!4050 fix unsortedgrad clipbynorm boundingboxdecode

Merge pull request !4050 from fangzehua/unsortedgrad

mindspore/nn/layer/basic.py

@@ -250,6 +250,10 @@ def _is_equal_one(x):
         return False
     return bool(x.asnumpy().mean() == 1.0)
 
+@constexpr
+def _dtype_check(x_dtype):
+    if x_dtype not in [mstype.float32, mstype.float16]:
+        raise  TypeError("The input type must be float32 or float16.")
 
 class ClipByNorm(Cell):
     r"""
@@ -264,12 +268,11 @@ class ClipByNorm(Cell):
     where :math:`L_2(X)` is the :math:`L_2`-norm of :math:`X`.
 
     Inputs:
-        - **input** (Tensor) - Tensor of shape N-D.
-        - **clip_norm** (Tensor) - A scalar Tensor of shape :math:`()` or :math:`(1)` and of
-          the same type as the input Tensor.
+        - **input** (Tensor) - Tensor of shape N-D. The type should be float32 or float16.
+        - **clip_norm** (Tensor) - A scalar Tensor of shape :math:`()` or :math:`(1)`.
 
     Outputs:
-        Tensor, clipped tensor with the same shape as the input.
+        Tensor, clipped tensor with the same shape as the input, whose type is float32.
 
     Examples:
         >>> net = nn.ClipByNorm()
@@ -300,10 +303,10 @@ class ClipByNorm(Cell):
         l2sum = self.cast(self.reduce_sum(mul_x), mstype.float32)
         cond = self.greater_(l2sum, 0)
         ones_ = self.fill(self.dtype(cond), self.shape(cond), 1.0)
-
         l2sum_safe = self.select_(cond, l2sum, self.cast(ones_, self.dtype(l2sum)))
         l2norm = self.select_(cond, self.sqrt(l2sum_safe), l2sum)
 
+        _dtype_check(self.dtype(x))
         if _is_equal_one(clip_norm):
             intermediate = x
         else:

mindspore/ops/_grad/grad_array_ops.py

@@ -827,13 +827,3 @@ def get_bprop_unique(self):
         dx = op(dout, out)
         return (dx,)
     return bprop
-
-
-@bprop_getters.register(P.UnsortedSegmentSum)
-def get_bprop_unsorted_segment_sum(self):
-    """Generate bprop for UnsortedSegmentSum"""
-    op = G.UnsortedSegmentSumGrad()
-    def bprop(x, segment_ids, num_segments, out, dout):
-        dx = op(dout, segment_ids)
-        return (dx, zeros_like(segment_ids), zeros_like(num_segments))
-    return bprop

mindspore/ops/operations/_grad_ops.py

@@ -502,20 +502,6 @@ class UniqueGrad(Primitive):
         raise NotImplementedError
 
 
-class UnsortedSegmentSumGrad(PrimitiveWithInfer):
-    """Gradients of UnsortedSegmentSum operation."""
-
-    @prim_attr_register
-    def __init__(self):
-        self.init_prim_io_names(inputs=['grads', 'ids'], outputs=['y'])
-
-    def infer_shape(self, grads, ids):
-        return ids + grads[len(ids):]
-
-    def infer_dtype(self, grads, ids):
-        return grads
-
-
 class BNTrainingReduceGrad(PrimitiveWithInfer):
     """Gradients of FusedBatchNorm operation."""
 

mindspore/ops/operations/other_ops.py

@@ -93,8 +93,12 @@ class BoundingBoxEncode(PrimitiveWithInfer):
 
     @prim_attr_register
     def __init__(self, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0)):
-        validator.check_value_type('means', means, [tuple], self.name)
-        validator.check_value_type('stds', stds, [tuple], self.name)
+        validator.check_value_type('means', means, [tuple, list], self.name)
+        validator.check_value_type('stds', stds, [tuple, list], self.name)
+        for i, value in enumerate(means):
+            validator.check_value_type("means[%d]" % i, value, [float], self.name)
+        for i, value in enumerate(stds):
+            validator.check_value_type("stds[%d]" % i, value, [float], self.name)
         validator.check_integer("means len", len(means), 4, Rel.EQ, self.name)
         validator.check_integer("stds len", len(stds), 4, Rel.EQ, self.name)
 
@@ -143,8 +147,12 @@ class BoundingBoxDecode(PrimitiveWithInfer):
 
     @prim_attr_register
     def __init__(self, max_shape, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0), wh_ratio_clip=0.016):
-        validator.check_value_type('means', means, [tuple], self.name)
-        validator.check_value_type('stds', stds, [tuple], self.name)
+        validator.check_value_type('means', means, [tuple, list], self.name)
+        validator.check_value_type('stds', stds, [tuple, list], self.name)
+        for i, value in enumerate(means):
+            validator.check_value_type("means[%d]" % i, value, [float], self.name)
+        for i, value in enumerate(stds):
+            validator.check_value_type("stds[%d]" % i, value, [float], self.name)
         validator.check_value_type('wh_ratio_clip', wh_ratio_clip, [float], self.name)
         validator.check_integer("means len", len(means), 4, Rel.EQ, self.name)
         validator.check_integer("stds len", len(stds), 4, Rel.EQ, self.name)