fix nn.CentralCrop calulation result.

mindspore/nn/layer/image.py

@@ -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

mindspore/ops/operations/nn_ops.py

@@ -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)