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RMBG-2.0/birefnet.py
ai-modelscope 839b3a2e36 Update birefnet.py
2025-02-20 06:29:05 +08:00

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### config.py
import os
import math
from transformers import PretrainedConfig
class Config(PretrainedConfig):
def __init__(self) -> None:
# PATH settings
self.sys_home_dir = os.path.expanduser('~') # Make up your file system as: SYS_HOME_DIR/codes/dis/BiRefNet, SYS_HOME_DIR/datasets/dis/xx, SYS_HOME_DIR/weights/xx
# TASK settings
self.task = ['DIS5K', 'COD', 'HRSOD', 'DIS5K+HRSOD+HRS10K', 'P3M-10k'][0]
self.training_set = {
'DIS5K': ['DIS-TR', 'DIS-TR+DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4'][0],
'COD': 'TR-COD10K+TR-CAMO',
'HRSOD': ['TR-DUTS', 'TR-HRSOD', 'TR-UHRSD', 'TR-DUTS+TR-HRSOD', 'TR-DUTS+TR-UHRSD', 'TR-HRSOD+TR-UHRSD', 'TR-DUTS+TR-HRSOD+TR-UHRSD'][5],
'DIS5K+HRSOD+HRS10K': 'DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4+DIS-TR+TE-HRS10K+TE-HRSOD+TE-UHRSD+TR-HRS10K+TR-HRSOD+TR-UHRSD', # leave DIS-VD for evaluation.
'P3M-10k': 'TR-P3M-10k',
}[self.task]
self.prompt4loc = ['dense', 'sparse'][0]
# Faster-Training settings
self.load_all = True
self.compile = True # 1. Trigger CPU memory leak in some extend, which is an inherent problem of PyTorch.
# Machines with > 70GB CPU memory can run the whole training on DIS5K with default setting.
# 2. Higher PyTorch version may fix it: https://github.com/pytorch/pytorch/issues/119607.
# 3. But compile in Pytorch > 2.0.1 seems to bring no acceleration for training.
self.precisionHigh = True
# MODEL settings
self.ms_supervision = True
self.out_ref = self.ms_supervision and True
self.dec_ipt = True
self.dec_ipt_split = True
self.cxt_num = [0, 3][1] # multi-scale skip connections from encoder
self.mul_scl_ipt = ['', 'add', 'cat'][2]
self.dec_att = ['', 'ASPP', 'ASPPDeformable'][2]
self.squeeze_block = ['', 'BasicDecBlk_x1', 'ResBlk_x4', 'ASPP_x3', 'ASPPDeformable_x3'][1]
self.dec_blk = ['BasicDecBlk', 'ResBlk', 'HierarAttDecBlk'][0]
# TRAINING settings
self.batch_size = 4
self.IoU_finetune_last_epochs = [
0,
{
'DIS5K': -50,
'COD': -20,
'HRSOD': -20,
'DIS5K+HRSOD+HRS10K': -20,
'P3M-10k': -20,
}[self.task]
][1] # choose 0 to skip
self.lr = (1e-4 if 'DIS5K' in self.task else 1e-5) * math.sqrt(self.batch_size / 4) # DIS needs high lr to converge faster. Adapt the lr linearly
self.size = 1024
self.num_workers = max(4, self.batch_size) # will be decrease to min(it, batch_size) at the initialization of the data_loader
# Backbone settings
self.bb = [
'vgg16', 'vgg16bn', 'resnet50', # 0, 1, 2
'swin_v1_t', 'swin_v1_s', # 3, 4
'swin_v1_b', 'swin_v1_l', # 5-bs9, 6-bs4
'pvt_v2_b0', 'pvt_v2_b1', # 7, 8
'pvt_v2_b2', 'pvt_v2_b5', # 9-bs10, 10-bs5
][6]
self.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],
'swin_v1_t': [768, 384, 192, 96], 'swin_v1_s': [768, 384, 192, 96],
'pvt_v2_b0': [256, 160, 64, 32], 'pvt_v2_b1': [512, 320, 128, 64],
}[self.bb]
if self.mul_scl_ipt == 'cat':
self.lateral_channels_in_collection = [channel * 2 for channel in self.lateral_channels_in_collection]
self.cxt = self.lateral_channels_in_collection[1:][::-1][-self.cxt_num:] if self.cxt_num else []
# MODEL settings - inactive
self.lat_blk = ['BasicLatBlk'][0]
self.dec_channels_inter = ['fixed', 'adap'][0]
self.refine = ['', 'itself', 'RefUNet', 'Refiner', 'RefinerPVTInChannels4'][0]
self.progressive_ref = self.refine and True
self.ender = self.progressive_ref and False
self.scale = self.progressive_ref and 2
self.auxiliary_classification = False # Only for DIS5K, where class labels are saved in `dataset.py`.
self.refine_iteration = 1
self.freeze_bb = False
self.model = [
'BiRefNet',
][0]
if self.dec_blk == 'HierarAttDecBlk':
self.batch_size = 2 ** [0, 1, 2, 3, 4][2]
# TRAINING settings - inactive
self.preproc_methods = ['flip', 'enhance', 'rotate', 'pepper', 'crop'][:4]
self.optimizer = ['Adam', 'AdamW'][1]
self.lr_decay_epochs = [1e5] # Set to negative N to decay the lr in the last N-th epoch.
self.lr_decay_rate = 0.5
# Loss
self.lambdas_pix_last = {
# not 0 means opening this loss
# original rate -- 1 : 30 : 1.5 : 0.2, bce x 30
'bce': 30 * 1, # high performance
'iou': 0.5 * 1, # 0 / 255
'iou_patch': 0.5 * 0, # 0 / 255, win_size = (64, 64)
'mse': 150 * 0, # can smooth the saliency map
'triplet': 3 * 0,
'reg': 100 * 0,
'ssim': 10 * 1, # help contours,
'cnt': 5 * 0, # help contours
'structure': 5 * 0, # structure loss from codes of MVANet. A little improvement on DIS-TE[1,2,3], a bit more decrease on DIS-TE4.
}
self.lambdas_cls = {
'ce': 5.0
}
# Adv
self.lambda_adv_g = 10. * 0 # turn to 0 to avoid adv training
self.lambda_adv_d = 3. * (self.lambda_adv_g > 0)
# PATH settings - inactive
self.data_root_dir = os.path.join(self.sys_home_dir, 'datasets/dis')
self.weights_root_dir = os.path.join(self.sys_home_dir, 'weights')
self.weights = {
'pvt_v2_b2': os.path.join(self.weights_root_dir, 'pvt_v2_b2.pth'),
'pvt_v2_b5': os.path.join(self.weights_root_dir, ['pvt_v2_b5.pth', 'pvt_v2_b5_22k.pth'][0]),
'swin_v1_b': os.path.join(self.weights_root_dir, ['swin_base_patch4_window12_384_22kto1k.pth', 'swin_base_patch4_window12_384_22k.pth'][0]),
'swin_v1_l': os.path.join(self.weights_root_dir, ['swin_large_patch4_window12_384_22kto1k.pth', 'swin_large_patch4_window12_384_22k.pth'][0]),
'swin_v1_t': os.path.join(self.weights_root_dir, ['swin_tiny_patch4_window7_224_22kto1k_finetune.pth'][0]),
'swin_v1_s': os.path.join(self.weights_root_dir, ['swin_small_patch4_window7_224_22kto1k_finetune.pth'][0]),
'pvt_v2_b0': os.path.join(self.weights_root_dir, ['pvt_v2_b0.pth'][0]),
'pvt_v2_b1': os.path.join(self.weights_root_dir, ['pvt_v2_b1.pth'][0]),
}
# Callbacks - inactive
self.verbose_eval = True
self.only_S_MAE = False
self.use_fp16 = False # Bugs. It may cause nan in training.
self.SDPA_enabled = False # Bugs. Slower and errors occur in multi-GPUs
# others
self.device = [0, 'cpu'][0] # .to(0) == .to('cuda:0')
self.batch_size_valid = 1
self.rand_seed = 7
# run_sh_file = [f for f in os.listdir('.') if 'train.sh' == f] + [os.path.join('..', f) for f in os.listdir('..') if 'train.sh' == f]
# with open(run_sh_file[0], 'r') as f:
# lines = f.readlines()
# self.save_last = int([l.strip() for l in lines if '"{}")'.format(self.task) in l and 'val_last=' in l][0].split('val_last=')[-1].split()[0])
# self.save_step = int([l.strip() for l in lines if '"{}")'.format(self.task) in l and 'step=' in l][0].split('step=')[-1].split()[0])
# self.val_step = [0, self.save_step][0]
def print_task(self) -> None:
# Return task for choosing settings in shell scripts.
print(self.task)
### models/backbones/pvt_v2.py
import torch
import torch.nn as nn
from functools import partial
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
import math
# from config import Config
# config = Config()
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.dwconv = DWConv(hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
x = self.fc1(x)
x = self.dwconv(x, H, W)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
super().__init__()
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
self.dim = dim
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.q = nn.Linear(dim, dim, bias=qkv_bias)
self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
self.attn_drop_prob = attn_drop
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.sr_ratio = sr_ratio
if sr_ratio > 1:
self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
self.norm = nn.LayerNorm(dim)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
B, N, C = x.shape
q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
if self.sr_ratio > 1:
x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
x_ = self.norm(x_)
kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
else:
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
k, v = kv[0], kv[1]
if config.SDPA_enabled:
x = torch.nn.functional.scaled_dot_product_attention(
q, k, v,
attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False
).transpose(1, 2).reshape(B, N, C)
else:
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
x = x + self.drop_path(self.attn(self.norm1(x), H, W))
x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
return x
class OverlapPatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=7, stride=4, in_channels=3, embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
self.img_size = img_size
self.patch_size = patch_size
self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
self.num_patches = self.H * self.W
self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=stride,
padding=(patch_size[0] // 2, patch_size[1] // 2))
self.norm = nn.LayerNorm(embed_dim)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
x = self.proj(x)
_, _, H, W = x.shape
x = x.flatten(2).transpose(1, 2)
x = self.norm(x)
return x, H, W
class PyramidVisionTransformerImpr(nn.Module):
def __init__(self, img_size=224, patch_size=16, in_channels=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]):
super().__init__()
self.num_classes = num_classes
self.depths = depths
# patch_embed
self.patch_embed1 = OverlapPatchEmbed(img_size=img_size, patch_size=7, stride=4, in_channels=in_channels,
embed_dim=embed_dims[0])
self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_channels=embed_dims[0],
embed_dim=embed_dims[1])
self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_channels=embed_dims[1],
embed_dim=embed_dims[2])
self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_channels=embed_dims[2],
embed_dim=embed_dims[3])
# transformer encoder
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
cur = 0
self.block1 = nn.ModuleList([Block(
dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
sr_ratio=sr_ratios[0])
for i in range(depths[0])])
self.norm1 = norm_layer(embed_dims[0])
cur += depths[0]
self.block2 = nn.ModuleList([Block(
dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
sr_ratio=sr_ratios[1])
for i in range(depths[1])])
self.norm2 = norm_layer(embed_dims[1])
cur += depths[1]
self.block3 = nn.ModuleList([Block(
dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
sr_ratio=sr_ratios[2])
for i in range(depths[2])])
self.norm3 = norm_layer(embed_dims[2])
cur += depths[2]
self.block4 = nn.ModuleList([Block(
dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
sr_ratio=sr_ratios[3])
for i in range(depths[3])])
self.norm4 = norm_layer(embed_dims[3])
# classification head
# self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
logger = 1
#load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
def reset_drop_path(self, drop_path_rate):
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
cur = 0
for i in range(self.depths[0]):
self.block1[i].drop_path.drop_prob = dpr[cur + i]
cur += self.depths[0]
for i in range(self.depths[1]):
self.block2[i].drop_path.drop_prob = dpr[cur + i]
cur += self.depths[1]
for i in range(self.depths[2]):
self.block3[i].drop_path.drop_prob = dpr[cur + i]
cur += self.depths[2]
for i in range(self.depths[3]):
self.block4[i].drop_path.drop_prob = dpr[cur + i]
def freeze_patch_emb(self):
self.patch_embed1.requires_grad = False
@torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'} # has pos_embed may be better
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=''):
self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
B = x.shape[0]
outs = []
# stage 1
x, H, W = self.patch_embed1(x)
for i, blk in enumerate(self.block1):
x = blk(x, H, W)
x = self.norm1(x)
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
outs.append(x)
# stage 2
x, H, W = self.patch_embed2(x)
for i, blk in enumerate(self.block2):
x = blk(x, H, W)
x = self.norm2(x)
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
outs.append(x)
# stage 3
x, H, W = self.patch_embed3(x)
for i, blk in enumerate(self.block3):
x = blk(x, H, W)
x = self.norm3(x)
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
outs.append(x)
# stage 4
x, H, W = self.patch_embed4(x)
for i, blk in enumerate(self.block4):
x = blk(x, H, W)
x = self.norm4(x)
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
outs.append(x)
return outs
# return x.mean(dim=1)
def forward(self, x):
x = self.forward_features(x)
# x = self.head(x)
return x
class DWConv(nn.Module):
def __init__(self, dim=768):
super(DWConv, self).__init__()
self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
def forward(self, x, H, W):
B, N, C = x.shape
x = x.transpose(1, 2).view(B, C, H, W).contiguous()
x = self.dwconv(x)
x = x.flatten(2).transpose(1, 2)
return x
def _conv_filter(state_dict, patch_size=16):
""" convert patch embedding weight from manual patchify + linear proj to conv"""
out_dict = {}
for k, v in state_dict.items():
if 'patch_embed.proj.weight' in k:
v = v.reshape((v.shape[0], 3, patch_size, patch_size))
out_dict[k] = v
return out_dict
## @register_model
class pvt_v2_b0(PyramidVisionTransformerImpr):
def __init__(self, **kwargs):
super(pvt_v2_b0, self).__init__(
patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1)
## @register_model
class pvt_v2_b1(PyramidVisionTransformerImpr):
def __init__(self, **kwargs):
super(pvt_v2_b1, self).__init__(
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1)
## @register_model
class pvt_v2_b2(PyramidVisionTransformerImpr):
def __init__(self, in_channels=3, **kwargs):
super(pvt_v2_b2, self).__init__(
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1, in_channels=in_channels)
## @register_model
class pvt_v2_b3(PyramidVisionTransformerImpr):
def __init__(self, **kwargs):
super(pvt_v2_b3, self).__init__(
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1)
## @register_model
class pvt_v2_b4(PyramidVisionTransformerImpr):
def __init__(self, **kwargs):
super(pvt_v2_b4, self).__init__(
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1)
## @register_model
class pvt_v2_b5(PyramidVisionTransformerImpr):
def __init__(self, **kwargs):
super(pvt_v2_b5, self).__init__(
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1)
### models/backbones/swin_v1.py
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu, Yutong Lin, Yixuan Wei
# --------------------------------------------------------
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
# from config import Config
# config = Config()
class Mlp(nn.Module):
""" Multilayer perceptron."""
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
def window_partition(x, window_size):
"""
Args:
x: (B, H, W, C)
window_size (int): window size
Returns:
windows: (num_windows*B, window_size, window_size, C)
"""
B, H, W, C = x.shape
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
return windows
def window_reverse(windows, window_size, H, W):
"""
Args:
windows: (num_windows*B, window_size, window_size, C)
window_size (int): Window size
H (int): Height of image
W (int): Width of image
Returns:
x: (B, H, W, C)
"""
B = int(windows.shape[0] / (H * W / window_size / window_size))
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
return x
class WindowAttention(nn.Module):
""" Window based multi-head self attention (W-MSA) module with relative position bias.
It supports both of shifted and non-shifted window.
Args:
dim (int): Number of input channels.
window_size (tuple[int]): The height and width of the window.
num_heads (int): Number of attention heads.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
"""
def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
self.dim = dim
self.window_size = window_size # Wh, Ww
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
# define a parameter table of relative position bias
self.relative_position_bias_table = nn.Parameter(
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(self.window_size[0])
coords_w = torch.arange(self.window_size[1])
coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij')) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += self.window_size[1] - 1
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
self.register_buffer("relative_position_index", relative_position_index)
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop_prob = attn_drop
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
trunc_normal_(self.relative_position_bias_table, std=.02)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x, mask=None):
""" Forward function.
Args:
x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
"""
B_, N, C = x.shape
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
q = q * self.scale
if config.SDPA_enabled:
x = torch.nn.functional.scaled_dot_product_attention(
q, k, v,
attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False
).transpose(1, 2).reshape(B_, N, C)
else:
attn = (q @ k.transpose(-2, -1))
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
attn = attn + relative_position_bias.unsqueeze(0)
if mask is not None:
nW = mask.shape[0]
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
attn = attn.view(-1, self.num_heads, N, N)
attn = self.softmax(attn)
else:
attn = self.softmax(attn)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class SwinTransformerBlock(nn.Module):
""" Swin Transformer Block.
Args:
dim (int): Number of input channels.
num_heads (int): Number of attention heads.
window_size (int): Window size.
shift_size (int): Shift size for SW-MSA.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float, optional): Stochastic depth rate. Default: 0.0
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
def __init__(self, dim, num_heads, window_size=7, shift_size=0,
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
self.dim = dim
self.num_heads = num_heads
self.window_size = window_size
self.shift_size = shift_size
self.mlp_ratio = mlp_ratio
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
self.norm1 = norm_layer(dim)
self.attn = WindowAttention(
dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.H = None
self.W = None
def forward(self, x, mask_matrix):
""" Forward function.
Args:
x: Input feature, tensor size (B, H*W, C).
H, W: Spatial resolution of the input feature.
mask_matrix: Attention mask for cyclic shift.
"""
B, L, C = x.shape
H, W = self.H, self.W
assert L == H * W, "input feature has wrong size"
shortcut = x
x = self.norm1(x)
x = x.view(B, H, W, C)
# pad feature maps to multiples of window size
pad_l = pad_t = 0
pad_r = (self.window_size - W % self.window_size) % self.window_size
pad_b = (self.window_size - H % self.window_size) % self.window_size
x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
_, Hp, Wp, _ = x.shape
# cyclic shift
if self.shift_size > 0:
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
attn_mask = mask_matrix
else:
shifted_x = x
attn_mask = None
# partition windows
x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
# W-MSA/SW-MSA
attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
# merge windows
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
# reverse cyclic shift
if self.shift_size > 0:
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
else:
x = shifted_x
if pad_r > 0 or pad_b > 0:
x = x[:, :H, :W, :].contiguous()
x = x.view(B, H * W, C)
# FFN
x = shortcut + self.drop_path(x)
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class PatchMerging(nn.Module):
""" Patch Merging Layer
Args:
dim (int): Number of input channels.
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
def __init__(self, dim, norm_layer=nn.LayerNorm):
super().__init__()
self.dim = dim
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
self.norm = norm_layer(4 * dim)
def forward(self, x, H, W):
""" Forward function.
Args:
x: Input feature, tensor size (B, H*W, C).
H, W: Spatial resolution of the input feature.
"""
B, L, C = x.shape
assert L == H * W, "input feature has wrong size"
x = x.view(B, H, W, C)
# padding
pad_input = (H % 2 == 1) or (W % 2 == 1)
if pad_input:
x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
x = self.norm(x)
x = self.reduction(x)
return x
class BasicLayer(nn.Module):
""" A basic Swin Transformer layer for one stage.
Args:
dim (int): Number of feature channels
depth (int): Depths of this stage.
num_heads (int): Number of attention head.
window_size (int): Local window size. Default: 7.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
"""
def __init__(self,
dim,
depth,
num_heads,
window_size=7,
mlp_ratio=4.,
qkv_bias=True,
qk_scale=None,
drop=0.,
attn_drop=0.,
drop_path=0.,
norm_layer=nn.LayerNorm,
downsample=None,
use_checkpoint=False):
super().__init__()
self.window_size = window_size
self.shift_size = window_size // 2
self.depth = depth
self.use_checkpoint = use_checkpoint
# build blocks
self.blocks = nn.ModuleList([
SwinTransformerBlock(
dim=dim,
num_heads=num_heads,
window_size=window_size,
shift_size=0 if (i % 2 == 0) else window_size // 2,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop,
attn_drop=attn_drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
norm_layer=norm_layer)
for i in range(depth)])
# patch merging layer
if downsample is not None:
self.downsample = downsample(dim=dim, norm_layer=norm_layer)
else:
self.downsample = None
def forward(self, x, H, W):
""" Forward function.
Args:
x: Input feature, tensor size (B, H*W, C).
H, W: Spatial resolution of the input feature.
"""
# calculate attention mask for SW-MSA
Hp = int(np.ceil(H / self.window_size)) * self.window_size
Wp = int(np.ceil(W / self.window_size)) * self.window_size
img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1
h_slices = (slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None))
w_slices = (slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None))
cnt = 0
for h in h_slices:
for w in w_slices:
img_mask[:, h, w, :] = cnt
cnt += 1
mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
for blk in self.blocks:
blk.H, blk.W = H, W
if self.use_checkpoint:
x = checkpoint.checkpoint(blk, x, attn_mask)
else:
x = blk(x, attn_mask)
if self.downsample is not None:
x_down = self.downsample(x, H, W)
Wh, Ww = (H + 1) // 2, (W + 1) // 2
return x, H, W, x_down, Wh, Ww
else:
return x, H, W, x, H, W
class PatchEmbed(nn.Module):
""" Image to Patch Embedding
Args:
patch_size (int): Patch token size. Default: 4.
in_channels (int): Number of input image channels. Default: 3.
embed_dim (int): Number of linear projection output channels. Default: 96.
norm_layer (nn.Module, optional): Normalization layer. Default: None
"""
def __init__(self, patch_size=4, in_channels=3, embed_dim=96, norm_layer=None):
super().__init__()
patch_size = to_2tuple(patch_size)
self.patch_size = patch_size
self.in_channels = in_channels
self.embed_dim = embed_dim
self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
if norm_layer is not None:
self.norm = norm_layer(embed_dim)
else:
self.norm = None
def forward(self, x):
"""Forward function."""
# padding
_, _, H, W = x.size()
if W % self.patch_size[1] != 0:
x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
if H % self.patch_size[0] != 0:
x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
x = self.proj(x) # B C Wh Ww
if self.norm is not None:
Wh, Ww = x.size(2), x.size(3)
x = x.flatten(2).transpose(1, 2)
x = self.norm(x)
x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
return x
class SwinTransformer(nn.Module):
""" Swin Transformer backbone.
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
https://arxiv.org/pdf/2103.14030
Args:
pretrain_img_size (int): Input image size for training the pretrained model,
used in absolute postion embedding. Default 224.
patch_size (int | tuple(int)): Patch size. Default: 4.
in_channels (int): Number of input image channels. Default: 3.
embed_dim (int): Number of linear projection output channels. Default: 96.
depths (tuple[int]): Depths of each Swin Transformer stage.
num_heads (tuple[int]): Number of attention head of each stage.
window_size (int): Window size. Default: 7.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
drop_rate (float): Dropout rate.
attn_drop_rate (float): Attention dropout rate. Default: 0.
drop_path_rate (float): Stochastic depth rate. Default: 0.2.
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
patch_norm (bool): If True, add normalization after patch embedding. Default: True.
out_indices (Sequence[int]): Output from which stages.
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-1 means not freezing any parameters.
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
"""
def __init__(self,
pretrain_img_size=224,
patch_size=4,
in_channels=3,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4.,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.2,
norm_layer=nn.LayerNorm,
ape=False,
patch_norm=True,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
use_checkpoint=False):
super().__init__()
self.pretrain_img_size = pretrain_img_size
self.num_layers = len(depths)
self.embed_dim = embed_dim
self.ape = ape
self.patch_norm = patch_norm
self.out_indices = out_indices
self.frozen_stages = frozen_stages
# split image into non-overlapping patches
self.patch_embed = PatchEmbed(
patch_size=patch_size, in_channels=in_channels, embed_dim=embed_dim,
norm_layer=norm_layer if self.patch_norm else None)
# absolute position embedding
if self.ape:
pretrain_img_size = to_2tuple(pretrain_img_size)
patch_size = to_2tuple(patch_size)
patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
trunc_normal_(self.absolute_pos_embed, std=.02)
self.pos_drop = nn.Dropout(p=drop_rate)
# stochastic depth
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
# build layers
self.layers = nn.ModuleList()
for i_layer in range(self.num_layers):
layer = BasicLayer(
dim=int(embed_dim * 2 ** i_layer),
depth=depths[i_layer],
num_heads=num_heads[i_layer],
window_size=window_size,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
norm_layer=norm_layer,
downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
use_checkpoint=use_checkpoint)
self.layers.append(layer)
num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
self.num_features = num_features
# add a norm layer for each output
for i_layer in out_indices:
layer = norm_layer(num_features[i_layer])
layer_name = f'norm{i_layer}'
self.add_module(layer_name, layer)
self._freeze_stages()
def _freeze_stages(self):
if self.frozen_stages >= 0:
self.patch_embed.eval()
for param in self.patch_embed.parameters():
param.requires_grad = False
if self.frozen_stages >= 1 and self.ape:
self.absolute_pos_embed.requires_grad = False
if self.frozen_stages >= 2:
self.pos_drop.eval()
for i in range(0, self.frozen_stages - 1):
m = self.layers[i]
m.eval()
for param in m.parameters():
param.requires_grad = False
def forward(self, x):
"""Forward function."""
x = self.patch_embed(x)
Wh, Ww = x.size(2), x.size(3)
if self.ape:
# interpolate the position embedding to the corresponding size
absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
x = (x + absolute_pos_embed) # B Wh*Ww C
outs = []#x.contiguous()]
x = x.flatten(2).transpose(1, 2)
x = self.pos_drop(x)
for i in range(self.num_layers):
layer = self.layers[i]
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
if i in self.out_indices:
norm_layer = getattr(self, f'norm{i}')
x_out = norm_layer(x_out)
out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
outs.append(out)
return tuple(outs)
def train(self, mode=True):
"""Convert the model into training mode while keep layers freezed."""
super(SwinTransformer, self).train(mode)
self._freeze_stages()
def swin_v1_t():
model = SwinTransformer(embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7)
return model
def swin_v1_s():
model = SwinTransformer(embed_dim=96, depths=[2, 2, 18, 2], num_heads=[3, 6, 12, 24], window_size=7)
return model
def swin_v1_b():
model = SwinTransformer(embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12)
return model
def swin_v1_l():
model = SwinTransformer(embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12)
return model
### models/modules/deform_conv.py
import torch
import torch.nn as nn
from torchvision.ops import deform_conv2d
class DeformableConv2d(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1,
bias=False):
super(DeformableConv2d, self).__init__()
assert type(kernel_size) == tuple or type(kernel_size) == int
kernel_size = kernel_size if type(kernel_size) == tuple else (kernel_size, kernel_size)
self.stride = stride if type(stride) == tuple else (stride, stride)
self.padding = padding
self.offset_conv = nn.Conv2d(in_channels,
2 * kernel_size[0] * kernel_size[1],
kernel_size=kernel_size,
stride=stride,
padding=self.padding,
bias=True)
nn.init.constant_(self.offset_conv.weight, 0.)
nn.init.constant_(self.offset_conv.bias, 0.)
self.modulator_conv = nn.Conv2d(in_channels,
1 * kernel_size[0] * kernel_size[1],
kernel_size=kernel_size,
stride=stride,
padding=self.padding,
bias=True)
nn.init.constant_(self.modulator_conv.weight, 0.)
nn.init.constant_(self.modulator_conv.bias, 0.)
self.regular_conv = nn.Conv2d(in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=self.padding,
bias=bias)
def forward(self, x):
#h, w = x.shape[2:]
#max_offset = max(h, w)/4.
offset = self.offset_conv(x)#.clamp(-max_offset, max_offset)
modulator = 2. * torch.sigmoid(self.modulator_conv(x))
x = deform_conv2d(
input=x,
offset=offset,
weight=self.regular_conv.weight,
bias=self.regular_conv.bias,
padding=self.padding,
mask=modulator,
stride=self.stride,
)
return x
### utils.py
import torch.nn as nn
def build_act_layer(act_layer):
if act_layer == 'ReLU':
return nn.ReLU(inplace=True)
elif act_layer == 'SiLU':
return nn.SiLU(inplace=True)
elif act_layer == 'GELU':
return nn.GELU()
raise NotImplementedError(f'build_act_layer does not support {act_layer}')
def build_norm_layer(dim,
norm_layer,
in_format='channels_last',
out_format='channels_last',
eps=1e-6):
layers = []
if norm_layer == 'BN':
if in_format == 'channels_last':
layers.append(to_channels_first())
layers.append(nn.BatchNorm2d(dim))
if out_format == 'channels_last':
layers.append(to_channels_last())
elif norm_layer == 'LN':
if in_format == 'channels_first':
layers.append(to_channels_last())
layers.append(nn.LayerNorm(dim, eps=eps))
if out_format == 'channels_first':
layers.append(to_channels_first())
else:
raise NotImplementedError(
f'build_norm_layer does not support {norm_layer}')
return nn.Sequential(*layers)
class to_channels_first(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x.permute(0, 3, 1, 2)
class to_channels_last(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x.permute(0, 2, 3, 1)
### dataset.py
_class_labels_TR_sorted = (
'Airplane, Ant, Antenna, Archery, Axe, BabyCarriage, Bag, BalanceBeam, Balcony, Balloon, Basket, BasketballHoop, Beatle, Bed, Bee, Bench, Bicycle, '
'BicycleFrame, BicycleStand, Boat, Bonsai, BoomLift, Bridge, BunkBed, Butterfly, Button, Cable, CableLift, Cage, Camcorder, Cannon, Canoe, Car, '
'CarParkDropArm, Carriage, Cart, Caterpillar, CeilingLamp, Centipede, Chair, Clip, Clock, Clothes, CoatHanger, Comb, ConcretePumpTruck, Crack, Crane, '
'Cup, DentalChair, Desk, DeskChair, Diagram, DishRack, DoorHandle, Dragonfish, Dragonfly, Drum, Earphone, Easel, ElectricIron, Excavator, Eyeglasses, '
'Fan, Fence, Fencing, FerrisWheel, FireExtinguisher, Fishing, Flag, FloorLamp, Forklift, GasStation, Gate, Gear, Goal, Golf, GymEquipment, Hammock, '
'Handcart, Handcraft, Handrail, HangGlider, Harp, Harvester, Headset, Helicopter, Helmet, Hook, HorizontalBar, Hydrovalve, IroningTable, Jewelry, Key, '
'KidsPlayground, Kitchenware, Kite, Knife, Ladder, LaundryRack, Lightning, Lobster, Locust, Machine, MachineGun, MagazineRack, Mantis, Medal, MemorialArchway, '
'Microphone, Missile, MobileHolder, Monitor, Mosquito, Motorcycle, MovingTrolley, Mower, MusicPlayer, MusicStand, ObservationTower, Octopus, OilWell, '
'OlympicLogo, OperatingTable, OutdoorFitnessEquipment, Parachute, Pavilion, Piano, Pipe, PlowHarrow, PoleVault, Punchbag, Rack, Racket, Rifle, Ring, Robot, '
'RockClimbing, Rope, Sailboat, Satellite, Scaffold, Scale, Scissor, Scooter, Sculpture, Seadragon, Seahorse, Seal, SewingMachine, Ship, Shoe, ShoppingCart, '
'ShoppingTrolley, Shower, Shrimp, Signboard, Skateboarding, Skeleton, Skiing, Spade, SpeedBoat, Spider, Spoon, Stair, Stand, Stationary, SteeringWheel, '
'Stethoscope, Stool, Stove, StreetLamp, SweetStand, Swing, Sword, TV, Table, TableChair, TableLamp, TableTennis, Tank, Tapeline, Teapot, Telescope, Tent, '
'TobaccoPipe, Toy, Tractor, TrafficLight, TrafficSign, Trampoline, TransmissionTower, Tree, Tricycle, TrimmerCover, Tripod, Trombone, Truck, Trumpet, Tuba, '
'UAV, Umbrella, UnevenBars, UtilityPole, VacuumCleaner, Violin, Wakesurfing, Watch, WaterTower, WateringPot, Well, WellLid, Wheel, Wheelchair, WindTurbine, Windmill, WineGlass, WireWhisk, Yacht'
)
class_labels_TR_sorted = _class_labels_TR_sorted.split(', ')
### models/backbones/build_backbones.py
import torch
import torch.nn as nn
from collections import OrderedDict
from torchvision.models import vgg16, vgg16_bn, VGG16_Weights, VGG16_BN_Weights, resnet50, ResNet50_Weights
# from models.pvt_v2 import pvt_v2_b0, pvt_v2_b1, pvt_v2_b2, pvt_v2_b5
# from models.swin_v1 import swin_v1_t, swin_v1_s, swin_v1_b, swin_v1_l
# from config import Config
config = Config()
def build_backbone(bb_name, pretrained=True, params_settings=''):
if bb_name == 'vgg16':
bb_net = list(vgg16(pretrained=VGG16_Weights.DEFAULT if pretrained else None).children())[0]
bb = nn.Sequential(OrderedDict({'conv1': bb_net[:4], 'conv2': bb_net[4:9], 'conv3': bb_net[9:16], 'conv4': bb_net[16:23]}))
elif bb_name == 'vgg16bn':
bb_net = list(vgg16_bn(pretrained=VGG16_BN_Weights.DEFAULT if pretrained else None).children())[0]
bb = nn.Sequential(OrderedDict({'conv1': bb_net[:6], 'conv2': bb_net[6:13], 'conv3': bb_net[13:23], 'conv4': bb_net[23:33]}))
elif bb_name == 'resnet50':
bb_net = list(resnet50(pretrained=ResNet50_Weights.DEFAULT if pretrained else None).children())
bb = nn.Sequential(OrderedDict({'conv1': nn.Sequential(*bb_net[0:3]), 'conv2': bb_net[4], 'conv3': bb_net[5], 'conv4': bb_net[6]}))
else:
bb = eval('{}({})'.format(bb_name, params_settings))
if pretrained:
bb = load_weights(bb, bb_name)
return bb
def load_weights(model, model_name):
save_model = torch.load(config.weights[model_name], map_location='cpu')
model_dict = model.state_dict()
state_dict = {k: v if v.size() == model_dict[k].size() else model_dict[k] for k, v in save_model.items() if k in model_dict.keys()}
# to ignore the weights with mismatched size when I modify the backbone itself.
if not state_dict:
save_model_keys = list(save_model.keys())
sub_item = save_model_keys[0] if len(save_model_keys) == 1 else None
state_dict = {k: v if v.size() == model_dict[k].size() else model_dict[k] for k, v in save_model[sub_item].items() if k in model_dict.keys()}
if not state_dict or not sub_item:
print('Weights are not successully loaded. Check the state dict of weights file.')
return None
else:
print('Found correct weights in the "{}" item of loaded state_dict.'.format(sub_item))
model_dict.update(state_dict)
model.load_state_dict(model_dict)
return model
### models/modules/decoder_blocks.py
import torch
import torch.nn as nn
# from models.aspp import ASPP, ASPPDeformable
# from config import Config
# config = Config()
class BasicDecBlk(nn.Module):
def __init__(self, in_channels=64, out_channels=64, inter_channels=64):
super(BasicDecBlk, self).__init__()
inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1)
self.relu_in = nn.ReLU(inplace=True)
if config.dec_att == 'ASPP':
self.dec_att = ASPP(in_channels=inter_channels)
elif config.dec_att == 'ASPPDeformable':
self.dec_att = ASPPDeformable(in_channels=inter_channels)
self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1)
self.bn_in = nn.BatchNorm2d(inter_channels) if config.batch_size > 1 else nn.Identity()
self.bn_out = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity()
def forward(self, x):
x = self.conv_in(x)
x = self.bn_in(x)
x = self.relu_in(x)
if hasattr(self, 'dec_att'):
x = self.dec_att(x)
x = self.conv_out(x)
x = self.bn_out(x)
return x
class ResBlk(nn.Module):
def __init__(self, in_channels=64, out_channels=None, inter_channels=64):
super(ResBlk, self).__init__()
if out_channels is None:
out_channels = in_channels
inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1)
self.bn_in = nn.BatchNorm2d(inter_channels) if config.batch_size > 1 else nn.Identity()
self.relu_in = nn.ReLU(inplace=True)
if config.dec_att == 'ASPP':
self.dec_att = ASPP(in_channels=inter_channels)
elif config.dec_att == 'ASPPDeformable':
self.dec_att = ASPPDeformable(in_channels=inter_channels)
self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1)
self.bn_out = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity()
self.conv_resi = nn.Conv2d(in_channels, out_channels, 1, 1, 0)
def forward(self, x):
_x = self.conv_resi(x)
x = self.conv_in(x)
x = self.bn_in(x)
x = self.relu_in(x)
if hasattr(self, 'dec_att'):
x = self.dec_att(x)
x = self.conv_out(x)
x = self.bn_out(x)
return x + _x
### models/modules/lateral_blocks.py
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
# from config import Config
# config = Config()
class BasicLatBlk(nn.Module):
def __init__(self, in_channels=64, out_channels=64, inter_channels=64):
super(BasicLatBlk, self).__init__()
inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
self.conv = nn.Conv2d(in_channels, out_channels, 1, 1, 0)
def forward(self, x):
x = self.conv(x)
return x
### models/modules/aspp.py
import torch
import torch.nn as nn
import torch.nn.functional as F
# from models.deform_conv import DeformableConv2d
# from config import Config
# config = Config()
class _ASPPModule(nn.Module):
def __init__(self, in_channels, planes, kernel_size, padding, dilation):
super(_ASPPModule, self).__init__()
self.atrous_conv = nn.Conv2d(in_channels, planes, kernel_size=kernel_size,
stride=1, padding=padding, dilation=dilation, bias=False)
self.bn = nn.BatchNorm2d(planes) if config.batch_size > 1 else nn.Identity()
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.atrous_conv(x)
x = self.bn(x)
return self.relu(x)
class ASPP(nn.Module):
def __init__(self, in_channels=64, out_channels=None, output_stride=16):
super(ASPP, self).__init__()
self.down_scale = 1
if out_channels is None:
out_channels = in_channels
self.in_channelster = 256 // self.down_scale
if output_stride == 16:
dilations = [1, 6, 12, 18]
elif output_stride == 8:
dilations = [1, 12, 24, 36]
else:
raise NotImplementedError
self.aspp1 = _ASPPModule(in_channels, self.in_channelster, 1, padding=0, dilation=dilations[0])
self.aspp2 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[1], dilation=dilations[1])
self.aspp3 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[2], dilation=dilations[2])
self.aspp4 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[3], dilation=dilations[3])
self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
nn.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False),
nn.BatchNorm2d(self.in_channelster) if config.batch_size > 1 else nn.Identity(),
nn.ReLU(inplace=True))
self.conv1 = nn.Conv2d(self.in_channelster * 5, out_channels, 1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity()
self.relu = nn.ReLU(inplace=True)
self.dropout = nn.Dropout(0.5)
def forward(self, x):
x1 = self.aspp1(x)
x2 = self.aspp2(x)
x3 = self.aspp3(x)
x4 = self.aspp4(x)
x5 = self.global_avg_pool(x)
x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True)
x = torch.cat((x1, x2, x3, x4, x5), dim=1)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
return self.dropout(x)
##################### Deformable
class _ASPPModuleDeformable(nn.Module):
def __init__(self, in_channels, planes, kernel_size, padding):
super(_ASPPModuleDeformable, self).__init__()
self.atrous_conv = DeformableConv2d(in_channels, planes, kernel_size=kernel_size,
stride=1, padding=padding, bias=False)
self.bn = nn.BatchNorm2d(planes) if config.batch_size > 1 else nn.Identity()
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.atrous_conv(x)
x = self.bn(x)
return self.relu(x)
class ASPPDeformable(nn.Module):
def __init__(self, in_channels, out_channels=None, parallel_block_sizes=[1, 3, 7]):
super(ASPPDeformable, self).__init__()
self.down_scale = 1
if out_channels is None:
out_channels = in_channels
self.in_channelster = 256 // self.down_scale
self.aspp1 = _ASPPModuleDeformable(in_channels, self.in_channelster, 1, padding=0)
self.aspp_deforms = nn.ModuleList([
_ASPPModuleDeformable(in_channels, self.in_channelster, conv_size, padding=int(conv_size//2)) for conv_size in parallel_block_sizes
])
self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
nn.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False),
nn.BatchNorm2d(self.in_channelster) if config.batch_size > 1 else nn.Identity(),
nn.ReLU(inplace=True))
self.conv1 = nn.Conv2d(self.in_channelster * (2 + len(self.aspp_deforms)), out_channels, 1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels) if config.batch_size > 1 else nn.Identity()
self.relu = nn.ReLU(inplace=True)
self.dropout = nn.Dropout(0.5)
def forward(self, x):
x1 = self.aspp1(x)
x_aspp_deforms = [aspp_deform(x) for aspp_deform in self.aspp_deforms]
x5 = self.global_avg_pool(x)
x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True)
x = torch.cat((x1, *x_aspp_deforms, x5), dim=1)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
return self.dropout(x)
### models/refinement/refiner.py
import torch
import torch.nn as nn
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models import vgg16, vgg16_bn
from torchvision.models import resnet50
# from config import Config
# from dataset import class_labels_TR_sorted
# from models.build_backbone import build_backbone
# from models.decoder_blocks import BasicDecBlk
# from models.lateral_blocks import BasicLatBlk
# from models.ing import *
# from models.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)
########## Encoder ##########
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)
########## Decoder ##########
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)
########## Encoder ##########
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)
########## Decoder ##########
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):
# Refinement
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
### models/stem_layer.py
import torch.nn as nn
# from utils import build_act_layer, build_norm_layer
class StemLayer(nn.Module):
r""" Stem layer of InternImage
Args:
in_channels (int): number of input channels
out_channels (int): number of output channels
act_layer (str): activation layer
norm_layer (str): normalization layer
"""
def __init__(self,
in_channels=3+1,
inter_channels=48,
out_channels=96,
act_layer='GELU',
norm_layer='BN'):
super().__init__()
self.conv1 = nn.Conv2d(in_channels,
inter_channels,
kernel_size=3,
stride=1,
padding=1)
self.norm1 = build_norm_layer(
inter_channels, norm_layer, 'channels_first', 'channels_first'
)
self.act = build_act_layer(act_layer)
self.conv2 = nn.Conv2d(inter_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
self.norm2 = build_norm_layer(
out_channels, norm_layer, 'channels_first', 'channels_first'
)
def forward(self, x):
x = self.conv1(x)
x = self.norm1(x)
x = self.act(x)
x = self.conv2(x)
x = self.norm2(x)
return x
### models/birefnet.py
import torch
import torch.nn as nn
import torch.nn.functional as F
from kornia.filters import laplacian
from transformers import PreTrainedModel
# from config import Config
# from dataset import class_labels_TR_sorted
# from models.build_backbone import build_backbone
# from models.decoder_blocks import BasicDecBlk, ResBlk, HierarAttDecBlk
# from models.lateral_blocks import BasicLatBlk
# from models.aspp import ASPP, ASPPDeformable
# from models.ing import *
# from models.refiner import Refiner, RefinerPVTInChannels4, RefUNet
# from models.stem_layer import StemLayer
from .BiRefNet_config import BiRefNetConfig
class BiRefNet(
PreTrainedModel
):
config_class = BiRefNetConfig
def __init__(self, bb_pretrained=True, config=BiRefNetConfig()):
super(BiRefNet, self).__init__(config)
bb_pretrained = config.bb_pretrained
self.config = Config()
self.epoch = 1
self.bb = build_backbone(self.config.bb, pretrained=bb_pretrained)
channels = self.config.lateral_channels_in_collection
if self.config.auxiliary_classification:
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.cls_head = nn.Sequential(
nn.Linear(channels[0], len(class_labels_TR_sorted))
)
if self.config.squeeze_block:
self.squeeze_module = nn.Sequential(*[
eval(self.config.squeeze_block.split('_x')[0])(channels[0]+sum(self.config.cxt), channels[0])
for _ in range(eval(self.config.squeeze_block.split('_x')[1]))
])
self.decoder = Decoder(channels)
if self.config.ender:
self.dec_end = nn.Sequential(
nn.Conv2d(1, 16, 3, 1, 1),
nn.Conv2d(16, 1, 3, 1, 1),
nn.ReLU(inplace=True),
)
# refine patch-level segmentation
if self.config.refine:
if self.config.refine == 'itself':
self.stem_layer = StemLayer(in_channels=3+1, inter_channels=48, out_channels=3, norm_layer='BN' if self.config.batch_size > 1 else 'LN')
else:
self.refiner = eval('{}({})'.format(self.config.refine, 'in_channels=3+1'))
if self.config.freeze_bb:
# Freeze the backbone...
print(self.named_parameters())
for key, value in self.named_parameters():
if 'bb.' in key and 'refiner.' not in key:
value.requires_grad = False
def forward_enc(self, 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)
if self.config.mul_scl_ipt == 'cat':
B, C, H, W = x.shape
x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True))
x1 = torch.cat([x1, F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)], dim=1)
x2 = torch.cat([x2, F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)], dim=1)
x3 = torch.cat([x3, F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)], dim=1)
x4 = torch.cat([x4, F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)], dim=1)
elif self.config.mul_scl_ipt == 'add':
B, C, H, W = x.shape
x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True))
x1 = x1 + F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)
x2 = x2 + F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)
x3 = x3 + F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)
x4 = x4 + F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)
class_preds = self.cls_head(self.avgpool(x4).view(x4.shape[0], -1)) if self.training and self.config.auxiliary_classification else None
if self.config.cxt:
x4 = torch.cat(
(
*[
F.interpolate(x1, size=x4.shape[2:], mode='bilinear', align_corners=True),
F.interpolate(x2, size=x4.shape[2:], mode='bilinear', align_corners=True),
F.interpolate(x3, size=x4.shape[2:], mode='bilinear', align_corners=True),
][-len(self.config.cxt):],
x4
),
dim=1
)
return (x1, x2, x3, x4), class_preds
def forward_ori(self, x):
########## Encoder ##########
(x1, x2, x3, x4), class_preds = self.forward_enc(x)
if self.config.squeeze_block:
x4 = self.squeeze_module(x4)
########## Decoder ##########
features = [x, x1, x2, x3, x4]
if self.training and self.config.out_ref:
features.append(laplacian(torch.mean(x, dim=1).unsqueeze(1), kernel_size=5))
scaled_preds = self.decoder(features)
return scaled_preds, class_preds
def forward(self, x):
scaled_preds, class_preds = self.forward_ori(x)
class_preds_lst = [class_preds]
return [scaled_preds, class_preds_lst] if self.training else scaled_preds
class Decoder(nn.Module):
def __init__(self, channels):
super(Decoder, self).__init__()
self.config = Config()
DecoderBlock = eval(self.config.dec_blk)
LateralBlock = eval(self.config.lat_blk)
if self.config.dec_ipt:
self.split = self.config.dec_ipt_split
N_dec_ipt = 64
DBlock = SimpleConvs
ic = 64
ipt_cha_opt = 1
self.ipt_blk5 = DBlock(2**10*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
self.ipt_blk4 = DBlock(2**8*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
self.ipt_blk3 = DBlock(2**6*3 if self.split else 3, [N_dec_ipt, channels[1]//8][ipt_cha_opt], inter_channels=ic)
self.ipt_blk2 = DBlock(2**4*3 if self.split else 3, [N_dec_ipt, channels[2]//8][ipt_cha_opt], inter_channels=ic)
self.ipt_blk1 = DBlock(2**0*3 if self.split else 3, [N_dec_ipt, channels[3]//8][ipt_cha_opt], inter_channels=ic)
else:
self.split = None
self.decoder_block4 = DecoderBlock(channels[0]+([N_dec_ipt, channels[0]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[1])
self.decoder_block3 = DecoderBlock(channels[1]+([N_dec_ipt, channels[0]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[2])
self.decoder_block2 = DecoderBlock(channels[2]+([N_dec_ipt, channels[1]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[3])
self.decoder_block1 = DecoderBlock(channels[3]+([N_dec_ipt, channels[2]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[3]//2)
self.conv_out1 = nn.Sequential(nn.Conv2d(channels[3]//2+([N_dec_ipt, channels[3]//8][ipt_cha_opt] if self.config.dec_ipt else 0), 1, 1, 1, 0))
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)
if self.config.out_ref:
_N = 16
self.gdt_convs_4 = nn.Sequential(nn.Conv2d(channels[1], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True))
self.gdt_convs_3 = nn.Sequential(nn.Conv2d(channels[2], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True))
self.gdt_convs_2 = nn.Sequential(nn.Conv2d(channels[3], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True))
self.gdt_convs_pred_4 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
self.gdt_convs_pred_3 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
self.gdt_convs_pred_2 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
self.gdt_convs_attn_4 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
self.gdt_convs_attn_3 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
self.gdt_convs_attn_2 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
def get_patches_batch(self, x, p):
_size_h, _size_w = p.shape[2:]
patches_batch = []
for idx in range(x.shape[0]):
columns_x = torch.split(x[idx], split_size_or_sections=_size_w, dim=-1)
patches_x = []
for column_x in columns_x:
patches_x += [p.unsqueeze(0) for p in torch.split(column_x, split_size_or_sections=_size_h, dim=-2)]
patch_sample = torch.cat(patches_x, dim=1)
patches_batch.append(patch_sample)
return torch.cat(patches_batch, dim=0)
def forward(self, features):
if self.training and self.config.out_ref:
outs_gdt_pred = []
outs_gdt_label = []
x, x1, x2, x3, x4, gdt_gt = features
else:
x, x1, x2, x3, x4 = features
outs = []
if self.config.dec_ipt:
patches_batch = self.get_patches_batch(x, x4) if self.split else x
x4 = torch.cat((x4, self.ipt_blk5(F.interpolate(patches_batch, size=x4.shape[2:], mode='bilinear', align_corners=True))), 1)
p4 = self.decoder_block4(x4)
m4 = self.conv_ms_spvn_4(p4) if self.config.ms_supervision else None
if self.config.out_ref:
p4_gdt = self.gdt_convs_4(p4)
if self.training:
# >> GT:
m4_dia = m4
gdt_label_main_4 = gdt_gt * F.interpolate(m4_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True)
outs_gdt_label.append(gdt_label_main_4)
# >> Pred:
gdt_pred_4 = self.gdt_convs_pred_4(p4_gdt)
outs_gdt_pred.append(gdt_pred_4)
gdt_attn_4 = self.gdt_convs_attn_4(p4_gdt).sigmoid()
# >> Finally:
p4 = p4 * gdt_attn_4
_p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True)
_p3 = _p4 + self.lateral_block4(x3)
if self.config.dec_ipt:
patches_batch = self.get_patches_batch(x, _p3) if self.split else x
_p3 = torch.cat((_p3, self.ipt_blk4(F.interpolate(patches_batch, size=x3.shape[2:], mode='bilinear', align_corners=True))), 1)
p3 = self.decoder_block3(_p3)
m3 = self.conv_ms_spvn_3(p3) if self.config.ms_supervision else None
if self.config.out_ref:
p3_gdt = self.gdt_convs_3(p3)
if self.training:
# >> GT:
# m3 --dilation--> m3_dia
# G_3^gt * m3_dia --> G_3^m, which is the label of gradient
m3_dia = m3
gdt_label_main_3 = gdt_gt * F.interpolate(m3_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True)
outs_gdt_label.append(gdt_label_main_3)
# >> Pred:
# p3 --conv--BN--> F_3^G, where F_3^G predicts the \hat{G_3} with xx
# F_3^G --sigmoid--> A_3^G
gdt_pred_3 = self.gdt_convs_pred_3(p3_gdt)
outs_gdt_pred.append(gdt_pred_3)
gdt_attn_3 = self.gdt_convs_attn_3(p3_gdt).sigmoid()
# >> Finally:
# p3 = p3 * A_3^G
p3 = p3 * gdt_attn_3
_p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True)
_p2 = _p3 + self.lateral_block3(x2)
if self.config.dec_ipt:
patches_batch = self.get_patches_batch(x, _p2) if self.split else x
_p2 = torch.cat((_p2, self.ipt_blk3(F.interpolate(patches_batch, size=x2.shape[2:], mode='bilinear', align_corners=True))), 1)
p2 = self.decoder_block2(_p2)
m2 = self.conv_ms_spvn_2(p2) if self.config.ms_supervision else None
if self.config.out_ref:
p2_gdt = self.gdt_convs_2(p2)
if self.training:
# >> GT:
m2_dia = m2
gdt_label_main_2 = gdt_gt * F.interpolate(m2_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True)
outs_gdt_label.append(gdt_label_main_2)
# >> Pred:
gdt_pred_2 = self.gdt_convs_pred_2(p2_gdt)
outs_gdt_pred.append(gdt_pred_2)
gdt_attn_2 = self.gdt_convs_attn_2(p2_gdt).sigmoid()
# >> Finally:
p2 = p2 * gdt_attn_2
_p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True)
_p1 = _p2 + self.lateral_block2(x1)
if self.config.dec_ipt:
patches_batch = self.get_patches_batch(x, _p1) if self.split else x
_p1 = torch.cat((_p1, self.ipt_blk2(F.interpolate(patches_batch, size=x1.shape[2:], mode='bilinear', align_corners=True))), 1)
_p1 = self.decoder_block1(_p1)
_p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True)
if self.config.dec_ipt:
patches_batch = self.get_patches_batch(x, _p1) if self.split else x
_p1 = torch.cat((_p1, self.ipt_blk1(F.interpolate(patches_batch, size=x.shape[2:], mode='bilinear', align_corners=True))), 1)
p1_out = self.conv_out1(_p1)
if self.config.ms_supervision:
outs.append(m4)
outs.append(m3)
outs.append(m2)
outs.append(p1_out)
return outs if not (self.config.out_ref and self.training) else ([outs_gdt_pred, outs_gdt_label], outs)
class SimpleConvs(nn.Module):
def __init__(
self, in_channels: int, out_channels: int, inter_channels=64
) -> None:
super().__init__()
self.conv1 = nn.Conv2d(in_channels, inter_channels, 3, 1, 1)
self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, 1)
def forward(self, x):
return self.conv_out(self.conv1(x))
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