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32 changes: 21 additions & 11 deletions benchmarks/BM_resnet50/model_repository/dali/pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,24 +24,34 @@

def parse_args():
import argparse
parser = argparse.ArgumentParser(description="Serialize the pipeline and save it to a file")
parser.add_argument('file_path', type=str, help='The path where to save the serialized pipeline')
parser = argparse.ArgumentParser(
description="Serialize the pipeline and save it to a file")
parser.add_argument('file_path',
type=str,
help='The path where to save the serialized pipeline')
return parser.parse_args()


def preprocessing(images, device='gpu'):
images = dali.fn.decoders.image(images, device="mixed" if device == 'gpu' else 'cpu', output_type=types.RGB)
images = dali.fn.resize(images, resize_x=224, resize_y=224)
return dali.fn.crop_mirror_normalize(images,
dtype=types.FLOAT,
output_layout="HWC",
crop=(224, 224),
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
images = dali.fn.decoders.image(
images,
device="mixed" if device == 'gpu' else 'cpu',
output_type=types.RGB)
images = dali.fn.resize(images, resize_x=224, resize_y=224)
return dali.fn.crop_mirror_normalize(
images,
dtype=types.FLOAT,
output_layout="HWC",
crop=(224, 224),
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255])


@dali.pipeline_def(batch_size=1, num_threads=1, device_id=0)
def pipe():
images = dali.fn.external_source(device="cpu", name="DALI_INPUT_0", no_copy=True)
images = dali.fn.external_source(device="cpu",
name="DALI_INPUT_0",
no_copy=True)
return preprocessing(images)


Expand Down
63 changes: 36 additions & 27 deletions benchmarks/BM_resnet50/scripts/dataset_preprocess.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,7 +46,11 @@ def parse_meta_mat(metafile) -> Dict[int, str]:

meta = scipy.io.loadmat(metafile, squeeze_me=True)["synsets"]
nums_children = list(zip(*meta))[4]
meta = [meta[idx] for idx, num_children in enumerate(nums_children) if num_children == 0]
meta = [
meta[idx]
for idx, num_children in enumerate(nums_children)
if num_children == 0
]
idcs, wnids = list(zip(*meta))[:2]
idx_to_wnid = {idx: wnid for idx, wnid in zip(idcs, wnids)}
return idx_to_wnid
Expand All @@ -57,13 +61,17 @@ def _process_image(image_file, target_size):
original_size = image.size

# scale image to size where minimal size is _RESIZE_MIN
scale_factor = max(_RESIZE_MIN / original_size[0], _RESIZE_MIN / original_size[1])
resize_to = int(original_size[0] * scale_factor), int(original_size[1] * scale_factor)
scale_factor = max(_RESIZE_MIN / original_size[0],
_RESIZE_MIN / original_size[1])
resize_to = int(original_size[0] * scale_factor), int(original_size[1] *
scale_factor)
resized_image = image.resize(resize_to)

# central crop of image to target_size
left, upper = (resize_to[0] - target_size[0]) // 2, (resize_to[1] - target_size[1]) // 2
cropped_image = resized_image.crop((left, upper, left + target_size[0], upper + target_size[1]))
left, upper = (resize_to[0] - target_size[0]) // 2, (resize_to[1] -
target_size[1]) // 2
cropped_image = resized_image.crop(
(left, upper, left + target_size[0], upper + target_size[1]))
return cropped_image


Expand All @@ -73,27 +81,25 @@ def main():
parser = argparse.ArgumentParser(description="short_description")
parser.add_argument(
"--dataset-dir",
help="Path to dataset directory where imagenet archives are stored and processed files will be saved.",
help=
"Path to dataset directory where imagenet archives are stored and processed files will be saved.",
required=False,
default=DATASETS_DIR,
)
parser.add_argument(
'--save',
help='Save processed images.',
required=False, default=False
)
parser.add_argument('--save',
help='Save processed images.',
required=False,
default=False)
parser.add_argument(
"--target-size",
help="Size of target image. Format it as <width>,<height>.",
required=False,
default=",".join(map(str, TARGET_SIZE)),
)
parser.add_argument(
'--perf-file',
required=False,
default=None,
help='Path to save a file with time measurements.'
)
parser.add_argument('--perf-file',
required=False,
default=None,
help='Path to save a file with time measurements.')
args = parser.parse_args()

if args.dataset_dir is None:
Expand Down Expand Up @@ -130,32 +136,35 @@ def main():

# remap WNID into index in sorted list of all WNIDs - this is how network outputs class
available_wnids = sorted(set(labels_wnid))
wnid_to_newidx = {wnid: new_cls for new_cls, wnid in enumerate(available_wnids)}
wnid_to_newidx = {
wnid: new_cls for new_cls, wnid in enumerate(available_wnids)
}
labels = [wnid_to_newidx[wnid] for wnid in labels_wnid]
if args.perf_file is None:
perf = False
perf = False
else:
times = []
perf = True
times = []
perf = True
output_dir = datasets_dir / IMAGENET_DIRNAME
with tarfile.open(image_archive_path, mode="r") as image_archive_file:
image_rel_paths = sorted(image_archive_file.getnames())
for cls, image_rel_path in tqdm(zip(labels, image_rel_paths), total=len(image_rel_paths)):
for cls, image_rel_path in tqdm(zip(labels, image_rel_paths),
total=len(image_rel_paths)):
output_path = output_dir / str(cls) / image_rel_path
original_image_file = image_archive_file.extractfile(image_rel_path)
file_data = original_image_file.read()
start = time.perf_counter()
processed_image = _process_image(io.BytesIO(file_data), target_size)
end = time.perf_counter()
if perf:
times.append(end-start)
times.append(end - start)
if args.save:
output_path.parent.mkdir(parents=True, exist_ok=True)
processed_image.save(output_path.as_posix())
output_path.parent.mkdir(parents=True, exist_ok=True)
processed_image.save(output_path.as_posix())

if perf:
with open(args.perf_file, 'w') as perf_file:
print(times, file=perf_file)
with open(args.perf_file, 'w') as perf_file:
print(times, file=perf_file)


if __name__ == "__main__":
Expand Down
41 changes: 27 additions & 14 deletions benchmarks/BM_resnet50/scripts/model-loader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,25 +23,38 @@
import argparse
import sys


def get_args():
parser = argparse.ArgumentParser(description='Load or unload a model in Triton server.')
parser.add_argument('action', action='store', choices=['load', 'unload', 'reload'])
parser.add_argument('-u', '--url', required=False, action='store', default='localhost:8001', help='Server url.')
parser.add_argument('-m', '--model', required=True, action='store', help='Model name.')
return parser.parse_args()
parser = argparse.ArgumentParser(
description='Load or unload a model in Triton server.')
parser.add_argument('action',
action='store',
choices=['load', 'unload', 'reload'])
parser.add_argument('-u',
'--url',
required=False,
action='store',
default='localhost:8001',
help='Server url.')
parser.add_argument('-m',
'--model',
required=True,
action='store',
help='Model name.')
return parser.parse_args()


def main(args):
client = t_client.InferenceServerClient(url=args.url)
if args.action in ['reload', 'unload']:
client.unload_model(args.model)
print('Successfully unloaded model', args.model)
client = t_client.InferenceServerClient(url=args.url)
if args.action in ['reload', 'unload']:
client.unload_model(args.model)
print('Successfully unloaded model', args.model)

if args.action in ['reload', 'load']:
client.load_model(args.model)
print('Successfully loaded model', args.model)
if args.action in ['reload', 'load']:
client.load_model(args.model)
print('Successfully loaded model', args.model)


if __name__ == '__main__':
args = get_args()
main(args)
args = get_args()
main(args)
76 changes: 47 additions & 29 deletions benchmarks/BM_resnet50/scripts/prepare-input-data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,6 @@
import base64 as b64
import json


dali_extra_path = os.getenv('DALI_EXTRA_PATH', None)
assert dali_extra_path is not None, "Please set DALI_EXTRA_PATH env variable."

Expand All @@ -39,44 +38,63 @@
# choose 16 smallest samples
chosen_set = [p for (_, p) in sized_images[:16]]

# choose medium sized image
# choose medium sized image
chosen_sample = sized_images[8][1]


def save_sample_input(sample, dir_name, input_name):
Path(dir_name).mkdir(exist_ok=True)
shutil.copy(sample, Path(dir_name) / Path(input_name))
Path(dir_name).mkdir(exist_ok=True)
shutil.copy(sample, Path(dir_name) / Path(input_name))


def get_content(fpath):
with fpath.open("rb") as f:
content = f.read()
return {
'content' : {
'b64': b64.b64encode(content).decode('utf-8')
},
'shape': [len(content)]
}
with fpath.open("rb") as f:
content = f.read()
return {
'content': {
'b64': b64.b64encode(content).decode('utf-8')
},
'shape': [len(content)]
}


def save_json_dataset(files, dataset_filename, input_name):
contents = [get_content(fpath) for fpath in files]
inputs = [{input_name: content} for content in contents]
result_dict = {'data': inputs}
with open(dataset_filename, 'w') as dataset_file:
json.dump(result_dict, dataset_file)
contents = [get_content(fpath) for fpath in files]
inputs = [{input_name: content} for content in contents]
result_dict = {'data': inputs}
with open(dataset_filename, 'w') as dataset_file:
json.dump(result_dict, dataset_file)


def get_args():
parser = argparse.ArgumentParser(description='Prepare perf_analyzer input data.')
parser.add_argument('-d', '--directory-name', required=False, action='store', default='inputs-data',
help='Directory name to store a single sample data.')
parser.add_argument('-i', '--input-name', required=False, action='store', default='input',
help='Input name.')
parser.add_argument('-f', '--dataset-filename', required=False, action='store', default='dataset.json',
help='Name of the created JSON dataset.')
return parser.parse_args()
parser = argparse.ArgumentParser(
description='Prepare perf_analyzer input data.')
parser.add_argument('-d',
'--directory-name',
required=False,
action='store',
default='inputs-data',
help='Directory name to store a single sample data.')
parser.add_argument('-i',
'--input-name',
required=False,
action='store',
default='input',
help='Input name.')
parser.add_argument('-f',
'--dataset-filename',
required=False,
action='store',
default='dataset.json',
help='Name of the created JSON dataset.')
return parser.parse_args()


def main(args):
save_sample_input(chosen_sample, args.directory_name, args.input_name)
save_json_dataset(chosen_set, args.dataset_filename, args.input_name)
save_sample_input(chosen_sample, args.directory_name, args.input_name)
save_json_dataset(chosen_set, args.dataset_filename, args.input_name)


if __name__ == '__main__':
args = get_args()
main(args)
args = get_args()
main(args)
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