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Type cast before normalize mobilevit #21

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Merged
amyeroberts merged 3 commits into from
Sep 2, 2022
Merged

Type cast before normalize mobilevit #21

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d49aec3
Cast images to numpy arrays in call to enable consistent behaviour wi…
amyeroberts Aug 5, 2022
6d08db7
Remove accidental clip changes
amyeroberts Aug 5, 2022
a31e59e
Merge branch 'type-cast-before-normalize-update-methods' into type-ca…
amyeroberts Aug 5, 2022
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16 changes: 9 additions & 7 deletions src/transformers/models/mobilevit/feature_extraction_mobilevit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -93,13 +93,15 @@ def __call__(
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
number of channels, H and W are image height and width.

return_tensors (`str` or [`~utils.TensorType`], *optional*, defaults to `'np'`):
If set, will return tensors of a particular framework. Acceptable values are:

- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
return_tensors (`str` or [`~utils.TensorType`], *optional*, defaults to `None`):
If set, will return a tensor of a particular framework.

Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` object.
- `'pt'`: Return PyTorch `torch.Tensor` object.
- `'np'`: Return NumPy `np.ndarray` object.
- `'jax'`: Return JAX `jnp.ndarray` object.
- None: Return list of `np.ndarray` objects.

Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
Expand Down
53 changes: 53 additions & 0 deletions tests/models/mobilevit/test_feature_extraction_mobilevit.py
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Expand Up @@ -18,6 +18,7 @@

import numpy as np

from parameterized import parameterized
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

Expand Down Expand Up @@ -189,3 +190,55 @@ def test_call_pytorch(self):
self.feature_extract_tester.crop_size,
),
)

@parameterized.expand(
[
("do_resize_True_do_center_crop_True_do_flip_channel_order_True", True, True, True),
("do_resize_True_do_center_crop_True_do_flip_channel_order_False", True, True, False),
("do_resize_True_do_center_crop_False_do_flip_channel_order_True", True, False, True),
("do_resize_True_do_center_crop_False_do_flip_channel_order_False", True, False, False),
("do_resize_False_do_center_crop_True_do_flip_channel_order_True", False, True, True),
("do_resize_False_do_center_crop_True_do_flip_channel_order_False", False, True, False),
("do_resize_False_do_center_crop_False_do_flip_channel_order_True", False, False, True),
("do_resize_False_do_center_crop_False_do_flip_channel_order_False", False, False, False),
]
)
def test_call_flags(self, _, do_resize, do_center_crop, do_flip_channel_order):
# Initialize feature_extractor
feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
feature_extractor.do_center_crop = do_center_crop
feature_extractor.do_resize = do_resize
feature_extractor.do_flip_channel_order = do_flip_channel_order
# create random PIL images
image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False)

expected_shapes = [(3, *x.size[::-1]) for x in image_inputs]
if do_resize:
# Same size logic inside resized
resized_shapes = []
for shape in expected_shapes:
c, h, w = shape
short, long = (w, h) if w <= h else (h, w)
min_size = self.feature_extract_tester.size
if short == min_size:
resized_shapes.append((c, h, w))
else:
short, long = min_size, int(long * min_size / short)
resized_shape = (c, long, short) if w <= h else (c, short, long)
resized_shapes.append(resized_shape)
expected_shapes = resized_shapes
if do_center_crop:
expected_shapes = [
(
self.feature_extract_tester.num_channels,
self.feature_extract_tester.crop_size,
self.feature_extract_tester.crop_size,
)
for _ in range(self.feature_extract_tester.batch_size)
]

pixel_values = feature_extractor(image_inputs, return_tensors=None)["pixel_values"]
self.assertEqual(len(pixel_values), self.feature_extract_tester.batch_size)
for idx, image in enumerate(pixel_values):
self.assertEqual(image.shape, expected_shapes[idx])
self.assertIsInstance(image, np.ndarray)