train.py 12.9 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import time
import numpy as np
X
xiegegege 已提交
22
import random
23 24
import datetime
from collections import deque
H
hysunflower 已提交
25
from paddle.fluid import profiler
26

27

28 29 30 31 32
def set_paddle_flags(**kwargs):
    for key, value in kwargs.items():
        if os.environ.get(key, None) is None:
            os.environ[key] = str(value)

33

34
# NOTE(paddle-dev): All of these flags should be set before
35
# `import paddle`. Otherwise, it would not take any effect.
36 37 38 39 40
set_paddle_flags(
    FLAGS_eager_delete_tensor_gb=0,  # enable GC to save memory
)

from paddle import fluid
41 42

from ppdet.experimental import mixed_precision_context
43
from ppdet.core.workspace import load_config, merge_config, create
44
from ppdet.data.reader import create_reader
45

46
from ppdet.utils import dist_utils
47 48 49
from ppdet.utils.eval_utils import parse_fetches, eval_run, eval_results
from ppdet.utils.stats import TrainingStats
from ppdet.utils.cli import ArgsParser
W
wangguanzhong 已提交
50
from ppdet.utils.check import check_gpu, check_version
51 52 53 54 55 56 57 58 59
import ppdet.utils.checkpoint as checkpoint

import logging
FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
logging.basicConfig(level=logging.INFO, format=FORMAT)
logger = logging.getLogger(__name__)


def main():
60 61 62 63 64 65 66 67
    env = os.environ
    FLAGS.dist = 'PADDLE_TRAINER_ID' in env and 'PADDLE_TRAINERS_NUM' in env
    if FLAGS.dist:
        trainer_id = int(env['PADDLE_TRAINER_ID'])
        local_seed = (99 + trainer_id)
        random.seed(local_seed)
        np.random.seed(local_seed)

X
xiegegege 已提交
68 69 70 71
    if FLAGS.enable_ce:
        random.seed(0)
        np.random.seed(0)

72 73 74 75 76 77 78
    cfg = load_config(FLAGS.config)
    if 'architecture' in cfg:
        main_arch = cfg.architecture
    else:
        raise ValueError("'architecture' not specified in config file.")

    merge_config(FLAGS.opt)
W
wangguanzhong 已提交
79

80 81
    if 'log_iter' not in cfg:
        cfg.log_iter = 20
82

83 84
    # check if set use_gpu=True in paddlepaddle cpu version
    check_gpu(cfg.use_gpu)
W
wangguanzhong 已提交
85 86
    # check if paddlepaddle version is satisfied
    check_version()
87

88 89 90
    if cfg.use_gpu:
        devices_num = fluid.core.get_cuda_device_count()
    else:
91
        devices_num = int(os.environ.get('CPU_NUM', 1))
92

93 94 95 96
    if 'FLAGS_selected_gpus' in env:
        device_id = int(env['FLAGS_selected_gpus'])
    else:
        device_id = 0
W
wangguanzhong 已提交
97
    place = fluid.CUDAPlace(device_id) if cfg.use_gpu else fluid.CPUPlace()
98 99 100 101 102 103 104 105
    exe = fluid.Executor(place)

    lr_builder = create('LearningRate')
    optim_builder = create('OptimizerBuilder')

    # build program
    startup_prog = fluid.Program()
    train_prog = fluid.Program()
X
xiegegege 已提交
106 107 108
    if FLAGS.enable_ce:
        startup_prog.random_seed = 1000
        train_prog.random_seed = 1000
109 110
    with fluid.program_guard(train_prog, startup_prog):
        with fluid.unique_name.guard():
111
            model = create(main_arch)
112 113 114 115 116 117
            if FLAGS.fp16:
                assert (getattr(model.backbone, 'norm_type', None)
                        != 'affine_channel'), \
                    '--fp16 currently does not support affine channel, ' \
                    ' please modify backbone settings to use batch norm'

118
            with mixed_precision_context(FLAGS.loss_scale, FLAGS.fp16) as ctx:
119 120
                inputs_def = cfg['TrainReader']['inputs_def']
                feed_vars, train_loader = model.build_inputs(**inputs_def)
121 122 123 124 125 126 127 128 129
                train_fetches = model.train(feed_vars)
                loss = train_fetches['loss']
                if FLAGS.fp16:
                    loss *= ctx.get_loss_scale_var()
                lr = lr_builder()
                optimizer = optim_builder(lr)
                optimizer.minimize(loss)
                if FLAGS.fp16:
                    loss /= ctx.get_loss_scale_var()
130 131 132 133 134 135 136 137 138

    # parse train fetches
    train_keys, train_values, _ = parse_fetches(train_fetches)
    train_values.append(lr)

    if FLAGS.eval:
        eval_prog = fluid.Program()
        with fluid.program_guard(eval_prog, startup_prog):
            with fluid.unique_name.guard():
139
                model = create(main_arch)
140 141
                inputs_def = cfg['EvalReader']['inputs_def']
                feed_vars, eval_loader = model.build_inputs(**inputs_def)
142
                fetches = model.eval(feed_vars)
143 144
        eval_prog = eval_prog.clone(True)

145
        eval_reader = create_reader(cfg.EvalReader, devices_num=1)
W
wangguanzhong 已提交
146
        eval_loader.set_sample_list_generator(eval_reader, place)
147

148
        # parse eval fetches
149 150 151 152
        extra_keys = []
        if cfg.metric == 'COCO':
            extra_keys = ['im_info', 'im_id', 'im_shape']
        if cfg.metric == 'VOC':
153
            extra_keys = ['gt_bbox', 'gt_class', 'is_difficult']
154
        if cfg.metric == 'WIDERFACE':
155
            extra_keys = ['im_id', 'im_shape', 'gt_bbox']
156 157 158 159 160
        eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
                                                         extra_keys)

    # compile program for multi-devices
    build_strategy = fluid.BuildStrategy()
161
    build_strategy.fuse_all_optimizer_ops = False
K
Kaipeng Deng 已提交
162
    # only enable sync_bn in multi GPU devices
163
    sync_bn = getattr(model.backbone, 'norm_type', None) == 'sync_bn'
164 165
    build_strategy.sync_batch_norm = sync_bn and devices_num > 1 \
        and cfg.use_gpu
166 167 168 169 170 171

    exec_strategy = fluid.ExecutionStrategy()
    # iteration number when CompiledProgram tries to drop local execution scopes.
    # Set it to be 1 to save memory usages, so that unused variables in
    # local execution scopes can be deleted after each iteration.
    exec_strategy.num_iteration_per_drop_scope = 1
172
    if FLAGS.dist:
W
wangguanzhong 已提交
173 174
        dist_utils.prepare_for_multi_process(exe, build_strategy, startup_prog,
                                             train_prog)
175
        exec_strategy.num_threads = 1
176 177

    exe.run(startup_prog)
178 179 180 181 182 183 184
    compiled_train_prog = fluid.CompiledProgram(train_prog).with_data_parallel(
        loss_name=loss.name,
        build_strategy=build_strategy,
        exec_strategy=exec_strategy)

    if FLAGS.eval:
        compiled_eval_prog = fluid.compiler.CompiledProgram(eval_prog)
185

186
    fuse_bn = getattr(model.backbone, 'norm_type', None) == 'affine_channel'
187

Q
qingqing01 已提交
188 189 190 191
    ignore_params = cfg.finetune_exclude_pretrained_params \
                 if 'finetune_exclude_pretrained_params' in cfg else []

    start_iter = 0
192 193
    if FLAGS.resume_checkpoint:
        checkpoint.load_checkpoint(exe, train_prog, FLAGS.resume_checkpoint)
Q
qingqing01 已提交
194
        start_iter = checkpoint.global_step()
195
    elif cfg.pretrain_weights and fuse_bn and not ignore_params:
196 197
        checkpoint.load_and_fusebn(exe, train_prog, cfg.pretrain_weights)
    elif cfg.pretrain_weights:
198 199
        checkpoint.load_params(
            exe, train_prog, cfg.pretrain_weights, ignore_params=ignore_params)
200

201 202 203 204
    train_reader = create_reader(
        cfg.TrainReader, (cfg.max_iters - start_iter) * devices_num,
        cfg,
        devices_num=devices_num)
W
wangguanzhong 已提交
205
    train_loader.set_sample_list_generator(train_reader, place)
206

207 208 209 210 211 212
    # whether output bbox is normalized in model output layer
    is_bbox_normalized = False
    if hasattr(model, 'is_bbox_normalized') and \
            callable(model.is_bbox_normalized):
        is_bbox_normalized = model.is_bbox_normalized()

K
Kaipeng Deng 已提交
213 214 215
    # if map_type not set, use default 11point, only use in VOC eval
    map_type = cfg.map_type if 'map_type' in cfg else '11point'

216
    train_stats = TrainingStats(cfg.log_smooth_window, train_keys)
W
wangguanzhong 已提交
217
    train_loader.start()
218 219 220 221 222
    start_time = time.time()
    end_time = time.time()

    cfg_name = os.path.basename(FLAGS.config).split('.')[0]
    save_dir = os.path.join(cfg.save_dir, cfg_name)
223
    time_stat = deque(maxlen=cfg.log_smooth_window)
224
    best_box_ap_list = [0.0, 0]  #[map, iter]
225 226 227 228 229 230 231 232

    # use tb-paddle to log data
    if FLAGS.use_tb:
        from tb_paddle import SummaryWriter
        tb_writer = SummaryWriter(FLAGS.tb_log_dir)
        tb_loss_step = 0
        tb_mAP_step = 0

Q
qingqing01 已提交
233
    for it in range(start_iter, cfg.max_iters):
234 235
        start_time = end_time
        end_time = time.time()
236 237 238 239
        time_stat.append(end_time - start_time)
        time_cost = np.mean(time_stat)
        eta_sec = (cfg.max_iters - it) * time_cost
        eta = str(datetime.timedelta(seconds=int(eta_sec)))
240
        outs = exe.run(compiled_train_prog, fetch_list=train_values)
241
        stats = {k: np.array(v).mean() for k, v in zip(train_keys, outs[:-1])}
242 243 244 245 246 247 248 249

        # use tb-paddle to log loss
        if FLAGS.use_tb:
            if it % cfg.log_iter == 0:
                for loss_name, loss_value in stats.items():
                    tb_writer.add_scalar(loss_name, loss_value, tb_loss_step)
                tb_loss_step += 1

250 251
        train_stats.update(stats)
        logs = train_stats.log()
252
        if it % cfg.log_iter == 0 and (not FLAGS.dist or trainer_id == 0):
253 254 255
            strs = 'iter: {}, lr: {:.6f}, {}, time: {:.3f}, eta: {}'.format(
                it, np.mean(outs[-1]), logs, time_cost, eta)
            logger.info(strs)
256

H
hysunflower 已提交
257 258 259 260 261 262 263
        # NOTE : profiler tools, used for benchmark
        if FLAGS.is_profiler and it == 5:
            profiler.start_profiler("All")
        elif FLAGS.is_profiler and it == 10:
            profiler.stop_profiler("total", FLAGS.profiler_path)
            return

littletomatodonkey's avatar
littletomatodonkey 已提交
264

265 266
        if (it > 0 and it % cfg.snapshot_iter == 0 or it == cfg.max_iters - 1) \
           and (not FLAGS.dist or trainer_id == 0):
267 268
            save_name = str(it) if it != cfg.max_iters - 1 else "model_final"
            checkpoint.save(exe, train_prog, os.path.join(save_dir, save_name))
269 270 271 272

            if FLAGS.eval:
                # evaluation
                resolution = None
W
wangguanzhong 已提交
273
                if 'Mask' in cfg.architecture:
274
                    resolution = model.mask_head.resolution
W
wangguanzhong 已提交
275 276 277 278 279 280 281 282
                results = eval_run(
                    exe,
                    compiled_eval_prog,
                    eval_loader,
                    eval_keys,
                    eval_values,
                    eval_cls,
                    resolution=resolution)
283
                box_ap_stats = eval_results(
284 285 286
                    results, cfg.metric, cfg.num_classes, resolution,
                    is_bbox_normalized, FLAGS.output_eval, map_type,
                    cfg['EvalReader']['dataset'])
287

288 289 290 291
                # use tb_paddle to log mAP
                if FLAGS.use_tb:
                    tb_writer.add_scalar("mAP", box_ap_stats[0], tb_mAP_step)
                    tb_mAP_step += 1
292

293 294 295
                if box_ap_stats[0] > best_box_ap_list[0]:
                    best_box_ap_list[0] = box_ap_stats[0]
                    best_box_ap_list[1] = it
296 297
                    checkpoint.save(exe, train_prog,
                                    os.path.join(save_dir, "best_model"))
298
                logger.info("Best test box ap: {}, in iter: {}".format(
299
                    best_box_ap_list[0], best_box_ap_list[1]))
300

W
wangguanzhong 已提交
301
    train_loader.reset()
302 303 304 305


if __name__ == '__main__':
    parser = ArgsParser()
306 307 308 309 310 311
    parser.add_argument(
        "-r",
        "--resume_checkpoint",
        default=None,
        type=str,
        help="Checkpoint path for resuming training.")
312 313 314 315 316 317 318 319 320 321
    parser.add_argument(
        "--fp16",
        action='store_true',
        default=False,
        help="Enable mixed precision training.")
    parser.add_argument(
        "--loss_scale",
        default=8.,
        type=float,
        help="Mixed precision training loss scale.")
322 323 324 325 326 327
    parser.add_argument(
        "--eval",
        action='store_true',
        default=False,
        help="Whether to perform evaluation in train")
    parser.add_argument(
328
        "--output_eval",
329 330
        default=None,
        type=str,
331
        help="Evaluation directory, default is current directory.")
332 333 334 335 336 337 338 339 340 341
    parser.add_argument(
        "--use_tb",
        type=bool,
        default=False,
        help="whether to record the data to Tensorboard.")
    parser.add_argument(
        '--tb_log_dir',
        type=str,
        default="tb_log_dir/scalar",
        help='Tensorboard logging directory for scalar.')
X
xiegegege 已提交
342 343 344 345 346 347
    parser.add_argument(
        "--enable_ce",
        type=bool,
        default=False,
        help="If set True, enable continuous evaluation job."
        "This flag is only used for internal test.")
H
hysunflower 已提交
348 349 350 351 352 353 354 355 356 357 358 359

    #NOTE:args for profiler tools, used for benchmark
    parser.add_argument(
        '--is_profiler',
        type=int,
        default=0,
        help='The switch of profiler tools. (used for benchmark)')
    parser.add_argument(
        '--profiler_path',
        type=str,
        default="./detection.profiler",
        help='The profiler output file path. (used for benchmark)')
360 361
    FLAGS = parser.parse_args()
    main()