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save_feat.py
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save_feat.py
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import pickle
from utils.logger import logger
import torch.nn.parallel
import torch.optim
import torch
from utils.loaders import EpicKitchensDataset
from utils.args import args
from utils.utils import pformat_dict
import utils
import numpy as np
import os
import models as model_list
import tasks
# global variables among training functions
modalities = None
np.random.seed(13696641)
torch.manual_seed(13696641)
def init_operations():
"""
parse all the arguments, generate the logger, check gpus to be used and wandb
"""
logger.info("Feature Extraction")
logger.info("Running with parameters: " + pformat_dict(args, indent=1))
if args.gpus is not None:
logger.debug('Using only these GPUs: {}'.format(args.gpus))
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpus
def main():
global modalities
init_operations()
modalities = args.modality
# recover valid paths, domains, classes
# this will output the domain conversion (D1 -> 8, et cetera) and the label list
num_classes, valid_labels, source_domain, target_domain = utils.utils.get_domains_and_labels(args)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
models = {}
train_augmentations = {}
test_augmentations = {}
logger.info("Instantiating models per modality")
for m in modalities:
logger.info('{} Net\tModality: {}'.format(args.models[m].model, m))
models[m] = getattr(model_list, args.models[m].model)(num_classes, m, args.models[m], **args.models[m].kwargs)
train_augmentations[m], test_augmentations[m] = models[m].get_augmentation(m)
action_classifier = tasks.ActionRecognition("action-classifier", models, 1,
args.total_batch, args.models_dir, num_classes,
args.test.num_clips, args.models, args=args)
action_classifier.load_on_gpu(device)
if args.resume_from is not None:
action_classifier.load_last_model(args.resume_from)
if args.action == "save":
augmentations = {"train": train_augmentations, "test": test_augmentations}
# the only action possible with this script is "save"
loader = torch.utils.data.DataLoader(EpicKitchensDataset(args.dataset.shift.split("-")[1], modalities,
args.split, args.dataset,
args.save.num_frames_per_clip,
args.save.num_clips, args.save.dense_sampling,
augmentations[args.split], additional_info=True,
**{"save": args.split}),
batch_size=1, shuffle=False,
num_workers=args.dataset.workers, pin_memory=True, drop_last=False)
save_feat(action_classifier, loader, device, action_classifier.current_iter, num_classes)
else:
raise NotImplementedError
def save_feat(model, loader, device, it, num_classes):
"""
function to validate the model on the test set
model: Task containing the model to be tested
val_loader: dataloader containing the validation data
device: device on which you want to test
it: int, iteration among the training num_iter at which the model is tested
num_classes: int, number of classes in the classification problem
"""
global modalities
model.reset_acc()
model.train(False)
results_dict = {"features": []}
num_samples = 0
logits = {}
features = {}
# Iterate over the models
with torch.no_grad():
for i_val, (data, label, video_name, uid) in enumerate(loader):
label = label.to(device)
for m in modalities:
batch, _, height, width = data[m].shape
data[m] = data[m].reshape(batch, args.test.num_clips,
args.test.num_frames_per_clip[m], -1, height, width)
data[m] = data[m].permute(1, 0, 3, 2, 4, 5)
logits[m] = torch.zeros((args.test.num_clips, batch, num_classes)).to(device)
features[m] = torch.zeros((args.test.num_clips, batch, model.task_models[m]
.module.feat_dim)).to(device)
clip = {}
for i_c in range(args.test.num_clips):
for m in modalities:
clip[m] = data[m][i_c].to(device)
output, feat = model(clip)
feat = feat["features"]
for m in modalities:
logits[m][i_c] = output[m]
features[m][i_c] = feat[m]
for m in modalities:
logits[m] = torch.mean(logits[m], dim=0)
for i in range(batch):
sample = {"uid": int(uid[i].cpu().detach().numpy()), "video_name": video_name[i]}
for m in modalities:
sample["features_" + m] = features[m][:, i].cpu().detach().numpy()
results_dict["features"].append(sample)
num_samples += batch
model.compute_accuracy(logits, label)
if (i_val + 1) % (len(loader) // 5) == 0:
logger.info("[{}/{}] top1= {:.3f}% top5 = {:.3f}%".format(i_val + 1, len(loader),
model.accuracy.avg[1], model.accuracy.avg[5]))
os.makedirs("saved_features", exist_ok=True)
pickle.dump(results_dict, open(os.path.join("saved_features", args.name + "_" +
args.dataset.shift.split("-")[1] + "_" +
args.split + ".pkl"), 'wb'))
class_accuracies = [(x / y) * 100 for x, y in zip(model.accuracy.correct, model.accuracy.total)]
logger.info('Final accuracy: top1 = %.2f%%\ttop5 = %.2f%%' % (model.accuracy.avg[1],
model.accuracy.avg[5]))
for i_class, class_acc in enumerate(class_accuracies):
logger.info('Class %d = [%d/%d] = %.2f%%' % (i_class,
int(model.accuracy.correct[i_class]),
int(model.accuracy.total[i_class]),
class_acc))
logger.info('Accuracy by averaging class accuracies (same weight for each class): {}%'
.format(np.array(class_accuracies).mean(axis=0)))
test_results = {'top1': model.accuracy.avg[1], 'top5': model.accuracy.avg[5],
'class_accuracies': np.array(class_accuracies)}
with open(os.path.join(args.log_dir, f'val_precision_{args.dataset.shift.split("-")[0]}-'
f'{args.dataset.shift.split("-")[-1]}.txt'), 'a+') as f:
f.write("[%d/%d]\tAcc@top1: %.2f%%\n" % (it, args.train.num_iter, test_results['top1']))
return test_results
if __name__ == '__main__':
main()