Add unit test

paddle/operators/conv3dtranspose_op.cc

@@ -42,12 +42,12 @@ void Conv3DTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
   PADDLE_ENFORCE_EQ(in_dims[1], filter_dims[0],
                     "input and kernel input dimension should be equal.");
 
-  std::vector<int64_t> output_shape({in_dims[0], in_dims[1]});
-  for (size_t i = 0; i < filter_dims.size(); ++i) {
+  std::vector<int64_t> output_shape({in_dims[0], filter_dims[1]});
+  for (size_t i = 0; i < paddings.size(); ++i) {
     output_shape.push_back((in_dims[i + 2] - 1) * strides[i] +
                            filter_dims[i + 2]);
   }
-  ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
+  ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
 }
 
 Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(

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

@@ -43,8 +43,8 @@ class TestConv2dTransposeOp(OpTest):
         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)
+        output = conv2dtranspose_forward_naive(
+            input_, filter_, conv2dtranspose_param).astype("float32")
         # print 'deconv output py', output, output.shape
 
         self.inputs = {'Input': input_, 'Filter': filter_}

python/paddle/v2/framework/tests/test_conv3dtranspose_op.py

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+def conv3dtranspose_forward_naive(input_, filter_, conv3dtranspose_param):
+    # [2, 3, 5, 5, 5]
+    in_n, in_c, in_d, in_h, in_w = input_.shape
+    # [3, 6, 3, 3, 3]
+    f_c, out_c, f_d, f_h, f_w = filter_.shape
+    assert in_c == f_c
+
+    stride, pad = conv3dtranspose_param['stride'], conv3dtranspose_param['pad']
+    out_d = (in_d - 1) * stride[0] + f_d
+    out_h = (in_h - 1) * stride[1] + f_h
+    out_w = (in_w - 1) * stride[2] + f_w
+
+    out = np.zeros((in_n, out_c, out_d, out_h, out_w))
+
+    for n in range(in_n):
+        for d in range(in_d):
+            for i in range(in_h):
+                for j in range(in_w):
+                    input_masked = input_[n, :, d, i, j]  # (c)
+                    input_masked = np.reshape(input_masked, (in_c, 1, 1, 1))
+                    input_masked = np.tile(input_masked, (1, f_d, f_h, f_w))
+
+                    for k in range(out_c):
+                        tmp_out = np.sum(input_masked * filter_[:, k, :, :, :],
+                                         axis=0)
+                        d1, d2 = d * stride[0], d * stride[0] + f_d
+                        i1, i2 = i * stride[1], i * stride[1] + f_h
+                        j1, j2 = j * stride[2], j * stride[2] + f_w
+                        out[n, k, d1:d2, i1:i2, j1:j2] += tmp_out
+
+    return out
+
+
+class TestConv3dTransposeOp(OpTest):
+    def setUp(self):
+        # init as conv transpose
+        self.init_op_type()
+
+        # [2, 3, 5, 5, 5] -> kernel [3, 6, 3, 3, 3] -> output [2, 6, 7, 7, 7]
+        self.init_test_case()
+
+        conv3dtranspose_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 = conv3dtranspose_forward_naive(
+            input_, filter_, conv3dtranspose_param).astype("float32")
+        # 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, 0]
+        self.stride = [1, 1, 1]
+        self.dilations = [1, 1, 1]
+        self.input_size = [2, 3, 5, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3, 3]
+
+    def init_op_type(self):
+        self.op_type = "conv3dtranspose"
+
+
+if __name__ == '__main__':
+    unittest.main()