diff --git a/paddle/fluid/operators/conv_transpose_op_npu.cc b/paddle/fluid/operators/conv_transpose_op_npu.cc
new file mode 100644
index 0000000000000000000000000000000000000000..6cb431b873a35070d59cafcae7d2a27b8d7e9547
--- /dev/null
+++ b/paddle/fluid/operators/conv_transpose_op_npu.cc
@@ -0,0 +1,114 @@
+/* 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. */
+
+#include "paddle/fluid/operators/conv_transpose_op.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class Conv2DTransposeNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    // input
+    const Tensor* input = context.Input<Tensor>("Input");
+    const Tensor* filter = context.Input<Tensor>("Filter");
+    // output
+    Tensor* output = context.Output<Tensor>("Output");
+    output->mutable_data<T>(context.GetPlace());
+    // attr
+    std::vector<int> output_padding =
+        context.Attr<std::vector<int>>("output_padding");
+    const std::vector<int> stride = context.Attr<std::vector<int>>("strides");
+    std::vector<int> padding = context.Attr<std::vector<int>>("paddings");
+    std::vector<int> dilation = context.Attr<std::vector<int>>("dilations");
+    const std::string data_format = context.Attr<std::string>("data_format");
+    int groups = context.Attr<int>("groups");
+    const std::string padding_algorithm =
+        context.Attr<std::string>("padding_algorithm");
+
+    // npu stream
+    auto stream =
+        context.template device_context<platform::NPUDeviceContext>().stream();
+
+    // check dimension
+    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(&padding, &dilation, padding_algorithm,
+                             in_data_dims, stride, ksize);
+
+    // construct NPU attr
+    std::vector<int> strides(4, 1);
+    std::vector<int> dilations(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[1] = stride[0];
+      strides[2] = stride[1];
+      dilations[1] = dilation[0];
+      dilations[2] = dilation[1];
+    } else {
+      strides[2] = stride[0];
+      strides[3] = stride[1];
+      dilations[2] = dilation[0];
+      dilations[3] = dilation[1];
+    }
+
+    for (auto i = output_padding.size(); i < 4; ++i) {
+      output_padding.insert(output_padding.begin(), 0);
+    }
+    auto output_dim_vec = framework::vectorize(output_tensor.dims());
+    // CANN OP
+    const auto& runner =
+        NpuOpRunner("Conv2DTransposeD", {input_tensor, *filter},
+                    {output_tensor}, {{"input_size", output_dim_vec},
+                                      {"strides", strides},
+                                      {"dilations", dilations},
+                                      {"output_padding", output_padding},
+                                      {"groups", groups},
+                                      {"pads", padding},
+                                      {"data_format", data_format}});
+    runner.Run(stream);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+// conv2d
+REGISTER_OP_NPU_KERNEL(conv2d_transpose, ops::Conv2DTransposeNPUKernel<float>,
+                       ops::Conv2DTransposeNPUKernel<plat::float16>);
diff --git a/python/paddle/fluid/tests/unittests/npu/test_conv2d_transpose_op_npu.py b/python/paddle/fluid/tests/unittests/npu/test_conv2d_transpose_op_npu.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cb94cb98f1e5d8c2d530d89757ea548ce505c7b
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/npu/test_conv2d_transpose_op_npu.py
@@ -0,0 +1,698 @@
+#   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.nn as nn
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from op_test import OpTest, skip_check_grad_ci
+
+from test_conv2d_transpose_op import conv2dtranspose_forward_naive
+
+paddle.enable_static()
+
+
+@skip_check_grad_ci(
+    reason='''Inference only, it doesn't need to call check_grad.''')
+class TestConv2DTransposeOp(OpTest):
+    def set_npu(self):
+        self.__class__.use_npu = True
+        self.place = paddle.NPUPlace(0)
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+    def init_data_format(self):
+        self.data_format = "NCHW"
+
+    def setUp(self):
+        self.init_op_type()
+        self.init_dtype()
+        self.set_npu()
+        self.init_data_format()
+        self.output_padding = []
+        self.pad = [0, 0]
+        self.padding_algorithm = "EXPLICIT"
+        self.init_test_case()
+        self.output_size = None
+
+        input_ = np.random.random(self.input_size).astype(self.dtype)
+        filter_ = np.random.random(self.filter_size).astype(self.dtype)
+
+        self.inputs = {'Input': input_, 'Filter': filter_}
+        self.attrs = {
+            'strides': self.stride,
+            'paddings': self.pad,
+            'padding_algorithm': self.padding_algorithm,
+            'groups': self.groups,
+            'dilations': self.dilations,
+            'use_cudnn': False,
+            'is_test': False,
+            'use_mkldnn': False,
+            'data_format': self.data_format
+        }
+        if self.output_size is not None:
+            self.attrs['output_size'] = self.output_size
+
+        if len(self.output_padding) > 0:
+            self.attrs['output_padding'] = self.output_padding
+        output = conv2dtranspose_forward_naive(input_, filter_,
+                                               self.attrs).astype(self.dtype)
+
+        self.outputs = {'Output': output}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, atol=1e-2)
+
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+    def init_op_type(self):
+        self.op_type = "conv2d_transpose"
+
+
+class TestWithSymmetricPad_FP32(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestWithSymmetricPad(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+
+class TestWithAsymmetricPad_FP32(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 0, 1, 2]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestWithAsymmetricPad(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 0, 1, 2]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+
+class TestWithSAMEPad_FP32(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.stride = [2, 1]
+        self.dilations = [1, 2]
+        self.groups = 1
+        self.input_size = [2, 3, 6, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 4, 3]
+        self.padding_algorithm = 'SAME'
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestWithSAMEPad(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.stride = [2, 1]
+        self.dilations = [1, 2]
+        self.groups = 1
+        self.input_size = [2, 3, 6, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 4, 3]
+        self.padding_algorithm = 'SAME'
+
+
+class TestWithVALIDPad_FP32(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.padding_algorithm = 'VALID'
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestWithVALIDPad(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.padding_algorithm = 'VALID'
+
+
+class TestWithGroups_FP32(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 2
+        self.input_size = [2, 4, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 3, 3, 3]
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestWithGroups(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 2
+        self.input_size = [2, 4, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 3, 3, 3]
+
+
+class TestWithStride_FP32(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [2, 2]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestWithStride(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [2, 2]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+
+class TestWithDilation_FP32(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.groups = 1
+        self.dilations = [2, 2]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestWithDilation(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.groups = 1
+        self.dilations = [2, 2]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+
+class TestWithEvenUpsample_FP32(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [2, 2]
+        self.stride = [2, 2]
+        self.groups = 1
+        self.dilations = [1, 1]
+        self.output_size = [14, 14]
+        self.input_size = [2, 3, 7, 7]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 5, 5]
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestWithEvenUpsample(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [2, 2]
+        self.stride = [2, 2]
+        self.groups = 1
+        self.dilations = [1, 1]
+        self.output_size = [14, 14]
+        self.input_size = [2, 3, 7, 7]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 5, 5]
+
+
+class TestWithEvenUpsampleOutputPadding_FP32(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [2, 2]
+        self.stride = [2, 2]
+        self.groups = 1
+        self.dilations = [1, 1]
+        self.output_padding = [1, 1]
+        self.input_size = [2, 3, 7, 7]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 5, 5]
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestWithEvenUpsampleOutputPadding(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [2, 2]
+        self.stride = [2, 2]
+        self.groups = 1
+        self.dilations = [1, 1]
+        self.output_padding = [1, 1]
+        self.input_size = [2, 3, 7, 7]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 5, 5]
+
+
+class Test_NHWC_FP32(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 5, 5, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.data_format = 'NHWC'
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class Test_NHWC(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 5, 5, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.data_format = 'NHWC'
+
+
+class TestWithSymmetricPad_NHWC_FP32(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 5, 5, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.data_format = 'NHWC'
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestWithSymmetricPad_NHWC(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 5, 5, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.data_format = 'NHWC'
+
+
+class TestWithAsymmetricPad_NHWC_FP32(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 0, 1, 2]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 5, 5, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.data_format = 'NHWC'
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestWithAsymmetricPad_NHWC(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 0, 1, 2]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 5, 5, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.data_format = 'NHWC'
+
+
+class TestWithGroups_NHWC_FP32(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 2
+        self.input_size = [2, 5, 5, 4]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 3, 3, 3]
+        self.data_format = 'NHWC'
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestWithGroups_NHWC(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 2
+        self.input_size = [2, 5, 5, 4]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 3, 3, 3]
+        self.data_format = 'NHWC'
+
+
+class TestWithStride_NHWC_FP32(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [2, 2]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 5, 5, 3]  # NCHW
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.data_format = 'NHWC'
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestWithStride_NHWC(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [2, 2]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 5, 5, 3]  # NCHW
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.data_format = 'NHWC'
+
+
+class TestWithDilation_NHWC_FP32(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.groups = 1
+        self.dilations = [2, 2]
+        self.input_size = [2, 5, 5, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.data_format = 'NHWC'
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestWithDilation_NHWC(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.groups = 1
+        self.dilations = [2, 2]
+        self.input_size = [2, 5, 5, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.data_format = 'NHWC'
+
+
+class TestWithEvenUpsample_NHWC_FP32(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [2, 2]
+        self.stride = [2, 2]
+        self.groups = 1
+        self.dilations = [1, 1]
+        self.output_size = [14, 14]
+        self.input_size = [2, 7, 7, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 5, 5]
+        self.data_format = 'NHWC'
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestWithEvenUpsample_NHWC(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [2, 2]
+        self.stride = [2, 2]
+        self.groups = 1
+        self.dilations = [1, 1]
+        self.output_size = [14, 14]
+        self.input_size = [2, 7, 7, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 5, 5]
+        self.data_format = 'NHWC'
+
+
+class TestWithEvenUpsample_NHWC_output_padding_FP32(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [2, 2]
+        self.stride = [2, 2]
+        self.groups = 1
+        self.dilations = [1, 1]
+        self.output_padding = [1, 1]
+        self.input_size = [2, 7, 7, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 5, 5]
+        self.data_format = 'NHWC'
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestWithEvenUpsample_NHWC_output_padding(TestConv2DTransposeOp):
+    def init_test_case(self):
+        self.pad = [2, 2]
+        self.stride = [2, 2]
+        self.groups = 1
+        self.dilations = [1, 1]
+        self.output_padding = [1, 1]
+        self.input_size = [2, 7, 7, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 5, 5]
+        self.data_format = 'NHWC'
+
+
+class TestConv2DTransposeAPI(unittest.TestCase):
+    def test_case1(self):
+        data1 = fluid.layers.data(
+            name='data1', shape=[3, 5, 5], dtype='float32')
+        data2 = fluid.layers.data(
+            name='data2', shape=[5, 5, 3], dtype='float32')
+        out1 = fluid.layers.conv2d_transpose(
+            input=data1,
+            groups=1,
+            num_filters=6,
+            filter_size=3,
+            data_format='NCHW')
+        out2 = fluid.layers.conv2d_transpose(
+            input=data2,
+            groups=1,
+            num_filters=6,
+            filter_size=3,
+            data_format='NHWC')
+        out3 = fluid.layers.conv2d_transpose(
+            input=data1,
+            groups=1,
+            num_filters=6,
+            filter_size=3,
+            padding=[[0, 0], [1, 1], [1, 1], [0, 0]],
+            data_format='NHWC')
+        out4 = fluid.layers.conv2d_transpose(
+            input=data1,
+            groups=3,
+            num_filters=6,
+            filter_size=3,
+            padding=[[0, 0], [0, 0], [2, 1], [0, 0]],
+            data_format='NCHW')
+        out5 = fluid.layers.conv2d_transpose(
+            input=data2,
+            groups=1,
+            num_filters=6,
+            filter_size=3,
+            padding='SAME',
+            data_format='NCHW')
+        out6 = fluid.layers.conv2d_transpose(
+            input=data1,
+            groups=1,
+            num_filters=6,
+            filter_size=3,
+            padding='VALID',
+            data_format='NHWC')
+        out7 = fluid.layers.conv2d_transpose(
+            input=data1,
+            groups=1,
+            num_filters=6,
+            output_size=[7, 7],
+            padding=[0, 0],
+            data_format='NHWC')
+
+        data1_np = np.random.random((2, 3, 5, 5)).astype("float32")
+        data2_np = np.random.random((2, 5, 5, 3)).astype("float32")
+
+        place = core.NPUPlace(0)
+        exe = fluid.Executor(place)
+        exe.run(fluid.default_startup_program())
+        results = exe.run(
+            fluid.default_main_program(),
+            feed={"data1": data1_np,
+                  "data2": data2_np},
+            fetch_list=[out1, out2, out3, out4, out5, out6, out7],
+            return_numpy=True)
+        self.assertIsNotNone(results[0])
+        self.assertIsNotNone(results[1])
+        self.assertIsNotNone(results[2])
+        self.assertIsNotNone(results[3])
+        self.assertIsNotNone(results[4])
+        self.assertIsNotNone(results[5])
+        self.assertIsNotNone(results[6])
+
+
+class TestConv2DTransposeOpException(unittest.TestCase):
+    def test_exception(self):
+        data = fluid.layers.data(name='data', shape=[3, 5, 5], dtype="float32")
+
+        def attr_data_format():
+            out = fluid.layers.conv2d_transpose(
+                input=data,
+                groups=1,
+                num_filters=6,
+                filter_size=3,
+                data_format="NCDHW")
+
+        self.assertRaises(ValueError, attr_data_format)
+
+        def attr_padding_str():
+            out = fluid.layers.conv2d_transpose(
+                input=data,
+                groups=1,
+                num_filters=6,
+                filter_size=3,
+                padding='Vald')
+
+        self.assertRaises(ValueError, attr_padding_str)
+
+        def attr_padding_list():
+            out = fluid.layers.conv2d_transpose(
+                input=data,
+                groups=1,
+                num_filters=6,
+                filter_size=3,
+                padding=[[1, 1], [1, 1], [0, 0], [0, 0]])
+
+        self.assertRaises(ValueError, attr_padding_list)
+
+        def attr_padding_with_data_format():
+            out = fluid.layers.conv2d_transpose(
+                input=data,
+                groups=1,
+                num_filters=6,
+                filter_size=3,
+                padding=[[1, 1], [0, 0], [0, 0], [1, 1]],
+                data_format='NHWC')
+
+        self.assertRaises(ValueError, attr_padding_with_data_format)
+
+        error_input = fluid.layers.data(
+            name='error_data', shape=[1], dtype="float32")
+
+        def error_input_size():
+            out = fluid.layers.conv2d_transpose(
+                input=error_input, groups=1, num_filters=6, filter_size=3)
+
+        self.assertRaises(ValueError, error_input_size)
+
+        def error_groups():
+            out = fluid.layers.conv2d_transpose(
+                input=data,
+                groups=0,
+                num_filters=6,
+                filter_size=3,
+                data_format='NHWC')
+
+        self.assertRaises(ValueError, error_groups)
+
+
+class TestConv2DTransposeRepr(unittest.TestCase):
+    def test_case(self):
+        paddle.disable_static(paddle.NPUPlace(0))
+        x_var = paddle.uniform((2, 4, 8, 8), dtype='float32', min=-1., max=1.)
+        conv = nn.Conv2DTranspose(4, 6, (3, 3), output_padding=1, stride=2)
+        print(conv)
+        y_var = conv(x_var)
+        y_np = y_var.numpy()
+        self.assertIsNotNone(y_np)
+        paddle.enable_static()
+
+
+if __name__ == '__main__':
+    unittest.main()