inference_cli.py

#!/usr/bin/env python3
"""
Standalone SeedVR2 Video Upscaler CLI Script
(MODIFICADO PARA SUPORTE ROBUSTO A CALLBACKS EM MULTIPROCESSING)
"""

import sys
import os
import argparse
import time
import multiprocessing as mp
import queue # Importa a classe de exceção para filas vazias

# Garante o uso seguro de CUDA com multiprocessing, essencial para estabilidade.
if mp.get_start_method(allow_none=True) != 'spawn':
    mp.set_start_method('spawn', force=True)

# -------------------------------------------------------------
# 1) Configuração de alocação de memória da VRAM
os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "backend:cudaMallocAsync")

# 2) Pré-análise dos argumentos para configurar a visibilidade dos dispositivos CUDA
_pre_parser = argparse.ArgumentParser(add_help=False)
_pre_parser.add_argument("--cuda_device", type=str, default=None)
_pre_args, _ = _pre_parser.parse_known_args()
if _pre_args.cuda_device is not None:
    device_list_env = [x.strip() for x in _pre_args.cuda_device.split(',') if x.strip()!='']
    if len(device_list_env) == 1:
        os.environ["CUDA_VISIBLE_DEVICES"] = device_list_env[0]

# -------------------------------------------------------------
# 3) Importações pesadas (torch, etc.) são feitas após a configuração do ambiente.
import torch
import cv2
import numpy as np
from datetime import datetime
from pathlib import Path

# Adiciona o diretório raiz do projeto ao path do sistema para permitir importações de `src`
# Isso assume que o script está dentro do repositório clonado.
script_dir = os.path.dirname(os.path.abspath(__file__))
if script_dir not in sys.path:
    sys.path.insert(0, script_dir)

# Importa as funções do SeedVR DEPOIS de ajustar o path.
from src.utils.downloads import download_weight

def extract_frames_from_video(video_path, debug=False, skip_first_frames=0, load_cap=None):
    """
    Extrai quadros de um vídeo e os converte para o formato de tensor.
    """
    if debug:
        print(f"🎬 Extracting frames from video: {video_path}")
    
    if not os.path.exists(video_path):
        raise FileNotFoundError(f"Video file not found: {video_path}")
    
    cap = cv2.VideoCapture(video_path)
    if not cap.isOpened():
        raise ValueError(f"Cannot open video file: {video_path}")
    
    fps = cap.get(cv2.CAP_PROP_FPS)
    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    
    if debug:
        print(f"📊 Video info: {frame_count} frames, {width}x{height}, {fps:.2f} FPS")
        if skip_first_frames:
            print(f"⏭️ Will skip first {skip_first_frames} frames")
        if load_cap and load_cap > 0:
            print(f"🔢 Will load maximum {load_cap} frames")
    
    frames = []
    frame_idx = 0
    frames_loaded = 0
    
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        
        if frame_idx < skip_first_frames:
            frame_idx += 1
            continue
        
        if load_cap is not None and load_cap > 0 and frames_loaded >= load_cap:
            break
        
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        frame = frame.astype(np.float32) / 255.0
        
        frames.append(frame)
        frame_idx += 1
        frames_loaded += 1
        
        if debug and frames_loaded % 100 == 0:
            total_to_load = min(frame_count, load_cap) if (load_cap and load_cap > 0) else frame_count
            print(f"📹 Extracted {frames_loaded}/{total_to_load} frames")
    
    cap.release()
    
    if len(frames) == 0:
        raise ValueError(f"No frames extracted from video: {video_path}")
    
    if debug:
        print(f"✅ Extracted {len(frames)} frames")
    
    frames_tensor = torch.from_numpy(np.stack(frames)).to(torch.float16)
    
    if debug:
        print(f"📊 Frames tensor shape: {frames_tensor.shape}, dtype: {frames_tensor.dtype}")
    
    return frames_tensor, fps


def save_frames_to_video(frames_tensor, output_path, fps=30.0, debug=False):
    """
    Salva um tensor de quadros em um arquivo de vídeo.
    """
    if debug:
        print(f"🎬 Saving {frames_tensor.shape[0]} frames to video: {output_path}")
    
    os.makedirs(os.path.dirname(output_path), exist_ok=True)
    
    frames_np = (frames_tensor.cpu().numpy() * 255.0).astype(np.uint8)
    
    T, H, W, C = frames_np.shape
    
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(output_path, fourcc, fps, (W, H))
    
    if not out.isOpened():
        raise ValueError(f"Cannot create video writer for: {output_path}")
    
    for i, frame in enumerate(frames_np):
        frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
        out.write(frame_bgr)
        
        if debug and (i + 1) % 100 == 0:
            print(f"💾 Saved {i + 1}/{T} frames")
    
    out.release()
    
    if debug:
        print(f"✅ Video saved successfully: {output_path}")

def _worker_process(proc_idx, device_id, frames_np, shared_args, return_queue, progress_queue=None):
    """
    Processo filho (worker) que executa o upscaling em uma GPU dedicada.
    """
    os.environ["CUDA_VISIBLE_DEVICES"] = str(device_id)
    os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "backend:cudaMallocAsync")

    import torch
    from src.core.model_manager import configure_runner
    from src.core.generation import generation_loop
    
    frames_tensor = torch.from_numpy(frames_np).to(torch.float16)
    
    local_progress_callback = None
    if progress_queue:
        def callback_wrapper(batch_idx, total_batches, current_frames, message):
            # Envia uma tupla com informações de progresso para a fila
            progress_queue.put((proc_idx, batch_idx, total_batches, message))
        local_progress_callback = callback_wrapper

    try:
        runner = configure_runner(shared_args["model"], shared_args["model_dir"], shared_args["preserve_vram"], shared_args["debug"])
        result_tensor = generation_loop(
            runner=runner, images=frames_tensor, cfg_scale=shared_args["cfg_scale"],
            seed=shared_args["seed"], res_w=shared_args["res_w"], batch_size=shared_args["batch_size"],
            preserve_vram=shared_args["preserve_vram"], temporal_overlap=shared_args["temporal_overlap"],
            debug=shared_args["debug"],
            progress_callback=local_progress_callback 
        )
        return_queue.put((proc_idx, result_tensor.cpu().numpy()))
    except Exception as e:
        import traceback
        error_msg = f"ERROR in worker {proc_idx}: {e}\n{traceback.format_exc()}"
        print(error_msg)
        if progress_queue:
            progress_queue.put((proc_idx, -1, -1, error_msg))
        return_queue.put((proc_idx, error_msg))

def _gpu_processing(frames_tensor, device_list, args, progress_callback=None):
    """
    Divide os quadros, gerencia os workers e monitora o progresso de forma robusta.
    """
    num_devices = len(device_list)
    chunks = torch.chunk(frames_tensor, num_devices, dim=0)

    manager = mp.Manager()
    return_queue = manager.Queue()
    progress_queue = manager.Queue() if progress_callback else None
    workers = []

    shared_args = {
        "model": args.model, "model_dir": args.model_dir or "./models/SEEDVR2",
        "preserve_vram": args.preserve_vram, "debug": args.debug, "cfg_scale": 1.0,
        "seed": args.seed, "res_w": args.resolution, "batch_size": args.batch_size, "temporal_overlap": 0,
    }

    for idx, (device_id, chunk_tensor) in enumerate(zip(device_list, chunks)):
        p = mp.Process(target=_worker_process, args=(idx, device_id, chunk_tensor.cpu().numpy(), shared_args, return_queue, progress_queue))
        p.start()
        workers.append(p)

    results_np = [None] * num_devices
    finished_workers_count = 0
    worker_progress = [0.0] * num_devices
    
    while finished_workers_count < num_devices:
        if progress_queue:
            while not progress_queue.empty():
                try:
                    proc_idx, batch_idx, total_batches, message = progress_queue.get_nowait()
                    if batch_idx == -1:
                        raise RuntimeError(f"Worker {proc_idx} error: {message}")
                    if total_batches > 0:
                        worker_progress[proc_idx] = batch_idx / total_batches
                    total_progress = sum(worker_progress) / num_devices
                    progress_callback(total_progress, desc=f"GPU {proc_idx+1}/{num_devices}: {message}")
                except queue.Empty:
                    break

        try:
            proc_idx, result = return_queue.get(timeout=0.2)
            if isinstance(result, str) and result.startswith("ERROR"):
                 raise RuntimeError(f"Worker {proc_idx} failed: {result}")
            results_np[proc_idx] = result
            worker_progress[proc_idx] = 1.0
            finished_workers_count += 1
            if progress_callback:
                total_progress = sum(worker_progress) / num_devices
                progress_callback(total_progress, desc=f"GPU {proc_idx+1}/{num_devices}: Completed!")
        except queue.Empty:
            pass

    for p in workers: p.join()

    if any(r is None for r in results_np):
        raise RuntimeError("One or more workers failed to return a result.")

    return torch.from_numpy(np.concatenate(results_np, axis=0)).to(torch.float16)

def parse_arguments():
    """Analisa os argumentos da linha de comando."""
    parser = argparse.ArgumentParser(description="SeedVR2 Video Upscaler CLI")
    parser.add_argument("--video_path", type=str, required=True, help="Path to input video file")
    parser.add_argument("--seed", type=int, default=100, help="Random seed for generation (default: 100)")
    parser.add_argument("--resolution", type=int, default=1072, help="Target resolution of the short side (default: 1072)")
    parser.add_argument("--batch_size", type=int, default=5, help="Number of frames per batch (default: 5)")
    parser.add_argument("--model", type=str, default="seedvr2_ema_3b_fp16.safetensors",
                        choices=["seedvr2_ema_3b_fp16.safetensors", "seedvr2_ema_3b_fp8_e4m3fn.safetensors", 
                                 "seedvr2_ema_7b_fp16.safetensors", "seedvr2_ema_7b_fp8_e4m3fn.safetensors"],
                        help="Model to use")
    parser.add_argument("--model_dir", type=str, default=None, help="Directory containing the model files")
    parser.add_argument("--skip_first_frames", type=int, default=0, help="Skip the first frames during processing")
    parser.add_argument("--load_cap", type=int, default=0, help="Maximum number of frames to load from video (default: load all)")
    parser.add_argument("--output", type=str, default=None, help="Output path")
    parser.add_argument("--output_format", type=str, default="video", choices=["video", "png"], help="Output format: 'video' (mp4) or 'png' images")
    parser.add_argument("--preserve_vram", action="store_true", help="Enable VRAM preservation mode")
    parser.add_argument("--debug", action="store_true", help="Enable debug logging")
    parser.add_argument("--cuda_device", type=str, default=None, help="CUDA device id(s). e.g., '0' or '0,1' for multi-GPU")
    
    return parser.parse_args()

def run_inference_logic(args, progress_callback=None):
    """
    Função principal que executa o pipeline de upscaling. Pode ser importada e chamada por outros scripts.
    """
    if args.debug:
        print(f"📋 Argumentos da Lógica de Inferência: {vars(args)}")

    if progress_callback: progress_callback(0.05, "Extracting frames...")
    print("🎬 Extraindo frames do vídeo...")
    start_time = time.time()
    frames_tensor, original_fps = extract_frames_from_video(
        args.video_path, args.debug, args.skip_first_frames, args.load_cap
    )
    if args.debug:
        print(f"🔄 Tempo de extração de frames: {time.time() - start_time:.2f}s")
        
    device_list = [d.strip() for d in str(args.cuda_device).split(',') if d.strip()] if args.cuda_device else ["0"]
    if args.debug:
        print(f"🚀 Usando dispositivos: {device_list}")
    
    if progress_callback: progress_callback(0.1, "Starting generation...")
    processing_start = time.time()
    download_weight(args.model, args.model_dir or "seedvr_models")
    
    result_tensor = _gpu_processing(frames_tensor, device_list, args, progress_callback)

    generation_time = time.time() - processing_start
    if args.debug:
        print(f"🔄 Tempo de Geração: {generation_time:.2f}s")
        print(f"📊 Resultado: {result_tensor.shape}, dtype: {result_tensor.dtype}")
        
    return result_tensor, original_fps, generation_time, len(frames_tensor)

def main():
    """
    Função principal para execução via linha de comando (CLI).
    """
    print(f"🚀 SeedVR2 Video Upscaler CLI iniciado às {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
    args = parse_arguments()
    try:
        result_tensor, original_fps, _, _ = run_inference_logic(args)
        
        if args.output is None:
            args.output = f"result_{Path(args.video_path).stem}.mp4"
            
        print(f"💾 Salvando vídeo em: {args.output}")
        save_frames_to_video(result_tensor, args.output, original_fps, args.debug)
        print("✅ Upscaling via CLI concluído com sucesso!")
        
    except Exception as e:
        print(f"❌ Erro durante o processamento via CLI: {e}")
        import traceback
        traceback.print_exc()
        sys.exit(1)

if __name__ == "__main__":
    main()