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import os
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from tqdm import tqdm
from open_clip import get_input_dtype, get_tokenizer
from open_clip.factory import HF_HUB_PREFIX
from .precision import get_autocast
from .stats import calculate_stats, d_prime
from .zero_shot_classifier import build_zero_shot_classifier
from .zero_shot_metadata import CLASSNAMES, OPENAI_IMAGENET_TEMPLATES
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.no_grad():
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)
images = images.unsqueeze(2)
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 validate(audio_model, classifier, val_loader, args, epoch):
epoch = epoch - 1 ########################
# switch to evaluate mode
audio_model.eval()
autocast = get_autocast(args.precision)
input_dtype = get_input_dtype(args.precision)
A_predictions = []
A_targets = []
A_loss = []
with torch.no_grad():
for i, (audio_input, labels) in enumerate(tqdm(val_loader)):
audio_input = audio_input.to(device=args.device, dtype=input_dtype)
# compute output
with autocast():
# predict
output = audio_model(image=audio_input)
image_features = output['image_features'] if isinstance(output, dict) else output[0]
logits = 100. * image_features @ classifier
audio_output = logits
# audio_output = torch.sigmoid(audio_output)
predictions = audio_output.to('cpu').detach()
A_predictions.append(predictions)
A_targets.append(labels)
# compute the loss
labels = labels.to(args.device)
loss = nn.CrossEntropyLoss()(audio_output, torch.argmax(labels.long(), dim=1))
A_loss.append(loss.to('cpu').detach())
audio_output = torch.cat(A_predictions)
target = torch.cat(A_targets)
loss = np.mean(A_loss)
stats = calculate_stats(audio_output, target)
# save the prediction here
args.a_cls_output_dir = os.path.join(args.log_base_path, f'a_cls/{args.val_a_cls_data.lower()}')
os.makedirs(args.a_cls_output_dir, exist_ok=True)
if os.path.exists(args.a_cls_output_dir + '/predictions') == False:
os.mkdir(args.a_cls_output_dir + '/predictions')
np.savetxt(args.a_cls_output_dir + '/predictions/target.csv', target, delimiter=',')
np.savetxt(args.a_cls_output_dir + '/predictions/predictions_' + str(epoch) + '.csv', audio_output,
delimiter=',')
valid_loss = loss
main_metrics = 'mAP'
metrics = {}
if args.do_train:
# ensemble results
cum_stats = validate_ensemble(args, epoch)
cum_mAP = np.mean([stat['AP'] for stat in cum_stats])
cum_mAUC = np.mean([stat['auc'] for stat in cum_stats])
cum_acc = cum_stats[0]['acc']
mAP = np.mean([stat['AP'] for stat in stats])
mAUC = np.mean([stat['auc'] for stat in stats])
acc = stats[0]['acc']
middle_ps = [stat['precisions'][int(len(stat['precisions']) / 2)] for stat in stats]
middle_rs = [stat['recalls'][int(len(stat['recalls']) / 2)] for stat in stats]
average_precision = np.mean(middle_ps)
average_recall = np.mean(middle_rs)
if main_metrics == 'mAP':
logging.info("mAP: {:.6f}".format(mAP))
else:
logging.info("acc: {:.6f}".format(acc))
logging.info("AUC: {:.6f}".format(mAUC))
logging.info("Avg Precision: {:.6f}".format(average_precision))
logging.info("Avg Recall: {:.6f}".format(average_recall))
logging.info("d_prime: {:.6f}".format(d_prime(mAUC)))
logging.info("valid_loss: {:.6f}".format(valid_loss))
if args.do_train:
logging.info("cum_mAP: {:.6f}".format(cum_mAP))
logging.info("cum_mAUC: {:.6f}".format(cum_mAUC))
if main_metrics == 'mAP':
metrics['mAP'] = float(mAP)
else:
metrics['acc'] = float(acc)
metrics['mAUC'] = float(mAUC)
metrics['average_precision'] = float(average_precision)
metrics['average_recall'] = float(average_recall)
metrics['d_prime_mAUC'] = float(d_prime(mAUC))
metrics['valid_loss'] = float(valid_loss)
if args.do_train:
metrics['cum_mAP'] = float(cum_mAP)
metrics['cum_mAUC'] = float(cum_mAUC)
return metrics
def validate_ensemble(args, epoch):
exp_dir = args.a_cls_output_dir
target = np.loadtxt(exp_dir + '/predictions/target.csv', delimiter=',')
if epoch == 0:
cum_predictions = np.loadtxt(exp_dir + '/predictions/predictions_0.csv', delimiter=',')
else:
cum_predictions = np.loadtxt(exp_dir + '/predictions/cum_predictions.csv', delimiter=',') * (epoch - 1)
predictions = np.loadtxt(exp_dir + '/predictions/predictions_' + str(epoch) + '.csv', delimiter=',')
cum_predictions = cum_predictions + predictions
# remove the prediction file to save storage space
os.remove(exp_dir + '/predictions/predictions_' + str(epoch - 1) + '.csv')
cum_predictions = cum_predictions / (epoch + 1)
np.savetxt(exp_dir + '/predictions/cum_predictions.csv', cum_predictions, delimiter=',')
stats = calculate_stats(cum_predictions, target)
return stats
def zero_shot_eval(model, data, epoch, args):
temp_val_a_cls_data = args.val_a_cls_data
args.val_a_cls_data = list(data.keys())
assert len(args.val_a_cls_data) == 1
args.val_a_cls_data = args.val_a_cls_data[0]
if args.val_a_cls_data 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(f'Starting zero-shot {args.val_a_cls_data.upper()}.')
logging.info('Building zero-shot classifier')
autocast = get_autocast(args.precision)
with autocast():
tokenizer = get_tokenizer(HF_HUB_PREFIX+args.model, cache_dir=args.cache_dir)
# tokenizer = get_tokenizer("ViT-L-14")
classifier = build_zero_shot_classifier(
model,
tokenizer=tokenizer,
classnames=CLASSNAMES[args.val_a_cls_data],
templates=OPENAI_IMAGENET_TEMPLATES,
num_classes_per_batch=10,
device=args.device,
use_tqdm=True,
)
logging.info('Using classifier')
results = {}
if args.val_a_cls_data.lower() == 'audioset':
if args.val_a_cls_data in data:
stats = validate(model, classifier, data[args.val_a_cls_data].dataloader, args, epoch)
results.update(stats)
else:
if args.val_a_cls_data in data:
top1, top5 = run(model, classifier, data[args.val_a_cls_data].dataloader, args)
results[f'{args.val_a_cls_data}-zeroshot-val-top1'] = top1
results[f'{args.val_a_cls_data}-zeroshot-val-top5'] = top5
logging.info(f'Finished zero-shot {args.val_a_cls_data.upper()}.')
args.val_a_cls_data = temp_val_a_cls_data
return results
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