support data_parallel training and ucf101 dataset #4819
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| CUDA_VISIBLE_DEVICES=0,1,2,3 python3.7 -m paddle.distributed.launch --started_port 38989 --log_dir ./mylog.ucf101.frames tsm.py --config=./tsm_ucf101.yaml --use_gpu=True --use_data_parallel=True |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -24,6 +24,7 @@ | |
| from model import TSM_ResNet | ||
| from config_utils import * | ||
| from reader import KineticsReader | ||
| from ucf101_reader import UCF101Reader | ||
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| logging.root.handlers = [] | ||
| FORMAT = '[%(levelname)s: %(filename)s: %(lineno)4d]: %(message)s' | ||
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@@ -65,12 +66,39 @@ def parse_args(): | |
| type=int, | ||
| default=None, | ||
| help='epoch number, 0 for read from config file') | ||
| parser.add_argument( | ||
| '--use_data_parallel', | ||
| type=ast.literal_eval, | ||
| default=True, | ||
| help='default use data parallel.') | ||
| parser.add_argument( | ||
| '--model_save_dir', | ||
| type=str, | ||
| default='./output', | ||
| help='default model save in ./output.') | ||
| parser.add_argument( | ||
| '--checkpoint', | ||
| type=str, | ||
| default=None, | ||
| help='path to resume training based on previous checkpoints. ' | ||
| 'None for not resuming any checkpoints.') | ||
| parser.add_argument( | ||
| '--model_path_pre', | ||
| type=str, | ||
| default='tsm', | ||
| help='default model path pre is tsm.') | ||
| parser.add_argument( | ||
| '--resnet50_dir', | ||
| type=str, | ||
| default='./ResNet50_pretrained/', | ||
| help='default resnet50 dir is ./ResNet50_pretrained/.') | ||
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| args = parser.parse_args() | ||
| return args | ||
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| def val(epoch, model, cfg, args): | ||
| reader = UCF101Reader(name="TSM", mode="valid", cfg=cfg) | ||
| reader = reader.create_reader() | ||
| total_loss = 0.0 | ||
| total_acc1 = 0.0 | ||
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@@ -101,9 +129,9 @@ def val(epoch, model, cfg, args): | |
| epoch, batch_id, | ||
| avg_loss.numpy()[0], acc_top1.numpy()[0], acc_top5.numpy()[0])) | ||
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| print('TEST Epoch {}, iter {}, Finish loss {} , acc1 {} , acc5 {}'.format( | ||
| epoch, batch_id, total_loss / total_sample, total_acc1 / total_sample, | ||
| total_acc5 / total_sample)) | ||
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| def create_optimizer(cfg, params): | ||
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@@ -132,26 +160,62 @@ def train(args): | |
| valid_config = merge_configs(config, 'valid', vars(args)) | ||
| print_configs(train_config, 'Train') | ||
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| use_data_parallel = args.use_data_parallel | ||
| trainer_count = fluid.dygraph.parallel.Env().nranks | ||
| if not args.use_gpu: | ||
| place = fluid.CPUPlace() | ||
| elif not args.use_data_parallel: | ||
| place = fluid.CUDAPlace(0) | ||
| else: | ||
| #(data_parallel step1/6) | ||
| place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) | ||
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| #load resnet50 pretrain | ||
| pre_state_dict = fluid.load_program_state(args.resnet50_dir) | ||
| for key in pre_state_dict.keys(): | ||
| print('pre_state_dict.key: {}'.format(key)) | ||
|
There was a problem hiding this comment. print是调试代码？所有参数名打印出来太长了 建议注释或删除 |
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| with fluid.dygraph.guard(place): | ||
| #1. init model | ||
| video_model = TSM_ResNet("TSM", train_config) | ||
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| #2. set resnet50 backbone weights | ||
| param_state_dict = {} | ||
| model_dict = video_model.state_dict() | ||
| for key in model_dict.keys(): | ||
| weight_name = model_dict[key].name | ||
| if weight_name in pre_state_dict.keys( | ||
| ) and weight_name != "fc_0.w_0" and weight_name != "fc_0.b_0": | ||
| print('succ Load weight: {}, shape: {}'.format( | ||
| weight_name, pre_state_dict[weight_name].shape)) | ||
| param_state_dict[key] = pre_state_dict[weight_name] | ||
| else: | ||
| print('fail Load weight: {}'.format(weight_name)) | ||
| param_state_dict[key] = model_dict[key] | ||
| video_model.set_dict(param_state_dict) | ||
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| #3. init optim | ||
| optimizer = create_optimizer(train_config.TRAIN, | ||
| video_model.parameters()) | ||
| if use_data_parallel: | ||
| #(data_parallel step2,3/6) | ||
| strategy = fluid.dygraph.parallel.prepare_context() | ||
| video_model = fluid.dygraph.parallel.DataParallel(video_model, | ||
| strategy) | ||
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| # 4. load checkpoint | ||
| if args.checkpoint: | ||
|
There was a problem hiding this comment. resume阶段， epoch计数是否对应调整下？ |
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| assert os.path.exists(args.checkpoint + ".pdparams"), \ | ||
| "Given dir {}.pdparams not exist.".format(args.checkpoint) | ||
| assert os.path.exists(args.checkpoint + ".pdopt"), \ | ||
| "Given dir {}.pdopt not exist.".format(args.checkpoint) | ||
| para_dict, opti_dict = fluid.dygraph.load_dygraph(args.checkpoint) | ||
| video_model.set_dict(para_dict) | ||
| optimizer.set_dict(opti_dict) | ||
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| # 5. reader | ||
| bs_denominator = 1 | ||
| if args.use_gpu: | ||
| gpus = os.getenv("CUDA_VISIBLE_DEVICES", "") | ||
| if gpus == "": | ||
| pass | ||
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@@ -168,27 +232,36 @@ def train(args): | |
| train_config.TRAIN.batch_size = int(train_config.TRAIN.batch_size / | ||
| bs_denominator) | ||
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| train_reader = UCF101Reader(name="TSM", mode="train", cfg=train_config) | ||
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| train_reader = train_reader.create_reader() | ||
| if use_data_parallel: | ||
| #(data_parallel step4/6) | ||
| train_reader = fluid.contrib.reader.distributed_batch_reader( | ||
| train_reader) | ||
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| # 6. train loop | ||
| for epoch in range(train_config.TRAIN.epoch): | ||
| video_model.train() | ||
| total_loss = 0.0 | ||
| total_acc1 = 0.0 | ||
| total_acc5 = 0.0 | ||
| total_sample = 0 | ||
| t_last = time.time() | ||
| # 6.1 for each batch, call model() , backward(), and minimize() | ||
| for batch_id, data in enumerate(train_reader()): | ||
| t1 = time.time() | ||
|
There was a problem hiding this comment. t1-t5重新命名一下？ 比如batch_start_time |
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| x_data = np.array([item[0] for item in data]) | ||
| y_data = np.array([item[1] for item in data]).reshape([-1, 1]) | ||
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| imgs = to_variable(x_data) | ||
| labels = to_variable(y_data) | ||
| labels.stop_gradient = True | ||
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| t2 = time.time() | ||
| outputs = video_model(imgs) | ||
| t3 = time.time() | ||
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| loss = fluid.layers.cross_entropy( | ||
| input=outputs, label=labels, ignore_index=-1) | ||
| avg_loss = fluid.layers.mean(loss) | ||
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There was a problem hiding this comment. copy avg_loss to a new variable , and output(print) it instead of avg_loss, in avoid to print avg_loss after scale_loss function, which is already divided by the number of cards |
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@@ -198,34 +271,69 @@ def train(args): | |
| acc_top5 = fluid.layers.accuracy( | ||
| input=outputs, label=labels, k=5) | ||
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| current_step_lr = optimizer.current_step_lr() | ||
| if use_data_parallel: | ||
| #(data_parallel step5/6) | ||
| avg_loss = video_model.scale_loss(avg_loss) | ||
| avg_loss.backward() | ||
| video_model.apply_collective_grads() | ||
| else: | ||
| avg_loss.backward() | ||
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| t4 = time.time() | ||
| optimizer.minimize(avg_loss) | ||
| video_model.clear_gradients() | ||
| t5 = time.time() | ||
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| total_loss += avg_loss.numpy()[0] | ||
| total_acc1 += acc_top1.numpy()[0] | ||
| total_acc5 += acc_top5.numpy()[0] | ||
| total_sample += 1 | ||
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| print( | ||
| 'TRAIN Epoch: %d, iter: %d, loss: %.5f, acc1: %.5f, acc5: %.5f, lr: %.5f, forward_cost:%.5f s, backward_cost:%.5f s, minimize_cost:%.5f s, to_variable_cost: %.5f s, batch_cost: %.5f s, reader_cost: %.5f s' | ||
| % (epoch, batch_id, avg_loss.numpy()[0], | ||
| acc_top1.numpy()[0], acc_top5.numpy()[0], | ||
| current_step_lr, t3 - t2, t4 - t3, t5 - t4, t2 - t1, | ||
| t5 - t_last, t2 - t_last)) | ||
| t_last = time.time() | ||
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| print( | ||
| 'TRAIN End, Epoch {}, avg_loss= {}, avg_acc1= {}, avg_acc5= {}, lr={}'. | ||
| format(epoch, total_loss / total_sample, total_acc1 / | ||
| total_sample, total_acc5 / total_sample, | ||
| current_step_lr)) | ||
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| # 6.2 save checkpoint | ||
| save_parameters = (not use_data_parallel) or ( | ||
|
There was a problem hiding this comment. 不用走or逻辑？单卡时也可以local_rank==0保存，参考 |
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| use_data_parallel and | ||
| fluid.dygraph.parallel.Env().local_rank == 0 | ||
| ) #(data_parallel step6/6) | ||
| if save_parameters: | ||
| if not os.path.isdir(args.model_save_dir): | ||
| os.makedirs(args.model_save_dir) | ||
| model_path = os.path.join( | ||
| args.model_save_dir, | ||
| args.model_path_pre + "_epoch{}".format(epoch)) | ||
| fluid.dygraph.save_dygraph(video_model.state_dict(), model_path) | ||
| fluid.dygraph.save_dygraph(optimizer.state_dict(), model_path) | ||
| print('save_dygraph End, Epoch {}/{} '.format( | ||
| epoch, train_config.TRAIN.epoch)) | ||
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| # 6.3 validation | ||
| video_model.eval() | ||
| val(epoch, video_model, valid_config, args) | ||
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| # 7. save final model | ||
| save_parameters = (not args.use_data_parallel) or ( | ||
| args.use_data_parallel and | ||
| fluid.dygraph.parallel.Env().local_rank == 0) | ||
| if save_parameters: | ||
| model_path = os.path.join(args.model_save_dir, | ||
| args.model_path_pre + "_final") | ||
| fluid.dygraph.save_dygraph(video_model.state_dict(), model_path) | ||
| fluid.dygraph.save_dygraph(optimizer.state_dict(), model_path) | ||
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| logger.info('[TRAIN] training finished') | ||
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what the meaning of model path pre?