# 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. from __future__ import print_function 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.dygraph.nn import Conv2D, Pool2D, Linear from paddle.fluid.dygraph.base import to_variable from test_imperative_base import new_program_scope from utils import DyGraphProgramDescTracerTestHelper, is_equal_program class SimpleImgConvPool(fluid.dygraph.Layer): def __init__(self, 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__() self._conv2d = Conv2D( 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( 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.dygraph.Layer): def __init__(self): super(MNIST, self).__init__() self._simple_img_conv_pool_1 = SimpleImgConvPool( 1, 20, 5, 2, 2, act="relu") self._simple_img_conv_pool_2 = SimpleImgConvPool( 20, 50, 5, 2, 2, act="relu") self.pool_2_shape = 50 * 4 * 4 SIZE = 10 scale = (2.0 / (self.pool_2_shape**2 * SIZE))**0.5 self._fc = Linear( self.pool_2_shape, 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 = fluid.layers.reshape(x, shape=[-1, self.pool_2_shape]) x = self._fc(x) return x class TestImperativeMnist(unittest.TestCase): def reader_decorator(self, reader): def _reader_imple(): for item in reader(): image = np.array(item[0]).reshape(1, 28, 28) label = np.array(item[1]).astype('int64').reshape(1) yield image, label return _reader_imple def test_mnist_float32(self): seed = 90 epoch_num = 1 batch_size = 128 batch_num = 50 traced_layer = None with fluid.dygraph.guard(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed mnist = MNIST() sgd = SGDOptimizer( learning_rate=1e-3, parameter_list=mnist.parameters()) batch_py_reader = fluid.io.PyReader(capacity=1) batch_py_reader.decorate_sample_list_generator( paddle.batch( self.reader_decorator(paddle.dataset.mnist.train()), batch_size=batch_size, drop_last=True), places=fluid.CPUPlace()) mnist.train() dy_param_init_value = {} helper = DyGraphProgramDescTracerTestHelper(self) program = None for epoch in range(epoch_num): for batch_id, data in enumerate(batch_py_reader()): if batch_id >= batch_num: break img = data[0] dy_x_data = img.numpy() label = data[1] label.stop_gradient = True if batch_id % 10 == 0: cost, traced_layer = paddle.imperative.TracedLayer.trace( mnist, inputs=img) if program is not None: self.assertTrue(program, traced_layer.program) program = traced_layer.program traced_layer.save_inference_model( './infer_imperative_mnist') else: cost = mnist(img) if traced_layer is not None: cost_static = traced_layer([img]) helper.assertEachVar(cost, cost_static) 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() sgd = SGDOptimizer(learning_rate=1e-3) train_reader = paddle.batch( paddle.dataset.mnist.train(), batch_size=batch_size, 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()): if batch_id >= batch_num: break 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( [batch_size, 1]) fetch_list = [avg_loss.name] fetch_list.extend(static_param_name_list) if traced_layer is not None: traced_layer([static_x_data]) 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()