fix bug in pool/conv/conv_transpose: UpdatePaddingAndDilation, _get_padding_with_SAME and conv2dtranspose_forward_naive. (#20997)

* fix bug in pool/conv/conv_transpose:
1. It should be stride[i] not stride[0] in UpdatePaddingAndDilation;
2. fix bug of func  _get_padding_with_SAME in test_conv/conv_transpose_op.py;
3. fix bug of the computation process in function conv2dtranspose_forward_naive.
test=develop

* change test to make the data of different dimensions different. test=develop

paddle/fluid/operators/conv_op.h

@@ -83,10 +83,10 @@ inline void UpdatePaddingAndDilation(std::vector<int>* paddings,
         "Paddings size should be the same or twice as the input data size.");
   }
 
-  // when padding_desc is "VALID" or "SAME"
+  // when padding_algorithm is "VALID" or "SAME"
   if (padding_algorithm == "SAME") {
     for (size_t i = 0; i < data_dims.size(); ++i) {
-      int out_size = (data_dims[i] + strides[i] - 1) / strides[0];
+      int out_size = (data_dims[i] + strides[i] - 1) / strides[i];
       int pad_sum =
           std::max((out_size - 1) * strides[i] + ksize[i] - data_shape[i], 0);
       int pad_0 = pad_sum / 2;

paddle/fluid/operators/conv_transpose_op.h

@@ -18,6 +18,7 @@ limitations under the License. */
 #include <vector>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/conv_op.h"
 #include "paddle/fluid/operators/math/blas.h"
 #include "paddle/fluid/operators/math/concat_and_split.h"
 #include "paddle/fluid/operators/math/depthwise_conv.h"
@@ -77,47 +78,6 @@ static void Slice(const framework::ExecutionContext& context,
   Slice<DeviceContext, T, D>(context, input, out, begin_vec, end_vec, axes_vec);
 }
 
-inline void UpdatePaddingAndDilation(std::vector<int>* paddings,
-                                     std::vector<int>* dilation,
-                                     const std::string padding_algorithm,
-                                     const framework::DDim data_dims,
-                                     const std::vector<int>& strides,
-                                     const std::vector<int>& ksize) {
-  // set padding size == data_dims.size() * 2
-  auto data_shape = framework::vectorize<int>(data_dims);
-  if (paddings->size() == data_dims.size()) {
-    for (size_t i = 0; i < data_dims.size(); ++i) {
-      int copy_pad = *(paddings->begin() + 2 * i);
-      paddings->insert(paddings->begin() + 2 * i + 1, copy_pad);
-    }
-  } else {
-    PADDLE_ENFORCE_EQ(
-        data_dims.size() * 2, paddings->size(),
-        "Paddings size should be the same or twice as the input data size.");
-  }
-
-  // when padding_algorithm is "VALID" or "SAME"
-  if (padding_algorithm == "SAME") {
-    for (size_t i = 0; i < data_dims.size(); ++i) {
-      int out_size = (data_dims[i] + strides[i] - 1) / strides[0];
-      int pad_sum =
-          std::max((out_size - 1) * strides[i] + ksize[i] - data_shape[i], 0);
-      int pad_0 = pad_sum / 2;
-      int pad_1 = pad_sum - pad_0;
-      *(paddings->begin() + i * 2) = pad_0;
-      *(paddings->begin() + i * 2 + 1) = pad_1;
-
-      // dilation
-      *(dilation->begin() + i) = 1;
-    }
-
-  } else if (padding_algorithm == "VALID") {
-    for (auto it = paddings->begin(); it != paddings->end(); it++) {
-      *it = 0;
-    }
-  }
-}
-
 // Define Op classes in .h file so that other conv transpose
 // operator implementations can reuse the code.
 class Conv2DTransposeOpMaker : public framework::OpProtoAndCheckerMaker {

paddle/fluid/operators/pool_op.h

@@ -66,7 +66,7 @@ inline void UpdatePadding(std::vector<int>* paddings, const bool global_pooling,
   // set padding size == data_dims.size() * 2
   auto data_shape = framework::vectorize<int>(data_dims);
   if (paddings->size() == data_dims.size()) {
-    for (int i = 0; i < data_dims.size(); ++i) {
+    for (size_t i = 0; i < data_dims.size(); ++i) {
       int copy_pad = *(paddings->begin() + 2 * i);
       paddings->insert(paddings->begin() + 2 * i + 1, copy_pad);
     }
@@ -76,10 +76,10 @@ inline void UpdatePadding(std::vector<int>* paddings, const bool global_pooling,
         "Paddings size should be the same or twice as the pooling size.");
   }
 
-  // when padding_desc is "VALID" or "SAME"
+  // when padding_algorithm is "VALID" or "SAME"
   if (padding_algorithm == "SAME") {
     for (int i = 0; i < data_dims.size(); ++i) {
-      int out_size = (data_dims[i] + strides[i] - 1) / strides[0];
+      int out_size = (data_dims[i] + strides[i] - 1) / strides[i];
       int pad_sum =
           std::max((out_size - 1) * strides[i] + ksize[i] - data_shape[i], 0);
       int pad_0 = pad_sum / 2;

python/paddle/fluid/tests/unittests/test_conv2d_op.py

@@ -73,11 +73,7 @@ def conv2d_forward_naive(input,
         pad = [0, 0, 0, 0]
     elif padding_algorithm == "SAME":
         dilation = [1, 1]
-        input_data_shape = []
-        if data_format == "NCHW":
-            input_data_shape = input.shape[2:4]
-        elif data_format == "NHWC":
-            input_data_shape = input.shape[1:3]
+        input_data_shape = input.shape[2:4]
         pad = _get_padding_with_SAME(input_data_shape, ksize, stride)
 
     pad_h_0, pad_h_1 = pad[0], pad[0]
@@ -775,11 +771,11 @@ class TestConv2dOp_v2(OpTest):
 
     def init_test_case(self):
         self.pad = [0, 0]
-        self.stride = [1, 1]
+        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, 3, 3]
+        self.filter_size = [6, f_c, 4, 3]
 
     def init_dilation(self):
         self.dilations = [1, 1]

python/paddle/fluid/tests/unittests/test_conv2d_transpose_op.py

@@ -59,12 +59,8 @@ def conv2dtranspose_forward_naive(input_, filter_, attrs):
     if padding_algorithm == "VALID":
         pad = [0, 0, 0, 0]
     elif padding_algorithm == "SAME":
-        dilation = [1, 1]
-        input_data_shape = []
-        if attrs['data_format'] == "NCHW":
-            input_data_shape = input_.shape[2:4]
-        elif attrs['data_format'] == "NHWC":
-            input_data_shape = input_.shape[1:3]
+        dilations = [1, 1]
+        input_data_shape = input_.shape[2:4]
         pad = _get_padding_with_SAME(input_data_shape, ksize, stride)
 
     pad_h_0, pad_h_1 = pad[0], pad[0]
@@ -99,7 +95,7 @@ def conv2dtranspose_forward_naive(input_, filter_, attrs):
                             filter_[g * sub_in_c:(g + 1) * sub_in_c, k, :, :],
                             axis=0)
                         i1, i2 = i * stride[0], i * stride[0] + d_bolck_h
-                        j1, j2 = j * stride[0], j * stride[0] + d_bolck_h
+                        j1, j2 = j * stride[1], j * stride[1] + d_bolck_w
                         out[n, g * f_out_c + k, i1:i2:dilations[0], j1:j2:
                             dilations[1]] += tmp_out
 
@@ -231,12 +227,12 @@ class TestWithAsymmetricPad(TestConv2dTransposeOp):
 
 class TestWithSAMEPad(TestConv2dTransposeOp):
     def init_test_case(self):
-        self.stride = [1, 1]
-        self.dilations = [1, 1]
+        self.stride = [2, 1]
+        self.dilations = [1, 2]
         self.groups = 1
-        self.input_size = [2, 3, 5, 5]  # NCHW
+        self.input_size = [2, 3, 6, 5]  # NCHW
         f_c = self.input_size[1]
-        self.filter_size = [f_c, 6, 3, 3]
+        self.filter_size = [f_c, 6, 4, 3]
         self.padding_algorithm = 'SAME'
 
 
@@ -429,7 +425,7 @@ class TestCUDNNWithAsymmetricPad(TestWithAsymmetricPad):
 class TestCUDNNWithSAMEPad(TestWithSAMEPad):
     def init_test_case(self):
         self.pad = [1, 0, 1, 2]
-        self.stride = [1, 1]
+        self.stride = [1, 2]
         self.groups = 1
         self.dilations = [1, 1]
         self.input_size = [2, 3, 5, 5]  # NCHW

python/paddle/fluid/tests/unittests/test_conv3d_op.py

@@ -75,11 +75,7 @@ def conv3d_forward_naive(input,
         pad = [0, 0, 0, 0, 0, 0]
     elif padding_algorithm == "SAME":
         dilation = [1, 1, 1]
-        input_data_shape = []
-        if data_format == "NCDHW":
-            input_data_shape = input.shape[2:5]
-        elif data_format == "NDHWC":
-            input_data_shape = input.shape[1:4]
+        input_data_shape = input.shape[2:5]
         pad = _get_padding_with_SAME(input_data_shape, ksize, stride)
 
     pad_d_0, pad_d_1 = pad[0], pad[0]
@@ -597,11 +593,36 @@ class TestConv3dOp_2(OpTest):
 
 
 class TestConv3dOp_AsyPadding(TestConv3dOp_2):
+    def init_test_case(self):
+        self.stride = [1, 1, 2]
+        self.input_size = [2, 3, 4, 4, 4]  # NCDHW
+        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, 3]
+
     def init_paddings(self):
         self.pad = [1, 0, 1, 0, 0, 2]
         self.padding_algorithm = "EXPLICIT"
 
 
+class TestConv3dOp_DiffDataInDiffDim(TestConv3dOp_2):
+    def init_test_case(self):
+        self.stride = [1, 1, 2]
+        self.input_size = [2, 3, 4, 5, 5]  # NCDHW
+        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, 4, 3]
+
+    def init_paddings(self):
+        self.pad = [1, 0, 1, 0, 0, 2]
+        self.padding_algorithm = "EXPLICIT"
+
+
+create_test_padding_SAME_class(TestConv3dOp_DiffDataInDiffDim)
+create_test_padding_VALID_class(TestConv3dOp_DiffDataInDiffDim)
+create_test_channel_last_class(TestConv3dOp_DiffDataInDiffDim)
+
+
 class TestCase1_AsyPadding(TestConv3dOp_2):
     def init_test_case(self):
         self.stride = [1, 1, 1]

python/paddle/fluid/tests/unittests/test_conv3d_transpose_op.py

@@ -58,12 +58,8 @@ def conv3dtranspose_forward_naive(input_, filter_, attrs):
     if padding_algorithm == "VALID":
         pad = [0, 0, 0, 0, 0, 0]
     elif padding_algorithm == "SAME":
-        dilation = [1, 1, 1]
-        input_data_shape = []
-        if attrs['data_format'] == "NCHW":
-            input_data_shape = input_.shape[2:5]
-        elif attrs['data_format'] == "NHWC":
-            input_data_shape = input_.shape[1:4]
+        dilations = [1, 1, 1]
+        input_data_shape = input_.shape[2:5]
         pad = _get_padding_with_SAME(input_data_shape, ksize, stride)
 
     pad_d_0, pad_d_1 = pad[0], pad[0]
@@ -226,23 +222,23 @@ class TestWithAsymmetricPad(TestConv3dTransposeOp):
 
 class TestWithSAMEPad(TestConv3dTransposeOp):
     def init_test_case(self):
-        self.stride = [1, 1, 1]
-        self.dilations = [1, 1, 1]
+        self.stride = [1, 1, 2]
+        self.dilations = [1, 2, 1]
         self.groups = 1
-        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
+        self.input_size = [2, 3, 5, 5, 6]  # NCDHW
         f_c = self.input_size[1]
-        self.filter_size = [f_c, 6, 3, 3, 3]
+        self.filter_size = [f_c, 6, 3, 3, 4]
         self.padding_algorithm = 'SAME'
 
 
 class TestWithVALIDPad(TestConv3dTransposeOp):
     def init_test_case(self):
-        self.stride = [1, 1, 1]
+        self.stride = [2, 1, 1]
         self.dilations = [1, 1, 1]
         self.groups = 1
         self.input_size = [2, 3, 5, 5, 5]  # NCDHW
         f_c = self.input_size[1]
-        self.filter_size = [f_c, 6, 3, 3, 3]
+        self.filter_size = [f_c, 6, 3, 4, 3]
         self.padding_algorithm = 'VALID'
 
 
@@ -398,12 +394,12 @@ class TestCUDNNWithAsymmetricPad(TestWithAsymmetricPad):
                  "core is not compiled with CUDA")
 class TestCUDNNWithSAMEPad(TestWithSAMEPad):
     def init_test_case(self):
-        self.stride = [1, 1, 1]
-        self.dilations = [1, 1, 1]
+        self.stride = [1, 1, 2]
+        self.dilations = [1, 2, 1]
         self.groups = 1
         self.input_size = [2, 3, 5, 5, 5]  # NCDHW
         f_c = self.input_size[1]
-        self.filter_size = [f_c, 6, 3, 3, 3]
+        self.filter_size = [f_c, 6, 3, 4, 3]
         self.padding_algorithm = 'SAME'
 
     def init_op_type(self):

python/paddle/fluid/tests/unittests/test_pool2d_op.py

@@ -950,6 +950,20 @@ create_test_cudnn_padding_VALID_class(TestCase4_channel_last)
 create_test_cudnn_padding_VALID_class(TestCase5_channel_last)
 
 
+class TestCase1_strides(TestCase1):
+    def init_test_case(self):
+        self.ksize = [3, 3]
+        self.strides = [1, 2]
+
+    def init_shape(self):
+        self.shape = [2, 3, 4, 5]
+
+
+create_test_cudnn_class(TestCase1_strides)
+create_test_padding_SAME_class(TestCase1_strides)
+create_test_cudnn_padding_SAME_class(TestCase1_strides)
+
+
 # ----- test API
 class TestPool2dAPI(OpTest):
     def test_api(self):