def initialize(self, source, target, batch_size1, batch_size2, scale=32):
transform = transforms.Compose([
transforms.Scale(scale),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset_source = Dataset(source['imgs'],
source['labels'],
transform=transform)
dataset_target = Dataset(target['imgs'],
target['labels'],
transform=transform)
# dataset_source = tnt.dataset.TensorDataset([source['imgs'], source['labels']])
# dataset_target = tnt.dataset.TensorDataset([target['imgs'], target['labels']])
data_loader_s = torch.utils.data.DataLoader(dataset_source,
batch_size=batch_size1,
shuffle=True,
num_workers=4)
data_loader_t = torch.utils.data.DataLoader(dataset_target,
batch_size=batch_size2,
shuffle=True,
num_workers=4)
self.dataset_s = dataset_source
self.dataset_t = dataset_target
self.paired_data = PairedData(data_loader_s, data_loader_t,
float("inf"))
def initialize(self, source, target, batch_size1, batch_size2, scale=32):
# transform = transforms.Compose([
# transforms.Scale(scale),
# transforms.ToTensor(),
# transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
# ])
dataset_source = Dataset(source['imgs'], source['labels'])
dataset_target = Dataset(target['imgs'], target['labels'])
data_loader_s = torch.utils.data.DataLoader(dataset_source,
batch_size=batch_size1,
shuffle=True,
num_workers=0)
data_loader_t = torch.utils.data.DataLoader(dataset_target,
batch_size=batch_size2,
shuffle=True,
num_workers=0)
# print('Source shape: {}, target shape: {}'.format(len(data_loader_s), len(data_loader_t)))
self.dataset_s = dataset_source
self.dataset_t = dataset_target
self.paired_data = PairedData(data_loader_s, data_loader_t,
float("inf"))
if __name__ == "__main__":
from config import config
from datasets.datasets import Dataset
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
ii = r'/home/wei/Deep_learning_pytorch/Data/UCAS/ucas_train.txt'
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
transform = transforms.Compose([
transforms.ToTensor(),
])
img_size = 256
da = Dataset(ii, transform=transform, img_size=img_size, train=False)
dataloader = torch.utils.data.DataLoader(da, batch_size=1, shuffle=False)
#x = torch.randn(1,3,128,128)
f = FCOS(config)
#checkpoint = torch.load('./checkpoint/ckpt.pth')
#f.load_state_dict(checkpoint['weights'])
for batch_i, (_, imgs, targets) in enumerate(dataloader):
images = imgs
targets = targets
#loss = f(images, targets)
detections = f(images)
break
