# 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. import contextlib import unittest import numpy as np import six import paddle import paddle.fluid as fluid from paddle.fluid import core from paddle.fluid.optimizer import SGDOptimizer from paddle.fluid.imperative.nn import Conv2D, Pool2D, FC from paddle.fluid.imperative.base import to_variable from test_imperative_base import new_program_scope class SimpleImgConvPool(fluid.imperative.Layer): def __init__(self, name_scope, num_channels, num_filters, filter_size, pool_size, pool_stride, pool_padding=0, pool_type='max', global_pooling=False, conv_stride=1, conv_padding=0, conv_dilation=1, conv_groups=1, act=None, use_cudnn=False, param_attr=None, bias_attr=None): super(SimpleImgConvPool, self).__init__(name_scope) self._conv2d = Conv2D( self.full_name(), num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=conv_stride, padding=conv_padding, dilation=conv_dilation, groups=conv_groups, param_attr=None, bias_attr=None, use_cudnn=use_cudnn) self._pool2d = Pool2D( self.full_name(), pool_size=pool_size, pool_type=pool_type, pool_stride=pool_stride, pool_padding=pool_padding, global_pooling=global_pooling, use_cudnn=use_cudnn) def forward(self, inputs): x = self._conv2d(inputs) x = self._pool2d(x) return x class MNIST(fluid.imperative.Layer): def __init__(self, name_scope, param_attr=None, bias_attr=None): super(MNIST, self).__init__(name_scope) self._simple_img_conv_pool_1 = SimpleImgConvPool( self.full_name(), 1, 20, 5, 2, 2, act="relu") self._simple_img_conv_pool_2 = SimpleImgConvPool( self.full_name(), 20, 50, 5, 2, 2, act="relu") pool_2_shape = 50 * 4 * 4 SIZE = 10 scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5 self._fc = FC(self.full_name(), 10, param_attr=fluid.param_attr.ParamAttr( initializer=fluid.initializer.NormalInitializer( loc=0.0, scale=scale)), act="softmax") def forward(self, inputs): x = self._simple_img_conv_pool_1(inputs) x = self._simple_img_conv_pool_2(x) x = self._fc(x) return x class TestImperativeMnist(unittest.TestCase): def test_mnist_float32(self): seed = 90 epoch_num = 1 with fluid.imperative.guard(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed mnist = MNIST("mnist") sgd = SGDOptimizer(learning_rate=1e-3) train_reader = paddle.batch( paddle.dataset.mnist.train(), batch_size=128, drop_last=True) dy_param_init_value = {} for epoch in range(epoch_num): for batch_id, data in enumerate(train_reader()): dy_x_data = np.array( [x[0].reshape(1, 28, 28) for x in data]).astype('float32') y_data = np.array( [x[1] for x in data]).astype('int64').reshape(128, 1) img = to_variable(dy_x_data) label = to_variable(y_data) label._stop_gradient = True cost = mnist(img) loss = fluid.layers.cross_entropy(cost, label) avg_loss = fluid.layers.mean(loss) dy_out = avg_loss._numpy() if epoch == 0 and batch_id == 0: for param in mnist.parameters(): dy_param_init_value[param.name] = param._numpy() avg_loss._backward() sgd.minimize(avg_loss) mnist.clear_gradients() dy_param_value = {} for param in mnist.parameters(): dy_param_value[param.name] = param._numpy() with new_program_scope(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed exe = fluid.Executor(fluid.CPUPlace( ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0)) mnist = MNIST("mnist") sgd = SGDOptimizer(learning_rate=1e-3) train_reader = paddle.batch( paddle.dataset.mnist.train(), batch_size=128, drop_last=True) img = fluid.layers.data( name='pixel', shape=[1, 28, 28], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') cost = mnist(img) loss = fluid.layers.cross_entropy(cost, label) avg_loss = fluid.layers.mean(loss) sgd.minimize(avg_loss) # initialize params and fetch them static_param_init_value = {} static_param_name_list = [] for param in mnist.parameters(): static_param_name_list.append(param.name) out = exe.run(fluid.default_startup_program(), fetch_list=static_param_name_list) for i in range(len(static_param_name_list)): static_param_init_value[static_param_name_list[i]] = out[i] for epoch in range(epoch_num): for batch_id, data in enumerate(train_reader()): static_x_data = np.array( [x[0].reshape(1, 28, 28) for x in data]).astype('float32') y_data = np.array( [x[1] for x in data]).astype('int64').reshape([128, 1]) fetch_list = [avg_loss.name] fetch_list.extend(static_param_name_list) out = exe.run( fluid.default_main_program(), feed={"pixel": static_x_data, "label": y_data}, fetch_list=fetch_list) static_param_value = {} static_out = out[0] for i in range(1, len(out)): static_param_value[static_param_name_list[i - 1]] = out[ i] self.assertTrue(np.allclose(dy_x_data.all(), static_x_data.all())) for key, value in six.iteritems(static_param_init_value): self.assertTrue(np.allclose(value, dy_param_init_value[key])) self.assertTrue(np.allclose(static_out, dy_out)) for key, value in six.iteritems(static_param_value): self.assertTrue(np.allclose(value, dy_param_value[key], atol=1e-5)) if __name__ == '__main__': unittest.main()