def test_center_crop_with_incorrectly_large_crop_size():
img = np.ones((4, 4), dtype=np.uint8)
with pytest.raises(ValueError) as exc_info:
F.center_crop(img, 8, 8)
assert str(
exc_info.value
) == 'Requested crop size (8, 8) is larger than the image size (4, 4)'
def test_center_crop_with_incorrectly_large_crop_size():
img = np.ones((4, 4), dtype=np.uint8)
with pytest.raises(
ValueError,
message=
'Requested crop size (8, 8) is larger than the image size (4, 4)'):
F.center_crop(img, 8, 8)
def fixed_size_rotate_crop(
img,
angle,
interpolation=cv2.INTER_LINEAR,
border_mode=cv2.BORDER_REFLECT_101,
value=None,
):
original_height, original_width = img.shape[:2]
rot_img = rotate_no_crop(img, angle, interpolation, border_mode, value)
new_height, new_width = rotatedRectWithMaxArea(original_height,
original_width, angle)
cropped_img = F.center_crop(rot_img, new_height, new_width)
resize_height, resize_width = get_resized_max_ratio(
original_height, original_width, new_height, new_width)
resized_img = F.resize(
cropped_img,
height=resize_height,
width=resize_width,
interpolation=interpolation,
)
return F.center_crop(resized_img, original_height, original_width)
def predict(model, from_file_names, batch_size, to_path, img_transform):
loader = DataLoader(dataset=SaltDataset(from_file_names,
transform=img_transform,
mode='predict'),
shuffle=False,
batch_size=batch_size,
num_workers=args.workers,
pin_memory=torch.cuda.is_available())
with torch.no_grad():
for _, (inputs, paths) in enumerate(tqdm(loader, desc='Predict')):
inputs = utils.cuda(inputs)
outputs = model(inputs)
for i, _ in enumerate(paths):
factor = prepare_data.binary_factor
t_mask = (F.sigmoid(outputs[i, 0]).data.cpu().numpy() *
factor).astype(np.uint8)
final_mask = center_crop(t_mask, 202, 202)
final_mask = resize(final_mask,
original_height,
original_width,
interpolation=cv2.INTER_AREA)
to_path.mkdir(exist_ok=True, parents=True)
cv2.imwrite(str(to_path / (Path(paths[i]).stem + '.png')),
final_mask)
def wrap_border(mask_src: np.ndarray) -> np.ndarray:
mask = mask_src.copy()
mask[14:27, :] = (mask[14:27, :] + vflip(mask[:13, :])) / 2
mask[:, 14:27] = (mask[:, 14:27] + hflip(mask[:, :13])) / 2
mask[99:113, :] = (mask[99:113, :] + vflip(mask[114:, :])) / 2
mask[:, 99:113] = (mask[:, 99:113] + hflip(mask[:, 114:])) / 2
return center_crop(mask, 101, 101)
def test_center_crop(target):
img = np.array([[1, 1, 1, 1], [0, 1, 1, 1], [0, 0, 1, 1], [0, 0, 0, 1]],
dtype=np.uint8)
expected = np.array([[1, 1], [0, 1]], dtype=np.uint8)
img, expected = convert_2d_to_target_format([img, expected], target=target)
cropped_img = F.center_crop(img, 2, 2)
assert np.array_equal(cropped_img, expected)
def test_center_crop_float(target):
img = np.array([[0.4, 0.4, 0.4, 0.4], [0.0, 0.4, 0.4, 0.4],
[0.0, 0.0, 0.4, 0.4], [0.0, 0.0, 0.0, 0.4]],
dtype=np.float32)
expected = np.array([[0.4, 0.4], [0.0, 0.4]], dtype=np.float32)
img, expected = convert_2d_to_target_format([img, expected], target=target)
cropped_img = F.center_crop(img, 2, 2)
assert_array_almost_equal_nulp(cropped_img, expected)
def test_center_crop(target):
img = np.array([[1, 1, 1, 1], [0, 1, 1, 1], [0, 0, 1, 1], [0, 0, 0, 1]],
dtype=np.uint8)
expected_output = np.array([[1, 1], [0, 1]], dtype=np.uint8)
if target == 'image':
img = convert_2d_to_3d(img)
expected_output = convert_2d_to_3d(expected_output)
cropped_img = F.center_crop(img, 2, 2)
assert np.array_equal(cropped_img, expected_output)
def apply(self, img, **params):
w, h = img.shape[:2]
crop_w, crop_h = self.width, self.height
if self.crop_pos == 0:
return F.crop(img, 0, 0, crop_w, crop_h)
elif self.crop_pos == 1:
return F.crop(img, w - crop_w, 0, w, crop_h)
elif self.crop_pos == 2:
return F.crop(img, 0, h - crop_h, crop_w, h)
elif self.crop_pos == 3:
return F.crop(img, w - crop_w, h - crop_h, w, h)
else:
return F.center_crop(img, crop_h, crop_w)
def test_center_crop(target):
img = np.array(
[[1, 1, 1, 1],
[0, 1, 1, 1],
[0, 0, 1, 1],
[0, 0, 0, 1]], dtype=np.uint8)
expected_output = np.array(
[[1, 1],
[0, 1]], dtype=np.uint8)
if target == 'image':
img = convert_2d_to_3d(img)
expected_output = convert_2d_to_3d(expected_output)
cropped_img = F.center_crop(img, 2, 2)
assert np.array_equal(cropped_img, expected_output)
def avg_masks(mask_fns: List[str], fn: str, th: float):
logger = configure_logger('prediction', logging.INFO, './logs')
test = pd.read_csv(os.path.join(DATA_DIR, 'sample_submission.csv'))
masks = []
for mask_fn in mask_fns:
masks.append(np.load(mask_fn))
masks = np.mean(masks, axis=0)
np.save(os.path.join(SUBMISSION_DIR, 'avg_masks_fold_0256'), masks)
masks = [(center_crop(mask, 101, 101) > th).astype(int) for mask in masks]
rle_masks = [get_mask_rle(mask) for mask in masks]
test['rle_mask'] = rle_masks
submission_fn = os.path.join(SUBMISSION_DIR,
f'{fn}_{get_current_datetime()}.csv')
test.to_csv(submission_fn, index=None)
logger.info(f'Saved submission to {submission_fn}')
def five_crop(
img: numpy.ndarray, size: List[int]
) -> Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray,
numpy.ndarray]:
"""Crop the given image into four corners and the central crop.
Args:
img (numpy.ndarray): Image to be cropped.
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made. If provided a sequence of length 1, it will be interpreted
as (size[0], size[0]).
Returns:
tuple: tuple (tl, tr, bl, br, center)
Corresponding top left, top right, bottom left, bottom right and center crop.
"""
image_height, image_width = img.shape[:2]
crop_height, crop_width = size
if crop_width > image_width or crop_height > image_height:
msg = "Requested crop size {} is bigger than input size {}"
raise ValueError(msg.format(size, (image_height, image_width)))
tl = F.crop(img, 0, 0, crop_width, crop_height)
tr = F.crop(img, image_width - crop_width, 0, image_width, crop_height)
bl = F.crop(img, 0, image_height - crop_height, crop_width, image_height)
br = F.crop(
img,
image_width - crop_width,
image_height - crop_height,
image_width,
image_height,
)
center = F.center_crop(img, crop_height, crop_width)
return tl, tr, bl, br, center
def _prep_func(image: np.array):
image_ = center_crop(image, self.input_size[1], self.input_size[0])
image_ = imagenet_normalize(image_)
return to_tensor(image_).squeeze().unsqueeze(0)
def predict_with_snapshot(snapshot_dir: str, arch: str, device: str, fn: str,
th: float, masks_fn: str):
logger = configure_logger('prediction', logging.INFO, './logs')
test = pd.read_csv(os.path.join(DATA_DIR, 'sample_submission.csv'))
if masks_fn is None:
test_images = np.load(os.path.join(DATA_DIR, 'test_images.npy'))
test_ids = test.index.tolist()
model = architectures[arch]().to(device)
test = pd.read_csv(os.path.join(DATA_DIR, 'sample_submission.csv'))
test_images = np.load(os.path.join(DATA_DIR, 'test_images.npy'))
test_ids = test.index.tolist()
tta_augs = [val_augmentations, flip_pad]
tta_predictions = []
for cycle_dir in os.listdir(snapshot_dir):
snapshots = os.listdir(os.path.join(snapshot_dir, cycle_dir))
best_snapshot = sorted(snapshots,
key=lambda x: int(x.split('.')[-1]),
reverse=True)[0]
state_dict = torch.load(
os.path.join(snapshot_dir, cycle_dir, best_snapshot))
model.load_state_dict(state_dict)
model.eval()
logger.info(f'Loaded model from {best_snapshot}')
for i, aug in enumerate(tta_augs):
test_dataset = SaltTestDataset(test_ids, test_images, aug)
test_loader = DataLoader(test_dataset, 30, shuffle=False)
# actual prediction is made here
masks = []
with torch.no_grad():
for batch in tqdm.tqdm(test_loader):
image = batch['image'].to(device)
y_pred = torch.sigmoid(model(image)).cpu().numpy()
masks.append(y_pred)
# postprocess masks (crop, threshold, rle)
masks = np.concatenate(masks).reshape(
(len(test), NET_INPUT_SIZE, NET_INPUT_SIZE))
# TODO: replace that with smth that makes more sens
if i == 1:
masks = [hflip(mask) for mask in masks]
tta_predictions.append(masks)
masks = np.mean(tta_predictions, axis=0)
np.save(os.path.join(SUBMISSION_DIR, 'raw_masks_fold1_scnd.npy'),
masks)
else:
masks = np.load(os.path.join(SUBMISSION_DIR, masks_fn))
masks = [(center_crop(mask, SRC_SIZE, SRC_SIZE) > th).astype(int)
for mask in masks]
rle_masks = [get_mask_rle(mask) for mask in masks]
test['rle_mask'] = rle_masks
# TODO: get some stats on empty masks etc. there and log it too
submission_fn = os.path.join(SUBMISSION_DIR,
f'{fn}_{get_current_datetime()}.csv')
test.to_csv(submission_fn, index=None)
logger.info(f'Saved submission to {submission_fn}')
def test_center_crop_with_incorrectly_large_crop_size():
img = np.ones((4, 4), dtype=np.uint8)
with pytest.raises(ValueError, message='Requested crop size (8, 8) is larger than the image size (4, 4)'):
F.center_crop(img, 8, 8)
def _load_images(path):
image = np.array(Image.open(path))
image = center_crop(image, height, width)
image = torch_tools.imagenet_normalize(image)
return to_tensor(image).squeeze().unsqueeze(0)
def _load_mask(path):
mask = np.array(Image.open(path))
mask = center_crop(mask, height, width)
mask = surface_types.convert_mask(mask, category_type)
return mask
def apply(self, img, **params):
h, w, _ = img.shape
return F.center_crop(img, min(self.height, h), min(self.width, w))
def crop_from_128(image: np.ndarray) -> np.ndarray:
return center_crop(image, 101, 101)
def crop_resize_from_256(image: np.ndarray) -> np.ndarray:
cropped = center_crop(image, 202, 202)
return resize(cropped, 101, 101)