import sys sys.path.append('..') import numpy as np import argparse import ast import logging import time import paddle import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.analysis import flops from paddleslim.nas import SANAS from paddleslim.common import get_logger from optimizer import create_optimizer import imagenet_reader _logger = get_logger(__name__, level=logging.INFO) reduce_rate = 0.85 init_temperature = 10.24 max_flops = 321208544 server_address = "" port = 8979 retain_epoch = 5 def create_data_loader(image_shape): data_shape = [-1] + image_shape data = fluid.data(name='data', shape=data_shape, dtype='float32') label = fluid.data(name='label', shape=[-1, 1], dtype='int64') data_loader = fluid.io.DataLoader.from_generator( feed_list=[data, label], capacity=1024, use_double_buffer=True, iterable=True) return data_loader, data, label def conv_bn_layer(input, filter_size, num_filters, stride, padding='SAME', num_groups=1, act=None, name=None, use_cudnn=True): conv = fluid.layers.conv2d( input, num_filters=num_filters, filter_size=filter_size, stride=stride, padding=padding, groups=num_groups, act=None, use_cudnn=use_cudnn, param_attr=ParamAttr(name=name + '_weights'), bias_attr=False) bn_name = name + '_bn' return fluid.layers.batch_norm( input=conv, act=act, param_attr=ParamAttr(name=bn_name + '_scale'), bias_attr=ParamAttr(name=bn_name + '_offset'), moving_mean_name=bn_name + '_mean', moving_variance_name=bn_name + '_variance') def search_mobilenetv2_block(config, args, image_size): image_shape = [3, image_size, image_size] if args.is_server: sa_nas = SANAS( config, server_addr=("", port), init_temperature=init_temperature, reduce_rate=reduce_rate, search_steps=args.search_steps, is_server=True) else: sa_nas = SANAS( config, server_addr=(server_address, port), init_temperature=init_temperature, reduce_rate=reduce_rate, search_steps=args.search_steps, is_server=False) for step in range(args.search_steps): archs = sa_nas.next_archs()[0] train_program = fluid.Program() test_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(train_program, startup_program): train_loader, data, label = create_data_loader(image_shape) data = conv_bn_layer( input=data, num_filters=32, filter_size=3, stride=2, padding='SAME', act='relu6', name='mobilenetv2_conv1') data = archs(data)[0] data = conv_bn_layer( input=data, num_filters=1280, filter_size=1, stride=1, padding='SAME', act='relu6', name='mobilenetv2_last_conv') data = fluid.layers.pool2d( input=data, pool_size=7, pool_stride=1, pool_type='avg', global_pooling=True, name='mobilenetv2_last_pool') output = fluid.layers.fc( input=data, size=args.class_dim, param_attr=ParamAttr(name='mobilenetv2_fc_weights'), bias_attr=ParamAttr(name='mobilenetv2_fc_offset')) softmax_out = fluid.layers.softmax(input=output, use_cudnn=False) cost = fluid.layers.cross_entropy(input=softmax_out, label=label) avg_cost = fluid.layers.mean(cost) acc_top1 = fluid.layers.accuracy( input=softmax_out, label=label, k=1) acc_top5 = fluid.layers.accuracy( input=softmax_out, label=label, k=5) test_program = train_program.clone(for_test=True) optimizer = fluid.optimizer.Momentum( learning_rate=0.1, momentum=0.9, regularization=fluid.regularizer.L2Decay(1e-4)) optimizer.minimize(avg_cost) current_flops = flops(train_program) print('step: {}, current_flops: {}'.format(step, current_flops)) if current_flops > max_flops: continue place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(startup_program) if args.data == 'cifar10': train_reader = paddle.batch( paddle.reader.shuffle( paddle.dataset.cifar.train10(cycle=False), buf_size=1024), batch_size=args.batch_size, drop_last=True) test_reader = paddle.batch( paddle.dataset.cifar.test10(cycle=False), batch_size=args.batch_size, drop_last=False) elif args.data == 'imagenet': train_reader = paddle.batch( imagenet_reader.train(), batch_size=args.batch_size, drop_last=True) test_reader = paddle.batch( imagenet_reader.val(), batch_size=args.batch_size, drop_last=False) test_loader, _, _ = create_data_loader(image_shape) train_loader.set_sample_list_generator( train_reader, places=fluid.cuda_places() if args.use_gpu else fluid.cpu_places()) test_loader.set_sample_list_generator(test_reader, places=place) build_strategy = fluid.BuildStrategy() train_compiled_program = fluid.CompiledProgram( train_program).with_data_parallel( loss_name=avg_cost.name, build_strategy=build_strategy) for epoch_id in range(retain_epoch): for batch_id, data in enumerate(train_loader()): fetches = [avg_cost.name] s_time = time.time() outs = exe.run(train_compiled_program, feed=data, fetch_list=fetches)[0] batch_time = time.time() - s_time if batch_id % 10 == 0: _logger.info( 'TRAIN: steps: {}, epoch: {}, batch: {}, cost: {}, batch_time: {}ms'. format(step, epoch_id, batch_id, outs[0], batch_time)) reward = [] for batch_id, data in enumerate(test_loader()): test_fetches = [avg_cost.name, acc_top1.name, acc_top5.name] batch_reward = exe.run(test_program, feed=data, fetch_list=test_fetches) reward_avg = np.mean(np.array(batch_reward), axis=1) reward.append(reward_avg) _logger.info( 'TEST: step: {}, batch: {}, avg_cost: {}, acc_top1: {}, acc_top5: {}'. format(step, batch_id, batch_reward[0], batch_reward[1], batch_reward[2])) finally_reward = np.mean(np.array(reward), axis=0) _logger.info( 'FINAL TEST: avg_cost: {}, acc_top1: {}, acc_top5: {}'.format( finally_reward[0], finally_reward[1], finally_reward[2])) sa_nas.reward(float(finally_reward[1])) if __name__ == '__main__': parser = argparse.ArgumentParser( description='SA NAS MobileNetV2 cifar10 argparase') parser.add_argument( '--use_gpu', type=ast.literal_eval, default=True, help='Whether to use GPU in train/test model.') parser.add_argument( '--class_dim', type=int, default=1000, help='classify number.') parser.add_argument( '--batch_size', type=int, default=256, help='batch size.') parser.add_argument( '--data', type=str, default='cifar10', choices=['cifar10', 'imagenet'], help='dataset name.') parser.add_argument( '--is_server', type=ast.literal_eval, default=True, help='Whether to start a server.') # nas args parser.add_argument( '--search_steps', type=int, default=100, help='controller server number.') parser.add_argument('--lr', type=float, default=0.1, help='learning rate.') args = parser.parse_args() print(args) if args.data == 'cifar10': image_size = 32 elif args.data == 'imagenet': image_size = 224 else: raise NotImplementedError( 'data must in [cifar10, imagenet], but received: {}'.format( args.data)) # block mask means block number, 1 mean downsample, 0 means the size of feature map don't change after this block config_info = {'block_mask': [0, 1, 1, 1, 1, 0, 1, 0]} config = [('MobileNetV2BlockSpace', config_info)] search_mobilenetv2_block(config, args, image_size)