# Copyright (c) 2019 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 division from __future__ import print_function import argparse import contextlib import os import numpy as np import paddle from paddle import fluid from paddle.fluid.optimizer import Momentum from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear from model import Model, CrossEntropy, Input 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=None, act=None, use_cudnn=False, param_attr=None, bias_attr=None): super(SimpleImgConvPool, self).__init__('SimpleConv') 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(Model): def __init__(self, inputs): super(MNIST, self).__init__(inputs) 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") pool_2_shape = 50 * 4 * 4 SIZE = 10 scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5 self._fc = Linear( 800, 10, param_attr=fluid.param_attr.ParamAttr( initializer=fluid.initializer.NormalInitializer( loc=0.0, scale=scale)), act="softmax") def forward(self, inputs, label): x = self._simple_img_conv_pool_1(inputs) x = self._simple_img_conv_pool_2(x) x = fluid.layers.flatten(x, axis=1) x = self._fc(x) loss = fluid.layers.cross_entropy(x, label) loss = fluid.layers.mean(loss) self.set_loss(loss) return x, loss def accuracy(pred, label, topk=(1, )): maxk = max(topk) pred = np.argsort(pred)[:, ::-1][:, :maxk] correct = (pred == np.repeat(label, maxk, 1)) batch_size = label.shape[0] res = [] for k in topk: correct_k = correct[:, :k].sum() res.append(100.0 * correct_k / batch_size) return res def main(): @contextlib.contextmanager def null_guard(): yield guard = fluid.dygraph.guard() if FLAGS.dynamic else null_guard() if not os.path.exists('mnist_checkpoints'): os.mkdir('mnist_checkpoints') train_loader = fluid.io.xmap_readers( lambda b: [np.array([x[0] for x in b]).reshape(-1, 1, 28, 28), np.array([x[1] for x in b]).reshape(-1, 1)], paddle.batch(fluid.io.shuffle(paddle.dataset.mnist.train(), 6e4), batch_size=FLAGS.batch_size, drop_last=True), 1, 1) val_loader = fluid.io.xmap_readers( lambda b: [np.array([x[0] for x in b]).reshape(-1, 1, 28, 28), np.array([x[1] for x in b]).reshape(-1, 1)], paddle.batch(paddle.dataset.mnist.test(), batch_size=FLAGS.batch_size, drop_last=True), 1, 1) device_ids = list(range(FLAGS.num_devices)) add_loss = True with guard: inputs = [ Input( [None, 1, 28, 28], 'float32', name='image'), Input( [None, 1], 'int64', name='label') ] model = MNIST(inputs) optim = Momentum( learning_rate=FLAGS.lr, momentum=.9, parameter_list=model.parameters()) model.prepare(optim) if FLAGS.resume is not None: model.load(FLAGS.resume) for e in range(FLAGS.epoch): train_loss = 0.0 train_acc = 0.0 val_loss = 0.0 val_acc = 0.0 print("======== train epoch {} ========".format(e)) for idx, batch in enumerate(train_loader()): outputs, losses = model.train( batch, device='gpu', device_ids=device_ids) acc = accuracy(outputs[0], batch[1])[0] train_loss += np.sum(losses) train_acc += acc if idx % 10 == 0: print("{:04d}: loss {:0.3f} top1: {:0.3f}%".format( idx, train_loss / (idx + 1), train_acc / (idx + 1))) print("======== eval epoch {} ========".format(e)) for idx, batch in enumerate(val_loader()): outputs, losses = model.eval( batch, device='gpu', device_ids=device_ids) acc = accuracy(outputs[0], batch[1])[0] val_loss += np.sum(losses) val_acc += acc if idx % 10 == 0: print("{:04d}: loss {:0.3f} top1: {:0.3f}%".format( idx, val_loss / (idx + 1), val_acc / (idx + 1))) model.save('mnist_checkpoints/{:02d}'.format(e)) if __name__ == '__main__': parser = argparse.ArgumentParser("CNN training on MNIST") parser.add_argument( "-d", "--dynamic", action='store_true', help="enable dygraph mode") parser.add_argument( "-e", "--epoch", default=100, type=int, help="number of epoch") parser.add_argument( '--lr', '--learning-rate', default=1e-3, type=float, metavar='LR', help='initial learning rate') parser.add_argument( "-b", "--batch_size", default=128, type=int, help="batch size") parser.add_argument( "-n", "--num_devices", default=4, type=int, help="number of devices") parser.add_argument( "-r", "--resume", default=None, type=str, help="checkpoint path to resume") FLAGS = parser.parse_args() main()