add unittest for nearest_neighbor_interp_op

paddle/fluid/operators/nearest_neighbor_interp_op.cc

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 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

paddle/fluid/operators/nearest_neighbor_interp_op.cu

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 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

paddle/fluid/operators/nearest_neighbor_interp_op.h

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 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
@@ -37,12 +37,12 @@ class NearestNeighborInterpKernel : public framework::OpKernel<T> {
       out_w = out_size_data[1];
     }
 
-    const int in_n = input->dims()[0];
-    const int in_c = input->dims()[1];
+    const int n = input->dims()[0];
+    const int c = input->dims()[1];
     const int in_h = input->dims()[2];
     const int in_w = input->dims()[3];
 
-    output->mutable_data<T>({in_n, in_c, out_h, out_w}, ctx.GetPlace());
+    output->mutable_data<T>({n, c, out_h, out_w}, ctx.GetPlace());
     auto& device_ctx =
         ctx.template device_context<platform::CPUDeviceContext>();
     math::SetConstant<platform::CPUDeviceContext, T> zero;
@@ -61,11 +61,11 @@ class NearestNeighborInterpKernel : public framework::OpKernel<T> {
     auto input_t = EigenTensor<T, 4>::From(*input);
     auto output_t = EigenTensor<T, 4>::From(*output);
     for (int k = 0; k < out_h; k++) {  // loop for images
+      int in_k = static_cast<int>(round(ratio_h * k));
       for (int l = 0; l < out_w; l++) {
-        int in_k = static_cast<int>(round(ratio_h * k));
         int in_l = static_cast<int>(round(ratio_w * l));
-        for (int i = 0; i < in_n; i++) {    // loop for batches
-          for (int j = 0; j < in_c; j++) {  // loop for channels
+        for (int i = 0; i < n; i++) {    // loop for batches
+          for (int j = 0; j < c; j++) {  // loop for channels
             output_t(i, j, k, l) = input_t(i, j, in_k, in_l);
           }
         }
@@ -78,6 +78,7 @@ template <typename T>
 class NearestNeighborInterpGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<Tensor>("X");
     auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
 
@@ -90,11 +91,12 @@ class NearestNeighborInterpGradKernel : public framework::OpKernel<T> {
       out_w = out_size_data[1];
     }
 
-    const int in_n = input_grad->dims()[0];
-    const int in_c = input_grad->dims()[1];
-    const int in_h = input_grad->dims()[2];
-    const int in_w = input_grad->dims()[3];
+    const int n = input->dims()[0];
+    const int c = input->dims()[1];
+    const int in_h = input->dims()[2];
+    const int in_w = input->dims()[3];
 
+    input_grad->mutable_data<T>({n, c, in_h, in_w}, ctx.GetPlace());
     auto& device_ctx =
         ctx.template device_context<platform::CPUDeviceContext>();
     math::SetConstant<platform::CPUDeviceContext, T> zero;
@@ -113,11 +115,11 @@ class NearestNeighborInterpGradKernel : public framework::OpKernel<T> {
     auto input_grad_t = EigenTensor<T, 4>::From(*input_grad);
     auto output_grad_t = EigenTensor<T, 4>::From(*output_grad);
     for (int k = 0; k < out_h; k++) {  // loop for images
+      int in_k = static_cast<int>(round(ratio_h * k));
       for (int l = 0; l < out_w; l++) {
-        int in_k = static_cast<int>(round(ratio_h * k));
         int in_l = static_cast<int>(round(ratio_w * l));
-        for (int i = 0; i < in_n; i++) {    // loop for batches
-          for (int j = 0; j < in_c; j++) {  // loop for channels
+        for (int i = 0; i < n; i++) {    // loop for batches
+          for (int j = 0; j < c; j++) {  // loop for channels
             input_grad_t(i, j, in_k, in_l) += output_grad_t(i, j, k, l);
           }
         }

python/paddle/fluid/tests/unittests/test_nearest_neighbor_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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from op_test import OpTest
+import paddle.fluid.core as core
+
+
+def nearest_neighbor_interp_np(X, out_h, out_w, out_size=None):
+    """nearest neighbor interpolation implement in shape [N, C, H, W]"""
+    if out_size is not None:
+        out_h = out_size[0]
+        out_w = out_size[1]
+    n, c, in_h, in_w = X.shape
+
+    ratio_h = ratio_w = 0.0
+    if out_h > 1:
+        ratio_h = (in_h - 1.0) / (out_h - 1.0)
+    if out_w > 1:
+        ratio_w = (in_w - 1.0) / (out_w - 1.0)
+
+    out = np.zeros((n, c, out_h, out_w))
+    for i in range(out_h):
+        in_i = int(round(ratio_h * i))
+        for j in range(out_w):
+            in_j = int(round(ratio_w * j))
+            out[:, :, i, j] = X[:, :, in_i, in_j]
+
+    return out.astype(X.dtype)
+
+
+class TestBilinearInterpOp(OpTest):
+    def setUp(self):
+        self.out_size = None
+        self.init_test_case()
+        self.op_type = "nearest_neighbor_interp"
+        input_np = np.random.random(self.input_shape).astype("float32")
+        output_np = nearest_neighbor_interp_np(input_np, self.out_h, self.out_w,
+                                               self.out_size)
+        self.inputs = {'X': input_np}
+        if self.out_size is not None:
+            self.inputs['OutSize'] = self.out_size
+        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
+        self.out_size = np.array([3, 3]).astype("int32")
+
+
+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
+
+
+class TestCase3(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.input_shape = [1, 1, 128, 64]
+        self.out_h = 64
+        self.out_w = 128
+
+
+class TestCase4(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 1
+        self.out_w = 1
+        self.out_size = np.array([2, 2]).astype("int32")
+
+
+class TestCase5(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+        self.out_size = np.array([11, 11]).astype("int32")
+
+
+class TestCase6(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.input_shape = [1, 1, 128, 64]
+        self.out_h = 64
+        self.out_w = 128
+        self.out_size = np.array([65, 129]).astype("int32")
+
+
+class TestBilinearInterpOpUint8(OpTest):
+    def setUp(self):
+        self.out_size = None
+        self.init_test_case()
+        self.op_type = "nearest_neighbor_interp"
+        input_np = np.random.randint(
+            low=0, high=256, size=self.input_shape).astype("uint8")
+        output_np = nearest_neighbor_interp_np(input_np, self.out_h, self.out_w,
+                                               self.out_size)
+        self.inputs = {'X': input_np}
+        if self.out_size is not None:
+            self.inputs['OutSize'] = self.out_size
+        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_with_place(place=core.CPUPlace(), atol=1)
+
+    def init_test_case(self):
+        self.input_shape = [1, 3, 9, 6]
+        self.out_h = 10
+        self.out_w = 9
+
+
+class TestCase1Uint8(TestBilinearInterpOpUint8):
+    def init_test_case(self):
+        self.input_shape = [2, 3, 128, 64]
+        self.out_h = 120
+        self.out_w = 50
+
+
+class TestCase2Uint8(TestBilinearInterpOpUint8):
+    def init_test_case(self):
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 5
+        self.out_w = 13
+        self.out_size = np.array([6, 15]).astype("int32")
+
+
+if __name__ == "__main__":
+    unittest.main()