def main(args):
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
if args.opts:
cfg.update_from_list(args.opts)
if args.enable_ce:
random.seed(0)
np.random.seed(0)
cfg.TRAINER_ID = int(os.getenv("PADDLE_TRAINER_ID", 0))
cfg.NUM_TRAINERS = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
cfg.check_and_infer()
print_info(pprint.pformat(cfg))
name = cfg.TRAIN.MODEL_SAVE_DIR.replace("output/", "")
store_config = {
"model_name": cfg.MODEL.MODEL_NAME,
"solver": cfg.SOLVER.OPTIMIZER,
"lr": cfg.SOLVER.LR,
"lr_policy": cfg.SOLVER.LR_POLICY,
}
wandb.init(project="rs_segmentation",
name=name,
config=store_config,
dir=cfg.LOG_DIR,
notes="")
train(cfg)
def main(args):
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
if args.opts is not None:
cfg.update_from_list(args.opts)
cfg.check_and_infer(reset_dataset=True)
print(pprint.pformat(cfg))
train(cfg)
def main():
args = parse_args()
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
if args.opts:
cfg.update_from_list(args.opts)
cfg.check_and_infer()
print(pprint.pformat(cfg))
export_inference_model(args)
def main():
args = parse_args()
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
if args.opts:
cfg.update_from_list(args.opts)
cfg.check_and_infer()
print(pprint.pformat(cfg))
evaluate(cfg, **args.__dict__)
def parse_args():
parser = argparse.ArgumentParser(description="数据预处理")
parser.add_argument("-c", "--cfg_file", type=str, help="配置文件路径")
parser.add_argument("opts", nargs=argparse.REMAINDER)
args = parser.parse_args()
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
if args.opts:
cfg.update_from_list(args.opts)
def main(args):
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
if args.opts:
cfg.update_from_list(args.opts)
cfg.TRAINER_ID = int(os.getenv("PADDLE_TRAINER_ID", 0))
cfg.NUM_TRAINERS = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
cfg.check_and_infer()
print_info(pprint.pformat(cfg))
train(cfg)
def main(args):
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
cfg.check_and_infer(reset_dataset=True)
logger.info(pprint.pformat(cfg))
init_global_variable()
check_train_dataset()
init_global_variable()
check_val_dataset()
init_global_variable()
check_test_dataset()
inf_resize_value_check()
def main(args):
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
cfg.check_and_infer()
logger.info(pprint.pformat(cfg))
init_global_variable()
check_train_dataset()
init_global_variable()
check_val_dataset()
init_global_variable()
check_test_dataset()
inf_resize_value_check()
print("\nDetailed error information can be viewed in detail.log file.")
def parse_args():
parser = argparse.ArgumentParser(description="预测")
parser.add_argument("-c", "--cfg_file", type=str, help="配置文件路径")
parser.add_argument("--use_gpu",
action="store_true",
default=False,
help="使用GPU推理")
parser.add_argument("opts", nargs=argparse.REMAINDER)
args = parser.parse_args()
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
if args.opts:
cfg.update_from_list(args.opts)
if args.use_gpu: # 命令行参数只能从false改成true，不能声明false
cfg.TRAIN.USE_GPU = True
cfg.set_immutable(True)
# BGR->RGB
img = cv2.imread(
os.path.join(cfg.DATASET.DATA_DIR,
img_names[i]))[..., ::-1]
log_writer.add_image("Images/{}".format(img_names[i]),
img,
epoch,
dataformats='HWC')
#add ground truth (label) images
if grt is not None:
log_writer.add_image("Label/{}".format(img_names[i]),
grt[..., ::-1],
epoch,
dataformats='HWC')
# If in local_test mode, only visualize 5 images just for testing
# procedure
if local_test and img_cnt >= 5:
break
if __name__ == '__main__':
args = parse_args()
if args.cfg_file is not None:
cfg.update_from_file(args.cfg_file)
if args.opts:
cfg.update_from_list(args.opts)
cfg.check_and_infer()
print(pprint.pformat(cfg))
visualize(cfg, **args.__dict__)
def train(cfg):
# startup_prog = fluid.Program()
# train_prog = fluid.Program()
drop_last = True
dataset = SegDataset(
file_list=cfg.DATASET.TRAIN_FILE_LIST,
mode=ModelPhase.TRAIN,
shuffle=True,
data_dir=cfg.DATASET.DATA_DIR)
def data_generator():
if args.use_mpio:
data_gen = dataset.multiprocess_generator(
num_processes=cfg.DATALOADER.NUM_WORKERS,
max_queue_size=cfg.DATALOADER.BUF_SIZE)
else:
data_gen = dataset.generator()
batch_data = []
for b in data_gen:
batch_data.append(b)
if len(batch_data) == (cfg.BATCH_SIZE // cfg.NUM_TRAINERS):
for item in batch_data:
yield item[0], item[1], item[2]
batch_data = []
# If use sync batch norm strategy, drop last batch if number of samples
# in batch_data is less then cfg.BATCH_SIZE to avoid NCCL hang issues
if not cfg.TRAIN.SYNC_BATCH_NORM:
for item in batch_data:
yield item[0], item[1], item[2]
# Get device environment
# places = fluid.cuda_places() if args.use_gpu else fluid.cpu_places()
# place = places[0]
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id) if args.use_gpu else fluid.CPUPlace()
places = fluid.cuda_places() if args.use_gpu else fluid.cpu_places()
# Get number of GPU
dev_count = cfg.NUM_TRAINERS if cfg.NUM_TRAINERS > 1 else len(places)
print_info("#Device count: {}".format(dev_count))
# Make sure BATCH_SIZE can divided by GPU cards
assert cfg.BATCH_SIZE % dev_count == 0, (
'BATCH_SIZE:{} not divisble by number of GPUs:{}'.format(
cfg.BATCH_SIZE, dev_count))
# If use multi-gpu training mode, batch data will allocated to each GPU evenly
batch_size_per_dev = cfg.BATCH_SIZE // dev_count
print_info("batch_size_per_dev: {}".format(batch_size_per_dev))
data_loader, loss, lr, pred, grts, masks, image = build_model(
phase=ModelPhase.TRAIN)
data_loader.set_sample_generator(
data_generator, batch_size=batch_size_per_dev, drop_last=drop_last)
exe = fluid.Executor(place)
cfg.update_from_file(args.teacher_cfg_file)
# teacher_arch = teacher_cfg.architecture
teacher_program = fluid.Program()
teacher_startup_program = fluid.Program()
with fluid.program_guard(teacher_program, teacher_startup_program):
with fluid.unique_name.guard():
_, teacher_loss, _, _, _, _, _ = build_model(
teacher_program,
teacher_startup_program,
phase=ModelPhase.TRAIN,
image=image,
label=grts,
mask=masks)
exe.run(teacher_startup_program)
teacher_program = teacher_program.clone(for_test=True)
ckpt_dir = cfg.SLIM.KNOWLEDGE_DISTILL_TEACHER_MODEL_DIR
assert ckpt_dir is not None
print('load teacher model:', ckpt_dir)
if os.path.exists(ckpt_dir):
try:
fluid.load(teacher_program, os.path.join(ckpt_dir, 'model'), exe)
except:
fluid.io.load_params(exe, ckpt_dir, main_program=teacher_program)
# cfg = load_config(FLAGS.config)
cfg.update_from_file(args.cfg_file)
data_name_map = {
'image': 'image',
'label': 'label',
'mask': 'mask',
}
merge(teacher_program, fluid.default_main_program(), data_name_map, place)
distill_pairs = [[
'teacher_bilinear_interp_2.tmp_0', 'bilinear_interp_0.tmp_0'
]]
def distill(pairs, weight):
"""
Add 3 pairs of distillation losses, each pair of feature maps is the
input of teacher and student's yolov3_loss respectively
"""
loss = l2_loss(pairs[0][0], pairs[0][1])
weighted_loss = loss * weight
return weighted_loss
distill_loss = distill(distill_pairs, 0.1)
cfg.update_from_file(args.cfg_file)
optimizer = solver.Solver(None, None)
all_loss = loss + distill_loss
lr = optimizer.optimise(all_loss)
exe.run(fluid.default_startup_program())
exec_strategy = fluid.ExecutionStrategy()
# Clear temporary variables every 100 iteration
if args.use_gpu:
exec_strategy.num_threads = fluid.core.get_cuda_device_count()
exec_strategy.num_iteration_per_drop_scope = 100
build_strategy = fluid.BuildStrategy()
build_strategy.fuse_all_reduce_ops = False
build_strategy.fuse_all_optimizer_ops = False
build_strategy.fuse_elewise_add_act_ops = True
if cfg.NUM_TRAINERS > 1 and args.use_gpu:
dist_utils.prepare_for_multi_process(exe, build_strategy,
fluid.default_main_program())
exec_strategy.num_threads = 1
if cfg.TRAIN.SYNC_BATCH_NORM and args.use_gpu:
if dev_count > 1:
# Apply sync batch norm strategy
print_info("Sync BatchNorm strategy is effective.")
build_strategy.sync_batch_norm = True
else:
print_info(
"Sync BatchNorm strategy will not be effective if GPU device"
" count <= 1")
compiled_train_prog = fluid.CompiledProgram(
fluid.default_main_program()).with_data_parallel(
loss_name=all_loss.name,
exec_strategy=exec_strategy,
build_strategy=build_strategy)
# Resume training
begin_epoch = cfg.SOLVER.BEGIN_EPOCH
if cfg.TRAIN.RESUME_MODEL_DIR:
begin_epoch = load_checkpoint(exe, fluid.default_main_program())
# Load pretrained model
elif os.path.exists(cfg.TRAIN.PRETRAINED_MODEL_DIR):
load_pretrained_weights(exe, fluid.default_main_program(),
cfg.TRAIN.PRETRAINED_MODEL_DIR)
else:
print_info(
'Pretrained model dir {} not exists, training from scratch...'.
format(cfg.TRAIN.PRETRAINED_MODEL_DIR))
#fetch_list = [avg_loss.name, lr.name]
fetch_list = [
loss.name, 'teacher_' + teacher_loss.name, distill_loss.name, lr.name
]
if args.debug:
# Fetch more variable info and use streaming confusion matrix to
# calculate IoU results if in debug mode
np.set_printoptions(
precision=4, suppress=True, linewidth=160, floatmode="fixed")
fetch_list.extend([pred.name, grts.name, masks.name])
cm = ConfusionMatrix(cfg.DATASET.NUM_CLASSES, streaming=True)
if args.use_vdl:
if not args.vdl_log_dir:
print_info("Please specify the log directory by --vdl_log_dir.")
exit(1)
from visualdl import LogWriter
log_writer = LogWriter(args.vdl_log_dir)
# trainer_id = int(os.getenv("PADDLE_TRAINER_ID", 0))
# num_trainers = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
step = 0
all_step = cfg.DATASET.TRAIN_TOTAL_IMAGES // cfg.BATCH_SIZE
if cfg.DATASET.TRAIN_TOTAL_IMAGES % cfg.BATCH_SIZE and drop_last != True:
all_step += 1
all_step *= (cfg.SOLVER.NUM_EPOCHS - begin_epoch + 1)
avg_loss = 0.0
avg_t_loss = 0.0
avg_d_loss = 0.0
best_mIoU = 0.0
timer = Timer()
timer.start()
if begin_epoch > cfg.SOLVER.NUM_EPOCHS:
raise ValueError(
("begin epoch[{}] is larger than cfg.SOLVER.NUM_EPOCHS[{}]").format(
begin_epoch, cfg.SOLVER.NUM_EPOCHS))
if args.use_mpio:
print_info("Use multiprocess reader")
else:
print_info("Use multi-thread reader")
for epoch in range(begin_epoch, cfg.SOLVER.NUM_EPOCHS + 1):
data_loader.start()
while True:
try:
if args.debug:
# Print category IoU and accuracy to check whether the
# traning process is corresponed to expectation
loss, lr, pred, grts, masks = exe.run(
program=compiled_train_prog,
fetch_list=fetch_list,
return_numpy=True)
cm.calculate(pred, grts, masks)
avg_loss += np.mean(np.array(loss))
step += 1
if step % args.log_steps == 0:
speed = args.log_steps / timer.elapsed_time()
avg_loss /= args.log_steps
category_acc, mean_acc = cm.accuracy()
category_iou, mean_iou = cm.mean_iou()
print_info((
"epoch={} step={} lr={:.5f} loss={:.4f} acc={:.5f} mIoU={:.5f} step/sec={:.3f} | ETA {}"
).format(epoch, step, lr[0], avg_loss, mean_acc,
mean_iou, speed,
calculate_eta(all_step - step, speed)))
print_info("Category IoU: ", category_iou)
print_info("Category Acc: ", category_acc)
if args.use_vdl:
log_writer.add_scalar('Train/mean_iou', mean_iou,
step)
log_writer.add_scalar('Train/mean_acc', mean_acc,
step)
log_writer.add_scalar('Train/loss', avg_loss, step)
log_writer.add_scalar('Train/lr', lr[0], step)
log_writer.add_scalar('Train/step/sec', speed, step)
sys.stdout.flush()
avg_loss = 0.0
cm.zero_matrix()
timer.restart()
else:
# If not in debug mode, avoid unnessary log and calculate
loss, t_loss, d_loss, lr = exe.run(
program=compiled_train_prog,
fetch_list=fetch_list,
return_numpy=True)
avg_loss += np.mean(np.array(loss))
avg_t_loss += np.mean(np.array(t_loss))
avg_d_loss += np.mean(np.array(d_loss))
step += 1
if step % args.log_steps == 0 and cfg.TRAINER_ID == 0:
avg_loss /= args.log_steps
avg_t_loss /= args.log_steps
avg_d_loss /= args.log_steps
speed = args.log_steps / timer.elapsed_time()
print((
"epoch={} step={} lr={:.5f} loss={:.4f} teacher loss={:.4f} distill loss={:.4f} step/sec={:.3f} | ETA {}"
).format(epoch, step, lr[0], avg_loss, avg_t_loss,
avg_d_loss, speed,
calculate_eta(all_step - step, speed)))
if args.use_vdl:
log_writer.add_scalar('Train/loss', avg_loss, step)
log_writer.add_scalar('Train/lr', lr[0], step)
log_writer.add_scalar('Train/speed', speed, step)
sys.stdout.flush()
avg_loss = 0.0
avg_t_loss = 0.0
avg_d_loss = 0.0
timer.restart()
except fluid.core.EOFException:
data_loader.reset()
break
except Exception as e:
print(e)
if (epoch % cfg.TRAIN.SNAPSHOT_EPOCH == 0
or epoch == cfg.SOLVER.NUM_EPOCHS) and cfg.TRAINER_ID == 0:
ckpt_dir = save_checkpoint(fluid.default_main_program(), epoch)
if args.do_eval:
print("Evaluation start")
_, mean_iou, _, mean_acc = evaluate(
cfg=cfg,
ckpt_dir=ckpt_dir,
use_gpu=args.use_gpu,
use_mpio=args.use_mpio)
if args.use_vdl:
log_writer.add_scalar('Evaluate/mean_iou', mean_iou, step)
log_writer.add_scalar('Evaluate/mean_acc', mean_acc, step)
if mean_iou > best_mIoU:
best_mIoU = mean_iou
update_best_model(ckpt_dir)
print_info("Save best model {} to {}, mIoU = {:.4f}".format(
ckpt_dir,
os.path.join(cfg.TRAIN.MODEL_SAVE_DIR, 'best_model'),
mean_iou))
# Use VisualDL to visualize results
if args.use_vdl and cfg.DATASET.VIS_FILE_LIST is not None:
visualize(
cfg=cfg,
use_gpu=args.use_gpu,
vis_file_list=cfg.DATASET.VIS_FILE_LIST,
vis_dir="visual",
ckpt_dir=ckpt_dir,
log_writer=log_writer)
if cfg.TRAINER_ID == 0:
ckpt_dir = save_checkpoint(fluid.default_main_program(), epoch)
# save final model
if cfg.TRAINER_ID == 0:
save_checkpoint(fluid.default_main_program(), 'final')
from __future__ import print_function
import os
import json
from flask import Flask
from flask import request
# GPU memory garbage collection optimization flags
os.environ['FLAGS_eager_delete_tensor_gb'] = "0.0"
import numpy as np
from PIL import Image
import paddle.fluid as fluid
from utils.config import cfg
cfg.update_from_file("demo_server.yaml")
from models.model_builder import build_model
from models.model_builder import ModelPhase
server = Flask(__name__)
# ###### global init ######
def load_model():
startup_prog = fluid.Program()
test_prog = fluid.Program()
pred, logit = build_model(test_prog, startup_prog, phase=ModelPhase.VISUAL)
fetch_list = [pred.name]
# Clone forward graph
test_prog = test_prog.clone(for_test=True)