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# 2022.06.08-Changed for implementation of TokenFusion
# Huawei Technologies Co., Ltd. <[email protected]>
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import mix_transformer
from mmcv.cnn import ConvModule
from .modules import num_parallel
class MLP(nn.Module):
"""
Linear Embedding
"""
def __init__(self, input_dim=2048, embed_dim=768):
super().__init__()
self.proj = nn.Linear(input_dim, embed_dim)
def forward(self, x):
x = x.flatten(2).transpose(1, 2).contiguous()
x = self.proj(x)
return x
class SegFormerHead(nn.Module):
"""
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
"""
def __init__(self, feature_strides=None, in_channels=128, embedding_dim=256, num_classes=20, **kwargs):
super(SegFormerHead, self).__init__()
self.in_channels = in_channels
self.num_classes = num_classes
assert len(feature_strides) == len(self.in_channels)
assert min(feature_strides) == feature_strides[0]
self.feature_strides = feature_strides
c1_in_channels, c2_in_channels, c3_in_channels, c4_in_channels = self.in_channels
#decoder_params = kwargs['decoder_params']
#embedding_dim = decoder_params['embed_dim']
self.linear_c4 = MLP(input_dim=c4_in_channels, embed_dim=embedding_dim)
self.linear_c3 = MLP(input_dim=c3_in_channels, embed_dim=embedding_dim)
self.linear_c2 = MLP(input_dim=c2_in_channels, embed_dim=embedding_dim)
self.linear_c1 = MLP(input_dim=c1_in_channels, embed_dim=embedding_dim)
self.dropout = nn.Dropout2d(0.1)
self.linear_fuse = ConvModule(
in_channels=embedding_dim*4,
out_channels=embedding_dim,
kernel_size=1,
norm_cfg=dict(type='BN', requires_grad=True)
)
self.linear_pred = nn.Conv2d(embedding_dim, self.num_classes, kernel_size=1)
def forward(self, x):
c1, c2, c3, c4 = x
############## MLP decoder on C1-C4 ###########
n, _, h, w = c4.shape
_c4 = self.linear_c4(c4).permute(0,2,1).reshape(n, -1, c4.shape[2], c4.shape[3]).contiguous()
_c4 = F.interpolate(_c4, size=c1.size()[2:],mode='bilinear',align_corners=False)
_c3 = self.linear_c3(c3).permute(0,2,1).reshape(n, -1, c3.shape[2], c3.shape[3]).contiguous()
_c3 = F.interpolate(_c3, size=c1.size()[2:],mode='bilinear',align_corners=False)
_c2 = self.linear_c2(c2).permute(0,2,1).reshape(n, -1, c2.shape[2], c2.shape[3]).contiguous()
_c2 = F.interpolate(_c2, size=c1.size()[2:],mode='bilinear',align_corners=False)
_c1 = self.linear_c1(c1).permute(0,2,1).reshape(n, -1, c1.shape[2], c1.shape[3]).contiguous()
_c = self.linear_fuse(torch.cat([_c4, _c3, _c2, _c1], dim=1))
x = self.dropout(_c)
x = self.linear_pred(x)
return x
class SegFormerReconstructionHead(nn.Module):
"""
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
"""
def __init__(self, feature_strides=None, in_channels=128, embedding_dim=256, **kwargs):
super(SegFormerReconstructionHead, self).__init__()
self.in_channels = in_channels
assert len(feature_strides) == len(self.in_channels)
assert min(feature_strides) == feature_strides[0]
self.feature_strides = feature_strides
c1_in_channels, c2_in_channels, c3_in_channels, c4_in_channels = self.in_channels
#decoder_params = kwargs['decoder_params']
#embedding_dim = decoder_params['embed_dim']
self.linear_c4 = MLP(input_dim=c4_in_channels, embed_dim=embedding_dim)
self.linear_c3 = MLP(input_dim=c3_in_channels, embed_dim=embedding_dim)
self.linear_c2 = MLP(input_dim=c2_in_channels, embed_dim=embedding_dim)
self.linear_c1 = MLP(input_dim=c1_in_channels, embed_dim=embedding_dim)
self.dropout = nn.Dropout2d(0.1)
self.linear_fuse = ConvModule(
in_channels=embedding_dim*4,
out_channels=embedding_dim,
kernel_size=1,
norm_cfg=dict(type='BN', requires_grad=True)
)
self.linear_pred = nn.Conv2d(embedding_dim, 3, kernel_size=1)
def forward(self, x):
c1, c2, c3, c4 = x
############## MLP decoder on C1-C4 ###########
n, _, h, w = c4.shape
_c4 = self.linear_c4(c4).permute(0,2,1).reshape(n, -1, c4.shape[2], c4.shape[3]).contiguous()
_c4 = F.interpolate(_c4, size=c1.size()[2:],mode='bilinear',align_corners=False)
_c3 = self.linear_c3(c3).permute(0,2,1).reshape(n, -1, c3.shape[2], c3.shape[3]).contiguous()
_c3 = F.interpolate(_c3, size=c1.size()[2:],mode='bilinear',align_corners=False)
_c2 = self.linear_c2(c2).permute(0,2,1).reshape(n, -1, c2.shape[2], c2.shape[3]).contiguous()
_c2 = F.interpolate(_c2, size=c1.size()[2:],mode='bilinear',align_corners=False)
_c1 = self.linear_c1(c1).permute(0,2,1).reshape(n, -1, c1.shape[2], c1.shape[3]).contiguous()
_c = self.linear_fuse(torch.cat([_c4, _c3, _c2, _c1], dim=1))
x = self.dropout(_c)
x = self.linear_pred(x)
return x
class LinearFusionMAEMaskedConsistency(nn.Module):
def __init__(self, backbone, config, num_classes=20, embedding_dim=256, pretrained=True):
super().__init__()
self.num_classes = num_classes
self.embedding_dim = embedding_dim
self.feature_strides = [4, 8, 16, 32]
self.num_parallel = num_parallel
#self.in_channels = [32, 64, 160, 256]
#self.in_channels = [64, 128, 320, 512]
self.encoder = getattr(mix_transformer, backbone)(ratio = config.ratio, masking_ratio = config.masking_ratio)
self.in_channels = self.encoder.embed_dims
## initilize encoder
if pretrained:
state_dict = torch.load(config.root_dir+'/data/pytorch-weight/' + backbone + '.pth')
state_dict.pop('head.weight')
state_dict.pop('head.bias')
state_dict = expand_state_dict(self.encoder.state_dict(), state_dict, self.num_parallel)
self.encoder.load_state_dict(state_dict, strict=True)
print("Load pretrained weights from " + config.root_dir+'/data/pytorch-weight/' + backbone + '.pth')
else:
print("Train from scratch")
self.decoder = SegFormerHead(feature_strides=self.feature_strides, in_channels=self.in_channels,
embedding_dim=self.embedding_dim, num_classes=self.num_classes)
self.decoder_reconstruct_rgb = SegFormerReconstructionHead(feature_strides=self.feature_strides, in_channels=self.in_channels,
embedding_dim=self.embedding_dim, num_classes=self.num_classes)
self.decoder_reconstruct_depth = SegFormerReconstructionHead(feature_strides=self.feature_strides, in_channels=self.in_channels,
embedding_dim=self.embedding_dim, num_classes=self.num_classes)
self.alpha = nn.Parameter(torch.ones(self.num_parallel, requires_grad=True))
self.register_parameter('alpha', self.alpha)
self.ratio = config.ratio
def get_params(self):
param_groups = [[], [], []]
for name, param in list(self.encoder.named_parameters()):
if "norm" in name:
param_groups[1].append(param)
else:
param_groups[0].append(param)
for param in list(self.decoder.parameters()):
param_groups[2].append(param)
return param_groups
# def get_params(self):
# param_groups = [[], []]
# for param in list(self.encoder.parameters()):
# param_groups[0].append(param)
# for param in list(self.decoder.parameters()):
# param_groups[1].append(param)
# return param_groups
def forward(self, data, get_sup_loss = False, gt = None, criterion = None, mask = False, range_batches_to_mask = None):
b, c, h, w = data[0].shape #rgb is the 0th element
if not mask:
masking_branch = -1
else:
masking_branch = int((torch.rand(1)<0.5)*1)
x = self.encoder(data, masking_branch, range_batches_to_mask)
if self.training:
#Reconstruction branch
reconstruction_criterion = nn.MSELoss()
encoder_output_0 = [x[0][i][range_batches_to_mask[0]:range_batches_to_mask[1]] for i in range(len(x[0]))]
pred_reconstruct_0 = self.decoder_reconstruct_rgb(encoder_output_0)
pred_reconstruct_0 = F.interpolate(pred_reconstruct_0, size=(h, w), mode='bilinear', align_corners=True)
encoder_output_1 = [x[1][i][range_batches_to_mask[0]:range_batches_to_mask[1]] for i in range(len(x[1]))]
pred_reconstruct_1 = self.decoder_reconstruct_depth(encoder_output_1)
pred_reconstruct_1 = F.interpolate(pred_reconstruct_1, size=(h, w), mode='bilinear', align_corners=True)
reconstruction_loss = (1 - masking_branch) * reconstruction_criterion(pred_reconstruct_0, data[masking_branch][range_batches_to_mask[0]:range_batches_to_mask[1]])
reconstruction_loss += masking_branch * reconstruction_criterion(pred_reconstruct_1, data[masking_branch][range_batches_to_mask[0]:range_batches_to_mask[1]])
# if masking_branch == 0:
# masked_encoder_output = [x[0][i][range_batches_to_mask[0]:range_batches_to_mask[1]] for i in range(len(x[0]))]
# pred_reconstruct = self.decoder_reconstruct_rgb(masked_encoder_output)
# pred_reconstruct = F.interpolate(pred_reconstruct, size=(h, w), mode='bilinear', align_corners=True)
# else:
# masked_encoder_output = [x[1][i][range_batches_to_mask[0]:range_batches_to_mask[1]] for i in range(len(x[1]))]
# pred_reconstruct = self.decoder_reconstruct_depth(masked_encoder_output)
# pred_reconstruct = F.interpolate(pred_reconstruct, size=(h, w), mode='bilinear', align_corners=True)
# reconstruction_criterion = nn.MSELoss()
# reconstruction_loss = reconstruction_criterion(pred_reconstruct, data[masking_branch][range_batches_to_mask[0]:range_batches_to_mask[1]])
#Segmentation branch
pred = [self.decoder(x[0]), self.decoder(x[1])]
ens = 0
alpha_soft = F.softmax(self.alpha)
for l in range(self.num_parallel):
ens += alpha_soft[l] * pred[l].detach()
pred.append(ens)
for i in range(len(pred)):
pred[i] = F.interpolate(pred[i], size=(h, w), mode='bilinear', align_corners=True)
if not self.training:
return pred
else: # training
if get_sup_loss:
sup_loss = self.get_sup_loss(pred, gt, criterion)
# print(sup_loss, l1, l1_loss, sup_loss + l1_loss, "losses")
return pred, sup_loss, reconstruction_loss, masking_branch
else:
return pred, reconstruction_loss, masking_branch
def get_sup_loss(self, pred, gt, criterion):
sup_loss = 0
for p in pred:
p = p[:gt.shape[0]] #Getting loss for only those examples in batch where gt exists. Won't get sup loss for unlabeled data.
# soft_output = nn.LogSoftmax()(p)
sup_loss += criterion(p, gt)
return sup_loss / len(pred)
def expand_state_dict(model_dict, state_dict, num_parallel):
model_dict_keys = model_dict.keys()
state_dict_keys = state_dict.keys()
for model_dict_key in model_dict_keys:
model_dict_key_re = model_dict_key.replace('module.', '')
if model_dict_key_re in state_dict_keys:
model_dict[model_dict_key] = state_dict[model_dict_key_re]
for i in range(num_parallel):
ln = '.ln_%d' % i
replace = True if ln in model_dict_key_re else False
model_dict_key_re = model_dict_key_re.replace(ln, '')
if replace and model_dict_key_re in state_dict_keys:
model_dict[model_dict_key] = state_dict[model_dict_key_re]
return model_dict
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