LOG_DIR = args.log_dir + '/run-test_{}-n{}-lr{}-wd{}-m{}-embeddings{}-msceleb-alpha10' \
.format(args.optimizer, args.n_triplets, args.lr, args.wd,
args.margin, args.embedding_size)
# create logger
logger = Logger(LOG_DIR)
# Define visulaize SummaryWriter instance
writer = SummaryWriter('Log/amsoftmax_res10', comment='margin0.3')
kwargs = {'num_workers': 0, 'pin_memory': True} if args.cuda else {}
if args.cos_sim:
l2_dist = nn.CosineSimilarity(dim=1, eps=1e-6)
else:
l2_dist = PairwiseDistance(2)
voxceleb, voxceleb_dev = wav_list_reader(args.dataroot)
if args.makemfb:
# pbar = tqdm(voxceleb)
for datum in voxceleb:
mk_MFB(
(args.dataroot + '/voxceleb1_wav/' + datum['filename'] + '.wav'))
print("Complete convert")
if args.mfb:
transform = transforms.Compose([
concateinputfromMFB(),
# truncatedinputfromMFB(),
totensor()
])
transform_T = transforms.Compose([
concateinputfromMFB(input_per_file=args.test_input_per_file),
def main():
# Views the training images and displays the distance on anchor-negative and anchor-positive
voxceleb, train_set, valid_set = wav_list_reader(args.dataroot, split=True)
class_to_idx = np.load('Data/dataset/voxceleb1/Fbank64_Norm/class2idx.npy').item()
print('Number of Speakers: {}.\n'.format(len(class_to_idx)))
test_dir = VoxcelebTestset(dir=args.dataroot, pairs_path=args.test_pairs_path, loader=file_loader,
transform=transform_T)
indices = list(range(len(test_dir)))
random.shuffle(indices)
indices = indices[:4000]
test_part = torch.utils.data.Subset(test_dir, indices)
valid_dir = ValidationDataset(voxceleb=valid_set, dir=args.dataroot, loader=file_loader, class_to_idx=class_to_idx,
transform=transform)
del voxceleb
del train_set
del valid_set
# print the experiment configuration
print('\nCurrent time is \33[91m{}\33[0m.'.format(str(time.asctime())))
print('Parsed options: {}'.format(vars(args)))
# instantiate model and initialize weights
model = SuperficialResCNN(layers=[1, 1, 1, 0], embedding_size=args.embedding_size, n_classes=len(class_to_idx), m=3)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print('=> loading checkpoint {}'.format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
checkpoint = torch.load(args.resume)
filtered = {k: v for k, v in checkpoint['state_dict'].items() if 'num_batches_tracked' not in k}
model.load_state_dict(filtered)
else:
print('=> no checkpoint found at {}'.format(args.resume))
# train_loader = torch.utils.data.DataLoader(train_dir, batch_size=args.batch_size, shuffle=True,
# # collate_fn=PadCollate(dim=2),
# **kwargs)
valid_loader = torch.utils.data.DataLoader(valid_dir, batch_size=args.test_batch_size, shuffle=False,
# collate_fn=PadCollate(dim=2),
**kwargs)
test_loader = torch.utils.data.DataLoader(test_part, batch_size=args.test_batch_size, shuffle=False, **kwargs)
# train(train_loader, model, ce, optimizer, scheduler, epoch)
# train(train_loader, model, ce)
# test(test_loader, valid_loader, model)
model.eval()
valid_pbar = tqdm(enumerate(valid_loader))
softmax = nn.Softmax(dim=1)
correct = 0.
total_datasize = 0.
for batch_idx, (data, label) in valid_pbar:
# data = Variable(data.cuda())
# true_labels = Variable(label.cuda())
pdb.set_trace()
explainer = lime_image.LimeImageExplainer()
explanation = explainer.explain_instance(data,
batch_predict, # classification function
top_labels=5,
hide_color=0,
num_samples=len(data))
temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=False, num_features=10,
hide_rest=False)
img_boundry2 = mark_boundaries(temp / 255.0, mask)
plt.imshow(img_boundry2)
break
writer.close()
kwargs = {'num_workers': 2, 'pin_memory': True} if args.cuda else {}
opt_kwargs = {
'lr': args.lr,
'lr_decay': args.lr_decay,
'weight_decay': args.weight_decay,
'dampening': args.dampening,
'momentum': args.momentum
}
if args.cos_sim:
l2_dist = nn.CosineSimilarity(dim=1, eps=1e-6)
else:
l2_dist = PairwiseDistance(2)
voxceleb, train_set, valid_set = wav_list_reader(args.dataroot, split=True)
# voxceleb2, voxceleb2_dev = voxceleb2_list_reader(args.dataroot)
# if args.makemfb:
# #pbar = tqdm(voxceleb)
# for datum in voxceleb:
# mk_MFB((args.dataroot +'/voxceleb1_wav/' + datum['filename']+'.wav'))
# print("Complete convert")
#
# if args.makespec:
# num_pro = 1.
# for datum in voxceleb:
# # Data/voxceleb1/
# # /data/voxceleb/voxceleb1_wav/
# GenerateSpect(wav_path='/data/voxceleb/voxceleb1_wav/' + datum['filename']+'.wav',
# write_path=args.dataroot +'/spectrogram/voxceleb1_wav/' + datum['filename']+'.npy')
vox2['duration'] = len(item)
vox_duration.append(vox2)
share_lock.release()
# print('')
except Exception:
error_queue.put(vox2)
#share_lock.release()
print('\rProcess {}: There are {:6d} features left.'.format(cpid, queue.qsize()), end='\r')
pass
if __name__ == '__main__':
queue = Queue()
que_queue = Queue()
# voxceleb2, voxceleb2_dev = voxceleb2_list_reader(dataroot)
vox1, vox1_dev = wav_list_reader(dataroot)
vox_duration = multiprocessing.Manager().list()
# spk_utt_duration = multiprocessing.Manager().dict()
share_lock = multiprocessing.Manager().Lock()
for i in range(len(vox1)):
queue.put(vox1[i])
#check_from_queue(queue, que_queue, 1)
process_list = []
for i in range(15):
# pro = Process(target=check_from_queue, args=(queue, que_queue, spk_utt_duration, i, share_lock))
pro = Process(target=add_duration_vox, args=(queue, que_queue, vox_duration, i, share_lock))
process_list.append(pro)