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153 lines
5.0 KiB
153 lines
5.0 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 contextlib
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import unittest
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import numpy as np
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import paddle.fluid as fluid
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from paddle.fluid import core
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from paddle.fluid.imperative.nn import Conv2D, Pool2D, FC
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from paddle.fluid.imperative.base import to_variable
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class SimpleImgConvPool(fluid.imperative.PyLayer):
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def __init__(self,
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num_channels,
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filter_size,
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num_filters,
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pool_size,
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pool_stride,
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pool_padding=0,
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pool_type='max',
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global_pooling=False,
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conv_stride=1,
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conv_padding=0,
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conv_dilation=1,
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conv_groups=1,
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act=None,
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use_cudnn=False,
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param_attr=None,
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bias_attr=None):
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super(SimpleImgConvPool, self).__init__()
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# groups = 1
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# dilation = [1, 1]
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# pad = [0, 0]
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# stride = [1, 1]
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# input_size = [2, 3, 5, 5] # NCHW
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# assert np.mod(input_size[1], groups) == 0
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# f_c = input_size[1] // groups
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# filter_size = [6, f_c, 3, 3]
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self._conv2d = Conv2D(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=conv_stride,
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padding=conv_padding,
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dilation=conv_dilation,
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groups=conv_groups,
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param_attr=None,
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bias_attr=None,
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use_cudnn=use_cudnn)
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self._pool2d = Pool2D(
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pool_size=pool_size,
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pool_type=pool_type,
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pool_stride=pool_stride,
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pool_padding=pool_padding,
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global_pooling=global_pooling,
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use_cudnn=use_cudnn)
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def forward(self, inputs):
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x = self._conv2d(inputs)
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x = self._pool2d(x)
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return x
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class MNIST(fluid.imperative.PyLayer):
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def __init__(self, param_attr=None, bias_attr=None):
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super(MNIST, self).__init__(param_attr=param_attr, bias_attr=bias_attr)
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self._simple_img_conv_pool_1 = SimpleImgConvPool(
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1, 5, 20, 2, 2, act="relu")
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self._simple_img_conv_pool_2 = SimpleImgConvPool(
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20, 5, 50, 2, 2, act="relu")
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pool_2_shape = 50 * 8 * 8
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SIZE = 10
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scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5
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self._fc = FC(-1,
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10,
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.NormalInitializer(
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loc=0.0, scale=scale)))
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def forward(self, inputs):
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x = self._simple_img_conv_pool_1(inputs)
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x = self._simple_img_conv_pool_2(x)
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x = self._fc(x)
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return x
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class TestImperativeMnist(unittest.TestCase):
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def test_mnist_cpu_float32(self):
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with fluid.imperative.guard():
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mnist = MNIST()
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x_data = np.random.rand(128, 1, 28, 28).astype('float32')
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img = to_variable(x_data)
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y_data = np.random.rand(128, 1).astype('int64')
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label = to_variable(y_data)
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label._stop_gradient = True
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predict = mnist(img)
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print(predict.shape, predict.dtype, label.shape, label.dtype)
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out = fluid.layers.cross_entropy(predict, label)
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print(out.shape, out.dtype)
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out._backward()
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filter_grad = mnist._simple_img_conv_pool_1._conv2d._filter_param._gradient(
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)
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print(filter_grad)
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# np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
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# with fluid.imperative.guard():
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# mlp = MLP()
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# out = mlp(np_inp)
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# dy_out = out._numpy()
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# out._backward()
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# dy_grad = mlp._fc1._w._gradient()
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# with new_program_scope():
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# inp = fluid.layers.data(
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# name="inp", shape=[2, 2], append_batch_size=False)
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# mlp = MLP()
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# out = mlp(inp)
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# param_grads = fluid.backward.append_backward(
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# out, parameter_list=[mlp._fc1._w.name])[0]
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# exe = fluid.Executor(fluid.CPUPlace())
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# exe.run(fluid.default_startup_program())
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# static_out, static_grad = exe.run(
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# feed={inp.name: np_inp},
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# fetch_list=[out.name, param_grads[1].name])
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# self.assertTrue(np.allclose(dy_out, static_out))
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# self.assertTrue(np.allclose(dy_grad, static_grad))
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if __name__ == '__main__':
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unittest.main()
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