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get_evaluation.py
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get_evaluation.py
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from src.dataset import DefaultCollator
from src.args import args_main
from torch.utils import data
from src.dataset import (
ActivityNetDataset,
VGGSoundDataset,
UCFDataset,
ContrastiveDataset,
)
from src.AVDiff import AVDiff
from src.test import test
from src.utils import (
fix_seeds,
load_args,
setup_evaluation,
load_model_weights,
)
from src.utils_improvements import get_model_params
def get_evaluation(args, dictionary_stage_1, dictionary_stage_2, iteration_test, beta):
config = load_args(args.load_path_stage_B)
config.root_dir = args.root_dir
if config.input_size is not None:
config.input_size_audio = config.input_size
config.input_size_video = config.input_size
assert (
config.retrain_all
), f"--retrain_all flag is not set in load_path_stage_B. Are you sure this is the correct path?. {args.load_path_stage_B}"
fix_seeds(config.seed)
logger, eval_dir, test_stats, tb_writer = setup_evaluation(
args, config.__dict__.keys()
)
if args.dataset_name == "VGGSound":
val_all_dataset = VGGSoundDataset(
args=config,
dataset_split="val",
zero_shot_mode=None,
)
test_dataset = VGGSoundDataset(
args=config,
dataset_split="test" + iteration_test,
zero_shot_mode=None,
)
elif args.dataset_name == "UCF":
val_all_dataset = UCFDataset(
args=config,
dataset_split="val",
zero_shot_mode=None,
)
test_dataset = UCFDataset(
args=config,
dataset_split="test" + iteration_test,
zero_shot_mode=None,
)
elif args.dataset_name == "ActivityNet":
val_all_dataset = ActivityNetDataset(
args=config,
dataset_split="val",
zero_shot_mode=None,
)
test_dataset = ActivityNetDataset(
args=config,
dataset_split="test" + iteration_test,
zero_shot_mode=None,
)
else:
raise NotImplementedError()
contrastive_val_dataset = ContrastiveDataset(val_all_dataset)
contrastive_test_dataset = ContrastiveDataset(test_dataset)
if config.selavi == False:
collator_test = DefaultCollator(
mode=args.batch_seqlen_test,
max_len=args.batch_seqlen_test_maxlen,
trim=args.batch_seqlen_test_trim,
)
collator_val = DefaultCollator(
mode=args.batch_seqlen_test,
max_len=args.batch_seqlen_val_maxlen,
trim=args.batch_seqlen_test_trim,
)
elif config.selavi == True:
collator_test = DefaultCollator(
mode=args.batch_seqlen_test,
max_len=args.batch_seqlen_test_maxlen,
trim=args.batch_seqlen_test_trim,
rate_video=1,
rate_audio=1,
)
collator_val = DefaultCollator(
mode=args.batch_seqlen_test,
max_len=args.batch_seqlen_val_maxlen,
trim=args.batch_seqlen_test_trim,
rate_video=1,
rate_audio=1,
)
final_val_loader = data.DataLoader(
dataset=contrastive_val_dataset,
collate_fn=collator_val,
batch_size=args.eval_bs,
num_workers=args.eval_num_workers,
)
final_test_loader = data.DataLoader(
dataset=contrastive_test_dataset,
collate_fn=collator_test,
batch_size=args.eval_bs,
num_workers=args.eval_num_workers,
)
model_params = get_model_params(
config.lr,
config.reg_loss,
config.embedding_dropout,
config.decoder_dropout,
config.additional_dropout,
config.embeddings_hidden_size,
config.decoder_hidden_size,
config.embeddings_batch_norm,
config.rec_loss,
config.cross_entropy_loss,
config.transformer_use_embedding_net,
config.transformer_dim,
config.transformer_depth,
config.transformer_heads,
config.transformer_dim_head,
config.transformer_mlp_dim,
config.transformer_dropout,
config.transformer_embedding_dim,
config.transformer_embedding_time_len,
config.transformer_embedding_dropout,
config.transformer_embedding_time_embed_type,
config.transformer_embedding_fourier_scale,
config.transformer_embedding_embed_augment_position,
config.lr_scheduler,
config.optimizer,
config.use_self_attention,
config.use_cross_attention,
config.transformer_average_features,
config.audio_only,
config.video_only,
config.transformer_use_class_token,
config.transformer_embedding_modality,
config.latent_generator,
config.discriminator_hidden_size,
config.generator_hidden_size,
config.calibration_net_hidden_size,
config.learn_calibration_fake,
config.use_calibration,
config.detach_output,
config.attention_type,
config.use_diffusion_model,
config.diffusion_steps,
config.layer_change_attention,
config.use_mixup,
config.mixup_parameter,
config.output_dimension_transformer,
config.use_diffusion_batch_norm,
config.diffusion_dropout_value,
config.embedding_type,
config.number_layers_diffusion,
)
if config.final_model == True:
model_A = AVDiff(
params_model=model_params,
input_size_audio=config.input_size_audio,
input_size_video=config.input_size_video,
length_logits=len(dictionary_stage_1[1]),
)
model_B = AVDiff(
params_model=model_params,
input_size_audio=config.input_size_audio,
input_size_video=config.input_size_video,
length_logits=len(dictionary_stage_2[1]),
)
else:
raise AttributeError("No correct model_A name.")
weights_path_stage_A = list(args.load_path_stage_A.glob("*_score.pt"))[0]
epoch_A = load_model_weights(weights_path_stage_A, model_A)
weights_path_stage_B = list(
(args.load_path_stage_B / ("checkpoints" + str(iteration_test))).glob(
f"*_ckpt_{epoch_A - 1}.pt"
)
)[0]
_ = load_model_weights(weights_path_stage_B, model_B)
model_A.to(config.device)
model_B.to(config.device)
results = test(
test_dataset=(test_dataset, final_test_loader),
model_A=model_A,
model_B=model_B,
device=args.device,
distance_fn=config.distance_fn,
args=config,
dictionary=dictionary_stage_2[1],
save_performances=args.eval_save_performances,
best_beta=beta,
)
# Tensorboard HParam logging
logger.info("FINISHED evaluation")
logger.info("Test results", results)
return results, logger, [tb_writer, config]
if __name__ == "__main__":
args, eval_args = args_main()
get_evaluation(eval_args)