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104 lines
4.1 KiB
104 lines
4.1 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import paddle.fluid.core as core
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import paddle.fluid as fluid
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class TestElementWiseAddOp(unittest.TestCase):
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def __assert_close(self, tensor, np_array, msg, atol=1e-4):
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self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg)
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def check_forward_backward(self):
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def test_with_place(place):
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out_grad = np.random.random_sample(self.x.shape).astype(np.float32)
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x_grad = out_grad
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sum_axis = range(0, len(self.x.shape))
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del sum_axis[self.axis]
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y_grad = np.sum(out_grad, axis=tuple(sum_axis))
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var_dict = locals()
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var_dict['y'] = self.y
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var_dict['x'] = self.x
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var_dict['out'] = self.out
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var_dict['y@GRAD'] = y_grad
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var_dict['x@GRAD'] = x_grad
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var_dict['out@GRAD'] = out_grad
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var_names = ['x', 'y', 'out', 'y@GRAD', 'x@GRAD', 'out@GRAD']
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ground_truth = {name: var_dict[name] for name in var_names}
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program = fluid.Program()
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with fluid.program_guard(program):
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block = program.global_block()
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for name in ground_truth:
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block.create_var(
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name=name,
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dtype='float32',
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shape=ground_truth[name].shape)
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elementwise_add_op = block.append_op(
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type="elementwise_add",
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inputs={
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"X": block.var('x'),
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"Y": block.var('y'),
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},
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outputs={"Out": block.var('out'), },
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attrs={"axis": self.axis, })
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# generate backward op_desc
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grad_op_desc_list, op_grad_to_var = core.get_grad_op_desc(
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elementwise_add_op.desc, set(), [])
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grad_op_desc = grad_op_desc_list[0]
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new_op_desc = block.desc.append_op()
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new_op_desc.copy_from(grad_op_desc)
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for var_name in grad_op_desc.output_arg_names():
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block.desc.var(var_name.encode("ascii"))
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grad_op_desc.infer_var_type(block.desc)
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grad_op_desc.infer_shape(block.desc)
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for arg in grad_op_desc.output_arg_names():
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grad_var = block.desc.find_var(arg.encode("ascii"))
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grad_var.set_dtype(core.VarDesc.VarType.FP32)
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exe = fluid.Executor(place)
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out = exe.run(program,
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feed={
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name: var_dict[name]
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for name in ['x', 'y', 'out@GRAD']
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},
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fetch_list=['x@GRAD', 'y@GRAD'])
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self.__assert_close(x_grad, out[0], "x@GRAD")
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self.__assert_close(y_grad, out[1], "y@GRAD", atol=1.4)
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places = [core.CPUPlace()]
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if core.is_compiled_with_cuda() and core.op_support_gpu(
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"elementwise_add"):
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places.append(core.CUDAPlace(0))
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for place in places:
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test_with_place(place)
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def test_check_forward_backward_with_scale_and_bias(self):
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np.random.seed(123)
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self.x = np.random.random((4, 32, 220, 220)).astype(np.float32)
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self.y = np.random.random((32)).astype(np.float32)
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self.out = self.x + self.y.reshape(1, 32, 1, 1)
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self.axis = 1
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self.check_forward_backward()
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if __name__ == '__main__':
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unittest.main()
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