def tester(cfg):
print('testing')
dataloader_test, dataset_size_test = data.make_dataloader(cfg,
is_train=False)
model = modeling.build(cfg)
if cfg.TEST.MODEL.startswith('.'):
load_path = cfg.TEST.MODEL.replace(".", os.path.realpath("."))
else:
load_path = cfg.TEST.MODEL
model = torch.load(load_path)
model.cuda()
vis_test = Visualization(cfg, dataset_size_test, is_train=False)
writer_path = os.path.join(cfg.VISUALIZATION_DIRECTORY,
cfg.EXPERIMENT_NAME)
writer = SummaryWriter(writer_path)
total_iterations = 0
total_iterations_val = 0
model.eval()
epoch = 1
for iteration, batch in enumerate(dataloader_test):
index = batch[0]
videoFeat = batch[1].cuda()
videoFeat_lengths = batch[2].cuda()
tokens = batch[3].cuda()
tokens_lengths = batch[4].cuda()
start = batch[5].cuda()
end = batch[6].cuda()
localiz = batch[7].cuda()
localiz_lengths = batch[8]
time_starts = batch[9]
time_ends = batch[10]
factors = batch[11]
fps = batch[12]
frame_start = batch[13]
frame_end = batch[14]
loss, individual_loss, pred_start, pred_end, attention, atten_loss = model(
videoFeat, videoFeat_lengths, tokens, tokens_lengths, start, end,
localiz, frame_start, frame_end)
aux = vis_test.run(index, pred_start,
pred_end, start, end, videoFeat_lengths, epoch,
loss.detach(), individual_loss, attention,
atten_loss, time_starts, time_ends, factors, fps)
total_iterations_val += 1
a = vis_test.plot(epoch)
def trainer(cfg):
print('trainer')
dataloader_train, dataset_size_train = data.make_dataloader(cfg,
is_train=True)
dataloader_test, dataset_size_test = data.make_dataloader(cfg,
is_train=False)
model = modeling.build(cfg)
model.cuda()
#model = torch.load("/home/crodriguezo/projects/phd/moment-localization-with-NLP/mlnlp_lastversion/checkpoints/anet_config7/model_epoch_80")
optimizer = solver.make_optimizer(cfg, model)
vis_train = Visualization(cfg, dataset_size_train)
vis_test = Visualization(cfg, dataset_size_test, is_train=False)
writer_path = os.path.join(cfg.VISUALIZATION_DIRECTORY,
cfg.EXPERIMENT_NAME)
writer = SummaryWriter(writer_path)
total_iterations = 0
total_iterations_val = 0
for epoch in range(cfg.EPOCHS):
print("Epoch {}".format(epoch))
model.train()
for iteration, batch in enumerate(dataloader_train):
index = batch[0]
videoFeat = batch[1].cuda()
videoFeat_lengths = batch[2].cuda()
tokens = batch[3].cuda()
tokens_lengths = batch[4].cuda()
start = batch[5].cuda()
end = batch[6].cuda()
localiz = batch[7].cuda()
localiz_lengths = batch[8]
time_starts = batch[9]
time_ends = batch[10]
factors = batch[11]
fps = batch[12]
loss, individual_loss, pred_start, pred_end, attention, atten_loss = model(
videoFeat, videoFeat_lengths, tokens, tokens_lengths, start,
end, localiz)
print("Loss :{}".format(loss))
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 5)
optimizer.step()
vis_train.run(index, pred_start,
pred_end, start, end, videoFeat_lengths, epoch,
loss.detach(), individual_loss, attention,
atten_loss, time_starts, time_ends, factors, fps)
writer.add_scalar(f'mlnlp/Progress_Loss', loss.item(),
total_iterations)
writer.add_scalar(f'mlnlp/Progress_Attention_Loss',
atten_loss.item(), total_iterations)
writer.add_scalar(f'mlnlp/Progress_Mean_IoU', vis_train.mIoU[-1],
total_iterations)
total_iterations += 1.
writer.add_scalar(f'mlnlp/Train_Loss', np.mean(vis_train.loss), epoch)
writer.add_scalar(f'mlnlp/Train_Mean_IoU', np.mean(vis_train.mIoU),
epoch)
vis_train.plot(epoch)
torch.save(
model,
"./checkpoints/{}/model_epoch_{}".format(cfg.EXPERIMENT_NAME,
epoch))
model.eval()
for iteration, batch in enumerate(dataloader_test):
index = batch[0]
videoFeat = batch[1].cuda()
videoFeat_lengths = batch[2].cuda()
tokens = batch[3].cuda()
tokens_lengths = batch[4].cuda()
start = batch[5].cuda()
end = batch[6].cuda()
localiz = batch[7].cuda()
localiz_lengths = batch[8]
time_starts = batch[9]
time_ends = batch[10]
factors = batch[11]
fps = batch[12]
loss, individual_loss, pred_start, pred_end, attention, atten_loss = model(
videoFeat, videoFeat_lengths, tokens, tokens_lengths, start,
end, localiz)
vis_test.run(index, pred_start,
pred_end, start, end, videoFeat_lengths, epoch,
loss.detach(), individual_loss, attention, atten_loss,
time_starts, time_ends, factors, fps)
#print(loss)
writer.add_scalar(f'mlnlp/Progress_Valid_Loss', loss.item(),
total_iterations_val)
writer.add_scalar(f'mlnlp/Progress_Valid_Atten_Loss',
atten_loss.item(), total_iterations_val)
writer.add_scalar(f'mlnlp/Progress_Valid_Mean_IoU',
vis_test.mIoU[-1], total_iterations_val)
total_iterations_val += 1
writer.add_scalar(f'mlnlp/Valid_Loss', np.mean(vis_test.loss), epoch)
writer.add_scalar(f'mlnlp/Valid_Mean_IoU', np.mean(vis_test.mIoU),
epoch)
a = vis_test.plot(epoch)
writer.add_scalars(f'mlnlp/Valid_tIoU_th', a, epoch)
def tester(cfg):
print('testing')
dataloader_test, dataset_size_test = data.make_dataloader(cfg,
is_train=False)
model = modeling.build(cfg)
# torch.nn.Module.dump_patches = True
model = torch.load(cfg.TEST.MODEL)
# print(model)
model.cuda()
vis_test = Visualization(cfg, dataset_size_test, is_train=False)
writer_path = os.path.join(cfg.VISUALIZATION_DIRECTORY,
cfg.EXPERIMENT_NAME)
writer = SummaryWriter(writer_path)
total_iterations = 0
total_iterations_val = 0
model.eval()
epoch = 1
results_data = {}
for iteration, batch in enumerate(dataloader_test):
index = batch[0]
videoFeat = batch[1].cuda()
videoFeat_lengths = batch[2].cuda()
tokens = batch[3].cuda()
tokens_lengths = batch[4].cuda()
start = batch[5].cuda()
end = batch[6].cuda()
localiz = batch[7].cuda()
localiz_lengths = batch[8]
time_starts = batch[9]
time_ends = batch[10]
factors = batch[11]
fps = batch[12]
objects = batch[13].cuda()
objects_lengths = batch[14].cuda()
humans = batch[15].cuda()
humans_lengths = batch[16].cuda()
loss, individual_loss, pred_start, pred_end, attention,atten_loss, attentionNodeQueryHO, attentionNodeQueryVH, attentionNodeQueryVO = model(videoFeat, videoFeat_lengths, \
objects, objects_lengths, \
humans, humans_lengths, \
tokens, tokens_lengths, \
start, end, localiz)
aux = vis_test.run(index, pred_start,
pred_end, start, end, videoFeat_lengths, epoch,
loss.detach(), individual_loss, attention,
atten_loss, time_starts, time_ends, factors, fps,
attentionNodeQueryHO, attentionNodeQueryVH,
attentionNodeQueryVO)
total_iterations_val += 1
for k, v in aux.items():
results_data[k] = v
def trainer(cfg):
print('trainer')
dataloader_train, dataset_size_train = data.make_dataloader(cfg,
is_train=True)
dataloader_test, dataset_size_test = data.make_dataloader(cfg,
is_train=False)
model = modeling.build(cfg)
model.cuda()
optimizer = solver.make_optimizer(cfg, model)
scheduler = StepLR(optimizer, step_size=6, gamma=0.01)
vis_train = Visualization(cfg, dataset_size_train)
vis_test = Visualization(cfg, dataset_size_test, is_train=False)
writer_path = os.path.join(cfg.VISUALIZATION_DIRECTORY,
cfg.EXPERIMENT_NAME)
writer = SummaryWriter(writer_path)
total_iterations = 0
total_iterations_val = 0
for epoch in range(cfg.EPOCHS):
# Decay Learning Rate
# print("Epoch {}".format(epoch))
print('Epoch:', epoch, 'LR:', scheduler.get_lr())
model.train()
for iteration, batch in enumerate(dataloader_train):
index = batch[0]
videoFeat = batch[1].cuda()
videoFeat_lengths = batch[2].cuda()
tokens = batch[3].cuda()
tokens_lengths = batch[4].cuda()
start = batch[5].cuda()
end = batch[6].cuda()
localiz = batch[7].cuda()
localiz_lengths = batch[8]
time_starts = batch[9]
time_ends = batch[10]
factors = batch[11]
fps = batch[12]
objects = batch[13].cuda()
objects_lengths = batch[14].cuda()
humans = batch[15].cuda()
humans_lengths = batch[16].cuda()
loss, individual_loss, pred_start, pred_end, attention, atten_loss, attentionNodeQueryHO, attentionNodeQueryVH, attentionNodeQueryVO = model(videoFeat, videoFeat_lengths, \
objects, objects_lengths, \
humans, humans_lengths, \
tokens, tokens_lengths, \
start, end, localiz)
# print("Loss :{}".format(loss))
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 5)
optimizer.step()
vis_train.run(index, pred_start,
pred_end, start, end, videoFeat_lengths, epoch,
loss.detach(), individual_loss, attention,
atten_loss, time_starts, time_ends, factors, fps,
attentionNodeQueryHO, attentionNodeQueryVH,
attentionNodeQueryVO)
writer.add_scalar(f'mlnlp/Progress_Loss', loss.item(),
total_iterations)
writer.add_scalar(f'mlnlp/Progress_Attention_Loss',
atten_loss.item(), total_iterations)
writer.add_scalar(f'mlnlp/Progress_Mean_IoU', vis_train.mIoU[-1],
total_iterations)
total_iterations += 1.
writer.add_scalar(f'mlnlp/Train_Loss', np.mean(vis_train.loss), epoch)
writer.add_scalar(f'mlnlp/Train_Mean_IoU', np.mean(vis_train.mIoU),
epoch)
scheduler.step()
vis_train.plot(epoch)
torch.save(
model,
"./checkpoints/{}/model_epoch_{}".format(cfg.EXPERIMENT_NAME,
epoch))
model.eval()
for iteration, batch in enumerate(dataloader_test):
index = batch[0]
videoFeat = batch[1].cuda()
videoFeat_lengths = batch[2].cuda()
tokens = batch[3].cuda()
tokens_lengths = batch[4].cuda()
start = batch[5].cuda()
end = batch[6].cuda()
localiz = batch[7].cuda()
localiz_lengths = batch[8]
time_starts = batch[9]
time_ends = batch[10]
factors = batch[11]
fps = batch[12]
objects = batch[13].cuda()
objects_lengths = batch[14].cuda()
humans = batch[15].cuda()
humans_lengths = batch[16].cuda()
loss, individual_loss, pred_start, pred_end, attention,atten_loss, attentionNodeQueryHO, attentionNodeQueryVH, attentionNodeQueryVO = model(videoFeat, videoFeat_lengths, \
objects, objects_lengths, \
humans, humans_lengths, \
tokens, tokens_lengths, \
start, end, localiz)
vis_test.run(index, pred_start,
pred_end, start, end, videoFeat_lengths, epoch,
loss.detach(), individual_loss, attention, atten_loss,
time_starts, time_ends, factors, fps,
attentionNodeQueryHO, attentionNodeQueryVH,
attentionNodeQueryVO)
#print(index)
writer.add_scalar(f'mlnlp/Progress_Valid_Loss', loss.item(),
total_iterations_val)
writer.add_scalar(f'mlnlp/Progress_Valid_Atten_Loss',
atten_loss.item(), total_iterations_val)
writer.add_scalar(f'mlnlp/Progress_Valid_Mean_IoU',
vis_test.mIoU[-1], total_iterations_val)
total_iterations_val += 1
writer.add_scalar(f'mlnlp/Valid_Loss', np.mean(vis_test.loss), epoch)
writer.add_scalar(f'mlnlp/Valid_Mean_IoU', np.mean(vis_test.mIoU),
epoch)
a = vis_test.plot(epoch)
writer.add_scalars(f'mlnlp/Valid_tIoU_th', a, epoch)
async def train():
print(df.shape)
X, y = preprocess(df)
model = build(c.ModelName.lgbm)
model = train_and_validate(model, X, y)
return "Training is done."
def trainer(cfg):
print('trainer')
dataloader_train, dataset_size_train = data.make_dataloader(cfg,
is_train=True)
dataloader_test, dataset_size_test = data.make_dataloader(cfg,
is_train=False)
print(dataset_size_train)
print(dataset_size_test)
model = modeling.build(cfg)
if cfg.MODE_TRAIN == 'resume':
model = torch.load("./checkpoints/{}/model_{}_epoch_{}".format(
cfg.EXPERIMENT_NAME, cfg.MODEL_NAME, cfg.MODE_TRAIN_RESUME_EPOCH))
model = torch.load("./checkpoints/{}/model_{}".format(
cfg.EXPERIMENT_NAME, cfg.MODEL_NAME))
model.cuda()
optimizer = solver.make_optimizer(cfg, model)
#model = torch.load("/home/crodriguezo/projects/phd/moment-localization-with-NLP/mlnlp_lastversion/checkpoints/anet_config7/model_epoch_80")
vis_train = Visualization(cfg, dataset_size_train)
vis_test = Visualization(cfg, dataset_size_test, is_train=False)
writer_path = os.path.join(cfg.VISUALIZATION_DIRECTORY,
cfg.EXPERIMENT_NAME)
writer = SummaryWriter(writer_path)
total_iterations = 0
total_iterations_val = 0
cfg.EPOCHS = 1
for epoch in range(cfg.EPOCHS):
model.eval()
sumloss = 0
sumsample = 0
with torch.no_grad():
for iteration, batch in enumerate(dataloader_test):
index = batch[0]
videoFeat = batch[1].cuda()
videoFeat_lengths = batch[2].cuda()
tokens = batch[3].cuda()
tokens_lengths = batch[4].cuda()
if cfg.MODEL_NAME == 'TMLGA':
start = batch[5].cuda()
end = batch[6].cuda()
localiz = batch[7].cuda()
frame_start = batch[13]
frame_end = batch[14]
else:
start = batch[5]
end = batch[6]
localiz = batch[7]
frame_start = batch[13].cuda()
frame_end = batch[14].cuda()
localiz_lengths = batch[8]
time_starts = batch[9]
time_ends = batch[10]
factors = batch[11]
fps = batch[12]
duration = batch[15]
vid_names = batch[16]
loss, individual_loss, pred_start, pred_end, attention, atten_loss = model(
videoFeat, videoFeat_lengths, tokens, tokens_lengths,
start, end, localiz, frame_start, frame_end)
sumloss += loss.item() * float(videoFeat.shape[0])
sumsample += videoFeat.shape[0]
# print("Test_Loss :{}".format(loss))
vis_test.run(index, pred_start, pred_end, start, end, videoFeat_lengths, epoch, loss.detach(), individual_loss, \
attention,atten_loss, time_starts, time_ends, factors, fps, duration,vid_names)
#print(loss)
writer.add_scalar(f'mlnlp/Progress_Valid_Loss', loss.item(),
total_iterations_val)
writer.add_scalar(f'mlnlp/Progress_Valid_Atten_Loss',
atten_loss.item(), total_iterations_val)
writer.add_scalar(f'mlnlp/Progress_Valid_Mean_IoU',
vis_test.mIoU[-1], total_iterations_val)
total_iterations_val += 1
# del videoFeat,videoFeat_lengths,tokens,tokens_lengths,start,end,localiz
# torch.cuda.empty_cache()
print("Test_Loss :{}".format(sumloss / sumsample))
writer.add_scalar(f'mlnlp/Valid_Loss', np.mean(vis_test.loss), epoch)
writer.add_scalar(f'mlnlp/Valid_Mean_IoU', np.mean(vis_test.mIoU),
epoch)
a = vis_test.plot(epoch)
writer.add_scalars(f'mlnlp/Valid_tIoU_th', a, epoch)