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CUB_Model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from backbone import Get_vit, conv_ln, ArcMarginProduct, load_DINO
class UnsupervisedPart_CUB(nn.Module):
def __init__(self, num_point, d_model, pretrain,
cfg):
super(UnsupervisedPart_CUB, self).__init__()
self.num_point = num_point
self.d_model = d_model
self.background = cfg.MODEL.BACKGROUND
self.Reconstruct_Block = nn.Sequential(
conv_ln(64, 64, 1),
conv_ln(64, 64, 1),
nn.Upsample(scale_factor=2.0, mode='bilinear', align_corners=False),
conv_ln(64, 64, 1),
conv_ln(64, 64, 1),
nn.Upsample(scale_factor=2.0, mode='bilinear', align_corners=False),
conv_ln(64, 64, 1),
nn.Conv2d(64, 3, (1, 1), (1, 1))
)
self.compress_layer = self.compress_layer = nn.Sequential(
nn.Conv2d(384, 256, (1, 1), (1, 1)),
nn.ReLU(inplace=True),
nn.BatchNorm2d(256),
nn.Conv2d(256, 128, (1, 1), (1, 1)),
nn.ReLU(inplace=True),
nn.BatchNorm2d(128),
nn.Conv2d(128, 64, (1, 1), (1, 1)),
)
self.ArcMarginProduct = ArcMarginProduct(d_model, self.num_point-self.background, self.num_point-self.background)
self.apply(self._init_weights)
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
x_map = torch.arange(cfg.MODEL.IMG_SIZE // 8, dtype=torch.float32).cuda() / (cfg.MODEL.IMG_SIZE // 8) * 2.0 - 1.0
self.x_map = x_map.unsqueeze(0).repeat(cfg.MODEL.IMG_SIZE // 8, 1)
self.x_map = self.x_map.unsqueeze(0).unsqueeze(0)
y_map = torch.arange(cfg.MODEL.IMG_SIZE // 8, dtype=torch.float32).cuda() / (cfg.MODEL.IMG_SIZE // 8) * 2.0 - 1.0
self.y_map = y_map.unsqueeze(1).repeat(1, cfg.MODEL.IMG_SIZE // 8)
self.y_map = self.y_map.unsqueeze(0).unsqueeze(0)
self.backbone1 = load_DINO("small", pretrain, 8)
self.Transformer = Get_vit(num_point, 128, 16, 256, 6, 8, 64)
def _reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def Concentration_loss(self, pred, epsilon=0.5):
SumWeight = torch.sum(pred, dim=(2, 3), keepdim=True) + 1e-8
X_sum = torch.sum(pred * self.x_map, dim=(2, 3), keepdim=True)
Y_sum = torch.sum(pred * self.y_map, dim=(2, 3), keepdim=True)
# [B, N, 1, 1]
X_coord = X_sum / SumWeight
Y_coord = Y_sum / SumWeight
X_var = torch.square(self.x_map - X_coord)
Y_var = torch.square(self.y_map - Y_coord)
X_var = torch.sum(X_var * pred / SumWeight, dim=(1, 2, 3))
Y_var = torch.sum(Y_var * pred / SumWeight, dim=(1, 2, 3))
size_constrain = 1 / (1 + SumWeight / epsilon)
size_constrain = torch.sum(size_constrain, dim=(1, 2, 3))
return torch.mean(X_var + Y_var), torch.mean(size_constrain)
def high_level_loss(self, input_feature, img, vgg, return_img=False):
Bs = img.size(0)
R_img = self.Reconstruct_Block(input_feature)
vgg_features = vgg(torch.cat((R_img, img), dim=0))
loss_weight = [1.0 / 32.0, 1.0 / 16.0, 1.0 / 8.0, 1.0 / 4.0, 1.0]
loss = 0
for i in range(len(vgg_features)):
feature_diff = (vgg_features[i][0:Bs] - vgg_features[i][Bs:])
value = torch.abs(feature_diff).mean()
loss += value * loss_weight[i]
if return_img is False:
return loss
else:
return loss, R_img
def _init_weights(self, m):
if isinstance(m, nn.Linear):
# we use xavier_uniform following official JAX ViT:
torch.nn.init.xavier_uniform_(m.weight)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Conv2d):
torch.nn.init.xavier_uniform_(m.weight)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, input, input_pair=None, VGG=None, trainable=False):
# 产生特征图
Large_input = input
Large_input_pair = input_pair
input = F.interpolate(input, scale_factor=0.5)
Local_Features = self.backbone1(Large_input)
Local_Features = self.compress_layer(Local_Features)
Global_Feature = self.Transformer(input)
if trainable:
input_pair = F.interpolate(input_pair, scale_factor=0.5)
Local_Features_pair = self.backbone1(Large_input_pair)
Local_Features_pair = self.compress_layer(Local_Features_pair)
Global_Feature_pair = self.Transformer(input_pair)
output_distribution_raw_pair = torch.einsum("bqc,bchw->bqhw", Global_Feature, Local_Features_pair)
output_distribution_raw_pair = F.softmax(output_distribution_raw_pair * 0.8, dim=1)
R_img_pair = torch.einsum("bln,bnc->blc", Global_Feature.permute(0, 2, 1), output_distribution_raw_pair.flatten(2))
output_distribution_pair_raw = torch.einsum("bqc,bchw->bqhw", Global_Feature_pair, Local_Features)
output_distribution_pair_raw = F.softmax(output_distribution_pair_raw * 0.8, dim=1)
R_img = torch.einsum("bln,bnc->blc", Global_Feature_pair.permute(0, 2, 1), output_distribution_pair_raw.flatten(2))
Bs = R_img_pair.size(0)
R_img_pair = R_img_pair.view(Bs, self.d_model, 32, 32)
R_img = R_img.view(Bs, self.d_model, 32, 32)
Highlevel_loss_raw_pair = self.high_level_loss(R_img_pair, input_pair, VGG)
concentration_loss_raw_pair, area_loss_raw_pair = self.Concentration_loss(output_distribution_raw_pair[:, :self.num_point-self.background, :, :])
Highlevel_loss_pair_raw = self.high_level_loss(R_img, input, VGG)
concentration_loss_pair_raw, area_loss_pair_raw = self.Concentration_loss(output_distribution_pair_raw[:, :self.num_point-self.background, :, :])
Highlevel_loss = (Highlevel_loss_raw_pair + Highlevel_loss_pair_raw) / 2.0
Concentration_loss = (concentration_loss_raw_pair + concentration_loss_pair_raw) / 2.0
area_loss = (area_loss_raw_pair + area_loss_pair_raw) / 2.0
semantic_loss = self.ArcMarginProduct(Global_Feature[:, :self.num_point-self.background, :]) \
+ self.ArcMarginProduct(Global_Feature_pair[:, :self.num_point-self.background, :])
semantic_loss = semantic_loss / 2.0
return area_loss, Highlevel_loss, Concentration_loss, semantic_loss
else:
Local_Features = F.interpolate(Local_Features, scale_factor=4.0, mode='bilinear')
output_distribution_raw_raw = torch.einsum("bqc,bchw->bqhw", Global_Feature, Local_Features)
output_distribution_raw_raw = F.softmax(output_distribution_raw_raw * 0.8, dim=1)
return output_distribution_raw_raw