!19127 add st test: test_Conv2dBnFoldQuant

Merge pull request !19127 from zhang_sss/add_st_test

mindspore/nn/layer/quant.py

@@ -1351,7 +1351,7 @@ class DenseQuant(Cell):
 
     Inputs:
         - **x** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
-           The input dimension is preferably 2D or 4D.
+          The input dimension is preferably 2D or 4D.
 
     Outputs:
         Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.

tests/st/quantization/ops/test_Conv2dBnFoldQuant.py

@@ -0,0 +1,58 @@
+# Copyright 2021 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.
+# ============================================================================
+"""
+train Conv2dBnFoldQuant Cell
+"""
+
+import pytest
+import numpy as np
+from mindspore import nn
+from mindspore import context
+from mindspore import Tensor
+from mindspore.common import set_seed
+from mindspore.compression.quant import create_quant_config
+
+class Net(nn.Cell):
+    def __init__(self, qconfig):
+        super(Net, self).__init__()
+        self.conv = nn.Conv2dBnFoldQuant(2, 3, kernel_size=(2, 2), stride=(1, 1),
+                                         pad_mode='valid', quant_config=qconfig)
+    def construct(self, x):
+        return self.conv(x)
+
+def test_conv2d_bn_fold_quant():
+    set_seed(1)
+    quant_config = create_quant_config()
+    network = Net(quant_config)
+    inputs = Tensor(np.ones([1, 2, 5, 5]).astype(np.float32))
+    label = Tensor(np.ones([1, 3, 4, 4]).astype(np.int32))
+    opt = nn.Momentum(filter(lambda x: x.requires_grad, network.get_parameters()), learning_rate=0.1, momentum=0.9)
+    loss = nn.MSELoss()
+    net_with_loss = nn.WithLossCell(network, loss)
+    train_network = nn.TrainOneStepCell(net_with_loss, opt)
+    train_network.set_train()
+    out_loss = train_network(inputs, label)
+    expect_loss = np.array([0.940427])
+    error = np.array([0.1])
+    diff = out_loss.asnumpy() - expect_loss
+    assert np.all(abs(diff) < error)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_conv2d_bn_fold_quant_ascend():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    test_conv2d_bn_fold_quant()