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import torch
import os
import csv
from tqdm import tqdm
from imagebind import data
from imagebind.models import imagebind_model
from imagebind.models.imagebind_model import ModalityType
from pathlib import Path
import pandas as pd
import random
import torch.nn.functional as F
# Set device: Use GPU if available, otherwise CPU
device = ""
# Load ImageBind model
try:
model = imagebind_model.imagebind_huge(pretrained=True)
model.eval()
model.to(device)
except Exception as e:
print(f"Error loading the model: {e}")
exit(1) # Exit if model loading fails
# Set audio and video folder paths
audio_folder = ""
video_folder = ""
# Read CSV file
csv_file = ''
try:
df = pd.read_csv(csv_file)
except Exception as e:
print(f"Error reading CSV file {csv_file}: {e}")
exit(1)
# Prepare output CSV and error log files
output_csv = 'output.csv'
error_log_file = 'test.log'
# Track processed files by reading existing output (if any)
processed_files = set()
if os.path.exists(output_csv):
try:
with open(output_csv, mode='r') as file:
reader = csv.reader(file)
for row in reader:
file_name = row[0] # Full filename
processed_files.add(file_name) # Record processed files
except Exception as e:
print(f"Error reading the output CSV file {output_csv}: {e}")
exit(1)
# Initialize lists for matched audio-video pairs
paired_audio_paths = []
paired_video_paths = []
# Open error log for writing
with open(error_log_file, mode='a') as error_log:
# Process each file_id, skipping already processed files
for file_id in df['file_id']:
audio_file = f"{file_id}.flac"
video_file = f"000040.jpg"
video_path = os.path.join(video_folder, file_id, video_file)
audio_path = os.path.join(audio_folder, audio_file)
# Get basenames without extensions
video_name = os.path.basename(video_path)
audio_name = os.path.basename(audio_path)
video_name_no_ext = os.path.splitext(video_name)[0]
audio_name_no_ext = os.path.splitext(audio_name)[0]
# Skip if already processed
if video_name_no_ext in processed_files or audio_name_no_ext in processed_files:
continue
# Validate file existence
if not os.path.exists(video_path):
error_log.write(f"Video directory not found: {video_path}\n")
continue
if not os.path.exists(audio_path):
error_log.write(f"Audio file not found: {audio_path}\n")
continue
paired_audio_paths.append(audio_path)
paired_video_paths.append(video_path)
print(f"Successfully matched {len(paired_audio_paths)} audio-video pairs.")
# Batch processing configuration
batch_size = 16
num_batches = len(paired_video_paths) // batch_size + 1
# Process and write results
try:
with open(output_csv, mode='a', newline='') as file:
writer = csv.writer(file)
with torch.no_grad():
for i in tqdm(range(num_batches), desc="Processing Batches"):
start_idx = i * batch_size
end_idx = min((i + 1) * batch_size, len(paired_video_paths))
# Get current batch paths
video_batch_paths = paired_video_paths[start_idx:end_idx]
audio_batch_paths = paired_audio_paths[start_idx:end_idx]
try:
# Load batch data
video_batch = data.load_and_transform_vision_data(video_batch_paths, device)
audio_batch = data.load_and_transform_audio_data(audio_batch_paths, device)
except RuntimeError as e:
print(f"Error loading video data in batch {i}: {e}")
continue
except Exception as e:
print(f"Unexpected error in batch {i}: {e}")
continue
try:
# Model inference
inputs = {
ModalityType.VISION: video_batch,
ModalityType.AUDIO: audio_batch,
}
embeddings = model(inputs)
# Calculate similarity
audio_embedding = embeddings[ModalityType.AUDIO]
video_embedding = embeddings[ModalityType.VISION]
batch_similarity = F.cosine_similarity(video_embedding, audio_embedding) * 10
# Write results
for video_path, similarity in zip(video_batch_paths, batch_similarity.tolist()):
video_name = os.path.basename(os.path.dirname(video_path))
writer.writerow([video_name, similarity])
except Exception as e:
print(f"Error processing batch {i}: {e}")
continue
except Exception as e:
print(f"Error writing to the output CSV file {output_csv}: {e}")
exit(1)
print(f"Similarity scores have been saved to {output_csv}.")
print(f"Any missing files have been logged in {error_log_file}.") |