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Preprocessing.py
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import torch
import os
from torchvision import datasets, transforms
from utils import get_yaml_value
from Uncertainties_Imgaug import AddBlock, Weather
def Create_Training_Datasets(train_data_path, batch_size, image_size):
training_data_loader = {}
transform_drone_list = [
transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC),
transforms.RandomCrop((image_size, image_size)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
transforms_satellite_list = [
transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC),
transforms.RandomCrop((image_size, image_size)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
drone_train_datasets = datasets.ImageFolder(os.path.join(train_data_path, "drone"),
transform=transforms.Compose(transform_drone_list))
satellite_train_datasets = datasets.ImageFolder(os.path.join(train_data_path, "satellite"),
transform=transforms.Compose(transforms_satellite_list))
training_data_loader["drone_train"] = torch.utils.data.DataLoader(drone_train_datasets,
batch_size=batch_size,
shuffle=True,
num_workers=4, # 多进程
pin_memory=True) # 锁页内存
training_data_loader["satellite_train"] = torch.utils.data.DataLoader(satellite_train_datasets,
batch_size=batch_size,
shuffle=True,
num_workers=4, # 多进程
pin_memory=True) # 锁页内存
return training_data_loader
def Create_Testing_Datasets(test_data_path, batch_size, image_size):
testing_data_loader = {}
image_datasets = {}
transforms_test_list = [
transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC),
# iaa.Sequential([seq]),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
transforms_list = [
transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
image_datasets['query_drone'] = datasets.ImageFolder(os.path.join(test_data_path, "query_drone"),
transform=transforms.Compose(transforms_test_list))
image_datasets['query_satellite'] = datasets.ImageFolder(os.path.join(test_data_path, "query_satellite"),
transform=transforms.Compose(transforms_test_list))
image_datasets['gallery_drone'] = datasets.ImageFolder(os.path.join(test_data_path, "gallery_drone"),
transform=transforms.Compose(transforms_list))
image_datasets['gallery_satellite'] = datasets.ImageFolder(os.path.join(test_data_path, "gallery_satellite"),
transform=transforms.Compose(transforms_list))
testing_data_loader["query_drone"] = torch.utils.data.DataLoader(image_datasets['query_drone'],
batch_size=batch_size,
shuffle=False,
num_workers=4, # 多进程
pin_memory=True)
testing_data_loader["query_satellite"] = torch.utils.data.DataLoader(image_datasets['query_satellite'],
batch_size=batch_size,
shuffle=False,
num_workers=4, # 多进程
pin_memory=True) # 锁页内存
testing_data_loader["gallery_drone"] = torch.utils.data.DataLoader(image_datasets['gallery_drone'],
batch_size=batch_size,
shuffle=False,
num_workers=4, # 多进程
pin_memory=True) # 锁页内存
testing_data_loader["gallery_satellite"] = torch.utils.data.DataLoader(image_datasets['gallery_satellite'],
batch_size=batch_size,
shuffle=False,
num_workers=4, # 多进程
pin_memory=True) # 锁页内存
return image_datasets, testing_data_loader
def Create_Testing_Datasets_uncertainties(test_data_path, batch_size, image_size, gap, type):
testing_data_loader = {}
image_datasets = {}
if type in ['snow', 'rain', 'fog']:
transforms_test_list = [
transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC),
Weather(type),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
elif type in ['flip', 'black']:
transforms_test_list = [
transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC),
AddBlock(gap=gap, type=type),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
elif type == "normal" or "":
transforms_test_list = [
transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
transforms_list = [
transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
image_datasets['query_drone'] = datasets.ImageFolder(os.path.join(test_data_path, "query_drone"),
transform=transforms.Compose(transforms_test_list),
)
image_datasets['query_satellite'] = datasets.ImageFolder(os.path.join(test_data_path, "query_satellite"),
transform=transforms.Compose(transforms_list),
)
image_datasets['gallery_drone'] = datasets.ImageFolder(os.path.join(test_data_path, "gallery_drone"),
transform=transforms.Compose(transforms_test_list))
image_datasets['gallery_satellite'] = datasets.ImageFolder(os.path.join(test_data_path, "gallery_satellite"),
transform=transforms.Compose(transforms_list))
testing_data_loader["query_drone"] = torch.utils.data.DataLoader(image_datasets['query_drone'],
batch_size=batch_size,
shuffle=False,
num_workers=4, # 多进程
pin_memory=True)
testing_data_loader["query_satellite"] = torch.utils.data.DataLoader(image_datasets['query_satellite'],
batch_size=batch_size,
shuffle=False,
num_workers=4, # 多进程
pin_memory=True) # 锁页内存
testing_data_loader["gallery_drone"] = torch.utils.data.DataLoader(image_datasets['gallery_drone'],
batch_size=batch_size,
shuffle=False,
num_workers=4, # 多进程
pin_memory=True) # 锁页内存
testing_data_loader["gallery_satellite"] = torch.utils.data.DataLoader(image_datasets['gallery_satellite'],
batch_size=batch_size,
shuffle=False,
num_workers=4, # 多进程
pin_memory=True) # 锁页内存
return image_datasets, testing_data_loader
if __name__ == "__main__":
params = get_yaml_value("settings.yaml")
data_path = params["dataset_path"]
data_path = data_path + "/Testing/{}".format(params["height"])
image_datasets, data_loader = Create_Testing_Datasets_uncertainties(test_data_path=data_path,
batch_size=params["batch_size"],
image_size=params["image_size"],
gap=50, type="fog")
for data in data_loader['query_drone']:
img, label = data
print(img.shape, label.shape)
# break