def main_custom_vgg(start_from_best_model=True, with_data_aug=True):
model = Custom_vgg(1, len(config["catslist"]), DEVICE)
if start_from_best_model:
print("Loading model from current best model")
model.load_state_dict(
torch.load(config["current_best_model"], map_location=DEVICE))
return main(
model, lambda pixelstring_batch, emotions_batch, DEVICE:
preprocess_batch_custom_vgg(pixelstring_batch, emotions_batch, DEVICE,
with_data_aug, config["loss_mode"]))
def preprocess_batch(pixelstring_batch, emotions_batch, DEVICE):
if model_type == "CustomVGG":
return preprocess_batch_custom_vgg(pixelstring_batch,
emotions_batch, DEVICE, False,
config["loss_mode"])
elif model_type == "DenseSIFTHybrid":
return preprocess_batch_dense_sift_hybrid(pixelstring_batch,
emotions_batch, DEVICE,
False,
config["loss_mode"])
elif model_type == "SIFTHybrid":
return preprocess_batch_sift_hybrid(pixelstring_batch,
emotions_batch, DEVICE, False,
config["loss_mode"])
def preprocess_batch(pixelstring_batch, emotions_batch, DEVICE):
return preprocess_batch_custom_vgg(pixelstring_batch, emotions_batch,
DEVICE, False, config["loss_mode"])