diff --git a/mmengine/model/base_model/data_preprocessor.py b/mmengine/model/base_model/data_preprocessor.py
index 9fa5b48e1b904a5c3f1bff0b91be8dd4e7e650ca..8b5f79824c9d178b043db7ed02c997c28969bcf4 100644
--- a/mmengine/model/base_model/data_preprocessor.py
+++ b/mmengine/model/base_model/data_preprocessor.py
@@ -153,11 +153,15 @@ class ImgDataPreprocessor(BaseDataPreprocessor):
Args:
mean (Sequence[float or int], optional): The pixel mean of image
channels. If ``bgr_to_rgb=True`` it means the mean value of R,
- G, B channels. If it is not specified, images will not be
- normalized. Defaults None.
+ G, B channels. If the length of `mean` is 1, it means all
+ channels have the same mean value, or the input is a gray image.
+ If it is not specified, images will not be normalized. Defaults
+ None.
std (Sequence[float or int], optional): The pixel standard deviation of
image channels. If ``bgr_to_rgb=True`` it means the standard
- deviation of R, G, B channels. If it is not specified, images will
+ deviation of R, G, B channels. If the length of `std` is 1,
+ it means all channels have the same standard deviation, or the
+ input is a gray image. If it is not specified, images will
not be normalized. Defaults None.
pad_size_divisor (int): The size of padded image should be
divisible by ``pad_size_divisor``. Defaults to 1.
@@ -187,11 +191,11 @@ class ImgDataPreprocessor(BaseDataPreprocessor):
'mean and std should be both None or tuple')
if mean is not None:
assert len(mean) == 3 or len(mean) == 1, (
- 'The length of mean should be 1 or 3 to be compatible with '
- f'RGB or gray image, but got {len(mean)}')
+ '`mean` should have 1 or 3 values, to be compatible with '
+ f'RGB or gray image, but got {len(mean)} values')
assert len(std) == 3 or len(std) == 1, ( # type: ignore
- 'The length of std should be 1 or 3 to be compatible with RGB ' # type: ignore # noqa: E501
- f'or gray image, but got {len(std)}') # type: ignore
+ '`std` should have 1 or 3 values, to be compatible with RGB ' # type: ignore # noqa: E501
+ f'or gray image, but got {len(std)} values')
self._enable_normalize = True
self.register_buffer('mean',
torch.tensor(mean).view(-1, 1, 1), False)
@@ -221,12 +225,19 @@ class ImgDataPreprocessor(BaseDataPreprocessor):
model input.
"""
inputs, batch_data_samples = self.collate_data(data)
- # channel transform
- if self.channel_conversion:
- inputs = [_input[[2, 1, 0], ...] for _input in inputs]
- # Normalization.
- if self._enable_normalize:
- inputs = [(_input - self.mean) / self.std for _input in inputs]
+ for idx, _input in enumerate(inputs):
+ # channel transform
+ if self.channel_conversion:
+ _input = _input[[2, 1, 0], ...]
+ # Normalization.
+ if self._enable_normalize:
+ if self.mean.shape[0] == 3:
+ assert _input.dim() == 3 and _input.shape[0] == 3, (
+ 'If the mean has 3 values, the input tensor should in '
+ 'shape of (3, H, W), but got the tensor with shape '
+ f'{_input.shape}')
+ _input = (_input - self.mean) / self.std
+ inputs[idx] = _input
# Pad and stack Tensor.
batch_inputs = stack_batch(inputs, self.pad_size_divisor,
self.pad_value)
diff --git a/tests/test_model/test_base_model/test_data_preprocessor.py b/tests/test_model/test_base_model/test_data_preprocessor.py
index 6af6075210054c3ee2850c6c2b564be43834b992..99cbf2895a7f3760705f36ad5ea59e8ab2bbcb17 100644
--- a/tests/test_model/test_base_model/test_data_preprocessor.py
+++ b/tests/test_model/test_base_model/test_data_preprocessor.py
@@ -53,7 +53,7 @@ class TestBaseDataPreprocessor(TestCase):
self.assertEqual(batch_inputs.device.type, 'cuda')
-class TestImageDataPreprocessor(TestBaseDataPreprocessor):
+class TestImgataPreprocessor(TestBaseDataPreprocessor):
def test_init(self):
# initiate model without `preprocess_cfg`
@@ -79,10 +79,10 @@ class TestImageDataPreprocessor(TestBaseDataPreprocessor):
assert_allclose(data_processor.pad_value, torch.tensor(10))
self.assertEqual(data_processor.pad_size_divisor, 16)
- with self.assertRaisesRegex(AssertionError, 'The length of mean'):
+ with self.assertRaisesRegex(AssertionError, '`mean` should have'):
ImgDataPreprocessor(mean=(1, 2), std=(1, 2, 3))
- with self.assertRaisesRegex(AssertionError, 'The length of std'):
+ with self.assertRaisesRegex(AssertionError, '`std` should have'):
ImgDataPreprocessor(mean=(1, 2, 3), std=(1, 2))
with self.assertRaisesRegex(AssertionError, '`bgr2rgb` and `rgb2bgr`'):
@@ -121,6 +121,7 @@ class TestImageDataPreprocessor(TestBaseDataPreprocessor):
std = torch.tensor([1, 2, 3]).view(-1, 1, 1)
target_inputs1 = (inputs1.clone()[[2, 1, 0], ...] - 127.5) / std
target_inputs2 = (inputs2.clone()[[2, 1, 0], ...] - 127.5) / std
+
target_inputs1 = F.pad(target_inputs1, (0, 6, 0, 6), value=10)
target_inputs2 = F.pad(target_inputs2, (0, 1, 0, 1), value=10)
@@ -155,6 +156,25 @@ class TestImageDataPreprocessor(TestBaseDataPreprocessor):
assert_allclose(input_, target_input)
assert_allclose(data_sample.bboxes, target_data_sample.bboxes)
+ # Test gray image with 3 dim mean will raise error
+ data_preprocessor = ImgDataPreprocessor(
+ mean=(127.5, 127.5, 127.5), std=(127.5, 127.5, 127.5))
+ data = [
+ dict(inputs=torch.ones(10, 10)),
+ dict(inputs=torch.ones(10, 10))
+ ]
+ with self.assertRaisesRegex(AssertionError,
+ 'If the mean has 3 values'):
+ data_preprocessor(data)
+
+ data = [
+ dict(inputs=torch.ones(1, 10, 10)),
+ dict(inputs=torch.ones(1, 10, 10))
+ ]
+ with self.assertRaisesRegex(AssertionError,
+ 'If the mean has 3 values'):
+ data_preprocessor(data)
+
# Test empty `data_sample`
data = [dict(inputs=inputs1.clone()), dict(inputs=inputs2.clone())]
data_preprocessor(data, True)