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Significant ugprade to inference.py, support for different formats, formatting, etc.

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pull/1520/head
Ross Wightman 1 year ago
parent ec6921fcb0
commit c7a07e9ee6
1 changed files with 227 additions and 39 deletions
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266
inference.py
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@ -9,12 +9,44 @@ import os
import time
import argparse
import logging
from contextlib import suppress
from functools import partial
import numpy as np
import pandas as pd
import torch
from timm.models import create_model, apply_test_time_pool
from timm.data import ImageDataset, create_loader, resolve_data_config
from timm.utils import AverageMeter, setup_default_logging
from timm.models import create_model, apply_test_time_pool, load_checkpoint
from timm.data import create_dataset, create_loader, resolve_data_config
from timm.utils import AverageMeter, setup_default_logging, set_jit_fuser
try:
from apex import amp
has_apex = True
except ImportError:
has_apex = False
has_native_amp = False
try:
if getattr(torch.cuda.amp, 'autocast') is not None:
has_native_amp = True
except AttributeError:
pass
try:
from functorch.compile import memory_efficient_fusion
has_functorch = True
except ImportError as e:
has_functorch = False
try:
import torch._dynamo
has_dynamo = True
except ImportError:
has_dynamo = False
torch.backends.cudnn.benchmark = True
_logger = logging.getLogger('inference')
@ -23,8 +55,10 @@ _logger = logging.getLogger('inference')
parser = argparse.ArgumentParser(description='PyTorch ImageNet Inference')
parser.add_argument('data', metavar='DIR',
help='path to dataset')
parser.add_argument('--output_dir', metavar='DIR', default='./',
help='path to output files')
parser.add_argument('--dataset', '-d', metavar='NAME', default='',
help='dataset type (default: ImageFolder/ImageTar if empty)')
parser.add_argument('--split', metavar='NAME', default='validation',
help='dataset split (default: validation)')
parser.add_argument('--model', '-m', metavar='MODEL', default='dpn92',
help='model architecture (default: dpn92)')
parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
@ -32,17 +66,25 @@ parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
parser.add_argument('-b', '--batch-size', default=256, type=int,
metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--img-size', default=None, type=int,
metavar='N', help='Input image dimension')
metavar='N', help='Input image dimension, uses model default if empty')
parser.add_argument('--input-size', default=None, nargs=3, type=int,
metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
parser.add_argument('--use-train-size', action='store_true', default=False,
help='force use of train input size, even when test size is specified in pretrained cfg')
parser.add_argument('--crop-pct', default=None, type=float,
metavar='N', help='Input image center crop pct')
parser.add_argument('--crop-mode', default=None, type=str,
metavar='N', help='Input image crop mode (squash, border, center). Model default if None.')
parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
help='Override mean pixel value of dataset')
parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
help='Override std deviation of of dataset')
parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
help='Image resize interpolation type (overrides model)')
parser.add_argument('--num-classes', type=int, default=1000,
parser.add_argument('--num-classes', type=int, default=None,
help='Number classes in dataset')
parser.add_argument('--class-map', default='', type=str, metavar='FILENAME',
help='path to class to idx mapping file (default: "")')
parser.add_argument('--log-freq', default=10, type=int,
metavar='N', help='batch logging frequency (default: 10)')
parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
@ -51,10 +93,51 @@ parser.add_argument('--pretrained', dest='pretrained', action='store_true',
help='use pre-trained model')
parser.add_argument('--num-gpu', type=int, default=1,
help='Number of GPUS to use')
parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true',
help='disable test time pool')
parser.add_argument('--topk', default=5, type=int,
parser.add_argument('--test-pool', dest='test_pool', action='store_true',
help='enable test time pool')
parser.add_argument('--channels-last', action='store_true', default=False,
help='Use channels_last memory layout')
parser.add_argument('--device', default='cuda', type=str,
help="Device (accelerator) to use.")
parser.add_argument('--amp', action='store_true', default=False,
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('--use-ema', dest='use_ema', action='store_true',
help='use ema version of weights if present')
parser.add_argument('--fuser', default='', type=str,
help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
parser.add_argument('--dynamo-backend', default=None, type=str,
help="Select dynamo backend. Default: None")
scripting_group = parser.add_mutually_exclusive_group()
scripting_group.add_argument('--torchscript', default=False, action='store_true',
help='torch.jit.script the full model')
scripting_group.add_argument('--aot-autograd', default=False, action='store_true',
help="Enable AOT Autograd support.")
scripting_group.add_argument('--dynamo', default=False, action='store_true',
help="Enable Dynamo optimization.")
parser.add_argument('--results-dir',type=str, default=None,
help='folder for output results')
parser.add_argument('--results-file', type=str, default=None,
help='results filename (relative to results-dir)')
parser.add_argument('--results-format', type=str, default='csv',
help='results format (one of "csv", "json", "json-split", "parquet")')
parser.add_argument('--topk', default=1, type=int,
metavar='N', help='Top-k to output to CSV')
parser.add_argument('--fullname', action='store_true', default=False,
help='use full sample name in output (not just basename).')
parser.add_argument('--indices-name', default='index',
help='name for output indices column(s)')
parser.add_argument('--outputs-name', default=None,
help='name for logit/probs output column(s)')
parser.add_argument('--outputs-type', default='prob',
help='output type colum ("prob" for probabilities, "logit" for raw logits)')
parser.add_argument('--separate-columns', action='store_true', default=False,
help='separate output columns per result index.')
parser.add_argument('--exclude-outputs', action='store_true', default=False,
help='exclude logits/probs from results, just indices. topk must be set !=0.')
def main():
@ -63,48 +146,109 @@ def main():
# might as well try to do something useful...
args.pretrained = args.pretrained or not args.checkpoint
if torch.cuda.is_available():
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.benchmark = True
device = torch.device(args.device)
# resolve AMP arguments based on PyTorch / Apex availability
use_amp = None
amp_autocast = suppress
if args.amp:
assert has_native_amp, 'Please update PyTorch to a version with native AMP (or use APEX).'
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)
_logger.info('Running inference in mixed precision with native PyTorch AMP.')
else:
_logger.info('Running inference in float32. AMP not enabled.')
if args.fuser:
set_jit_fuser(args.fuser)
# create model
model = create_model(
args.model,
num_classes=args.num_classes,
in_chans=3,
pretrained=args.pretrained,
checkpoint_path=args.checkpoint)
checkpoint_path=args.checkpoint,
)
if args.num_classes is None:
assert hasattr(model, 'num_classes'), 'Model must have `num_classes` attr if not set on cmd line/config.'
args.num_classes = model.num_classes
if args.checkpoint:
load_checkpoint(model, args.checkpoint, args.use_ema)
_logger.info(
f'Model {args.model} created, param count: {sum([m.numel() for m in model.parameters()])}')
_logger.info('Model %s created, param count: %d' %
(args.model, sum([m.numel() for m in model.parameters()])))
data_config = resolve_data_config(vars(args), model=model)
test_time_pool = False
if args.test_pool:
model, test_time_pool = apply_test_time_pool(model, data_config)
config = resolve_data_config(vars(args), model=model)
model, test_time_pool = (model, False) if args.no_test_pool else apply_test_time_pool(model, config)
model = model.to(device)
model.eval()
if args.channels_last:
model = model.to(memory_format=torch.channels_last)
if args.torchscript:
model = torch.jit.script(model)
elif args.aot_autograd:
assert has_functorch, "functorch is needed for --aot-autograd"
model = memory_efficient_fusion(model)
elif args.dynamo:
assert has_dynamo, "torch._dynamo is needed for --dynamo"
torch._dynamo.reset()
if args.dynamo_backend is not None:
model = torch._dynamo.optimize(args.dynamo_backend)(model)
else:
model = torch._dynamo.optimize()(model)
if args.num_gpu > 1:
model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
else:
model = model.cuda()
model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu)))
dataset = create_dataset(
root=args.data,
name=args.dataset,
split=args.split,
class_map=args.class_map,
)
if test_time_pool:
data_config['crop_pct'] = 1.0
loader = create_loader(
ImageDataset(args.data),
input_size=config['input_size'],
dataset,
batch_size=args.batch_size,
use_prefetcher=True,
interpolation=config['interpolation'],
mean=config['mean'],
std=config['std'],
num_workers=args.workers,
crop_pct=1.0 if test_time_pool else config['crop_pct'])
**data_config,
)
model.eval()
k = min(args.topk, args.num_classes)
top_k = min(args.topk, args.num_classes)
batch_time = AverageMeter()
end = time.time()
topk_ids = []
all_indices = []
all_outputs = []
use_probs = args.outputs_type == 'prob'
with torch.no_grad():
for batch_idx, (input, _) in enumerate(loader):
input = input.cuda()
labels = model(input)
topk = labels.topk(k)[1]
topk_ids.append(topk.cpu().numpy())
with amp_autocast():
output = model(input)
if use_probs:
output = output.softmax(-1)
if top_k:
output, indices = output.topk(top_k)
all_indices.append(indices.cpu().numpy())
all_outputs.append(output.cpu().numpy())
# measure elapsed time
batch_time.update(time.time() - end)
@ -114,13 +258,57 @@ def main():
_logger.info('Predict: [{0}/{1}] Time {batch_time.val:.3f} ({batch_time.avg:.3f})'.format(
batch_idx, len(loader), batch_time=batch_time))
topk_ids = np.concatenate(topk_ids, axis=0)
all_indices = np.concatenate(all_indices, axis=0) if all_indices else None
all_outputs = np.concatenate(all_outputs, axis=0).astype(np.float32)
filenames = loader.dataset.filenames(basename=not args.fullname)
outputs_name = args.outputs_name or ('prob' if use_probs else 'logit')
data_dict = {'filename': filenames}
if args.separate_columns and all_outputs.shape[-1] > 1:
if all_indices is not None:
for i in range(all_indices.shape[-1]):
data_dict[f'{args.indices_name}_{i}'] = all_indices[:, i]
for i in range(all_outputs.shape[-1]):
data_dict[f'{outputs_name}_{i}'] = all_outputs[:, i]
else:
if all_indices is not None:
if all_indices.shape[-1] == 1:
all_indices = all_indices.squeeze(-1)
data_dict[args.indices_name] = list(all_indices)
if all_outputs.shape[-1] == 1:
all_outputs = all_outputs.squeeze(-1)
data_dict[outputs_name] = list(all_outputs)
df = pd.DataFrame(data=data_dict)
results_filename = args.results_file
needs_ext = False
if not results_filename:
# base default filename on model name + img-size
img_size = data_config["input_size"][1]
results_filename = f'{args.model}-{img_size}'
needs_ext = True
with open(os.path.join(args.output_dir, './topk_ids.csv'), 'w') as out_file:
filenames = loader.dataset.filenames(basename=True)
for filename, label in zip(filenames, topk_ids):
out_file.write('{0},{1}\n'.format(
filename, ','.join([ str(v) for v in label])))
if args.results_dir:
results_filename = os.path.join(args.results_dir, results_filename)
if args.results_format == 'parquet':
if needs_ext:
results_filename += '.parquet'
df = df.set_index('filename')
df.to_parquet(results_filename)
elif args.results_format == 'json':
if needs_ext:
results_filename += '.json'
df.to_json(results_filename, lines=True, orient='records')
elif args.results_format == 'json-split':
if needs_ext:
results_filename += '.json'
df.to_json(results_filename, indent=4, orient='split', index=False)
else:
if needs_ext:
results_filename += '.csv'
df.to_csv(results_filename, index=False)
if __name__ == '__main__':

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