forked from PulseFocusPlatform/PulseFocusPlatform
385 lines
14 KiB
Python
385 lines
14 KiB
Python
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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# add python path of PadleDetection to sys.path
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parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
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if parent_path not in sys.path:
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sys.path.append(parent_path)
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import logging
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FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
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logging.basicConfig(level=logging.INFO, format=FORMAT)
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logger = logging.getLogger(__name__)
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import time
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import numpy as np
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import random
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import datetime
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import six
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from collections import deque
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from paddle.fluid import profiler
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from paddle import fluid
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from paddle.fluid.layers.learning_rate_scheduler import _decay_step_counter
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from paddle.fluid.optimizer import ExponentialMovingAverage
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from paddle.fluid.incubate.fleet.collective import fleet, DistributedStrategy # new line 1
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from paddle.fluid.incubate.fleet.base import role_maker # new line 2
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try:
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from ppdet.experimental import mixed_precision_context
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from ppdet.core.workspace import load_config, merge_config, create
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from ppdet.data.reader import create_reader
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from ppdet.utils import dist_utils
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from ppdet.utils.eval_utils import parse_fetches, eval_run, eval_results
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from ppdet.utils.stats import TrainingStats
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from ppdet.utils.cli import ArgsParser
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from ppdet.utils.check import check_gpu, check_version, check_config, enable_static_mode
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import ppdet.utils.checkpoint as checkpoint
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except ImportError as e:
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if sys.argv[0].find('static') >= 0:
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logger.error("Importing ppdet failed when running static model "
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"with error: {}\n"
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"please try:\n"
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"\t1. run static model under PaddleDetection/static "
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"directory\n"
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"\t2. run 'pip uninstall ppdet' to uninstall ppdet "
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"dynamic version firstly.".format(e))
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sys.exit(-1)
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else:
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raise e
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def main():
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role = role_maker.PaddleCloudRoleMaker(is_collective=True) # new line 3
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fleet.init(role) # new line 4
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env = os.environ
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num_trainers = int(env.get('PADDLE_TRAINERS_NUM', 0))
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assert num_trainers != 0, "multi-machine training process must be started using distributed.launch..."
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trainer_id = int(env.get("PADDLE_TRAINER_ID", 0))
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# set different seeds for different trainers
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random.seed(trainer_id)
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np.random.seed(trainer_id)
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if FLAGS.enable_ce:
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random.seed(0)
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np.random.seed(0)
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cfg = load_config(FLAGS.config)
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merge_config(FLAGS.opt)
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check_config(cfg)
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# check if set use_gpu=True in paddlepaddle cpu version
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check_gpu(cfg.use_gpu)
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# check if paddlepaddle version is satisfied
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check_version()
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save_only = getattr(cfg, 'save_prediction_only', False)
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if save_only:
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raise NotImplementedError('The config file only support prediction,'
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' training stage is not implemented now')
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main_arch = cfg.architecture
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assert cfg.use_gpu == True, "GPU must be supported for multi-machine training..."
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devices_num = fluid.core.get_cuda_device_count()
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if 'FLAGS_selected_gpus' in env:
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device_id = int(env['FLAGS_selected_gpus'])
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else:
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device_id = 0
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place = fluid.CUDAPlace(device_id) if cfg.use_gpu else fluid.CPUPlace()
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exe = fluid.Executor(place)
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lr_builder = create('LearningRate')
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optim_builder = create('OptimizerBuilder')
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# build program
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startup_prog = fluid.Program()
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train_prog = fluid.Program()
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if FLAGS.enable_ce:
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startup_prog.random_seed = 1000
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train_prog.random_seed = 1000
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with fluid.program_guard(train_prog, startup_prog):
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with fluid.unique_name.guard():
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model = create(main_arch)
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if FLAGS.fp16:
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assert (getattr(model.backbone, 'norm_type', None)
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!= 'affine_channel'), \
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'--fp16 currently does not support affine channel, ' \
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' please modify backbone settings to use batch norm'
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with mixed_precision_context(FLAGS.loss_scale, FLAGS.fp16) as ctx:
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inputs_def = cfg['TrainReader']['inputs_def']
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feed_vars, train_loader = model.build_inputs(**inputs_def)
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train_fetches = model.train(feed_vars)
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loss = train_fetches['loss']
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if FLAGS.fp16:
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loss *= ctx.get_loss_scale_var()
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lr = lr_builder()
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optimizer = optim_builder(lr)
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dist_strategy = DistributedStrategy()
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sync_bn = getattr(model.backbone, 'norm_type',
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None) == 'sync_bn'
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dist_strategy.sync_batch_norm = sync_bn
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dist_strategy.nccl_comm_num = 1
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exec_strategy = fluid.ExecutionStrategy()
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exec_strategy.num_threads = 3
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exec_strategy.num_iteration_per_drop_scope = 30
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dist_strategy.exec_strategy = exec_strategy
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dist_strategy.fuse_all_reduce_ops = True
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optimizer = fleet.distributed_optimizer(
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optimizer, strategy=dist_strategy) # new line 5
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optimizer.minimize(loss)
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if FLAGS.fp16:
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loss /= ctx.get_loss_scale_var()
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if 'use_ema' in cfg and cfg['use_ema']:
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global_steps = _decay_step_counter()
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ema = ExponentialMovingAverage(
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cfg['ema_decay'], thres_steps=global_steps)
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ema.update()
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# parse train fetches
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train_keys, train_values, _ = parse_fetches(train_fetches)
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train_values.append(lr)
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if FLAGS.eval:
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eval_prog = fluid.Program()
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with fluid.program_guard(eval_prog, startup_prog):
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with fluid.unique_name.guard():
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model = create(main_arch)
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inputs_def = cfg['EvalReader']['inputs_def']
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feed_vars, eval_loader = model.build_inputs(**inputs_def)
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fetches = model.eval(feed_vars)
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eval_prog = eval_prog.clone(True)
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eval_reader = create_reader(cfg.EvalReader, devices_num=1)
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# When iterable mode, set set_sample_list_generator(eval_reader, place)
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eval_loader.set_sample_list_generator(eval_reader)
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# parse eval fetches
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extra_keys = []
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if cfg.metric == 'COCO':
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extra_keys = ['im_info', 'im_id', 'im_shape']
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if cfg.metric == 'VOC':
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extra_keys = ['gt_bbox', 'gt_class', 'is_difficult']
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if cfg.metric == 'WIDERFACE':
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extra_keys = ['im_id', 'im_shape', 'gt_bbox']
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eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
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extra_keys)
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exe.run(startup_prog)
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compiled_train_prog = fleet.main_program
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if FLAGS.eval:
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compiled_eval_prog = fluid.CompiledProgram(eval_prog)
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fuse_bn = getattr(model.backbone, 'norm_type', None) == 'affine_channel'
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ignore_params = cfg.finetune_exclude_pretrained_params \
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if 'finetune_exclude_pretrained_params' in cfg else []
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start_iter = 0
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if FLAGS.resume_checkpoint:
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checkpoint.load_checkpoint(exe, train_prog, FLAGS.resume_checkpoint)
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start_iter = checkpoint.global_step()
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elif cfg.pretrain_weights and fuse_bn and not ignore_params:
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checkpoint.load_and_fusebn(exe, train_prog, cfg.pretrain_weights)
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elif cfg.pretrain_weights:
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checkpoint.load_params(
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exe, train_prog, cfg.pretrain_weights, ignore_params=ignore_params)
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train_reader = create_reader(
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cfg.TrainReader, (cfg.max_iters - start_iter) * devices_num,
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cfg,
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devices_num=devices_num)
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# When iterable mode, set set_sample_list_generator(train_reader, place)
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train_loader.set_sample_list_generator(train_reader)
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# whether output bbox is normalized in model output layer
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is_bbox_normalized = False
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if hasattr(model, 'is_bbox_normalized') and \
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callable(model.is_bbox_normalized):
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is_bbox_normalized = model.is_bbox_normalized()
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# if map_type not set, use default 11point, only use in VOC eval
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map_type = cfg.map_type if 'map_type' in cfg else '11point'
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train_stats = TrainingStats(cfg.log_iter, train_keys)
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train_loader.start()
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start_time = time.time()
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end_time = time.time()
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cfg_name = os.path.basename(FLAGS.config).split('.')[0]
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save_dir = os.path.join(cfg.save_dir, cfg_name)
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time_stat = deque(maxlen=cfg.log_iter)
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best_box_ap_list = [0.0, 0] #[map, iter]
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# use VisualDL to log data
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if FLAGS.use_vdl:
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assert six.PY3, "VisualDL requires Python >= 3.5"
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from visualdl import LogWriter
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vdl_writer = LogWriter(FLAGS.vdl_log_dir)
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vdl_loss_step = 0
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vdl_mAP_step = 0
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for it in range(start_iter, cfg.max_iters):
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start_time = end_time
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end_time = time.time()
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time_stat.append(end_time - start_time)
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time_cost = np.mean(time_stat)
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eta_sec = (cfg.max_iters - it) * time_cost
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eta = str(datetime.timedelta(seconds=int(eta_sec)))
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outs = exe.run(compiled_train_prog, fetch_list=train_values)
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stats = {k: np.array(v).mean() for k, v in zip(train_keys, outs[:-1])}
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# use vdl-paddle to log loss
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if FLAGS.use_vdl:
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if it % cfg.log_iter == 0:
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for loss_name, loss_value in stats.items():
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vdl_writer.add_scalar(loss_name, loss_value, vdl_loss_step)
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vdl_loss_step += 1
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train_stats.update(stats)
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logs = train_stats.log()
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if it % cfg.log_iter == 0 and trainer_id == 0:
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strs = 'iter: {}, lr: {:.6f}, {}, time: {:.3f}, eta: {}'.format(
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it, np.mean(outs[-1]), logs, time_cost, eta)
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logger.info(strs)
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# NOTE : profiler tools, used for benchmark
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if FLAGS.is_profiler and it == 5:
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profiler.start_profiler("All")
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elif FLAGS.is_profiler and it == 10:
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profiler.stop_profiler("total", FLAGS.profiler_path)
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return
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if (it > 0 and it % cfg.snapshot_iter == 0 or it == cfg.max_iters - 1) \
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and trainer_id == 0:
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save_name = str(it) if it != cfg.max_iters - 1 else "model_final"
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if 'use_ema' in cfg and cfg['use_ema']:
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exe.run(ema.apply_program)
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checkpoint.save(exe, train_prog, os.path.join(save_dir, save_name))
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if FLAGS.eval:
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# evaluation
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resolution = None
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if 'Mask' in cfg.architecture:
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resolution = model.mask_head.resolution
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results = eval_run(
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exe,
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compiled_eval_prog,
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eval_loader,
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eval_keys,
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eval_values,
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eval_cls,
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cfg,
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resolution=resolution)
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box_ap_stats = eval_results(
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results, cfg.metric, cfg.num_classes, resolution,
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is_bbox_normalized, FLAGS.output_eval, map_type,
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cfg['EvalReader']['dataset'])
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# use vdl_paddle to log mAP
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if FLAGS.use_vdl:
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vdl_writer.add_scalar("mAP", box_ap_stats[0], vdl_mAP_step)
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vdl_mAP_step += 1
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if box_ap_stats[0] > best_box_ap_list[0]:
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best_box_ap_list[0] = box_ap_stats[0]
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best_box_ap_list[1] = it
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checkpoint.save(exe, train_prog,
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os.path.join(save_dir, "best_model"))
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logger.info("Best test box ap: {}, in iter: {}".format(
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best_box_ap_list[0], best_box_ap_list[1]))
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if 'use_ema' in cfg and cfg['use_ema']:
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exe.run(ema.restore_program)
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train_loader.reset()
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if __name__ == '__main__':
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enable_static_mode()
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parser = ArgsParser()
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parser.add_argument(
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"-r",
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"--resume_checkpoint",
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default=None,
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type=str,
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help="Checkpoint path for resuming training.")
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parser.add_argument(
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"--fp16",
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action='store_true',
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default=False,
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help="Enable mixed precision training.")
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parser.add_argument(
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"--loss_scale",
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default=8.,
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type=float,
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help="Mixed precision training loss scale.")
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parser.add_argument(
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"--eval",
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action='store_true',
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default=False,
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help="Whether to perform evaluation in train")
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parser.add_argument(
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"--output_eval",
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default=None,
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type=str,
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help="Evaluation directory, default is current directory.")
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parser.add_argument(
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"--use_vdl",
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type=bool,
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default=False,
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help="whether to record the data to VisualDL.")
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parser.add_argument(
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'--vdl_log_dir',
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type=str,
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default="vdl_log_dir/scalar",
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help='VisualDL logging directory for scalar.')
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parser.add_argument(
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"--enable_ce",
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type=bool,
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default=False,
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help="If set True, enable continuous evaluation job."
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"This flag is only used for internal test.")
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#NOTE:args for profiler tools, used for benchmark
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parser.add_argument(
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'--is_profiler',
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type=int,
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default=0,
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help='The switch of profiler tools. (used for benchmark)')
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parser.add_argument(
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'--profiler_path',
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type=str,
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default="./detection.profiler",
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help='The profiler output file path. (used for benchmark)')
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FLAGS = parser.parse_args()
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main()
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