1556 lines
58 KiB
Python
1556 lines
58 KiB
Python
#!/usr/bin/python3
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import os
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import sys
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HOME_DIR = os.environ.get('HOME', '/root')
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MVANET_SOURCE_DIR = HOME_DIR + '/GITHUB/qianyu-dlut/MVANet'
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finetuned_MVANet_model_path = MVANET_SOURCE_DIR + '/model/Model_80.pth'
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pretrained_SwinB_model_path = MVANET_SOURCE_DIR + '/model/swin_base_patch4_window12_384_22kto1k.pth'
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import math
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import numpy as np
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import time
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import cv2
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import wget
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.checkpoint as checkpoint
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from torch.autograd import Variable
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from torch import nn
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from torchvision import transforms
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from einops import rearrange
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from timm.models import load_checkpoint
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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torch_device = 'cuda'
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torch_dtype = torch.float16
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def check_mkdir(dir_name):
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if not os.path.isdir(dir_name):
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os.makedirs(dir_name)
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def SwinT(pretrained=True):
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model = SwinTransformer(embed_dim=96,
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depths=[2, 2, 6, 2],
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num_heads=[3, 6, 12, 24],
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window_size=7)
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if pretrained is True:
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model.load_state_dict(torch.load(
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'data/backbone_ckpt/swin_tiny_patch4_window7_224.pth',
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map_location='cpu')['model'],
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strict=False)
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return model
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def SwinS(pretrained=True):
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model = SwinTransformer(embed_dim=96,
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depths=[2, 2, 18, 2],
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num_heads=[3, 6, 12, 24],
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window_size=7)
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if pretrained is True:
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model.load_state_dict(torch.load(
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'data/backbone_ckpt/swin_small_patch4_window7_224.pth',
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map_location='cpu')['model'],
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strict=False)
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return model
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def SwinB(pretrained=True):
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model = SwinTransformer(embed_dim=128,
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depths=[2, 2, 18, 2],
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num_heads=[4, 8, 16, 32],
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window_size=12)
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if pretrained is True:
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import os
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model.load_state_dict(torch.load(pretrained_SwinB_model_path,
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map_location='cpu')['model'],
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strict=False)
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return model
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def SwinL(pretrained=True):
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model = SwinTransformer(embed_dim=192,
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depths=[2, 2, 18, 2],
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num_heads=[6, 12, 24, 48],
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window_size=12)
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if pretrained is True:
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model.load_state_dict(torch.load(
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'data/backbone_ckpt/swin_large_patch4_window12_384_22kto1k.pth',
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map_location='cpu')['model'],
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strict=False)
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return model
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def get_activation_fn(activation):
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"""Return an activation function given a string"""
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if activation == "relu":
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return F.relu
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if activation == "gelu":
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return F.gelu
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if activation == "glu":
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return F.glu
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raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
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def make_cbr(in_dim, out_dim):
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return nn.Sequential(nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1),
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nn.BatchNorm2d(out_dim), nn.PReLU())
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def make_cbg(in_dim, out_dim):
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return nn.Sequential(nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1),
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nn.BatchNorm2d(out_dim), nn.GELU())
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def rescale_to(x, scale_factor: float = 2, interpolation='nearest'):
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return F.interpolate(x, scale_factor=scale_factor, mode=interpolation)
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def resize_as(x, y, interpolation='bilinear'):
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return F.interpolate(x, size=y.shape[-2:], mode=interpolation)
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def image2patches(x):
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"""b c (hg h) (wg w) -> (hg wg b) c h w"""
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x = rearrange(x, 'b c (hg h) (wg w) -> (hg wg b) c h w', hg=2, wg=2)
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return x
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def patches2image(x):
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"""(hg wg b) c h w -> b c (hg h) (wg w)"""
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x = rearrange(x, '(hg wg b) c h w -> b c (hg h) (wg w)', hg=2, wg=2)
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return x
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def window_partition(x, window_size):
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"""
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Args:
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x: (B, H, W, C)
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window_size (int): window size
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Returns:
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windows: (num_windows*B, window_size, window_size, C)
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"""
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B, H, W, C = x.shape
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x = x.view(B, H // window_size, window_size, W // window_size, window_size,
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C)
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windows = x.permute(0, 1, 3, 2, 4,
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5).contiguous().view(-1, window_size, window_size, C)
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return windows
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def window_reverse(windows, window_size, H, W):
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"""
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Args:
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windows: (num_windows*B, window_size, window_size, C)
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window_size (int): Window size
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H (int): Height of image
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W (int): Width of image
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Returns:
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x: (B, H, W, C)
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"""
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B = int(windows.shape[0] / (H * W / window_size / window_size))
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x = windows.view(B, H // window_size, W // window_size, window_size,
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window_size, -1)
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x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
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return x
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def mkdir_safe(out_path):
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if type(out_path) == str:
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if len(out_path) > 0:
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if not os.path.exists(out_path):
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os.mkdir(out_path)
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def get_model_path():
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import folder_paths
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from folder_paths import models_dir
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path_file_model = models_dir
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mkdir_safe(out_path=path_file_model)
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path_file_model = os.path.join(path_file_model, 'MVANet')
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mkdir_safe(out_path=path_file_model)
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path_file_model = os.path.join(path_file_model, 'Model_80.pth')
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return path_file_model
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def download_model(path):
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if not os.path.exists(path):
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wget.download(
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'https://huggingface.co/aravindhv10/Self-Correction-Human-Parsing/resolve/main/checkpoints/Model_80.pth',
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out=path)
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def load_model(model_checkpoint_path):
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download_model(path=model_checkpoint_path)
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torch.cuda.set_device(0)
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net = inf_MVANet().to(dtype=torch_dtype, device=torch_device)
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pretrained_dict = torch.load(model_checkpoint_path,
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map_location=torch_device)
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model_dict = net.state_dict()
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pretrained_dict = {
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k: v
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for k, v in pretrained_dict.items() if k in model_dict
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}
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model_dict.update(pretrained_dict)
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net.load_state_dict(model_dict)
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net = net.to(dtype=torch_dtype, device=torch_device)
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net.eval()
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return net
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def do_infer_tensor2tensor(img, net):
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img_transform = transforms.Compose(
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[transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
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h_, w_ = img.shape[1], img.shape[2]
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with torch.no_grad():
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img = rearrange(img, 'B H W C -> B C H W')
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img_resize = torch.nn.functional.interpolate(input=img,
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size=(1024, 1024),
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mode='bicubic',
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antialias=True)
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img_var = img_transform(img_resize)
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img_var = Variable(img_var)
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img_var = img_var.to(dtype=torch_dtype, device=torch_device)
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mask = []
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mask.append(net(img_var))
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prediction = torch.mean(torch.stack(mask, dim=0), dim=0)
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prediction = prediction.sigmoid()
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prediction = torch.nn.functional.interpolate(input=prediction,
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size=(h_, w_),
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mode='bicubic',
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antialias=True)
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prediction = prediction.squeeze(0)
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prediction = prediction.clamp(0, 1)
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prediction = prediction.detach()
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prediction = prediction.to(dtype=torch.float32, device='cpu')
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return prediction
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class Mlp(nn.Module):
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""" Multilayer perceptron."""
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def __init__(self,
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in_features,
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hidden_features=None,
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out_features=None,
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act_layer=nn.GELU,
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drop=0.):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.act = act_layer()
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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class WindowAttention(nn.Module):
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""" Window based multi-head self attention (W-MSA) module with relative position bias.
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It supports both of shifted and non-shifted window.
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Args:
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dim (int): Number of input channels.
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window_size (tuple[int]): The height and width of the window.
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num_heads (int): Number of attention heads.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
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attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
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proj_drop (float, optional): Dropout ratio of output. Default: 0.0
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"""
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def __init__(self,
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dim,
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window_size,
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num_heads,
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qkv_bias=True,
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qk_scale=None,
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attn_drop=0.,
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proj_drop=0.):
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super().__init__()
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self.dim = dim
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self.window_size = window_size # Wh, Ww
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or head_dim**-0.5
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# define a parameter table of relative position bias
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self.relative_position_bias_table = nn.Parameter(
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torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1),
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num_heads)) # 2*Wh-1 * 2*Ww-1, nH
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# get pair-wise relative position index for each token inside the window
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coords_h = torch.arange(self.window_size[0])
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coords_w = torch.arange(self.window_size[1])
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coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
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coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
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relative_coords = coords_flatten[:, :,
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None] - coords_flatten[:,
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None, :] # 2, Wh*Ww, Wh*Ww
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relative_coords = relative_coords.permute(
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1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
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relative_coords[:, :,
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0] += self.window_size[0] - 1 # shift to start from 0
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relative_coords[:, :, 1] += self.window_size[1] - 1
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relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
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relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
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self.register_buffer("relative_position_index",
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relative_position_index)
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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trunc_normal_(self.relative_position_bias_table, std=.02)
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self.softmax = nn.Softmax(dim=-1)
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def forward(self, x, mask=None):
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""" Forward function.
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Args:
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x: input features with shape of (num_windows*B, N, C)
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mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
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"""
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x = x.to(dtype=torch_dtype, device=torch_device)
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B_, N, C = x.shape
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qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads,
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C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[
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2] # make torchscript happy (cannot use tensor as tuple)
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q = q * self.scale
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attn = (q @ k.transpose(-2, -1))
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relative_position_bias = self.relative_position_bias_table[
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self.relative_position_index.view(-1)].view(
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self.window_size[0] * self.window_size[1],
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self.window_size[0] * self.window_size[1],
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-1) # Wh*Ww,Wh*Ww,nH
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relative_position_bias = relative_position_bias.permute(
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2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
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attn = attn + relative_position_bias.unsqueeze(0)
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if mask is not None:
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nW = mask.shape[0]
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attn = attn.view(B_ // nW, nW, self.num_heads, N,
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N) + mask.unsqueeze(1).unsqueeze(0)
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attn = attn.view(-1, self.num_heads, N, N)
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attn = self.softmax(attn)
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else:
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attn = self.softmax(attn)
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attn = self.attn_drop(attn)
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attn = attn.to(dtype=torch_dtype, device=torch_device)
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v = v.to(dtype=torch_dtype, device=torch_device)
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x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class SwinTransformerBlock(nn.Module):
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""" Swin Transformer Block.
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Args:
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dim (int): Number of input channels.
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num_heads (int): Number of attention heads.
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window_size (int): Window size.
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shift_size (int): Shift size for SW-MSA.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
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drop (float, optional): Dropout rate. Default: 0.0
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attn_drop (float, optional): Attention dropout rate. Default: 0.0
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drop_path (float, optional): Stochastic depth rate. Default: 0.0
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act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
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"""
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def __init__(self,
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dim,
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num_heads,
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window_size=7,
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shift_size=0,
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mlp_ratio=4.,
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qkv_bias=True,
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qk_scale=None,
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drop=0.,
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attn_drop=0.,
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drop_path=0.,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm):
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super().__init__()
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self.dim = dim
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self.num_heads = num_heads
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self.window_size = window_size
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self.shift_size = shift_size
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self.mlp_ratio = mlp_ratio
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assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
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self.norm1 = norm_layer(dim)
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self.attn = WindowAttention(dim,
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window_size=to_2tuple(self.window_size),
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num_heads=num_heads,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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attn_drop=attn_drop,
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proj_drop=drop)
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self.drop_path = DropPath(
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drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp(in_features=dim,
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hidden_features=mlp_hidden_dim,
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act_layer=act_layer,
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drop=drop)
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self.H = None
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self.W = None
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def forward(self, x, mask_matrix):
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""" Forward function.
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Args:
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x: Input feature, tensor size (B, H*W, C).
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H, W: Spatial resolution of the input feature.
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mask_matrix: Attention mask for cyclic shift.
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"""
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B, L, C = x.shape
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H, W = self.H, self.W
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assert L == H * W, "input feature has wrong size"
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shortcut = x
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x = self.norm1(x)
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x = x.view(B, H, W, C)
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# pad feature maps to multiples of window size
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pad_l = pad_t = 0
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pad_r = (self.window_size - W % self.window_size) % self.window_size
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pad_b = (self.window_size - H % self.window_size) % self.window_size
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x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
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_, Hp, Wp, _ = x.shape
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# cyclic shift
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if self.shift_size > 0:
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shifted_x = torch.roll(x,
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shifts=(-self.shift_size, -self.shift_size),
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dims=(1, 2))
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attn_mask = mask_matrix
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else:
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shifted_x = x
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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_chans (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_chans=3,
|
|
embed_dim=96,
|
|
norm_layer=None):
|
|
super().__init__()
|
|
patch_size = to_2tuple(patch_size)
|
|
self.patch_size = patch_size
|
|
|
|
self.in_chans = in_chans
|
|
self.embed_dim = embed_dim
|
|
|
|
self.proj = nn.Conv2d(in_chans,
|
|
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_chans (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_chans=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_chans=in_chans,
|
|
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 init_weights(self, pretrained=None):
|
|
"""Initialize the weights in backbone.
|
|
|
|
Args:
|
|
pretrained (str, optional): Path to pre-trained weights.
|
|
Defaults to None.
|
|
"""
|
|
|
|
def _init_weights(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)
|
|
|
|
if isinstance(pretrained, str):
|
|
self.apply(_init_weights)
|
|
load_checkpoint(self, pretrained, strict=False, logger=None)
|
|
elif pretrained is None:
|
|
self.apply(_init_weights)
|
|
else:
|
|
raise TypeError('pretrained must be a str or None')
|
|
|
|
def forward(self, x):
|
|
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()
|
|
|
|
|
|
class PositionEmbeddingSine:
|
|
|
|
def __init__(self,
|
|
num_pos_feats=64,
|
|
temperature=10000,
|
|
normalize=False,
|
|
scale=None):
|
|
super().__init__()
|
|
self.num_pos_feats = num_pos_feats
|
|
self.temperature = temperature
|
|
self.normalize = normalize
|
|
if scale is not None and normalize is False:
|
|
raise ValueError("normalize should be True if scale is passed")
|
|
if scale is None:
|
|
scale = 2 * math.pi
|
|
self.scale = scale
|
|
self.dim_t = torch.arange(0,
|
|
self.num_pos_feats,
|
|
dtype=torch_dtype,
|
|
device=torch_device)
|
|
|
|
def __call__(self, b, h, w):
|
|
mask = torch.zeros([b, h, w], dtype=torch.bool, device=torch_device)
|
|
assert mask is not None
|
|
not_mask = ~mask
|
|
y_embed = not_mask.cumsum(dim=1, dtype=torch_dtype)
|
|
x_embed = not_mask.cumsum(dim=2, dtype=torch_dtype)
|
|
if self.normalize:
|
|
eps = 1e-6
|
|
y_embed = ((y_embed - 0.5) / (y_embed[:, -1:, :] + eps) *
|
|
self.scale).to(device=torch_device, dtype=torch_dtype)
|
|
x_embed = ((x_embed - 0.5) / (x_embed[:, :, -1:] + eps) *
|
|
self.scale).to(device=torch_device, dtype=torch_dtype)
|
|
|
|
dim_t = self.temperature**(2 * (self.dim_t // 2) / self.num_pos_feats)
|
|
|
|
pos_x = x_embed[:, :, :, None] / dim_t
|
|
pos_y = y_embed[:, :, :, None] / dim_t
|
|
pos_x = torch.stack(
|
|
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
|
|
dim=4).flatten(3)
|
|
pos_y = torch.stack(
|
|
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
|
|
dim=4).flatten(3)
|
|
return torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
|
|
|
|
|
|
class MCLM(nn.Module):
|
|
|
|
def __init__(self, d_model, num_heads, pool_ratios=[1, 4, 8]):
|
|
super(MCLM, self).__init__()
|
|
self.attention = nn.ModuleList([
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
|
|
])
|
|
|
|
self.linear1 = nn.Linear(d_model, d_model * 2)
|
|
self.linear2 = nn.Linear(d_model * 2, d_model)
|
|
self.linear3 = nn.Linear(d_model, d_model * 2)
|
|
self.linear4 = nn.Linear(d_model * 2, d_model)
|
|
self.norm1 = nn.LayerNorm(d_model)
|
|
self.norm2 = nn.LayerNorm(d_model)
|
|
self.dropout = nn.Dropout(0.1)
|
|
self.dropout1 = nn.Dropout(0.1)
|
|
self.dropout2 = nn.Dropout(0.1)
|
|
self.activation = get_activation_fn('relu')
|
|
self.pool_ratios = pool_ratios
|
|
self.p_poses = []
|
|
self.g_pos = None
|
|
self.positional_encoding = PositionEmbeddingSine(
|
|
num_pos_feats=d_model // 2, normalize=True)
|
|
|
|
def forward(self, l, g):
|
|
"""
|
|
l: 4,c,h,w
|
|
g: 1,c,h,w
|
|
"""
|
|
b, c, h, w = l.size()
|
|
# 4,c,h,w -> 1,c,2h,2w
|
|
concated_locs = rearrange(l,
|
|
'(hg wg b) c h w -> b c (hg h) (wg w)',
|
|
hg=2,
|
|
wg=2)
|
|
|
|
pools = []
|
|
for pool_ratio in self.pool_ratios:
|
|
# b,c,h,w
|
|
tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
|
|
pool = F.adaptive_avg_pool2d(concated_locs, tgt_hw)
|
|
pools.append(rearrange(pool, 'b c h w -> (h w) b c'))
|
|
if self.g_pos is None:
|
|
pos_emb = self.positional_encoding(pool.shape[0],
|
|
pool.shape[2],
|
|
pool.shape[3])
|
|
pos_emb = rearrange(pos_emb, 'b c h w -> (h w) b c')
|
|
self.p_poses.append(pos_emb)
|
|
pools = torch.cat(pools, 0)
|
|
if self.g_pos is None:
|
|
self.p_poses = torch.cat(self.p_poses, dim=0)
|
|
pos_emb = self.positional_encoding(g.shape[0], g.shape[2],
|
|
g.shape[3])
|
|
self.g_pos = rearrange(pos_emb, 'b c h w -> (h w) b c')
|
|
|
|
# attention between glb (q) & multisensory concated-locs (k,v)
|
|
g_hw_b_c = rearrange(g, 'b c h w -> (h w) b c')
|
|
g_hw_b_c = g_hw_b_c + self.dropout1(self.attention[0](
|
|
g_hw_b_c + self.g_pos, pools + self.p_poses, pools)[0])
|
|
g_hw_b_c = self.norm1(g_hw_b_c)
|
|
g_hw_b_c = g_hw_b_c + self.dropout2(
|
|
self.linear2(
|
|
self.dropout(self.activation(self.linear1(g_hw_b_c)).clone())))
|
|
g_hw_b_c = self.norm2(g_hw_b_c)
|
|
|
|
# attention between origin locs (q) & freashed glb (k,v)
|
|
l_hw_b_c = rearrange(l, "b c h w -> (h w) b c")
|
|
_g_hw_b_c = rearrange(g_hw_b_c, '(h w) b c -> h w b c', h=h, w=w)
|
|
_g_hw_b_c = rearrange(_g_hw_b_c,
|
|
"(ng h) (nw w) b c -> (h w) (ng nw b) c",
|
|
ng=2,
|
|
nw=2)
|
|
outputs_re = []
|
|
for i, (_l, _g) in enumerate(
|
|
zip(l_hw_b_c.chunk(4, dim=1), _g_hw_b_c.chunk(4, dim=1))):
|
|
outputs_re.append(self.attention[i + 1](_l, _g,
|
|
_g)[0]) # (h w) 1 c
|
|
outputs_re = torch.cat(outputs_re, 1) # (h w) 4 c
|
|
|
|
l_hw_b_c = l_hw_b_c + self.dropout1(outputs_re)
|
|
l_hw_b_c = self.norm1(l_hw_b_c)
|
|
l_hw_b_c = l_hw_b_c + self.dropout2(
|
|
self.linear4(
|
|
self.dropout(self.activation(self.linear3(l_hw_b_c)).clone())))
|
|
l_hw_b_c = self.norm2(l_hw_b_c)
|
|
|
|
l = torch.cat((l_hw_b_c, g_hw_b_c), 1) # hw,b(5),c
|
|
return rearrange(l, "(h w) b c -> b c h w", h=h, w=w) ## (5,c,h*w)
|
|
|
|
|
|
class inf_MCLM(nn.Module):
|
|
|
|
def __init__(self, d_model, num_heads, pool_ratios=[1, 4, 8]):
|
|
super(inf_MCLM, self).__init__()
|
|
self.attention = nn.ModuleList([
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
|
|
])
|
|
|
|
self.linear1 = nn.Linear(d_model, d_model * 2)
|
|
self.linear2 = nn.Linear(d_model * 2, d_model)
|
|
self.linear3 = nn.Linear(d_model, d_model * 2)
|
|
self.linear4 = nn.Linear(d_model * 2, d_model)
|
|
self.norm1 = nn.LayerNorm(d_model)
|
|
self.norm2 = nn.LayerNorm(d_model)
|
|
self.dropout = nn.Dropout(0.1)
|
|
self.dropout1 = nn.Dropout(0.1)
|
|
self.dropout2 = nn.Dropout(0.1)
|
|
self.activation = get_activation_fn('relu')
|
|
self.pool_ratios = pool_ratios
|
|
self.p_poses = []
|
|
self.g_pos = None
|
|
self.positional_encoding = PositionEmbeddingSine(
|
|
num_pos_feats=d_model // 2, normalize=True)
|
|
|
|
def forward(self, l, g):
|
|
"""
|
|
l: 4,c,h,w
|
|
g: 1,c,h,w
|
|
"""
|
|
b, c, h, w = l.size()
|
|
# 4,c,h,w -> 1,c,2h,2w
|
|
concated_locs = rearrange(l,
|
|
'(hg wg b) c h w -> b c (hg h) (wg w)',
|
|
hg=2,
|
|
wg=2)
|
|
self.p_poses = []
|
|
pools = []
|
|
for pool_ratio in self.pool_ratios:
|
|
# b,c,h,w
|
|
tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
|
|
pool = F.adaptive_avg_pool2d(concated_locs, tgt_hw)
|
|
pools.append(rearrange(pool, 'b c h w -> (h w) b c'))
|
|
# if self.g_pos is None:
|
|
pos_emb = self.positional_encoding(pool.shape[0], pool.shape[2],
|
|
pool.shape[3])
|
|
pos_emb = rearrange(pos_emb, 'b c h w -> (h w) b c')
|
|
self.p_poses.append(pos_emb)
|
|
pools = torch.cat(pools, 0)
|
|
# if self.g_pos is None:
|
|
self.p_poses = torch.cat(self.p_poses, dim=0)
|
|
pos_emb = self.positional_encoding(g.shape[0], g.shape[2], g.shape[3])
|
|
self.g_pos = rearrange(pos_emb, 'b c h w -> (h w) b c')
|
|
|
|
# attention between glb (q) & multisensory concated-locs (k,v)
|
|
g_hw_b_c = rearrange(g, 'b c h w -> (h w) b c')
|
|
g_hw_b_c = g_hw_b_c + self.dropout1(self.attention[0](
|
|
g_hw_b_c + self.g_pos, pools + self.p_poses, pools)[0])
|
|
g_hw_b_c = self.norm1(g_hw_b_c)
|
|
g_hw_b_c = g_hw_b_c + self.dropout2(
|
|
self.linear2(
|
|
self.dropout(self.activation(self.linear1(g_hw_b_c)).clone())))
|
|
g_hw_b_c = self.norm2(g_hw_b_c)
|
|
|
|
# attention between origin locs (q) & freashed glb (k,v)
|
|
l_hw_b_c = rearrange(l, "b c h w -> (h w) b c")
|
|
_g_hw_b_c = rearrange(g_hw_b_c, '(h w) b c -> h w b c', h=h, w=w)
|
|
_g_hw_b_c = rearrange(_g_hw_b_c,
|
|
"(ng h) (nw w) b c -> (h w) (ng nw b) c",
|
|
ng=2,
|
|
nw=2)
|
|
outputs_re = []
|
|
for i, (_l, _g) in enumerate(
|
|
zip(l_hw_b_c.chunk(4, dim=1), _g_hw_b_c.chunk(4, dim=1))):
|
|
outputs_re.append(self.attention[i + 1](_l, _g,
|
|
_g)[0]) # (h w) 1 c
|
|
outputs_re = torch.cat(outputs_re, 1) # (h w) 4 c
|
|
|
|
l_hw_b_c = l_hw_b_c + self.dropout1(outputs_re)
|
|
l_hw_b_c = self.norm1(l_hw_b_c)
|
|
l_hw_b_c = l_hw_b_c + self.dropout2(
|
|
self.linear4(
|
|
self.dropout(self.activation(self.linear3(l_hw_b_c)).clone())))
|
|
l_hw_b_c = self.norm2(l_hw_b_c)
|
|
|
|
l = torch.cat((l_hw_b_c, g_hw_b_c), 1) # hw,b(5),c
|
|
return rearrange(l, "(h w) b c -> b c h w", h=h, w=w) ## (5,c,h*w)
|
|
|
|
|
|
class MCRM(nn.Module):
|
|
|
|
def __init__(self, d_model, num_heads, pool_ratios=[4, 8, 16], h=None):
|
|
super(MCRM, self).__init__()
|
|
self.attention = nn.ModuleList([
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
|
|
])
|
|
|
|
self.linear3 = nn.Linear(d_model, d_model * 2)
|
|
self.linear4 = nn.Linear(d_model * 2, d_model)
|
|
self.norm1 = nn.LayerNorm(d_model)
|
|
self.norm2 = nn.LayerNorm(d_model)
|
|
self.dropout = nn.Dropout(0.1)
|
|
self.dropout1 = nn.Dropout(0.1)
|
|
self.dropout2 = nn.Dropout(0.1)
|
|
self.sigmoid = nn.Sigmoid()
|
|
self.activation = get_activation_fn('relu')
|
|
self.sal_conv = nn.Conv2d(d_model, 1, 1)
|
|
self.pool_ratios = pool_ratios
|
|
self.positional_encoding = PositionEmbeddingSine(
|
|
num_pos_feats=d_model // 2, normalize=True)
|
|
|
|
def forward(self, x):
|
|
b, c, h, w = x.size()
|
|
loc, glb = x.split([4, 1], dim=0) # 4,c,h,w; 1,c,h,w
|
|
# b(4),c,h,w
|
|
patched_glb = rearrange(glb,
|
|
'b c (hg h) (wg w) -> (hg wg b) c h w',
|
|
hg=2,
|
|
wg=2)
|
|
|
|
# generate token attention map
|
|
token_attention_map = self.sigmoid(self.sal_conv(glb))
|
|
token_attention_map = F.interpolate(token_attention_map,
|
|
size=patches2image(loc).shape[-2:],
|
|
mode='nearest')
|
|
loc = loc * rearrange(token_attention_map,
|
|
'b c (hg h) (wg w) -> (hg wg b) c h w',
|
|
hg=2,
|
|
wg=2)
|
|
pools = []
|
|
for pool_ratio in self.pool_ratios:
|
|
tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
|
|
pool = F.adaptive_avg_pool2d(patched_glb, tgt_hw)
|
|
pools.append(rearrange(pool,
|
|
'nl c h w -> nl c (h w)')) # nl(4),c,hw
|
|
# nl(4),c,nphw -> nl(4),nphw,1,c
|
|
pools = rearrange(torch.cat(pools, 2), "nl c nphw -> nl nphw 1 c")
|
|
loc_ = rearrange(loc, 'nl c h w -> nl (h w) 1 c')
|
|
outputs = []
|
|
for i, q in enumerate(
|
|
loc_.unbind(dim=0)): # traverse all local patches
|
|
# np*hw,1,c
|
|
v = pools[i]
|
|
k = v
|
|
outputs.append(self.attention[i](q, k, v)[0])
|
|
outputs = torch.cat(outputs, 1)
|
|
src = loc.view(4, c, -1).permute(2, 0, 1) + self.dropout1(outputs)
|
|
src = self.norm1(src)
|
|
src = src + self.dropout2(
|
|
self.linear4(
|
|
self.dropout(self.activation(self.linear3(src)).clone())))
|
|
src = self.norm2(src)
|
|
|
|
src = src.permute(1, 2, 0).reshape(4, c, h, w) # freshed loc
|
|
glb = glb + F.interpolate(patches2image(src),
|
|
size=glb.shape[-2:],
|
|
mode='nearest') # freshed glb
|
|
return torch.cat((src, glb), 0), token_attention_map
|
|
|
|
|
|
class inf_MCRM(nn.Module):
|
|
|
|
def __init__(self, d_model, num_heads, pool_ratios=[4, 8, 16], h=None):
|
|
super(inf_MCRM, self).__init__()
|
|
self.attention = nn.ModuleList([
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1),
|
|
nn.MultiheadAttention(d_model, num_heads, dropout=0.1)
|
|
])
|
|
|
|
self.linear3 = nn.Linear(d_model, d_model * 2)
|
|
self.linear4 = nn.Linear(d_model * 2, d_model)
|
|
self.norm1 = nn.LayerNorm(d_model)
|
|
self.norm2 = nn.LayerNorm(d_model)
|
|
self.dropout = nn.Dropout(0.1)
|
|
self.dropout1 = nn.Dropout(0.1)
|
|
self.dropout2 = nn.Dropout(0.1)
|
|
self.sigmoid = nn.Sigmoid()
|
|
self.activation = get_activation_fn('relu')
|
|
self.sal_conv = nn.Conv2d(d_model, 1, 1)
|
|
self.pool_ratios = pool_ratios
|
|
self.positional_encoding = PositionEmbeddingSine(
|
|
num_pos_feats=d_model // 2, normalize=True)
|
|
|
|
def forward(self, x):
|
|
b, c, h, w = x.size()
|
|
loc, glb = x.split([4, 1], dim=0) # 4,c,h,w; 1,c,h,w
|
|
# b(4),c,h,w
|
|
patched_glb = rearrange(glb,
|
|
'b c (hg h) (wg w) -> (hg wg b) c h w',
|
|
hg=2,
|
|
wg=2)
|
|
|
|
# generate token attention map
|
|
token_attention_map = self.sigmoid(self.sal_conv(glb))
|
|
token_attention_map = F.interpolate(token_attention_map,
|
|
size=patches2image(loc).shape[-2:],
|
|
mode='nearest')
|
|
loc = loc * rearrange(token_attention_map,
|
|
'b c (hg h) (wg w) -> (hg wg b) c h w',
|
|
hg=2,
|
|
wg=2)
|
|
pools = []
|
|
for pool_ratio in self.pool_ratios:
|
|
tgt_hw = (round(h / pool_ratio), round(w / pool_ratio))
|
|
pool = F.adaptive_avg_pool2d(patched_glb, tgt_hw)
|
|
pools.append(rearrange(pool,
|
|
'nl c h w -> nl c (h w)')) # nl(4),c,hw
|
|
# nl(4),c,nphw -> nl(4),nphw,1,c
|
|
pools = rearrange(torch.cat(pools, 2), "nl c nphw -> nl nphw 1 c")
|
|
loc_ = rearrange(loc, 'nl c h w -> nl (h w) 1 c')
|
|
outputs = []
|
|
for i, q in enumerate(
|
|
loc_.unbind(dim=0)): # traverse all local patches
|
|
# np*hw,1,c
|
|
v = pools[i]
|
|
k = v
|
|
outputs.append(self.attention[i](q, k, v)[0])
|
|
outputs = torch.cat(outputs, 1)
|
|
src = loc.view(4, c, -1).permute(2, 0, 1) + self.dropout1(outputs)
|
|
src = self.norm1(src)
|
|
src = src + self.dropout2(
|
|
self.linear4(
|
|
self.dropout(self.activation(self.linear3(src)).clone())))
|
|
src = self.norm2(src)
|
|
|
|
src = src.permute(1, 2, 0).reshape(4, c, h, w) # freshed loc
|
|
glb = glb + F.interpolate(patches2image(src),
|
|
size=glb.shape[-2:],
|
|
mode='nearest') # freshed glb
|
|
return torch.cat((src, glb), 0)
|
|
|
|
|
|
# model for single-scale training
|
|
class MVANet(nn.Module):
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.backbone = SwinB(pretrained=True)
|
|
emb_dim = 128
|
|
self.sideout5 = nn.Sequential(
|
|
nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
|
|
self.sideout4 = nn.Sequential(
|
|
nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
|
|
self.sideout3 = nn.Sequential(
|
|
nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
|
|
self.sideout2 = nn.Sequential(
|
|
nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
|
|
self.sideout1 = nn.Sequential(
|
|
nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
|
|
|
|
self.output5 = make_cbr(1024, emb_dim)
|
|
self.output4 = make_cbr(512, emb_dim)
|
|
self.output3 = make_cbr(256, emb_dim)
|
|
self.output2 = make_cbr(128, emb_dim)
|
|
self.output1 = make_cbr(128, emb_dim)
|
|
|
|
self.multifieldcrossatt = MCLM(emb_dim, 1, [1, 4, 8])
|
|
self.conv1 = make_cbr(emb_dim, emb_dim)
|
|
self.conv2 = make_cbr(emb_dim, emb_dim)
|
|
self.conv3 = make_cbr(emb_dim, emb_dim)
|
|
self.conv4 = make_cbr(emb_dim, emb_dim)
|
|
self.dec_blk1 = MCRM(emb_dim, 1, [2, 4, 8])
|
|
self.dec_blk2 = MCRM(emb_dim, 1, [2, 4, 8])
|
|
self.dec_blk3 = MCRM(emb_dim, 1, [2, 4, 8])
|
|
self.dec_blk4 = MCRM(emb_dim, 1, [2, 4, 8])
|
|
|
|
self.insmask_head = nn.Sequential(
|
|
nn.Conv2d(emb_dim, 384, kernel_size=3, padding=1),
|
|
nn.BatchNorm2d(384), nn.PReLU(),
|
|
nn.Conv2d(384, 384, kernel_size=3, padding=1), nn.BatchNorm2d(384),
|
|
nn.PReLU(), nn.Conv2d(384, emb_dim, kernel_size=3, padding=1))
|
|
|
|
self.shallow = nn.Sequential(
|
|
nn.Conv2d(3, emb_dim, kernel_size=3, padding=1))
|
|
self.upsample1 = make_cbg(emb_dim, emb_dim)
|
|
self.upsample2 = make_cbg(emb_dim, emb_dim)
|
|
self.output = nn.Sequential(
|
|
nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
|
|
|
|
for m in self.modules():
|
|
if isinstance(m, nn.ReLU) or isinstance(m, nn.Dropout):
|
|
m.inplace = True
|
|
|
|
def forward(self, x):
|
|
x = x.to(dtype=torch_dtype, device=torch_device)
|
|
shallow = self.shallow(x)
|
|
glb = rescale_to(x, scale_factor=0.5, interpolation='bilinear')
|
|
loc = image2patches(x)
|
|
input = torch.cat((loc, glb), dim=0)
|
|
feature = self.backbone(input)
|
|
e5 = self.output5(feature[4]) # (5,128,16,16)
|
|
e4 = self.output4(feature[3]) # (5,128,32,32)
|
|
e3 = self.output3(feature[2]) # (5,128,64,64)
|
|
e2 = self.output2(feature[1]) # (5,128,128,128)
|
|
e1 = self.output1(feature[0]) # (5,128,128,128)
|
|
loc_e5, glb_e5 = e5.split([4, 1], dim=0)
|
|
e5 = self.multifieldcrossatt(loc_e5, glb_e5) # (4,128,16,16)
|
|
|
|
e4, tokenattmap4 = self.dec_blk4(e4 + resize_as(e5, e4))
|
|
e4 = self.conv4(e4)
|
|
e3, tokenattmap3 = self.dec_blk3(e3 + resize_as(e4, e3))
|
|
e3 = self.conv3(e3)
|
|
e2, tokenattmap2 = self.dec_blk2(e2 + resize_as(e3, e2))
|
|
e2 = self.conv2(e2)
|
|
e1, tokenattmap1 = self.dec_blk1(e1 + resize_as(e2, e1))
|
|
e1 = self.conv1(e1)
|
|
loc_e1, glb_e1 = e1.split([4, 1], dim=0)
|
|
output1_cat = patches2image(loc_e1) # (1,128,256,256)
|
|
# add glb feat in
|
|
output1_cat = output1_cat + resize_as(glb_e1, output1_cat)
|
|
# merge
|
|
final_output = self.insmask_head(output1_cat) # (1,128,256,256)
|
|
# shallow feature merge
|
|
final_output = final_output + resize_as(shallow, final_output)
|
|
final_output = self.upsample1(rescale_to(final_output))
|
|
final_output = rescale_to(final_output +
|
|
resize_as(shallow, final_output))
|
|
final_output = self.upsample2(final_output)
|
|
final_output = self.output(final_output)
|
|
####
|
|
sideout5 = self.sideout5(e5).to(dtype=torch_dtype, device=torch_device)
|
|
sideout4 = self.sideout4(e4)
|
|
sideout3 = self.sideout3(e3)
|
|
sideout2 = self.sideout2(e2)
|
|
sideout1 = self.sideout1(e1)
|
|
#######glb_sideouts ######
|
|
glb5 = self.sideout5(glb_e5)
|
|
glb4 = sideout4[-1, :, :, :].unsqueeze(0)
|
|
glb3 = sideout3[-1, :, :, :].unsqueeze(0)
|
|
glb2 = sideout2[-1, :, :, :].unsqueeze(0)
|
|
glb1 = sideout1[-1, :, :, :].unsqueeze(0)
|
|
####### concat 4 to 1 #######
|
|
sideout1 = patches2image(sideout1[:-1]).to(dtype=torch_dtype,
|
|
device=torch_device)
|
|
sideout2 = patches2image(sideout2[:-1]).to(
|
|
dtype=torch_dtype,
|
|
device=torch_device) ####(5,c,h,w) -> (1 c 2h,2w)
|
|
sideout3 = patches2image(sideout3[:-1]).to(dtype=torch_dtype,
|
|
device=torch_device)
|
|
sideout4 = patches2image(sideout4[:-1]).to(dtype=torch_dtype,
|
|
device=torch_device)
|
|
sideout5 = patches2image(sideout5[:-1]).to(dtype=torch_dtype,
|
|
device=torch_device)
|
|
if self.training:
|
|
return sideout5, sideout4, sideout3, sideout2, sideout1, final_output, glb5, glb4, glb3, glb2, glb1, tokenattmap4, tokenattmap3, tokenattmap2, tokenattmap1
|
|
else:
|
|
return final_output
|
|
|
|
|
|
# model for multi-scale testing
|
|
class inf_MVANet(nn.Module):
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
# self.backbone = SwinB(pretrained=True)
|
|
self.backbone = SwinB(pretrained=False)
|
|
|
|
emb_dim = 128
|
|
self.output5 = make_cbr(1024, emb_dim)
|
|
self.output4 = make_cbr(512, emb_dim)
|
|
self.output3 = make_cbr(256, emb_dim)
|
|
self.output2 = make_cbr(128, emb_dim)
|
|
self.output1 = make_cbr(128, emb_dim)
|
|
|
|
self.multifieldcrossatt = inf_MCLM(emb_dim, 1, [1, 4, 8])
|
|
self.conv1 = make_cbr(emb_dim, emb_dim)
|
|
self.conv2 = make_cbr(emb_dim, emb_dim)
|
|
self.conv3 = make_cbr(emb_dim, emb_dim)
|
|
self.conv4 = make_cbr(emb_dim, emb_dim)
|
|
self.dec_blk1 = inf_MCRM(emb_dim, 1, [2, 4, 8])
|
|
self.dec_blk2 = inf_MCRM(emb_dim, 1, [2, 4, 8])
|
|
self.dec_blk3 = inf_MCRM(emb_dim, 1, [2, 4, 8])
|
|
self.dec_blk4 = inf_MCRM(emb_dim, 1, [2, 4, 8])
|
|
|
|
self.insmask_head = nn.Sequential(
|
|
nn.Conv2d(emb_dim, 384, kernel_size=3, padding=1),
|
|
nn.BatchNorm2d(384), nn.PReLU(),
|
|
nn.Conv2d(384, 384, kernel_size=3, padding=1), nn.BatchNorm2d(384),
|
|
nn.PReLU(), nn.Conv2d(384, emb_dim, kernel_size=3, padding=1))
|
|
|
|
self.shallow = nn.Sequential(
|
|
nn.Conv2d(3, emb_dim, kernel_size=3, padding=1))
|
|
self.upsample1 = make_cbg(emb_dim, emb_dim)
|
|
self.upsample2 = make_cbg(emb_dim, emb_dim)
|
|
self.output = nn.Sequential(
|
|
nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1))
|
|
|
|
for m in self.modules():
|
|
if isinstance(m, nn.ReLU) or isinstance(m, nn.Dropout):
|
|
m.inplace = True
|
|
|
|
def forward(self, x):
|
|
shallow = self.shallow(x)
|
|
glb = rescale_to(x, scale_factor=0.5, interpolation='bilinear')
|
|
loc = image2patches(x)
|
|
input = torch.cat((loc, glb), dim=0)
|
|
feature = self.backbone(input)
|
|
e5 = self.output5(feature[4])
|
|
e4 = self.output4(feature[3])
|
|
e3 = self.output3(feature[2])
|
|
e2 = self.output2(feature[1])
|
|
e1 = self.output1(feature[0])
|
|
loc_e5, glb_e5 = e5.split([4, 1], dim=0)
|
|
e5_cat = self.multifieldcrossatt(loc_e5, glb_e5)
|
|
|
|
e4 = self.conv4(self.dec_blk4(e4 + resize_as(e5_cat, e4)))
|
|
e3 = self.conv3(self.dec_blk3(e3 + resize_as(e4, e3)))
|
|
e2 = self.conv2(self.dec_blk2(e2 + resize_as(e3, e2)))
|
|
e1 = self.conv1(self.dec_blk1(e1 + resize_as(e2, e1)))
|
|
loc_e1, glb_e1 = e1.split([4, 1], dim=0)
|
|
# after decoder, concat loc features to a whole one, and merge
|
|
output1_cat = patches2image(loc_e1)
|
|
# add glb feat in
|
|
output1_cat = output1_cat + resize_as(glb_e1, output1_cat)
|
|
# merge
|
|
final_output = self.insmask_head(output1_cat)
|
|
# shallow feature merge
|
|
final_output = final_output + resize_as(shallow, final_output)
|
|
final_output = self.upsample1(rescale_to(final_output))
|
|
final_output = rescale_to(final_output +
|
|
resize_as(shallow, final_output))
|
|
final_output = self.upsample2(final_output)
|
|
final_output = self.output(final_output)
|
|
return final_output
|
|
|
|
|
|
class load_MVANet_Model:
|
|
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {},
|
|
}
|
|
|
|
RETURN_TYPES = ("MVANet_Model", )
|
|
FUNCTION = "test"
|
|
CATEGORY = "MVANet"
|
|
|
|
def test(self):
|
|
return (load_model(get_model_path()), )
|
|
|
|
|
|
class run_MVANet_inference:
|
|
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE", ),
|
|
"MVANet_Model": ("MVANet_Model", ),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("MASK", )
|
|
FUNCTION = "test"
|
|
CATEGORY = "MVANet"
|
|
|
|
def test(
|
|
self,
|
|
image,
|
|
MVANet_Model,
|
|
):
|
|
ret = do_infer_tensor2tensor(img=image, net=MVANet_Model)
|
|
|
|
return (ret, )
|
|
|
|
|
|
# NODE_CLASS_MAPPINGS = {
|
|
# "load_MVANet_Model": load_MVANet_Model,
|
|
# "run_MVANet_inference": run_MVANet_inference
|
|
# }
|
|
|
|
# NODE_DISPLAY_NAME_MAPPINGS = {
|
|
# "load_MVANet_Model": "load MVANet Model",
|
|
# "run_MVANet_inference": "run MVANet inference"
|
|
# }
|