def sample(args, exp_num):
assert args.load, '-load should not be None'
assert os.path.exists(args.load), '-load should exist'
args_subset = ['exp', 'cpk', 'model']
book = BookKeeper(args,
args_subset,
args_dict_update={
'batch_size': 1,
'stft_window': None
},
tensorboard=args.tb)
dir_name = book.name.dir(args.save_dir)
args = book.args
## Training parameters
path2data = args.path2data
dataset = args.dataset
split = (args.train_frac, args.dev_frac)
batch_size = args.batch_size
stft_window = args.stft_window
stft_hop = args.stft_hop
n_fft = args.n_fft
hop_length = args.hop_length
win_length = args.win_length
## Load data iterables
data = Data(path2data,
dataset,
split,
batch_size=batch_size,
stft_window=stft_window,
stft_hop=stft_hop,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
shuffle=False)
train = data.train
dev = data.dev
test = data.test
print('Data Loaded')
## Create a model
device = torch.device('cuda:{}'.format(
args.cuda)) if args.cuda >= 0 else torch.device('cpu')
modelKwargs = {'feat_dim': data.shape}
modelKwargs.update(args.modelKwargs)
model = eval(args.model)(**modelKwargs)
model.to(device).double()
book._copy_best_model(model)
print('Model Created')
## Load model
if args.load:
print('Loading Model')
book._load_model(model)
## Loss function
criterion = Loss(args.losses, args.lossKwargs)
## Transforms
pre = Transforms(args.transforms, n_fft, hop_length, win_length, args)
def loop(model, data_cls, data, pre, desc='train'):
running_loss = 0
running_count = 0
model.eval()
for count, batch in tqdm(enumerate(data),
desc=desc,
leave=False,
ncols=20):
x = batch.double()
y = x.clone()
batch_size = x.shape[0]
x = x.to(device)
y = y.to(device)
## Transform before the model
x = pre.transform(x)
y = pre.transform(y)
#y_cap = x
#internal_losses = []
_, _, _, _, _, _, y_cap, internal_losses = model(x)
## save spectrogram as wavfiles
save_path = Path(dir_name) / Path(desc) / Path(
data.dataset.datasets[count].audiopath).relative_to(
args.path2data)
os.makedirs(save_path.parent, exist_ok=True)
save_path = save_path.as_posix()
sr = data.dataset.datasets[count].sr
time_shape = data.dataset.datasets[count].time_shape
spectrogram = pre.inv_transform(y_cap).squeeze().transpose(
1, 0).contiguous().cpu().detach().numpy()
spec2wav(save_path,
sr,
spectrogram,
time_shape,
n_fft,
hop_length,
win_length,
num_iter=500)
loss = criterion(y_cap, y)
for i_loss in internal_losses:
loss += i_loss
running_loss += loss.item() * batch_size
running_count += batch_size
if count >= 0 and args.debug: ## debugging by overfitting
break
return running_loss / running_count
train_loss = loop(model, data, train, pre, 'train')
dev_loss = loop(model, data, dev, pre, 'dev')
test_loss = loop(model, data, test, pre, 'test')
## update results but not save them
book.update_res({'train': train_loss, 'dev': dev_loss, 'test': test_loss})
## print results
book.print_res(0,
key_order=['train', 'dev', 'test'],
exp=exp_num,
lr=optim.param_groups[0]['lr'])
def train(args, exp_num):
args_subset = ['exp', 'cpk', 'model', 'time']
book = BookKeeper(args, args_subset, args_dict_update={},
tensorboard=args.tb)
args = book.args
global ARGS
ARGS = args
## Start Log
book._start_log()
## Training parameters
path2data = args.path2data
dataset = args.dataset
lmksSubset = args.lmksSubset
desc = args.desc
split = (args.train_frac, args.dev_frac)
idx_dependent = args.idx_dependent
batch_size = args.batch_size
time = args.time
chunks = args.chunks
offset = args.offset
mask = args.mask
feats_kind = args.feats_kind
s2v = args.s2v
f_new = args.f_new
curriculum = args.curriculum
kl_anneal = args.kl_anneal
## Load data iterables
data = Data(path2data, dataset, lmksSubset, desc,
split, batch_size=batch_size,
time=time,
chunks=chunks,
offset=offset,
shuffle=True,
mask=mask,
feats_kind=feats_kind,
s2v=s2v,
f_new=f_new)
print('Data Loaded')
## Create a model
device = torch.device('cuda:{}'.format(args.cuda)) if args.cuda>=0 else torch.device('cpu')
input_shape = data.input_shape
kwargs_keys = ['pose_size', 'trajectory_size']
modelKwargs = {key:input_shape[key] for key in kwargs_keys}
modelKwargs.update(args.modelKwargs)
model = eval(args.model)(**modelKwargs)
model.to(device).double()
book._copy_best_model(model)
print('Model Created')
## Load model
if args.load:
print('Loading Model')
book._load_model(model)
## Loss function
criterion = Loss(args.losses, args.lossKwargs)
## Optimizers
optim = torch.optim.Adam(model.parameters(), lr=args.lr)
#optim = torch.optim.RMSprop(model.parameters(), lr=args.lr)
## LR scheduler
scheduler = lr_scheduler.ExponentialLR(optim, gamma=0.99)
## Transforms
columns = get_columns(feats_kind, data)
pre = Transforms(args.transforms, columns, args.seed, mask, feats_kind, dataset, f_new)
def loop(model, data, pre, desc='train', epoch=0):
running_loss = 0
running_internal_loss = 0
running_count = 0
# if kl_anneal > 0:
# kl_weight = lambda x: min((x+1)/(kl_anneal+1.), 2)
# else:
# kl_weight = lambda x: 1
count = 0
if desc == 'train':
model.train(True)
else:
model.eval()
Tqdm = tqdm(data, desc=desc+' {:.4f}'.format(running_loss/(count+1.)), leave=False, ncols=20)
for count, batch in enumerate(Tqdm):
model.zero_grad()
optim.zero_grad()
X, Y = batch['input'], batch['output']
pose, trajectory, start_trajectory = X
pose_gt, trajectory_gt, start_trajectory_gt = Y
x = torch.cat((trajectory, pose), dim=-1)
y = torch.cat((trajectory_gt, pose_gt), dim=-1)
x = x.to(device)
y = y.to(device)
## Transform before the model
x = pre.transform(x)
y = pre.transform(y)
if desc=='train':
y_cap, internal_losses = model(x, train=True)
else:
y_cap, internal_losses = model(x, train=False)
loss = 0
loss_ = 0
if y_cap is not None: ## if model returns only internal losses
loss = criterion(y_cap, y)
loss_ = loss.item()
for i_loss in internal_losses:
loss += i_loss
loss_ += i_loss.item()
running_internal_loss += i_loss.item()
running_count += np.prod(y.shape)
running_loss += loss_
# loss = criterion(y_cap, y)
# loss_= loss.item()
# #if count == 0 and desc == 'train':
# # pdb.set_trace()
# for i_loss in internal_losses:
# loss += kl_weight(epoch) * i_loss
# loss_ += i_loss.item()
# running_internal_loss += i_loss
# #running_loss += loss.item()
# running_loss += loss_
## update tqdm
Tqdm.set_description(desc+' {:.4f} {:.4f}'.format(running_loss/running_count, running_internal_loss/running_count))
Tqdm.refresh()
if desc == 'train':
loss.backward()
optim.step()
# if kl_anneal == 0:
# y_cap, internal_losses = model(x, train=True, epoch=epoch)
# sum(internal_losses).backward()
# optim.step()
x = x.detach()
y = y.detach()
loss = loss.detach()
if y_cap is not None:
y_cap = y_cap.detach()
internal_losses = [i.detach() for i in internal_losses]
if count>=0 and args.debug: ## debugging by overfitting
break
return running_loss/running_count
num_epochs = args.num_epochs
## set up curriculum learning for training
time_list = []
time_list_idx = 0
if curriculum:
for power in range(1, int(np.log2(time-1)) + 1):
time_list.append(2**power)
data.update_dataloaders(time_list[0])
time_list.append(time)
## Training Loop
for epoch in tqdm(range(num_epochs), ncols=20):
train_loss = loop(model, data.train, pre, 'train', epoch)
dev_loss = loop(model, data.dev, pre, 'dev')
test_loss = loop(model, data.test, pre, 'test')
scheduler.step() ## Change the Learning Rate
## save results
book.update_res({'train':train_loss,
'dev':dev_loss,
'test':test_loss})
book._save_res()
## update tensorboard
book.update_tb({'scalar':[[f'{args.cpk}/train', train_loss, epoch],
[f'{args.cpk}/dev', dev_loss, epoch],
[f'{args.cpk}/test', test_loss, epoch]]})
# 'histogram':[[f'{args.cpk}/'+name, param.clone().cpu().detach().numpy(), epoch]
# for name, param in model.named_parameters()]})
## print results
book.print_res(epoch, key_order=['train','dev','test'], exp=exp_num, lr=scheduler.get_lr())
## ignore increasing dev loss till the annealing occurs
# if epoch < kl_anneal:
# book.stop_count = 0
if book.stop_training(model, epoch):
## if early_stopping criterion is met,
## start training with more time steps
time_list_idx += 1
book.stop_count = 0 ## reset the threshold counter
book.best_dev_score = np.inf
model.load_state_dict(copy.deepcopy(book.best_model))
if len(time_list) > time_list_idx:
time_ = time_list[time_list_idx]
data.update_dataloaders(time_)
tqdm.write('Training up to time: {}'.format(time_))
else:
break
# End Log
book._stop_log()
# ## Sample
print('Loading the best model and training with language input as well')
args.__dict__.update({'load':book.name(book.weights_ext[0], book.weights_ext[1], args.save_dir),
'model':'Seq2SeqConditioned10'})
train_wordConditioned(args, exp_num, data)
def sample(args, exp_num, data=None):
assert args.load, 'Model name not provided'
assert os.path.isfile(args.load), 'Model file not found'
args_subset = ['exp', 'cpk', 'model', 'time', 'chunks']
book = BookKeeper(args, args_subset, args_dict_update={})
args = book.args
dir_name = book.name.dir(args.save_dir)
## Training parameters
path2data = args.path2data
dataset = args.dataset
lmksSubset = args.lmksSubset
desc = args.desc
split = (args.train_frac, args.dev_frac)
idx_dependent = args.idx_dependent
f_new = args.f_new
## hardcoded for sampling
batch_size = args.batch_size
time = args.time
offset = args.offset
## mask for delta
mask = args.mask
global feats_kind
feats_kind = args.feats_kind
## Load data iterables
if data is None:
data = Data(path2data,
dataset,
lmksSubset,
desc,
split,
batch_size=batch_size,
time=time,
offset=offset,
shuffle=False,
mask=mask,
feats_kind=feats_kind,
f_new=f_new)
train = data.train.dataset.datasets
dev = data.dev.dataset.datasets
test = data.test.dataset.datasets
print('Data Loaded')
## Create a model
device = torch.device('cuda:{}'.format(
args.cuda)) if args.cuda >= 0 else torch.device('cpu')
input_shape = data.input_shape
modelKwargs = {}
modelKwargs.update(input_shape)
modelKwargs.update(args.modelKwargs)
model = eval(args.model)(**modelKwargs)
model.to(device).double()
print('Model Created')
## Load model
if args.load:
print('Loading Model')
book._load_model(model)
## Loss function
criterion = Loss(args.losses, args.lossKwargs)
## Transforms
global columns
columns = get_columns(feats_kind, data)
pre = Transforms(args.transforms, columns, args.seed, mask, feats_kind,
dataset, f_new)
def loop(model, data, dataLoaders, pre, batch_size, desc='train'):
running_loss = 0
count = 0
model.eval()
Tqdm = tqdm(dataLoaders,
desc=desc + ' {:.4f}'.format(running_loss / (count + 1.)),
leave=False,
ncols=20)
for count, loader in enumerate(Tqdm):
loader = DataLoader(loader, batch_size=batch_size, shuffle=False)
outputs_list = []
start_trajectory_list = []
for _, batch in enumerate(loader):
model.zero_grad()
X, Y = batch['input'], batch['output']
pose, trajectory, start_trajectory = X
pose_gt, trajectory_gt, start_trajectory_gt = Y
x = torch.cat((trajectory, pose), dim=-1)
y = torch.cat((trajectory_gt, pose_gt), dim=-1)
x = x.to(device)
y = y.to(device)
start_trajectory_gt = start_trajectory_gt.to(device)
## Transform before the model
x = pre.transform(x)
y = pre.transform(y)
if offset == 0:
y_cap, internal_losses = model(x, train=False)
#y_cap = x
#internal_losses = []
else:
assert 0, 'offset = {}, it must be 0 for now'.format(
offset)
input_shape = sum(
[data.input_shape[key] for key in data.input_shape])
trajectory_size = data.input_shape['trajectory_size']
#y_output = y_cap.repeat(1,1,loader.dataset.f_ratio).view(new_size)
#mask = torch.Tensor(data.mask[:3]).to(y_cap.device).double().view(1,1,-1)
#y_output[..., :3] = (y_output[..., :3] * mask) * 1./loader.dataset.f_ratio + y_output[..., :3] * (1-mask)
outputs_list.append(pre.inv_transform(y_cap))
start_trajectory_list.append(start_trajectory_gt)
loss = criterion(y_cap, y)
for i_loss in internal_losses:
loss += i_loss
running_loss += loss.item()
## update tqdm
Tqdm.set_description(desc + ' {:.4f}'.format(running_loss /
(count + 1.)))
Tqdm.refresh()
x = x.detach()
y = y.detach()
loss = loss.detach()
y_cap = y_cap.detach()
if outputs_list:
## Collect all outputs
outputs = local2global(outputs_list, start_trajectory_list,
input_shape, trajectory_size, data.mask)
new_size = list(outputs.shape)
new_size[0] *= loader.dataset.f_ratio
outputs = outputs.repeat(1,
loader.dataset.f_ratio).view(new_size)
outputs = outputs.detach().cpu().numpy()
## copy outputs in the dataframe format
mat_full_temp = pd.DataFrame(
data=np.zeros((outputs.shape[0], len(columns))),
columns=loader.dataset.mat_full.columns)
## copy all joints
mat_full_temp.loc[:, columns] = outputs
if dataset == 'KITMocap':
filename = Path(dir_name) / Path(desc) / Path(
loader.dataset.path2csv).relative_to(
path2data).with_suffix('.csv')
os.makedirs(filename.parent, exist_ok=True)
if feats_kind == 'quaternion':
data.raw_data.mat2csv(mat_full_temp.values, filename,
columns)
elif feats_kind == 'rifke':
toFKE(mat_full_temp, data,
filename.with_suffix('.fke'))
elif dataset == 'CMUMocap':
filename = Path(dir_name) / Path(desc) / Path(
loader.dataset.path2csv).relative_to(
path2data).with_suffix('.amc')
os.makedirs(filename.parent, exist_ok=True)
if feats_kind in {'quaternion'}:
data.raw_data.mat2csv(mat_full_temp.values, filename,
columns)
mat_full = toEuler(M=mat_full_temp,
joints=data.raw_data.joints,
euler_columns=data.raw_data.columns)
data.raw_data.mat2amc(mat_full.values, filename)
elif feats_kind == 'rifke':
toFKE(mat_full_temp, data,
filename.with_suffix('.fke'))
if count >= 0 and args.debug: ## debugging by overfitting
break
return running_loss / (count + 1.)
## Sample
train_loss = loop(model, data, train, pre, batch_size, 'train')
dev_loss = loop(model, data, dev, pre, batch_size, 'dev')
test_loss = loop(model, data, test, pre, batch_size, 'test')
## update results but not save them (just to print)
book.update_res({'train': train_loss, 'dev': dev_loss, 'test': test_loss})
## print results
book.print_res(0, key_order=['train', 'dev', 'test'], exp=exp_num)
def train(args, exp_num):
args_subset = ['exp', 'cpk']
book = BookKeeper(args,
args_subset,
args_dict_update={},
tensorboard=args.tb)
args = book.args
## Start Log
book._start_log()
## Training parameters
num_epochs = args.num_epochs
## Load data iterables
print('Data Loaded')
## Create a model
model = Wavenet(num_channels,
kernel_size,
num_layers,
num_lmks,
sample_rate_lmks,
samples_per_frame,
dilation_limit,
mfccFlag=mfccFlag)
if args.cuda >= 0:
model.cuda(args.cuda)
book._copy_best_model(model)
print('Model Created')
## Load model
if args.load:
print('Loading Model')
book._load_model(model)
## Loss function
criterion = torch.nn.MSELoss()
## Optimizers
optim = torch.optim.Adam(model.parameters(), lr=args.lr)
def loop(model, data, desc='train', epoch=0):
running_loss = 0
running_loss_partial = 0
if desc == 'train':
model.train(True)
else:
model.eval()
for count, batch in tqdm(enumerate(data),
desc=desc,
leave=False,
ncols=20):
pdb.set_trace()
## Training Loop
for epoch in tqdm(range(num_epochs), ncols=20):
train_loss = loop(model, train, 'train', epoch)
dev_loss = loop(model, dev, 'dev')
test_loss = loop(model, test, 'test')
## save results
book.update_res({
'train': train_loss,
'dev': dev_loss,
'test': test_loss
})
book._save_res()
## print results
book.print_res(epoch, key_order=['train', 'dev', 'test'], exp=exp_num)
if book.stop_training(model, epoch):
break
# End Log
book._stop_log()
def sample(args, exp_num, data=None):
assert args.load, 'Model name not provided'
assert os.path.isfile(args.load), 'Model file not found'
args_subset = ['exp', 'cpk', 'model', 'time', 'chunks']
book = BookKeeper(args, args_subset, args_dict_update={'view': args.view})
args = book.args
dir_name = book.name.dir(args.save_dir)
## Training parameters
path2data = args.path2data
dataset = args.dataset
lmksSubset = args.lmksSubset
desc = args.desc
split = (args.train_frac, args.dev_frac)
idx_dependent = args.idx_dependent
## hardcoded for sampling
batch_size = args.batch_size
time = args.time
chunks = args.chunks
offset = args.offset
## mask for delta
mask = args.mask
global feats_kind
feats_kind = args.feats_kind
global render_feats_kind
render_feats_kind = {
'rifke': 'fke',
'quaternion': 'quaternion',
'fke': 'fke'
}
s2v = args.s2v
f_new = args.f_new
curriculum = args.curriculum
## Load data iterables
if data is None:
data = Data(path2data,
dataset,
lmksSubset,
desc,
split,
batch_size=batch_size,
time=time,
chunks=chunks,
offset=offset,
shuffle=False,
mask=mask,
feats_kind=feats_kind,
s2v=s2v,
f_new=f_new)
print('Data Loaded')
else:
print('Data already loaded!! Yessss!')
train = data.train.dataset.datasets
dev = data.dev.dataset.datasets
test = data.test.dataset.datasets
## Create a model
global device
device = torch.device('cuda:{}'.format(
args.cuda)) if args.cuda >= 0 else torch.device('cpu')
input_shape = data.input_shape
modelKwargs = {}
modelKwargs.update(input_shape)
modelKwargs.update(args.modelKwargs)
## getting the input_size
if args.s2v:
input_size = 300
elif args.desc:
input_size = len(args.desc)
else:
input_size = 0
model = eval(args.model)(chunks,
input_size=input_size,
Seq2SeqKwargs=modelKwargs,
load=None)
model.to(device).double()
print('Model Created')
## Load model
if args.load:
print('Loading Model')
book._load_model(model)
## Transforms
global columns
columns = get_columns(feats_kind, data)
pre = Transforms(args.transforms, columns, args.seed, mask, feats_kind,
dataset, f_new)
def loop(model, data, loader, sentences, pre, batch_size, desc='train'):
filenames = []
output_filenames = []
descriptions = sentences
model.eval()
for count, s2v in enumerate(tqdm(sentences)):
try:
row = data.df[data.df['descriptions'] == s2v].iloc[0]
minidata = MiniData(row[feats_kind],
args.lmksSubset,
args.time,
sentence_vector=row['descriptions'],
mask=args.mask,
dataset=args.dataset,
f_ratio=int(data.raw_data._get_f() /
args.f_new),
feats_kind=feats_kind)
start = minidata[0]['input']
start = np.concatenate([start[1], start[0]], axis=-1)
start = pre.transform(
torch.from_numpy(start).to(device).double())[0, 0:1]
except:
start = torch.zeros(
1, data.input_shape['pose_size'] +
data.input_shape['trajectory_size']).to(device).double()
y_cap, internal_losses = model.sample([s2v],
time_steps=32,
start=start)
#outputs_list.append(pre.inv_transform(y_cap))
#outputs = torch.cat(outputs_list, dim=0)
outputs = pre.inv_transform(y_cap).squeeze(0)
new_size = list(outputs.shape)
new_size[0] *= loader[0].f_ratio
outputs = outputs.repeat(1, loader[0].f_ratio).view(new_size)
outputs = outputs.detach().cpu().numpy()
## copy outputs in the dataframe format
mat_full_temp = pd.DataFrame(data=np.zeros(
(outputs.shape[0], len(columns))),
columns=columns)
if feats_kind == 'rifke':
mat_full_temp['root_tx'] = 0
mat_full_temp['root_tz'] = 0
## copy all joints
mat_full_temp.loc[:, columns] = outputs
if dataset == 'KITMocap':
filename = Path(dir_name) / Path('new') / Path(
'{}.csv'.format(count))
filenames.append(filename.with_suffix('.fke').as_posix())
output_filenames.append(
filename.with_suffix('.mp4').as_posix())
os.makedirs(filename.parent, exist_ok=True)
if feats_kind == 'quaternion':
data.raw_data.mat2csv(mat_full_temp.values, filename,
columns)
elif feats_kind == 'rifke':
toFKE(mat_full_temp, data, filename.with_suffix('.fke'))
## Render
#parallelRender(filenames, descriptions, output_filenames, data.raw_data.skel, render_feats_kind[feats_kind])
## Render on slurm (cpu-node only)
# print('Rendering')
# render = Slurm('render_new', slurm_kwargs={'partition':'cpu_long', 'time':'10-00:00', 'n':10})
# python_cmd = ['source activate torch',
# 'python render_newSentence.py -load {} -view {}'.format(
# args.load,
# args.view)]
# render.run('\n'.join(python_cmd))
## Sample
with open(args.view, 'r') as f:
sentences = f.readlines()
sentences = [s.strip() for s in sentences]
loop(model, data, train, sentences, pre, batch_size, 'train')