inference_davis.py

import argparse
import json
import random
import time
from pathlib import Path

import numpy as np
import torch


import util.misc as utils
from models import build_model
import torchvision.transforms as T
import matplotlib.pyplot as plt
import os
import cv2
from PIL import Image, ImageDraw
import math
import torch.nn.functional as F
import json


import opts
from tqdm import tqdm

import multiprocessing as mp
import threading

from tools.colormap import colormap


# colormap
color_list = colormap()
color_list = color_list.astype('uint8').tolist()

# build transform
transform = T.Compose([
    T.Resize(360),
    T.ToTensor(),
    T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
    

def main(args):
    args.dataset_file = "davis"
    args.masks = True
    args.batch_size == 1
    print("Inference only supports for batch size = 1") 
    print(args)

    # fix the seed for reproducibility
    seed = args.seed + utils.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)

    split = args.split
    # save path
    output_dir = args.output_dir
    save_path_prefix = os.path.join(output_dir, split)
    if not os.path.exists(save_path_prefix):
        os.makedirs(save_path_prefix)

    save_visualize_path_prefix = os.path.join(output_dir, split + '_images')
    if args.visualize:
        if not os.path.exists(save_visualize_path_prefix):
            os.makedirs(save_visualize_path_prefix)

    # load data
    root = Path(args.davis_path) # data/ref-davis
    img_folder = os.path.join(root, split, "JPEGImages")
    meta_file = os.path.join(root, "meta_expressions", split, "meta_expressions.json")
    with open(meta_file, "r") as f:
        data = json.load(f)["videos"]
    video_list = list(data.keys())

    # create subprocess
    thread_num = args.ngpu
    global result_dict
    result_dict = mp.Manager().dict()

    processes = []
    lock = threading.Lock()

    video_num = len(video_list)
    per_thread_video_num = math.ceil(float(video_num) / float(thread_num))

    start_time = time.time()
    print('Start inference')
    for i in range(thread_num):
        if i == thread_num - 1:
            sub_video_list = video_list[i * per_thread_video_num:]
        else:
            sub_video_list = video_list[i * per_thread_video_num: (i + 1) * per_thread_video_num]
        p = mp.Process(target=sub_processor, args=(lock, i, args, data, 
                                                   save_path_prefix, save_visualize_path_prefix, 
                                                   img_folder, sub_video_list))
        p.start()
        processes.append(p)

    for p in processes:
        p.join()

    end_time = time.time()
    total_time = end_time - start_time

    result_dict = dict(result_dict)
    num_all_frames_gpus = 0
    for pid, num_all_frames in result_dict.items():
        num_all_frames_gpus += num_all_frames

    print("Total inference time: %.4f s" %(total_time))


def sub_processor(lock, pid, args, data, save_path_prefix, save_visualize_path_prefix, img_folder, video_list):
    text = 'processor %d' % pid
    with lock:
        progress = tqdm(
            total=len(video_list),
            position=pid,
            desc=text,
            ncols=0
        )
    torch.cuda.set_device(pid)

    # model
    model, criterion, _ = build_model(args)  
    device = args.device
    model.to(device)

    model_without_ddp = model
    n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)

    if pid == 0:
        print('number of params:', n_parameters)

    if args.resume:
        checkpoint = torch.load(args.resume, map_location='cpu')
        missing_keys, unexpected_keys = model_without_ddp.load_state_dict(checkpoint['model'], strict=False)
        unexpected_keys = [k for k in unexpected_keys if not (k.endswith('total_params') or k.endswith('total_ops'))]
        if len(missing_keys) > 0:
            print('Missing Keys: {}'.format(missing_keys))
        if len(unexpected_keys) > 0:
            print('Unexpected Keys: {}'.format(unexpected_keys))
    else:
    	raise ValueError('Please specify the checkpoint for inference.')

    # get palette
    palette_img = os.path.join(args.davis_path, "valid/Annotations/blackswan/00000.png")
    palette = Image.open(palette_img).getpalette()

    # start inference
    num_all_frames = 0
    model.eval()
    
    # 1. for each video
    for video in video_list:
        metas = []

        expressions = data[video]["expressions"]   
        expression_list = list(expressions.keys()) 
        num_expressions = len(expression_list)
        video_len = len(data[video]["frames"])

        # read all the anno meta
        for i in range(num_expressions):
            meta = {}
            meta["video"] = video
            meta["exp"] = expressions[expression_list[i]]["exp"]
            meta["exp_id"] = expression_list[i] # start from 0
            meta["frames"] = data[video]["frames"]
            metas.append(meta)
        meta = metas

        # since there are 4 annotations
        num_obj = num_expressions // 4

        # 2. for each annotator
        for anno_id in range(4): # 4 annotators
            anno_logits = []  
            anno_masks = []   # [num_obj+1, video_len, h, w], +1 for background

            for obj_id in range(num_obj): 
                i = obj_id * 4 + anno_id
                video_name = meta[i]["video"]
                exp = meta[i]["exp"]
                exp_id = meta[i]["exp_id"]
                frames = meta[i]["frames"]

                video_len = len(frames)
                # NOTE: the im2col_step for MSDeformAttention is set as 64
                # so the max length for a clip is 64
                # store the video pred results
                all_pred_logits = []
                all_pred_masks = []

                # 3. for each clip
                for clip_id in range(0, video_len, 36):
                    frames_ids = [x for x in range(video_len)]
                    clip_frames_ids = frames_ids[clip_id : clip_id + 36]
                    clip_len = len(clip_frames_ids)

                    # load the clip images
                    imgs = []
                    for t in clip_frames_ids:
                        frame = frames[t]
                        img_path = os.path.join(img_folder, video_name, frame + ".jpg")
                        img = Image.open(img_path).convert('RGB')
                        origin_w, origin_h = img.size
                        imgs.append(transform(img)) # list[Img]
                    
                    imgs = torch.stack(imgs, dim=0).to(args.device) # [video_len, 3, H, W]
                    img_h, img_w = imgs.shape[-2:]
                    size = torch.as_tensor([int(img_h), int(img_w)]).to(args.device)
                    target = {"size": size}

                    with torch.no_grad():
                        outputs = model([imgs], [exp], [target])
                    
                    pred_logits = outputs["pred_logits"][0] # [t, q, k]
                    pred_masks = outputs["pred_masks"][0]   # [t, q, h, w]

                    # according to pred_logits, select the query index
                    pred_scores = pred_logits.sigmoid() # [t, q, k]
                    pred_scores = pred_scores.mean(0)   # [q, K]
                    max_scores, _ = pred_scores.max(-1) # [q,]
                    _, max_ind = max_scores.max(-1)     # [1,]
                    max_inds = max_ind.repeat(clip_len)
                    pred_masks = pred_masks[range(clip_len), max_inds, ...] # [t, h, w]
                    pred_masks = pred_masks.unsqueeze(0)

                    pred_masks = F.interpolate(pred_masks, size=(origin_h, origin_w), mode='bilinear', align_corners=False)
                    pred_masks = pred_masks.sigmoid()[0] # [t, h, w], NOTE: here mask is score

                    # store the clip results
                    pred_logits = pred_logits[range(clip_len), max_inds] # [t, k]
                    all_pred_logits.append(pred_logits)
                    all_pred_masks.append(pred_masks)
                
                all_pred_logits = torch.cat(all_pred_logits, dim=0) # (video_len, K)
                all_pred_masks = torch.cat(all_pred_masks, dim=0)   # (video_len, h, w) 
                anno_logits.append(all_pred_logits) 
                anno_masks.append(all_pred_masks)   
            
            # handle a complete image (all objects of a annotator)
            anno_logits = torch.stack(anno_logits) # [num_obj, video_len, k]
            anno_masks = torch.stack(anno_masks)   # [num_obj, video_len, h, w]
            t, h, w = anno_masks.shape[-3:]
            anno_masks[anno_masks < 0.5] = 0.0
            background = 0.1 * torch.ones(1, t, h, w).to(args.device)
            anno_masks = torch.cat([background, anno_masks], dim=0) # [num_obj+1, video_len, h, w]
            out_masks = torch.argmax(anno_masks, dim=0) # int, the value indicate which object, [video_len, h, w]

            out_masks = out_masks.detach().cpu().numpy().astype(np.uint8) # [video_len, h, w]
    
            # save results
            anno_save_path = os.path.join(save_path_prefix, f"anno_{anno_id}", video)
            if not os.path.exists(anno_save_path):
                os.makedirs(anno_save_path)
            for f in range(out_masks.shape[0]):
                img_E = Image.fromarray(out_masks[f])
                img_E.putpalette(palette)
                img_E.save(os.path.join(anno_save_path, '{:05d}.png'.format(f)))


        with lock:
            progress.update(1)
    result_dict[str(pid)] = num_all_frames
    with lock:
        progress.close()



# Post-process functions
def box_cxcywh_to_xyxy(x):
    x_c, y_c, w, h = x.unbind(1)
    b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
         (x_c + 0.5 * w), (y_c + 0.5 * h)]
    return torch.stack(b, dim=1)

def rescale_bboxes(out_bbox, size):
    img_w, img_h = size
    b = box_cxcywh_to_xyxy(out_bbox)
    b = b.cpu() * torch.tensor([img_w, img_h, img_w, img_h], dtype=torch.float32)
    return b


# Visualization functions
def draw_reference_points(draw, reference_points, img_size, color):
    W, H = img_size
    for i, ref_point in enumerate(reference_points):
        init_x, init_y = ref_point
        x, y = W * init_x, H * init_y
        cur_color = color
        draw.line((x-10, y, x+10, y), tuple(cur_color), width=4)
        draw.line((x, y-10, x, y+10), tuple(cur_color), width=4)

def draw_sample_points(draw, sample_points, img_size, color_list):
    alpha = 255
    for i, samples in enumerate(sample_points):
        for sample in samples:
            x, y = sample
            cur_color = color_list[i % len(color_list)][::-1]
            cur_color += [alpha]
            draw.ellipse((x-2, y-2, x+2, y+2), 
                            fill=tuple(cur_color), outline=tuple(cur_color), width=1)

def vis_add_mask(img, mask, color):
    origin_img = np.asarray(img.convert('RGB')).copy()
    color = np.array(color)

    mask = mask.reshape(mask.shape[0], mask.shape[1]).astype('uint8') # np
    mask = mask > 0.5

    origin_img[mask] = origin_img[mask] * 0.5 + color * 0.5
    origin_img = Image.fromarray(origin_img)
    return origin_img

  

if __name__ == '__main__':
    parser = argparse.ArgumentParser('STHQ inference script', parents=[opts.get_args_parser()])
    args = parser.parse_args()
    main(args)