Add Squeeze

mindspore/ccsrc/backend/kernel_compiler/cpu/reshape_cpu_kernel.h

@@ -62,6 +62,14 @@ MS_REG_CPU_KERNEL(ExpandDims, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOut
                   ReshapeCPUKernel);
 MS_REG_CPU_KERNEL(ExpandDims, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
                   ReshapeCPUKernel);
+
+MS_REG_CPU_KERNEL(Squeeze, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+                  ReshapeCPUKernel);
+MS_REG_CPU_KERNEL(Squeeze, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+                  ReshapeCPUKernel);
+MS_REG_CPU_KERNEL(Squeeze, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
+                  ReshapeCPUKernel);
+MS_REG_CPU_KERNEL(Squeeze, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool), ReshapeCPUKernel);
 }  // namespace kernel
 }  // namespace mindspore
 

tests/st/ops/cpu/test_squeeze_op.py

@@ -0,0 +1,65 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.nn import Cell
+import mindspore.ops as P
+
+context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+
+
+class SqueezeNet(Cell):
+    def __init__(self):
+        super(SqueezeNet, self).__init__()
+        self.squeeze = P.Squeeze()
+
+    def construct(self, x):
+        return self.squeeze(x)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_squeeze_shape_float32():
+    x = np.ones(shape=[1, 2, 1, 1, 8, 3, 1]).astype(np.float32)
+    expect = np.ones(shape=[2, 8, 3]).astype(np.float32)
+    net = SqueezeNet()
+    result = net(Tensor(x))
+    assert np.allclose(result.asnumpy(), expect, rtol=1.e-4, atol=1.e-8, equal_nan=True)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_squeeze_shape_int32():
+    x = np.array([[7], [11]]).astype(np.int32)
+    expect = np.array([7, 11]).astype(np.int32)
+    net = SqueezeNet()
+    result = net(Tensor(x))
+    assert np.allclose(result.asnumpy(), expect, rtol=1.e-4, atol=1.e-8, equal_nan=True)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_squeeze_shape_bool():
+    x = np.array([[True], [False]]).astype(np.bool_)
+    expect = np.array([True, False]).astype(np.bool_)
+    net = SqueezeNet()
+    result = net(Tensor(x))
+    assert np.allclose(result.asnumpy(), expect, rtol=1.e-4, atol=1.e-8, equal_nan=True)