def initialize(self, source, target, batch_size_source, batch_size_target, 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 = []
dataloader_source = []
self.max_len = 0
for i in range(len(source)):
dataset_source.append(Dataset(source[i]['imgs'], source[i]['labels'], transform=transform))
self.max_len = max(self.max_len, len(dataset_source))
dataloader_source.append(
torch.utils.data.DataLoader(dataset_source[i], batch_size=batch_size_source, shuffle=True,
num_workers=4))
self.dataset_s = dataset_source
dataset_target = Dataset(target['imgs'], target['labels'], transform=transform)
self.max_len = max(self.max_len, len(dataset_target))
dataloader_target = torch.utils.data.DataLoader(dataset_target, batch_size=batch_size_target, shuffle=True,
num_workers=4)
self.dataset_t = dataset_target
self.paired_data = PairedData(dataloader_source, dataloader_target, 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_source1 = Dataset(source[1]['imgs'], source['labels'], transform=transform)
dataset_source1 = Dataset(source[0]['imgs'], source[0]['labels'], transform=transform)
data_loader_s1 = torch.utils.data.DataLoader(dataset_source1, batch_size=batch_size1, shuffle=True, num_workers=4)
self.dataset_s1 = dataset_source1
dataset_source2 = Dataset(source[1]['imgs'], source[1]['labels'], transform=transform)
data_loader_s2 = torch.utils.data.DataLoader(dataset_source2, batch_size=batch_size1, shuffle=True, num_workers=4)
self.dataset_s2 = dataset_source2
dataset_source3 = Dataset(source[2]['imgs'], source[2]['labels'], transform=transform)
data_loader_s3 = torch.utils.data.DataLoader(dataset_source3, batch_size=batch_size1, shuffle=True, num_workers=4)
self.dataset_s3 = dataset_source3
dataset_source4 = Dataset(source[3]['imgs'], source[3]['labels'], transform=transform)
data_loader_s4 = torch.utils.data.DataLoader(dataset_source4, batch_size=batch_size1, shuffle=True, num_workers=4)
self.dataset_s4 = dataset_source4
#for i in range(len(source)):
# dataset_source[i] = Dataset(source[i]['imgs'], source[i]['labels'], transform=transform)
dataset_target = Dataset(target['imgs'], target['labels'], transform=transform)
data_loader_t = torch.utils.data.DataLoader(dataset_target, batch_size=batch_size2, shuffle=True, num_workers=4)
self.dataset_t = dataset_target
self.paired_data = CombinedData(data_loader_s1, data_loader_s2, data_loader_s3,data_loader_s4, data_loader_t,
float("inf"))
self.num_datasets = 4
self.num_samples = min(max(len(self.dataset_s1),len(self.dataset_s2),len(self.dataset_s3), len(self.dataset_s4),len(self.dataset_t)), float("inf"))*self.num_datasets
def __init__(self, source, target, bs_source, bs_target, scale=32):
transform = transforms.Compose([
transforms.Resize(scale),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
self.dataset_s = Dataset(source['imgs'], source['labels'], transform=transform)
self.dataset_t = Dataset(target['imgs'], target['labels'], transform=transform)
loader_s = torch.utils.data.DataLoader(
self.dataset_s, batch_size=bs_source,
shuffle=True, num_workers=4)
loader_t = torch.utils.data.DataLoader(
self.dataset_t, batch_size=bs_target,
shuffle=True, num_workers=4)
self.paired_data = PairedData(loader_s, 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'], transform=transform)
dataset_target = Dataset(target['imgs'], target['labels'], transform=transform)
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"))