import os import numpy as np import time import sys import paddle.v2 as paddle import paddle.fluid as fluid from resnet import TSN_ResNet import reader import argparse import functools from paddle.fluid.framework import Parameter from utility import add_arguments, print_arguments parser = argparse.ArgumentParser(description=__doc__) add_arg = functools.partial(add_arguments, argparser=parser) # yapf: disable add_arg('batch_size', int, 128, "Minibatch size.") add_arg('num_layers', int, 50, "How many layers for ResNet model.") add_arg('with_mem_opt', bool, True, "Whether to use memory optimization or not.") add_arg('num_epochs', int, 60, "Number of epochs.") add_arg('class_dim', int, 101, "Number of class.") add_arg('seg_num', int, 7, "Number of segments.") add_arg('image_shape', str, "3,224,224", "Input image size.") add_arg('model_save_dir', str, "output", "Model save directory.") add_arg('pretrained_model', str, None, "Whether to use pretrained model.") add_arg('total_videos', int, 9537, "Training video number.") add_arg('lr_init', float, 0.01, "Set initial learning rate.") # yapf: enable def train(args): # parameters from arguments seg_num = args.seg_num class_dim = args.class_dim num_layers = args.num_layers num_epochs = args.num_epochs batch_size = args.batch_size pretrained_model = args.pretrained_model model_save_dir = args.model_save_dir image_shape = [int(m) for m in args.image_shape.split(",")] image_shape = [seg_num] + image_shape # model definition model = TSN_ResNet(layers=num_layers, seg_num=seg_num) image = fluid.layers.data(name='image', shape=image_shape, dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') out = model.net(input=image, class_dim=class_dim) cost = fluid.layers.cross_entropy(input=out, label=label) avg_cost = fluid.layers.mean(x=cost) acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1) acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) # for test inference_program = fluid.default_main_program().clone(for_test=True) # learning rate strategy epoch_points = [num_epochs / 3, num_epochs * 2 / 3] total_videos = args.total_videos step = int(total_videos / batch_size + 1) bd = [e * step for e in epoch_points] lr_init = args.lr_init lr = [lr_init, lr_init / 10, lr_init / 100] # initialize optimizer optimizer = fluid.optimizer.Momentum( learning_rate=fluid.layers.piecewise_decay( boundaries=bd, values=lr), momentum=0.9, regularization=fluid.regularizer.L2Decay(1e-4)) opts = optimizer.minimize(avg_cost) if args.with_mem_opt: fluid.memory_optimize(fluid.default_main_program()) place = fluid.CUDAPlace(0) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) def is_parameter(var): if isinstance(var, Parameter): return isinstance(var, Parameter) and (not ("fc_0" in var.name)) if pretrained_model is not None: vars = filter(is_parameter, inference_program.list_vars()) fluid.io.load_vars(exe, pretrained_model, vars=vars) # reader train_reader = paddle.batch(reader.train(seg_num), batch_size=batch_size) # test in single GPU test_reader = paddle.batch(reader.test(seg_num), batch_size=batch_size / 16) feeder = fluid.DataFeeder(place=place, feed_list=[image, label]) train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name) fetch_list = [avg_cost.name, acc_top1.name, acc_top5.name] # train for pass_id in range(num_epochs): train_info = [[], [], []] test_info = [[], [], []] for batch_id, data in enumerate(train_reader()): t1 = time.time() loss, acc1, acc5 = train_exe.run(fetch_list, feed=feeder.feed(data)) t2 = time.time() period = t2 - t1 loss = np.mean(np.array(loss)) acc1 = np.mean(np.array(acc1)) acc5 = np.mean(np.array(acc5)) train_info[0].append(loss) train_info[1].append(acc1) train_info[2].append(acc5) if batch_id % 10 == 0: print( "[TRAIN] Pass: {0}\ttrainbatch: {1}\tloss: {2}\tacc1: {3}\tacc5: {4}\ttime: {5}" .format(pass_id, batch_id, '%.6f' % loss, acc1, acc5, "%2.2f sec" % period)) sys.stdout.flush() train_loss = np.array(train_info[0]).mean() train_acc1 = np.array(train_info[1]).mean() train_acc5 = np.array(train_info[2]).mean() # test cnt = 0 for batch_id, data in enumerate(test_reader()): t1 = time.time() loss, acc1, acc5 = exe.run(inference_program, fetch_list=fetch_list, feed=feeder.feed(data)) t2 = time.time() period = t2 - t1 loss = np.mean(loss) acc1 = np.mean(acc1) acc5 = np.mean(acc5) test_info[0].append(loss * len(data)) test_info[1].append(acc1 * len(data)) test_info[2].append(acc5 * len(data)) cnt += len(data) if batch_id % 10 == 0: print( "[TEST] Pass: {0}\ttestbatch: {1}\tloss: {2}\tacc1: {3}\tacc5: {4}\ttime: {5}" .format(pass_id, batch_id, '%.6f' % loss, acc1, acc5, "%2.2f sec" % period)) sys.stdout.flush() test_loss = np.sum(test_info[0]) / cnt test_acc1 = np.sum(test_info[1]) / cnt test_acc5 = np.sum(test_info[2]) / cnt print( "+ End pass: {0}, train_loss: {1}, train_acc1: {2}, train_acc5: {3}" .format(pass_id, '%.3f' % train_loss, '%.3f' % train_acc1, '%.3f' % train_acc5)) print("+ End pass: {0}, test_loss: {1}, test_acc1: {2}, test_acc5: {3}" .format(pass_id, '%.3f' % test_loss, '%.3f' % test_acc1, '%.3f' % test_acc5)) sys.stdout.flush() # save model model_path = os.path.join(model_save_dir, str(pass_id)) if not os.path.isdir(model_path): os.makedirs(model_path) fluid.io.save_persistables(exe, model_path) def main(): args = parser.parse_args() print_arguments(args) train(args) if __name__ == '__main__': main()