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add pse to benchmark

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This commit is contained in:
WenmuZhou
2021-10-27 12:16:36 +00:00
parent cc01a59b82
commit 46c170bc12
4 changed files with 37 additions and 29 deletions
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tools/program.py
+18 -12
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@@ -212,15 +212,15 @@ def train(config,
for epoch in range(start_epoch, epoch_num + 1):
train_dataloader = build_dataloader(
config, 'Train', device, logger, seed=epoch)
train_batch_cost = 0.0
train_reader_cost = 0.0
batch_sum = 0
batch_start = time.time()
train_run_cost = 0.0
total_samples = 0
reader_start = time.time()
max_iter = len(train_dataloader) - 1 if platform.system(
) == "Windows" else len(train_dataloader)
for idx, batch in enumerate(train_dataloader):
profiler.add_profiler_step(profiler_options)
train_reader_cost += time.time() - batch_start
train_reader_cost += time.time() - reader_start
if idx >= max_iter:
break
lr = optimizer.get_lr()
@@ -228,6 +228,7 @@ def train(config,
if use_srn:
model_average = True
train_start = time.time()
# use amp
if scaler:
with paddle.amp.auto_cast():
@@ -252,8 +253,8 @@ def train(config,
optimizer.step()
optimizer.clear_grad()
train_batch_cost += time.time() - batch_start
batch_sum += len(images)
train_run_cost += time.time() - train_start
total_samples += len(images)
if not isinstance(lr_scheduler, float):
lr_scheduler.step()
@@ -284,12 +285,13 @@ def train(config,
logs = train_stats.log()
strs = 'epoch: [{}/{}], iter: {}, {}, reader_cost: {:.5f} s, batch_cost: {:.5f} s, samples: {}, ips: {:.5f}'.format(
epoch, epoch_num, global_step, logs, train_reader_cost /
print_batch_step, train_batch_cost / print_batch_step,
batch_sum, batch_sum / train_batch_cost)
print_batch_step, (train_reader_cost + train_run_cost) /
print_batch_step, total_samples,
total_samples / (train_reader_cost + train_run_cost))
logger.info(strs)
train_batch_cost = 0.0
train_reader_cost = 0.0
batch_sum = 0
train_run_cost = 0.0
total_samples = 0
# eval
if global_step > start_eval_step and \
(global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0:
@@ -342,7 +344,7 @@ def train(config,
global_step)
global_step += 1
optimizer.clear_grad()
batch_start = time.time()
reader_start = time.time()
if dist.get_rank() == 0:
save_model(
model,
@@ -383,7 +385,11 @@ def eval(model,
with paddle.no_grad():
total_frame = 0.0
total_time = 0.0
pbar = tqdm(total=len(valid_dataloader), desc='eval model:')
pbar = tqdm(
total=len(valid_dataloader),
desc='eval model:',
position=0,
leave=True)
max_iter = len(valid_dataloader) - 1 if platform.system(
) == "Windows" else len(valid_dataloader)
for idx, batch in enumerate(valid_dataloader):