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import os
import time
import datetime as datetime
import json
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchvision import transforms
from dataloaders.eval_datasets import YOUTUBEVOS_Test, YOUTUBEVOS_DenseTest, DAVIS_Test, EVAL_TEST
import dataloaders.video_transforms as tr
from utils.image import flip_tensor, save_mask
from utils.checkpoint import load_network
from utils.eval import zip_folder
from networks.models import build_vos_model
from networks.engines import build_engine
class Evaluator(object):
def __init__(self, cfg, rank=0, seq_queue=None, info_queue=None):
self.gpu = cfg.TEST_GPU_ID + rank
self.gpu_num = cfg.TEST_GPU_NUM
self.rank = rank
self.cfg = cfg
self.seq_queue = seq_queue
self.info_queue = info_queue
self.print_log("Exp {}:".format(cfg.EXP_NAME))
self.print_log(json.dumps(cfg.__dict__, indent=4, sort_keys=True))
print("Use GPU {} for evaluating.".format(self.gpu))
torch.cuda.set_device(self.gpu)
self.print_log('Build VOS model.')
self.model = build_vos_model(cfg.MODEL_VOS, cfg).cuda(self.gpu)
self.process_pretrained_model()
self.prepare_dataset()
def process_pretrained_model(self):
cfg = self.cfg
if cfg.TEST_CKPT_PATH == 'test':
self.ckpt = 'test'
self.print_log('Test evaluation.')
return
if cfg.TEST_CKPT_PATH is None:
if cfg.TEST_CKPT_STEP is not None:
ckpt = str(cfg.TEST_CKPT_STEP)
else:
ckpts = os.listdir(cfg.DIR_CKPT)
if len(ckpts) > 0:
ckpts = list(
map(lambda x: int(x.split('_')[-1].split('.')[0]),
ckpts))
ckpt = np.sort(ckpts)[-1]
else:
self.print_log('No checkpoint in {}.'.format(cfg.DIR_CKPT))
exit()
self.ckpt = ckpt
if cfg.TEST_EMA:
cfg.DIR_CKPT = os.path.join(cfg.DIR_RESULT, 'ema_ckpt')
cfg.TEST_CKPT_PATH = os.path.join(cfg.DIR_CKPT,
'save_step_%s.pth' % ckpt)
try:
self.model, removed_dict = load_network(
self.model, cfg.TEST_CKPT_PATH, self.gpu)
except Exception as inst:
self.print_log(inst)
self.print_log('Try to use backup checkpoint.')
DIR_RESULT = './backup/{}/{}'.format(cfg.EXP_NAME,
cfg.STAGE_NAME)
DIR_CKPT = os.path.join(DIR_RESULT, 'ema_ckpt')
TEST_CKPT_PATH = os.path.join(DIR_CKPT,
'save_step_%s.pth' % ckpt)
self.model, removed_dict = load_network(
self.model, TEST_CKPT_PATH, self.gpu)
if len(removed_dict) > 0:
self.print_log(
'Remove {} from pretrained model.'.format(removed_dict))
self.print_log('Load latest checkpoint from {}'.format(
cfg.TEST_CKPT_PATH))
else:
self.ckpt = 'unknown'
self.model, removed_dict = load_network(self.model,
cfg.TEST_CKPT_PATH,
self.gpu)
if len(removed_dict) > 0:
self.print_log(
'Remove {} from pretrained model.'.format(removed_dict))
self.print_log('Load checkpoint from {}'.format(
cfg.TEST_CKPT_PATH))
def prepare_dataset(self):
cfg = self.cfg
self.print_log('Process dataset...')
eval_transforms = transforms.Compose([
tr.MultiRestrictSize(cfg.TEST_MAX_SHORT_EDGE,
cfg.TEST_MAX_LONG_EDGE, cfg.TEST_FLIP,
cfg.TEST_MULTISCALE, cfg.MODEL_ALIGN_CORNERS),
tr.MultiToTensor()
])
exp_name = cfg.EXP_NAME
if 'aost' in cfg.MODEL_VOS:
exp_name += '_L{}'.format(int(cfg.MODEL_LSTT_NUM))
eval_name = '{}_{}_{}_{}_ckpt_{}'.format(cfg.TEST_DATASET,
cfg.TEST_DATASET_SPLIT,
exp_name, cfg.STAGE_NAME,
self.ckpt)
if cfg.TEST_EMA:
eval_name += '_ema'
if cfg.TEST_FLIP:
eval_name += '_flip'
if len(cfg.TEST_MULTISCALE) > 1:
eval_name += '_ms_' + str(cfg.TEST_MULTISCALE).replace(
'.', 'dot').replace('[', '').replace(']', '').replace(
', ', '_')
if 'youtubevos' in cfg.TEST_DATASET:
year = int(cfg.TEST_DATASET[-4:])
self.result_root = os.path.join(cfg.DIR_EVALUATION,
cfg.TEST_DATASET, eval_name,
'Annotations')
if '_all_frames' in cfg.TEST_DATASET_SPLIT:
split = cfg.TEST_DATASET_SPLIT.split('_')[0]
youtubevos_test = YOUTUBEVOS_DenseTest
self.result_root_sparse = os.path.join(cfg.DIR_EVALUATION,
cfg.TEST_DATASET,
eval_name + '_sparse',
'Annotations')
self.zip_dir_sparse = os.path.join(
cfg.DIR_EVALUATION, cfg.TEST_DATASET,
'{}_sparse.zip'.format(eval_name))
else:
split = cfg.TEST_DATASET_SPLIT
youtubevos_test = YOUTUBEVOS_Test
self.dataset = youtubevos_test(root=cfg.DIR_YTB,
year=year,
split=split,
transform=eval_transforms,
result_root=self.result_root)
elif cfg.TEST_DATASET == 'davis2017':
resolution = 'Full-Resolution' if cfg.TEST_DATASET_FULL_RESOLUTION else '480p'
self.result_root = os.path.join(cfg.DIR_EVALUATION,
cfg.TEST_DATASET, eval_name,
'Annotations', resolution)
self.dataset = DAVIS_Test(
split=[cfg.TEST_DATASET_SPLIT],
root=cfg.DIR_DAVIS,
year=2017,
transform=eval_transforms,
full_resolution=cfg.TEST_DATASET_FULL_RESOLUTION,
result_root=self.result_root)
elif cfg.TEST_DATASET == 'davis2016':
resolution = 'Full-Resolution' if cfg.TEST_DATASET_FULL_RESOLUTION else '480p'
self.result_root = os.path.join(cfg.DIR_EVALUATION,
cfg.TEST_DATASET, eval_name,
'Annotations', resolution)
self.dataset = DAVIS_Test(
split=[cfg.TEST_DATASET_SPLIT],
root=cfg.DIR_DAVIS,
year=2016,
transform=eval_transforms,
full_resolution=cfg.TEST_DATASET_FULL_RESOLUTION,
result_root=self.result_root)
elif cfg.TEST_DATASET == 'test':
self.result_root = os.path.join(cfg.DIR_EVALUATION,
cfg.TEST_DATASET, eval_name,
'Annotations')
self.dataset = EVAL_TEST(eval_transforms, self.result_root)
else:
self.print_log('Unknown dataset!')
exit()
self.print_log('Eval {} on {} {}:'.format(cfg.EXP_NAME,
cfg.TEST_DATASET,
cfg.TEST_DATASET_SPLIT))
self.source_folder = os.path.join(cfg.DIR_EVALUATION, cfg.TEST_DATASET,
eval_name, 'Annotations')
self.zip_dir = os.path.join(cfg.DIR_EVALUATION, cfg.TEST_DATASET,
'{}.zip'.format(eval_name))
if not os.path.exists(self.result_root):
try:
os.makedirs(self.result_root)
except Exception as inst:
self.print_log(inst)
self.print_log('Failed to mask dir: {}.'.format(
self.result_root))
self.print_log('Done!')
def evaluating(self):
cfg = self.cfg
self.model.eval()
video_num = 0
processed_video_num = 0
total_time = 0
total_frame = 0
total_sfps = 0
total_video_num = len(self.dataset)
start_eval_time = time.time()
if self.seq_queue is not None:
if self.rank == 0:
for seq_idx in range(total_video_num):
self.seq_queue.put(seq_idx)
for _ in range(self.gpu_num):
self.seq_queue.put('END')
coming_seq_idx = self.seq_queue.get()
all_engines = []
with torch.no_grad():
for seq_idx, seq_dataset in enumerate(self.dataset):
video_num += 1
if self.seq_queue is not None:
if coming_seq_idx == 'END':
break
elif coming_seq_idx != seq_idx:
continue
else:
coming_seq_idx = self.seq_queue.get()
processed_video_num += 1
for engine in all_engines:
engine.restart_engine()
seq_name = seq_dataset.seq_name
print('GPU {} - Processing Seq {} [{}/{}]:'.format(
self.gpu, seq_name, video_num, total_video_num))
torch.cuda.empty_cache()
seq_dataloader = DataLoader(seq_dataset,
batch_size=1,
shuffle=False,
num_workers=cfg.TEST_WORKERS,
pin_memory=True)
if 'all_frames' in cfg.TEST_DATASET_SPLIT:
images_sparse = seq_dataset.images_sparse
seq_dir_sparse = os.path.join(self.result_root_sparse,
seq_name)
if not os.path.exists(seq_dir_sparse):
os.makedirs(seq_dir_sparse)
seq_total_time = 0
seq_total_frame = 0
seq_pred_masks = {'dense': [], 'sparse': []}
seq_timers = []
for frame_idx, samples in enumerate(seq_dataloader):
all_preds = []
new_obj_label = None
aug_num = len(samples)
for aug_idx in range(aug_num):
if len(all_engines) <= aug_idx:
all_engines.append(
build_engine(cfg.MODEL_ENGINE,
phase='eval',
aot_model=self.model,
gpu_id=self.gpu,
long_term_mem_gap=self.cfg.
TEST_LONG_TERM_MEM_GAP,
short_term_mem_skip=self.cfg.
TEST_SHORT_TERM_MEM_SKIP))
all_engines[-1].eval()
if aug_num > 1: # if use test-time augmentation
torch.cuda.empty_cache() # release GPU memory
engine = all_engines[aug_idx]
sample = samples[aug_idx]
is_flipped = sample['meta']['flip']
obj_nums = sample['meta']['obj_num']
imgname = sample['meta']['current_name']
ori_height = sample['meta']['height']
ori_width = sample['meta']['width']
obj_idx = sample['meta']['obj_idx']
obj_nums = [int(obj_num) for obj_num in obj_nums]
obj_idx = [int(_obj_idx) for _obj_idx in obj_idx]
current_img = sample['current_img']
current_img = current_img.cuda(self.gpu,
non_blocking=True)
sample['current_img'] = current_img
if 'current_label' in sample.keys():
current_label = sample['current_label'].cuda(
self.gpu, non_blocking=True).float()
else:
current_label = None
#############################################################
if frame_idx == 0:
_current_label = F.interpolate(
current_label,
size=current_img.size()[2:],
mode="nearest")
engine.add_reference_frame(current_img,
_current_label,
frame_step=0,
obj_nums=obj_nums)
else:
if aug_idx == 0:
seq_timers.append([])
now_timer = torch.cuda.Event(
enable_timing=True)
now_timer.record()
seq_timers[-1].append(now_timer)
engine.match_propogate_one_frame(current_img)
pred_logit = engine.decode_current_logits(
(ori_height, ori_width))
if is_flipped:
pred_logit = flip_tensor(pred_logit, 3)
pred_prob = torch.softmax(pred_logit, dim=1)
all_preds.append(pred_prob)
if not is_flipped and current_label is not None and new_obj_label is None:
new_obj_label = current_label
if frame_idx > 0:
all_pred_probs = [
torch.mean(pred, dim=0, keepdim=True)
for pred in all_preds
]
all_pred_labels = [
torch.argmax(prob, dim=1, keepdim=True).float()
for prob in all_pred_probs
]
cat_all_preds = torch.cat(all_preds, dim=0)
pred_prob = torch.mean(cat_all_preds,
dim=0,
keepdim=True)
pred_label = torch.argmax(pred_prob,
dim=1,
keepdim=True).float()
if new_obj_label is not None:
keep = (new_obj_label == 0).float()
all_pred_labels = [label * \
keep + new_obj_label * (1 - keep) for label in all_pred_labels]
pred_label = pred_label * \
keep + new_obj_label * (1 - keep)
new_obj_nums = [int(pred_label.max().item())]
if cfg.TEST_FLIP:
all_flip_pred_labels = [
flip_tensor(label, 3)
for label in all_pred_labels
]
flip_pred_label = flip_tensor(pred_label, 3)
for aug_idx in range(len(samples)):
engine = all_engines[aug_idx]
current_img = samples[aug_idx]['current_img']
# current_label = flip_pred_label if samples[
# aug_idx]['meta']['flip'] else pred_label
current_label = all_flip_pred_labels[
aug_idx] if samples[aug_idx]['meta'][
'flip'] else all_pred_labels[aug_idx]
current_label = F.interpolate(
current_label,
size=engine.input_size_2d,
mode="nearest")
engine.add_reference_frame(
current_img,
current_label,
obj_nums=new_obj_nums,
frame_step=frame_idx)
engine.decode_current_logits(
(ori_height, ori_width))
engine.update_memory(current_label)
else:
if not cfg.MODEL_USE_PREV_PROB:
if cfg.TEST_FLIP:
all_flip_pred_labels = [
flip_tensor(label, 3)
for label in all_pred_labels
]
flip_pred_label = flip_tensor(
pred_label, 3)
for aug_idx in range(len(samples)):
engine = all_engines[aug_idx]
# current_label = flip_pred_label if samples[
# aug_idx]['meta']['flip'] else pred_label
current_label = all_flip_pred_labels[
aug_idx] if samples[aug_idx]['meta'][
'flip'] else all_pred_labels[
aug_idx]
current_label = F.interpolate(
current_label,
size=engine.input_size_2d,
mode="nearest")
engine.update_memory(current_label)
else:
if cfg.TEST_FLIP:
all_flip_pred_probs = [
flip_tensor(prob, 3)
for prob in all_pred_probs
]
flip_pred_prob = flip_tensor(pred_prob, 3)
for aug_idx in range(len(samples)):
engine = all_engines[aug_idx]
# current_prob = flip_pred_prob if samples[
# aug_idx]['meta']['flip'] else pred_prob
current_label = all_flip_pred_probs[
aug_idx] if samples[aug_idx]['meta'][
'flip'] else all_pred_probs[aug_idx]
current_prob = F.interpolate(
current_prob,
size=engine.input_size_2d,
mode="nearest")
engine.update_memory(current_prob)
now_timer = torch.cuda.Event(enable_timing=True)
now_timer.record()
seq_timers[-1].append((now_timer))
if cfg.TEST_FRAME_LOG:
torch.cuda.synchronize()
one_frametime = seq_timers[-1][0].elapsed_time(
seq_timers[-1][1]) / 1e3
obj_num = obj_nums[0]
print(
'GPU {} - Frame: {} - Obj Num: {}, Time: {}ms'.
format(self.gpu, imgname[0].split('.')[0],
obj_num, int(one_frametime * 1e3)))
# Save result
seq_pred_masks['dense'].append({
'path':
os.path.join(self.result_root, seq_name,
imgname[0].split('.')[0] + '.png'),
'mask':
pred_label,
'obj_idx':
obj_idx
})
if 'all_frames' in cfg.TEST_DATASET_SPLIT and imgname in images_sparse:
seq_pred_masks['sparse'].append({
'path':
os.path.join(self.result_root_sparse, seq_name,
imgname[0].split('.')[0] +
'.png'),
'mask':
pred_label,
'obj_idx':
obj_idx
})
# Save result
for mask_result in seq_pred_masks['dense'] + seq_pred_masks[
'sparse']:
save_mask(mask_result['mask'].squeeze(0).squeeze(0),
mask_result['path'], mask_result['obj_idx'])
del (seq_pred_masks)
for timer in seq_timers:
torch.cuda.synchronize()
one_frametime = timer[0].elapsed_time(timer[1]) / 1e3
seq_total_time += one_frametime
seq_total_frame += 1
del (seq_timers)
seq_avg_time_per_frame = seq_total_time / seq_total_frame
total_time += seq_total_time
total_frame += seq_total_frame
total_avg_time_per_frame = total_time / total_frame
total_sfps += seq_avg_time_per_frame
avg_sfps = total_sfps / processed_video_num
max_mem = torch.cuda.max_memory_allocated(
device=self.gpu) / (1024.**3)
print(
"GPU {} - Seq {} - FPS: {:.2f}. All-Frame FPS: {:.2f}, All-Seq FPS: {:.2f}, Max Mem: {:.2f}G"
.format(self.gpu, seq_name, 1. / seq_avg_time_per_frame,
1. / total_avg_time_per_frame, 1. / avg_sfps,
max_mem))
if self.seq_queue is not None:
if self.rank != 0:
self.info_queue.put({
'total_time': total_time,
'total_frame': total_frame,
'total_sfps': total_sfps,
'processed_video_num': processed_video_num,
'max_mem': max_mem
})
print('Finished the evaluation on GPU {}.'.format(self.gpu))
if self.rank == 0:
for _ in range(self.gpu_num - 1):
info_dict = self.info_queue.get()
total_time += info_dict['total_time']
total_frame += info_dict['total_frame']
total_sfps += info_dict['total_sfps']
processed_video_num += info_dict['processed_video_num']
max_mem = max(max_mem, info_dict['max_mem'])
all_reduced_total_avg_time_per_frame = total_time / total_frame
all_reduced_avg_sfps = total_sfps / processed_video_num
print(
"GPU {} - All-Frame FPS: {:.2f}, All-Seq FPS: {:.2f}, Max Mem: {:.2f}G"
.format(list(range(self.gpu_num)),
1. / all_reduced_total_avg_time_per_frame,
1. / all_reduced_avg_sfps, max_mem))
else:
print(
"GPU {} - All-Frame FPS: {:.2f}, All-Seq FPS: {:.2f}, Max Mem: {:.2f}G"
.format(self.gpu, 1. / total_avg_time_per_frame, 1. / avg_sfps,
max_mem))
if self.rank == 0:
zip_folder(self.source_folder, self.zip_dir)
self.print_log('Saving result to {}.'.format(self.zip_dir))
if 'all_frames' in cfg.TEST_DATASET_SPLIT:
zip_folder(self.result_root_sparse, self.zip_dir_sparse)
end_eval_time = time.time()
total_eval_time = str(
datetime.timedelta(seconds=int(end_eval_time -
start_eval_time)))
self.print_log("Total evaluation time: {}".format(total_eval_time))
def print_log(self, string):
if self.rank == 0:
print(string)
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