2272 lines
91 KiB
Python
2272 lines
91 KiB
Python
### config.py
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import os
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import math
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from transformers import PretrainedConfig
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# BiRefNetConfig for transformers
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class BiRefNetConfig(PretrainedConfig):
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"""BiRefNet 模型配置类（用于 transformers）"""
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model_type = "SegformerForSemanticSegmentation"
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def __init__(
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self,
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bb_pretrained=False,
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**kwargs
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):
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self.bb_pretrained = bb_pretrained
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super().__init__(**kwargs)
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class Config(PretrainedConfig):
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def __init__(self) -> None:
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# Compatible with the latest version of transformers.
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# Error source: https://github.com/huggingface/transformers/commit/9568b506ed511c76ab4d0c6ed591c7fce8e048a5
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# Previous solution in the users' end: https://github.com/ZhengPeng7/BiRefNet/issues/189#issuecomment-2716688688
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super().__init__()
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# PATH settings
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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
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# TASK settings
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self.task = ['DIS5K', 'COD', 'HRSOD', 'DIS5K+HRSOD+HRS10K', 'P3M-10k'][0]
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self.training_set = {
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'DIS5K': ['DIS-TR', 'DIS-TR+DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4'][0],
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'COD': 'TR-COD10K+TR-CAMO',
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'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],
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'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.
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'P3M-10k': 'TR-P3M-10k',
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}[self.task]
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self.prompt4loc = ['dense', 'sparse'][0]
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# Faster-Training settings
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self.load_all = True
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self.compile = True # 1. Trigger CPU memory leak in some extend, which is an inherent problem of PyTorch.
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# Machines with > 70GB CPU memory can run the whole training on DIS5K with default setting.
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# 2. Higher PyTorch version may fix it: https://github.com/pytorch/pytorch/issues/119607.
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# 3. But compile in Pytorch > 2.0.1 seems to bring no acceleration for training.
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self.precisionHigh = True
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# MODEL settings
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self.ms_supervision = True
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self.out_ref = self.ms_supervision and True
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self.dec_ipt = True
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self.dec_ipt_split = True
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self.cxt_num = [0, 3][1] # multi-scale skip connections from encoder
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self.mul_scl_ipt = ['', 'add', 'cat'][2]
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self.dec_att = ['', 'ASPP', 'ASPPDeformable'][2]
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self.squeeze_block = ['', 'BasicDecBlk_x1', 'ResBlk_x4', 'ASPP_x3', 'ASPPDeformable_x3'][1]
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self.dec_blk = ['BasicDecBlk', 'ResBlk', 'HierarAttDecBlk'][0]
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# TRAINING settings
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self.batch_size = 4
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self.IoU_finetune_last_epochs = [
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0,
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{
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'DIS5K': -50,
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'COD': -20,
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'HRSOD': -20,
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'DIS5K+HRSOD+HRS10K': -20,
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'P3M-10k': -20,
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}[self.task]
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][1] # choose 0 to skip
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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
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self.size = 1024
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self.num_workers = max(4, self.batch_size) # will be decrease to min(it, batch_size) at the initialization of the data_loader
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# Backbone settings
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self.bb = [
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'vgg16', 'vgg16bn', 'resnet50', # 0, 1, 2
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'swin_v1_t', 'swin_v1_s', # 3, 4
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'swin_v1_b', 'swin_v1_l', # 5-bs9, 6-bs4
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'pvt_v2_b0', 'pvt_v2_b1', # 7, 8
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'pvt_v2_b2', 'pvt_v2_b5', # 9-bs10, 10-bs5
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][6]
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self.lateral_channels_in_collection = {
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'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
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'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
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'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
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'swin_v1_t': [768, 384, 192, 96], 'swin_v1_s': [768, 384, 192, 96],
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'pvt_v2_b0': [256, 160, 64, 32], 'pvt_v2_b1': [512, 320, 128, 64],
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}[self.bb]
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if self.mul_scl_ipt == 'cat':
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self.lateral_channels_in_collection = [channel * 2 for channel in self.lateral_channels_in_collection]
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self.cxt = self.lateral_channels_in_collection[1:][::-1][-self.cxt_num:] if self.cxt_num else []
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# MODEL settings - inactive
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self.lat_blk = ['BasicLatBlk'][0]
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self.dec_channels_inter = ['fixed', 'adap'][0]
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self.refine = ['', 'itself', 'RefUNet', 'Refiner', 'RefinerPVTInChannels4'][0]
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self.progressive_ref = self.refine and True
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self.ender = self.progressive_ref and False
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self.scale = self.progressive_ref and 2
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self.auxiliary_classification = False # Only for DIS5K, where class labels are saved in `dataset.py`.
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self.refine_iteration = 1
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self.freeze_bb = False
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self.model = [
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'BiRefNet',
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][0]
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if self.dec_blk == 'HierarAttDecBlk':
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self.batch_size = 2 ** [0, 1, 2, 3, 4][2]
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# TRAINING settings - inactive
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self.preproc_methods = ['flip', 'enhance', 'rotate', 'pepper', 'crop'][:4]
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self.optimizer = ['Adam', 'AdamW'][1]
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self.lr_decay_epochs = [1e5] # Set to negative N to decay the lr in the last N-th epoch.
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self.lr_decay_rate = 0.5
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# Loss
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self.lambdas_pix_last = {
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# not 0 means opening this loss
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# original rate -- 1 : 30 : 1.5 : 0.2, bce x 30
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'bce': 30 * 1, # high performance
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'iou': 0.5 * 1, # 0 / 255
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'iou_patch': 0.5 * 0, # 0 / 255, win_size = (64, 64)
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'mse': 150 * 0, # can smooth the saliency map
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'triplet': 3 * 0,
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'reg': 100 * 0,
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'ssim': 10 * 1, # help contours,
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'cnt': 5 * 0, # help contours
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'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.
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}
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self.lambdas_cls = {
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'ce': 5.0
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}
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# Adv
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self.lambda_adv_g = 10. * 0 # turn to 0 to avoid adv training
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self.lambda_adv_d = 3. * (self.lambda_adv_g > 0)
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# PATH settings - inactive
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self.data_root_dir = os.path.join(self.sys_home_dir, 'datasets/dis')
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self.weights_root_dir = os.path.join(self.sys_home_dir, 'weights')
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self.weights = {
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'pvt_v2_b2': os.path.join(self.weights_root_dir, 'pvt_v2_b2.pth'),
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'pvt_v2_b5': os.path.join(self.weights_root_dir, ['pvt_v2_b5.pth', 'pvt_v2_b5_22k.pth'][0]),
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'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]),
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'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]),
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'swin_v1_t': os.path.join(self.weights_root_dir, ['swin_tiny_patch4_window7_224_22kto1k_finetune.pth'][0]),
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'swin_v1_s': os.path.join(self.weights_root_dir, ['swin_small_patch4_window7_224_22kto1k_finetune.pth'][0]),
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'pvt_v2_b0': os.path.join(self.weights_root_dir, ['pvt_v2_b0.pth'][0]),
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'pvt_v2_b1': os.path.join(self.weights_root_dir, ['pvt_v2_b1.pth'][0]),
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}
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# Callbacks - inactive
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self.verbose_eval = True
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self.only_S_MAE = False
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self.use_fp16 = False # Bugs. It may cause nan in training.
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self.SDPA_enabled = False # Bugs. Slower and errors occur in multi-GPUs
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# others
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self.device = [0, 'cpu'][0] # .to(0) == .to('cuda:0')
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self.batch_size_valid = 1
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self.rand_seed = 7
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# 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]
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# with open(run_sh_file[0], 'r') as f:
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# lines = f.readlines()
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# 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])
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# 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])
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# self.val_step = [0, self.save_step][0]
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def print_task(self) -> None:
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# Return task for choosing settings in shell scripts.
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print(self.task)
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### models/backbones/pvt_v2.py
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import torch
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import torch.nn as nn
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from functools import partial
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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from timm.models.registry import register_model
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import math
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# from config import Config
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# config = Config()
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class Mlp(nn.Module):
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.dwconv = DWConv(hidden_features)
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self.act = act_layer()
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop = nn.Dropout(drop)
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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elif isinstance(m, nn.Conv2d):
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fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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fan_out //= m.groups
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m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
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if m.bias is not None:
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m.bias.data.zero_()
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def forward(self, x, H, W):
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x = self.fc1(x)
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x = self.dwconv(x, H, W)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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class Attention(nn.Module):
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def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
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super().__init__()
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assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
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self.dim = dim
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or head_dim ** -0.5
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self.q = nn.Linear(dim, dim, bias=qkv_bias)
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self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
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self.attn_drop_prob = attn_drop
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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self.sr_ratio = sr_ratio
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if sr_ratio > 1:
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self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
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self.norm = nn.LayerNorm(dim)
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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elif isinstance(m, nn.Conv2d):
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fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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fan_out //= m.groups
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m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
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if m.bias is not None:
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m.bias.data.zero_()
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def forward(self, x, H, W):
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B, N, C = x.shape
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q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
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if self.sr_ratio > 1:
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x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
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x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
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x_ = self.norm(x_)
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kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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else:
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kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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k, v = kv[0], kv[1]
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if config.SDPA_enabled:
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x = torch.nn.functional.scaled_dot_product_attention(
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q, k, v,
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attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False
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).transpose(1, 2).reshape(B, N, C)
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else:
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attn = (q @ k.transpose(-2, -1)) * self.scale
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class Block(nn.Module):
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def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
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drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention(
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dim,
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num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
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attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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elif isinstance(m, nn.Conv2d):
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fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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fan_out //= m.groups
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m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
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if m.bias is not None:
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m.bias.data.zero_()
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def forward(self, x, H, W):
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x = x + self.drop_path(self.attn(self.norm1(x), H, W))
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x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
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return x
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class OverlapPatchEmbed(nn.Module):
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""" Image to Patch Embedding
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"""
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def __init__(self, img_size=224, patch_size=7, stride=4, in_channels=3, embed_dim=768):
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super().__init__()
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img_size = to_2tuple(img_size)
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patch_size = to_2tuple(patch_size)
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self.img_size = img_size
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self.patch_size = patch_size
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self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
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self.num_patches = self.H * self.W
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self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=stride,
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padding=(patch_size[0] // 2, patch_size[1] // 2))
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self.norm = nn.LayerNorm(embed_dim)
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
|
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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elif isinstance(m, nn.Conv2d):
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fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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fan_out //= m.groups
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m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
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if m.bias is not None:
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m.bias.data.zero_()
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def forward(self, x):
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x = self.proj(x)
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_, _, H, W = x.shape
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x = x.flatten(2).transpose(1, 2)
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x = self.norm(x)
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return x, H, W
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|
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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
|
||
# Turn int to torch.tensor for the compatiability with torch.compile in PyTorch 2.5.
|
||
Hp = torch.ceil(torch.tensor(H) / self.window_size).to(torch.int64) * self.window_size
|
||
Wp = torch.ceil(torch.tensor(W) / self.window_size).to(torch.int64) * 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)).to(x.dtype)
|
||
|
||
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 einops import rearrange
|
||
|
||
# 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
|
||
# BiRefNetConfig 已在文件开头定义
|
||
|
||
|
||
def image2patches(image, grid_h=2, grid_w=2, patch_ref=None, transformation='b c (hg h) (wg w) -> (b hg wg) c h w'):
|
||
if patch_ref is not None:
|
||
grid_h, grid_w = image.shape[-2] // patch_ref.shape[-2], image.shape[-1] // patch_ref.shape[-1]
|
||
patches = rearrange(image, transformation, hg=grid_h, wg=grid_w)
|
||
return patches
|
||
|
||
def patches2image(patches, grid_h=2, grid_w=2, patch_ref=None, transformation='(b hg wg) c h w -> b c (hg h) (wg w)'):
|
||
if patch_ref is not None:
|
||
grid_h, grid_w = patch_ref.shape[-2] // patches[0].shape[-2], patch_ref.shape[-1] // patches[0].shape[-1]
|
||
image = rearrange(patches, transformation, hg=grid_h, wg=grid_w)
|
||
return image
|
||
|
||
class BiRefNet(
|
||
PreTrainedModel
|
||
):
|
||
config_class = BiRefNetConfig
|
||
def __init__(self, bb_pretrained=True, config=None):
|
||
if config is None:
|
||
config = BiRefNetConfig()
|
||
super(BiRefNet, self).__init__(config)
|
||
bb_pretrained = config.bb_pretrained if hasattr(config, 'bb_pretrained') else bb_pretrained
|
||
# 创建训练配置实例（保持原有功能）
|
||
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 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 = image2patches(x, patch_ref=x4, transformation='b c (hg h) (wg w) -> b (c hg wg) h w') 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 and self.training 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 = image2patches(x, patch_ref=_p3, transformation='b c (hg h) (wg w) -> b (c hg wg) h w') 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 and self.training 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 = image2patches(x, patch_ref=_p2, transformation='b c (hg h) (wg w) -> b (c hg wg) h w') 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 and self.training 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 = image2patches(x, patch_ref=_p1, transformation='b c (hg h) (wg w) -> b (c hg wg) h w') 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 = image2patches(x, patch_ref=_p1, transformation='b c (hg h) (wg w) -> b (c hg wg) h w') 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 and self.training:
|
||
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))
|