def training_step(self, batch, batch_idx):
a, v, t, label, urls = batch
if a.shape[0] < self.batch_size:
a = pad_batch(a, self.batch_size)
v = pad_batch(v, self.batch_size)
t = pad_batch(t, self.batch_size)
label = pad_batch(label, self.batch_size)
logits, similarity = self.classifier(a, v, t)
loss = self.loss(logits, label)
top_1_accuracy = compute_accuracy(logits, label, top_k=1)
top_5_accuracy = compute_accuracy(logits, label, top_k=5)
logs = {
'loss': loss,
'train_top_1': top_1_accuracy,
'train_top_5': top_5_accuracy}
for k in logs:
prog_bar = True if "top" in k else False
self.log('train/{}'.format(k), logs[k], prog_bar=prog_bar)
if similarity is not None and batch_idx % 100 == 0:
visualize_batch_downstream(similarity, prefix="train", dataset="k700")
return {'loss': loss,
'logs': logs}
def training_step(self, batch, batch_idx):
if self.multiple_video:
a, v1, v2, t, urls, t_mask = batch
if a.shape[0] < self.batch_size:
a = pad_batch(a, self.batch_size)
if v1.shape[0] < self.batch_size:
v1 = pad_batch(v1, self.batch_size)
if v2.shape[0] < self.batch_size:
v2 = pad_batch(v2, self.batch_size)
if t.shape[0] < self.batch_size:
t = pad_batch(t, self.batch_size)
t_mask = pad_batch(t_mask, self.batch_size)
a, v1, v2, t = self.encoder( a, v1, v2, t)
loss, metrics = self.encoder.loss( a, v1, v2, t, t_mask.to(dtype=torch.float32))
else:
# a, v, t, urls, t_mask = batch
a, v, t, i, urls, t_mask = batch
if a.shape[0] < self.batch_size:
a = pad_batch(a, self.batch_size)
if v.shape[0] < self.batch_size:
v = pad_batch(v, self.batch_size)
if t.shape[0] < self.batch_size:
t = pad_batch(t, self.batch_size)
t_mask = pad_batch(t_mask, self.batch_size)
if i.shape[0] < self.batch_size:
i = pad_batch(i, self.batch_size)
a, v, t, i = self.encoder(a, v, t, i)
loss, metrics = self.encoder.loss(a, v, t, i, t_mask.to(dtype=torch.float32))
# a, v, t = self.encoder(a, v, t)
# loss, metrics = self.encoder.loss(a, v, t, t_mask.to(dtype=torch.float32))
for k in metrics:
if 'matrix' not in k:
prog_bar = 'av_top1' in k
self.log('train/{}'.format(k), metrics[k], prog_bar=prog_bar)
self.log('lr', self.loggable_lr)
torch.cuda.empty_cache()
try:
if batch_idx % 100 == 0:
q = 'st' if self.pretrained_text else 'c'
q += 'vq'
if 'vt_matrix' in metrics:
visualize_batch(urls, metrics['vt_matrix'], prefix="train", qualifier=q, mode='vt')
else:
visualize_batch(urls, metrics['av_matrix'], prefix="train", qualifier=q, mode='av')
except:
a = batch_idx % 100
return {'loss': loss,
'logs': metrics}
def test_step(self, batch, batch_idx):
if self.multiple_video:
a, v1, v2, t, urls, t_mask = batch
if a.shape[0] < self.batch_size:
a = pad_batch(a, self.batch_size)
if v1.shape[0] < self.batch_size:
v1 = pad_batch(v1, self.batch_size)
if v2.shape[0] < self.batch_size:
v2 = pad_batch(v2, self.batch_size)
if t.shape[0] < self.batch_size:
t = pad_batch(t, self.batch_size)
t_mask = pad_batch(t_mask, self.batch_size)
a, v1, v2, t = self.encoder( a, v1, v2, t)
loss, metrics = self.encoder.loss( a, v1, v2, t, t_mask.to(dtype=torch.float32))
else:
a, v, t, urls, t_mask = batch
if a.shape[0] < self.batch_size:
a = pad_batch(a, self.batch_size)
if v.shape[0] < self.batch_size:
v = pad_batch(v, self.batch_size)
if t.shape[0] < self.batch_size:
t = pad_batch(t, self.batch_size)
t_mask = pad_batch(t_mask, self.batch_size)
a, v, t = self.encoder(a, v, t)
loss, metrics = self.encoder.loss(a, v, t, t_mask.to(dtype=torch.float32))
return {'test_total_loss': metrics['total_loss'],}
def validation_step(self, batch, batch_idx):
a, v, t, urls, t_mask = batch
# a, v, t, i, urls, t_mask = batch
if a.shape[0] < self.batch_size:
a = pad_batch(a, self.batch_size)
if v.shape[0] < self.batch_size:
v = pad_batch(v, self.batch_size)
if t.shape[0] < self.batch_size:
t = pad_batch(t, self.batch_size)
t_mask = pad_batch(t_mask, self.batch_size)
# if i.shape[0] < self.batch_size:
# i = pad_batch(i, self.batch_size)
# a, v, t, i = self.encoder(a, v, t, i)
# loss, metrics = self.encoder.loss(a, v, t, i, t_mask.to(dtype=torch.float32))
a, v, t = self.encoder(a, v, t)
loss, metrics = self.encoder.loss(a, v, t,
t_mask.to(dtype=torch.float32))
for k in metrics:
if 'matrix' not in k:
prog_bar = 'av_top1' in k
self.log('val/{}'.format(k), metrics[k], prog_bar=prog_bar)
torch.cuda.empty_cache()
try:
if batch_idx % 100 == 0:
q = 'st' if self.pretrained_text else 'c'
q += 'vq'
if 'vt_matrix' in metrics:
visualize_batch(urls,
metrics['vt_matrix'],
prefix="val",
qualifier=q,
mode='vt')
else:
visualize_batch(urls,
metrics['av_matrix'],
prefix="val",
qualifier=q,
mode='av')
except:
a = batch_idx % 100
return {'val_total_loss': loss}
def validation_step(self, batch, batch_idx):
a, v, t = batch
if a.shape[0] < self.batch_size:
a = pad_batch(a, self.batch_size)
if v.shape[0] < self.batch_size:
v = pad_batch(v, self.batch_size)
if t.shape[0] < self.batch_size:
t = pad_batch(t, self.batch_size)
t_mask = pad_batch(t_mask, self.batch_size)
a, v, t = self.encoder(a, v, t)
loss, metrics = self.encoder.loss(a, v, t)
for k in metrics:
if 'matrix' not in k:
prog_bar = 'av_top1' in k
self.log('val/{}'.format(k), metrics[k], prog_bar=prog_bar)
torch.cuda.empty_cache()
return {'val_total_loss': loss}
def test_step(self, batch, batch_idx):
v, label = batch
v = v.permute(0, 2, 1, 3, 4)
if v.shape[0] < self.batch_size:
v = pad_batch(v, self.batch_size)
label = pad_batch(label, self.batch_size)
features = self.model.encoder.v_encoder(v)
logits = self.fc(features)
loss = self.loss(logits, label)
top_1_accuracy = compute_accuracy(logits, label, top_k=1)
top_5_accuracy = compute_accuracy(logits, label, top_k=5)
logs = {
'test_loss': loss,
'test_top_1': top_1_accuracy,
'test_top_5': top_5_accuracy
}
for k in logs:
self.log('test/{}'.format(k), logs[k], prog_bar=False)
def training_step(self, batch, batch_idx):
a, v, t = batch
if a.shape[0] < self.batch_size:
a = pad_batch(a, self.batch_size)
if v.shape[0] < self.batch_size:
v = pad_batch(v, self.batch_size)
if t.shape[0] < self.batch_size:
t = pad_batch(t, self.batch_size)
t_mask = pad_batch(t_mask, self.batch_size)
a, v, t = self.encoder(a, v, t)
loss, metrics = self.encoder.loss(a, v, t)
for k in metrics:
if 'matrix' not in k:
prog_bar = 'av_top1' in k
self.log('train/{}'.format(k), metrics[k], prog_bar=prog_bar)
self.log('lr', self.loggable_lr)
torch.cuda.empty_cache()
return {'loss': loss, 'logs': metrics}
