add files
This commit is contained in:
刘雪峰
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MIT License
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Copyright (c) 2024 lldacing
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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---
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The code and models of BiRefNet are released under the MIT License.
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## Install
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- Manual
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```shell
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cd custom_nodes
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git clone https://github.com/lldacing/ComfyUI_BiRefNet_ll.git
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cd custom_nodes/ComfyUI_BiRefNet_ll
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pip install -r requirements.txt
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# restart ComfyUI
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```
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## Models
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The available models are:
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- General: A pre-trained model for general use cases.
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- General-Lite: A light pre-trained model for general use cases.
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- Portrait: A pre-trained model for human portraits.
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- DIS: A pre-trained model for dichotomous image segmentation (DIS).
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- HRSOD: A pre-trained model for high-resolution salient object detection (HRSOD).
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- COD: A pre-trained model for concealed object detection (COD).
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- DIS-TR_TEs: A pre-trained model with massive dataset.
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Model files go here (automatically downloaded if the folder is not present during first run): `models/BiRefNet`.
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If necessary, they can be downloaded from:
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- [General](https://huggingface.co/ZhengPeng7/BiRefNet/resolve/main/model.safetensors) ➔ `model.safetensors` must be renamed `General.safetensors`
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- [General-Lite](https://huggingface.co/ZhengPeng7/BiRefNet_T/resolve/main/model.safetensors) ➔ `model.safetensors` must be renamed `General-Lite.safetensors`
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- [Portrait](https://huggingface.co/ZhengPeng7/BiRefNet-portrait/resolve/main/model.safetensors) ➔ `model.safetensors` must be renamed `Portrait.safetensors`
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- [DIS](https://huggingface.co/ZhengPeng7/BiRefNet-DIS5K/resolve/main/model.safetensors) ➔ `model.safetensors` must be renamed `DIS.safetensors`
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- [HRSOD](https://huggingface.co/ZhengPeng7/BiRefNet-HRSOD/resolve/main/model.safetensors) ➔ `model.safetensors` must be renamed `HRSOD.safetensors`
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- [COD](https://huggingface.co/ZhengPeng7/BiRefNet-COD/resolve/main/model.safetensors) ➔ `model.safetensors` must be renamed `COD.safetensors`
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- [DIS-TR_TEs](https://huggingface.co/ZhengPeng7/BiRefNet-DIS5K-TR_TEs/resolve/main/model.safetensors) ➔ `model.safetensors` must be renamed `DIS-TR_TEs.safetensors`
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## Nodes
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- AutoDownloadBiRefNetModel
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- Automatically download the model into models/BiRefNet
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- LoadRembgByBiRefNetModel
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- Can select model from "models/BiRefNet" or the path of "birefnet" configured in the extra YAML file
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- You can download model from [BiRefNet Releases](https://github.com/ZhengPeng7/BiRefNet/releases)
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- RembgByBiRefNet
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## Thanks
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[BiRefNet](https://github.com/zhengpeng7/birefnet)
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[dimitribarbot/sd-webui-birefnet](https://github.com/dimitribarbot/sd-webui-birefnet)
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+13
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import os
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import sys
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# 获取当前目录的父目录的父目录
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parent_dir = os.path.dirname(os.path.abspath(__file__))
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# 添加父目录的父目录到系统路径
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sys.path.insert(0, parent_dir)
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from . import birefnetNode
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NODE_CLASS_MAPPINGS = {**birefnetNode.NODE_CLASS_MAPPINGS}
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NODE_DISPLAY_NAME_MAPPINGS = {**birefnetNode.NODE_DISPLAY_NAME_MAPPINGS}
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import os
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import math
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CUR_DIR = os.path.dirname(__file__)
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class Config():
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def __init__(self, bb_index: int = 6) -> None:
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# PATH settings
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# 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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# if os.name == 'nt':
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# self.sys_home_dir = os.environ['USERPROFILE'] # For windows system
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# else:
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# self.sys_home_dir = os.environ['HOME'] # For Linux system
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# TASK settings
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self.task = ['DIS5K', 'COD', 'HRSOD', 'General', 'Matting'][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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'General': 'DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4+DIS-TR+TR-HRSOD+TE-HRSOD+TR-HRS10K+TE-HRS10K+TR-UHRSD+TE-UHRSD+TR-P3M-10k+TE-P3M-500-NP+TE-P3M-500-P+TR-humans', # leave DIS-VD for evaluation.
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'Matting': 'TR-P3M-10k+TE-P3M-500-NP+TR-humans+TR-Distrinctions-646',
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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 = False # Turn it on/off by your case. It may consume a lot of CPU memory. And for multi-GPU (N), it would cost N times the CPU memory to load the data.
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self.use_fp16 = False # It may cause nan in training.
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self.compile = True and (not self.use_fp16) # 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'][0]
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# TRAINING settings
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self.batch_size = 4
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self.finetune_last_epochs = [
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('IoU', 0),
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{
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'DIS5K': ('IoU', -30),
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'COD': ('IoU', -20),
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'HRSOD': ('IoU', -20),
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'General': ('MAE', -10),
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'Matting': ('MAE', -10),
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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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][bb_index]
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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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# 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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if self.task not in ['Matting']:
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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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'mae': 30 * 0,
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'mse': 30 * 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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else:
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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 * 0, # high performance
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'iou': 0.5 * 0, # 0 / 255
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'iou_patch': 0.5 * 0, # 0 / 255, win_size = (64, 64)
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'mae': 100 * 1,
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'mse': 30 * 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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self.weights = {}
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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.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(CUR_DIR) if 'train.sh' == f] + [os.path.join(CUR_DIR, '..', f) for f in os.listdir('..') if 'train.sh' == f]
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# if run_sh_file:
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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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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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# if __name__ == '__main__':
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# config = Config()
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# config.print_task()
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import os
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import cv2
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from tqdm import tqdm
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from PIL import Image
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from torch.utils import data
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from torchvision import transforms
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from birefnet.image_proc import preproc
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from birefnet.config import Config
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from birefnet.utils import path_to_image
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Image.MAX_IMAGE_PIXELS = None # remove DecompressionBombWarning
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config = Config()
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_class_labels_TR_sorted = (
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'Airplane, Ant, Antenna, Archery, Axe, BabyCarriage, Bag, BalanceBeam, Balcony, Balloon, Basket, BasketballHoop, Beatle, Bed, Bee, Bench, Bicycle, '
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'BicycleFrame, BicycleStand, Boat, Bonsai, BoomLift, Bridge, BunkBed, Butterfly, Button, Cable, CableLift, Cage, Camcorder, Cannon, Canoe, Car, '
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'CarParkDropArm, Carriage, Cart, Caterpillar, CeilingLamp, Centipede, Chair, Clip, Clock, Clothes, CoatHanger, Comb, ConcretePumpTruck, Crack, Crane, '
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||||
'Cup, DentalChair, Desk, DeskChair, Diagram, DishRack, DoorHandle, Dragonfish, Dragonfly, Drum, Earphone, Easel, ElectricIron, Excavator, Eyeglasses, '
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'Fan, Fence, Fencing, FerrisWheel, FireExtinguisher, Fishing, Flag, FloorLamp, Forklift, GasStation, Gate, Gear, Goal, Golf, GymEquipment, Hammock, '
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'Handcart, Handcraft, Handrail, HangGlider, Harp, Harvester, Headset, Helicopter, Helmet, Hook, HorizontalBar, Hydrovalve, IroningTable, Jewelry, Key, '
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'KidsPlayground, Kitchenware, Kite, Knife, Ladder, LaundryRack, Lightning, Lobster, Locust, Machine, MachineGun, MagazineRack, Mantis, Medal, MemorialArchway, '
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||||
'Microphone, Missile, MobileHolder, Monitor, Mosquito, Motorcycle, MovingTrolley, Mower, MusicPlayer, MusicStand, ObservationTower, Octopus, OilWell, '
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'OlympicLogo, OperatingTable, OutdoorFitnessEquipment, Parachute, Pavilion, Piano, Pipe, PlowHarrow, PoleVault, Punchbag, Rack, Racket, Rifle, Ring, Robot, '
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||||
'RockClimbing, Rope, Sailboat, Satellite, Scaffold, Scale, Scissor, Scooter, Sculpture, Seadragon, Seahorse, Seal, SewingMachine, Ship, Shoe, ShoppingCart, '
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'ShoppingTrolley, Shower, Shrimp, Signboard, Skateboarding, Skeleton, Skiing, Spade, SpeedBoat, Spider, Spoon, Stair, Stand, Stationary, SteeringWheel, '
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||||
'Stethoscope, Stool, Stove, StreetLamp, SweetStand, Swing, Sword, TV, Table, TableChair, TableLamp, TableTennis, Tank, Tapeline, Teapot, Telescope, Tent, '
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||||
'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'
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)
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class_labels_TR_sorted = _class_labels_TR_sorted.split(', ')
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class MyData(data.Dataset):
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def __init__(self, datasets, image_size, is_train=True):
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self.size_train = image_size
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self.size_test = image_size
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self.keep_size = not config.size
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self.data_size = (config.size, config.size)
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self.is_train = is_train
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self.load_all = config.load_all
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self.device = config.device
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valid_extensions = ['.png', '.jpg', '.PNG', '.JPG', '.JPEG']
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||||
if self.is_train and config.auxiliary_classification:
|
||||
self.cls_name2id = {_name: _id for _id, _name in enumerate(class_labels_TR_sorted)}
|
||||
self.transform_image = transforms.Compose([
|
||||
transforms.Resize(self.data_size),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
|
||||
][self.load_all or self.keep_size:])
|
||||
self.transform_label = transforms.Compose([
|
||||
transforms.Resize(self.data_size),
|
||||
transforms.ToTensor(),
|
||||
][self.load_all or self.keep_size:])
|
||||
dataset_root = os.path.join(config.data_root_dir, config.task)
|
||||
# datasets can be a list of different datasets for training on combined sets.
|
||||
self.image_paths = []
|
||||
for dataset in datasets.split('+'):
|
||||
image_root = os.path.join(dataset_root, dataset, 'im')
|
||||
self.image_paths += [os.path.join(image_root, p) for p in os.listdir(image_root) if any(p.endswith(ext) for ext in valid_extensions)]
|
||||
self.label_paths = []
|
||||
for p in self.image_paths:
|
||||
for ext in valid_extensions:
|
||||
## 'im' and 'gt' may need modifying
|
||||
p_gt = p.replace('/im/', '/gt/')[:-(len(p.split('.')[-1])+1)] + ext
|
||||
file_exists = False
|
||||
if os.path.exists(p_gt):
|
||||
self.label_paths.append(p_gt)
|
||||
file_exists = True
|
||||
break
|
||||
if not file_exists:
|
||||
print('Not exists:', p_gt)
|
||||
|
||||
if len(self.label_paths) != len(self.image_paths):
|
||||
raise ValueError(f"There are different numbers of images ({len(self.label_paths)}) and labels ({len(self.image_paths)})")
|
||||
|
||||
if self.load_all:
|
||||
self.images_loaded, self.labels_loaded = [], []
|
||||
self.class_labels_loaded = []
|
||||
# for image_path, label_path in zip(self.image_paths, self.label_paths):
|
||||
for image_path, label_path in tqdm(zip(self.image_paths, self.label_paths), total=len(self.image_paths)):
|
||||
_image = path_to_image(image_path, size=(config.size, config.size), color_type='rgb')
|
||||
_label = path_to_image(label_path, size=(config.size, config.size), color_type='gray')
|
||||
self.images_loaded.append(_image)
|
||||
self.labels_loaded.append(_label)
|
||||
self.class_labels_loaded.append(
|
||||
self.cls_name2id[label_path.split('/')[-1].split('#')[3]] if self.is_train and config.auxiliary_classification else -1
|
||||
)
|
||||
|
||||
def __getitem__(self, index):
|
||||
|
||||
if self.load_all:
|
||||
image = self.images_loaded[index]
|
||||
label = self.labels_loaded[index]
|
||||
class_label = self.class_labels_loaded[index] if self.is_train and config.auxiliary_classification else -1
|
||||
else:
|
||||
image = path_to_image(self.image_paths[index], size=(config.size, config.size), color_type='rgb')
|
||||
label = path_to_image(self.label_paths[index], size=(config.size, config.size), color_type='gray')
|
||||
class_label = self.cls_name2id[self.label_paths[index].split('/')[-1].split('#')[3]] if self.is_train and config.auxiliary_classification else -1
|
||||
|
||||
# loading image and label
|
||||
if self.is_train:
|
||||
image, label = preproc(image, label, preproc_methods=config.preproc_methods)
|
||||
# else:
|
||||
# if _label.shape[0] > 2048 or _label.shape[1] > 2048:
|
||||
# _image = cv2.resize(_image, (2048, 2048), interpolation=cv2.INTER_LINEAR)
|
||||
# _label = cv2.resize(_label, (2048, 2048), interpolation=cv2.INTER_LINEAR)
|
||||
|
||||
image, label = self.transform_image(image), self.transform_label(label)
|
||||
|
||||
if self.is_train:
|
||||
return image, label, class_label
|
||||
else:
|
||||
return image, label, self.label_paths[index]
|
||||
|
||||
def __len__(self):
|
||||
return len(self.image_paths)
|
||||
@@ -0,0 +1,119 @@
|
||||
import random
|
||||
from PIL import Image, ImageEnhance
|
||||
import numpy as np
|
||||
import cv2
|
||||
|
||||
|
||||
def refine_foreground(image, mask, r=90):
|
||||
if mask.size != image.size:
|
||||
mask = mask.resize(image.size)
|
||||
image = np.array(image) / 255.0
|
||||
mask = np.array(mask) / 255.0
|
||||
estimated_foreground = FB_blur_fusion_foreground_estimator_2(image, mask, r=r)
|
||||
image_masked = Image.fromarray((estimated_foreground * 255.0).astype(np.uint8))
|
||||
return image_masked
|
||||
|
||||
|
||||
def FB_blur_fusion_foreground_estimator_2(image, alpha, r=90):
|
||||
# Thanks to the source: https://github.com/Photoroom/fast-foreground-estimation
|
||||
alpha = alpha[:, :, None]
|
||||
F, blur_B = FB_blur_fusion_foreground_estimator(
|
||||
image, image, image, alpha, r)
|
||||
return FB_blur_fusion_foreground_estimator(image, F, blur_B, alpha, r=6)[0]
|
||||
|
||||
|
||||
def FB_blur_fusion_foreground_estimator(image, F, B, alpha, r=90):
|
||||
if isinstance(image, Image.Image):
|
||||
image = np.array(image) / 255.0
|
||||
blurred_alpha = cv2.blur(alpha, (r, r))[:, :, None]
|
||||
|
||||
blurred_FA = cv2.blur(F * alpha, (r, r))
|
||||
blurred_F = blurred_FA / (blurred_alpha + 1e-5)
|
||||
|
||||
blurred_B1A = cv2.blur(B * (1 - alpha), (r, r))
|
||||
blurred_B = blurred_B1A / ((1 - blurred_alpha) + 1e-5)
|
||||
F = blurred_F + alpha * \
|
||||
(image - alpha * blurred_F - (1 - alpha) * blurred_B)
|
||||
F = np.clip(F, 0, 1)
|
||||
return F, blurred_B
|
||||
|
||||
|
||||
def preproc(image, label, preproc_methods=['flip']):
|
||||
if 'flip' in preproc_methods:
|
||||
image, label = cv_random_flip(image, label)
|
||||
if 'crop' in preproc_methods:
|
||||
image, label = random_crop(image, label)
|
||||
if 'rotate' in preproc_methods:
|
||||
image, label = random_rotate(image, label)
|
||||
if 'enhance' in preproc_methods:
|
||||
image = color_enhance(image)
|
||||
if 'pepper' in preproc_methods:
|
||||
label = random_pepper(label)
|
||||
return image, label
|
||||
|
||||
|
||||
def cv_random_flip(img, label):
|
||||
if random.random() > 0.5:
|
||||
img = img.transpose(Image.FLIP_LEFT_RIGHT)
|
||||
label = label.transpose(Image.FLIP_LEFT_RIGHT)
|
||||
return img, label
|
||||
|
||||
|
||||
def random_crop(image, label):
|
||||
border = 30
|
||||
image_width = image.size[0]
|
||||
image_height = image.size[1]
|
||||
border = int(min(image_width, image_height) * 0.1)
|
||||
crop_win_width = np.random.randint(image_width - border, image_width)
|
||||
crop_win_height = np.random.randint(image_height - border, image_height)
|
||||
random_region = (
|
||||
(image_width - crop_win_width) >> 1, (image_height - crop_win_height) >> 1, (image_width + crop_win_width) >> 1,
|
||||
(image_height + crop_win_height) >> 1)
|
||||
return image.crop(random_region), label.crop(random_region)
|
||||
|
||||
|
||||
def random_rotate(image, label, angle=15):
|
||||
mode = Image.BICUBIC
|
||||
if random.random() > 0.8:
|
||||
random_angle = np.random.randint(-angle, angle)
|
||||
image = image.rotate(random_angle, mode)
|
||||
label = label.rotate(random_angle, mode)
|
||||
return image, label
|
||||
|
||||
|
||||
def color_enhance(image):
|
||||
bright_intensity = random.randint(5, 15) / 10.0
|
||||
image = ImageEnhance.Brightness(image).enhance(bright_intensity)
|
||||
contrast_intensity = random.randint(5, 15) / 10.0
|
||||
image = ImageEnhance.Contrast(image).enhance(contrast_intensity)
|
||||
color_intensity = random.randint(0, 20) / 10.0
|
||||
image = ImageEnhance.Color(image).enhance(color_intensity)
|
||||
sharp_intensity = random.randint(0, 30) / 10.0
|
||||
image = ImageEnhance.Sharpness(image).enhance(sharp_intensity)
|
||||
return image
|
||||
|
||||
|
||||
def random_gaussian(image, mean=0.1, sigma=0.35):
|
||||
def gaussianNoisy(im, mean=mean, sigma=sigma):
|
||||
for _i in range(len(im)):
|
||||
im[_i] += random.gauss(mean, sigma)
|
||||
return im
|
||||
|
||||
img = np.asarray(image)
|
||||
width, height = img.shape
|
||||
img = gaussianNoisy(img[:].flatten(), mean, sigma)
|
||||
img = img.reshape([width, height])
|
||||
return Image.fromarray(np.uint8(img))
|
||||
|
||||
|
||||
def random_pepper(img, N=0.0015):
|
||||
img = np.array(img)
|
||||
noiseNum = int(N * img.shape[0] * img.shape[1])
|
||||
for i in range(noiseNum):
|
||||
randX = random.randint(0, img.shape[0] - 1)
|
||||
randY = random.randint(0, img.shape[1] - 1)
|
||||
if random.randint(0, 1) == 0:
|
||||
img[randX, randY] = 0
|
||||
else:
|
||||
img[randX, randY] = 255
|
||||
return Image.fromarray(img)
|
||||
@@ -0,0 +1,46 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import safetensors.torch
|
||||
from collections import OrderedDict
|
||||
from torchvision.models import vgg16, vgg16_bn, VGG16_Weights, VGG16_BN_Weights, resnet50, ResNet50_Weights
|
||||
from birefnet.models.backbones.pvt_v2 import pvt_v2_b0, pvt_v2_b1, pvt_v2_b2, pvt_v2_b5
|
||||
from birefnet.models.backbones.swin_v1 import swin_v1_t, swin_v1_s, swin_v1_b, swin_v1_l
|
||||
from birefnet.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):
|
||||
safetensors.torch.load_file
|
||||
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
|
||||
@@ -0,0 +1,435 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from functools import partial
|
||||
|
||||
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
|
||||
from timm.models.registry import register_model
|
||||
|
||||
import math
|
||||
|
||||
from birefnet.config import Config
|
||||
|
||||
config = Config()
|
||||
|
||||
class Mlp(nn.Module):
|
||||
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||
self.dwconv = DWConv(hidden_features)
|
||||
self.act = act_layer()
|
||||
self.fc2 = nn.Linear(hidden_features, out_features)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
elif isinstance(m, nn.Conv2d):
|
||||
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
fan_out //= m.groups
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
||||
if m.bias is not None:
|
||||
m.bias.data.zero_()
|
||||
|
||||
def forward(self, x, H, W):
|
||||
x = self.fc1(x)
|
||||
x = self.dwconv(x, H, W)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
|
||||
super().__init__()
|
||||
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
|
||||
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = qk_scale or head_dim ** -0.5
|
||||
|
||||
self.q = nn.Linear(dim, dim, bias=qkv_bias)
|
||||
self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
|
||||
self.attn_drop_prob = attn_drop
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
self.sr_ratio = sr_ratio
|
||||
if sr_ratio > 1:
|
||||
self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
elif isinstance(m, nn.Conv2d):
|
||||
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
fan_out //= m.groups
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
||||
if m.bias is not None:
|
||||
m.bias.data.zero_()
|
||||
|
||||
def forward(self, x, H, W):
|
||||
B, N, C = x.shape
|
||||
q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
|
||||
|
||||
if self.sr_ratio > 1:
|
||||
x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
|
||||
x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
|
||||
x_ = self.norm(x_)
|
||||
kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
else:
|
||||
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
k, v = kv[0], kv[1]
|
||||
|
||||
if config.SDPA_enabled:
|
||||
x = torch.nn.functional.scaled_dot_product_attention(
|
||||
q, k, v,
|
||||
attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False
|
||||
).transpose(1, 2).reshape(B, N, C)
|
||||
else:
|
||||
attn = (q @ k.transpose(-2, -1)) * self.scale
|
||||
attn = attn.softmax(dim=-1)
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
|
||||
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
|
||||
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
|
||||
super().__init__()
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = Attention(
|
||||
dim,
|
||||
num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
|
||||
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
elif isinstance(m, nn.Conv2d):
|
||||
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
fan_out //= m.groups
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
||||
if m.bias is not None:
|
||||
m.bias.data.zero_()
|
||||
|
||||
def forward(self, x, H, W):
|
||||
x = x + self.drop_path(self.attn(self.norm1(x), H, W))
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class OverlapPatchEmbed(nn.Module):
|
||||
""" Image to Patch Embedding
|
||||
"""
|
||||
|
||||
def __init__(self, img_size=224, patch_size=7, stride=4, in_channels=3, embed_dim=768):
|
||||
super().__init__()
|
||||
img_size = to_2tuple(img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
|
||||
self.img_size = img_size
|
||||
self.patch_size = patch_size
|
||||
self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
|
||||
self.num_patches = self.H * self.W
|
||||
self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=stride,
|
||||
padding=(patch_size[0] // 2, patch_size[1] // 2))
|
||||
self.norm = nn.LayerNorm(embed_dim)
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
elif isinstance(m, nn.Conv2d):
|
||||
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
fan_out //= m.groups
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
||||
if m.bias is not None:
|
||||
m.bias.data.zero_()
|
||||
|
||||
def forward(self, x):
|
||||
x = self.proj(x)
|
||||
_, _, H, W = x.shape
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.norm(x)
|
||||
|
||||
return x, H, W
|
||||
|
||||
|
||||
class PyramidVisionTransformerImpr(nn.Module):
|
||||
def __init__(self, img_size=224, patch_size=16, in_channels=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
|
||||
num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
|
||||
attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
|
||||
depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]):
|
||||
super().__init__()
|
||||
self.num_classes = num_classes
|
||||
self.depths = depths
|
||||
|
||||
# patch_embed
|
||||
self.patch_embed1 = OverlapPatchEmbed(img_size=img_size, patch_size=7, stride=4, in_channels=in_channels,
|
||||
embed_dim=embed_dims[0])
|
||||
self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_channels=embed_dims[0],
|
||||
embed_dim=embed_dims[1])
|
||||
self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_channels=embed_dims[1],
|
||||
embed_dim=embed_dims[2])
|
||||
self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_channels=embed_dims[2],
|
||||
embed_dim=embed_dims[3])
|
||||
|
||||
# transformer encoder
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
||||
cur = 0
|
||||
self.block1 = nn.ModuleList([Block(
|
||||
dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
|
||||
sr_ratio=sr_ratios[0])
|
||||
for i in range(depths[0])])
|
||||
self.norm1 = norm_layer(embed_dims[0])
|
||||
|
||||
cur += depths[0]
|
||||
self.block2 = nn.ModuleList([Block(
|
||||
dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
|
||||
sr_ratio=sr_ratios[1])
|
||||
for i in range(depths[1])])
|
||||
self.norm2 = norm_layer(embed_dims[1])
|
||||
|
||||
cur += depths[1]
|
||||
self.block3 = nn.ModuleList([Block(
|
||||
dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
|
||||
sr_ratio=sr_ratios[2])
|
||||
for i in range(depths[2])])
|
||||
self.norm3 = norm_layer(embed_dims[2])
|
||||
|
||||
cur += depths[2]
|
||||
self.block4 = nn.ModuleList([Block(
|
||||
dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
|
||||
sr_ratio=sr_ratios[3])
|
||||
for i in range(depths[3])])
|
||||
self.norm4 = norm_layer(embed_dims[3])
|
||||
|
||||
# classification head
|
||||
# self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
elif isinstance(m, nn.Conv2d):
|
||||
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
fan_out //= m.groups
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
||||
if m.bias is not None:
|
||||
m.bias.data.zero_()
|
||||
|
||||
def init_weights(self, pretrained=None):
|
||||
if isinstance(pretrained, str):
|
||||
logger = 1
|
||||
#load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
|
||||
|
||||
def reset_drop_path(self, drop_path_rate):
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
|
||||
cur = 0
|
||||
for i in range(self.depths[0]):
|
||||
self.block1[i].drop_path.drop_prob = dpr[cur + i]
|
||||
|
||||
cur += self.depths[0]
|
||||
for i in range(self.depths[1]):
|
||||
self.block2[i].drop_path.drop_prob = dpr[cur + i]
|
||||
|
||||
cur += self.depths[1]
|
||||
for i in range(self.depths[2]):
|
||||
self.block3[i].drop_path.drop_prob = dpr[cur + i]
|
||||
|
||||
cur += self.depths[2]
|
||||
for i in range(self.depths[3]):
|
||||
self.block4[i].drop_path.drop_prob = dpr[cur + i]
|
||||
|
||||
def freeze_patch_emb(self):
|
||||
self.patch_embed1.requires_grad = False
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay(self):
|
||||
return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'} # has pos_embed may be better
|
||||
|
||||
def get_classifier(self):
|
||||
return self.head
|
||||
|
||||
def reset_classifier(self, num_classes, global_pool=''):
|
||||
self.num_classes = num_classes
|
||||
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
|
||||
|
||||
def forward_features(self, x):
|
||||
B = x.shape[0]
|
||||
outs = []
|
||||
|
||||
# stage 1
|
||||
x, H, W = self.patch_embed1(x)
|
||||
for i, blk in enumerate(self.block1):
|
||||
x = blk(x, H, W)
|
||||
x = self.norm1(x)
|
||||
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(x)
|
||||
|
||||
# stage 2
|
||||
x, H, W = self.patch_embed2(x)
|
||||
for i, blk in enumerate(self.block2):
|
||||
x = blk(x, H, W)
|
||||
x = self.norm2(x)
|
||||
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(x)
|
||||
|
||||
# stage 3
|
||||
x, H, W = self.patch_embed3(x)
|
||||
for i, blk in enumerate(self.block3):
|
||||
x = blk(x, H, W)
|
||||
x = self.norm3(x)
|
||||
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(x)
|
||||
|
||||
# stage 4
|
||||
x, H, W = self.patch_embed4(x)
|
||||
for i, blk in enumerate(self.block4):
|
||||
x = blk(x, H, W)
|
||||
x = self.norm4(x)
|
||||
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(x)
|
||||
|
||||
return outs
|
||||
|
||||
# return x.mean(dim=1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.forward_features(x)
|
||||
# x = self.head(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class DWConv(nn.Module):
|
||||
def __init__(self, dim=768):
|
||||
super(DWConv, self).__init__()
|
||||
self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
|
||||
|
||||
def forward(self, x, H, W):
|
||||
B, N, C = x.shape
|
||||
x = x.transpose(1, 2).view(B, C, H, W).contiguous()
|
||||
x = self.dwconv(x)
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
def _conv_filter(state_dict, patch_size=16):
|
||||
""" convert patch embedding weight from manual patchify + linear proj to conv"""
|
||||
out_dict = {}
|
||||
for k, v in state_dict.items():
|
||||
if 'patch_embed.proj.weight' in k:
|
||||
v = v.reshape((v.shape[0], 3, patch_size, patch_size))
|
||||
out_dict[k] = v
|
||||
|
||||
return out_dict
|
||||
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b0(PyramidVisionTransformerImpr):
|
||||
def __init__(self, **kwargs):
|
||||
super(pvt_v2_b0, self).__init__(
|
||||
patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1)
|
||||
|
||||
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b1(PyramidVisionTransformerImpr):
|
||||
def __init__(self, **kwargs):
|
||||
super(pvt_v2_b1, self).__init__(
|
||||
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1)
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b2(PyramidVisionTransformerImpr):
|
||||
def __init__(self, in_channels=3, **kwargs):
|
||||
super(pvt_v2_b2, self).__init__(
|
||||
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1, in_channels=in_channels)
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b3(PyramidVisionTransformerImpr):
|
||||
def __init__(self, **kwargs):
|
||||
super(pvt_v2_b3, self).__init__(
|
||||
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1)
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b4(PyramidVisionTransformerImpr):
|
||||
def __init__(self, **kwargs):
|
||||
super(pvt_v2_b4, self).__init__(
|
||||
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1)
|
||||
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b5(PyramidVisionTransformerImpr):
|
||||
def __init__(self, **kwargs):
|
||||
super(pvt_v2_b5, self).__init__(
|
||||
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1)
|
||||
@@ -0,0 +1,627 @@
|
||||
# --------------------------------------------------------
|
||||
# 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 birefnet.config import Config
|
||||
|
||||
|
||||
config = Config()
|
||||
|
||||
class Mlp(nn.Module):
|
||||
""" Multilayer perceptron."""
|
||||
|
||||
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||
self.act = act_layer()
|
||||
self.fc2 = nn.Linear(hidden_features, out_features)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
|
||||
|
||||
def window_partition(x, window_size):
|
||||
"""
|
||||
Args:
|
||||
x: (B, H, W, C)
|
||||
window_size (int): window size
|
||||
|
||||
Returns:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
"""
|
||||
B, H, W, C = x.shape
|
||||
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
|
||||
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
|
||||
return windows
|
||||
|
||||
|
||||
def window_reverse(windows, window_size, H, W):
|
||||
"""
|
||||
Args:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
window_size (int): Window size
|
||||
H (int): Height of image
|
||||
W (int): Width of image
|
||||
|
||||
Returns:
|
||||
x: (B, H, W, C)
|
||||
"""
|
||||
B = int(windows.shape[0] / (H * W / window_size / window_size))
|
||||
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
|
||||
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
|
||||
return x
|
||||
|
||||
|
||||
class WindowAttention(nn.Module):
|
||||
""" Window based multi-head self attention (W-MSA) module with relative position bias.
|
||||
It supports both of shifted and non-shifted window.
|
||||
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
window_size (tuple[int]): The height and width of the window.
|
||||
num_heads (int): Number of attention heads.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
|
||||
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
|
||||
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
|
||||
"""
|
||||
|
||||
def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
|
||||
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.window_size = window_size # Wh, Ww
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = qk_scale or head_dim ** -0.5
|
||||
|
||||
# define a parameter table of relative position bias
|
||||
self.relative_position_bias_table = nn.Parameter(
|
||||
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
|
||||
|
||||
# get pair-wise relative position index for each token inside the window
|
||||
coords_h = torch.arange(self.window_size[0])
|
||||
coords_w = torch.arange(self.window_size[1])
|
||||
coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij')) # 2, Wh, Ww
|
||||
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
|
||||
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
|
||||
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
|
||||
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
|
||||
relative_coords[:, :, 1] += self.window_size[1] - 1
|
||||
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
|
||||
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
||||
self.register_buffer("relative_position_index", relative_position_index)
|
||||
|
||||
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
||||
self.attn_drop_prob = attn_drop
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
trunc_normal_(self.relative_position_bias_table, std=.02)
|
||||
self.softmax = nn.Softmax(dim=-1)
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
""" Forward function.
|
||||
|
||||
Args:
|
||||
x: input features with shape of (num_windows*B, N, C)
|
||||
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
|
||||
"""
|
||||
B_, N, C = x.shape
|
||||
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
|
||||
|
||||
q = q * self.scale
|
||||
|
||||
if config.SDPA_enabled:
|
||||
x = torch.nn.functional.scaled_dot_product_attention(
|
||||
q, k, v,
|
||||
attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False
|
||||
).transpose(1, 2).reshape(B_, N, C)
|
||||
else:
|
||||
attn = (q @ k.transpose(-2, -1))
|
||||
|
||||
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
|
||||
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
|
||||
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
|
||||
attn = attn + relative_position_bias.unsqueeze(0)
|
||||
|
||||
if mask is not None:
|
||||
nW = mask.shape[0]
|
||||
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
|
||||
attn = attn.view(-1, self.num_heads, N, N)
|
||||
attn = self.softmax(attn)
|
||||
else:
|
||||
attn = self.softmax(attn)
|
||||
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class SwinTransformerBlock(nn.Module):
|
||||
""" Swin Transformer Block.
|
||||
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
num_heads (int): Number of attention heads.
|
||||
window_size (int): Window size.
|
||||
shift_size (int): Shift size for SW-MSA.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float, optional): Stochastic depth rate. Default: 0.0
|
||||
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
|
||||
def __init__(self, dim, num_heads, window_size=7, shift_size=0,
|
||||
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
|
||||
act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.window_size = window_size
|
||||
self.shift_size = shift_size
|
||||
self.mlp_ratio = mlp_ratio
|
||||
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
|
||||
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = WindowAttention(
|
||||
dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
|
||||
qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
|
||||
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
||||
|
||||
self.H = None
|
||||
self.W = None
|
||||
|
||||
def forward(self, x, mask_matrix):
|
||||
""" Forward function.
|
||||
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
mask_matrix: Attention mask for cyclic shift.
|
||||
"""
|
||||
B, L, C = x.shape
|
||||
H, W = self.H, self.W
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
|
||||
shortcut = x
|
||||
x = self.norm1(x)
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
# pad feature maps to multiples of window size
|
||||
pad_l = pad_t = 0
|
||||
pad_r = (self.window_size - W % self.window_size) % self.window_size
|
||||
pad_b = (self.window_size - H % self.window_size) % self.window_size
|
||||
x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
|
||||
_, Hp, Wp, _ = x.shape
|
||||
|
||||
# cyclic shift
|
||||
if self.shift_size > 0:
|
||||
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
|
||||
attn_mask = mask_matrix
|
||||
else:
|
||||
shifted_x = x
|
||||
attn_mask = None
|
||||
|
||||
# partition windows
|
||||
x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
|
||||
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
|
||||
|
||||
# W-MSA/SW-MSA
|
||||
attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
|
||||
|
||||
# merge windows
|
||||
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
|
||||
shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
|
||||
|
||||
# reverse cyclic shift
|
||||
if self.shift_size > 0:
|
||||
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
|
||||
else:
|
||||
x = shifted_x
|
||||
|
||||
if pad_r > 0 or pad_b > 0:
|
||||
x = x[:, :H, :W, :].contiguous()
|
||||
|
||||
x = x.view(B, H * W, C)
|
||||
|
||||
# FFN
|
||||
x = shortcut + self.drop_path(x)
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class PatchMerging(nn.Module):
|
||||
""" Patch Merging Layer
|
||||
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
def __init__(self, dim, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
|
||||
self.norm = norm_layer(4 * dim)
|
||||
|
||||
def forward(self, x, H, W):
|
||||
""" Forward function.
|
||||
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
"""
|
||||
B, L, C = x.shape
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
# padding
|
||||
pad_input = (H % 2 == 1) or (W % 2 == 1)
|
||||
if pad_input:
|
||||
x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
|
||||
|
||||
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
|
||||
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
|
||||
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
|
||||
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
|
||||
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
|
||||
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
|
||||
|
||||
x = self.norm(x)
|
||||
x = self.reduction(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class BasicLayer(nn.Module):
|
||||
""" A basic Swin Transformer layer for one stage.
|
||||
|
||||
Args:
|
||||
dim (int): Number of feature channels
|
||||
depth (int): Depths of this stage.
|
||||
num_heads (int): Number of attention head.
|
||||
window_size (int): Local window size. Default: 7.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
dim,
|
||||
depth,
|
||||
num_heads,
|
||||
window_size=7,
|
||||
mlp_ratio=4.,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop=0.,
|
||||
attn_drop=0.,
|
||||
drop_path=0.,
|
||||
norm_layer=nn.LayerNorm,
|
||||
downsample=None,
|
||||
use_checkpoint=False):
|
||||
super().__init__()
|
||||
self.window_size = window_size
|
||||
self.shift_size = window_size // 2
|
||||
self.depth = depth
|
||||
self.use_checkpoint = use_checkpoint
|
||||
|
||||
# build blocks
|
||||
self.blocks = nn.ModuleList([
|
||||
SwinTransformerBlock(
|
||||
dim=dim,
|
||||
num_heads=num_heads,
|
||||
window_size=window_size,
|
||||
shift_size=0 if (i % 2 == 0) else window_size // 2,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop,
|
||||
attn_drop=attn_drop,
|
||||
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
|
||||
norm_layer=norm_layer)
|
||||
for i in range(depth)])
|
||||
|
||||
# patch merging layer
|
||||
if downsample is not None:
|
||||
self.downsample = downsample(dim=dim, norm_layer=norm_layer)
|
||||
else:
|
||||
self.downsample = None
|
||||
|
||||
def forward(self, x, H, W):
|
||||
""" Forward function.
|
||||
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
"""
|
||||
|
||||
# calculate attention mask for SW-MSA
|
||||
Hp = int(np.ceil(H / self.window_size)) * self.window_size
|
||||
Wp = int(np.ceil(W / self.window_size)) * self.window_size
|
||||
img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1
|
||||
h_slices = (slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None))
|
||||
w_slices = (slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None))
|
||||
cnt = 0
|
||||
for h in h_slices:
|
||||
for w in w_slices:
|
||||
img_mask[:, h, w, :] = cnt
|
||||
cnt += 1
|
||||
|
||||
mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
|
||||
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
|
||||
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
||||
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
|
||||
|
||||
for blk in self.blocks:
|
||||
blk.H, blk.W = H, W
|
||||
if self.use_checkpoint:
|
||||
x = checkpoint.checkpoint(blk, x, attn_mask)
|
||||
else:
|
||||
x = blk(x, attn_mask)
|
||||
if self.downsample is not None:
|
||||
x_down = self.downsample(x, H, W)
|
||||
Wh, Ww = (H + 1) // 2, (W + 1) // 2
|
||||
return x, H, W, x_down, Wh, Ww
|
||||
else:
|
||||
return x, H, W, x, H, W
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
""" Image to Patch Embedding
|
||||
|
||||
Args:
|
||||
patch_size (int): Patch token size. Default: 4.
|
||||
in_channels (int): Number of input image channels. Default: 3.
|
||||
embed_dim (int): Number of linear projection output channels. Default: 96.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: None
|
||||
"""
|
||||
|
||||
def __init__(self, patch_size=4, in_channels=3, embed_dim=96, norm_layer=None):
|
||||
super().__init__()
|
||||
patch_size = to_2tuple(patch_size)
|
||||
self.patch_size = patch_size
|
||||
|
||||
self.in_channels = in_channels
|
||||
self.embed_dim = embed_dim
|
||||
|
||||
self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
|
||||
if norm_layer is not None:
|
||||
self.norm = norm_layer(embed_dim)
|
||||
else:
|
||||
self.norm = None
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward function."""
|
||||
# padding
|
||||
_, _, H, W = x.size()
|
||||
if W % self.patch_size[1] != 0:
|
||||
x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
|
||||
if H % self.patch_size[0] != 0:
|
||||
x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
|
||||
|
||||
x = self.proj(x) # B C Wh Ww
|
||||
if self.norm is not None:
|
||||
Wh, Ww = x.size(2), x.size(3)
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.norm(x)
|
||||
x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class SwinTransformer(nn.Module):
|
||||
""" Swin Transformer backbone.
|
||||
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
|
||||
https://arxiv.org/pdf/2103.14030
|
||||
|
||||
Args:
|
||||
pretrain_img_size (int): Input image size for training the pretrained model,
|
||||
used in absolute postion embedding. Default 224.
|
||||
patch_size (int | tuple(int)): Patch size. Default: 4.
|
||||
in_channels (int): Number of input image channels. Default: 3.
|
||||
embed_dim (int): Number of linear projection output channels. Default: 96.
|
||||
depths (tuple[int]): Depths of each Swin Transformer stage.
|
||||
num_heads (tuple[int]): Number of attention head of each stage.
|
||||
window_size (int): Window size. Default: 7.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
||||
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop_rate (float): Dropout rate.
|
||||
attn_drop_rate (float): Attention dropout rate. Default: 0.
|
||||
drop_path_rate (float): Stochastic depth rate. Default: 0.2.
|
||||
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
|
||||
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
|
||||
patch_norm (bool): If True, add normalization after patch embedding. Default: True.
|
||||
out_indices (Sequence[int]): Output from which stages.
|
||||
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
|
||||
-1 means not freezing any parameters.
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
pretrain_img_size=224,
|
||||
patch_size=4,
|
||||
in_channels=3,
|
||||
embed_dim=96,
|
||||
depths=[2, 2, 6, 2],
|
||||
num_heads=[3, 6, 12, 24],
|
||||
window_size=7,
|
||||
mlp_ratio=4.,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop_rate=0.,
|
||||
attn_drop_rate=0.,
|
||||
drop_path_rate=0.2,
|
||||
norm_layer=nn.LayerNorm,
|
||||
ape=False,
|
||||
patch_norm=True,
|
||||
out_indices=(0, 1, 2, 3),
|
||||
frozen_stages=-1,
|
||||
use_checkpoint=False):
|
||||
super().__init__()
|
||||
|
||||
self.pretrain_img_size = pretrain_img_size
|
||||
self.num_layers = len(depths)
|
||||
self.embed_dim = embed_dim
|
||||
self.ape = ape
|
||||
self.patch_norm = patch_norm
|
||||
self.out_indices = out_indices
|
||||
self.frozen_stages = frozen_stages
|
||||
|
||||
# split image into non-overlapping patches
|
||||
self.patch_embed = PatchEmbed(
|
||||
patch_size=patch_size, in_channels=in_channels, embed_dim=embed_dim,
|
||||
norm_layer=norm_layer if self.patch_norm else None)
|
||||
|
||||
# absolute position embedding
|
||||
if self.ape:
|
||||
pretrain_img_size = to_2tuple(pretrain_img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
|
||||
|
||||
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
|
||||
trunc_normal_(self.absolute_pos_embed, std=.02)
|
||||
|
||||
self.pos_drop = nn.Dropout(p=drop_rate)
|
||||
|
||||
# stochastic depth
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
||||
|
||||
# build layers
|
||||
self.layers = nn.ModuleList()
|
||||
for i_layer in range(self.num_layers):
|
||||
layer = BasicLayer(
|
||||
dim=int(embed_dim * 2 ** i_layer),
|
||||
depth=depths[i_layer],
|
||||
num_heads=num_heads[i_layer],
|
||||
window_size=window_size,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop_rate,
|
||||
attn_drop=attn_drop_rate,
|
||||
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
|
||||
norm_layer=norm_layer,
|
||||
downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
|
||||
use_checkpoint=use_checkpoint)
|
||||
self.layers.append(layer)
|
||||
|
||||
num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
|
||||
self.num_features = num_features
|
||||
|
||||
# add a norm layer for each output
|
||||
for i_layer in out_indices:
|
||||
layer = norm_layer(num_features[i_layer])
|
||||
layer_name = f'norm{i_layer}'
|
||||
self.add_module(layer_name, layer)
|
||||
|
||||
self._freeze_stages()
|
||||
|
||||
def _freeze_stages(self):
|
||||
if self.frozen_stages >= 0:
|
||||
self.patch_embed.eval()
|
||||
for param in self.patch_embed.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
if self.frozen_stages >= 1 and self.ape:
|
||||
self.absolute_pos_embed.requires_grad = False
|
||||
|
||||
if self.frozen_stages >= 2:
|
||||
self.pos_drop.eval()
|
||||
for i in range(0, self.frozen_stages - 1):
|
||||
m = self.layers[i]
|
||||
m.eval()
|
||||
for param in m.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward function."""
|
||||
x = self.patch_embed(x)
|
||||
|
||||
Wh, Ww = x.size(2), x.size(3)
|
||||
if self.ape:
|
||||
# interpolate the position embedding to the corresponding size
|
||||
absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
|
||||
x = (x + absolute_pos_embed) # B Wh*Ww C
|
||||
|
||||
outs = []#x.contiguous()]
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.pos_drop(x)
|
||||
for i in range(self.num_layers):
|
||||
layer = self.layers[i]
|
||||
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
|
||||
|
||||
if i in self.out_indices:
|
||||
norm_layer = getattr(self, f'norm{i}')
|
||||
x_out = norm_layer(x_out)
|
||||
|
||||
out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(out)
|
||||
|
||||
return tuple(outs)
|
||||
|
||||
def train(self, mode=True):
|
||||
"""Convert the model into training mode while keep layers freezed."""
|
||||
super(SwinTransformer, self).train(mode)
|
||||
self._freeze_stages()
|
||||
|
||||
def swin_v1_t():
|
||||
model = SwinTransformer(embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7)
|
||||
return model
|
||||
|
||||
def swin_v1_s():
|
||||
model = SwinTransformer(embed_dim=96, depths=[2, 2, 18, 2], num_heads=[3, 6, 12, 24], window_size=7)
|
||||
return model
|
||||
|
||||
def swin_v1_b():
|
||||
model = SwinTransformer(embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12)
|
||||
return model
|
||||
|
||||
def swin_v1_l():
|
||||
model = SwinTransformer(embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12)
|
||||
return model
|
||||
@@ -0,0 +1,279 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from kornia.filters import laplacian
|
||||
|
||||
from birefnet.config import Config
|
||||
from birefnet.dataset import class_labels_TR_sorted
|
||||
from birefnet.models.backbones.build_backbone import build_backbone
|
||||
from birefnet.models.modules.decoder_blocks import BasicDecBlk, ResBlk
|
||||
from birefnet.models.modules.lateral_blocks import BasicLatBlk
|
||||
from birefnet.models.modules.aspp import ASPP, ASPPDeformable
|
||||
from birefnet.models.refinement.refiner import Refiner, RefinerPVTInChannels4, RefUNet
|
||||
from birefnet.models.refinement.stem_layer import StemLayer
|
||||
|
||||
|
||||
class BiRefNet(nn.Module):
|
||||
def __init__(self, bb_pretrained=True, bb_index=6):
|
||||
super(BiRefNet, self).__init__()
|
||||
self.config = Config(bb_index)
|
||||
self.epoch = 1
|
||||
self.bb = build_backbone(self.config.bb, pretrained=bb_pretrained)
|
||||
|
||||
channels = self.config.lateral_channels_in_collection
|
||||
|
||||
if self.config.auxiliary_classification:
|
||||
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
|
||||
self.cls_head = nn.Sequential(
|
||||
nn.Linear(channels[0], len(class_labels_TR_sorted))
|
||||
)
|
||||
|
||||
if self.config.squeeze_block:
|
||||
self.squeeze_module = nn.Sequential(*[
|
||||
eval(self.config.squeeze_block.split('_x')[0])(channels[0]+sum(self.config.cxt), channels[0])
|
||||
for _ in range(eval(self.config.squeeze_block.split('_x')[1]))
|
||||
])
|
||||
|
||||
self.decoder = Decoder(channels)
|
||||
|
||||
if self.config.ender:
|
||||
self.dec_end = nn.Sequential(
|
||||
nn.Conv2d(1, 16, 3, 1, 1),
|
||||
nn.Conv2d(16, 1, 3, 1, 1),
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
|
||||
# refine patch-level segmentation
|
||||
if self.config.refine:
|
||||
if self.config.refine == 'itself':
|
||||
self.stem_layer = StemLayer(in_channels=3+1, inter_channels=48, out_channels=3, norm_layer='BN' if self.config.batch_size > 1 else 'LN')
|
||||
else:
|
||||
self.refiner = eval('{}({})'.format(self.config.refine, 'in_channels=3+1'))
|
||||
|
||||
if self.config.freeze_bb:
|
||||
# Freeze the backbone...
|
||||
print(self.named_parameters())
|
||||
for key, value in self.named_parameters():
|
||||
if 'bb.' in key and 'refiner.' not in key:
|
||||
value.requires_grad = False
|
||||
|
||||
def forward_enc(self, x):
|
||||
if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']:
|
||||
x1 = self.bb.conv1(x); x2 = self.bb.conv2(x1); x3 = self.bb.conv3(x2); x4 = self.bb.conv4(x3)
|
||||
else:
|
||||
x1, x2, x3, x4 = self.bb(x)
|
||||
if self.config.mul_scl_ipt == 'cat':
|
||||
B, C, H, W = x.shape
|
||||
x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True))
|
||||
x1 = torch.cat([x1, F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)], dim=1)
|
||||
x2 = torch.cat([x2, F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)], dim=1)
|
||||
x3 = torch.cat([x3, F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)], dim=1)
|
||||
x4 = torch.cat([x4, F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)], dim=1)
|
||||
elif self.config.mul_scl_ipt == 'add':
|
||||
B, C, H, W = x.shape
|
||||
x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True))
|
||||
x1 = x1 + F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)
|
||||
x2 = x2 + F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)
|
||||
x3 = x3 + F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)
|
||||
x4 = x4 + F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)
|
||||
class_preds = self.cls_head(self.avgpool(x4).view(x4.shape[0], -1)) if self.training and self.config.auxiliary_classification else None
|
||||
if self.config.cxt:
|
||||
x4 = torch.cat(
|
||||
(
|
||||
*[
|
||||
F.interpolate(x1, size=x4.shape[2:], mode='bilinear', align_corners=True),
|
||||
F.interpolate(x2, size=x4.shape[2:], mode='bilinear', align_corners=True),
|
||||
F.interpolate(x3, size=x4.shape[2:], mode='bilinear', align_corners=True),
|
||||
][-len(self.config.cxt):],
|
||||
x4
|
||||
),
|
||||
dim=1
|
||||
)
|
||||
return (x1, x2, x3, x4), class_preds
|
||||
|
||||
def forward_ori(self, x):
|
||||
########## Encoder ##########
|
||||
(x1, x2, x3, x4), class_preds = self.forward_enc(x)
|
||||
if self.config.squeeze_block:
|
||||
x4 = self.squeeze_module(x4)
|
||||
########## Decoder ##########
|
||||
features = [x, x1, x2, x3, x4]
|
||||
if self.training and self.config.out_ref:
|
||||
features.append(laplacian(torch.mean(x, dim=1).unsqueeze(1), kernel_size=5))
|
||||
scaled_preds = self.decoder(features)
|
||||
return scaled_preds, class_preds
|
||||
|
||||
def forward(self, x):
|
||||
scaled_preds, class_preds = self.forward_ori(x)
|
||||
class_preds_lst = [class_preds]
|
||||
return [scaled_preds, class_preds_lst] if self.training else scaled_preds
|
||||
|
||||
|
||||
class Decoder(nn.Module):
|
||||
def __init__(self, channels):
|
||||
super(Decoder, self).__init__()
|
||||
self.config = Config()
|
||||
DecoderBlock = eval(self.config.dec_blk)
|
||||
LateralBlock = eval(self.config.lat_blk)
|
||||
|
||||
if self.config.dec_ipt:
|
||||
self.split = self.config.dec_ipt_split
|
||||
N_dec_ipt = 64
|
||||
DBlock = SimpleConvs
|
||||
ic = 64
|
||||
ipt_cha_opt = 1
|
||||
self.ipt_blk5 = DBlock(2**10*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
|
||||
self.ipt_blk4 = DBlock(2**8*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
|
||||
self.ipt_blk3 = DBlock(2**6*3 if self.split else 3, [N_dec_ipt, channels[1]//8][ipt_cha_opt], inter_channels=ic)
|
||||
self.ipt_blk2 = DBlock(2**4*3 if self.split else 3, [N_dec_ipt, channels[2]//8][ipt_cha_opt], inter_channels=ic)
|
||||
self.ipt_blk1 = DBlock(2**0*3 if self.split else 3, [N_dec_ipt, channels[3]//8][ipt_cha_opt], inter_channels=ic)
|
||||
else:
|
||||
self.split = None
|
||||
|
||||
self.decoder_block4 = DecoderBlock(channels[0]+([N_dec_ipt, channels[0]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[1])
|
||||
self.decoder_block3 = DecoderBlock(channels[1]+([N_dec_ipt, channels[0]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[2])
|
||||
self.decoder_block2 = DecoderBlock(channels[2]+([N_dec_ipt, channels[1]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[3])
|
||||
self.decoder_block1 = DecoderBlock(channels[3]+([N_dec_ipt, channels[2]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[3]//2)
|
||||
self.conv_out1 = nn.Sequential(nn.Conv2d(channels[3]//2+([N_dec_ipt, channels[3]//8][ipt_cha_opt] if self.config.dec_ipt else 0), 1, 1, 1, 0))
|
||||
|
||||
self.lateral_block4 = LateralBlock(channels[1], channels[1])
|
||||
self.lateral_block3 = LateralBlock(channels[2], channels[2])
|
||||
self.lateral_block2 = LateralBlock(channels[3], channels[3])
|
||||
|
||||
if self.config.ms_supervision:
|
||||
self.conv_ms_spvn_4 = nn.Conv2d(channels[1], 1, 1, 1, 0)
|
||||
self.conv_ms_spvn_3 = nn.Conv2d(channels[2], 1, 1, 1, 0)
|
||||
self.conv_ms_spvn_2 = nn.Conv2d(channels[3], 1, 1, 1, 0)
|
||||
|
||||
if self.config.out_ref:
|
||||
_N = 16
|
||||
self.gdt_convs_4 = nn.Sequential(nn.Conv2d(channels[1], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True))
|
||||
self.gdt_convs_3 = nn.Sequential(nn.Conv2d(channels[2], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True))
|
||||
self.gdt_convs_2 = nn.Sequential(nn.Conv2d(channels[3], _N, 3, 1, 1), nn.BatchNorm2d(_N) if self.config.batch_size > 1 else nn.Identity(), nn.ReLU(inplace=True))
|
||||
|
||||
self.gdt_convs_pred_4 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
self.gdt_convs_pred_3 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
self.gdt_convs_pred_2 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
|
||||
self.gdt_convs_attn_4 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
self.gdt_convs_attn_3 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
self.gdt_convs_attn_2 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
|
||||
def get_patches_batch(self, x, p):
|
||||
_size_h, _size_w = p.shape[2:]
|
||||
patches_batch = []
|
||||
for idx in range(x.shape[0]):
|
||||
columns_x = torch.split(x[idx], split_size_or_sections=_size_w, dim=-1)
|
||||
patches_x = []
|
||||
for column_x in columns_x:
|
||||
patches_x += [p.unsqueeze(0) for p in torch.split(column_x, split_size_or_sections=_size_h, dim=-2)]
|
||||
patch_sample = torch.cat(patches_x, dim=1)
|
||||
patches_batch.append(patch_sample)
|
||||
return torch.cat(patches_batch, dim=0)
|
||||
|
||||
def forward(self, features):
|
||||
if self.training and self.config.out_ref:
|
||||
outs_gdt_pred = []
|
||||
outs_gdt_label = []
|
||||
x, x1, x2, x3, x4, gdt_gt = features
|
||||
else:
|
||||
x, x1, x2, x3, x4 = features
|
||||
outs = []
|
||||
|
||||
if self.config.dec_ipt:
|
||||
patches_batch = self.get_patches_batch(x, x4) if self.split else x
|
||||
x4 = torch.cat((x4, self.ipt_blk5(F.interpolate(patches_batch, size=x4.shape[2:], mode='bilinear', align_corners=True))), 1)
|
||||
p4 = self.decoder_block4(x4)
|
||||
m4 = self.conv_ms_spvn_4(p4) if self.config.ms_supervision 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 = self.get_patches_batch(x, _p3) if self.split else x
|
||||
_p3 = torch.cat((_p3, self.ipt_blk4(F.interpolate(patches_batch, size=x3.shape[2:], mode='bilinear', align_corners=True))), 1)
|
||||
p3 = self.decoder_block3(_p3)
|
||||
m3 = self.conv_ms_spvn_3(p3) if self.config.ms_supervision 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 = self.get_patches_batch(x, _p2) if self.split else x
|
||||
_p2 = torch.cat((_p2, self.ipt_blk3(F.interpolate(patches_batch, size=x2.shape[2:], mode='bilinear', align_corners=True))), 1)
|
||||
p2 = self.decoder_block2(_p2)
|
||||
m2 = self.conv_ms_spvn_2(p2) if self.config.ms_supervision 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 = self.get_patches_batch(x, _p1) if self.split else x
|
||||
_p1 = torch.cat((_p1, self.ipt_blk2(F.interpolate(patches_batch, size=x1.shape[2:], mode='bilinear', align_corners=True))), 1)
|
||||
_p1 = self.decoder_block1(_p1)
|
||||
_p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True)
|
||||
|
||||
if self.config.dec_ipt:
|
||||
patches_batch = self.get_patches_batch(x, _p1) if self.split else x
|
||||
_p1 = torch.cat((_p1, self.ipt_blk1(F.interpolate(patches_batch, size=x.shape[2:], mode='bilinear', align_corners=True))), 1)
|
||||
p1_out = self.conv_out1(_p1)
|
||||
|
||||
if self.config.ms_supervision 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))
|
||||
@@ -0,0 +1,119 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from birefnet.models.modules.deform_conv import DeformableConv2d
|
||||
from birefnet.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)
|
||||
@@ -0,0 +1,65 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from birefnet.models.modules.aspp import ASPP, ASPPDeformable
|
||||
from birefnet.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
|
||||
@@ -0,0 +1,66 @@
|
||||
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
|
||||
@@ -0,0 +1,21 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from functools import partial
|
||||
|
||||
from birefnet.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
|
||||
@@ -0,0 +1,118 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from functools import partial
|
||||
|
||||
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
|
||||
from timm.models.registry import register_model
|
||||
|
||||
import math
|
||||
|
||||
|
||||
class MLPLayer(nn.Module):
|
||||
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||
self.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
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
|
||||
super().__init__()
|
||||
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
|
||||
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = qk_scale or head_dim ** -0.5
|
||||
|
||||
self.q = nn.Linear(dim, dim, bias=qkv_bias)
|
||||
self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
self.sr_ratio = sr_ratio
|
||||
if sr_ratio > 1:
|
||||
self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
|
||||
def forward(self, x, H, W):
|
||||
B, N, C = x.shape
|
||||
q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
|
||||
|
||||
if self.sr_ratio > 1:
|
||||
x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
|
||||
x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
|
||||
x_ = self.norm(x_)
|
||||
kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
else:
|
||||
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
k, v = kv[0], kv[1]
|
||||
|
||||
attn = (q @ k.transpose(-2, -1)) * self.scale
|
||||
attn = attn.softmax(dim=-1)
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
|
||||
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
|
||||
super().__init__()
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = Attention(
|
||||
dim,
|
||||
num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
|
||||
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = MLPLayer(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
||||
|
||||
def forward(self, x, H, W):
|
||||
x = x + self.drop_path(self.attn(self.norm1(x), H, W))
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
|
||||
return x
|
||||
|
||||
|
||||
class OverlapPatchEmbed(nn.Module):
|
||||
""" Image to Patch Embedding
|
||||
"""
|
||||
|
||||
def __init__(self, img_size=224, patch_size=7, stride=4, in_channels=3, embed_dim=768):
|
||||
super().__init__()
|
||||
img_size = to_2tuple(img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
|
||||
self.img_size = img_size
|
||||
self.patch_size = patch_size
|
||||
self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
|
||||
self.num_patches = self.H * self.W
|
||||
self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=stride,
|
||||
padding=(patch_size[0] // 2, patch_size[1] // 2))
|
||||
self.norm = nn.LayerNorm(embed_dim)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.proj(x)
|
||||
_, _, H, W = x.shape
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.norm(x)
|
||||
return x, H, W
|
||||
|
||||
@@ -0,0 +1,222 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from typing import Any, Optional, Tuple, Type
|
||||
|
||||
|
||||
class PromptEncoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
embed_dim=256,
|
||||
image_embedding_size=1024,
|
||||
input_image_size=(1024, 1024),
|
||||
mask_in_chans=16,
|
||||
activation=nn.GELU
|
||||
) -> None:
|
||||
super().__init__()
|
||||
"""
|
||||
Codes are partially from SAM: https://github.com/facebookresearch/segment-anything/blob/6fdee8f2727f4506cfbbe553e23b895e27956588/segment_anything/modeling/prompt_encoder.py.
|
||||
|
||||
Arguments:
|
||||
embed_dim (int): The prompts' embedding dimension
|
||||
image_embedding_size (tuple(int, int)): The spatial size of the
|
||||
image embedding, as (H, W).
|
||||
input_image_size (int): The padded size of the image as input
|
||||
to the image encoder, as (H, W).
|
||||
mask_in_chans (int): The number of hidden channels used for
|
||||
encoding input masks.
|
||||
activation (nn.Module): The activation to use when encoding
|
||||
input masks.
|
||||
"""
|
||||
super().__init__()
|
||||
self.embed_dim = embed_dim
|
||||
self.input_image_size = input_image_size
|
||||
self.image_embedding_size = image_embedding_size
|
||||
self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
|
||||
|
||||
self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners
|
||||
point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
|
||||
self.point_embeddings = nn.ModuleList(point_embeddings)
|
||||
self.not_a_point_embed = nn.Embedding(1, embed_dim)
|
||||
|
||||
self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
|
||||
self.mask_downscaling = nn.Sequential(
|
||||
nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
|
||||
LayerNorm2d(mask_in_chans // 4),
|
||||
activation(),
|
||||
nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
|
||||
LayerNorm2d(mask_in_chans),
|
||||
activation(),
|
||||
nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
|
||||
)
|
||||
self.no_mask_embed = nn.Embedding(1, embed_dim)
|
||||
|
||||
def get_dense_pe(self) -> torch.Tensor:
|
||||
"""
|
||||
Returns the positional encoding used to encode point prompts,
|
||||
applied to a dense set of points the shape of the image encoding.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Positional encoding with shape
|
||||
1x(embed_dim)x(embedding_h)x(embedding_w)
|
||||
"""
|
||||
return self.pe_layer(self.image_embedding_size).unsqueeze(0)
|
||||
|
||||
def _embed_points(
|
||||
self,
|
||||
points: torch.Tensor,
|
||||
labels: torch.Tensor,
|
||||
pad: bool,
|
||||
) -> torch.Tensor:
|
||||
"""Embeds point prompts."""
|
||||
points = points + 0.5 # Shift to center of pixel
|
||||
if pad:
|
||||
padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
|
||||
padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
|
||||
points = torch.cat([points, padding_point], dim=1)
|
||||
labels = torch.cat([labels, padding_label], dim=1)
|
||||
point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
|
||||
point_embedding[labels == -1] = 0.0
|
||||
point_embedding[labels == -1] += self.not_a_point_embed.weight
|
||||
point_embedding[labels == 0] += self.point_embeddings[0].weight
|
||||
point_embedding[labels == 1] += self.point_embeddings[1].weight
|
||||
return point_embedding
|
||||
|
||||
def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
|
||||
"""Embeds box prompts."""
|
||||
boxes = boxes + 0.5 # Shift to center of pixel
|
||||
coords = boxes.reshape(-1, 2, 2)
|
||||
corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
|
||||
corner_embedding[:, 0, :] += self.point_embeddings[2].weight
|
||||
corner_embedding[:, 1, :] += self.point_embeddings[3].weight
|
||||
return corner_embedding
|
||||
|
||||
def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
|
||||
"""Embeds mask inputs."""
|
||||
mask_embedding = self.mask_downscaling(masks)
|
||||
return mask_embedding
|
||||
|
||||
def _get_batch_size(
|
||||
self,
|
||||
points: Optional[Tuple[torch.Tensor, torch.Tensor]],
|
||||
boxes: Optional[torch.Tensor],
|
||||
masks: Optional[torch.Tensor],
|
||||
) -> int:
|
||||
"""
|
||||
Gets the batch size of the output given the batch size of the input prompts.
|
||||
"""
|
||||
if points is not None:
|
||||
return points[0].shape[0]
|
||||
elif boxes is not None:
|
||||
return boxes.shape[0]
|
||||
elif masks is not None:
|
||||
return masks.shape[0]
|
||||
else:
|
||||
return 1
|
||||
|
||||
def _get_device(self) -> torch.device:
|
||||
return self.point_embeddings[0].weight.device
|
||||
|
||||
def forward(
|
||||
self,
|
||||
points: Optional[Tuple[torch.Tensor, torch.Tensor]],
|
||||
boxes: Optional[torch.Tensor],
|
||||
masks: Optional[torch.Tensor],
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""
|
||||
Embeds different types of prompts, returning both sparse and dense
|
||||
embeddings.
|
||||
|
||||
Arguments:
|
||||
points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
|
||||
and labels to embed.
|
||||
boxes (torch.Tensor or none): boxes to embed
|
||||
masks (torch.Tensor or none): masks to embed
|
||||
|
||||
Returns:
|
||||
torch.Tensor: sparse embeddings for the points and boxes, with shape
|
||||
BxNx(embed_dim), where N is determined by the number of input points
|
||||
and boxes.
|
||||
torch.Tensor: dense embeddings for the masks, in the shape
|
||||
Bx(embed_dim)x(embed_H)x(embed_W)
|
||||
"""
|
||||
bs = self._get_batch_size(points, boxes, masks)
|
||||
sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
|
||||
if points is not None:
|
||||
coords, labels = points
|
||||
point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
|
||||
sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
|
||||
if boxes is not None:
|
||||
box_embeddings = self._embed_boxes(boxes)
|
||||
sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
|
||||
|
||||
if masks is not None:
|
||||
dense_embeddings = self._embed_masks(masks)
|
||||
else:
|
||||
dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
|
||||
bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
|
||||
)
|
||||
|
||||
return sparse_embeddings, dense_embeddings
|
||||
|
||||
|
||||
class PositionEmbeddingRandom(nn.Module):
|
||||
"""
|
||||
Positional encoding using random spatial frequencies.
|
||||
"""
|
||||
|
||||
def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
|
||||
super().__init__()
|
||||
if scale is None or scale <= 0.0:
|
||||
scale = 1.0
|
||||
self.register_buffer(
|
||||
"positional_encoding_gaussian_matrix",
|
||||
scale * torch.randn((2, num_pos_feats)),
|
||||
)
|
||||
|
||||
def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
|
||||
"""Positionally encode points that are normalized to [0,1]."""
|
||||
# assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
|
||||
coords = 2 * coords - 1
|
||||
coords = coords @ self.positional_encoding_gaussian_matrix
|
||||
coords = 2 * np.pi * coords
|
||||
# outputs d_1 x ... x d_n x C shape
|
||||
return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
|
||||
|
||||
def forward(self, size: Tuple[int, int]) -> torch.Tensor:
|
||||
"""Generate positional encoding for a grid of the specified size."""
|
||||
h, w = size
|
||||
device: Any = self.positional_encoding_gaussian_matrix.device
|
||||
grid = torch.ones((h, w), device=device, dtype=torch.float32)
|
||||
y_embed = grid.cumsum(dim=0) - 0.5
|
||||
x_embed = grid.cumsum(dim=1) - 0.5
|
||||
y_embed = y_embed / h
|
||||
x_embed = x_embed / w
|
||||
|
||||
pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
|
||||
return pe.permute(2, 0, 1) # C x H x W
|
||||
|
||||
def forward_with_coords(
|
||||
self, coords_input: torch.Tensor, image_size: Tuple[int, int]
|
||||
) -> torch.Tensor:
|
||||
"""Positionally encode points that are not normalized to [0,1]."""
|
||||
coords = coords_input.clone()
|
||||
coords[:, :, 0] = coords[:, :, 0] / image_size[1]
|
||||
coords[:, :, 1] = coords[:, :, 1] / image_size[0]
|
||||
return self._pe_encoding(coords.to(torch.float)) # B x N x C
|
||||
|
||||
|
||||
class LayerNorm2d(nn.Module):
|
||||
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
|
||||
super().__init__()
|
||||
self.weight = nn.Parameter(torch.ones(num_channels))
|
||||
self.bias = nn.Parameter(torch.zeros(num_channels))
|
||||
self.eps = eps
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
u = x.mean(1, keepdim=True)
|
||||
s = (x - u).pow(2).mean(1, keepdim=True)
|
||||
x = (x - u) / torch.sqrt(s + self.eps)
|
||||
x = self.weight[:, None, None] * x + self.bias[:, None, None]
|
||||
return x
|
||||
|
||||
@@ -0,0 +1,54 @@
|
||||
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)
|
||||
@@ -0,0 +1,252 @@
|
||||
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 birefnet.config import Config
|
||||
from birefnet.dataset import class_labels_TR_sorted
|
||||
from birefnet.models.backbones.build_backbone import build_backbone
|
||||
from birefnet.models.modules.decoder_blocks import BasicDecBlk
|
||||
from birefnet.models.modules.lateral_blocks import BasicLatBlk
|
||||
from birefnet.models.refinement.stem_layer import StemLayer
|
||||
|
||||
|
||||
class RefinerPVTInChannels4(nn.Module):
|
||||
def __init__(self, in_channels=3+1):
|
||||
super(RefinerPVTInChannels4, self).__init__()
|
||||
self.config = Config()
|
||||
self.epoch = 1
|
||||
self.bb = build_backbone(self.config.bb, params_settings='in_channels=4')
|
||||
|
||||
lateral_channels_in_collection = {
|
||||
'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
|
||||
'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
|
||||
'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
|
||||
}
|
||||
channels = lateral_channels_in_collection[self.config.bb]
|
||||
self.squeeze_module = BasicDecBlk(channels[0], channels[0])
|
||||
|
||||
self.decoder = Decoder(channels)
|
||||
|
||||
if 0:
|
||||
for key, value in self.named_parameters():
|
||||
if 'bb.' in key:
|
||||
value.requires_grad = False
|
||||
|
||||
def forward(self, x):
|
||||
if isinstance(x, list):
|
||||
x = torch.cat(x, dim=1)
|
||||
########## 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
|
||||
@@ -0,0 +1,45 @@
|
||||
import torch.nn as nn
|
||||
from birefnet.models.modules.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
|
||||
@@ -0,0 +1,97 @@
|
||||
import logging
|
||||
import os
|
||||
import torch
|
||||
from torchvision import transforms
|
||||
import numpy as np
|
||||
import random
|
||||
import cv2
|
||||
from PIL import Image
|
||||
|
||||
|
||||
def path_to_image(path, size=(1024, 1024), color_type=['rgb', 'gray'][0]):
|
||||
if color_type.lower() == 'rgb':
|
||||
image = cv2.imread(path)
|
||||
elif color_type.lower() == 'gray':
|
||||
image = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
|
||||
else:
|
||||
print('Select the color_type to return, either to RGB or gray image.')
|
||||
return
|
||||
if size:
|
||||
image = cv2.resize(image, size, interpolation=cv2.INTER_LINEAR)
|
||||
if color_type.lower() == 'rgb':
|
||||
image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)).convert('RGB')
|
||||
else:
|
||||
image = Image.fromarray(image).convert('L')
|
||||
return image
|
||||
|
||||
|
||||
|
||||
def check_state_dict(state_dict, unwanted_prefix='_orig_mod.'):
|
||||
for k, v in list(state_dict.items()):
|
||||
if k.startswith(unwanted_prefix):
|
||||
state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
|
||||
return state_dict
|
||||
|
||||
|
||||
def generate_smoothed_gt(gts):
|
||||
epsilon = 0.001
|
||||
new_gts = (1-epsilon)*gts+epsilon/2
|
||||
return new_gts
|
||||
|
||||
|
||||
class Logger():
|
||||
def __init__(self, path="log.txt"):
|
||||
self.logger = logging.getLogger('BiRefNet')
|
||||
self.file_handler = logging.FileHandler(path, "w")
|
||||
self.stdout_handler = logging.StreamHandler()
|
||||
self.stdout_handler.setFormatter(logging.Formatter('%(asctime)s %(levelname)s %(message)s'))
|
||||
self.file_handler.setFormatter(logging.Formatter('%(asctime)s %(levelname)s %(message)s'))
|
||||
self.logger.addHandler(self.file_handler)
|
||||
self.logger.addHandler(self.stdout_handler)
|
||||
self.logger.setLevel(logging.INFO)
|
||||
self.logger.propagate = False
|
||||
|
||||
def info(self, txt):
|
||||
self.logger.info(txt)
|
||||
|
||||
def close(self):
|
||||
self.file_handler.close()
|
||||
self.stdout_handler.close()
|
||||
|
||||
|
||||
class AverageMeter(object):
|
||||
"""Computes and stores the average and current value"""
|
||||
def __init__(self):
|
||||
self.reset()
|
||||
|
||||
def reset(self):
|
||||
self.val = 0.0
|
||||
self.avg = 0.0
|
||||
self.sum = 0.0
|
||||
self.count = 0.0
|
||||
|
||||
def update(self, val, n=1):
|
||||
self.val = val
|
||||
self.sum += val * n
|
||||
self.count += n
|
||||
self.avg = self.sum / self.count
|
||||
|
||||
|
||||
def save_checkpoint(state, path, filename="latest.pth"):
|
||||
torch.save(state, os.path.join(path, filename))
|
||||
|
||||
|
||||
def save_tensor_img(tenor_im, path):
|
||||
im = tenor_im.cpu().clone()
|
||||
im = im.squeeze(0)
|
||||
tensor2pil = transforms.ToPILImage()
|
||||
im = tensor2pil(im)
|
||||
im.save(path)
|
||||
|
||||
|
||||
def set_seed(seed):
|
||||
torch.manual_seed(seed)
|
||||
torch.cuda.manual_seed_all(seed)
|
||||
np.random.seed(seed)
|
||||
random.seed(seed)
|
||||
torch.backends.cudnn.deterministic = True
|
||||
+205
@@ -0,0 +1,205 @@
|
||||
import os
|
||||
import safetensors.torch
|
||||
import torch
|
||||
from torchvision import transforms
|
||||
from torch.hub import download_url_to_file
|
||||
import comfy
|
||||
from comfy import model_management
|
||||
import folder_paths
|
||||
from birefnet.config import Config
|
||||
from birefnet.models.birefnet import BiRefNet
|
||||
from birefnet.utils import check_state_dict
|
||||
from .util import tensor_to_pil, apply_mask_to_image
|
||||
|
||||
config = Config()
|
||||
|
||||
deviceType = model_management.get_torch_device().type
|
||||
|
||||
models_dir_key = "birefnet"
|
||||
models_dir_default = os.path.join(folder_paths.models_dir, "BiRefNet")
|
||||
if models_dir_key not in folder_paths.folder_names_and_paths:
|
||||
folder_paths.folder_names_and_paths[models_dir_key] = (
|
||||
[os.path.join(folder_paths.models_dir, "BiRefNet")], folder_paths.supported_pt_extensions)
|
||||
else:
|
||||
if not os.path.exists(models_dir_default):
|
||||
os.makedirs(models_dir_default, exist_ok=True)
|
||||
folder_paths.add_model_folder_path(models_dir_key, models_dir_default)
|
||||
|
||||
models_path_default = folder_paths.get_folder_paths(models_dir_key)[0]
|
||||
|
||||
usage_to_weights_file = {
|
||||
'General': 'BiRefNet',
|
||||
'General-Lite': 'BiRefNet_T',
|
||||
'Portrait': 'BiRefNet-portrait',
|
||||
'DIS': 'BiRefNet-DIS5K',
|
||||
'HRSOD': 'BiRefNet-HRSOD',
|
||||
'COD': 'BiRefNet-COD',
|
||||
'DIS-TR_TEs': 'BiRefNet-DIS5K-TR_TEs'
|
||||
}
|
||||
|
||||
modelNameList = ['General', 'General-Lite', 'Portrait', 'DIS', 'HRSOD', 'COD', 'DIS-TR_TEs']
|
||||
|
||||
|
||||
def get_model_path(model_name):
|
||||
return os.path.join(models_path_default, f"{model_name}.safetensors")
|
||||
|
||||
|
||||
def download_models(model_root, model_urls):
|
||||
if not os.path.exists(model_root):
|
||||
os.makedirs(model_root, exist_ok=True)
|
||||
|
||||
for local_file, url in model_urls:
|
||||
local_path = os.path.join(model_root, local_file)
|
||||
if not os.path.exists(local_path):
|
||||
local_path = os.path.abspath(os.path.join(model_root, local_file))
|
||||
download_url_to_file(url, dst=local_path)
|
||||
|
||||
|
||||
def download_birefnet_model(model_name):
|
||||
"""
|
||||
Downloading model from huggingface.
|
||||
"""
|
||||
model_root = os.path.join(models_path_default)
|
||||
model_urls = (
|
||||
(f"{model_name}.safetensors",
|
||||
f"https://huggingface.co/ZhengPeng7/{usage_to_weights_file[model_name]}/resolve/main/model.safetensors"),
|
||||
)
|
||||
download_models(model_root, model_urls)
|
||||
|
||||
|
||||
proc_img = transforms.Compose(
|
||||
[
|
||||
transforms.Resize((1024, 1024)),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
class AutoDownloadBiRefNetModel:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"model_name": (modelNameList,),
|
||||
"device": (["AUTO", "CPU"],)
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("BiRefNetMODEL",)
|
||||
RETURN_NAMES = ("model",)
|
||||
FUNCTION = "load_model"
|
||||
CATEGORY = "image/BiRefNet"
|
||||
DESCRIPTION = "Auto download BiRefNet model from huggingface to models/BiRefNet/{model_name}.safetensors"
|
||||
|
||||
def load_model(self, model_name, device):
|
||||
bb_index = 3 if model_name == "General-Lite" else 6
|
||||
biRefNet_model = BiRefNet(bb_pretrained=False, bb_index=bb_index)
|
||||
model_file_name = f'{model_name}.safetensors'
|
||||
model_full_path = folder_paths.get_full_path(models_dir_key, model_file_name)
|
||||
if model_full_path is None:
|
||||
download_birefnet_model(model_name)
|
||||
model_full_path = folder_paths.get_full_path(models_dir_key, model_file_name)
|
||||
if device == "AUTO":
|
||||
device_type = deviceType
|
||||
else:
|
||||
device_type = "cpu"
|
||||
state_dict = safetensors.torch.load_file(model_full_path, device=device_type)
|
||||
biRefNet_model.load_state_dict(state_dict)
|
||||
biRefNet_model.to(device_type)
|
||||
biRefNet_model.eval()
|
||||
return biRefNet_model,
|
||||
|
||||
|
||||
class LoadRembgByBiRefNetModel:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"model": (folder_paths.get_filename_list(models_dir_key),),
|
||||
"device": (["AUTO", "CPU"], )
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("BiRefNetMODEL",)
|
||||
RETURN_NAMES = ("model",)
|
||||
FUNCTION = "load_model"
|
||||
CATEGORY = "rembg/BiRefNet"
|
||||
DESCRIPTION = "Load BiRefNet model from folder models/BiRefNet or the path of birefnet configured in the extra YAML file"
|
||||
|
||||
def load_model(self, model, device):
|
||||
biRefNet_model = BiRefNet(bb_pretrained=False, bb_index=6)
|
||||
model_path = folder_paths.get_full_path(models_dir_key, model)
|
||||
if device == "AUTO":
|
||||
device_type = deviceType
|
||||
else:
|
||||
device_type = "cpu"
|
||||
if model_path.endswith(".safetensors"):
|
||||
state_dict = safetensors.torch.load_file(model_path, device=device_type)
|
||||
else:
|
||||
state_dict = torch.load(model_path, map_location=device_type)
|
||||
state_dict = check_state_dict(state_dict)
|
||||
|
||||
biRefNet_model.load_state_dict(state_dict)
|
||||
biRefNet_model.to(device_type)
|
||||
biRefNet_model.eval()
|
||||
return [biRefNet_model]
|
||||
|
||||
|
||||
class RembgByBiRefNet:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"model": ("BiRefNetMODEL",),
|
||||
"images": ("IMAGE",),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE", "MASK",)
|
||||
RETURN_NAMES = ("image", "mask",)
|
||||
FUNCTION = "rem_bg"
|
||||
CATEGORY = "rembg/BiRefNet"
|
||||
|
||||
def rem_bg(self, model, images):
|
||||
_images = []
|
||||
_masks = []
|
||||
|
||||
for image in images:
|
||||
h, w, c = image.shape
|
||||
pil_image = tensor_to_pil(image)
|
||||
|
||||
im_tensor = proc_img(pil_image).unsqueeze(0)
|
||||
|
||||
with torch.no_grad():
|
||||
mask = model(im_tensor.to(deviceType))[-1].sigmoid().cpu()
|
||||
|
||||
# 遮罩大小需还原为与原图一致
|
||||
mask = comfy.utils.common_upscale(mask, w, h, 'bilinear', "disabled")
|
||||
|
||||
# image的mask对应部分设为透明
|
||||
image = apply_mask_to_image(image.cpu(), mask.cpu())
|
||||
|
||||
_images.append(image)
|
||||
_masks.append(mask)
|
||||
|
||||
out_images = torch.cat(_images, dim=0)
|
||||
out_masks = torch.cat(_masks, dim=0)
|
||||
|
||||
return out_images, out_masks
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"LoadBiRefNetModelByName": AutoDownloadBiRefNetModel,
|
||||
"LoadRembgByBiRefNetModel": LoadRembgByBiRefNetModel,
|
||||
"RembgByBiRefNet": RembgByBiRefNet,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"LoadBiRefNetModelByName": "LoadBiRefNetModelByName",
|
||||
"LoadRembgByBiRefNetModel": "LoadRembgByBiRefNetModel",
|
||||
"RembgByBiRefNet": "RembgByBiRefNet",
|
||||
}
|
||||
@@ -0,0 +1,4 @@
|
||||
numpy<2
|
||||
opencv-python
|
||||
scipy
|
||||
timm
|
||||
@@ -0,0 +1,57 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image
|
||||
|
||||
|
||||
def tensor_to_pil(image):
|
||||
return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
|
||||
|
||||
|
||||
def pil_to_tensor(image):
|
||||
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
|
||||
|
||||
|
||||
def apply_mask_to_image(image, mask):
|
||||
"""
|
||||
Apply a mask to an image and set non-masked parts to transparent.
|
||||
|
||||
Args:
|
||||
image (torch.Tensor): Image tensor of shape (h, w, c) or (1, h, w, c).
|
||||
mask (torch.Tensor): Mask tensor of shape (1, 1, h, w) or (h, w).
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Masked image tensor of shape (h, w, c+1) with transparency.
|
||||
"""
|
||||
# 判断 image 的形状
|
||||
if image.dim() == 3:
|
||||
pass
|
||||
elif image.dim() == 4:
|
||||
image = image.squeeze(0)
|
||||
else:
|
||||
raise ValueError("Image should be of shape (h, w, c) or (1, h, w, c).")
|
||||
|
||||
h, w, c = image.shape
|
||||
# 判断 mask 的形状
|
||||
if mask.dim() == 4:
|
||||
mask = mask.squeeze(0).squeeze(0) # 去掉前2个维度 (h,w)
|
||||
elif mask.dim() == 3:
|
||||
mask = mask.squeeze(0)
|
||||
elif mask.dim() == 2:
|
||||
pass
|
||||
else:
|
||||
raise ValueError("Mask should be of shape (1, 1, h, w) or (h, w).")
|
||||
|
||||
assert mask.shape == (h, w), "Mask shape does not match image shape."
|
||||
|
||||
# 将 mask 扩展到与 image 相同的通道数
|
||||
image_mask = mask.unsqueeze(-1).expand(h, w, c)
|
||||
|
||||
# 应用遮罩，黑色部分是0，相乘后白色1的部分会被保留，其它部分变为了黑色
|
||||
masked_image = image * image_mask
|
||||
|
||||
# 遮罩的黑白当做alpha通道的不透明度，黑色是0表示透明，白色是1表示不透明
|
||||
alpha = mask
|
||||
# alpha通道拼接到原图像的RGB中
|
||||
masked_image_with_alpha = torch.cat((masked_image[:, :, :3], alpha.unsqueeze(2)), dim=2)
|
||||
|
||||
return masked_image_with_alpha.unsqueeze(0)
|
||||
Reference in New Issue
Block a user