def test_encode_decode_from_loaded_tokenizer_with_1_maxlen(self, temp_dir):
tokenizer = Tokenizer(self.captions, 1)
tokenizer.save_dictionaries(temp_dir.path)
loading_tokenizer = Tokenizer()
loading_tokenizer.load_dictionaries(temp_dir.path)
phrase_encoded = loading_tokenizer.encode_caption(self.phrase)
phrase_decoded = tokenizer.decode_caption(phrase_encoded)
self.assertEqual(phrase_decoded, [Tokenizer.END])
def test_save_load(self, temp_dir):
for value in [None, 1, 5, 10]:
with self.subTest(captions=self.captions, user_maxlen=value):
tokenizer = Tokenizer(self.captions, value)
tokenizer.save_dictionaries(temp_dir.path)
loading_tokenizer = Tokenizer()
loading_tokenizer.load_dictionaries(temp_dir.path)
self.assertEqual(tokenizer.caption_dict,
loading_tokenizer.caption_dict)
self.assertEqual(tokenizer.inv_caption_dict,
loading_tokenizer.inv_caption_dict)
def test_encode_decode_from_loaded_tokenizer(self, temp_dir):
for value in [None, 5, 10]:
with self.subTest(captions=self.captions, user_maxlen=value):
tokenizer = Tokenizer(self.captions, value)
tokenizer.save_dictionaries(temp_dir.path)
loading_tokenizer = Tokenizer()
loading_tokenizer.load_dictionaries(temp_dir.path)
phrase_encoded = loading_tokenizer.encode_caption(self.phrase)
phrase_decoded = tokenizer.decode_caption(phrase_encoded)
expected_string = ["THERE", Tokenizer.UNK, "ONE", "HAND"]
pad_length = self.max_phraselen if (value is None) or (
value >= self.max_phraselen) else value
self.assertEqual(
phrase_decoded[:pad_length], expected_string +
[Tokenizer.END] * (pad_length - len(expected_string)))
def train_model(config_obj, relative_path=""):
# Find paths to training, validation and test sets
training_path = os.path.join(relative_path,
config_obj.get("paths", "train_annot"))
validation_path = os.path.join(relative_path,
config_obj.get("paths", "validation_annot"))
# Load attributes of config file
caption_type = config_obj.get("targets", "caption_type")
checkpoint_folder = os.path.join(
relative_path, config_obj.get("paths", "checkpoint_folder"))
higher_is_better = config_obj.get("criteria", "higher_is_better")
clip_grad = config_obj.get("training", "clip_grad")
frequency_valid = config_obj.get("validation", "frequency")
gpus = config_obj.get("device", "gpus")
num_epoch = config_obj.get("training", "num_epochs")
pretrained_folder = config_obj.get("pretrained", "pretrained_folder")
pretrained_file = config_obj.get("pretrained", "pretrained_file")
pretrained_folder = os.path.join(
relative_path, pretrained_folder) if pretrained_folder else None
teacher_force_train = config_obj.get("training", "teacher_force")
teacher_force_valid = config_obj.get("validation", "teacher_force")
verbose_train = config_obj.get("training", "verbose")
verbose_valid = config_obj.get("validation", "verbose")
# Get model, loss, optimizer, scheduler, and criteria from config_file
model_type = config_obj.get("model", "type")
loss_type = config_obj.get("loss", "type")
classif_loss_type = "CrossEntropy" #TODO: FIX reading from config file
optimizer_type = config_obj.get("optimizer", "type")
scheduler_type = config_obj.get("scheduler", "type")
criteria = config_obj.get("criteria", "score")
videos_folder = config_obj.get("paths", "videos_folder")
# Preprocess
crop_size = config_obj.get("preprocess", "crop_size")
scale = config_obj.get("preprocess", "scale")
input_resize = config_obj.get("preprocess", "input_resize")
# Load Json annotation files
training_parser = JsonParser(training_path,
os.path.join(relative_path, videos_folder),
caption_type=caption_type)
validation_parser = JsonParser(validation_path,
os.path.join(relative_path, videos_folder),
caption_type=caption_type)
# Build a tokenizer that contains all captions from annotation files
tokenizer = Tokenizer(**config_obj.get("tokenizer", "kwargs"))
if pretrained_folder:
tokenizer.load_dictionaries(pretrained_folder)
print("Inside pretrained", tokenizer.get_vocab_size())
else:
tokenizer.build_dictionaries(
training_parser.get_captions_from_tmp_and_lbl())
#tokenizer.build_dictionaries(training_parser.get_captions())
preprocessor = Compose([
prep.RandomCrop(crop_size),
prep.PadVideo(crop_size),
prep.Float32Converter(scale),
prep.PytorchTransposer()
])
val_preprocessor = Compose([
CenterCropper(crop_size),
prep.PadVideo(crop_size),
prep.Float32Converter(scale),
prep.PytorchTransposer()
])
training_set = GulpVideoDataset(annotation_parser=training_parser,
tokenizer=tokenizer,
preprocess=preprocessor,
gulp_dir=videos_folder,
size=input_resize)
validation_set = GulpVideoDataset(annotation_parser=validation_parser,
tokenizer=tokenizer,
preprocess=val_preprocessor,
gulp_dir=videos_folder,
size=input_resize)
dataloader = DataLoader(training_set,
shuffle=True,
drop_last=False,
**config_obj.get("dataloaders", "kwargs"))
val_dataloader = DataLoader(validation_set,
shuffle=True,
drop_last=False,
**config_obj.get("dataloaders", "kwargs"))
encoder_type = config_obj.get("model", "encoder")
decoder_type = config_obj.get("model", "decoder")
encoder_args = config_obj.get("model", "encoder_args")
encoder_kwargs = config_obj.get("model", "encoder_kwargs")
decoder_args = config_obj.get("model", "decoder_args")
decoder_kwargs = config_obj.get("model", "decoder_kwargs")
decoder_kwargs["vocab_size"] = tokenizer.get_vocab_size()
# decoder_kwargs["go_token"] = tokenizer.encode_token(tokenizer.GO)
# TODO: Remove GPUs?
gpus = config_obj.get("device", "gpus")
# decoder_kwargs["gpus"] = gpus
# Create model, loss, and optimizer objects
model = getattr(ptcap.model.captioners,
model_type)(encoder=getattr(all_models, encoder_type),
decoder=getattr(all_models, decoder_type),
encoder_args=encoder_args,
encoder_kwargs=encoder_kwargs,
decoder_args=decoder_args,
decoder_kwargs=decoder_kwargs,
gpus=gpus)
loss_function = getattr(ptcap.losses, loss_type)()
classif_loss_function = getattr(ptcap.losses, classif_loss_type)()
params = filter(lambda p: p.requires_grad, model.parameters())
optimizer = getattr(torch.optim, optimizer_type)(params=params,
**config_obj.get(
"optimizer",
"kwargs"))
scheduler_kwargs = copy.deepcopy(config_obj.get("scheduler", "kwargs"))
scheduler_kwargs["optimizer"] = optimizer
scheduler_kwargs["mode"] = "max" if higher_is_better else "min"
scheduler = getattr(torch.optim.lr_scheduler,
scheduler_type)(**scheduler_kwargs)
writer = Seq2seqAdapter(os.path.join(checkpoint_folder, "runs"),
config_obj.get("logging", "tensorboard_frequency"))
# Prepare checkpoint directory and save config
Checkpointer.save_meta(checkpoint_folder, config_obj, tokenizer)
checkpointer = Checkpointer(checkpoint_folder, higher_is_better)
# Setup the logger
logger = CustomLogger(folder=checkpoint_folder, tokenizer=tokenizer)
# Trainer
trainer = Trainer(model,
loss_function,
scheduler,
tokenizer,
logger,
writer,
checkpointer,
folder=pretrained_folder,
filename=pretrained_file,
gpus=gpus,
clip_grad=clip_grad,
classif_loss_function=classif_loss_function)
# Train the Model
trainer.train(dataloader, val_dataloader, criteria, num_epoch,
frequency_valid, teacher_force_train, teacher_force_valid,
verbose_train, verbose_valid)