def benchmark_all() -> pd.DataFrame:
save_path = Path("./benchmark.csv")
df = pd.DataFrame()
if save_path.exists():
df = pd.read_csv(str(save_path), index_col=0)
index = []
records = []
bar = tqdm(models)
try:
for key in bar:
if key not in df.index:
try:
model = AutoModel.from_pretrained(key)
tr = AutoTransform.from_name(key)
batch_size = 64
if key in batch_sizes:
batch_size = batch_sizes[key]
bar.set_description(
f"{key}, size={tr.transforms[0].size}, batch_size={batch_size}"
)
top1, top5, time = benchmark(model.to(device), tr,
batch_size)
index.append(key)
data = {
"top1": top1,
"top5": top5,
"time": time,
"batch_size": batch_size,
}
pprint(data)
records.append(data)
except KeyError:
continue
except Exception as e:
print(e)
pass
if len(records) > 0:
new_df = pd.DataFrame.from_records(records, index=index)
if df is not None:
df = pd.concat([df, new_df])
else:
df = new_df
df.to_csv("./benchmark.csv")
mk = df.sort_values("top1", ascending=False).to_markdown()
with open("./benchmark.md", "w") as f:
f.write(mk)
return df
def benchmark_all() -> pd.DataFrame:
save_path = Path('./benchmark.csv')
df = pd.DataFrame()
if save_path.exists():
df = pd.read_csv(str(save_path), index_col=0)
index = []
records = []
bar = tqdm(models)
try:
for key in bar:
if key not in df.index:
try:
model = AutoModel.from_pretrained(key)
cfg = AutoConfig.from_name(key)
tr = cfg.transform
batch_size = 64
# if key in batch_sizes:
# batch_size = batch_sizes[key]
bar.set_description(
f'{key}, size={cfg.input_size}, batch_size={batch_size}'
)
top1, top5, time = benchmark(model.to(device), tr,
batch_size)
index.append(key)
data = {
'top1': top1,
'top5': top5,
'time': time,
'batch_size': batch_size
}
pprint(data)
records.append(data)
except KeyError:
continue
except Exception as e:
print(e)
pass
new_df = pd.DataFrame.from_records(records, index=index)
if df is not None:
df = pd.concat([df, new_df])
else:
df = new_df
df.to_csv('./benchmark.csv')
print(df)