327 lines
14 KiB
Python
327 lines
14 KiB
Python
# ChangeDetection.py
|
||
|
||
import os
|
||
import torch
|
||
import numpy as np
|
||
from tqdm import tqdm
|
||
import itertools
|
||
import torch.optim as optim
|
||
from torch.utils.data import DataLoader
|
||
|
||
from CropLand_CD_module.cood_csv import PatchIndexer, PredictionAggregator
|
||
from CropLand_CD_module.data_utils import DynamicPatchDataset, LoadDatasetFromCoords, calMetric_iou, \
|
||
TestDatasetFromPic
|
||
from CropLand_CD_module.loss.losses import cross_entropy
|
||
from CropLand_CD_module.model.network import CDNet
|
||
# from model.network import CDNet
|
||
# from data_utils import DynamicPatchDataset, LoadDatasetFromCoords, TestDatasetFromCoords, calMetric_iou, get_transform, \
|
||
# TestDatasetFromPic
|
||
# from loss.losses import cross_entropy
|
||
from PIL import Image
|
||
# from cood_csv import PatchIndexer, PredictionAggregator
|
||
import torchvision.transforms as transforms
|
||
|
||
|
||
class ChangeDetectionModule:
|
||
|
||
def __init__(self, gpu_id,img_size,lr,batch_size=2):
|
||
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_id
|
||
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
||
self.best_iou = 0.0
|
||
self.batch_size=batch_size
|
||
# Initialize model
|
||
self.model = CDNet(img_size=img_size).to(self.device, dtype=torch.float)
|
||
if torch.cuda.device_count() > 1:
|
||
print(f"Using {torch.cuda.device_count()} GPUs!")
|
||
self.model = torch.nn.DataParallel(self.model, device_ids=range(torch.cuda.device_count()))
|
||
|
||
# Loss and optimizer
|
||
self.criterion = cross_entropy().to(self.device, dtype=torch.float)
|
||
self.optimizer = optim.Adam(itertools.chain(self.model.parameters()),
|
||
lr=lr, betas=(0.9, 0.999))
|
||
|
||
def train(self):
|
||
print("Starting training...")
|
||
# os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
|
||
|
||
# 自动推断 save_dir = 'val/', 自动保存为 'coords.csv'
|
||
# 1训练scv文件生成
|
||
indexer_train = PatchIndexer(
|
||
img_dir=self.args.hr1_train,
|
||
patch_size=256,
|
||
stride=256
|
||
)
|
||
indexer_train.index_all()
|
||
# 1验证scv文件生成
|
||
indexer_val = PatchIndexer(
|
||
img_dir=self.args.hr1_val,
|
||
patch_size=256,
|
||
stride=256
|
||
)
|
||
indexer_val.index_all()
|
||
|
||
# 调用相对路径
|
||
# 1获取上一级文件路径
|
||
parent_train_cood = os.path.dirname(self.args.hr1_train)
|
||
train_cood = os.path.join(parent_train_cood, "coords.csv")
|
||
parent_val_cood = os.path.dirname(self.args.hr1_val)
|
||
val_cood = os.path.join(parent_val_cood, "coords.csv")
|
||
|
||
train_set = DynamicPatchDataset(coords_csv=train_cood, img1_dir=self.args.hr1_train,
|
||
img2_dir=self.args.hr2_train, label_dir=self.args.lab_train, crop=True,
|
||
augment=True, angle=30, num_classes=2)
|
||
val_set = LoadDatasetFromCoords(coords_csv=val_cood, hr1_dir=self.args.hr1_val, hr2_dir=self.args.hr2_val,
|
||
label_dir=self.args.lab_val)
|
||
|
||
# train_set = DynamicPatchDataset(self.args.hr1_train, self.args.hr2_train, self.args.lab_train, crop=False)
|
||
# val_set = LoadDatasetFromFolder(self.args, self.args.hr1_val, self.args.hr2_val, self.args.lab_val)
|
||
train_loader = DataLoader(train_set, num_workers=self.args.num_workers,
|
||
batch_size=self.args.batchsize, shuffle=True)
|
||
val_loader = DataLoader(val_set, num_workers=self.args.num_workers,
|
||
batch_size=self.args.val_batchsize, shuffle=False)
|
||
|
||
for epoch in range(1, self.args.num_epochs + 1):
|
||
self.model.train()
|
||
train_bar = tqdm(train_loader, desc=f"Epoch [{epoch}/{self.args.num_epochs}]")
|
||
for hr_img1, hr_img2, label in train_bar:
|
||
hr_img1 = hr_img1.to(self.device, dtype=torch.float)
|
||
hr_img2 = hr_img2.to(self.device, dtype=torch.float)
|
||
label = label.to(self.device, dtype=torch.float)
|
||
label = torch.argmax(label, 1).unsqueeze(1).float()
|
||
|
||
self.optimizer.zero_grad()
|
||
out1, out2, out3 = self.model(hr_img1, hr_img2)
|
||
cd_loss = (self.criterion(out1, label) +
|
||
self.criterion(out2, label) +
|
||
self.criterion(out3, label))
|
||
cd_loss.backward()
|
||
self.optimizer.step()
|
||
train_bar.set_postfix(loss=cd_loss.item())
|
||
|
||
# Evaluate only on best epoch (IoU improved)
|
||
val_iou = self.validate(val_loader)
|
||
print(f"Validation IoU: {val_iou:.4f}")
|
||
|
||
if val_iou > self.best_iou:
|
||
self.best_iou = val_iou
|
||
self._save_best_model()
|
||
print(f"New best model saved with IoU: {val_iou:.4f}")
|
||
|
||
def validate(self, val_loader):
|
||
self.model.eval()
|
||
inter, union = 0, 0
|
||
with torch.no_grad():
|
||
for hr_img1, hr_img2, label in tqdm(val_loader, desc='Validating'):
|
||
hr_img1 = hr_img1.to(self.device, dtype=torch.float)
|
||
hr_img2 = hr_img2.to(self.device, dtype=torch.float)
|
||
label = label.to(self.device, dtype=torch.float)
|
||
label = torch.argmax(label, 1).unsqueeze(1).float()
|
||
|
||
output, _, _ = self.model(hr_img1, hr_img2)
|
||
pred = torch.argmax(output, 1).unsqueeze(1).float()
|
||
gt = (label > 0).float()
|
||
prob = (pred > 0).float()
|
||
|
||
gt_np = np.squeeze(gt.cpu().detach().numpy())
|
||
pred_np = np.squeeze(prob.cpu().detach().numpy())
|
||
intr, unn = calMetric_iou(gt_np, pred_np)
|
||
inter += intr
|
||
union += unn
|
||
|
||
iou = inter / union if union != 0 else 0
|
||
return iou
|
||
|
||
def _save_best_model(self):
|
||
if not os.path.exists(self.args.model_dir):
|
||
os.makedirs(self.args.model_dir)
|
||
# Remove any existing .pth files
|
||
for f in os.listdir(self.args.model_dir):
|
||
if f.endswith('.pth'):
|
||
os.remove(os.path.join(self.args.model_dir, f))
|
||
save_path = os.path.join(self.args.model_dir, 'best_model.pth')
|
||
torch.save(self.model.state_dict(), save_path)
|
||
print(f"Best model saved to {save_path}")
|
||
|
||
def load_model(self, model_path):
|
||
self.model.load_state_dict(torch.load(model_path, map_location=self.device))
|
||
self.model.eval()
|
||
print(f"Loaded model from {model_path}")
|
||
|
||
def predict(self):
|
||
print(f"结果将保存到目录: {self.args.save_dir}")
|
||
print("Starting prediction...")
|
||
indexer_test = PatchIndexer(
|
||
img_dir=self.args.path_img1,
|
||
patch_size=256,
|
||
stride=256
|
||
)
|
||
indexer_test.index_all()
|
||
# 调用scv相对路径
|
||
parent_test_cood = os.path.dirname(self.args.path_img1)
|
||
test_cood = os.path.join(parent_test_cood, "coords.csv")
|
||
|
||
if not os.path.exists(self.args.save_dir):
|
||
os.makedirs(self.args.save_dir)
|
||
|
||
transform = transforms.Compose([
|
||
transforms.ToTensor(),
|
||
transforms.Normalize(mean=[0.5] * 3, std=[0.5] * 3)
|
||
])
|
||
|
||
img1Url = r"D:\project\2025-7-10_data+model\val\image1\1.png"
|
||
img2Url = r"D:\project\2025-7-10_data+model\val\image2\1.png"
|
||
|
||
test_set = TestDatasetFromPic(img1Url, img2Url)
|
||
|
||
# test_set = TestDatasetFromCoords(coords_csv=test_cood, hr1_dir=self.args.path_img1, hr2_dir=self.args.path_img2,
|
||
# label_dir=None, transform=transform)
|
||
# test_set = TestDatasetFromFolder(self.args, self.args.path_img1,
|
||
# self.args.path_img2, self.args.path_lab)
|
||
test_loader = DataLoader(test_set, num_workers=24,
|
||
batch_size=self.args.batch_size, shuffle=False)
|
||
|
||
self.model.eval()
|
||
aggregator = PredictionAggregator(img_dir=self.args.path_img1)
|
||
# aggregator=PredictionAggregator()
|
||
test_bar = tqdm(test_loader)
|
||
inter, union = 0, 0
|
||
|
||
for img1, img2, label, patch_name, fname_list, x_list, y_list, h_list, w_list in test_bar:
|
||
img1 = img1.to(self.device)
|
||
img2 = img2.to(self.device)
|
||
label = label.to(self.device)
|
||
|
||
with torch.no_grad():
|
||
output = self.model(img1, img2)
|
||
if isinstance(output, tuple): # ← 检查是否是 tuple
|
||
output = output[0]
|
||
|
||
pred = torch.argmax(output, 1).unsqueeze(1).float()
|
||
# label = torch.argmax(label, 1).unsqueeze(1).float()
|
||
label = label.float()
|
||
|
||
for i in range(img1.size(0)):
|
||
# prob = pred[i].cpu().numpy()
|
||
# gt_value = label[i].cpu().numpy()
|
||
patch = pred[i][0].cpu().numpy().astype(np.uint8)
|
||
gt_value = label[i][0].cpu().numpy().astype(np.uint8)
|
||
|
||
# intr, unn = calMetric_iou(gt_value.squeeze(), prob.squeeze())
|
||
intr, unn = calMetric_iou(gt_value, patch)
|
||
inter += intr
|
||
union += unn
|
||
fname = fname_list[i]
|
||
x, y = x_list[i].item(), y_list[i].item()
|
||
# patch = pred[i][0].cpu().numpy().astype(np.uint8)
|
||
h, w = h_list[i].item(), w_list[i].item()
|
||
aggregator.add_patch(fname, x, y, patch, h, w)
|
||
# aggregator.add_patch(fname, x, y, patch)
|
||
|
||
test_bar.set_description('IoU: %.4f' % (inter / union if union > 0 else 0))
|
||
aggregator.save_all(self.args.save_dir)
|
||
iou = inter / union if union != 0 else 0
|
||
print(f"Test IoU: {iou:.4f}")
|
||
|
||
def predict_from_imgurl(self,img1Url,img2Url,save_dir):
|
||
print(f"结果将保存到目录: {save_dir}")
|
||
print("Starting prediction...")
|
||
# indexer_test = PatchIndexer(
|
||
# img_dir=self.args.path_img1,
|
||
# patch_size=256,
|
||
# stride=256
|
||
# )
|
||
# indexer_test.index_all()
|
||
# # 调用scv相对路径
|
||
# parent_test_cood = os.path.dirname(self.args.path_img1)
|
||
# # test_cood = os.path.join(parent_test_cood, "coords.csv")
|
||
|
||
# if not os.path.exists(self.args.save_dir):
|
||
# os.makedirs(self.args.save_dir)
|
||
|
||
transform = transforms.Compose([
|
||
transforms.ToTensor(),
|
||
transforms.Normalize(mean=[0.5] * 3, std=[0.5] * 3)
|
||
])
|
||
|
||
# img1Url = r"D:\project\2025-7-10_data+model\val\image1\1.png"
|
||
# img2Url = r"D:\project\2025-7-10_data+model\val\image2\1.png"
|
||
|
||
test_set = TestDatasetFromPic(img1Url, img2Url, None, transform)
|
||
|
||
# test_set = TestDatasetFromCoords(coords_csv=test_cood, hr1_dir=self.args.path_img1, hr2_dir=self.args.path_img2,
|
||
# label_dir=None, transform=transform)
|
||
# test_set = TestDatasetFromFolder(self.args, self.args.path_img1,
|
||
# self.args.path_img2, self.args.path_lab)
|
||
# DataLoader(num_workers=24) 尝试启动多进程加载数据。 Sanic 的 worker 进程默认是守护进程（daemon=True），导致无法再创建子进程。后续考虑将服务独立
|
||
test_loader = DataLoader(test_set, num_workers=0,
|
||
batch_size=self.batch_size, shuffle=False)
|
||
|
||
self.model.eval()
|
||
img1_dir=os.path.dirname(img1Url)
|
||
aggregator = PredictionAggregator(img_dir=img1_dir)
|
||
# aggregator=PredictionAggregator()
|
||
test_bar = tqdm(test_loader)
|
||
inter, union = 0, 0
|
||
|
||
for img1, img2, label, patch_name, fname_list, x_list, y_list, h_list, w_list in test_bar:
|
||
img1 = img1.to(self.device)
|
||
img2 = img2.to(self.device)
|
||
label = label.to(self.device)
|
||
|
||
with torch.no_grad():
|
||
output = self.model(img1, img2)
|
||
if isinstance(output, tuple): # ← 检查是否是 tuple
|
||
output = output[0]
|
||
|
||
pred = torch.argmax(output, 1).unsqueeze(1).float()
|
||
# label = torch.argmax(label, 1).unsqueeze(1).float()
|
||
label = label.float()
|
||
|
||
for i in range(img1.size(0)):
|
||
# prob = pred[i].cpu().numpy()
|
||
# gt_value = label[i].cpu().numpy()
|
||
patch = pred[i][0].cpu().numpy().astype(np.uint8)
|
||
gt_value = label[i][0].cpu().numpy().astype(np.uint8)
|
||
|
||
# intr, unn = calMetric_iou(gt_value.squeeze(), prob.squeeze())
|
||
intr, unn = calMetric_iou(gt_value, patch)
|
||
inter += intr
|
||
union += unn
|
||
fname = fname_list[i]
|
||
x, y = x_list[i].item(), y_list[i].item()
|
||
# patch = pred[i][0].cpu().numpy().astype(np.uint8)
|
||
h, w = h_list[i].item(), w_list[i].item()
|
||
aggregator.add_patch(fname, x, y, patch, h, w)
|
||
# aggregator.add_patch(fname, x, y, patch)
|
||
|
||
test_bar.set_description('IoU: %.4f' % (inter / union if union > 0 else 0))
|
||
out_save_path=aggregator.save_all(save_dir)
|
||
print(f"out_save_pathout_save_path {out_save_path}")
|
||
iou = inter / union if union != 0 else 0
|
||
print(f"Test IoU: {iou:.4f}")
|
||
return out_save_path
|
||
|
||
# for image1, image2, label, image_names in tqdm(test_loader, desc='Testing'):
|
||
# image1 = image1.to(self.device, dtype=torch.float)
|
||
# image2 = image2.to(self.device, dtype=torch.float)
|
||
# label = label.to(self.device, dtype=torch.float)
|
||
|
||
# output, _, _ = self.model(image1, image2)
|
||
# pred = torch.argmax(output, 1).unsqueeze(1)
|
||
# label = torch.argmax(label, 1).unsqueeze(1)
|
||
|
||
# for i in range(pred.size(0)):
|
||
# gt_value = (label[i] > 0).float()
|
||
# prob = (pred[i] > 0).float()
|
||
# gt_np = np.squeeze(gt_value.cpu().detach().numpy())
|
||
# prob_np = np.squeeze(prob.cpu().detach().numpy())
|
||
# intr, unn = calMetric_iou(gt_np, prob_np)
|
||
# inter += intr
|
||
# union += unn
|
||
# binary_result = np.where(prob_np > 0.5, 255, 0).astype('uint8') # 将预测值转换为0和255
|
||
|
||
# result = Image.fromarray(binary_result) # 使用转换后的二值图像
|
||
# result.save(os.path.join(self.args.save_dir, image_names[i]))
|
||
|
||
# iou = inter / union if union != 0 else 0
|
||
# print(f"Test IoU: {iou:.4f}" |