[NPU] add conv_op_npu and test (#34055)

* add conv_op_npu and test

* add more tests

* clean headers & support fp16

* update

paddle/fluid/operators/conv_op_npu.cc

@@ -126,6 +126,169 @@ class DepthwiseConvNPUKernel : public framework::OpKernel<T> {
   }
 };
 
+template <typename T>
+class NPUConvOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto& dev_ctx = ctx.template device_context<platform::NPUDeviceContext>();
+    const Tensor* input = ctx.Input<Tensor>("Input");
+    auto* filter = ctx.Input<Tensor>("Filter");
+    auto* output = ctx.Output<Tensor>("Output");
+    output->mutable_data<T>(ctx.GetPlace());
+    const std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
+    std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
+    std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
+    int groups = ctx.Attr<int>("groups");
+    const std::string padding_algorithm =
+        ctx.Attr<std::string>("padding_algorithm");
+    const std::string data_format = ctx.Attr<std::string>("data_format");
+
+    const bool channel_last = data_format == "NHWC";
+
+    // update padding and dilation
+    auto in_dims = input->dims();
+    auto filter_dims = filter->dims();
+    framework::DDim in_data_dims;
+    framework::DDim filter_data_dims;
+
+    if (channel_last) {
+      in_data_dims = framework::slice_ddim(in_dims, 1, in_dims.size() - 1);
+    } else {
+      in_data_dims = framework::slice_ddim(in_dims, 2, in_dims.size());
+    }
+    filter_data_dims = framework::slice_ddim(filter_dims, 2, in_dims.size());
+
+    std::vector<int> ksize = framework::vectorize<int>(filter_data_dims);
+    UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
+                             in_data_dims, strides, ksize);
+
+    std::vector<int> strides_vec(4, 1);
+    std::vector<int> dilations_vec(4, 1);
+
+    Tensor input_tensor, output_tensor;
+    input_tensor.ShareDataWith(*input);
+    output_tensor.ShareDataWith(*output);
+    if (channel_last) {
+      input_tensor.set_layout(DataLayout::kNHWC);
+      output_tensor.set_layout(DataLayout::kNHWC);
+      strides_vec[1] = strides[0];
+      strides_vec[2] = strides[1];
+      dilations_vec[1] = dilations[0];
+      dilations_vec[2] = dilations[1];
+    } else {
+      strides_vec[2] = strides[0];
+      strides_vec[3] = strides[1];
+      dilations_vec[2] = dilations[0];
+      dilations_vec[3] = dilations[1];
+    }
+
+    const auto& runner =
+        NpuOpRunner("Conv2D", {input_tensor, *filter}, {output_tensor},
+                    {{"strides", strides_vec},
+                     {"pads", paddings},
+                     {"dilations", dilations_vec},
+                     {"groups", groups},
+                     {"data_format", data_format}});
+    runner.Run(dev_ctx.stream());
+  }
+};
+
+template <typename T>
+class NPUConvGradOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto& dev_ctx = ctx.template device_context<platform::NPUDeviceContext>();
+
+    auto input = ctx.Input<Tensor>("Input");
+    auto filter = ctx.Input<Tensor>("Filter");
+    auto output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
+    auto input_grad = ctx.Output<Tensor>(framework::GradVarName("Input"));
+    auto filter_grad = ctx.Output<Tensor>(framework::GradVarName("Filter"));
+
+    const std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
+    std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
+    std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
+    int groups = ctx.Attr<int>("groups");
+    const std::string padding_algorithm =
+        ctx.Attr<std::string>("padding_algorithm");
+    const std::string data_format = ctx.Attr<std::string>("data_format");
+
+    const bool channel_last = data_format == "NHWC";
+
+    // update padding and dilation
+    auto in_dims = input->dims();
+    auto filter_dims = filter->dims();
+    framework::DDim in_data_dims;
+    framework::DDim filter_data_dims;
+
+    if (channel_last) {
+      in_data_dims = framework::slice_ddim(in_dims, 1, in_dims.size() - 1);
+    } else {
+      in_data_dims = framework::slice_ddim(in_dims, 2, in_dims.size());
+    }
+    filter_data_dims = framework::slice_ddim(filter_dims, 2, in_dims.size());
+
+    std::vector<int> ksize = framework::vectorize<int>(filter_data_dims);
+    UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
+                             in_data_dims, strides, ksize);
+
+    std::vector<int> strides_vec(4, 1);
+    std::vector<int> dilations_vec(4, 1);
+
+    Tensor input_tensor, output_grad_tensor;
+    input_tensor.ShareDataWith(*input);
+    output_grad_tensor.ShareDataWith(*output_grad);
+    if (channel_last) {
+      input_tensor.set_layout(DataLayout::kNHWC);
+      output_grad_tensor.set_layout(DataLayout::kNHWC);
+      strides_vec[1] = strides[0];
+      strides_vec[2] = strides[1];
+      dilations_vec[1] = dilations[0];
+      dilations_vec[2] = dilations[1];
+    } else {
+      strides_vec[2] = strides[0];
+      strides_vec[3] = strides[1];
+      dilations_vec[2] = dilations[0];
+      dilations_vec[3] = dilations[1];
+    }
+
+    if (filter_grad) {
+      filter_grad->mutable_data<T>(ctx.GetPlace());
+      std::vector<int> filter_shape_vec =
+          framework::vectorize<int>(filter->dims());
+
+      const auto& runner = NpuOpRunner(
+          "Conv2DBackpropFilterD", {input_tensor, output_grad_tensor},
+          {*filter_grad}, {{"filter_size", filter_shape_vec},
+                           {"strides", strides_vec},
+                           {"pads", paddings},
+                           {"dilations", dilations_vec},
+                           {"groups", groups},
+                           {"data_format", data_format}});
+      runner.Run(dev_ctx.stream());
+    }
+    if (input_grad) {
+      input_grad->mutable_data<T>(ctx.GetPlace());
+      std::vector<int> input_shape_vec =
+          framework::vectorize<int>(input->dims());
+
+      Tensor input_grad_tensor;
+      input_grad_tensor.ShareDataWith(*input_grad);
+      if (channel_last) {
+        input_grad_tensor.set_layout(DataLayout::kNHWC);
+      }
+      const auto& runner =
+          NpuOpRunner("Conv2DBackpropInputD", {*filter, output_grad_tensor},
+                      {input_grad_tensor}, {{"input_size", input_shape_vec},
+                                            {"strides", strides_vec},
+                                            {"pads", paddings},
+                                            {"dilations", dilations_vec},
+                                            {"groups", groups},
+                                            {"data_format", data_format}});
+      runner.Run(dev_ctx.stream());
+    }
+  }
+};
 }  // namespace operators
 }  // namespace paddle
 
@@ -135,3 +298,7 @@ REGISTER_OP_NPU_KERNEL(
     depthwise_conv2d,
     ops::DepthwiseConvNPUKernel<paddle::platform::NPUDeviceContext,
                                 paddle::platform::float16>);
+REGISTER_OP_NPU_KERNEL(conv2d, ops::NPUConvOpKernel<float>,
+                       ops::NPUConvOpKernel<paddle::platform::float16>);
+REGISTER_OP_NPU_KERNEL(conv2d_grad, ops::NPUConvGradOpKernel<float>,
+                       ops::NPUConvGradOpKernel<paddle::platform::float16>);

python/paddle/fluid/tests/unittests/npu/test_conv2d_op_npu.py

+#   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import sys
+sys.path.append("..")
+import paddle
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from op_test import OpTest
+
+from test_conv2d_op import conv2d_forward_naive
+
+paddle.enable_static()
+
+
+def create_test_channel_last_class(parent):
+    class TestChannelLastCase(parent):
+        def init_data_format(self):
+            self.data_format = "NHWC"
+
+        def init_test_case_2(self):
+            N, C, H, W = self.input_size
+            self.input_size = [N, H, W, C]
+
+    cls_name = "{0}_{1}".format(parent.__name__, "ChannelLast")
+    TestChannelLastCase.__name__ = cls_name
+    globals()[cls_name] = TestChannelLastCase
+
+
+def create_test_padding_SAME_class(parent):
+    class TestPaddingSMAECase(parent):
+        def init_paddings(self):
+            self.pad = [0, 0]
+            self.padding_algorithm = "SAME"
+
+    cls_name = "{0}_{1}".format(parent.__name__, "PaddingSAMEOp")
+    TestPaddingSMAECase.__name__ = cls_name
+    globals()[cls_name] = TestPaddingSMAECase
+
+
+def create_test_padding_VALID_class(parent):
+    class TestPaddingVALIDCase(parent):
+        def init_paddings(self):
+            self.pad = [1, 1]
+            self.padding_algorithm = "VALID"
+
+    cls_name = "{0}_{1}".format(parent.__name__, "PaddingVALIDOp")
+    TestPaddingVALIDCase.__name__ = cls_name
+    globals()[cls_name] = TestPaddingVALIDCase
+
+
+def create_test_fp16_class(parent):
+    class TestFp16Case(parent):
+        def init_dtype(self):
+            self.dtype = np.float16
+
+    cls_name = "{0}_{1}".format(parent.__name__, "Fp16")
+    TestFp16Case.__name__ = cls_name
+    globals()[cls_name] = TestFp16Case
+
+
+class TestConv2DOp(OpTest):
+    def set_npu(self):
+        self.__class__.use_npu = True
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def init_data_format(self):
+        self.data_format = "NCHW"
+
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "conv2d"
+        self.init_data_format()
+        self.init_dtype()
+        self.init_group()
+        self.init_dilation()
+        self.init_test_case()
+
+        conv2d_param = {
+            'stride': self.stride,
+            'pad': self.pad,
+            'dilation': self.dilations
+        }
+
+        input = np.random.random(self.input_size).astype(self.dtype)
+        filter = np.random.uniform(-1, 1, self.filter_size).astype(self.dtype)
+
+        output, _, _, _, _ = conv2d_forward_naive(
+            input,
+            filter,
+            self.groups,
+            conv2d_param,
+            data_format=self.data_format)
+        output = output.astype(self.dtype)
+
+        self.inputs = {
+            'Input': OpTest.np_dtype_to_fluid_dtype(input),
+            'Filter': OpTest.np_dtype_to_fluid_dtype(filter)
+        }
+        self.attrs = {
+            'strides': self.stride,
+            'paddings': self.pad,
+            'groups': self.groups,
+            'dilations': self.dilations,
+            'data_format': self.data_format,
+        }
+        self.outputs = {'Output': output}
+
+    def test_check_output(self):
+        self.check_output_with_place(fluid.NPUPlace(0), atol=1e-2)
+
+    def test_check_grad(self):
+        self.check_grad_with_place(
+            fluid.NPUPlace(0), {'Input', 'Filter'},
+            'Output',
+            max_relative_error=0.03)
+
+    def test_check_grad_no_filter(self):
+        self.check_grad_with_place(
+            fluid.NPUPlace(0), ['Input'],
+            'Output',
+            max_relative_error=0.03,
+            no_grad_set=set(['Filter']))
+
+    def test_check_grad_no_input(self):
+        self.check_grad_with_place(
+            fluid.NPUPlace(0), ['Filter'],
+            'Output',
+            max_relative_error=0.03,
+            no_grad_set=set(['Input']))
+
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [6, f_c, 3, 3]
+
+    def init_dilation(self):
+        self.dilations = [1, 1]
+
+    def init_group(self):
+        self.groups = 1
+
+
+class TestWithPad(TestConv2DOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [6, f_c, 3, 3]
+
+
+class TestWithStride(TestConv2DOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [2, 2]
+        self.input_size = [2, 3, 6, 6]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [6, f_c, 3, 3]
+
+
+class TestWithGroup(TestConv2DOp):
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        self.group = 3
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [18, f_c, 3, 3]
+
+
+class TestWith1x1(TestConv2DOp):
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [120, f_c, 1, 1]
+
+    def init_group(self):
+        # FIXME: Supporting group = 3 in this case.
+        # NOTE(wangran16): There is an unknown error (acl error code is : 507015) 
+        # when group = 3, which needs to be fixed.
+        self.groups = 1
+
+
+class TestWithDepthWise5x5(TestConv2DOp):
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.input_size = [2, 4, 10, 10]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [8, f_c, 5, 5]
+
+    def init_group(self):
+        self.groups = 4
+
+
+class TestWithDepthWise7x7(TestConv2DOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [2, 2]
+        self.input_size = [2, 8, 10, 10]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [16, f_c, 7, 7]
+
+    def init_group(self):
+        self.groups = 8
+
+
+class TestWithDilation(TestConv2DOp):
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.input_size = [2, 3, 10, 10]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [12, f_c, 3, 3]
+
+    def init_dilation(self):
+        self.dilations = [2, 2]
+
+    def init_group(self):
+        self.groups = 3
+
+
+class TestWithInput1x1Filter1x1(TestConv2DOp):
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.input_size = [100, 1, 1, 1]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [120, f_c, 1, 1]
+
+    def init_group(self):
+        self.groups = 1
+
+
+class TestConv2DOp_v2(OpTest):
+    def set_npu(self):
+        self.__class__.use_npu = True
+
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "conv2d"
+        self.dtype = np.float32
+        self.init_kernel_type()
+        self.init_group()
+        self.init_dilation()
+        self.init_data_format()
+        self.init_test_case()
+        self.init_paddings()
+        self.init_test_case_2()
+
+        conv2d_param = {
+            'stride': self.stride,
+            'pad': self.pad,
+            'dilation': self.dilations
+        }
+
+        input = np.random.random(self.input_size).astype(self.dtype)
+        filter = np.random.uniform(-1, 1, self.filter_size).astype(self.dtype)
+        output, _, _, _, _ = conv2d_forward_naive(
+            input, filter, self.groups, conv2d_param, self.padding_algorithm,
+            self.data_format)
+        output = output.astype(self.dtype)
+
+        self.inputs = {
+            'Input': OpTest.np_dtype_to_fluid_dtype(input),
+            'Filter': OpTest.np_dtype_to_fluid_dtype(filter)
+        }
+        self.attrs = {
+            'strides': self.stride,
+            'paddings': self.pad,
+            'padding_algorithm': self.padding_algorithm,
+            'groups': self.groups,
+            'dilations': self.dilations,
+            'data_format': self.data_format,
+        }
+        self.outputs = {'Output': output}
+
+    def test_check_output(self):
+        self.check_output_with_place(paddle.NPUPlace(0), atol=1e-2)
+
+    def test_check_grad(self):
+        self.check_grad_with_place(
+            paddle.NPUPlace(0), {'Input', 'Filter'},
+            'Output',
+            max_relative_error=0.02)
+
+    def test_check_grad_no_filter(self):
+        self.check_grad_with_place(
+            paddle.NPUPlace(0), ['Input'],
+            'Output',
+            max_relative_error=0.02,
+            no_grad_set=set(['Filter']))
+
+    def test_check_grad_no_input(self):
+        self.check_grad_with_place(
+            paddle.NPUPlace(0), ['Filter'],
+            'Output',
+            no_grad_set=set(['Input']))
+
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 2]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [6, f_c, 4, 3]
+
+    def init_dilation(self):
+        self.dilations = [1, 1]
+
+    def init_group(self):
+        self.groups = 1
+
+    def init_kernel_type(self):
+        pass
+
+    def init_paddings(self):
+        self.pad = [0, 0]
+        self.padding_algorithm = "EXPLICIT"
+
+    def init_data_format(self):
+        self.data_format = "NCHW"
+
+    def init_test_case_2(self):
+        pass
+
+
+class TestConv2DOp_AsyPadding(TestConv2DOp_v2):
+    def init_paddings(self):
+        self.pad = [0, 0, 1, 2]
+        self.padding_algorithm = "EXPLICIT"
+
+
+class TestWithPad_AsyPadding(TestConv2DOp_v2):
+    def init_test_case(self):
+        self.stride = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [6, f_c, 3, 3]
+
+    def init_paddings(self):
+        self.pad = [2, 1, 3, 2]
+        self.padding_algorithm = "EXPLICIT"
+
+
+class TestWithStride_AsyPadding(TestConv2DOp_v2):
+    def init_test_case(self):
+        self.stride = [2, 2]
+        self.input_size = [2, 3, 6, 6]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [6, f_c, 3, 3]
+
+    def init_paddings(self):
+        self.pad = [2, 1, 3, 2]
+        self.padding_algorithm = "EXPLICIT"
+
+
+class TestWithGroup_AsyPadding(TestConv2DOp_v2):
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 2]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        self.group = 3
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [24, f_c, 4, 3]
+
+
+class TestWith1x1_AsyPadding(TestConv2DOp_v2):
+    def init_test_case(self):
+        self.stride = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [120, f_c, 1, 1]
+
+    def init_group(self):
+        self.groups = 1
+
+    def init_paddings(self):
+        self.pad = [2, 2, 4, 0]
+        self.padding_algorithm = "EXPLICIT"
+
+
+class TestWithDepthWise3x3_AsyPadding(TestConv2DOp_v2):
+    def init_test_case(self):
+        self.stride = [1, 1]
+        self.input_size = [3, 4, 10, 10]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [16, f_c, 3, 3]
+
+    def init_dilation(self):
+        self.dilations = [2, 2]
+
+    def init_group(self):
+        self.groups = 4
+
+    def init_paddings(self):
+        self.pad = [1, 3, 2, 1]
+        self.padding_algorithm = "EXPLICIT"
+
+
+class TestWithDepthWise5x5_AsyPadding(TestConv2DOp_v2):
+    def init_test_case(self):
+        self.stride = [1, 1]
+        self.input_size = [2, 4, 10, 10]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [8, f_c, 5, 5]
+
+    def init_group(self):
+        self.groups = 4
+
+    def init_paddings(self):
+        self.pad = [0, 1, 1, 0]
+        self.padding_algorithm = "EXPLICIT"
+
+
+class TestWithDepthWise7x7_AsyPadding(TestConv2DOp_v2):
+    def init_test_case(self):
+        self.stride = [2, 2]
+        self.input_size = [2, 8, 10, 10]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [16, f_c, 7, 7]
+
+    def init_group(self):
+        self.groups = 8
+
+    def init_paddings(self):
+        self.pad = [1, 3, 4, 1]
+        self.padding_algorithm = "EXPLICIT"
+
+
+class TestWithDilation_AsyPadding(TestConv2DOp_v2):
+    def init_test_case(self):
+        self.stride = [1, 1]
+        self.input_size = [2, 3, 10, 10]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [24, f_c, 3, 3]
+
+    def init_dilation(self):
+        self.dilations = [2, 2]
+
+    def init_group(self):
+        self.groups = 3
+
+    def init_paddings(self):
+        self.pad = [0, 1, 3, 0]
+        self.padding_algorithm = "EXPLICIT"
+
+
+class TestWithInput1x1Filter1x1_AsyPadding(TestConv2DOp_v2):
+    def init_test_case(self):
+        self.stride = [1, 1]
+        self.input_size = [100, 1, 1, 1]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [120, f_c, 1, 1]
+
+    def init_group(self):
+        self.groups = 1
+
+    def init_paddings(self):
+        self.pad = [0, 3, 4, 0]
+        self.padding_algorithm = "EXPLICIT"
+
+
+create_test_padding_SAME_class(TestConv2DOp_AsyPadding)
+create_test_padding_SAME_class(TestWithPad_AsyPadding)
+create_test_padding_SAME_class(TestWithStride_AsyPadding)
+create_test_padding_SAME_class(TestWithGroup_AsyPadding)
+create_test_padding_SAME_class(TestWithInput1x1Filter1x1_AsyPadding)
+
+create_test_padding_VALID_class(TestConv2DOp_AsyPadding)
+create_test_padding_VALID_class(TestWithPad_AsyPadding)
+create_test_padding_VALID_class(TestWithStride_AsyPadding)
+create_test_padding_VALID_class(TestWithGroup_AsyPadding)
+create_test_padding_VALID_class(TestWithInput1x1Filter1x1_AsyPadding)
+
+create_test_channel_last_class(TestConv2DOp_AsyPadding)
+create_test_channel_last_class(TestWithPad_AsyPadding)
+create_test_channel_last_class(TestWithGroup_AsyPadding)
+create_test_channel_last_class(TestWith1x1_AsyPadding)
+create_test_channel_last_class(TestWithInput1x1Filter1x1_AsyPadding)
+
+create_test_fp16_class(TestConv2DOp_AsyPadding)
+create_test_fp16_class(TestWithPad_AsyPadding)
+create_test_fp16_class(TestWithStride_AsyPadding)
+create_test_fp16_class(TestWithGroup_AsyPadding)
+create_test_fp16_class(TestWithInput1x1Filter1x1_AsyPadding)
+
+if __name__ == "__main__":
+    unittest.main()