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!5805 Fix bug about nn.CentralCrop calulation result. 

Merge pull request !5805 from liuxiao93/central-crop
This commit is contained in:
mindspore-ci-bot 2020-09-07 17:43:45 +08:00 committed by Gitee
parent 506c1a0fd8 3045af13cb
commit 5389348148
2 changed files with 10 additions and 6 deletions
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11
mindspore/nn/layer/image.py
View File

@ -386,15 +386,15 @@ def _raise_dims_rank_error(input_shape, param_name, func_name):
raise ValueError(f"{func_name} {param_name} should be 3d or 4d, but got shape {input_shape}")
@constexpr
def _get_bbox(rank, shape, central_fraction):
def _get_bbox(rank, shape, size_h, size_w):
"""get bbox start and size for slice"""
if rank == 3:
c, h, w = shape
else:
n, c, h, w = shape
bbox_h_start = int(np.round((float(h) - float(h) * central_fraction) / 2))
bbox_w_start = int(np.round((float(w) - float(w) * central_fraction) / 2))
bbox_h_start = int((float(h) - size_h) / 2)
bbox_w_start = int((float(w) - size_w) / 2)
bbox_h_size = h - bbox_h_start * 2
bbox_w_size = w - bbox_w_start * 2
@ -436,12 +436,15 @@ class CentralCrop(Cell):
def construct(self, image):
image_shape = F.shape(image)
rank = len(image_shape)
h, w = image_shape[-2], image_shape[-1]
if not rank in (3, 4):
return _raise_dims_rank_error(image_shape, "image", self.cls_name)
if self.central_fraction == 1.0:
return image
bbox_begin, bbox_size = _get_bbox(rank, image_shape, self.central_fraction)
size_h = self.central_fraction * h
size_w = self.central_fraction * w
bbox_begin, bbox_size = _get_bbox(rank, image_shape, size_h, size_w)
image = self.slice(image, bbox_begin, bbox_size)
return image

5
mindspore/ops/operations/nn_ops.py
View File

@ -5298,7 +5298,7 @@ class CTCLoss(PrimitiveWithInfer):
- **inputs** (Tensor) - The input Tensor should be a `3-D` tensor whose shape is
:math:`(max_time, batch_size, num_classes)`. `num_classes` should be `num_labels + 1` classes, `num_labels`
indicates the number of actual labels. Blank labels are reserved. Default blank label is `num_classes - 1`.
Data type must be float32 or float64.
Data type must be float16, float32 or float64.
- **labels_indices** (Tensor) - The indices of labels. `labels_indices[i, :] == [b, t]` means `labels_values[i]`
stores the id for `(batch b, time t)`. The type must be int64 and rank must be 2.
- **labels_values** (Tensor) - A `1-D` input tensor. The values are associated with the given batch and time.
@ -5348,7 +5348,8 @@ class CTCLoss(PrimitiveWithInfer):
return batch_size, inputs
def infer_dtype(self, inputs, labels_indices, labels_values, sequence_length):
validator.check_tensor_type_same({"inputs_dtype": inputs}, [mstype.float32, mstype.double], self.name)
valid_dtype = [mstype.float16, mstype.float32, mstype.double]
validator.check_tensor_type_same({"inputs_dtype": inputs}, valid_dtype, self.name)
validator.check_tensor_type_same({"labels_indices_dtype": labels_indices}, [mstype.int64], self.name)
validator.check_tensor_type_same({"labels_values_dtype": labels_values}, [mstype.int32], self.name)
validator.check_tensor_type_same({"sequence_length_dtype": sequence_length}, [mstype.int32], self.name)