ids_train = [
image_name for image_name in os.listdir(train_path)
if is_image_file(image_name)
]
# ids_train = [image_name for image_name in os.listdir(os.path.join(data_path, "train_test", "Sat")) if is_image_file(image_name)]
ids_val = [
image_name for image_name in os.listdir(val_path)
if is_image_file(image_name)
]
ids_test = [
image_name for image_name in os.listdir(val_path)
if is_image_file(image_name)
]
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dataset_train = DeepGlobe(train_path, ids_train, label=True, transform=True)
dataloader_train = torch.utils.data.DataLoader(dataset=dataset_train,
batch_size=batch_size,
num_workers=10,
collate_fn=collate,
shuffle=True,
pin_memory=True)
dataset_val = DeepGlobe(val_path, ids_val, label=True)
dataloader_val = torch.utils.data.DataLoader(dataset=dataset_val,
batch_size=batch_size,
num_workers=10,
collate_fn=collate,
shuffle=False,
pin_memory=True)
dataset_test = DeepGlobe(test_path, ids_test, label=False)
]
# ids_train = [image_name for image_name in os.listdir(os.path.join(data_path, "train_test", "Sat")) if is_image_file(image_name)]
ids_val = [
image_name
for image_name in os.listdir(os.path.join(data_path, "crossvali", "Sat"))
if is_image_file(image_name)
]
ids_test = [
image_name for image_name in os.listdir(
os.path.join(data_path, "official_crossvali", "Sat"))
if is_image_file(image_name)
]
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dataset_train = DeepGlobe(os.path.join(data_path, "train"),
ids_train,
label=True,
transform=True)
dataloader_train = torch.utils.data.DataLoader(
dataset=dataset_train,
batch_size=batch_size,
num_workers=args.num_workers,
collate_fn=collate,
shuffle=True,
pin_memory=True,
)
dataset_val = DeepGlobe(os.path.join(data_path, "crossvali"),
ids_val,
label=True)
dataloader_val = torch.utils.data.DataLoader(
dataset=dataset_val,
batch_size=batch_size,
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# dataset_train = DeepGlobe(os.path.join(data_path, "train"), ids_train, label=True, transform=True)
# dataloader_train = torch.utils.data.DataLoader(dataset=dataset_train, batch_size=batch_size, num_workers=10, collate_fn=collate, shuffle=True, pin_memory=True)
# dataset_val = DeepGlobe(os.path.join(data_path, "crossvali"), ids_val, label=True)
# dataloader_val = torch.utils.data.DataLoader(dataset=dataset_val, batch_size=batch_size, num_workers=10, collate_fn=collate, shuffle=False, pin_memory=True)
# dataset_test = DeepGlobe(os.path.join(data_path, "offical_crossvali"), ids_test, label=False)
# dataloader_test = torch.utils.data.DataLoader(dataset=dataset_test, batch_size=batch_size, num_workers=10, collate_fn=collate_test, shuffle=False, pin_memory=True)
# ids_train = [image_name for image_name in os.listdir(os.path.join(data_path, "train", "Sat")) if is_image_file(image_name)]
# ids_val = [image_name for image_name in os.listdir(os.path.join(data_path, "crossvali", "Sat")) if is_image_file(image_name)]
# ids_test = [image_name for image_name in os.listdir(os.path.join(data_path, "offical_crossvali", "Sat")) if is_image_file(image_name)]
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dataset_train = DeepGlobe(os.path.join(data_path),
split='train',
label=True,
transform=True)
dataloader_train = torch.utils.data.DataLoader(dataset=dataset_train,
batch_size=batch_size,
num_workers=0,
collate_fn=collate,
shuffle=True,
pin_memory=True)
dataset_val = DeepGlobe(os.path.join(data_path), split='crossvali', label=True)
dataloader_val = torch.utils.data.DataLoader(dataset=dataset_val,
batch_size=batch_size,
num_workers=0,
collate_fn=collate,
shuffle=False,
pin_memory=True)
# dataset_test = DeepGlobe(os.path.join(data_path, "offical_crossvali"), ids_test, label=False)