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126 lines
4.5 KiB
126 lines
4.5 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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from __future__ import print_function
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import unittest
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import paddle.fluid.core as core
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from paddle.fluid.executor import Executor
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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from paddle.fluid.backward import append_backward
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from paddle.fluid.framework import switch_main_program
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from paddle.fluid.framework import Program
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import numpy as np
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from simple_nets import simple_fc_net, init_data
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class TestPrintOpCPU(unittest.TestCase):
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def setUp(self):
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self.place = core.CPUPlace()
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self.x_tensor = core.LoDTensor()
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tensor_np = np.random.random(size=(2, 3)).astype('float32')
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self.x_tensor.set(tensor_np, self.place)
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self.x_tensor.set_recursive_sequence_lengths([[1, 1]])
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def build_network(self, only_forward, **kargs):
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x = layers.data('x', shape=[3], dtype='float32', lod_level=1)
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x.stop_gradient = False
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layers.Print(input=x, **kargs)
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loss = layers.mean(x)
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append_backward(loss=loss)
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return loss
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def test_forward(self):
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switch_main_program(Program())
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printed = self.build_network(True, print_phase='forward')
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exe = Executor(self.place)
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outs = exe.run(feed={'x': self.x_tensor},
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fetch_list=[printed],
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return_numpy=False)
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def test_backward(self):
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switch_main_program(Program())
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loss = self.build_network(False, print_phase='backward')
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exe = Executor(self.place)
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outs = exe.run(feed={'x': self.x_tensor},
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fetch_list=[loss],
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return_numpy=False)
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def test_all_parameters(self):
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x = layers.data('x', shape=[3], dtype='float32', lod_level=1)
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x.stop_gradient = False
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for print_tensor_name in [True, False]:
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for print_tensor_type in [True, False]:
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for print_tensor_shape in [True, False]:
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for print_tensor_lod in [True, False]:
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layers.Print(
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input=x,
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print_tensor_name=print_tensor_name,
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print_tensor_type=print_tensor_type,
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print_tensor_shape=print_tensor_shape,
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print_tensor_lod=print_tensor_lod, )
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loss = layers.mean(x)
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append_backward(loss=loss)
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exe = Executor(self.place)
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outs = exe.run(feed={'x': self.x_tensor},
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fetch_list=[loss],
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return_numpy=False)
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestPrintOpGPU(TestPrintOpCPU):
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def setUp(self):
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self.place = core.CUDAPlace(0)
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self.x_tensor = core.LoDTensor()
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tensor_np = np.random.random(size=(2, 3)).astype('float32')
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self.x_tensor.set(tensor_np, self.place)
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self.x_tensor.set_recursive_sequence_lengths([[1, 1]])
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class TestPrintOpBackward(unittest.TestCase):
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def check_backward(self, use_cuda):
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main = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(main, startup):
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loss = simple_fc_net()
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loss = fluid.layers.Print(loss)
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fluid.optimizer.Adam().minimize(loss)
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print_ops = [op for op in main.blocks[0].ops if op.type == u'print']
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assert len(print_ops) == 2, "The number of print op should be 2"
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(startup)
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binary = fluid.compiler.CompiledProgram(main).with_data_parallel(
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loss_name=loss.name)
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img, label = init_data()
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feed_dict = {"image": img, "label": label}
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exe.run(binary, feed_dict)
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def test_fw_bw(self):
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if core.is_compiled_with_cuda():
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self.check_backward(use_cuda=True)
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self.check_backward(use_cuda=False)
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if __name__ == '__main__':
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unittest.main()
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