!794 fix issues image gradients 4d check

Merge pull request !794 from zhaozhenlong/fix-issues-image-gradients-complement-check

mindspore/ccsrc/operator/ops.cc

@@ -174,6 +174,8 @@ const PrimitivePtr kPrimAvgPoolGrad = std::make_shared<Primitive>("AvgPoolGrad")
 const PrimitivePtr kPrimFusedBatchNorm = std::make_shared<Primitive>("FusedBatchNorm");
 const PrimitivePtr kPrimConv2D = std::make_shared<Primitive>("Conv2D");
 const PrimitivePtr kPrimFusedBatchNormGrad = std::make_shared<Primitive>("FusedBatchNormGrad");
+const PrimitivePtr kPrimBatchNorm = std::make_shared<Primitive>("BatchNorm");
+const PrimitivePtr kPrimBatchNormGrad = std::make_shared<Primitive>("BatchNormGrad");
 const PrimitivePtr kPrimReluGrad = std::make_shared<Primitive>("ReluGrad");
 const PrimitivePtr kPrimConv2DBackpropInput = std::make_shared<Primitive>("Conv2DBackpropInput");
 const PrimitivePtr kPrimConv2DBackpropFilter = std::make_shared<Primitive>("Conv2DBackpropFilter");

mindspore/ccsrc/operator/ops.h

@@ -175,6 +175,8 @@ extern const PrimitivePtr kPrimTanhGrad;
 extern const PrimitivePtr kPrimPooling;
 extern const PrimitivePtr kPrimPoolingGrad;
 extern const PrimitivePtr kPrimFusedBatchNorm;
+extern const PrimitivePtr kPrimBatchNorm;
+extern const PrimitivePtr kPrimBatchNormGrad;
 extern const PrimitivePtr kPrimConv2D;
 extern const PrimitivePtr kPrimMaxPool;
 extern const PrimitivePtr kPrimMaxPoolGrad;

mindspore/ccsrc/transform/convert.cc

@@ -221,7 +221,6 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {prim::kPrimAssign->name(), ADPT_DESC(Assign)},
     {prim::kPrimStateSetItem->name(), ADPT_DESC(Assign)},
     {prim::kPrimReluGrad->name(), ADPT_DESC(ReluGrad)},
-    {prim::kPrimFusedBatchNormGrad->name(), ADPT_DESC(FusedBatchNormGrad)},
     {prim::kPrimBiasAddGrad->name(), ADPT_DESC(BiasAddGrad)},
     {prim::kPrimConv2D->name(), ADPT_DESC(Conv2D)},
     {prim::kPrimConv2DBackpropInput->name(), ADPT_DESC(Conv2DBackpropInputD)},
@@ -229,7 +228,6 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {prim::kPrimDepthwiseConv2dNative->name(), ADPT_DESC(DepthwiseConv2D)},
     {prim::kPrimDepthwiseConv2dNativeBackpropFilter->name(), ADPT_DESC(DepthwiseConv2DBackpropFilterD)},
     {prim::kPrimDepthwiseConv2dNativeBackpropInput->name(), ADPT_DESC(DepthwiseConv2DBackpropInputD)},
-    {prim::kPrimFusedBatchNorm->name(), ADPT_DESC(FusedBatchNorm, BatchNorm)},
     {string(kNameBatchNorm), ADPT_DESC(BatchNorm)},
     {string(kNameBatchNormGrad), ADPT_DESC(BatchNormGrad)},
     {string(kNameReshape), ADPT_DESC(Reshape)},

mindspore/ccsrc/transform/op_declare.cc

@@ -703,28 +703,6 @@ INPUT_MAP(ReluGrad) = {{1, INPUT_DESC(gradients)}, {2, INPUT_DESC(features)}};
 ATTR_MAP(ReluGrad) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(ReluGrad) = {{0, OUTPUT_DESC(backprops)}};
 
-// FusedBatchNorm
-INPUT_MAP(FusedBatchNorm) = {
-  {1, INPUT_DESC(x)}, {2, INPUT_DESC(scale)}, {3, INPUT_DESC(b)}, {4, INPUT_DESC(mean)}, {5, INPUT_DESC(variance)}};
-ATTR_MAP(FusedBatchNorm) = {{"mode", ATTR_DESC(mode, AnyTraits<int64_t>())},
-                            {"momentum", ATTR_DESC(moving_average_fraction, AnyTraits<float>())},
-                            {"epsilon", ATTR_DESC(epsilon, AnyTraits<float>())}};
-OUTPUT_MAP(FusedBatchNorm) = {{0, OUTPUT_DESC(y)},
-                              {1, OUTPUT_DESC(running_mean)},
-                              {2, OUTPUT_DESC(running_variance)},
-                              {3, OUTPUT_DESC(save_mean)},
-                              {4, OUTPUT_DESC(save_inv_variance)}};
-
-// FusedBatchNromGrad
-INPUT_MAP(FusedBatchNormGrad) = {{1, INPUT_DESC(dy)},
-                                 {2, INPUT_DESC(x)},
-                                 {3, INPUT_DESC(scale)},
-                                 {4, INPUT_DESC(save_mean)},
-                                 {5, INPUT_DESC(save_inv_variance)}};
-ATTR_MAP(FusedBatchNormGrad) = {{"momentum", ATTR_DESC(momentum, AnyTraits<float>())},
-                                {"epsilon", ATTR_DESC(epsilon, AnyTraits<float>())}};
-OUTPUT_MAP(FusedBatchNormGrad) = {{0, OUTPUT_DESC(dx)}, {1, OUTPUT_DESC(bn_scale)}, {2, OUTPUT_DESC(bn_bias)}};
-
 // BiasAddGrad
 INPUT_MAP(BiasAddGrad) = {{1, INPUT_DESC(x)}};
 ATTR_MAP(BiasAddGrad) = {{"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())}};

mindspore/ccsrc/transform/op_declare.h

@@ -82,10 +82,6 @@ DECLARE_OP_USE_OUTPUT(HcomAllGather)
 DECLARE_OP_ADAPTER(Variable)
 DECLARE_OP_ADAPTER(ReluGrad)
 DECLARE_OP_USE_OUTPUT(ReluGrad)
-DECLARE_OP_ADAPTER(FusedBatchNorm)
-DECLARE_OP_USE_OUTPUT(FusedBatchNorm)
-DECLARE_OP_ADAPTER(FusedBatchNormGrad)
-DECLARE_OP_USE_OUTPUT(FusedBatchNormGrad)
 DECLARE_OP_ADAPTER(BiasAddGrad)
 DECLARE_OP_USE_OUTPUT(BiasAddGrad)
 DECLARE_OP_ADAPTER(MaxPoolWithArgmax)

mindspore/nn/layer/image.py

@@ -58,6 +58,7 @@ class ImageGradients(Cell):
         super(ImageGradients, self).__init__()
 
     def construct(self, images):
+        _check_input_4d(F.shape(images), "images", self.cls_name)
         batch_size, depth, height, width = P.Shape()(images)
         dy = images[:, :, 1:, :] - images[:, :, :height - 1, :]
         dy_last = P.Fill()(P.DType()(images), (batch_size, depth, 1, width), 0)
@@ -151,8 +152,8 @@ class SSIM(Cell):
         self.mean = P.DepthwiseConv2dNative(channel_multiplier=1, kernel_size=filter_size)
 
     def construct(self, img1, img2):
-        _check_input_4d(F.shape(img1), "img1", "SSIM")
-        _check_input_4d(F.shape(img2), "img2", "SSIM")
+        _check_input_4d(F.shape(img1), "img1", self.cls_name)
+        _check_input_4d(F.shape(img2), "img2", self.cls_name)
         P.SameTypeShape()(img1, img2)
         max_val = _convert_img_dtype_to_float32(self.max_val, self.max_val)
         img1 = _convert_img_dtype_to_float32(img1, self.max_val)
@@ -244,8 +245,8 @@ class PSNR(Cell):
         self.max_val = max_val
 
     def construct(self, img1, img2):
-        _check_input_4d(F.shape(img1), "img1", "PSNR")
-        _check_input_4d(F.shape(img2), "img2", "PSNR")
+        _check_input_4d(F.shape(img1), "img1", self.cls_name)
+        _check_input_4d(F.shape(img2), "img2", self.cls_name)
         P.SameTypeShape()(img1, img2)
         max_val = _convert_img_dtype_to_float32(self.max_val, self.max_val)
         img1 = _convert_img_dtype_to_float32(img1, self.max_val)

mindspore/ops/operations/array_ops.py

@@ -1016,6 +1016,7 @@ class Argmin(PrimitiveWithInfer):
         """init Argmin"""
         self.init_prim_io_names(inputs=['x'], outputs=['output'])
         validator.check_value_type("axis", axis, [int], self.name)
+        validator.check_type_name("output_type", output_type, [mstype.int32, mstype.int64], self.name)
         self.axis = axis
         self.add_prim_attr('output_type', output_type)
 
@@ -1726,7 +1727,9 @@ class Diag(PrimitiveWithInfer):
     def infer_value(self, x):
         if x is None:
             return None
-        validator.check_integer("input x rank", len(x.shape()), 1, Rel.EQ, self.name)
+        # do constant-folding only when x rank is 1
+        if len(x.shape()) != 1:
+            return None
         ret = np.diag(x.asnumpy())
         return Tensor(ret)
 
@@ -1752,7 +1755,7 @@ class DiagPart(PrimitiveWithInfer):
         >>>                   [0, 0, 3, 0],
         >>>                   [0, 0, 0, 4]])
         >>> diag_part = P.DiagPart()
-        >>> diag_part(x)
+        >>> diag_part(input_x)
         [1, 2, 3, 4]
     """
 
@@ -1776,7 +1779,9 @@ class DiagPart(PrimitiveWithInfer):
     def infer_value(self, x):
         if x is None:
             return None
-        validator.check("x rank", len(x.shape()), "", 2, Rel.EQ, self.name)
+        # do constant-folding only when x rank is 2
+        if len(x.shape()) != 2:
+            return None
         ret = np.diag(x.asnumpy())
         return Tensor(ret)
 

mindspore/ops/operations/math_ops.py

@@ -2037,7 +2037,7 @@ class Atan2(_MathBinaryOp):
     r"""
     Returns arctangent of input_x/input_y element-wise.
 
-    It returns :math:`\theta\ \in\ (-\frac{\pi}{2}, \frac{\pi}{2})`
+    It returns :math:`\theta\ \in\ [-\pi, \pi]`
     such that :math:`x = r*\sin(\theta), y = r*\cos(\theta)`, where :math:`r = \sqrt{x^2 + y^2}`.
 
     Inputs:

tests/ut/cpp/transform/convert_test.cc

@@ -147,13 +147,13 @@ TEST_F(TestConvert, TestReluOps) {
 }
 
 TEST_F(TestConvert, TestConvertBatchNorm) {
-  PrimitivePtr fused_batch_norm = prim::kPrimFusedBatchNorm;
-  fused_batch_norm->AddAttr("epsilon", MakeValue(0.001f));
-  fused_batch_norm->AddAttr("momentum", MakeValue(0.1f));
+  PrimitivePtr batch_norm = prim::kPrimBatchNorm;
+  batch_norm->AddAttr("epsilon", MakeValue(0.001f));
+  batch_norm->AddAttr("momentum", MakeValue(0.1f));
 
   FuncGraphPtr anf_graph = std::make_shared<FuncGraph>();
   std::vector<AnfNodePtr> inputs;
-  inputs.push_back(NewValueNode(fused_batch_norm));
+  inputs.push_back(NewValueNode(batch_norm));
   for (unsigned int i = 0; i < 5; i++) {
     inputs.push_back(anf_graph->add_parameter());
   }

tests/ut/python/nn/test_image_gradients.py

@@ -14,6 +14,7 @@
 # ============================================================================
 """ test image gradients """
 import numpy as np
+import pytest
 import mindspore.nn as nn
 import mindspore.context as context
 import mindspore.common.dtype as mstype
@@ -47,3 +48,10 @@ def test_compile_multi_channel():
                              [[[10,20],[30,40]], [[50,60],[70,80]]]]), dtype=dtype)
     net = Net()
     _executor.compile(net, image)
+
+def test_invalid_5d_input():
+    dtype = mstype.float32
+    image = Tensor(np.random.random([4, 1, 16, 16, 1]), dtype=dtype)
+    net = Net()
+    with pytest.raises(ValueError):
+        _executor.compile(net, image)

tests/ut/python/ops/test_array_ops.py

@@ -14,16 +14,15 @@
 # ============================================================================
 """ test array ops """
 import functools
+import pytest
 import numpy as np
 import mindspore as ms
 from mindspore import Tensor
 from mindspore.nn import Cell
 from mindspore.ops import operations as P
-from mindspore.ops import functional as F
-from mindspore.ops import composite as C
 from mindspore.ops import prim_attr_register
+from mindspore.common import dtype as mstype
 from mindspore.ops.primitive import Primitive, PrimitiveWithInfer
-from mindspore.common.dtype import get_py_obj_dtype
 from mindspore._c_expression import signature_dtype as sig_dtype
 from mindspore._c_expression import signature_rw as sig_rw
 from mindspore._c_expression import signature_kind as sig_kind
@@ -96,6 +95,17 @@ def test_select():
     expect = np.array([[1, 8, 9], [10, 5, 6]])
     assert np.all(output.asnumpy() == expect)
 
+def test_argmin_invalid_output_type():
+    P.Argmin(-1, mstype.int64)
+    P.Argmin(-1, mstype.int32)
+    with pytest.raises(TypeError):
+        P.Argmin(-1, mstype.float32)
+    with pytest.raises(TypeError):
+        P.Argmin(-1, mstype.float64)
+    with pytest.raises(TypeError):
+        P.Argmin(-1, mstype.uint8)
+    with pytest.raises(TypeError):
+        P.Argmin(-1, mstype.bool_)
 
 class CustomOP(PrimitiveWithInfer):
     __mindspore_signature__ = (sig_dtype.T, sig_dtype.T, sig_dtype.T1,

tests/ut/python/ops/test_math_ops.py

@@ -17,6 +17,7 @@ import functools
 import numpy as np
 import mindspore as ms
 import mindspore.nn as nn
+from mindspore.common.api import _executor
 from mindspore.common import dtype as mstype
 from mindspore.ops import prim_attr_register, PrimitiveWithInfer
 from mindspore import Tensor