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import logging
import torch
from tqdm import tqdm
from open_clip import get_input_dtype, get_tokenizer, build_zero_shot_classifier, \
IMAGENET_CLASSNAMES, OPENAI_IMAGENET_TEMPLATES
from open_clip_train.precision import get_autocast
def accuracy(output, target, topk=(1,)):
pred = output.topk(max(topk), 1, True, True)[1].t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
return [float(correct[:k].reshape(-1).float().sum(0, keepdim=True).cpu().numpy()) for k in topk]
def run(model, classifier, dataloader, args):
autocast = get_autocast(args.precision)
input_dtype = get_input_dtype(args.precision)
with torch.inference_mode():
top1, top5, n = 0., 0., 0.
for images, target in tqdm(dataloader, unit_scale=args.batch_size):
images = images.to(device=args.device, dtype=input_dtype)
target = target.to(args.device)
with autocast():
# predict
output = model(image=images)
image_features = output['image_features'] if isinstance(output, dict) else output[0]
logits = 100. * image_features @ classifier
# measure accuracy
acc1, acc5 = accuracy(logits, target, topk=(1, 5))
top1 += acc1
top5 += acc5
n += images.size(0)
top1 = (top1 / n)
top5 = (top5 / n)
return top1, top5
def zero_shot_eval(model, data, epoch, args, tokenizer=None):
if 'imagenet-val' not in data and 'imagenet-v2' not in data:
return {}
if args.zeroshot_frequency == 0:
return {}
if (epoch % args.zeroshot_frequency) != 0 and epoch != args.epochs:
return {}
if args.distributed and not args.horovod:
model = model.module
logging.info('Starting zero-shot imagenet.')
if tokenizer is None:
tokenizer = get_tokenizer(args.model)
logging.info('Building zero-shot classifier')
autocast = get_autocast(args.precision)
with autocast():
classifier = build_zero_shot_classifier(
model,
tokenizer=tokenizer,
classnames=IMAGENET_CLASSNAMES,
templates=OPENAI_IMAGENET_TEMPLATES,
num_classes_per_batch=10,
device=args.device,
use_tqdm=True,
)
logging.info('Using classifier')
results = {}
if 'imagenet-val' in data:
top1, top5 = run(model, classifier, data['imagenet-val'].dataloader, args)
results['imagenet-zeroshot-val-top1'] = top1
results['imagenet-zeroshot-val-top5'] = top5
if 'imagenet-v2' in data:
top1, top5 = run(model, classifier, data['imagenet-v2'].dataloader, args)
results['imagenetv2-zeroshot-val-top1'] = top1
results['imagenetv2-zeroshot-val-top5'] = top5
logging.info('Finished zero-shot imagenet.')
return results
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