |
| hx = torch.cat((self.upscore2(hx), hx4), 1) |
|
|
| d4 = self.decoder_4(hx) |
| hx = torch.cat((self.upscore2(d4), hx3), 1) |
|
|
| d3 = self.decoder_3(hx) |
| hx = torch.cat((self.upscore2(d3), hx2), 1) |
|
|
| d2 = self.decoder_2(hx) |
| hx = torch.cat((self.upscore2(d2), hx1), 1) |
|
|
| d1 = self.decoder_1(hx) |
|
|
| x = self.conv_d0(d1) |
| outs.append(x) |
| return outs |
|
|
