pin_memory=True,
worker_init_fn=_init_fn)
##############
# LOAD MODEL #
##############
if args.restore is not None:
print("Restoring model from:", args.restore)
checkpoint = torch.load(
args.restore, map_location='cpu' if device.type == 'cpu' else None)
dims = checkpoint['model_dims']
hyperparams = checkpoint['model_hyperparams']
trainer_params = checkpoint['train_params']
model = autoregressive_model.AutoregressiveFR(dims=dims,
hyperparams=hyperparams,
dropout_p=args.dropout_p)
else:
checkpoint = args.restore
trainer_params = None
model = autoregressive_model.AutoregressiveFR(channels=args.channels,
dropout_p=args.dropout_p)
model.to(device)
################
# RUN TRAINING #
################
trainer = autoregressive_train.AutoregressiveTrainer(
model=model,
data_loader=loader,
self.set_parameter(layer.weight_g, layer_name + 'g')
self.set_parameter(layer.weight_v, layer_name + 'W', permute=(3, 2, 0, 1)) # HWIO to OIHW
def load_layer_norm(self, layer, layer_name):
self.set_parameter(layer.bias, layer_name + 'b')
self.set_parameter(layer.weight, layer_name + 'g')
def set_parameter(self, parameter, name='', permute=()):
new_data = self.reader.get_tensor(name)
new_data = torch.as_tensor(new_data, dtype=parameter.dtype, device=parameter.device)
new_data.requires_grad_(True)
if permute:
new_data = new_data.permute(*permute).contiguous()
if new_data.shape != parameter.shape:
raise ConversionError('mismatched shapes: {} to {} at {}'.format(
tuple(new_data.shape), tuple(parameter.shape), name))
parameter.data = new_data
self.unused_keys.remove(name)
if __name__ == '__main__':
import sys
import autoregressive_model
model_test = autoregressive_model.AutoregressiveFR()
reader = TFReader(sys.argv[1])
reader.load_autoregressive_fr(model_test)
print([key for key in reader.unused_keys if not key.startswith("Backprop")])