# Copyright (c) 2020 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 argparse import ast import numpy as np from PIL import Image import os import paddle import paddle.fluid as fluid from paddle.fluid.optimizer import AdamOptimizer from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear from paddle.fluid.dygraph.base import to_variable from paddleslim.nas.one_shot import SuperMnasnet from paddleslim.nas.one_shot import OneShotSearch def parse_args(): parser = argparse.ArgumentParser("Training for Mnist.") parser.add_argument( "--use_data_parallel", type=ast.literal_eval, default=False, help="The flag indicating whether to use data parallel mode to train the model." ) parser.add_argument("-e", "--epoch", default=5, type=int, help="set epoch") parser.add_argument("--ce", action="store_true", help="run ce") args = parser.parse_args() return args class SimpleImgConv(fluid.dygraph.Layer): def __init__(self, num_channels, num_filters, filter_size, conv_stride=1, conv_padding=0, conv_dilation=1, conv_groups=1, act=None, use_cudnn=False, param_attr=None, bias_attr=None): super(SimpleImgConv, 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, act=act, use_cudnn=use_cudnn) def forward(self, inputs): x = self._conv2d(inputs) return x class MNIST(fluid.dygraph.Layer): def __init__(self): super(MNIST, self).__init__() self._simple_img_conv_pool_1 = SimpleImgConv(1, 20, 2, act="relu") self.arch = SuperMnasnet( name_scope="super_net", input_channels=20, out_channels=20) self._simple_img_conv_pool_2 = SimpleImgConv(20, 50, 2, act="relu") self.pool_2_shape = 50 * 13 * 13 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, label=None, tokens=None): x = self._simple_img_conv_pool_1(inputs) x = self.arch(x, tokens=tokens) # addddddd x = self._simple_img_conv_pool_2(x) x = fluid.layers.reshape(x, shape=[-1, self.pool_2_shape]) x = self._fc(x) if label is not None: acc = fluid.layers.accuracy(input=x, label=label) return x, acc else: return x def test_mnist(model, tokens=None): acc_set = [] avg_loss_set = [] batch_size = 64 test_reader = paddle.fluid.io.batch( paddle.dataset.mnist.test(), batch_size=batch_size, drop_last=True) for batch_id, data in enumerate(test_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(batch_size, 1) img = to_variable(dy_x_data) label = to_variable(y_data) label.stop_gradient = True prediction, acc = model.forward(img, label, tokens=tokens) loss = fluid.layers.cross_entropy(input=prediction, label=label) avg_loss = fluid.layers.mean(loss) acc_set.append(float(acc.numpy())) avg_loss_set.append(float(avg_loss.numpy())) if batch_id % 100 == 0: print("Test - batch_id: {}".format(batch_id)) # get test acc and loss acc_val_mean = np.array(acc_set).mean() avg_loss_val_mean = np.array(avg_loss_set).mean() return acc_val_mean def train_mnist(args, model, tokens=None): epoch_num = args.epoch BATCH_SIZE = 64 adam = AdamOptimizer( learning_rate=0.001, parameter_list=model.parameters()) train_reader = paddle.fluid.io.batch( paddle.dataset.mnist.train(), batch_size=BATCH_SIZE, drop_last=True) if args.use_data_parallel: train_reader = fluid.contrib.reader.distributed_batch_reader( train_reader) 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(-1, 1) img = to_variable(dy_x_data) label = to_variable(y_data) label.stop_gradient = True cost, acc = model.forward(img, label, tokens=tokens) loss = fluid.layers.cross_entropy(cost, label) avg_loss = fluid.layers.mean(loss) if args.use_data_parallel: avg_loss = model.scale_loss(avg_loss) avg_loss.backward() model.apply_collective_grads() else: avg_loss.backward() adam.minimize(avg_loss) # save checkpoint model.clear_gradients() if batch_id % 1 == 0: print("Loss at epoch {} step {}: {:}".format(epoch, batch_id, avg_loss.numpy())) model.eval() test_acc = test_mnist(model, tokens=tokens) model.train() print("Loss at epoch {} , acc is: {}".format(epoch, test_acc)) save_parameters = (not args.use_data_parallel) or ( args.use_data_parallel and fluid.dygraph.parallel.Env().local_rank == 0) if save_parameters: fluid.save_dygraph(model.state_dict(), "save_temp") print("checkpoint saved") if __name__ == '__main__': args = parse_args() place = fluid.CPUPlace() with fluid.dygraph.guard(place): model = MNIST() # step 1: training super net #train_mnist(args, model) # step 2: search best_tokens = OneShotSearch(model, test_mnist) # step 3: final training # train_mnist(args, model, best_tokens)