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update batch evaluate #154

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merged 4 commits into from
Jul 27, 2023
Merged

update batch evaluate #154

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f0c49da
update batch evaluate
huilin16 May 28, 2023
d3b4b78
update batch evaluate
huilin16 Jun 6, 2023
0eacec7
update batch evaluate for classifier and object_detector
huilin16 Jul 26, 2023
3469cef
update style of slider_predict.py
huilin16 Jul 27, 2023
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31 changes: 15 additions & 16 deletions paddlers/tasks/change_detector.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@
import paddlers.utils.logging as logging
from paddlers.models import seg_losses
from paddlers.transforms import Resize, decode_image, construct_sample
from paddlers.utils import get_single_card_bs
from paddlers.utils import to_data_parallel
from paddlers.utils.checkpoint import cd_pretrain_weights_dict
from .base import BaseModel
from .utils import seg_metrics as metrics
Expand Down Expand Up @@ -447,25 +447,22 @@ def evaluate(self, eval_dataset, batch_size=1, return_details=False):
"""

self._check_transforms(eval_dataset.transforms)
net = self.net
net.eval()

self.net.eval()
# XXX: Hard-coding
nranks = paddle.distributed.get_world_size()
local_rank = paddle.distributed.get_rank()
if nranks > 1:
# Initialize parallel environment if not done.
if not (paddle.distributed.parallel.parallel_helper.
_is_parallel_ctx_initialized()):
if not paddle.distributed.parallel.parallel_helper._is_parallel_ctx_initialized(
):
paddle.distributed.init_parallel_env()
net = to_data_parallel(
net, find_unused_parameters=self.find_unused_parameters)
else:
net = to_data_parallel(
net, find_unused_parameters=self.find_unused_parameters)

batch_size_each_card = get_single_card_bs(batch_size)
if batch_size_each_card > 1:
batch_size_each_card = 1
batch_size = batch_size_each_card * paddlers.env_info['num']
logging.warning(
"ChangeDetector only supports batch_size=1 for each gpu/cpu card " \
"during evaluation, so batch_size " \
"is forcibly set to {}.".format(batch_size)
)
self.eval_data_loader = self.build_data_loader(
eval_dataset, batch_size=batch_size, mode='eval')

Expand All @@ -485,9 +482,9 @@ def evaluate(self, eval_dataset, batch_size=1, return_details=False):
enable=True,
custom_white_list=self.custom_white_list,
custom_black_list=self.custom_black_list):
outputs = self.run(self.net, data, 'eval')
outputs = self.run(net, data, 'eval')
else:
outputs = self.run(self.net, data, 'eval')
outputs = self.run(net, data, 'eval')
pred_area = outputs['pred_area']
label_area = outputs['label_area']
intersect_area = outputs['intersect_area']
Expand Down Expand Up @@ -694,6 +691,8 @@ def postprocess(self, batch_pred, batch_restore_list):
else:
raise RuntimeError
results.append(pred)
if len(results) > 1:
results = [paddle.concat(results, axis=0)]
return results

def _infer_postprocess(self, batch_label_map, batch_score_map,
Expand Down
94 changes: 54 additions & 40 deletions paddlers/tasks/classifier.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@
import paddlers.rs_models.clas as cmcls
import paddlers.utils.logging as logging
from paddlers.models.ppcls.metric import build_metrics
from paddlers.utils import to_data_parallel
from paddlers.models import clas_losses
from paddlers.models.ppcls.data.postprocess import build_postprocess
from paddlers.utils.checkpoint import cls_pretrain_weights_dict
Expand Down Expand Up @@ -402,54 +403,67 @@ def evaluate(self, eval_dataset, batch_size=1, return_details=False):
"""

self._check_transforms(eval_dataset.transforms)
net = self.net
net.eval()

self.net.eval()
# XXX: Hard-coding
nranks = paddle.distributed.get_world_size()
local_rank = paddle.distributed.get_rank()
if nranks > 1:
# Initialize parallel environment if not done.
if not paddle.distributed.parallel.parallel_helper._is_parallel_ctx_initialized(
):
paddle.distributed.init_parallel_env()
net = to_data_parallel(
net, find_unused_parameters=self.find_unused_parameters)
else:
net = to_data_parallel(
net, find_unused_parameters=self.find_unused_parameters)

self.eval_data_loader = self.build_data_loader(
eval_dataset, batch_size=batch_size, mode='eval')
logging.info("Start to evaluate (total_samples={}, total_steps={})...".
format(eval_dataset.num_samples, eval_dataset.num_samples))

top1s = []
top5s = []
with paddle.no_grad():
for step, data in enumerate(self.eval_data_loader):
if self.precision == 'fp16':
with paddle.amp.auto_cast(
level=self.amp_level,
enable=True,
custom_white_list=self.custom_white_list,
custom_black_list=self.custom_black_list):
outputs = self.run(net, data, 'eval')
else:
outputs = self.run(net, data, 'eval')
if nranks > 1:
t1 = outputs["top1"]
t5 = outputs["top5"]
t1s = []
t5s = []
paddle.distributed.all_gather(t1s, t1)
paddle.distributed.all_gather(t5s, t5)
for rank_id in range(nranks):
top1 = t1s[rank_id]
top5 = t5s[rank_id]
for i in range(data['image'].shape[0]):
top1s.append(top1)
top5s.append(top5)
else:
for i in range(data['image'].shape[0]):
top1s.append(outputs["top1"])
top5s.append(outputs["top5"])

top1 = np.mean(top1s)
top5 = np.mean(top5s)
eval_metrics = OrderedDict(zip(['top1', 'top5'], [top1, top5]))

if return_details:
# TODO: Add details
return eval_metrics, None

if batch_size > 1:
logging.warning(
"Classifier only supports single card evaluation with batch_size=1 "
"during evaluation, so batch_size is forcibly set to 1.")
batch_size = 1

if nranks < 2 or local_rank == 0:
self.eval_data_loader = self.build_data_loader(
eval_dataset, batch_size=batch_size, mode='eval')
logging.info(
"Start to evaluate (total_samples={}, total_steps={})...".
format(eval_dataset.num_samples, eval_dataset.num_samples))

top1s = []
top5s = []
with paddle.no_grad():
for step, data in enumerate(self.eval_data_loader):
if self.precision == 'fp16':
with paddle.amp.auto_cast(
level=self.amp_level,
enable=True,
custom_white_list=self.custom_white_list,
custom_black_list=self.custom_black_list):
outputs = self.run(self.net, data, 'eval')
else:
outputs = self.run(self.net, data, 'eval')
top1s.append(outputs["top1"])
top5s.append(outputs["top5"])

top1 = np.mean(top1s)
top5 = np.mean(top5s)
eval_metrics = OrderedDict(zip(['top1', 'top5'], [top1, top5]))

if return_details:
# TODO: Add details
return eval_metrics, None

return eval_metrics
return eval_metrics

@paddle.no_grad()
def predict(self, img_file, transforms=None):
Expand Down
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