def main():
"""
Main body of script.
"""
args = parse_args()
if args.disable_cudnn_autotune:
os.environ["MXNET_CUDNN_AUTOTUNE_DEFAULT"] = "0"
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=args.log_packages,
log_pip_packages=args.log_pip_packages)
if args.all:
args.use_pretrained = True
dataset_name_map = {
"in1k": "ImageNet1K",
"cub": "CUB200_2011",
"cf10": "CIFAR10",
"cf100": "CIFAR100",
"svhn": "SVHN",
"voc": "VOC",
"ade20k": "ADE20K",
"cs": "Cityscapes",
"cocoseg": "CocoSeg",
"cocohpe": "CocoHpe",
"hp": "HPatches",
}
for model_name, model_metainfo in (_model_sha1.items()
if version_info[0] >= 3 else
_model_sha1.iteritems()):
error, checksum, repo_release_tag, caption, paper, ds, img_size, scale, batch, rem = model_metainfo
if (ds != "in1k") or (img_size == 0) or ((len(rem) > 0) and
(rem[-1] == "*")):
continue
args.dataset = dataset_name_map[ds]
args.model = model_name
args.input_size = img_size
args.resize_inv_factor = scale
args.batch_size = batch
logging.info("==============")
logging.info("Checking model: {}".format(model_name))
acc_value = test_model(args=args)
if acc_value is not None:
exp_value = int(error) * 1e-4
if abs(acc_value - exp_value) > 2e-4:
logging.info(
"----> Wrong value detected (expected value: {})!".
format(exp_value))
else:
test_model(args=args)
from torchbench.image_classification import ImageNet
from pytorch.pytorchcv.models.model_store import _model_sha1
from pytorch.pytorchcv.model_provider import get_model as ptcv_get_model
import torchvision.transforms as transforms
import torch
import math
from sys import version_info
# import os
for model_name, model_metainfo in (_model_sha1.items() if version_info[0] >= 3
else _model_sha1.iteritems()):
net = ptcv_get_model(model_name, pretrained=True)
error, checksum, repo_release_tag, caption, paper, ds, img_size, scale, batch, rem = model_metainfo
if (ds != "in1k") or (img_size == 0) or ((len(rem) > 0) and
(rem[-1] == "*")):
continue
paper_model_name = caption
paper_arxiv_id = paper
input_image_size = img_size
resize_inv_factor = scale
batch_size = batch
model_description = "pytorch" + (rem if rem == "" else ", " + rem)
assert (not hasattr(net, "in_size")) or (input_image_size
== net.in_size[0])
ImageNet.benchmark(
model=net,
model_description=model_description,
paper_model_name=paper_model_name,
paper_arxiv_id=paper_arxiv_id,
input_transform=transforms.Compose([
transforms.Resize(