main

Author: caiyancheng

.idea/.gitignore

@@ -0,0 +1,100 @@
+from domain_ad import *
+
+def cheatbackbone_back(feature,d_1,d_2,d_3,d_4,MY_4,MY_1,TRAIN_GANLOSS,ADD):
+    d_out_first = d_1(feature[0])  # d_out_first:[b,2,8,8]
+    if not MY_4:  # 使用张铂师兄的方法
+        if MY_1:
+            source_label = 0.5
+        if TRAIN_GANLOSS == 'BCE':
+            loss_adv_trg_first = bce_loss(d_out_first, source_label)
+        elif TRAIN_GANLOSS == 'LS':
+            loss_adv_trg_first = ls_loss(d_out_first, source_label)
+        if ADD == 1:
+            d_out_first_add = d_first_add(feature_trg[0])  # d_out_first:[24,1,2,2]
+            if TRAIN_GANLOSS == 'BCE':
+                loss_adv_trg_first_add = bce_loss(d_out_first_add, source_label)
+            elif TRAIN_GANLOSS == 'LS':
+                loss_adv_trg_first_add = ls_loss(d_out_first_add, source_label)
+        if TRAIN_LAYER > 1:
+            if ddcpp_2 == 0:
+                d_out_second = d_second(feature_trg[1])  # ([24,1,1,1])
+            if ddcpp_2 == 1:
+                d_out_second = dis_model_2(feature_trg[1])  # ([24,1,1,1])
+            if TRAIN_GANLOSS == 'BCE':
+                loss_adv_trg_second = bce_loss(d_out_second, source_label)
+            elif TRAIN_GANLOSS == 'LS':
+                loss_adv_trg_second = ls_loss(d_out_second, source_label)
+        if TRAIN_LAYER > 2:
+            d_out_third = d_third(feature_trg[2])  # ([24,1,1,1])
+            if TRAIN_GANLOSS == 'BCE':
+                loss_adv_trg_third = bce_loss(d_out_third, source_label)
+            elif TRAIN_GANLOSS == 'LS':
+                loss_adv_trg_third = ls_loss(d_out_third, source_label)
+        if TRAIN_LAYER > 3:
+            d_out_forth = d_forth(feature_trg[3])  # ([24,1,1,1])
+            if TRAIN_GANLOSS == 'BCE':
+                loss_adv_trg_forth = bce_loss(d_out_forth, source_label)
+            elif TRAIN_GANLOSS == 'LS':
+                loss_adv_trg_forth = ls_loss(d_out_forth, source_label)
+        loss_adv = TRAIN_LAMBADA_ADV_FIRST * loss_adv_trg_first
+        if ADD == 1:
+            loss_adv = loss_adv + TRAIN_LAMBADA_ADV_FIRST * loss_adv_trg_first_add
+        if TRAIN_LAYER > 1:
+            loss_adv = loss_adv + TRAIN_LAMBADA_ADV_SECOND * loss_adv_trg_second
+        if TRAIN_LAYER > 2:
+            loss_adv = loss_adv + TRAIN_LAMBADA_ADV_THIRD * loss_adv_trg_third
+        if TRAIN_LAYER > 3:
+            loss_adv = loss_adv + TRAIN_LAMBADA_ADV_FORTH * loss_adv_trg_forth
+        if MY_6 and not MY_13:
+            loss_adv += loss_variance_trg
+        loss_adv = loss_adv
+        scaler.scale(loss_adv).backward()
+
+    if MY_4:  # 用反梯度
+        if MY_1:
+            target_label = 0.5
+        if TRAIN_GANLOSS == 'BCE':
+            loss_adv_trg_first = bce_loss(d_out_first, target_label)
+        elif TRAIN_GANLOSS == 'LS':
+            loss_adv_trg_first = ls_loss(d_out_first, target_label)
+        if ADD == 1:
+            d_out_first_add = d_first_add(feature_trg[0])  # d_out_first:[24,1,2,2]
+            if TRAIN_GANLOSS == 'BCE':
+                loss_adv_trg_first_add = bce_loss(d_out_first_add, target_label)
+            elif TRAIN_GANLOSS == 'LS':
+                loss_adv_trg_first_add = ls_loss(d_out_first_add, target_label)
+        if TRAIN_LAYER > 1:
+            if ddcpp_2 == 0:
+                d_out_second = d_second(feature_trg[1])  # ([24,1,1,1])
+            if ddcpp_2 == 1:
+                d_out_second = dis_model_2(feature_trg[1])  # ([24,1,1,1])
+            if TRAIN_GANLOSS == 'BCE':
+                loss_adv_trg_second = bce_loss(d_out_second, target_label)
+            elif TRAIN_GANLOSS == 'LS':
+                loss_adv_trg_second = ls_loss(d_out_second, target_label)
+        if TRAIN_LAYER > 2:
+            d_out_third = d_third(feature_trg[2])  # ([24,1,1,1])
+            if TRAIN_GANLOSS == 'BCE':
+                loss_adv_trg_third = bce_loss(d_out_third, target_label)
+            elif TRAIN_GANLOSS == 'LS':
+                loss_adv_trg_third = ls_loss(d_out_third, target_label)
+        if TRAIN_LAYER > 3:
+            d_out_forth = d_forth(feature_trg[3])  # ([24,1,1,1])
+            if TRAIN_GANLOSS == 'BCE':
+                loss_adv_trg_forth = bce_loss(d_out_forth, target_label)
+        elif TRAIN_GANLOSS == 'LS':
+            loss_adv_trg_forth = ls_loss(d_out_forth, target_label)
+        loss_adv = TRAIN_LAMBADA_ADV_FIRST * loss_adv_trg_first
+        if ADD == 1:
+            loss_adv = loss_adv + TRAIN_LAMBADA_ADV_FIRST * loss_adv_trg_first_add
+        if TRAIN_LAYER > 1:
+            loss_adv = loss_adv + TRAIN_LAMBADA_ADV_SECOND * loss_adv_trg_second
+        if TRAIN_LAYER > 2:
+            loss_adv = loss_adv + TRAIN_LAMBADA_ADV_THIRD * loss_adv_trg_third
+        if TRAIN_LAYER > 3:
+            loss_adv = loss_adv + TRAIN_LAMBADA_ADV_FORTH * loss_adv_trg_forth
+        loss_adv = - loss_adv
+        if MY_6 and not MY_13:
+            loss_adv += loss_variance_trg
+        loss_adv = loss_adv
+        scaler.scale(loss_adv).backward()

.idea/BFDA_old_yolov5.iml

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+import os
+from pycocotools.coco import COCO
+from eval_MR_multisetup import COCOeval
+
+def validate(annFile, dt_path):
+    mean_MR = []
+    for id_setup in range(0, 4):
+        cocoGt = COCO(annFile)#标注
+        cocoDt = cocoGt.loadRes(dt_path)#预测
+        imgIds = sorted(cocoGt.getImgIds())
+        cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
+        cocoEval.params.imgIds = imgIds
+        cocoEval.evaluate(id_setup)
+        cocoEval.accumulate()
+        mean_MR.append(cocoEval.summarize_nofile(id_setup))
+    return mean_MR

.idea/deployment.xml

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+import torch
+# import matplotlib
+# matplotlib.use('TkAgg')
+import matplotlib.pyplot as plt
+from PIL import Image
+import numpy as np
+from torchvision import models
+from torchvision import transforms
+import cv2
+import time
+
+size = (640,320)
+# 加载deeplabv3模型进行语义分割
+model = models.segmentation.deeplabv3_resnet101(pretrained=True)
+model = model.eval()
+
+img = cv2.imread(r'/remote-home/caiyancheng/BFDA_datasets/cityscapes/images/val_all/frankfurt/frankfurt_000001_057181_leftImg8bit.png')
+img = cv2.resize(img, size)
+img = torch.tensor(img).permute(2,0,1)[None,...]/255
+
+start_time = time.time()
+output = model(img)
+end_time = time.time()
+time_using = start_time - end_time
+print(time_using)
+print(output['out'].shape)
+output = torch.argmax(output['out'].squeeze(), dim=0).detach().cpu().numpy()
+
+output[output==15] = 255
+output[output!=255] = 0
+
+cv2.imwrite(f'/remote-home/caiyancheng/outlabel_frankfurt_000001_057181_leftImg8bit_size_{size[0]}_size{size[1]}_time_{time_using}.png', np.array(output))