def create_model_and_optimizer(opt, texts):
"""Builds the model and related optimizer."""
print('Creating model and optimizer for', opt.model)
if opt.model == 'imgonly':
model = img_text_composition_models.SimpleModelImageOnly(
texts, embed_dim=opt.embed_dim)
elif opt.model == 'textonly':
model = img_text_composition_models.SimpleModelTextOnly(
texts, embed_dim=opt.embed_dim)
elif opt.model == 'concat':
model = img_text_composition_models.Concat(texts,
embed_dim=opt.embed_dim)
elif opt.model == 'tirg':
model = img_text_composition_models.TIRG(texts,
embed_dim=opt.embed_dim)
elif opt.model == 'tirg_lastconv':
model = img_text_composition_models.TIRGLastConv(
texts, embed_dim=opt.embed_dim)
else:
print('Invalid model', opt.model)
print('available: imgonly, textonly, concat, tirg or tirg_lastconv')
sys.exit()
if torch.cuda.is_available():
model = model.cuda()
# create optimizer
params = []
# low learning rate for pretrained layers on real image datasets
if opt.dataset != 'css3d':
params.append({
'params': [p for p in model.img_model.fc.parameters()],
'lr': opt.learning_rate
})
params.append({
'params': [p for p in model.img_model.parameters()],
'lr': 0.1 * opt.learning_rate
})
params.append({'params': [p for p in model.parameters()]})
for _, p1 in enumerate(params): # remove duplicated params
for _, p2 in enumerate(params):
if p1 is not p2:
for p11 in p1['params']:
for j, p22 in enumerate(p2['params']):
if p11 is p22:
p2['params'][j] = torch.tensor(0.0,
requires_grad=True)
optimizer = torch.optim.SGD(params,
lr=opt.learning_rate,
momentum=0.9,
weight_decay=opt.weight_decay)
return model, optimizer
def create_model_and_optimizer(opt, texts):
"""Builds the model and related optimizer."""
print('Creating model and optimizer for', opt.model)
if opt.model == 'imgonly':
model = img_text_composition_models.SimpleModelImageOnly(
texts, opt)
elif opt.model == 'textonly':
model = img_text_composition_models.SimpleModelTextOnly(
texts, opt)
elif opt.model == 'add':
model = img_text_composition_models.Add(texts, opt)
elif opt.model == 'concat':
model = img_text_composition_models.Concat(texts, opt)
elif opt.model == 'tirg':
model = img_text_composition_models.TIRG(texts, opt)
elif opt.model == 'tirg_lastconv':
model = img_text_composition_models.TIRGLastConv(
texts, opt)
else:
print('Invalid model', opt.model)
print('available: imgonly, textonly, add, concat, tirg, tirg_lastconv')
sys.exit()
model = model.cuda()
# create optimizer
params = []
per_params = []
for name, param in model.named_parameters():
per_params.append(param)
params.append({'params': per_params})
if opt.optimizer == 'SGD':
optimizer = torch.optim.SGD(
params, lr=opt.learning_rate, momentum=0.9, weight_decay=opt.weight_decay)
elif opt.optimizer == 'Adam':
optimizer = torch.optim.Adam(params, lr=opt.learning_rate)
return model, optimizer
