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produce_reward.py
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produce_reward.py
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import copy
import logging
import os
import time
import sys
sys.path.append(os.path.join('VOS', 'ATNet'))
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from sacred import Experiment
from easydict import EasyDict as edict
from davisinteractive.dataset.davis import Davis
from davisinteractive import utils as interactive_utils
from davisinteractive.session import DavisInteractiveSession
from utils.utils_agent import (agent_business, gen_subseq, select_next_frame)
from utils.utils_atnet import run_VOS_singleiact
from utils.misc import (AverageMeter, save_agent_checkpoint, sequence_metric, set_random_seed)
from models.agent import Agent
from config import Config
from libs import utils
from networks.atnet import ATnet
cudnn.benchmark = False
cudnn.deterministic = True
def create_basic_stream_logger(format):
logger = logging.getLogger('')
logger.setLevel(logging.INFO)
logger.handlers = []
ch = logging.StreamHandler()
formatter = logging.Formatter(format)
ch.setFormatter(formatter)
logger.addHandler(ch)
return logger
ex = Experiment('train')
ex.add_config('./configs/config.yaml')
ex.logger = create_basic_stream_logger('%(name)s - %(message)s')
def davis_config(run, _log):
kwargs = dict()
cfg_yl = edict(run.config)
cfg_yl.phase = 'baseline'
# ====== configs ======
device = torch.device(f"cuda:{cfg_yl.gpu_id}" if torch.cuda.is_available() else "cpu")
subset = cfg_yl.data.subset
dataset_root_dir = cfg_yl.data.root_dir_davis
max_nb_interactions = int(cfg_yl.davis_interactive.max_nb_interactions)
max_time = None # Maximum time per object
davis = Davis(davis_root=dataset_root_dir)
# ------ Agent ------
agent = Agent(device=device, cfg=cfg_yl)
# ATNet
config = Config()
config.davis_dataset_dir = dataset_root_dir
net = ATnet()
net.cuda()
net.eval()
net.load_state_dict(torch.load(os.path.join('VOS', 'ATNet', config.test_load_state_dir)))
save_result_dir = cfg_yl.agent.save_result_dir
os.makedirs(save_result_dir, exist_ok=True)
df = None
agent.memory_pool.basename_csv = cfg_yl.agent.reward_csv
set_random_seed(2020)
cfg_yl.num_epochs = 30
cfg_yl.method = 'random'
report_save_dir = save_result_dir
kwargs['cfg_yl'] = cfg_yl
kwargs['config'] = config
kwargs['net'] = net
kwargs['agent'] = agent
kwargs['davis'] = davis
kwargs['dataset_root_dir'] = dataset_root_dir
kwargs['report_save_dir'] = report_save_dir
kwargs['subset'] = subset
kwargs['max_nb_interactions'] = max_nb_interactions
kwargs['max_time'] = max_time
kwargs['device'] = device
kwargs['df'] = df
return kwargs
@ex.automain
def main(_run, _log):
kwargs = davis_config(_run, _log)
auc_meter = AverageMeter()
metric_at_threshold_meter = AverageMeter()
seen_seq = {}
# ====== main loop ======
for epoch in range(1, kwargs['cfg_yl'].num_epochs+1):
# 'J', 'F', 'J_AND_F'
metric_to_optimize = kwargs['cfg_yl'].davis_interactive.metric
with DavisInteractiveSession(host='localhost', davis_root=kwargs['dataset_root_dir'], subset=kwargs['subset'],
metric_to_optimize=metric_to_optimize,
max_nb_interactions=kwargs['max_nb_interactions'], max_time=kwargs['max_time'],
report_save_dir=kwargs['report_save_dir']) as sess:
# per object per serquence
final_mask_iou_seq_obj_scb = AverageMeter()
final_time_seq_obj_scb = AverageMeter()
final_recommend_time_seq_obj_scb = AverageMeter()
final_agent_time_seq_obj_scb = AverageMeter()
final_seg_time_seq_obj_scb = AverageMeter()
final_reward_step_seq_obj_scb = AverageMeter()
final_reward_done_seq_obj_scb = AverageMeter()
agent_loss = AverageMeter()
i_seq = 0
sess.connector.service.robot.min_nb_nodes = kwargs['config'].test_min_nb_nodes
while sess.next():
# 1 ------ interaction initial ------
interaction_tic = time.time()
init_tic = time.time()
sequence, scribbles, first_scribble = sess.get_scribbles(only_last=False)
annotated_frames = interactive_utils.scribbles.annotated_frames(sess.sample_last_scribble)
if first_scribble:
i_seq = i_seq + 1
interaction_time = AverageMeter()
frame_recommend_time = AverageMeter()
segment_time = AverageMeter()
agent_time = AverageMeter()
assert len(annotated_frames) > 0
first_frame = annotated_frames[0]
next_frame = annotated_frames[0]
reward_step_acc = 0
reward_done_acc = 0
seen_seq[sequence] = 1 if sequence not in seen_seq.keys() else seen_seq[sequence] + 1
gt_masks_original = kwargs['davis'].load_annotations(sequence)
nb_objects = kwargs['davis'].dataset[sequence]['num_objects']
agent_train_loader = None
# make subsequence information
len_subseq = min(
kwargs['cfg_yl'].data.len_subseq, kwargs['davis'].dataset[sequence]['num_frames'])
subseq = gen_subseq(
first_frame, kwargs['davis'].dataset[sequence]['num_frames'], len_subseq)
_log.info(f'subseq: {subseq}')
_log.info(f"first_frame:{first_frame}, subseq:{subseq}")
n_frame = len_subseq
next_frame = subseq.index(next_frame)
gt_masks = gt_masks_original[subseq]
prev_frames = [next_frame]
annotated_frames_list = [next_frame]
# ATNet stuff
anno_dict = {'frames': [], 'annotated_masks': [],
'masks_tobe_modified': []}
info = kwargs['davis'].dataset[sequence]
img_size = info['image_size'][::-1]
n_objects = info['num_objects']
final_masks = np.zeros([n_frame, img_size[0], img_size[1]])
vos_kwargs = dict()
vos_kwargs['pad_info'] = utils.apply_pad(final_masks[0])[1]
vos_kwargs['hpad1'], vos_kwargs['wpad1'] = vos_kwargs['pad_info'][0][0], \
vos_kwargs['pad_info'][1][0]
vos_kwargs['hpad2'], vos_kwargs['wpad2'] = vos_kwargs['pad_info'][0][1], \
vos_kwargs['pad_info'][1][1]
h_ds, w_ds = int((img_size[0] + sum(vos_kwargs['pad_info'][0])) / 4), \
int((img_size[1] + sum(vos_kwargs['pad_info'][1])) / 4)
vos_kwargs['anno_6chEnc_r5_list'], vos_kwargs['anno_3chEnc_r5_list'] = [
], []
vos_kwargs['prob_map_of_frames'] = torch.zeros(
(n_frame, n_objects, 4 * h_ds, 4 * w_ds)).cuda()
vos_kwargs['num_frames'] = n_frame
vos_kwargs['n_objects'] = n_objects
vos_kwargs['subseq'] = subseq
vos_kwargs['n_interaction'] = 1
rec_kwargs = dict()
rec_kwargs['n_frame'] = n_frame
rec_kwargs['n_objects'] = n_objects
rec_kwargs['all_F'] = None
rec_kwargs['mask_quality'] = None
# dataset and VOS business
old_frame = None
old_masks_meta = None
old_masks_metric = None
repeat_selection = None
else:
annotated_frames_list_np = np.zeros(len(new_masks_metric))
for i in annotated_frames_list:
annotated_frames_list_np[i] += 1
repeat_selection = next_frame not in list(
np.where(annotated_frames_list_np == annotated_frames_list_np.min())[0])
annotated_frames_list.append(next_frame)
old_frame = next_frame
old_masks_meta = new_masks_meta
old_masks_metric = new_masks_metric
vos_kwargs['n_interaction'] += 1
# Where we save annotated frames
anno_dict['frames'].append(next_frame)
# mask before modefied at the annotated frame
anno_dict['masks_tobe_modified'].append(
final_masks[next_frame])
scribbles['annotated_frame'] = next_frame
scribbles_subseq = [scribbles['scribbles'][i] for i in subseq]
scribbles['scribbles'] = scribbles_subseq
init_time = time.time() - init_tic
# 2 ------ segmentation ------
segment_tic = time.time()
with torch.no_grad():
final_masks, all_P = run_VOS_singleiact(kwargs['net'], kwargs['config'], kwargs['subset'], scribbles,
anno_dict['frames'], final_masks, **vos_kwargs)
new_masks = final_masks
new_masks_metric = sequence_metric(metric_to_optimize, gt_masks, new_masks, nb_objects)
segment_time.update(time.time()-segment_tic)
# 3 ------ frame recommendation ------
frame_recommend_tic = time.time()
next_frame = select_next_frame(new_masks_metric, metric='random') # random
prev_frames.append(next_frame)
frame_recommend_time.update(time.time() - frame_recommend_tic)
# 4 ------ Submit prediction ------
new_masks_submit = copy.deepcopy(gt_masks_original)
new_masks_submit[subseq] = new_masks
sess.submit_masks(new_masks_submit, next_scribble_frame_candidates=[subseq[next_frame]])
# 5 ------ agent business ------
agent_tic = time.time()
new_masks_meta = dict(
sequence=sequence, scribble_iter=seen_seq[sequence], n_interaction=vos_kwargs['n_interaction'])
agent_kwargs = dict()
agent_kwargs['first_scribble'] = first_scribble
agent_kwargs['old_masks_metric'] = old_masks_metric
agent_kwargs['new_masks_metric'] = new_masks_metric
agent_kwargs['old_frame'] = old_frame
agent_kwargs['next_frame'] = next_frame
agent_kwargs['sequence'] = sequence
agent_kwargs['seen_seq'] = seen_seq
agent_kwargs['repeat_selection'] = repeat_selection
agent_kwargs['df'] = kwargs['df']
agent_kwargs['annotated_frames_list'] = annotated_frames_list
agent_kwargs['old_masks_meta'] = old_masks_meta
agent_kwargs['new_masks_meta'] = new_masks_meta
agent_kwargs['report_save_dir'] = kwargs['cfg_yl'].agent.save_result_dir
agent_kwargs['agent_train_loader'] = agent_train_loader
[agent_loss_iter, reward_step, reward_done] = \
agent_business(kwargs['cfg_yl'], kwargs['agent'], kwargs['max_nb_interactions'],
vos_kwargs['n_interaction'], **agent_kwargs)
reward_step_acc += reward_step
reward_done_acc += reward_done
agent_time.update(time.time() - agent_tic)
# 6 ------ print logs ------
interaction_time.update(time.time() - interaction_tic)
_log.info(
f"avg_{metric_to_optimize}: {(sum(new_masks_metric) / len(new_masks_metric) * 100):.2f} "
f"init_time:{init_time:.2f} "
f"rec_time:{frame_recommend_time.val:.2f} "
f"seg_time:{segment_time.val:.2f} ({segment_time.avg:.2f})\t"
f"next_frame: {next_frame:2d} [{int(sum(new_masks_metric < new_masks_metric[next_frame])) + 1:2d}/{new_masks_metric.shape[0]:2d}]\t"
f"reward_step:{reward_step:.2f} \t"
f"reward_done:{reward_done:.2f} \t"
f"seq: {sequence}_{seen_seq[sequence]:1d} [{vos_kwargs['n_interaction']:2d}/{kwargs['max_nb_interactions']:2d}]\t"
)
if vos_kwargs['n_interaction'] == kwargs['max_nb_interactions']:
final_mask_iou_seq_obj_scb.update(
(sum(new_masks_metric) / len(new_masks_metric)) * 100)
final_time_seq_obj_scb.update(interaction_time.avg)
final_recommend_time_seq_obj_scb.update(
frame_recommend_time.avg)
final_agent_time_seq_obj_scb.update(agent_time.avg)
final_seg_time_seq_obj_scb.update(segment_time.avg)
final_reward_step_seq_obj_scb.update(reward_step_acc)
final_reward_done_seq_obj_scb.update(reward_done_acc)
if agent_loss_iter > 0:
agent_loss.update(agent_loss_iter)
_log.info(
f"* avg_time: {final_time_seq_obj_scb.val:.2f} ({final_time_seq_obj_scb.avg:.2f})"
f" rec_time:{final_recommend_time_seq_obj_scb.val:.2f} ({final_recommend_time_seq_obj_scb.avg:.2f})"
f" agent_time:{final_agent_time_seq_obj_scb.val:.2f} ({final_agent_time_seq_obj_scb.avg:.2f})\t"
f"seg_time: {final_seg_time_seq_obj_scb.val:.2f} ({final_seg_time_seq_obj_scb.avg:.2f})\t"
f"{metric_to_optimize}: {final_mask_iou_seq_obj_scb.val:.2f} ({final_mask_iou_seq_obj_scb.avg:.2f})\t"
f"reward_step: {final_reward_step_seq_obj_scb.val:.2f} ({final_reward_step_seq_obj_scb.avg:.2f})\t"
f"reward_done: {final_reward_done_seq_obj_scb.val:.2f} ({final_reward_done_seq_obj_scb.avg:.2f})\t"
f"agent_loss: {agent_loss.val:.2f} ({agent_loss.avg:.2f})\t"
f"seq: [{i_seq}/{len(sess.samples)}] {sequence}_{seen_seq[sequence]:1d}"
)
global_summary = sess.get_global_summary()
_log.info(f"# final avg {metric_to_optimize}: {final_mask_iou_seq_obj_scb.avg:.4f}\t"
f"final agent loss: {agent_loss.avg:.2f}\t"
f"final avg reward_step: {final_reward_step_seq_obj_scb.avg:.4f}\t"
f"final avg reward_done: {final_reward_done_seq_obj_scb.avg:.4f}")
auc_round = np.trapz(global_summary['curve'][metric_to_optimize][:-1]) / \
(len(global_summary['curve'][metric_to_optimize][:-1]) - 1)
global_summary['auc'] = auc_round
auc = float(global_summary['auc'])
metric_at_threshold = float(
global_summary['metric_at_threshold'][metric_to_optimize])
auc_meter.update(auc)
metric_at_threshold_meter.update(metric_at_threshold)
_log.info(f"# global_summary: auc:{auc:.4f} ({auc_meter.avg:.4f})\t"
f"auc_t:{auc*100:.4f}\t"
f"{metric_to_optimize}@{int(global_summary['metric_at_threshold']['threshold']):2d}: "
f"{metric_at_threshold:.4f} ({metric_at_threshold_meter.avg:.4f})")
print(f"# time:\t", end=' ')
for i in range(len(global_summary['curve']['time'])):
print(f"{global_summary['curve']['time'][i]:.2f}\t", end=' ')
print(f"\n# {metric_to_optimize}:\t", end=' ')
for i in range(len(global_summary['curve'][metric_to_optimize])):
print(f"{global_summary['curve'][metric_to_optimize][i] * 100:.2f}\t", end=' ')
print('\n')