Skip to content

disable abbreviating csv inference output with ellipses #2402

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Jan 14, 2025
Merged

disable abbreviating csv inference output with ellipses #2402

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
8ce197e
disable abbreviating csv inference output with ellipses
JosuaRieder Jan 14, 2025
fc0609b
Add --model-dtype (pure bfloat16/float16) support to inference.py
rwightman Jan 14, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
15 changes: 13 additions & 2 deletions inference.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
import time
from contextlib import suppress
from functools import partial
from sys import maxsize

import numpy as np
import pandas as pd
Expand Down Expand Up @@ -104,6 +105,8 @@
help='use Native AMP for mixed precision training')
parser.add_argument('--amp-dtype', default='float16', type=str,
help='lower precision AMP dtype (default: float16)')
parser.add_argument('--model-dtype', default=None, type=str,
help='Model dtype override (non-AMP) (default: float32)')
parser.add_argument('--fuser', default='', type=str,
help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
parser.add_argument('--model-kwargs', nargs='*', default={}, action=ParseKwargs)
Expand Down Expand Up @@ -160,9 +163,15 @@ def main():

device = torch.device(args.device)

model_dtype = None
if args.model_dtype:
assert args.model_dtype in ('float32', 'float16', 'bfloat16')
model_dtype = getattr(torch, args.model_dtype)

# resolve AMP arguments based on PyTorch / Apex availability
amp_autocast = suppress
if args.amp:
assert model_dtype is None or model_dtype == torch.float32, 'float32 model dtype must be used with AMP'
assert args.amp_dtype in ('float16', 'bfloat16')
amp_dtype = torch.bfloat16 if args.amp_dtype == 'bfloat16' else torch.float16
amp_autocast = partial(torch.autocast, device_type=device.type, dtype=amp_dtype)
Expand Down Expand Up @@ -200,7 +209,7 @@ def main():
if args.test_pool:
model, test_time_pool = apply_test_time_pool(model, data_config)

model = model.to(device)
model = model.to(device=device, dtype=model_dtype)
model.eval()
if args.channels_last:
model = model.to(memory_format=torch.channels_last)
Expand Down Expand Up @@ -236,6 +245,7 @@ def main():
use_prefetcher=True,
num_workers=workers,
device=device,
img_dtype=model_dtype or torch.float32,
**data_config,
)

Expand Down Expand Up @@ -279,7 +289,7 @@ def main():
np_labels = to_label(np_indices)
all_labels.append(np_labels)

all_outputs.append(output.cpu().numpy())
all_outputs.append(output.float().cpu().numpy())

# measure elapsed time
batch_time.update(time.time() - end)
Expand Down Expand Up @@ -343,6 +353,7 @@ def main():


def save_results(df, results_filename, results_format='csv', filename_col='filename'):
np.set_printoptions(threshold=maxsize)
results_filename += _FMT_EXT[results_format]
if results_format == 'parquet':
df.set_index(filename_col).to_parquet(results_filename)
Expand Down