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import torch
import os
################################################### PARMETERS ##########################################################
################################################# PARAMETERS ###########################################################
class CFG:
max_length = 128
batch_size = 32
num_workers = 4
projection_dim = 256
dropout_rate = 0.1
num_head = 4
num_layers = 1
image_encoder_lr = 1e-4
radio_encoder_lr = 1e-5
video_encoder_lr = 1e-4
text_encoder_lr = 1e-5
audio_encoder_lr = 1e-5
modality_token_encoder_lr = 1e-3
universal_projection_lr = 1e-3
lr = 1e-3
weight_decay = 1e-3
patience = 10
factor = 0.8
token_size = 1
epochs = 100
image_size = 224
device = "cpu"
token_dim=3
data_directory = "datasets"
train_annotation_file = os.path.join(data_directory, "annotations", "captions_train2014.json")
val_annotation_file = os.path.join(data_directory, "annotations", "captions_val2014.json")
image_dir = os.path.join(data_directory, "train2014")
image_dir_val = os.path.join(data_directory, "val2014")
bert_name = "bert-base-uncased"
vit_name = "vit_base_patch16_224"
audio_name = "facebook/wav2vec2-base-960h"
radio_name = "microsoft/rad-dino"
video_name = "MCG-NJU/videomae-base"
sample_rate = 16000 |