def optimizer_setting(lr):
batch_size = 1024
iters = 150000 // batch_size
boundaries = [i * iters for i in [60,100,150]]
values = [ i * lr for i in [1,0.5,0.1,0.05]]
optimizer = fluid.optimizer.Adam(
#momentum=0.9,
learning_rate=exponential_with_warmup_decay(
learning_rate=lr,
boundaries=boundaries,
values=values,
warmup_iter=200,
warmup_factor=0.),
regularization=fluid.regularizer.L2Decay(0.00001), )
return optimizer
def train():
if cfg.debug or args.enable_ce:
fluid.default_startup_program().random_seed = 1000
fluid.default_main_program().random_seed = 1000
random.seed(0)
np.random.seed(0)
if not os.path.exists(cfg.model_save_dir):
os.makedirs(cfg.model_save_dir)
model = YOLOv3()
model.build_model()
input_size = cfg.input_size
loss = model.loss()
loss.persistable = True
devices_num = get_device_num()
print("Found {} CUDA devices.".format(devices_num))
learning_rate = cfg.learning_rate
boundaries = cfg.lr_steps
gamma = cfg.lr_gamma
step_num = len(cfg.lr_steps)
values = [learning_rate * (gamma**i) for i in range(step_num + 1)]
optimizer = fluid.optimizer.Momentum(
learning_rate=exponential_with_warmup_decay(
learning_rate=learning_rate,
boundaries=boundaries,
values=values,
warmup_iter=cfg.warm_up_iter,
warmup_factor=cfg.warm_up_factor),
regularization=fluid.regularizer.L2Decay(cfg.weight_decay),
momentum=cfg.momentum)
optimizer.minimize(loss)
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
if cfg.pretrain:
if not os.path.exists(cfg.pretrain):
print("Pretrain weights not found: {}".format(cfg.pretrain))
def if_exist(var):
return os.path.exists(os.path.join(cfg.pretrain, var.name)) \
and var.name.find('yolo_output') < 0
fluid.io.load_vars(exe, cfg.pretrain, predicate=if_exist)
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False #gc and memory optimize may conflict
syncbn = cfg.syncbn
if (syncbn and devices_num <= 1) or num_trainers > 1:
print("Disable syncbn in single device")
syncbn = False
build_strategy.sync_batch_norm = syncbn
exec_strategy = fluid.ExecutionStrategy()
if cfg.use_gpu and num_trainers > 1:
dist_utils.prepare_for_multi_process(exe, build_strategy,
fluid.default_main_program())
exec_strategy.num_threads = 1
compile_program = fluid.compiler.CompiledProgram(fluid.default_main_program(
)).with_data_parallel(
loss_name=loss.name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
random_sizes = [cfg.input_size]
if cfg.random_shape:
random_sizes = [32 * i for i in range(10, 20)]
total_iter = cfg.max_iter - cfg.start_iter
mixup_iter = total_iter - cfg.no_mixup_iter
shuffle = True
if args.enable_ce:
shuffle = False
shuffle_seed = None
# NOTE: yolov3 is a special model, if num_trainers > 1, each process
# trian the completed dataset.
# if num_trainers > 1: shuffle_seed = 1
train_reader = reader.train(
input_size,
batch_size=cfg.batch_size,
shuffle=shuffle,
shuffle_seed=shuffle_seed,
total_iter=total_iter * devices_num,
mixup_iter=mixup_iter * devices_num,
random_sizes=random_sizes,
use_multiprocess_reader=cfg.use_multiprocess_reader)
py_reader = model.py_reader
py_reader.decorate_paddle_reader(train_reader)
def save_model(postfix):
model_path = os.path.join(cfg.model_save_dir, postfix)
if os.path.isdir(model_path):
shutil.rmtree(model_path)
fluid.io.save_persistables(exe, model_path)
fetch_list = [loss]
py_reader.start()
smoothed_loss = SmoothedValue()
try:
start_time = time.time()
prev_start_time = start_time
snapshot_loss = 0
snapshot_time = 0
for iter_id in range(cfg.start_iter, cfg.max_iter):
prev_start_time = start_time
start_time = time.time()
losses = exe.run(compile_program,
fetch_list=[v.name for v in fetch_list])
smoothed_loss.add_value(np.mean(np.array(losses[0])))
snapshot_loss += np.mean(np.array(losses[0]))
snapshot_time += start_time - prev_start_time
lr = np.array(fluid.global_scope().find_var('learning_rate')
.get_tensor())
print("Iter {:d}, lr {:.6f}, loss {:.6f}, time {:.5f}".format(
iter_id, lr[0],
smoothed_loss.get_mean_value(), start_time - prev_start_time))
sys.stdout.flush()
if (iter_id + 1) % cfg.snapshot_iter == 0:
save_model("model_iter{}".format(iter_id))
print("Snapshot {} saved, average loss: {}, \
average time: {}".format(
iter_id + 1, snapshot_loss / float(cfg.snapshot_iter),
snapshot_time / float(cfg.snapshot_iter)))
if args.enable_ce and iter_id == cfg.max_iter - 1:
if devices_num == 1:
print("kpis\ttrain_cost_1card\t%f" %
(snapshot_loss / float(cfg.snapshot_iter)))
print("kpis\ttrain_duration_1card\t%f" %
(snapshot_time / float(cfg.snapshot_iter)))
else:
print("kpis\ttrain_cost_8card\t%f" %
(snapshot_loss / float(cfg.snapshot_iter)))
print("kpis\ttrain_duration_8card\t%f" %
(snapshot_time / float(cfg.snapshot_iter)))
snapshot_loss = 0
snapshot_time = 0
except fluid.core.EOFException:
py_reader.reset()
save_model('model_final')
def train():
learning_rate = cfg.learning_rate
image_shape = [3, cfg.TRAIN.max_size, cfg.TRAIN.max_size]
devices_num = get_device_num()
total_batch_size = devices_num * cfg.TRAIN.im_per_batch
use_random = True
model = model_builder.RCNN(
add_conv_body_func=resnet.
add_ResNet50_conv4_body, # res4: [-1, 1024, 84, 84]
add_roi_box_head_func=resnet.
add_ResNet_roi_conv5_head, # res5: [-1, 2048, 7, 7]
use_pyreader=cfg.use_pyreader,
use_random=use_random)
model.build_model(image_shape)
losses, keys = model.loss()
loss = losses[0]
fetch_list = losses
boundaries = cfg.lr_steps
gamma = cfg.lr_gamma
step_num = len(cfg.lr_steps)
values = [learning_rate * (gamma**i) for i in range(step_num + 1)]
lr = exponential_with_warmup_decay(learning_rate=learning_rate,
boundaries=boundaries,
values=values,
warmup_iter=cfg.warm_up_iter,
warmup_factor=cfg.warm_up_factor)
optimizer = fluid.optimizer.Momentum(
learning_rate=lr,
regularization=fluid.regularizer.L2Decay(cfg.weight_decay),
momentum=cfg.momentum)
optimizer.minimize(loss)
fetch_list = fetch_list + [lr]
for var in fetch_list:
var.persistable = True
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
if cfg.pretrained_model:
def if_exist(var):
return os.path.exists(os.path.join(cfg.pretrained_model, var.name))
fluid.io.load_vars(exe, cfg.pretrained_model, predicate=if_exist)
if cfg.parallel:
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = True
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.num_iteration_per_drop_scope = 10
if num_trainers > 1 and cfg.use_gpu:
dist_utils.prepare_for_multi_process(exe, build_strategy,
fluid.default_main_program())
# the process is fast when num_threads is 1 for multi-process training
exec_strategy.num_threads = 1
train_exe = fluid.ParallelExecutor(use_cuda=bool(cfg.use_gpu),
loss_name=loss.name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
else:
train_exe = exe
shuffle = True
# NOTE: do not shuffle dataset when using multi-process training
shuffle_seed = None
if num_trainers > 1:
shuffle_seed = 1
if cfg.use_pyreader:
train_reader = reader.train(batch_size=cfg.TRAIN.im_per_batch,
total_batch_size=total_batch_size,
padding_total=cfg.TRAIN.padding_minibatch,
shuffle=shuffle,
shuffle_seed=shuffle_seed)
if num_trainers > 1:
assert shuffle_seed is not None, "If num_trainers > 1, the shuffle_seed must be set, because the order of batch data generated by reader must be the same in the respective processes"
train_reader = fluid.contrib.reader.distributed_batch_reader(
train_reader)
py_reader = model.py_reader
py_reader.decorate_paddle_reader(train_reader)
else:
if num_trainers > 1:
shuffle = False
train_reader = reader.train(batch_size=total_batch_size,
shuffle=shuffle)
feeder = fluid.DataFeeder(place=place, feed_list=model.feeds())
def save_model(postfix):
model_path = os.path.join(cfg.model_save_dir, postfix)
if os.path.isdir(model_path):
shutil.rmtree(model_path)
fluid.io.save_persistables(exe, model_path)
def train_loop_pyreader():
py_reader.start()
train_stats = TrainingStats(cfg.log_window, keys)
try:
start_time = time.time()
for iter_id in range(cfg.max_iter):
prev_start_time = start_time
start_time = time.time()
outs = train_exe.run(fetch_list=[v.name for v in fetch_list])
stats = {
k: np.array(v).mean()
for k, v in zip(keys, outs[:-1])
}
train_stats.update(stats)
logs = train_stats.log()
strs = '{}, iter: {}, lr: {:.5f}, {}, time: {:.3f}'.format(
now_time(), iter_id, np.mean(outs[-1]), logs,
start_time - prev_start_time)
print(strs)
sys.stdout.flush()
if (iter_id + 1) % cfg.TRAIN.snapshot_iter == 0:
save_model("model_iter{}".format(iter_id))
end_time = time.time()
total_time = end_time - first_start_time
last_loss = np.array(outs[0]).mean()
except (StopIteration, fluid.core.EOFException):
py_reader.reset()
def train_loop():
train_stats = TrainingStats(cfg.log_window, keys)
start_time = time.time()
for iter_id, data in enumerate(train_reader()):
prev_start_time = start_time
start_time = time.time()
outs = train_exe.run(fetch_list=[v.name for v in fetch_list],
feed=feeder.feed(data))
stats = {k: np.array(v).mean() for k, v in zip(keys, outs[:-1])}
train_stats.update(stats)
logs = train_stats.log()
stats = '{}, iter: {}, lr: {:.5f}, {}, time: {:.3f}'.format(
now_time(), iter_id, np.mean(outs[-1]), logs,
start_time - prev_start_time)
print(stats)
sys.stdout.flush()
if (iter_id + 1) % cfg.TRAIN.snapshot_iter == 0:
save_model("model_iter{}".format(iter_id))
if (iter_id + 1) == cfg.max_iter:
break
end_time = time.time()
total_time = end_time - start_time
last_loss = np.array(outs[0]).mean()
if cfg.use_pyreader:
train_loop_pyreader()
else:
train_loop()
save_model('model_final')
def train():
learning_rate = cfg.learning_rate
image_shape = [3, cfg.TRAIN.max_size, cfg.TRAIN.max_size]
if cfg.enable_ce:
fluid.default_startup_program().random_seed = 1000
fluid.default_main_program().random_seed = 1000
import random
random.seed(0)
np.random.seed(0)
devices = os.getenv("CUDA_VISIBLE_DEVICES") or ""
devices_num = len(devices.split(","))
total_batch_size = devices_num * cfg.TRAIN.im_per_batch
use_random = True
if cfg.enable_ce:
use_random = False
model = model_builder.RCNN(
add_conv_body_func=resnet.add_ResNet50_conv4_body,
add_roi_box_head_func=resnet.add_ResNet_roi_conv5_head,
use_pyreader=cfg.use_pyreader,
use_random=use_random)
model.build_model(image_shape)
losses, keys = model.loss()
loss = losses[0]
fetch_list = losses
boundaries = cfg.lr_steps
gamma = cfg.lr_gamma
step_num = len(cfg.lr_steps)
values = [learning_rate * (gamma**i) for i in range(step_num + 1)]
lr = exponential_with_warmup_decay(learning_rate=learning_rate,
boundaries=boundaries,
values=values,
warmup_iter=cfg.warm_up_iter,
warmup_factor=cfg.warm_up_factor)
optimizer = fluid.optimizer.Momentum(
learning_rate=lr,
regularization=fluid.regularizer.L2Decay(cfg.weight_decay),
momentum=cfg.momentum)
optimizer.minimize(loss)
fetch_list = fetch_list + [lr]
for var in fetch_list:
var.persistable = True
#fluid.memory_optimize(fluid.default_main_program(), skip_opt_set=set(fetch_list))
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
if cfg.pretrained_model:
def if_exist(var):
return os.path.exists(os.path.join(cfg.pretrained_model, var.name))
fluid.io.load_vars(exe, cfg.pretrained_model, predicate=if_exist)
if cfg.parallel:
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = False
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.use_experimental_executor = True
train_exe = fluid.ParallelExecutor(use_cuda=bool(cfg.use_gpu),
loss_name=loss.name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
else:
train_exe = exe
shuffle = True
if cfg.enable_ce:
shuffle = False
if cfg.use_pyreader:
train_reader = reader.train(batch_size=cfg.TRAIN.im_per_batch,
total_batch_size=total_batch_size,
padding_total=cfg.TRAIN.padding_minibatch,
shuffle=shuffle)
py_reader = model.py_reader
py_reader.decorate_paddle_reader(train_reader)
else:
train_reader = reader.train(batch_size=total_batch_size,
shuffle=shuffle)
feeder = fluid.DataFeeder(place=place, feed_list=model.feeds())
def save_model(postfix):
model_path = os.path.join(cfg.model_save_dir, postfix)
if os.path.isdir(model_path):
shutil.rmtree(model_path)
fluid.io.save_persistables(exe, model_path)
def train_loop_pyreader():
py_reader.start()
train_stats = TrainingStats(cfg.log_window, keys)
try:
start_time = time.time()
prev_start_time = start_time
for iter_id in range(cfg.max_iter):
prev_start_time = start_time
start_time = time.time()
outs = train_exe.run(fetch_list=[v.name for v in fetch_list])
stats = {
k: np.array(v).mean()
for k, v in zip(keys, outs[:-1])
}
train_stats.update(stats)
logs = train_stats.log()
strs = '{}, iter: {}, lr: {:.5f}, {}, time: {:.3f}'.format(
now_time(), iter_id, np.mean(outs[-1]), logs,
start_time - prev_start_time)
print(strs)
sys.stdout.flush()
if (iter_id + 1) % cfg.TRAIN.snapshot_iter == 0:
save_model("model_iter{}".format(iter_id))
end_time = time.time()
total_time = end_time - start_time
last_loss = np.array(outs[0]).mean()
if cfg.enable_ce:
gpu_num = devices_num
epoch_idx = iter_id + 1
loss = last_loss
print("kpis\teach_pass_duration_card%s\t%s" %
(gpu_num, total_time / epoch_idx))
print("kpis\ttrain_loss_card%s\t%s" % (gpu_num, loss))
except (StopIteration, fluid.core.EOFException):
py_reader.reset()
def train_loop():
start_time = time.time()
prev_start_time = start_time
start = start_time
train_stats = TrainingStats(cfg.log_window, keys)
for iter_id, data in enumerate(train_reader()):
prev_start_time = start_time
start_time = time.time()
outs = train_exe.run(fetch_list=[v.name for v in fetch_list],
feed=feeder.feed(data))
stats = {k: np.array(v).mean() for k, v in zip(keys, outs[:-1])}
train_stats.update(stats)
logs = train_stats.log()
strs = '{}, iter: {}, lr: {:.5f}, {}, time: {:.3f}'.format(
now_time(), iter_id, np.mean(outs[-1]), logs,
start_time - prev_start_time)
print(strs)
sys.stdout.flush()
if (iter_id + 1) % cfg.TRAIN.snapshot_iter == 0:
save_model("model_iter{}".format(iter_id))
if (iter_id + 1) == cfg.max_iter:
break
end_time = time.time()
total_time = end_time - start_time
last_loss = np.array(outs[0]).mean()
# only for ce
if cfg.enable_ce:
gpu_num = devices_num
epoch_idx = iter_id + 1
loss = last_loss
print("kpis\teach_pass_duration_card%s\t%s" %
(gpu_num, total_time / epoch_idx))
print("kpis\ttrain_loss_card%s\t%s" % (gpu_num, loss))
return np.mean(every_pass_loss)
if cfg.use_pyreader:
train_loop_pyreader()
else:
train_loop()
save_model('model_final')
def train():
if cfg.debug:
fluid.default_startup_program().random_seed = 1000
fluid.default_main_program().random_seed = 1000
random.seed(0)
np.random.seed(0)
if not os.path.exists(cfg.model_save_dir):
os.makedirs(cfg.model_save_dir)
model = YOLOv3()
model.build_model()
input_size = cfg.input_size
loss = model.loss()
loss.persistable = True
devices = os.getenv("CUDA_VISIBLE_DEVICES") or ""
devices_num = len(devices.split(","))
print("Found {} CUDA devices.".format(devices_num))
learning_rate = cfg.learning_rate
boundaries = cfg.lr_steps
gamma = cfg.lr_gamma
step_num = len(cfg.lr_steps)
values = [learning_rate * (gamma**i) for i in range(step_num + 1)]
optimizer = fluid.optimizer.Momentum(
learning_rate=exponential_with_warmup_decay(
learning_rate=learning_rate,
boundaries=boundaries,
values=values,
warmup_iter=cfg.warm_up_iter,
warmup_factor=cfg.warm_up_factor),
regularization=fluid.regularizer.L2Decay(cfg.weight_decay),
momentum=cfg.momentum)
optimizer.minimize(loss)
if cfg.use_gpu:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
if cfg.pretrain:
if not os.path.exists(cfg.pretrain):
print("Pretrain weights not found: {}".format(cfg.pretrain))
def if_exist(var):
return os.path.exists(os.path.join(cfg.pretrain, var.name))
fluid.io.load_vars(exe, cfg.pretrain, predicate=if_exist)
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = True
build_strategy.sync_batch_norm = cfg.syncbn
compile_program = fluid.compiler.CompiledProgram(
fluid.default_main_program()).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
random_sizes = [cfg.input_size]
if cfg.random_shape:
random_sizes = [32 * i for i in range(10, 20)]
total_iter = cfg.max_iter - cfg.start_iter
mixup_iter = total_iter - cfg.no_mixup_iter
train_reader = reader.train(input_size,
batch_size=cfg.batch_size,
shuffle=True,
total_iter=total_iter * devices_num,
mixup_iter=mixup_iter * devices_num,
random_sizes=random_sizes,
use_multiprocessing=cfg.use_multiprocess)
py_reader = model.py_reader
py_reader.decorate_paddle_reader(train_reader)
def save_model(postfix):
model_path = os.path.join(cfg.model_save_dir, postfix)
if os.path.isdir(model_path):
shutil.rmtree(model_path)
fluid.io.save_persistables(exe, model_path)
fetch_list = [loss]
py_reader.start()
smoothed_loss = SmoothedValue()
try:
start_time = time.time()
prev_start_time = start_time
snapshot_loss = 0
snapshot_time = 0
for iter_id in range(cfg.start_iter, cfg.max_iter):
prev_start_time = start_time
start_time = time.time()
losses = exe.run(compile_program,
fetch_list=[v.name for v in fetch_list])
smoothed_loss.add_value(np.mean(np.array(losses[0])))
snapshot_loss += np.mean(np.array(losses[0]))
snapshot_time += start_time - prev_start_time
lr = np.array(
fluid.global_scope().find_var('learning_rate').get_tensor())
print("Iter {:d}, lr {:.6f}, loss {:.6f}, time {:.5f}".format(
iter_id, lr[0], smoothed_loss.get_mean_value(),
start_time - prev_start_time))
sys.stdout.flush()
if (iter_id + 1) % cfg.snapshot_iter == 0:
save_model("model_iter{}".format(iter_id))
print("Snapshot {} saved, average loss: {}, \
average time: {}".format(
iter_id + 1, snapshot_loss / float(cfg.snapshot_iter),
snapshot_time / float(cfg.snapshot_iter)))
snapshot_loss = 0
snapshot_time = 0
except fluid.core.EOFException:
py_reader.reset()
save_model('model_final')
def train():
learning_rate = cfg.learning_rate
image_shape = [3, cfg.TRAIN.max_size, cfg.TRAIN.max_size]
num_iterations = cfg.max_iter
devices = os.getenv("CUDA_VISIBLE_DEVICES") or ""
devices_num = len(devices.split(","))
total_batch_size = devices_num * cfg.TRAIN.im_per_batch
model = model_builder.RCNN(
add_conv_body_func=resnet.add_ResNet50_conv4_body,
add_roi_box_head_func=resnet.add_ResNet_roi_conv5_head,
use_pyreader=cfg.use_pyreader,
use_random=False)
model.build_model(image_shape)
losses, keys = model.loss()
loss = losses[0]
fetch_list = [loss]
boundaries = cfg.lr_steps
gamma = cfg.lr_gamma
step_num = len(cfg.lr_steps)
values = [learning_rate * (gamma**i) for i in range(step_num + 1)]
optimizer = fluid.optimizer.Momentum(
learning_rate=exponential_with_warmup_decay(
learning_rate=learning_rate,
boundaries=boundaries,
values=values,
warmup_iter=500,
warmup_factor=1.0 / 3.0),
regularization=fluid.regularizer.L2Decay(0.0001),
momentum=0.9)
optimizer.minimize(loss)
fluid.memory_optimize(fluid.default_main_program())
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
if cfg.pretrained_model:
def if_exist(var):
return os.path.exists(os.path.join(cfg.pretrained_model, var.name))
fluid.io.load_vars(exe, cfg.pretrained_model, predicate=if_exist)
if cfg.parallel:
train_exe = fluid.ParallelExecutor(use_cuda=bool(cfg.use_gpu),
loss_name=loss.name)
if cfg.use_pyreader:
train_reader = reader.train(batch_size=cfg.TRAIN.im_per_batch,
total_batch_size=total_batch_size,
padding_total=cfg.TRAIN.padding_minibatch,
shuffle=False)
py_reader = model.py_reader
py_reader.decorate_paddle_reader(train_reader)
else:
train_reader = reader.train(batch_size=total_batch_size, shuffle=False)
feeder = fluid.DataFeeder(place=place, feed_list=model.feeds())
def run(iterations):
reader_time = []
run_time = []
total_images = 0
for batch_id in range(iterations):
start_time = time.time()
data = next(train_reader())
end_time = time.time()
reader_time.append(end_time - start_time)
start_time = time.time()
if cfg.parallel:
outs = train_exe.run(fetch_list=[v.name for v in fetch_list],
feed=feeder.feed(data))
else:
outs = exe.run(fluid.default_main_program(),
fetch_list=[v.name for v in fetch_list],
feed=feeder.feed(data))
end_time = time.time()
run_time.append(end_time - start_time)
total_images += len(data)
print("Batch {:d}, loss {:.6f} ".format(batch_id,
np.mean(outs[0])))
return reader_time, run_time, total_images
def run_pyreader(iterations):
reader_time = [0]
run_time = []
total_images = 0
py_reader.start()
try:
for batch_id in range(iterations):
start_time = time.time()
if cfg.parallel:
outs = train_exe.run(
fetch_list=[v.name for v in fetch_list])
else:
outs = exe.run(fluid.default_main_program(),
fetch_list=[v.name for v in fetch_list])
end_time = time.time()
run_time.append(end_time - start_time)
total_images += devices_num
print("Batch {:d}, loss {:.6f} ".format(
batch_id, np.mean(outs[0])))
except fluid.core.EOFException:
py_reader.reset()
return reader_time, run_time, total_images
run_func = run if not cfg.use_pyreader else run_pyreader
# warm-up
run_func(2)
# profiling
start = time.time()
if cfg.use_profile:
with profiler.profiler('GPU', 'total', '/tmp/profile_file'):
reader_time, run_time, total_images = run_func(num_iterations)
else:
reader_time, run_time, total_images = run_func(num_iterations)
end = time.time()
total_time = end - start
print(
"Total time: {0}, reader time: {1} s, run time: {2} s, images/s: {3}".
format(total_time, np.sum(reader_time), np.sum(run_time),
total_images / total_time))
def train():
learning_rate = cfg.learning_rate
image_shape = [3, 512, 512]
if cfg.enable_ce:
fluid.default_startup_program().random_seed = 1000
fluid.default_main_program().random_seed = 1000
import random
random.seed(0)
np.random.seed(0)
devices_num = get_device_num()
total_batch_size = devices_num * cfg.TRAIN.im_per_batch
use_random = True
startup_prog = fluid.Program()
train_prog = fluid.Program()
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
model = model_builder.EAST(
add_conv_body_func=resnet.ResNet(),
use_random=use_random)
model.build_model(image_shape)
losses, keys = model.loss()
loss = losses[0]
fetch_list = losses
boundaries = cfg.lr_steps
gamma = cfg.lr_gamma
step_num = len(cfg.lr_steps)
values = [learning_rate * (gamma**i) for i in range(step_num + 1)]
lr = exponential_with_warmup_decay(
learning_rate=learning_rate,
boundaries=boundaries,
values=values,
warmup_iter=cfg.warm_up_iter,
warmup_factor=cfg.warm_up_factor)
optimizer = fluid.optimizer.AdamOptimizer(learning_rate=lr, regularization=fluid.regularizer.L2Decay(cfg.weight_decay))
optimizer.minimize(loss)
fetch_list = fetch_list + [lr]
for var in fetch_list:
var.persistable = True
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
build_strategy = fluid.BuildStrategy()
build_strategy.fuse_all_optimizer_ops = False
build_strategy.fuse_elewise_add_act_ops = True
build_strategy.sync_batch_norm=True
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.num_iteration_per_drop_scope = 1
exe.run(startup_prog)
if cfg.pretrained_model:
def if_exist(var):
return os.path.exists(os.path.join(cfg.pretrained_model, var.name))
fluid.io.load_vars(exe, cfg.pretrained_model, predicate=if_exist)
compiled_train_prog = fluid.CompiledProgram(train_prog).with_data_parallel(
loss_name=loss.name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
dataset = icdar.ICDAR2015Dataset()
data_generator = dataset.get_batch(num_workers=24,
input_size=512,
batch_size=14)
def train_loop():
start_time = time.time()
prev_start_time = start_time
start = start_time
train_stats = TrainingStats(cfg.log_window, keys)
#for iter_id, data in enumerate(next(data_generator)):
for iter_id in range(100000):
data = next(data_generator)
#for data in data_list:
prev_start_time = start_time
start_time = time.time()
outs = exe.run(compiled_train_prog, fetch_list=[v.name for v in fetch_list],
feed={"input_images": data[0],
"input_score_maps": data[2],
"input_geo_maps": data[3],
"input_training_masks": data[4]})
stats = {k: np.array(v).mean() for k, v in zip(keys, outs[:-1])}
train_stats.update(stats)
logs = train_stats.log()
strs = '{}, batch: {}, lr: {:.5f}, {}, time: {:.3f}'.format(
now_time(), iter_id,
np.mean(outs[-1]), logs, start_time - prev_start_time)
if iter_id % 10 == 0:
print(strs)
sys.stdout.flush()
if (iter_id + 1) % cfg.TRAIN.snapshot_iter == 0:
save_model(exe, "model_iter{}".format(iter_id), train_prog)
if (iter_id + 1) == cfg.max_iter:
break
end_time = time.time()
total_time = end_time - start_time
last_loss = np.array(outs[0]).mean()
train_loop()
