Merge pull request #4739 from zchen0211/develop

deconv op implementing ...

paddle/operators/conv2dtranspose_op.cc

@@ -0,0 +1,107 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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/operators/conv2dtranspose_op.h"
+
+namespace paddle {
+namespace operators {
+
+void Conv2DTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
+  PADDLE_ENFORCE(ctx->HasInput("Input"),
+                 "Input(Input) of Conv2DTransposeOp should not be null.");
+  PADDLE_ENFORCE(ctx->HasInput("Filter"),
+                 "Input(Filter) of Conv2DTransposeOp should not be null.");
+  PADDLE_ENFORCE(ctx->HasOutput("Output"),
+                 "Output(Output) of Conv2DTransposeOp should not be null.");
+
+  auto in_dims = ctx->GetInputDim("Input");
+  auto filter_dims = ctx->GetInputDim("Filter");
+  std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
+  std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
+
+  for (size_t i = 0; i < paddings.size(); ++i) {
+    PADDLE_ENFORCE_EQ(paddings[i], 0,
+                      "No Padding allowed in conv transpose op.");
+  }
+
+  PADDLE_ENFORCE_EQ(in_dims.size(), 4,
+                    "Conv2DTransposeOp input should be 4-D tensor.");
+  PADDLE_ENFORCE_EQ(filter_dims.size(), 4,
+                    "Conv2DTransposeOp filter should be 4-D tensor.");
+  PADDLE_ENFORCE_EQ(in_dims[1], filter_dims[0],
+                    "input and kernel input dimension should be equal.");
+
+  auto output_height = (in_dims[2] - 1) * strides[0] + filter_dims[2];
+  auto output_width = (in_dims[3] - 1) * strides[1] + filter_dims[3];
+  ctx->SetOutputDim("Output",
+                    {in_dims[0], filter_dims[1], output_height, output_width});
+}
+
+Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(
+    framework::OpProto* proto, framework::OpAttrChecker* op_checker)
+    : OpProtoAndCheckerMaker(proto, op_checker) {
+  AddInput(
+      "Input",
+      "(Tensor) The input tensor of convolution transpose operator. "
+      "The format of input tensor is NCHW. Where N is batch size, C is the "
+      "number of input channels, H and W is the height and width of image.");
+  AddInput("Filter",
+           "(Tensor) The filter tensor of convolution transpose operator."
+           "The format of the filter tensor is CMHW, where C is the number of "
+           "output image channels, M is the number of input image channels, "
+           "H and W is height and width of filter. "
+           "We enforce groups number == 1 and padding == 0 in "
+           "convolution transpose Scenario.");
+  AddOutput("Output",
+            "(Tensor) The output tensor of convolution transpose operator."
+            "The format of output tensor is also NCHW.");
+  AddAttr<std::vector<int>>("strides",
+                            "strides of convolution transpose operator.")
+      .SetDefault({1, 1});
+  AddAttr<std::vector<int>>("paddings",
+                            "paddings of convolution transpose operator.")
+      .SetDefault({0, 0});
+  AddComment(R"DOC(
+The convolution transpose operation calculates the output based on the input, filter
+and strides, paddings, groups parameters. The size of each dimension of the
+parameters is checked in the infer-shape.
+)DOC");
+}
+
+void Conv2DTransposeOpGrad::InferShape(
+    framework::InferShapeContext* ctx) const {
+  auto in_dims = ctx->GetInputDim("Input");
+  auto filter_dims = ctx->GetInputDim("Filter");
+  if (ctx->HasOutput(framework::GradVarName("Input"))) {
+    ctx->SetOutputDim(framework::GradVarName("Input"), in_dims);
+  }
+  if (ctx->HasOutput(framework::GradVarName("Filter"))) {
+    ctx->SetOutputDim(framework::GradVarName("Filter"), filter_dims);
+  }
+}
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(conv2dtranspose, ops::Conv2DTransposeOp,
+            ops::Conv2DTransposeOpMaker, conv2dtranspose_grad,
+            ops::Conv2DTransposeOpGrad);
+
+REGISTER_OP_CPU_KERNEL(
+    conv2dtranspose,
+    ops::GemmConv2DTransposeKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    conv2dtranspose_grad,
+    ops::GemmConv2DTransposeGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/conv2dtranspose_op.cu

@@ -0,0 +1,24 @@
+/* Copyright (c) 2016 PaddlePaddle Authors All Rights Reserve.
+
+   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/operators/conv2dtranspose_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_GPU_KERNEL(
+    conv2dtranspose,
+    ops::GemmConv2DTransposeKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    conv2dtranspose_grad,
+    ops::GemmConv2DTransposeGradKernel<paddle::platform::GPUPlace, float>);

python/paddle/v2/framework/tests/test_conv2dtranspose_op.py

@@ -0,0 +1,102 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+def conv2dtranspose_forward_naive(input_, filter_, conv2dtranspose_param):
+    # [2, 3, 5, 5]
+    in_n, in_c, in_h, in_w = input_.shape
+    # [3, 6, 3, 3]
+    f_c, out_c, f_h, f_w = filter_.shape
+    assert in_c == f_c
+
+    stride, pad = conv2dtranspose_param['stride'], conv2dtranspose_param['pad']
+    out_h = (in_h - 1) * stride[0] + f_h
+    out_w = (in_w - 1) * stride[1] + f_w
+
+    out = np.zeros((in_n, out_c, out_h, out_w))
+
+    for n in range(in_n):
+        for i in range(in_h):
+            for j in range(in_w):
+                input_masked = input_[n, :, i, j]  # (c)
+                input_masked = np.reshape(input_masked, (in_c, 1, 1))
+                input_masked = np.tile(input_masked, (1, f_h, f_w))
+
+                for k in range(out_c):
+                    tmp_out = np.sum(input_masked * filter_[:, k, :, :], axis=0)
+                    i1, i2 = i * stride[0], i * stride[0] + f_h
+                    j1, j2 = j * stride[0], j * stride[0] + f_w
+                    out[n, k, i1:i2, j1:j2] += tmp_out
+
+    return out
+
+
+class TestConv2dTransposeOp(OpTest):
+    def setUp(self):
+        # init as conv transpose
+        self.init_op_type()
+
+        # [2, 3, 5, 5] -> kernel [3, 6, 3, 3] -> output [2, 6, 7, 7]
+        self.init_test_case()
+
+        conv2dtranspose_param = {'stride': self.stride, 'pad': self.pad}
+        input_ = np.random.random(self.input_size).astype("float32")
+        filter_ = np.random.random(self.filter_size).astype("float32")
+        output = conv2dtranspose_forward_naive(input_, filter_,
+                                               conv2dtranspose_param)
+        # print 'deconv output py', output, output.shape
+
+        self.inputs = {'Input': input_, 'Filter': filter_}
+        self.attrs = {
+            'strides': self.stride,
+            'paddings': self.pad,
+            # 'dilations': self.dilations
+        }
+        self.outputs = {'Output': output}
+
+    def test_check_output(self):
+        print 'check output here'
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(
+            set(['Input', 'Filter']), 'Output', max_relative_error=0.05)
+
+    def test_check_grad_no_filter(self):
+        self.check_grad(
+            ['Input'],
+            'Output',
+            max_relative_error=0.05,
+            no_grad_set=set(['Filter']))
+
+    def test_check_grad_no_input(self):
+        self.check_grad(
+            ['Filter'],
+            'Output',
+            max_relative_error=0.05,
+            no_grad_set=set(['Input']))
+
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.dilations = [1, 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 = "conv2dtranspose"
+
+
+"""
+class TestCudnn(TestConv2dOp):
+    def init_group(self):
+        self.groups = 1
+
+    def init_op_type(self):
+        self.op_type = "conv_cudnn"
+"""
+
+if __name__ == '__main__':
+    unittest.main()