finished testing cpu bilinear_interp_op

paddle/fluid/operators/bilinear_interp_op.cc

@@ -27,13 +27,13 @@ class BilinearInterpOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of BilinearInterOp should not be null.");
 
-    auto dim_x = ctx->GetInputDim("Input");  // NCHW format
+    auto dim_x = ctx->GetInputDim("X");  // NCHW format
     int out_h = ctx->Attrs().Get<int>("out_h");
     int out_w = ctx->Attrs().Get<int>("out_w");
     PADDLE_ENFORCE_EQ(dim_x.size(), 4, "X's dimension must be 4");
 
     std::vector<int64_t> dim_out({dim_x[0], dim_x[1], out_h, out_w});
-    ctx->SetOutputDim("Output", framework::make_ddim(dim_out));
+    ctx->SetOutputDim("Out", framework::make_ddim(dim_out));
   }
 };
 
@@ -83,4 +83,5 @@ namespace ops = paddle::operators;
 REGISTER_OP(bilinear_interp, ops::BilinearInterpOp, ops::BilinearInterpOpMaker,
             bilinear_interp_grad, ops::BilinearInterpOpGrad);
 REGISTER_OP_CPU_KERNEL(bilinear_interp, ops::BilinearInterpKernel<float>);
-REGISTER_OP_CPU_KERNEL(bilinear_interp_grad, ops::BilinearInterpKernel<float>);
+REGISTER_OP_CPU_KERNEL(bilinear_interp_grad,
+                       ops::BilinearInterpGradKernel<float>);

paddle/fluid/operators/bilinear_interp_op.h

@@ -46,7 +46,7 @@ class BilinearInterpKernel : public framework::OpKernel<T> {
     T ratio_w = (out_w > 1) ? static_cast<T>(in_w - 1) / (out_w - 1) : 0.f;
 
     if (in_h == out_h && in_w == out_w) {
-      memcpy(output, input, product(input_t->dims()) * sizeof(T));
+      memcpy(output, input, input_t->numel() * sizeof(T));
     } else {
       for (int k = 0; k < batch_size; ++k) {  // loop for batches
         for (int i = 0; i < out_h; ++i) {     // loop for images
@@ -123,10 +123,10 @@ class BilinearInterpGradKernel : public framework::OpKernel<T> {
             const T* out_pos = &d_output[k * out_chw + i * out_w + j];
 
             for (int c = 0; c < channels; ++c) {  // loop for channels
-              in_pos[0] = h2lambda * w2lambda * out_pos[0];
-              in_pos[wid] = h2lambda * w1lambda * out_pos[0];
-              in_pos[hid * in_w] = h1lambda * w2lambda * out_pos[0];
-              in_pos[hid * in_w + wid] = h1lambda * w1lambda * out_pos[0];
+              in_pos[0] += h2lambda * w2lambda * out_pos[0];
+              in_pos[wid] += h2lambda * w1lambda * out_pos[0];
+              in_pos[hid * in_w] += h1lambda * w2lambda * out_pos[0];
+              in_pos[hid * in_w + wid] += h1lambda * w1lambda * out_pos[0];
               in_pos += in_hw;
               out_pos += out_hw;
             }

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

+#   Copyright (c) 2018 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.
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+def bilinear_interp_np(input, out_h, out_w):
+    batch_size, channel, in_h, in_w = input.shape
+    if out_h > 1:
+        ratio_h = (in_h - 1.0) / (out_h - 1.0)
+    else:
+        ratio_h = 0.0
+    if out_w > 1:
+        ratio_w = (in_w - 1.0) / (out_w - 1.0)
+    else:
+        ratio_w = 0.0
+
+    out = np.zeros((batch_size, channel, out_h, out_w))
+    for i in range(out_h):
+        h = int(ratio_h * i)
+        hid = 1 if h < in_h - 1 else 0
+        h1lambda = ratio_h * i - h
+        h2lambda = 1.0 - h1lambda
+        for j in range(out_w):
+            w = int(ratio_w * j)
+            wid = 1 if w < in_w - 1 else 0
+            w1lambda = ratio_w * j - w
+            w2lambda = 1.0 - w1lambda
+
+            out[:, :, i, j] = h2lambda*(w2lambda*input[:, :, h, w] +
+                                        w1lambda*input[:, :, h, w+wid]) + \
+                              h1lambda*(w2lambda*input[:, :, h+hid, w] +
+                                        w1lambda*input[:, :, h+hid, w+wid])
+    return out.astype("float32")
+
+
+class TestBilinearInterpOp(OpTest):
+    def setUp(self):
+        self.init_test_case()
+        self.op_type = "bilinear_interp"
+        input_np = np.random.random(self.input_shape).astype("float32")
+        output_np = bilinear_interp_np(input_np, self.out_h, self.out_w)
+
+        self.inputs = {'X': input_np}
+        self.attrs = {'out_h': self.out_h, 'out_w': self.out_w}
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out', in_place=True)
+
+    def init_test_case(self):
+        self.input_shape = [2, 3, 4, 4]
+        self.out_h = 2
+        self.out_w = 2
+
+
+class TestCase1(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 1
+        self.out_w = 1
+
+
+class TestCase2(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+
+
+if __name__ == "__main__":
+    unittest.main()