forked from mindspore-Ecosystem/mindspore
!2348 fix image.CenterCrop.
Merge pull request !2348 from liuxiao/central_crop
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c55b81e94f
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@ -267,11 +267,9 @@ class PSNR(Cell):
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@constexpr
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def _check_input_3d_or_4d(input_shape, param_name, func_name):
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"""check input 3d or 4d"""
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if len(input_shape) != 3 and len(input_shape) != 4:
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raise ValueError(f"{func_name} {param_name} should be 3d or 4d, but got shape {input_shape}")
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return True
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def _raise_dims_rank_error(input_shape, param_name, func_name):
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"""raise error if input is not 3d or 4d"""
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raise ValueError(f"{func_name} {param_name} should be 3d or 4d, but got shape {input_shape}")
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@constexpr
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def _get_bbox(rank, shape, central_fraction):
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@ -281,6 +279,7 @@ def _get_bbox(rank, shape, central_fraction):
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else:
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n, c, h, w = shape
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central_fraction = central_fraction.asnumpy()[0]
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bbox_h_start = int((float(h) - float(h) * central_fraction) / 2)
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bbox_w_start = int((float(w) - float(w) * central_fraction) / 2)
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bbox_h_size = h - bbox_h_start * 2
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@ -319,16 +318,18 @@ class CentralCrop(Cell):
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validator.check_value_type("central_fraction", central_fraction, [float], self.cls_name)
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self.central_fraction = validator.check_number_range('central_fraction', central_fraction,
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0.0, 1.0, Rel.INC_RIGHT, self.cls_name)
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self.central_fraction_tensor = Tensor(np.array([central_fraction]).astype(np.float64))
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self.slice = P.Slice()
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def construct(self, image):
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image_shape = F.shape(image)
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rank = len(image_shape)
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_check_input_3d_or_4d(image_shape, "image", self.cls_name)
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if not rank in (3, 4):
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return _raise_dims_rank_error(image_shape, "image", self.cls_name)
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if self.central_fraction == 1.0:
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return image
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bbox_begin, bbox_size = _get_bbox(rank, image_shape, self.central_fraction)
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bbox_begin, bbox_size = _get_bbox(rank, image_shape, self.central_fraction_tensor)
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image = self.slice(image, bbox_begin, bbox_size)
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return image
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