from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import sys import math import time import logging import argparse import functools import threading import subprocess import numpy as np import paddle import paddle.fluid as fluid import models import reader from losses import SoftmaxLoss from losses import ArcMarginLoss from utility import add_arguments, print_arguments from utility import fmt_time, recall_topk, get_gpu_num parser = argparse.ArgumentParser(description=__doc__) add_arg = functools.partial(add_arguments, argparser=parser) # yapf: disable add_arg('model', str, "ResNet50", "Set the network to use.") add_arg('embedding_size', int, 0, "Embedding size.") add_arg('train_batch_size', int, 256, "Minibatch size.") add_arg('test_batch_size', int, 50, "Minibatch size.") add_arg('image_shape', str, "3,224,224", "input image size") add_arg('class_dim', int, 11318 , "Class number.") add_arg('lr', float, 0.01, "set learning rate.") add_arg('lr_strategy', str, "piecewise_decay", "Set the learning rate decay strategy.") add_arg('lr_steps', str, "30000", "step of lr") add_arg('total_iter_num', int, 30000, "total_iter_num") add_arg('display_iter_step', int, 10, "display_iter_step.") add_arg('test_iter_step', int, 1000, "test_iter_step.") add_arg('save_iter_step', int, 1000, "save_iter_step.") add_arg('use_gpu', bool, True, "Whether to use GPU or not.") add_arg('with_mem_opt', bool, True, "Whether to use memory optimization or not.") add_arg('pretrained_model', str, None, "Whether to use pretrained model.") add_arg('checkpoint', str, None, "Whether to resume checkpoint.") add_arg('model_save_dir', str, "output", "model save directory") add_arg('loss_name', str, "softmax", "Set the loss type to use.") add_arg('arc_scale', float, 80.0, "arc scale.") add_arg('arc_margin', float, 0.15, "arc margin.") add_arg('arc_easy_margin', bool, False, "arc easy margin.") add_arg('enable_ce', bool, False, "If set True, enable continuous evaluation job.") # yapf: enable model_list = [m for m in dir(models) if "__" not in m] def optimizer_setting(params): ls = params["learning_strategy"] assert ls["name"] == "piecewise_decay", \ "learning rate strategy must be {}, \ but got {}".format("piecewise_decay", lr["name"]) bd = [int(e) for e in ls["lr_steps"].split(',')] base_lr = params["lr"] lr = [base_lr * (0.1 ** i) for i in range(len(bd) + 1)] optimizer = fluid.optimizer.Momentum( learning_rate=fluid.layers.piecewise_decay( boundaries=bd, values=lr), momentum=0.9, regularization=fluid.regularizer.L2Decay(1e-4)) return optimizer def net_config(image, label, model, args, is_train): assert args.model in model_list, "{} is not in lists: {}".format( args.model, model_list) out = model.net(input=image, embedding_size=args.embedding_size) if not is_train: return None, None, None, out if args.loss_name == "softmax": metricloss = SoftmaxLoss( class_dim=args.class_dim, ) elif args.loss_name == "arcmargin": metricloss = ArcMarginLoss( class_dim = args.class_dim, margin = args.arc_margin, scale = args.arc_scale, easy_margin = args.arc_easy_margin, ) cost, logit = metricloss.loss(out, label) avg_cost = fluid.layers.mean(x=cost) acc_top1 = fluid.layers.accuracy(input=logit, label=label, k=1) acc_top5 = fluid.layers.accuracy(input=logit, label=label, k=5) return avg_cost, acc_top1, acc_top5, out def build_program(is_train, main_prog, startup_prog, args): image_shape = [int(m) for m in args.image_shape.split(",")] model = models.__dict__[args.model]() with fluid.program_guard(main_prog, startup_prog): if is_train: queue_capacity = 64 py_reader = fluid.layers.py_reader( capacity=queue_capacity, shapes=[[-1] + image_shape, [-1, 1]], lod_levels=[0, 0], dtypes=["float32", "int64"], use_double_buffer=True) image, label = fluid.layers.read_file(py_reader) else: image = fluid.layers.data(name='image', shape=image_shape, dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') with fluid.unique_name.guard(): avg_cost, acc_top1, acc_top5, out = net_config(image, label, model, args, is_train) if is_train: params = model.params params["lr"] = args.lr params["learning_strategy"]["lr_steps"] = args.lr_steps params["learning_strategy"]["name"] = args.lr_strategy optimizer = optimizer_setting(params) optimizer.minimize(avg_cost) global_lr = optimizer._global_learning_rate() """ if not is_train: main_prog = main_prog.clone(for_test=True) """ if is_train: return py_reader, avg_cost, acc_top1, acc_top5, global_lr else: return out, image, label def train_async(args): # parameters from arguments logging.debug('enter train') model_name = args.model checkpoint = args.checkpoint pretrained_model = args.pretrained_model model_save_dir = args.model_save_dir startup_prog = fluid.Program() train_prog = fluid.Program() tmp_prog = fluid.Program() if args.enable_ce: assert args.model == "ResNet50" assert args.loss_name == "arcmargin" np.random.seed(0) startup_prog.random_seed = 1000 train_prog.random_seed = 1000 tmp_prog.random_seed = 1000 train_py_reader, train_cost, train_acc1, train_acc5, global_lr = build_program( is_train=True, main_prog=train_prog, startup_prog=startup_prog, args=args) test_feas, image, label = build_program( is_train=False, main_prog=tmp_prog, startup_prog=startup_prog, args=args) test_prog = tmp_prog.clone(for_test=True) train_fetch_list = [global_lr.name, train_cost.name, train_acc1.name, train_acc5.name] test_fetch_list = [test_feas.name] if args.with_mem_opt: fluid.memory_optimize(train_prog, skip_opt_set=set(train_fetch_list)) place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(startup_prog) logging.debug('after run startup program') if checkpoint is not None: fluid.io.load_persistables(exe, checkpoint, main_program=train_prog) if pretrained_model: def if_exist(var): return os.path.exists(os.path.join(pretrained_model, var.name)) fluid.io.load_vars( exe, pretrained_model, main_program=train_prog, predicate=if_exist) devicenum = get_gpu_num() assert (args.train_batch_size % devicenum) == 0 train_batch_size = args.train_batch_size // devicenum test_batch_size = args.test_batch_size train_reader = paddle.batch(reader.train(args), batch_size=train_batch_size, drop_last=True) test_reader = paddle.batch(reader.test(args), batch_size=test_batch_size, drop_last=False) test_feeder = fluid.DataFeeder(place=place, feed_list=[image, label]) train_py_reader.decorate_paddle_reader(train_reader) train_exe = fluid.ParallelExecutor( main_program=train_prog, use_cuda=args.use_gpu, loss_name=train_cost.name) totalruntime = 0 train_py_reader.start() iter_no = 0 train_info = [0, 0, 0, 0] while iter_no <= args.total_iter_num: t1 = time.time() lr, loss, acc1, acc5 = train_exe.run(fetch_list=train_fetch_list) t2 = time.time() period = t2 - t1 lr = np.mean(np.array(lr)) train_info[0] += np.mean(np.array(loss)) train_info[1] += np.mean(np.array(acc1)) train_info[2] += np.mean(np.array(acc5)) train_info[3] += 1 if iter_no % args.display_iter_step == 0: avgruntime = totalruntime / args.display_iter_step avg_loss = train_info[0] / train_info[3] avg_acc1 = train_info[1] / train_info[3] avg_acc5 = train_info[2] / train_info[3] print("[%s] trainbatch %d, lr %.6f, loss %.6f, "\ "acc1 %.4f, acc5 %.4f, time %2.2f sec" % \ (fmt_time(), iter_no, lr, avg_loss, avg_acc1, avg_acc5, avgruntime)) sys.stdout.flush() totalruntime = 0 if iter_no % 1000 == 0: train_info = [0, 0, 0, 0] totalruntime += period if iter_no % args.test_iter_step == 0 and iter_no != 0: f, l = [], [] for batch_id, data in enumerate(test_reader()): t1 = time.time() [feas] = exe.run(test_prog, fetch_list = test_fetch_list, feed=test_feeder.feed(data)) label = np.asarray([x[1] for x in data]) f.append(feas) l.append(label) t2 = time.time() period = t2 - t1 if batch_id % 20 == 0: print("[%s] testbatch %d, time %2.2f sec" % \ (fmt_time(), batch_id, period)) f = np.vstack(f) l = np.hstack(l) recall = recall_topk(f, l, k=1) print("[%s] test_img_num %d, trainbatch %d, test_recall %.5f" % \ (fmt_time(), len(f), iter_no, recall)) sys.stdout.flush() if iter_no % args.save_iter_step == 0 and iter_no != 0: model_path = os.path.join(model_save_dir + '/' + model_name, str(iter_no)) if not os.path.isdir(model_path): os.makedirs(model_path) fluid.io.save_persistables(exe, model_path, main_program=train_prog) iter_no += 1 # This is for continuous evaluation only if args.enable_ce: # Use the mean cost/acc for training print("kpis train_cost %s" % (avg_loss)) print("kpis test_recall %s" % (recall)) def initlogging(): for handler in logging.root.handlers[:]: logging.root.removeHandler(handler) loglevel = logging.DEBUG logging.basicConfig( level=loglevel, # logger.BASIC_FORMAT, format= "%(levelname)s:%(filename)s[%(lineno)s] %(name)s:%(funcName)s->%(message)s", datefmt='%a, %d %b %Y %H:%M:%S') def main(): args = parser.parse_args() print_arguments(args) train_async(args) if __name__ == '__main__': main()