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inference_davis.py
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'''
Inference code for MUTR, on Ref-Youtube-VOS
Modified from DETR (https://github.com/facebookresearch/detr)
Ref-Davis17 does not support visualize
'''
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(432),
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
if len(os.listdir(os.path.join('results', args.backbone, 'ckpt'))) == 1:
args.test_num_ckpt = -1
inference_ckpt = sorted(os.listdir(os.path.join('results', args.backbone, 'ckpt')))[args.test_num_ckpt]
print(inference_ckpt)
args.output_dir = os.path.join('results', args.backbone, 'eval', args.dataset_file, inference_ckpt[:-4])
os.makedirs(args.output_dir, exist_ok=True)
args.resume = os.path.join('results', args.backbone, 'ckpt', inference_ckpt)
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
save_name_expression = {0: 'Davis17_annot1', 1:'Davis17_annot1_full_video', 2:'Davis17_annot2', 3:'Davis17_annot2_full_video'}
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, 64):
frames_ids = [x for x in range(video_len)]
clip_frames_ids = frames_ids[clip_id : clip_id + 64]
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)
anno_save_path = os.path.join(save_path_prefix, f"{save_name_expression[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
def eval_davis(results_path):
import os
import sys
from time import time
import argparse
import numpy as np
import pandas as pd
from davis2017.evaluation import DAVISEvaluation
default_davis_path = './data/ref-davis/DAVIS'
time_start = time()
parser = argparse.ArgumentParser()
parser.add_argument('--davis_path', type=str, help='Path to the DAVIS folder containing the JPEGImages, Annotations, '
'ImageSets, Annotations_unsupervised folders',
required=False, default=default_davis_path)
parser.add_argument('--set', type=str, help='Subset to evaluate the results', default='val') # val subset
parser.add_argument('--task', type=str, help='Task to evaluate the results', default='unsupervised',
choices=['semi-supervised', 'unsupervised'])
argss, _ = parser.parse_known_args()
csv_name_global = f'global_results-{argss.set}.csv'
csv_name_per_sequence = f'per-sequence_results-{argss.set}.csv'
# Check if the method has been evaluated before, if so read the results, otherwise compute the results
csv_name_global_path = os.path.join(results_path, csv_name_global)
csv_name_per_sequence_path = os.path.join(results_path, csv_name_per_sequence)
print(f'Evaluating sequences for the {argss.task} task...')
# Create dataset and evaluate
dataset_eval = DAVISEvaluation(davis_root=argss.davis_path, task=argss.task, gt_set=argss.set)
metrics_res = dataset_eval.evaluate(results_path)
J, F = metrics_res['J'], metrics_res['F']
# Generate dataframe for the general results
g_measures = ['J&F-Mean', 'J-Mean', 'J-Recall', 'J-Decay', 'F-Mean', 'F-Recall', 'F-Decay']
final_mean = (np.mean(J["M"]) + np.mean(F["M"])) / 2.
g_res = np.array([final_mean, np.mean(J["M"]), np.mean(J["R"]), np.mean(J["D"]), np.mean(F["M"]), np.mean(F["R"]),
np.mean(F["D"])])
g_res = np.reshape(g_res, [1, len(g_res)])
table_g = pd.DataFrame(data=g_res, columns=g_measures)
with open(csv_name_global_path, 'w') as f:
table_g.to_csv(f, index=False, float_format="%.5f")
print(f'Global results saved in {csv_name_global_path}')
# Generate a dataframe for the per sequence results
seq_names = list(J['M_per_object'].keys())
seq_measures = ['Sequence', 'J-Mean', 'F-Mean']
J_per_object = [J['M_per_object'][x] for x in seq_names]
F_per_object = [F['M_per_object'][x] for x in seq_names]
table_seq = pd.DataFrame(data=list(zip(seq_names, J_per_object, F_per_object)), columns=seq_measures)
with open(csv_name_per_sequence_path, 'w') as f:
table_seq.to_csv(f, index=False, float_format="%.5f")
print(f'Per-sequence results saved in {csv_name_per_sequence_path}')
# Print the results
sys.stdout.write(f"--------------------------- Global results for {argss.set} ---------------------------\n")
print(table_g.to_string(index=False))
sys.stdout.write(f"\n---------- Per sequence results for {argss.set} ----------\n")
print(table_seq.to_string(index=False))
total_time = time() - time_start
sys.stdout.write('\nTotal time:' + str(total_time))
if __name__ == '__main__':
parser = argparse.ArgumentParser('MUTR inference script', parents=[opts.get_args_parser()])
args = parser.parse_args()
main(args)
eval_davis(os.path.join(args.output_dir, 'valid','Davis17_annot1'))
eval_davis(os.path.join(args.output_dir, 'valid','Davis17_annot1_full_video'))
eval_davis(os.path.join(args.output_dir, 'valid','Davis17_annot2'))
eval_davis(os.path.join(args.output_dir, 'valid','Davis17_annot2_full_video'))