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Remove use of eval in vision/classification_and_detection/tools/calibrate_torchvision_model.py #2514

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Remove use of eval in vision/classification_and_detection/tools/calibrate_torchvision_model.py #2514

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0ee8fb9
Add shebangs in bulk pre-testing
arav-agarwal2 Feb 2, 2026
3099c81
Update to exec mode
arav-agarwal2 Feb 3, 2026
134a652
Remove use of eval from torch quantization system
arav-agarwal2 Feb 9, 2026
9f64a04
Revert "Add shebangs in bulk pre-testing"
arav-agarwal2 Feb 9, 2026
0d77ceb
Revert "Update to exec mode"
arav-agarwal2 Feb 9, 2026
a8d3b97
Merge branch 'master' into arav-patch-010
arav-agarwal2 Feb 9, 2026
5fe3dcc
[Automated Commit] Format Codebase
github-actions[bot] Feb 9, 2026
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28 changes: 20 additions & 8 deletions text_to_video/wan-2.2-t2v-a14b/run_mlperf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,7 +46,8 @@ def load_prompts(dataset_path):


class Model:
def __init__(self, model_path, device, config, prompts, fixed_latent=None, rank=0):
def __init__(self, model_path, device, config,
prompts, fixed_latent=None, rank=0):
self.device = device
self.rank = rank
self.height = config["height"]
Expand Down Expand Up @@ -106,7 +107,8 @@ def flush_queries(self):


class DebugModel:
def __init__(self, model_path, device, config, prompts, fixed_latent=None, rank=0):
def __init__(self, model_path, device, config,
prompts, fixed_latent=None, rank=0):
self.prompts = prompts

def issue_queries(self, query_samples):
Expand Down Expand Up @@ -186,7 +188,8 @@ def get_args():
parser.add_argument(
"--scenario",
default="SingleStream",
help="mlperf benchmark scenario, one of " + str(list(SCENARIO_MAP.keys())),
help="mlperf benchmark scenario, one of " +
str(list(SCENARIO_MAP.keys())),
)
parser.add_argument(
"--user_conf",
Expand All @@ -202,7 +205,10 @@ def get_args():
help="performance sample count",
default=5000,
)
parser.add_argument("--accuracy", action="store_true", help="enable accuracy pass")
parser.add_argument(
"--accuracy",
action="store_true",
help="enable accuracy pass")
# Dont overwrite these for official submission
parser.add_argument("--count", type=int, help="dataset items to use")
parser.add_argument("--time", type=int, help="time to scan in seconds")
Expand Down Expand Up @@ -271,7 +277,10 @@ def run_mlperf(args, config):

audit_config = os.path.abspath(args.audit_conf)
if os.path.exists(audit_config):
settings.FromConfig(audit_config, "wan-2.2-t2v-a14b", args.scenario)
settings.FromConfig(
audit_config,
"wan-2.2-t2v-a14b",
args.scenario)
settings.scenario = SCENARIO_MAP[args.scenario]

settings.mode = lg.TestMode.PerformanceOnly
Expand All @@ -297,8 +306,10 @@ def run_mlperf(args, config):
if args.samples_per_query:
settings.multi_stream_samples_per_query = args.samples_per_query
if args.max_latency:
settings.server_target_latency_ns = int(args.max_latency * NANO_SEC)
settings.multi_stream_expected_latency_ns = int(args.max_latency * NANO_SEC)
settings.server_target_latency_ns = int(
args.max_latency * NANO_SEC)
settings.multi_stream_expected_latency_ns = int(
args.max_latency * NANO_SEC)

performance_sample_count = (
args.performance_sample_count
Expand All @@ -311,7 +322,8 @@ def run_mlperf(args, config):
count, performance_sample_count, load_query_samples, unload_query_samples
)

lg.StartTestWithLogSettings(sut, qsl, settings, log_settings, audit_config)
lg.StartTestWithLogSettings(
sut, qsl, settings, log_settings, audit_config)

lg.DestroyQSL(qsl)
lg.DestroySUT(sut)
Expand Down
8 changes: 6 additions & 2 deletions vision/classification_and_detection/tools/calibrate_torchvision_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@
from torch.utils.data import DataLoader, Dataset

import torchvision.transforms as transforms
from torchvision.models.quantization import *
import torchvision.models.quantization as torchvision_quantization_models


class CalibrationDataset(Dataset):
Expand Down Expand Up @@ -73,7 +73,11 @@ def main():
)
dataloader = DataLoader(dataset, batch_size=1)

model = eval(args.model)(pretrained=True, progress=True, quantize=False)
if not hasattr(torchvision_quantization_models, args.model):
raise ValueError(f"Model {args.model} not found in torchvision quantization models")


model = getattr(torchvision_quantization_models, args.model)(pretrained=True, progress=True, quantize=False)
quantize_model(model, dataloader)
print(model)

Expand Down