def get_train_loaders(ifold,
batch_size=8,
dev_mode=False,
pad_mode='edge',
meta_version=1,
pseudo_label=False,
depths=False):
train_shuffle = True
train_meta, val_meta = get_nfold_split(ifold,
nfold=10,
meta_version=meta_version)
if pseudo_label:
test_meta = get_test_meta()
train_meta = train_meta.append(test_meta, sort=True)
if dev_mode:
train_shuffle = False
train_meta = train_meta.iloc[:10]
val_meta = val_meta.iloc[:10]
#print(val_meta[X_COLUMN].values[:5])
#print(val_meta[Y_COLUMN].values[:5])
print(train_meta.shape, val_meta.shape)
img_mask_aug_train, img_mask_aug_val = get_img_mask_augments(
pad_mode, depths)
train_set = ImageDataset(True,
train_meta,
augment_with_target=img_mask_aug_train,
image_augment=transforms.ColorJitter(
0.2, 0.2, 0.2, 0.2),
image_transform=get_image_transform(pad_mode),
mask_transform=get_mask_transform(pad_mode))
train_loader = data.DataLoader(train_set,
batch_size=batch_size,
shuffle=train_shuffle,
num_workers=4,
collate_fn=train_set.collate_fn,
drop_last=True)
train_loader.num = len(train_set)
val_set = ImageDataset(True,
val_meta,
augment_with_target=img_mask_aug_val,
image_augment=None,
image_transform=get_image_transform(pad_mode),
mask_transform=get_mask_transform(pad_mode))
val_loader = data.DataLoader(val_set,
batch_size=batch_size,
shuffle=False,
num_workers=4,
collate_fn=val_set.collate_fn)
val_loader.num = len(val_set)
val_loader.y_true = read_masks(val_meta[ID_COLUMN].values)
return train_loader, val_loader
def get_test_loader(batch_size=16, index=0, dev_mode=False, pad_mode='edge'):
test_meta = get_test_meta()
if dev_mode:
test_meta = test_meta.iloc[:10]
test_set = ImageDataset(False, test_meta,
image_augment=None if pad_mode == 'resize' else transforms.Pad((13,13,14,14), padding_mode=pad_mode),
image_transform=get_tta_transforms(index, pad_mode))
test_loader = data.DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=4, collate_fn=test_set.collate_fn, drop_last=False)
test_loader.num = len(test_set)
test_loader.meta = test_set.meta
return test_loader
def get_test_loader(batch_size=16, index=0, dev_mode=False, img_sz=384):
test_meta = get_test_meta()
if dev_mode:
test_meta = test_meta.iloc[:10]
test_set = ImageDataset(False,
test_meta,
img_dir=settings.TEST_IMG_DIR,
image_augment=None,
image_transform=get_tta_transforms(index, img_sz))
test_loader = data.DataLoader(test_set,
batch_size=batch_size,
shuffle=False,
num_workers=4,
collate_fn=test_set.collate_fn,
drop_last=False)
test_loader.num = len(test_set)
test_loader.meta = test_set.meta
return test_loader
def get_test_loader(batch_size=16, index=0, dev_mode=False):
test_meta = get_test_meta()
if dev_mode:
test_meta = test_meta.iloc[:10]
test_set = ImageDataset(False,
test_meta,
image_augment=ImgAug(
aug.pad_to_fit_net(64, 'reflect')),
image_transform=get_tta_transforms(index))
test_loader = data.DataLoader(test_set,
batch_size=batch_size,
shuffle=False,
num_workers=4,
collate_fn=test_set.collate_fn,
drop_last=False)
test_loader.num = len(test_set)
test_loader.meta = test_set.meta
return test_loader