Polish RandomCropOp

Author: JiayiFeng

paddle/fluid/operators/random_crop_op.cc

@@ -74,8 +74,8 @@ namespace ops = paddle::operators;
 namespace f = paddle::framework;
 REGISTER_OPERATOR(random_crop, ops::RandomCropOp, ops::RandomCropOpMaker,
                   ops::RandomCropOpInferShape, f::EmptyGradOpMaker);
+
 template <typename T>
 using Kernel = ops::RandomCropKernel<paddle::platform::CPUDeviceContext, T>;
-
 REGISTER_OP_CPU_KERNEL(random_crop, Kernel<float>, Kernel<int>, Kernel<double>,
                        Kernel<uint8_t>, Kernel<int16_t>);

paddle/fluid/operators/random_crop_op.cu

@@ -0,0 +1,20 @@
+// 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.
+
+#include "paddle/fluid/operators/random_crop_op.h"
+
+template <typename T>
+using Kernel = ops::RandomCropKernel<paddle::platform::CUDADeviceContext, T>;
+REGISTER_OP_CUDA_KERNEL(random_crop, Kernel<float>, Kernel<int>, Kernel<double>,
+                        Kernel<uint8_t>, Kernel<int16_t>);

python/paddle/fluid/layers/nn.py

@@ -3990,15 +3990,16 @@ def _is_list_or_turple_(data):
     return out
 
 
-def random_crop(input, shape, seed=0):
+def random_crop(input, shape, seed=1):
     helper = LayerHelper("random_crop", **locals())
     dtype = helper.input_dtype()
     out = helper.create_tmp_variable(dtype)
     if isinstance(seed, int):
+        seed_value = seed
         seed = helper.create_global_variable(
             persistable=True, shape=[1], dtype="int32")
         helper.set_variable_initializer(
-            var=seed, initializer=Constant(value=seed))
+            var=seed, initializer=Constant(value=seed_value))
     elif not isinstance(seed, Variable):
         raise ValueError("'seed' must be a Variable or an int.")
     seed_out = helper.create_tmp_variable(dtype="int32")

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

@@ -336,8 +336,6 @@ def find_actual(target_name, fetch_list):
                 actual_t = np.array(actual)
                 expect = self.outputs[out_name]
                 expect_t = expect[0] if isinstance(expect, tuple) else expect
-                import pdb
-                pdb.set_trace()
                 self.assertTrue(
                     np.allclose(
                         actual_t, expect_t, atol=atol),