def train_rpn(cfg,
dataset,
image_set,
root_path,
dataset_path,
frequent,
kvstore,
flip,
shuffle,
resume,
ctx,
pretrained,
epoch,
prefix,
begin_epoch,
end_epoch,
train_shared,
lr,
lr_step,
logger=None,
output_path=None):
# set up logger
if not logger:
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# set up config
cfg.TRAIN.BATCH_IMAGES = cfg.TRAIN.ALTERNATE.RPN_BATCH_IMAGES
# load symbol
sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
sym = sym_instance.get_symbol_rpn(cfg, is_train=True)
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = cfg.TRAIN.BATCH_IMAGES * batch_size
# print cfg
pprint.pprint(cfg)
logger.info('training rpn cfg:{}\n'.format(pprint.pformat(cfg)))
# load dataset and prepare imdb for training
image_sets = [iset for iset in image_set.split('+')]
roidbs = [
load_gt_roidb(dataset,
image_set,
root_path,
dataset_path,
result_path=output_path,
flip=flip) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, cfg)
# load training data
train_data = AnchorLoader(feat_sym,
roidb,
cfg,
batch_size=input_batch_size,
shuffle=shuffle,
ctx=ctx,
feat_stride=cfg.network.RPN_FEAT_STRIDE,
anchor_scales=cfg.network.ANCHOR_SCALES,
anchor_ratios=cfg.network.ANCHOR_RATIOS,
aspect_grouping=cfg.TRAIN.ASPECT_GROUPING)
# infer max shape
max_data_shape = [('data', (cfg.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in cfg.SCALES]),
max([v[1] for v in cfg.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
print('providing maximum shape', max_data_shape, max_label_shape)
# infer shape
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if resume:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight_rpn(cfg, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# create solver
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
if train_shared:
fixed_param_prefix = cfg.network.FIXED_PARAMS_SHARED
else:
fixed_param_prefix = cfg.network.FIXED_PARAMS
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in xrange(batch_size)],
max_label_shapes=[max_label_shape for _ in xrange(batch_size)],
fixed_param_prefix=fixed_param_prefix)
# decide training params
# metric
eval_metric = metric.RPNAccMetric()
cls_metric = metric.RPNLogLossMetric()
bbox_metric = metric.RPNL1LossMetric()
eval_metrics = mx.metric.CompositeEvalMetric()
for child_metric in [eval_metric, cls_metric, bbox_metric]:
eval_metrics.add(child_metric)
# callback
batch_end_callback = callback.Speedometer(train_data.batch_size,
frequent=frequent)
# epoch_end_callback = mx.callback.do_checkpoint(prefix)
epoch_end_callback = mx.callback.module_checkpoint(
mod, prefix, period=1, save_optimizer_states=True)
# decide learning rate
base_lr = lr
lr_factor = cfg.TRAIN.lr_factor
lr_epoch = [int(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch
]
lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [
int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
cfg.TRAIN.warmup,
cfg.TRAIN.warmup_lr,
cfg.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': cfg.TRAIN.momentum,
'wd': cfg.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None
}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
def generate_proposals(predictor, test_data, imdb, cfg, vis=False, thresh=0.):
"""
Generate detections results using RPN.
:param predictor: Predictor
:param test_data: data iterator, must be non-shuffled
:param imdb: image database
:param vis: controls visualization
:param thresh: thresh for valid detections
:return: list of detected boxes
"""
assert vis or not test_data.shuffle
data_names = [k[0] for k in test_data.provide_data[0]]
if not isinstance(test_data, PrefetchingIter):
test_data = PrefetchingIter(test_data)
idx = 0
t = time.time()
imdb_boxes = list()
original_boxes = list()
for im_info, data_batch in test_data:
t1 = time.time() - t
t = time.time()
scales = [iim_info[0, 2] for iim_info in im_info]
scores_all, boxes_all, data_dict_all = im_proposal(
predictor, data_batch, data_names, scales)
t2 = time.time() - t
t = time.time()
for delta, (scores, boxes, data_dict, scale) in enumerate(
zip(scores_all, boxes_all, data_dict_all, scales)):
# assemble proposals
dets = np.hstack((boxes, scores))
original_boxes.append(dets)
# filter proposals
keep = np.where(dets[:, 4:] > thresh)[0]
dets = dets[keep, :]
imdb_boxes.append(dets)
if vis:
vis_all_detection(data_dict['data'].asnumpy(), [dets], ['obj'],
scale, cfg)
print 'generating %d/%d' % (idx + 1, imdb.num_images), 'proposal %d' % (dets.shape[0]), \
'data %.4fs net %.4fs' % (t1, t2 / test_data.batch_size)
idx += 1
assert len(imdb_boxes) == imdb.num_images, 'calculations not complete'
# save results
rpn_folder = os.path.join(imdb.result_path, 'rpn_data')
if not os.path.exists(rpn_folder):
os.mkdir(rpn_folder)
rpn_file = os.path.join(rpn_folder, imdb.name + '_rpn.pkl')
with open(rpn_file, 'wb') as f:
cPickle.dump(imdb_boxes, f, cPickle.HIGHEST_PROTOCOL)
if thresh > 0:
full_rpn_file = os.path.join(rpn_folder, imdb.name + '_full_rpn.pkl')
with open(full_rpn_file, 'wb') as f:
cPickle.dump(original_boxes, f, cPickle.HIGHEST_PROTOCOL)
print 'wrote rpn proposals to {}'.format(rpn_file)
return imdb_boxes
def pred_eval(predictor, test_data, imdb, cfg, vis=False, thresh=1e-3, logger=None, ignore_cache=False):
det_file = os.path.join(imdb.result_path, imdb.name + '_detections.pkl')
seg_file = os.path.join(imdb.result_path, imdb.name + '_masks.pkl')
if os.path.exists(det_file) and os.path.exists(seg_file) and not ignore_cache:
with open(det_file, 'rb') as f:
all_boxes = cPickle.load(f)
with open(seg_file, 'rb') as f:
all_masks = cPickle.load(f)
else:
assert vis or not test_data.shuffle
data_names = [k[0] for k in test_data.provide_data[0]]
if not isinstance(test_data, PrefetchingIter):
test_data = PrefetchingIter(test_data)
# function pointers
nms = py_nms_wrapper(cfg.TEST.NMS)
mask_voting = gpu_mask_voting if cfg.TEST.USE_GPU_MASK_MERGE else cpu_mask_voting
max_per_image = 100 if cfg.TEST.USE_MASK_MERGE else -1
num_images = imdb.num_images
all_boxes = [[[] for _ in xrange(num_images)]
for _ in xrange(imdb.num_classes)]
all_masks = [[[] for _ in xrange(num_images)]
for _ in xrange(imdb.num_classes)]
idx = 0
t = time.time()
for data_batch in test_data:
t1 = time.time() - t
t = time.time()
scales = [data_batch.data[i][1].asnumpy()[0, 2] for i in xrange(len(data_batch.data))]
scores_all, boxes_all, masks_all, data_dict_all = im_detect(predictor, data_batch, data_names, scales, cfg)
im_shapes = [data_batch.data[i][0].shape[2:4] for i in xrange(len(data_batch.data))]
t2 = time.time() - t
t = time.time()
# post processing
for delta, (scores, boxes, masks, data_dict) in enumerate(zip(scores_all, boxes_all, masks_all, data_dict_all)):
if not cfg.TEST.USE_MASK_MERGE:
for j in range(1, imdb.num_classes):
indexes = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[indexes, j, np.newaxis]
cls_masks = masks[indexes, 1, :, :]
try:
if cfg.CLASS_AGNOSTIC:
cls_boxes = boxes[indexes, :]
else:
raise Exception()
except:
cls_boxes = boxes[indexes, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores))
keep = nms(cls_dets)
all_boxes[j][idx + delta] = cls_dets[keep, :]
all_masks[j][idx + delta] = cls_masks[keep, :]
else:
masks = masks[:, 1:, :, :]
im_height = np.round(im_shapes[delta][0] / scales[delta]).astype('int')
im_width = np.round(im_shapes[delta][1] / scales[delta]).astype('int')
boxes = clip_boxes(boxes, (im_height, im_width))
result_mask, result_box = mask_voting(masks, boxes, scores, imdb.num_classes,
max_per_image, im_width, im_height,
cfg.TEST.NMS, cfg.TEST.MASK_MERGE_THRESH,
cfg.BINARY_THRESH)
for j in xrange(1, imdb.num_classes):
all_boxes[j][idx+delta] = result_box[j]
all_masks[j][idx+delta] = result_mask[j][:,0,:,:]
if vis:
boxes_this_image = [[]] + [all_boxes[j][idx + delta] for j in range(1, imdb.num_classes)]
masks_this_image = [[]] + [all_masks[j][idx + delta] for j in range(1, imdb.num_classes)]
vis_all_mask(data_dict['data'].asnumpy(), boxes_this_image, masks_this_image, imdb.classes, scales[delta], cfg)
idx += test_data.batch_size
t3 = time.time() - t
t = time.time()
print ('testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(idx, imdb.num_images, t1, t2, t3))
if logger:
logger.info('testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(idx, imdb.num_images, t1, t2, t3))
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, protocol=cPickle.HIGHEST_PROTOCOL)
with open(seg_file, 'wb') as f:
cPickle.dump(all_masks, f, protocol=cPickle.HIGHEST_PROTOCOL)
info_str = imdb.evaluate_sds(all_boxes, all_masks)
if logger:
logger.info('evaluate detections: \n{}'.format(info_str))
def train_net(args, ctx, pretrained, pretrained_base, pretrained_ec, epoch,
prefix, begin_epoch, end_epoch, lr, lr_step):
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_train_symbol(config)
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
segdbs = [
load_gt_segdb(config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
result_path=final_output_path,
flip=config.TRAIN.FLIP) for image_set in image_sets
]
segdb = merge_segdb(segdbs)
# load training data
train_data = TrainDataLoader(sym,
segdb,
config,
batch_size=input_batch_size,
crop_height=config.TRAIN.CROP_HEIGHT,
crop_width=config.TRAIN.CROP_WIDTH,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES]))),
('data_ref', (config.TRAIN.KEY_INTERVAL - 1, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES]))),
('eq_flag', (1, ))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
print 'providing maximum shape', max_data_shape, max_label_shape
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
print pretrained
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
arg_params_base, aux_params_base = load_param(pretrained_base,
epoch,
convert=True)
arg_params.update(arg_params_base)
aux_params.update(aux_params_base)
arg_params_ec, aux_params_ec = load_param(
pretrained_ec,
epoch,
convert=True,
argprefix=config.TRAIN.arg_prefix)
arg_params.update(arg_params_ec)
aux_params.update(aux_params_ec)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix)
if config.TRAIN.RESUME:
mod._preload_opt_states = '%s-%04d.states' % (prefix, begin_epoch)
# decide training params
# metric
fcn_loss_metric = metric.FCNLogLossMetric(config.default.frequent *
batch_size)
eval_metrics = mx.metric.CompositeEvalMetric()
for child_metric in [fcn_loss_metric]:
eval_metrics.add(child_metric)
# callback
batch_end_callback = callback.Speedometer(train_data.batch_size,
frequent=args.frequent)
epoch_end_callback = mx.callback.module_checkpoint(
mod, prefix, period=1, save_optimizer_states=True)
# decide learning rate
base_lr = lr
lr_factor = 0.1
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch
]
lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [
int(epoch * len(segdb) / batch_size) for epoch in lr_epoch_diff
]
print 'lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None
}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr,
lr_step):
# 创建logger和对应的输出路径
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
# 特征symbol，从网络sym中获取rpn_cls_score_output
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
# 使能多GPU训练，每一张卡训练一个batch
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
# 加载数据集同时准备训练的imdb，使用+分割不同的图像数据集，比如2007_trainval+2012_trainval
image_sets = [iset for iset in config.dataset.image_set.split('+')]
# load gt roidb加载gt roidb，根据数据集类型，图像集具体子类，数据集根目录和数据集路径，同时配置相关TRAIN为FLIP来增广数据
roidbs = [
load_gt_roidb(config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
flip=config.TRAIN.FLIP) for image_set in image_sets
]
# 合并不同的roidb
roidb = merge_roidb(roidbs)
# 根据配置文件中对应的过滤规则来滤出roi
roidb = filter_roidb(roidb, config)
# load training data
# 加载训练数据，anchor Loader为对应分类和回归的锚点加载，通过对应的roidb，查找对应的正负样本的锚点，该生成器需要参数锚点尺度，ratios和对应的feature的stride
train_data = AnchorLoader(feat_sym,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_stride=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
print('providing maximum shape', max_data_shape, max_label_shape)
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
# 加载并且初始化参数，如果训练中是继续上次的训练，也就是RESUME这一flag设置为True
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
# 从前缀和being_epoch中加载RESUME的arg参数和aux参数，同时需要转换为GPU NDArray
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
# 检查相关参数的shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# create solver
# 创造求解器
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix)
if config.TRAIN.RESUME:
mod._preload_opt_states = '%s-%04d.states' % (prefix, begin_epoch)
# decide training params
# metric
# 以下主要是RPN和RCNN相关的一些评价指标
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
eval_metric = metric.RCNNAccMetric(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
# mxnet中合成的评估指标，可以增加以上所有的评估指标，包括rpn_eval_metrix、rpn_cls_metric、rpn_bbox_metric和rcnn_eval_metric、rcnn_cls_metric、rcnn_bbox_metric
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
for child_metric in [
rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric,
cls_metric, bbox_metric
]:
eval_metrics.add(child_metric)
# callback
# batch后的callback回调以及epoch后的callback回调
# batch_end_callback是在训练一定batch_size后进行的相应回调，回调频率为frequent
batch_end_callback = callback.Speedometer(train_data.batch_size,
frequent=args.frequent)
# means和stds，如果BBOX是类无关的，那么means为复制means两个，否则复制数量为NUM_CLASSES
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
# epoch为一个周期结束后的回调
epoch_end_callback = [
mx.callback.module_checkpoint(mod,
prefix,
period=1,
save_optimizer_states=True),
callback.do_checkpoint(prefix, means, stds)
]
# decide learning rate
# 以下主要根据不同的学习率调整策略来决定学习率，这里如voc中默认的初始lr为0.0005
base_lr = lr
# 学习率调整因子
lr_factor = config.TRAIN.lr_factor
# 学习率调整周期，lr_step一般格式为3, 5，表示在3和5周期中进行学习率调整
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
# 如果当前周期大于begin_epoch那么lr_epoch_diff为epoch-begin_epoch
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch
]
print('lr_epoch', lr_epoch, 'begin_epoch', begin_epoch)
# 通过当前的epoch来计算当前应该具有的lr
lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [
int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
# learning rate调整机制，warmup multi factor scheduler预训练多因子调整器
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
# 优化器参数，包含momentum、wd、lr、lr_scheduler、rescale_grad和clip_gradient
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None
}
if not isinstance(train_data, PrefetchingIter):
print('!!!train_data is not PrefetchingIter!!!')
train_data = PrefetchingIter(train_data)
# train
# 模型训练过程，输入train_data，评估指标包括eval_metrics等一系列指标，每一个epoch结束后进入epoch_end_callback，每一个batch结束后进入batch_end_callback，优化器使用sgd，同时优化参数、输入参数和辅助参数以及begin周期和end周期
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
def pred_eval(predictor,
test_data,
imdb,
cfg,
vis=False,
thresh=1e-3,
logger=None,
ignore_cache=True):
"""
wrapper for calculating offline validation for faster data analysis
in this example, all threshold are set by hand
:param predictor: Predictor
:param test_data: data iterator, must be non-shuffle
:param imdb: image database
:param vis: controls visualization
:param thresh: valid detection threshold
:return:
"""
det_file = os.path.join(imdb.result_path, imdb.name + '_detections.pkl')
if os.path.exists(det_file) and not ignore_cache:
with open(det_file, 'rb') as fid:
all_boxes = cPickle.load(fid)
info_str = imdb.evaluate_detections(all_boxes)
if logger:
logger.info('evaluate detections: \n{}'.format(info_str))
return
assert vis or not test_data.shuffle
data_names = [k[0] for k in test_data.provide_data[0]]
if not isinstance(test_data, PrefetchingIter):
test_data = PrefetchingIter(test_data)
# limit detections to max_per_image over all classes
max_per_image = cfg.TEST.max_per_image
num_images = imdb.num_images
for test_scale_index, test_scale in enumerate(cfg.TEST_SCALES):
det_file_single_scale = os.path.join(
imdb.result_path,
imdb.name + '_detections_' + str(test_scale_index) + '.pkl')
# if os.path.exists(det_file_single_scale):
# continue
cfg.SCALES = [test_scale]
test_data.reset()
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes_single_scale = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
detect_at_single_scale(predictor, data_names, imdb, test_data, cfg,
thresh, vis, all_boxes_single_scale, logger)
with open(det_file_single_scale, 'wb') as f:
cPickle.dump(all_boxes_single_scale,
f,
protocol=cPickle.HIGHEST_PROTOCOL)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
for test_scale_index, test_scale in enumerate(cfg.TEST_SCALES):
det_file_single_scale = os.path.join(
imdb.result_path,
imdb.name + '_detections_' + str(test_scale_index) + '.pkl')
if os.path.exists(det_file_single_scale):
with open(det_file_single_scale, 'rb') as fid:
all_boxes_single_scale = cPickle.load(fid)
for idx_class in range(1, imdb.num_classes):
for idx_im in range(0, num_images):
if len(all_boxes[idx_class][idx_im]) == 0:
all_boxes[idx_class][idx_im] = all_boxes_single_scale[
idx_class][idx_im]
else:
all_boxes[idx_class][idx_im] = np.vstack(
(all_boxes[idx_class][idx_im],
all_boxes_single_scale[idx_class][idx_im]))
for idx_class in range(1, imdb.num_classes):
for idx_im in range(0, num_images):
if cfg.TEST.USE_SOFTNMS:
soft_nms = py_softnms_wrapper(cfg.TEST.SOFTNMS_THRESH,
max_dets=max_per_image)
all_boxes[idx_class][idx_im] = soft_nms(
all_boxes[idx_class][idx_im])
else:
nms = py_nms_wrapper(cfg.TEST.NMS)
keep = nms(all_boxes[idx_class][idx_im])
all_boxes[idx_class][idx_im] = all_boxes[idx_class][idx_im][
keep, :]
if max_per_image > 0:
for idx_im in range(0, num_images):
image_scores = np.hstack([
all_boxes[j][idx_im][:, -1]
for j in range(1, imdb.num_classes)
])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in range(1, imdb.num_classes):
keep = np.where(
all_boxes[j][idx_im][:, -1] >= image_thresh)[0]
all_boxes[j][idx_im] = all_boxes[j][idx_im][keep, :]
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, protocol=cPickle.HIGHEST_PROTOCOL)
info_str = imdb.evaluate_detections(all_boxes)
if logger:
logger.info('evaluate detections: \n{}'.format(info_str))
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr,
lr_step):
logger, final_output_path, _, tensorboard_path = create_env(
config.output_path, args.cfg, config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
print "config.symbol", config.symbol
sym_instance = eval(config.symbol + '.' + config.symbol)()
if config.TRAIN.use_dynamic:
sym_gen = sym_instance.sym_gen(config, is_train=True)
else:
sym = sym_instance.get_symbol(config, is_train=True)
# infer max shape
scales = [(config.TRAIN.crop_size[0], config.TRAIN.crop_size[1])
] if config.TRAIN.enable_crop else config.SCALES
label_stride = config.network.LABEL_STRIDE
network_ratio = config.network.ratio
if config.network.use_context:
if config.network.use_crop_context:
max_data_shape = [
('data',
(config.TRAIN.BATCH_IMAGES, 3, config.TRAIN.crop_size[0],
config.TRAIN.crop_size[1])),
('origin_data', (config.TRAIN.BATCH_IMAGES, 3, 736, 736)),
('rois', (config.TRAIN.BATCH_IMAGES, 5))
]
else:
max_data_shape = [
('data',
(config.TRAIN.BATCH_IMAGES, 3, config.TRAIN.crop_size[0],
config.TRAIN.crop_size[1])),
('origin_data', (config.TRAIN.BATCH_IMAGES, 3,
int(config.SCALES[0][0] * network_ratio),
int(config.SCALES[0][1] * network_ratio))),
('rois', (config.TRAIN.BATCH_IMAGES, 5))
]
else:
if config.TRAIN.enable_crop:
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
config.TRAIN.crop_size[0],
config.TRAIN.crop_size[1]))]
else:
max_data_shape = [
('data',
(config.TRAIN.BATCH_IMAGES, 3,
max([make_divisible(v[0], label_stride) for v in scales]),
max([make_divisible(v[1], label_stride) for v in scales])))
]
if config.network.use_mult_label:
if config.network.use_crop_context:
max_label_shape = [
('label',
(config.TRAIN.BATCH_IMAGES, 1,
make_divisible(config.TRAIN.crop_size[0], label_stride) //
config.network.LABEL_STRIDE,
make_divisible(config.TRAIN.crop_size[1], label_stride) //
config.network.LABEL_STRIDE)),
('origin_label', (config.TRAIN.BATCH_IMAGES, 1, 736, 736))
]
else:
max_label_shape = [
('label',
(config.TRAIN.BATCH_IMAGES, 1,
make_divisible(config.TRAIN.crop_size[0], label_stride) //
config.network.LABEL_STRIDE,
make_divisible(config.TRAIN.crop_size[1], label_stride) //
config.network.LABEL_STRIDE)),
('origin_label', (config.TRAIN.BATCH_IMAGES, 1,
int(config.SCALES[0][0] * network_ratio),
int(config.SCALES[0][1] * network_ratio)))
]
elif config.network.use_metric:
scale_list = config.network.scale_list
scale_name = ['a', 'b', 'c']
if config.network.scale_list == [1, 2, 4]:
scale_name = ['', '', '']
if config.TRAIN.enable_crop:
if config.TRAIN.use_mult_metric:
max_label_shape = [
('label', (config.TRAIN.BATCH_IMAGES, 1,
config.TRAIN.crop_size[0] // label_stride,
config.TRAIN.crop_size[1] // label_stride)),
('metric_label_' + str(scale_list[0]) + scale_name[0],
(config.TRAIN.BATCH_IMAGES, 9, 1,
config.TRAIN.crop_size[0] // label_stride,
config.TRAIN.crop_size[1] // label_stride)),
('metric_label_' + str(scale_list[1]) + scale_name[1],
(config.TRAIN.BATCH_IMAGES, 9, 1,
config.TRAIN.crop_size[0] // label_stride,
config.TRAIN.crop_size[1] // label_stride)),
('metric_label_' + str(scale_list[2]) + scale_name[2],
(config.TRAIN.BATCH_IMAGES, 9, 1,
config.TRAIN.crop_size[0] // label_stride,
config.TRAIN.crop_size[1] // label_stride))
]
else:
max_label_shape = [
('label', (config.TRAIN.BATCH_IMAGES, 1,
config.TRAIN.crop_size[0] // label_stride,
config.TRAIN.crop_size[1] // label_stride)),
('metric_label',
(config.TRAIN.BATCH_IMAGES, 9, 1,
config.TRAIN.crop_size[0] // label_stride,
config.TRAIN.crop_size[1] // label_stride))
]
else:
if config.TRAIN.use_mult_metric:
max_label_shape = [
('label',
(config.TRAIN.BATCH_IMAGES, 1,
max([make_divisible(v[0], label_stride)
for v in scales]) // config.network.LABEL_STRIDE,
max([make_divisible(v[1], label_stride)
for v in scales]) // config.network.LABEL_STRIDE)),
('metric_label_' + str(scale_list[0]) + scale_name[0],
(config.TRAIN.BATCH_IMAGES, 9, 1,
max([make_divisible(v[0], label_stride)
for v in scales]) // config.network.LABEL_STRIDE,
max([make_divisible(v[1], label_stride)
for v in scales]) // config.network.LABEL_STRIDE)),
('metric_label_' + str(scale_list[1]) + scale_name[1],
(config.TRAIN.BATCH_IMAGES, 9, 1,
max([make_divisible(v[0], label_stride)
for v in scales]) // config.network.LABEL_STRIDE,
max([make_divisible(v[1], label_stride)
for v in scales]) // config.network.LABEL_STRIDE)),
('metric_label_' + str(scale_list[2]) + scale_name[2],
(config.TRAIN.BATCH_IMAGES, 9, 1,
max([make_divisible(v[0], label_stride)
for v in scales]) // config.network.LABEL_STRIDE,
max([make_divisible(v[1], label_stride)
for v in scales]) // config.network.LABEL_STRIDE))
]
else:
max_label_shape = [
('label',
(config.TRAIN.BATCH_IMAGES, 1,
max([make_divisible(v[0], label_stride)
for v in scales]) // config.network.LABEL_STRIDE,
max([make_divisible(v[1], label_stride)
for v in scales]) // config.network.LABEL_STRIDE)),
('metric_label',
(config.TRAIN.BATCH_IMAGES, 9, 1,
max([make_divisible(v[0], label_stride)
for v in scales]) // config.network.LABEL_STRIDE,
max([make_divisible(v[1], label_stride)
for v in scales]) // config.network.LABEL_STRIDE))
]
else:
if config.TRAIN.enable_crop:
max_label_shape = [('label',
(config.TRAIN.BATCH_IMAGES, 1,
config.TRAIN.crop_size[0] // label_stride,
config.TRAIN.crop_size[1] // label_stride))]
else:
max_label_shape = [
('label',
(config.TRAIN.BATCH_IMAGES, 1,
max([make_divisible(v[0], label_stride)
for v in scales]) // config.network.LABEL_STRIDE,
max([make_divisible(v[1], label_stride)
for v in scales]) // config.network.LABEL_STRIDE))
]
print "max_label_shapes", max_label_shape
if config.TRAIN.use_dynamic:
sym = sym_gen([max_data_shape])
# setup multi-gpu
input_batch_size = config.TRAIN.BATCH_IMAGES * len(ctx)
NUM_GPUS = len(ctx)
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
segdbs = [
load_gt_segdb(config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
result_path=final_output_path,
flip=True) for image_set in image_sets
]
segdb = merge_segdb(segdbs)
# load training data
train_data = TrainDataLoader(sym,
segdb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
use_context=config.network.use_context,
use_mult_label=config.network.use_mult_label,
use_metric=config.network.use_metric)
# loading val data
if config.TRAIN.eval_data_frequency > 0:
val_image_set = config.dataset.test_image_set
val_root_path = config.dataset.root_path
val_dataset = config.dataset.dataset
val_dataset_path = config.dataset.dataset_path
val_imdb = eval(val_dataset)(val_image_set,
val_root_path,
val_dataset_path,
result_path=final_output_path)
val_segdb = val_imdb.gt_segdb()
val_data = TrainDataLoader(
sym,
val_segdb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
use_context=config.network.use_context,
use_mult_label=config.network.use_mult_label,
use_metric=config.network.use_metric)
else:
val_data = None
# print sym.list_arguments()
print 'providing maximum shape', max_data_shape, max_label_shape
max_data_shape, max_label_shape = train_data.infer_shape(
max_data_shape, max_label_shape)
print 'providing maximum shape', max_data_shape, max_label_shape
# infer shape
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
if config.TRAIN.use_dynamic:
sym = sym_gen([train_data.provide_data_single])
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
nset = set()
for nm in sym.list_arguments():
if nm in nset:
raise ValueError('Duplicate names detected, %s' % str(nm))
nset.add(nm)
# load and initialize params
if config.TRAIN.RESUME:
print 'continue training from ', begin_epoch
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
sym_instance.check_parameter_shapes(arg_params,
aux_params,
data_shape_dict,
is_train=True)
preload_opt_states = load_preload_opt_states(prefix, begin_epoch)
# preload_opt_states = None
else:
print pretrained
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
preload_opt_states = None
if not config.TRAIN.FINTUNE:
fixed_param_names = sym_instance.init_weights(
config, arg_params, aux_params)
sym_instance.check_parameter_shapes(arg_params,
aux_params,
data_shape_dict,
is_train=True)
# check parameter shapes
# sym_instance.check_parameter_shapes(arg_params, aux_params, data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in xrange(NUM_GPUS)],
max_label_shapes=[max_label_shape for _ in xrange(NUM_GPUS)],
fixed_param_prefix=fixed_param_prefix)
# metric
imagecrossentropylossmetric = metric.ImageCrossEntropyLossMetric()
localmetric = metric.LocalImageCrossEntropyLossMetric()
globalmetric = metric.GlobalImageCrossEntropyLossMetric()
pixcelAccMetric = metric.PixcelAccMetric()
eval_metrics = mx.metric.CompositeEvalMetric()
if config.network.use_mult_label:
metric_list = [
imagecrossentropylossmetric, localmetric, globalmetric,
pixcelAccMetric
]
elif config.network.use_metric:
if config.TRAIN.use_crl_ses:
metric_list = [
imagecrossentropylossmetric,
metric.SigmoidPixcelAccMetric(1),
metric.SigmoidPixcelAccMetric(2),
metric.SigmoidPixcelAccMetric(3),
metric.CenterLossMetric(4),
metric.CenterLossMetric(5),
metric.CenterLossMetric(6), pixcelAccMetric
]
elif config.network.use_sigmoid_metric:
if config.TRAIN.use_mult_metric:
metric_list = [
imagecrossentropylossmetric,
metric.SigmoidPixcelAccMetric(1),
metric.SigmoidPixcelAccMetric(2),
metric.SigmoidPixcelAccMetric(3), pixcelAccMetric
]
else:
metric_list = [
imagecrossentropylossmetric,
metric.SigmoidPixcelAccMetric(), pixcelAccMetric
]
else:
if config.TRAIN.use_mult_metric:
metric_list = [
imagecrossentropylossmetric,
metric.MetricLossMetric(1),
metric.MetricLossMetric(2),
metric.MetricLossMetric(3), pixcelAccMetric
]
else:
metric_list = [
imagecrossentropylossmetric,
metric.MetricLossMetric(1), pixcelAccMetric
]
elif config.network.mult_loss:
metric_list = [
imagecrossentropylossmetric,
metric.MImageCrossEntropyLossMetric(1),
metric.MImageCrossEntropyLossMetric(2), pixcelAccMetric
]
elif config.TRAIN.use_center:
if config.TRAIN.use_one_center:
metric_list = [
imagecrossentropylossmetric, pixcelAccMetric,
metric.CenterLossMetric(1)
]
else:
metric_list = [
imagecrossentropylossmetric, pixcelAccMetric,
metric.CenterLossMetric(1),
metric.CenterLossMetric(2),
metric.CenterLossMetric(3)
]
else:
metric_list = [imagecrossentropylossmetric, pixcelAccMetric]
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
for child_metric in metric_list:
eval_metrics.add(child_metric)
# callback
if False:
batch_end_callback = [
callback.Speedometer(train_data.batch_size,
frequent=args.frequent),
callback.TensorboardCallback(tensorboard_path,
prefix="train/batch")
]
epoch_end_callback = mx.callback.module_checkpoint(
mod, prefix, period=1, save_optimizer_states=True)
shared_tensorboard = batch_end_callback[1]
epoch_end_metric_callback = callback.TensorboardCallback(
tensorboard_path,
shared_tensorboard=shared_tensorboard,
prefix="train/epoch")
eval_end_callback = callback.TensorboardCallback(
tensorboard_path,
shared_tensorboard=shared_tensorboard,
prefix="val/epoch")
lr_callback = callback.LrCallback(
tensorboard_path,
shared_tensorboard=shared_tensorboard,
prefix='train/batch')
else:
batch_end_callback = [
callback.Speedometer(train_data.batch_size, frequent=args.frequent)
]
epoch_end_callback = mx.callback.module_checkpoint(
mod, prefix, period=1, save_optimizer_states=True)
epoch_end_metric_callback = None
eval_end_callback = None
#decide learning rate
base_lr = lr
lr_factor = 0.1
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch
]
lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [
int(epoch * len(segdb) / input_batch_size) for epoch in lr_epoch_diff
]
print 'lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters
if config.TRAIN.lr_type == "MultiStage":
lr_scheduler = LinearWarmupMultiStageScheduler(
lr_iters,
lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step,
args.frequent,
stop_lr=lr * 0.01)
elif config.TRAIN.lr_type == "MultiFactor":
lr_scheduler = LinearWarmupMultiFactorScheduler(
lr_iters, lr_factor, config.TRAIN.warmup, config.TRAIN.warmup_lr,
config.TRAIN.warmup_step, args.frequent)
if config.TRAIN.optimizer == "sgd":
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None
}
optimizer = SGD(**optimizer_params)
elif config.TRAIN.optimizer == "adam":
optimizer_params = {
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None
}
optimizer = Adam(**optimizer_params)
print "optimizer adam"
freeze_layer_pattern = config.TRAIN.FIXED_PARAMS_PATTERN
if freeze_layer_pattern.strip():
args_lr_mult = {}
re_prog = re.compile(freeze_layer_pattern)
if freeze_layer_pattern:
fixed_param_names = [
name for name in sym.list_arguments() if re_prog.match(name)
]
print "============================"
print "fixed_params_names:"
print(fixed_param_names)
for name in fixed_param_names:
args_lr_mult[name] = config.TRAIN.FIXED_PARAMS_PATTERN_LR_MULT
print "============================"
else:
args_lr_mult = {}
optimizer.set_lr_mult(args_lr_mult)
# data_shape_dict = dict(train_data.provide_data_single + train_data.provide_label_single)
# if config.TRAIN.use_dynamic:
# sym = sym_gen([train_data.provide_data_single])
# pprint.pprint(data_shape_dict)
# sym_instance.infer_shape(data_shape_dict)
if not isinstance(train_data, PrefetchingIter) and config.TRAIN.use_thread:
train_data = PrefetchingIter(train_data)
if val_data:
if not isinstance(val_data, PrefetchingIter):
val_data = PrefetchingIter(val_data)
if Debug:
monitor = mx.monitor.Monitor(1)
else:
monitor = None
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
eval_end_callback=eval_end_callback,
epoch_end_metric_callback=epoch_end_metric_callback,
optimizer=optimizer,
eval_data=val_data,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch,
allow_missing=begin_epoch == 0,
allow_extra=True,
monitor=monitor,
preload_opt_states=preload_opt_states,
eval_data_frequency=config.TRAIN.eval_data_frequency)
def pred_eval(gpu_id,
feat_predictors,
aggr_predictors_feat_array,
aggr_predictors_rfcn,
test_data,
imdb,
cfg,
vis=False,
thresh=1e-3,
logger=None,
ignore_cache=True):
"""
wrapper for calculating offline validation for faster data analysis
in this example, all threshold are set by hand
:param predictor: Predictor
:param test_data: data iterator, must be non-shuffle
:param imdb: image database
:param vis: controls visualization
:param thresh: valid detection threshold
:return:
"""
det_file = os.path.join(imdb.result_path, imdb.name + '_' + str(gpu_id))
if cfg.TEST.SEQ_NMS == True:
det_file += '_raw'
print 'det_file=', det_file
if os.path.exists(det_file) and not ignore_cache:
with open(det_file, 'rb') as fid:
all_boxes, frame_ids = cPickle.load(fid)
return all_boxes, frame_ids
assert vis or not test_data.shuffle
data_names = [k[0] for k in test_data.provide_data[0]]
num_images = test_data.size
roidb_frame_ids = [x['frame_id'] for x in test_data.roidb]
if not isinstance(test_data, PrefetchingIter):
test_data = PrefetchingIter(test_data)
nms = py_nms_wrapper(cfg.TEST.NMS)
# limit detections to max_per_image over all classes
max_per_image = cfg.TEST.max_per_image
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
frame_ids = np.zeros(num_images, dtype=np.int)
roidb_idx = -1
roidb_offset = -1
idx = 0
all_frame_interval = cfg.TEST.KEY_FRAME_INTERVAL * 2 + 1
data_time, net_time, post_time, seq_time = 0.0, 0.0, 0.0, 0.0
t = time.time()
# loop through all the test data
for im_info, key_frame_flag, data_batch in test_data:
t1 = time.time() - t
t = time.time()
#################################################
# new video #
#################################################
# empty lists and append padding images
# do not do prediction yet
if key_frame_flag == 0:
roidb_idx += 1
roidb_offset = -1
# init data_lsit and feat_list for a new video
data_list = deque(maxlen=all_frame_interval)
feat_list = deque(maxlen=all_frame_interval)
image, feat = get_resnet_output(feat_predictors, data_batch,
data_names)
# append cfg.TEST.KEY_FRAME_INTERVAL+1 padding images in the front (first frame)
while len(data_list) < cfg.TEST.KEY_FRAME_INTERVAL + 1:
data_list.append(image)
preprocess(feat, idx)
feat_list.append(feat)
get_feature_init()
#################################################
# main part of the loop #
#################################################
elif key_frame_flag == 2:
# keep appending data to the lists without doing prediction until the lists contain 2 * cfg.TEST.KEY_FRAME_INTERVAL objects
if len(data_list) < all_frame_interval - 1:
image, feat = get_resnet_output(feat_predictors, data_batch,
data_names)
data_list.append(image)
preprocess(feat, idx)
feat_list.append(feat)
else:
scales = [iim_info[0, 2] for iim_info in im_info]
image, feat = get_resnet_output(feat_predictors, data_batch,
data_names)
data_list.append(image)
preprocess(feat, idx)
feat_list.append(feat)
prepare_data(data_list, feat_list, data_batch)
aggr_feat = im_detect_feat(aggr_predictors_feat_array,
data_batch, data_names, scales, cfg,
cfg.TEST.INTERVALS)
if cfg.TEST.SELECT_FEATURES:
aggr_feat = get_feature(
[f[0].asnumpy() for f in aggr_feat],
[int(x) for x in im_info[0][0, :2]])
aggr_feat = [mx.nd.array(aggr_feat)]
else:
aggr_feat = list(aggr_feat)[-1]
prepare_aggregation(aggr_feat, data_batch)
pred_result = im_detect_rfcn(aggr_predictors_rfcn, data_batch,
data_names, scales, cfg)
roidb_offset += 1
frame_ids[idx] = roidb_frame_ids[roidb_idx] + roidb_offset
t2 = time.time() - t
t = time.time()
process_pred_result(
pred_result, imdb, thresh, cfg, nms, all_boxes, idx,
max_per_image, vis,
data_list[cfg.TEST.KEY_FRAME_INTERVAL].asnumpy(), scales)
idx += test_data.batch_size
t3 = time.time() - t
t = time.time()
data_time += t1
net_time += t2
post_time += t3
print 'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, num_images, data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size)
if logger:
logger.info(
'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.
format(idx, num_images,
data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size))
#################################################
# end part of a video #
#################################################
elif key_frame_flag == 1: # last frame of a video
end_counter = 0
image, feat = get_resnet_output(feat_predictors, data_batch,
data_names)
while end_counter < cfg.TEST.KEY_FRAME_INTERVAL + 1:
data_list.append(image)
preprocess(feat, idx)
feat_list.append(feat)
prepare_data(data_list, feat_list, data_batch)
aggr_feat = im_detect_feat(aggr_predictors_feat_array,
data_batch, data_names, scales, cfg,
cfg.TEST.INTERVALS)
if cfg.TEST.SELECT_FEATURES:
aggr_feat = get_feature(
[f[0].asnumpy() for f in aggr_feat],
[int(x) for x in im_info[0][0, :2]])
aggr_feat = [mx.nd.array(aggr_feat)]
else:
aggr_feat = list(aggr_feat)[-1]
prepare_aggregation(aggr_feat, data_batch)
pred_result = im_detect_rfcn(aggr_predictors_rfcn, data_batch,
data_names, scales, cfg)
roidb_offset += 1
frame_ids[idx] = roidb_frame_ids[roidb_idx] + roidb_offset
t2 = time.time() - t
t = time.time()
process_pred_result(
pred_result, imdb, thresh, cfg, nms, all_boxes, idx,
max_per_image, vis,
data_list[cfg.TEST.KEY_FRAME_INTERVAL].asnumpy(), scales)
idx += test_data.batch_size
t3 = time.time() - t
t = time.time()
data_time += t1
net_time += t2
post_time += t3
print 'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, num_images, data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size)
if logger:
logger.info(
'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.
format(idx, num_images,
data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size))
end_counter += 1
get_feature_init()
with open(det_file, 'wb') as f:
cPickle.dump((all_boxes, frame_ids),
f,
protocol=cPickle.HIGHEST_PROTOCOL)
return all_boxes, frame_ids
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr, lr_step):
# 创建logger和对应的输出路径
logger, final_output_path = create_logger(config.output_path, args.cfg, config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'), final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
# 特征symbol，从网络sym中获取rpn_cls_score_output
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
# 使能多GPU训练，每一张卡训练一个batch
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
# 加载数据集同时准备训练的imdb，使用+分割不同的图像数据集，比如2007_trainval+2012_trainval
image_sets = [iset for iset in config.dataset.image_set.split('+')]
# load gt roidb加载gt roidb，根据数据集类型，图像集具体子类，数据集根目录和数据集路径，同时配置相关TRAIN为FLIP来增广数据
roidbs = [load_gt_roidb(config.dataset.dataset, image_set, config.dataset.root_path, config.dataset.dataset_path,
flip=config.TRAIN.FLIP)
for image_set in image_sets]
# 合并不同的roidb
roidb = merge_roidb(roidbs)
# 根据配置文件中对应的过滤规则来滤出roi
roidb = filter_roidb(roidb, config)
# load training data
# 加载训练数据，anchor Loader为对应分类和回归的锚点加载，通过对应的roidb，查找对应的正负样本的锚点，该生成器需要参数锚点尺度，ratios和对应的feature的stride
train_data = AnchorLoader(feat_sym, roidb, config, batch_size=input_batch_size, shuffle=config.TRAIN.SHUFFLE, ctx=ctx,
feat_stride=config.network.RPN_FEAT_STRIDE, anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS, aspect_grouping=config.TRAIN.ASPECT_GROUPING)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
print('providing maximum shape', max_data_shape, max_label_shape)
data_shape_dict = dict(train_data.provide_data_single + train_data.provide_label_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params, data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(sym, data_names=data_names, label_names=label_names,
logger=logger, context=ctx, max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)], fixed_param_prefix=fixed_param_prefix)
if config.TRAIN.RESUME:
mod._preload_opt_states = '%s-%04d.states'%(prefix, begin_epoch)
# decide training params
# metric
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
eval_metric = metric.RCNNAccMetric(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
for child_metric in [rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric]:
eval_metrics.add(child_metric)
# callback
batch_end_callback = callback.Speedometer(train_data.batch_size, frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS), 2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS), 2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = [mx.callback.module_checkpoint(mod, prefix, period=1, save_optimizer_states=True), callback.do_checkpoint(prefix, means, stds)]
# decide learning rate
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch]
lr = base_lr * (lr_factor ** (len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor, config.TRAIN.warmup, config.TRAIN.warmup_lr, config.TRAIN.warmup_step)
# optimizer
optimizer_params = {'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# train
mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback, kvstore=config.default.kvstore,
optimizer='sgd', optimizer_params=optimizer_params,
arg_params=arg_params, aux_params=aux_params, begin_epoch=begin_epoch, num_epoch=end_epoch)
def pred_eval_dota_rotbox_Rroi(predictor,
test_data,
imdb,
cfg,
vis=False,
draw=False,
thresh=1e-3,
logger=None,
ignore_cache=True):
"""
wrapper for calculating offline validation for faster data analysis
in this example, all threshold are set by hand
:param predictor: Predictor
:param test_data: data iterator, must be non-shuffle
:param imdb: image database
:param vis: controls visualization
:param thresh: valid detection threshold
:return:
"""
# ignore_cache = True
# pdb.set_trace()
det_file = os.path.join(imdb.result_path, imdb.name + '_detections.pkl')
if os.path.exists(det_file) and not ignore_cache:
with open(det_file, 'rb') as fid:
all_boxes = cPickle.load(fid)
# imdb.count_ar()
#imdb.check_transform()
# imdb.draw_gt_and_detections(all_boxes, thresh=0.1)
info_str = imdb.evaluate_detections(all_boxes, ignore_cache)
if logger:
logger.info('evaluate detections: \n{}'.format(info_str))
return
assert vis or not test_data.shuffle
data_names = [k[0] for k in test_data.provide_data[0]]
if not isinstance(test_data, PrefetchingIter):
test_data = PrefetchingIter(test_data)
#nms = py_nms_wrapper(cfg.TEST.NMS)
# limit detections to max_per_image over all classes
max_per_image = cfg.TEST.max_per_image
num_images = imdb.num_images
# all detections are collected into:
# all_boxes[cls][image] = N x 9 array of detections in
# (x1, y1, x2, y2, x3, y3, x4, y4, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
idx = 0
data_time, net_time, post_time = 0.0, 0.0, 0.0
t = time.time()
for im_info, data_batch in test_data:
t1 = time.time() - t
t = time.time()
scales = [iim_info[0, 2] for iim_info in im_info]
# scores_all, boxes_all, data_dict_all= im_detect_poly(predictor, data_batch, data_names, scales, cfg)
scores_all, boxes_all, data_dict_all = im_detect_rotbox_Rroi(
predictor, data_batch, data_names, scales, cfg)
# pdb.set_trace()
t2 = time.time() - t
t = time.time()
for delta, (scores, boxes, data_dict) in enumerate(
zip(scores_all, boxes_all, data_dict_all)):
# idx = int(data_dict['im_index'])-1
for j in range(1, imdb.num_classes):
indexes = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[indexes, j, np.newaxis]
cls_boxes = boxes[
indexes,
8:16] if cfg.network.RRoI_CLASS_AGNOSTIC else boxes[
indexes, j * 8:(j + 1) * 8]
cls_quadrangle_dets = np.hstack((cls_boxes, cls_scores))
# keep = nms(cls_dets)
# TODO: check the thresh
keep = py_cpu_nms_poly(cls_quadrangle_dets, 0.3)
# pdb.set_trace()
all_boxes[j][idx + delta] = cls_quadrangle_dets[keep, :]
# all_boxes[j][idx+delta]=cls_quadrangle_dets
if max_per_image > 0:
image_scores = np.hstack([
all_boxes[j][idx + delta][:, -1]
for j in range(1, imdb.num_classes)
])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in range(1, imdb.num_classes):
keep = np.where(
all_boxes[j][idx + delta][:,
-1] >= image_thresh)[0]
all_boxes[j][idx +
delta] = all_boxes[j][idx +
delta][keep, :]
if vis:
boxes_this_image = [[]] + [
all_boxes[j][idx + delta]
for j in range(1, imdb.num_classes)
]
vis_all_detection(data_dict['data'].asnumpy(),
boxes_this_image, imdb.classes,
scales[delta], cfg)
if draw:
if not os.path.isdir(cfg.TEST.save_img_path):
os.mkdir(cfg.TEST.save_img_path)
path = os.path.join(cfg.TEST.save_img_path, str(idx) + '.jpg')
boxes_this_image = [[]] + [
all_boxes[j][idx + delta]
for j in range(1, imdb.num_classes)
]
im = draw_all_poly_detection(data_dict['data'].asnumpy(),
boxes_this_image,
imdb.classes,
scales[delta],
cfg,
threshold=0.2)
print path
cv2.imwrite(path, im)
idx += test_data.batch_size
t3 = time.time() - t
t = time.time()
data_time += t1
net_time += t2
post_time += t3
print 'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, imdb.num_images, data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size)
if logger:
logger.info(
'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, imdb.num_images,
data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size))
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, protocol=cPickle.HIGHEST_PROTOCOL)
# imdb.draw_gt_and_detections(all_boxes, thresh=0.1)
info_str = imdb.evaluate_detections(all_boxes, ignore_cache)
if logger:
logger.info('evaluate detections: \n{}'.format(info_str))
def pred_eval(gpu_id,
feat_predictors,
aggr_predictors,
test_data,
imdb,
cfg,
vis=False,
thresh=1e-3,
logger=None,
ignore_cache=True):
"""
wrapper for calculating offline validation for faster data analysis
in this example, all threshold are set by hand
:param predictor: Predictor
:param test_data: data iterator, must be non-shuffle
:param imdb: image database
:param vis: controls visualization
:param thresh: valid detection threshold
:return:
"""
det_file = os.path.join(imdb.result_path, imdb.name + '_' + str(gpu_id))
if cfg.TEST.SEQ_NMS == True:
det_file += '_raw'
print 'det_file=', det_file
if os.path.exists(det_file) and not ignore_cache:
with open(det_file, 'rb') as fid:
all_boxes, frame_ids = cPickle.load(fid)
return all_boxes, frame_ids
assert vis or not test_data.shuffle
data_names = [k[0] for k in test_data.provide_data[0]
] # data, img_info, data_cache, feat_cache
num_images = test_data.size # 43859, 44033, 43812, 44422
roidb_frame_ids = [x['frame_id'] for x in test_data.roidb]
print 'roidb_frame_ids:{}'.format(roidb_frame_ids)
if not isinstance(test_data, PrefetchingIter):
test_data = PrefetchingIter(test_data)
nms = py_nms_wrapper(cfg.TEST.NMS)
# limit detections to max_per_image over all classes
max_per_image = cfg.TEST.max_per_image
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
frame_ids = np.zeros(num_images, dtype=np.int)
roidb_idx = -1
roidb_offset = -1
idx = 0
if cfg.TRAIN.E2E_NAME == 'off':
all_frame_interval = 2
else:
all_frame_interval = cfg.TEST.KEY_FRAME_INTERVAL * 2 + 1
data_time, net_time, post_time, seq_time = 0.0, 0.0, 0.0, 0.0
t = time.time()
# loop through all the test data
for im_info, key_frame_flag, data_batch in test_data:
print 'key_frame_flag:{}'.format(key_frame_flag)
t1 = time.time() - t
t = time.time()
if cfg.TRAIN.E2E_NAME == 'off':
if key_frame_flag == 0:
roidb_idx += 1
roidb_offset = -1
data_list = deque(maxlen=all_frame_interval)
feat_list = deque(maxlen=all_frame_interval)
scales = [iim_info[0, 2] for iim_info in im_info]
image, feat = get_resnet_output(feat_predictors, data_batch,
data_names, cfg)
data_list.append(image)
feat_list.append(feat)
prepare_data(data_list, feat_list, data_batch, logger)
pred_result = im_detect(aggr_predictors, data_batch,
data_names, scales, cfg)
roidb_offset += 1
frame_ids[idx] = roidb_frame_ids[roidb_idx] + roidb_offset
print 'roidb_idx:{}'.format(roidb_idx)
print 'roidb_frames_ids[roidb_idx]:{}'.format(
roidb_frame_ids[roidb_idx])
print 'roidb_offset:{}'.format(roidb_offset)
print 'idx:{}'.format(idx)
print 'frame_ids[idx]:{}'.format(frame_ids[idx])
t2 = time.time() - t
t = time.time()
file_name = imdb.get_result_file_template().format('bind')
with open(file_name, 'a+') as f:
for scores, boxes, data_dict in pred_result:
for j in range(1, imdb.num_classes):
# indexes = np.where(scores[:, j] > thresh)[0]
# cls_scores = scores[indexes, j]
# if cls_scores is not None:
for m in range(len(scores[:, j])):
f.write(
'{:d} {:d} {:f} {:.2f} {:.2f} {:.2f} {:.2f}\n'
.format(frame_ids[idx], j, scores[m, j],
boxes[m, 4], boxes[m, 5],
boxes[m, 6], boxes[m, 7]))
if cfg.TRAIN.E2E_NAME == 'off':
process_pred_result(pred_result, imdb, thresh, cfg, nms,
all_boxes, idx, max_per_image, vis,
image.asnumpy(), scales)
else:
process_pred_result(
pred_result, imdb, thresh, cfg, nms, all_boxes, idx,
max_per_image, vis,
data_list[cfg.TEST.KEY_FRAME_INTERVAL].asnumpy(),
scales)
idx += test_data.batch_size #1
t3 = time.time() - t
t = time.time()
data_time += t1
net_time += t2
post_time += t3
print 'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, num_images, data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size)
if logger:
logger.info(
'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.
format(idx, num_images,
data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size))
elif key_frame_flag == 2:
data_list = deque(maxlen=all_frame_interval)
feat_list = deque(maxlen=all_frame_interval)
scales = [iim_info[0, 2] for iim_info in im_info]
image, feat = get_resnet_output(feat_predictors, data_batch,
data_names, cfg)
data_list.append(image)
feat_list.append(feat)
prepare_data(data_list, feat_list, data_batch, logger)
pred_result = im_detect(aggr_predictors, data_batch,
data_names, scales, cfg)
roidb_offset += 1
frame_ids[idx] = roidb_frame_ids[roidb_idx] + roidb_offset
print 'roidb_idx:{}'.format(roidb_idx)
print 'roidb_frames_ids[roidb_idx]:{}'.format(roidb_idx)
print 'roidb_offset:{}'.format(roidb_offset)
print 'frame_ids[idx]:{}'.format(idx)
t2 = time.time() - t
t = time.time()
file_name = imdb.get_result_file_template().format('bind')
with open(file_name, 'a+') as f:
for scores, boxes, data_dict in pred_result:
for j in range(1, imdb.num_classes):
# indexes = np.where(scores[:, j] > thresh)[0]
# cls_scores = scores[indexes, j]
# if cls_scores is not None:
for m in range(len(scores[:, j])):
f.write(
'{:d} {:d} {:f} {:.2f} {:.2f} {:.2f} {:.2f}\n'
.format(frame_ids[idx], j, scores[m, j],
boxes[m, 4], boxes[m, 5],
boxes[m, 6], boxes[m, 7]))
if cfg.TRAIN.E2E_NAME == 'off':
process_pred_result(pred_result, imdb, thresh, cfg, nms,
all_boxes, idx, max_per_image, vis,
image.asnumpy(), scales)
else:
process_pred_result(
pred_result, imdb, thresh, cfg, nms, all_boxes, idx,
max_per_image, vis,
data_list[cfg.TEST.KEY_FRAME_INTERVAL].asnumpy(),
scales)
idx += test_data.batch_size #1
t3 = time.time() - t
t = time.time()
data_time += t1
net_time += t2
post_time += t3
print 'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, num_images, data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size)
if logger:
logger.info(
'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.
format(idx, num_images,
data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size))
else:
#################################################
# new video #
#################################################
# empty lists and append padding images
# do not do prediction yet
if key_frame_flag == 0:
roidb_idx += 1
roidb_offset = -1
# init data_lsit and feat_list for a new video
data_list = deque(maxlen=all_frame_interval)
feat_list = deque(maxlen=all_frame_interval)
image, feat = get_resnet_output(feat_predictors, data_batch,
data_names, cfg)
# append cfg.TEST.KEY_FRAME_INTERVAL+1 padding images in the front (first frame)
if cfg.TRAIN.E2E_NAME == 'off': # changed by zy
key_frame_interval = 0
elif cfg.TRAIN.E2E_NAME == 'base':
key_frame_interval = -1
else:
key_frame_interval = cfg.TEST.KEY_FRAME_INTERVAL
if logger:
logger.info(
'key_frame_interval:{}\n'.format(key_frame_interval))
while len(data_list) < key_frame_interval + 1: #????
print "RR, no append___________________________________-"
data_list.append(image)
feat_list.append(feat)
#################################################
# main part of the loop #
#################################################
elif key_frame_flag == 2:
# keep appending data to the lists without doing prediction until the lists contain 2 * cfg.TEST.KEY_FRAME_INTERVAL objects
if len(data_list) < all_frame_interval - 1: # off:1
image, feat = get_resnet_output(feat_predictors,
data_batch, data_names,
cfg)
data_list.append(image)
feat_list.append(feat)
else:
scales = [iim_info[0, 2] for iim_info in im_info]
image, feat = get_resnet_output(feat_predictors,
data_batch, data_names,
cfg)
data_list.append(image)
feat_list.append(feat)
prepare_data(data_list, feat_list, data_batch, logger)
print 'pred_eval,flag=2:{}'.format(
data_batch.data[0][-2].shape)
if logger:
logger.info('pred_eval,flag=2:{}'.format(
data_batch.data[0][-2].shape))
pred_result = im_detect(aggr_predictors, data_batch,
data_names, scales, cfg)
roidb_offset += 1
frame_ids[idx] = roidb_frame_ids[roidb_idx] + roidb_offset
t2 = time.time() - t
t = time.time()
file_name = imdb.get_result_file_template().format('bind')
with open(file_name, 'a+') as f:
for scores, boxes, data_dict in pred_result:
for j in range(1, imdb.num_classes):
# indexes = np.where(scores[:, j] > thresh)[0]
# cls_scores = scores[indexes, j]
# if cls_scores is not None:
for m in range(len(scores[:, j])):
f.write(
'{:d} {:d} {:f} {:.2f} {:.2f} {:.2f} {:.2f}\n'
.format(frame_ids[idx], j, scores[m,
j],
boxes[m, 4], boxes[m, 5],
boxes[m, 6], boxes[m, 7]))
if cfg.TRAIN.E2E_NAME == 'off':
process_pred_result(pred_result, imdb, thresh, cfg,
nms, all_boxes, idx, max_per_image,
vis, data_list[0].asnumpy(),
scales)
else:
process_pred_result(
pred_result, imdb, thresh, cfg, nms, all_boxes,
idx, max_per_image, vis,
data_list[cfg.TEST.KEY_FRAME_INTERVAL].asnumpy(),
scales)
idx += test_data.batch_size
t3 = time.time() - t
t = time.time()
data_time += t1
net_time += t2
post_time += t3
print 'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, num_images,
data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size)
if logger:
logger.info(
'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'
.format(idx, num_images,
data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size))
#################################################
# end part of a video #
#################################################
elif key_frame_flag == 1: # last frame of a video
end_counter = 0
# image, feat = get_resnet_output(feat_predictors, data_batch, data_names, cfg)
if cfg.TRAIN.E2E_NAME == 'off':
key_frame_interval = 1
else:
key_frame_interval = cfg.TEST.KEY_FRAME_INTERVAL
if len(data_list) == all_frame_interval - 1:
# while end_counter < key_frame_interval + 1:
image, feat = get_resnet_output(feat_predictors,
data_batch, data_names,
cfg)
data_list.append(image)
feat_list.append(feat)
prepare_data(data_list, feat_list, data_batch, logger)
print 'pred_eval,flag=1:{}'.format(
data_batch.data[0][-2].shape)
if logger:
logger.info('pred_eval,flag=1:{}'.format(
data_batch.data[0][-2].shape))
pred_result = im_detect(aggr_predictors, data_batch,
data_names, scales, cfg)
roidb_offset += 1
frame_ids[idx] = roidb_frame_ids[roidb_idx] + roidb_offset
t2 = time.time() - t
t = time.time()
file_name = imdb.get_result_file_template().format('bind')
with open(file_name, 'a+') as f:
for scores, boxes, data_dict in pred_result:
for j in range(1, imdb.num_classes):
# indexes = np.where(scores[:, j] > thresh)[0]
# cls_scores = scores[indexes, j]
# if cls_scores is not None:
for m in range(len(scores[:, j])):
f.write(
'{:d} {:d} {:f} {:.2f} {:.2f} {:.2f} {:.2f}\n'
.format(frame_ids[idx], j, scores[m,
j],
boxes[m, 4], boxes[m, 5],
boxes[m, 6], boxes[m, 7]))
if cfg.TRAIN.E2E_NAME == 'off':
process_pred_result(pred_result, imdb, thresh, cfg,
nms, all_boxes, idx, max_per_image,
vis, data_list[0].asnumpy(),
scales)
else:
process_pred_result(
pred_result, imdb, thresh, cfg, nms, all_boxes,
idx, max_per_image, vis,
data_list[cfg.TEST.KEY_FRAME_INTERVAL].asnumpy(),
scales)
idx += test_data.batch_size
t3 = time.time() - t
t = time.time()
data_time += t1
net_time += t2
post_time += t3
print 'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, num_images,
data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size)
if logger:
logger.info(
'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'
.format(idx, num_images,
data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size))
# end_counter += 1
with open(det_file, 'wb') as f:
cPickle.dump((all_boxes, frame_ids),
f,
protocol=cPickle.HIGHEST_PROTOCOL)
return all_boxes, frame_ids
def train_rcnn(cfg, dataset, image_set, root_path, dataset_path,
frequent, kvstore, flip, shuffle, resume,
ctx, pretrained, epoch, prefix, begin_epoch, end_epoch,
train_shared, lr, lr_step, proposal, logger=None, output_path=None):
mx.random.seed(0)
np.random.seed(0)
# set up logger
if not logger:
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# load symbol
sym_instance = eval(cfg.symbol + '.' + cfg.symbol)()
sym = sym_instance.get_symbol_rcnn(cfg, is_train=True)
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = cfg.TRAIN.BATCH_IMAGES * batch_size
# print cfg
pprint.pprint(cfg)
logger.info('training rcnn cfg:{}\n'.format(pprint.pformat(cfg)))
rpn_path = cfg.dataset.proposal_cache
# load dataset and prepare imdb for training
image_sets = [iset for iset in image_set.split('+')]
roidbs = [load_proposal_roidb(dataset, image_set, root_path, dataset_path,
proposal=proposal, append_gt=True, flip=flip, result_path=output_path,
rpn_path=rpn_path, top_roi=cfg.TRAIN.TOP_ROIS)
for image_set in image_sets]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, cfg)
means, stds = add_bbox_regression_targets(roidb, cfg)
# load training data
train_data = ROIIter(roidb, cfg, batch_size=input_batch_size, shuffle=shuffle,
ctx=ctx, aspect_grouping=cfg.TRAIN.ASPECT_GROUPING)
# infer max shape
max_height = max([v[0] for v in cfg.SCALES])
max_width = max([v[1] for v in cfg.SCALES])
paded_max_height = max_height + cfg.network.IMAGE_STRIDE - max_height % cfg.network.IMAGE_STRIDE
paded_max_width = max_width + cfg.network.IMAGE_STRIDE - max_width % (cfg.network.IMAGE_STRIDE)
max_data_shape = [('data', (cfg.TRAIN.BATCH_IMAGES, 3, paded_max_height, paded_max_width))]
# infer shape
data_shape_dict = dict(train_data.provide_data_single + train_data.provide_label_single)
sym_instance.infer_shape(data_shape_dict)
# print shape
pprint.pprint(sym_instance.arg_shape_dict)
logging.info(pprint.pformat(sym_instance.arg_shape_dict))
max_batch_roi = cfg.TRAIN.TOP_ROIS if cfg.TRAIN.BATCH_ROIS == -1 else cfg.TRAIN.BATCH_ROIS
num_class = 2 if cfg.CLASS_AGNOSTIC else cfg.dataset.NUM_CLASSES
max_label_shape = [('label', (cfg.TRAIN.BATCH_IMAGES, max_batch_roi)),
('bbox_target', (cfg.TRAIN.BATCH_IMAGES, max_batch_roi, num_class * 4)),
('bbox_weight', (cfg.TRAIN.BATCH_IMAGES, max_batch_roi, num_class * 4))]
if cfg.network.USE_NONGT_INDEX:
max_label_shape.append(('nongt_index', (2000,)))
if cfg.network.ROIDispatch:
max_data_shape.append(('rois_0', (cfg.TRAIN.BATCH_IMAGES, max_batch_roi / 4, 5)))
max_data_shape.append(('rois_1', (cfg.TRAIN.BATCH_IMAGES, max_batch_roi / 4, 5)))
max_data_shape.append(('rois_2', (cfg.TRAIN.BATCH_IMAGES, max_batch_roi / 4, 5)))
max_data_shape.append(('rois_3', (cfg.TRAIN.BATCH_IMAGES, max_batch_roi / 4, 5)))
else:
max_data_shape.append(('rois', (cfg.TEST.PROPOSAL_POST_NMS_TOP_N + 30, 5)))
#dot = mx.viz.plot_network(sym, node_attrs={'shape': 'rect', 'fixedsize': 'false'})
#dot.render(os.path.join('./output/rcnn/network_vis', cfg.symbol + cfg.TRAIN.model_prefix))
# load and initialize params
if resume:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight_rcnn(cfg, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params, data_shape_dict)
# prepare training
# create solver
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
if train_shared:
fixed_param_prefix = cfg.network.FIXED_PARAMS_SHARED
else:
fixed_param_prefix = cfg.network.FIXED_PARAMS
if cfg.network.ROIDispatch:
mod = MutableModule(sym, data_names=data_names, label_names=label_names,
logger=logger, context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix)
else:
mod = MutableModule(sym, data_names=data_names, label_names=label_names,
logger=logger, context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix)
if cfg.TRAIN.RESUME:
mod._preload_opt_states = '%s-%04d.states' % (prefix, begin_epoch)
# decide training params
# metric
eval_metric = metric.RCNNAccMetric(cfg)
cls_metric = metric.RCNNLogLossMetric(cfg)
bbox_metric = metric.RCNNL1LossMetric(cfg)
eval_metrics = mx.metric.CompositeEvalMetric()
for child_metric in [eval_metric, cls_metric, bbox_metric]:
eval_metrics.add(child_metric)
if cfg.TRAIN.LEARN_NMS:
eval_metrics.add(metric.NMSLossMetric(cfg, 'pos'))
eval_metrics.add(metric.NMSLossMetric(cfg, 'neg'))
eval_metrics.add(metric.NMSAccMetric(cfg))
# callback
batch_end_callback = callback.Speedometer(train_data.batch_size, frequent=frequent)
epoch_end_callback = [mx.callback.module_checkpoint(mod, prefix, period=1, save_optimizer_states=True),
callback.do_checkpoint(prefix, means, stds)]
# decide learning rate
base_lr = lr
lr_factor = cfg.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch]
lr = base_lr * (lr_factor ** (len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor, cfg.TRAIN.warmup, cfg.TRAIN.warmup_lr,
cfg.TRAIN.warmup_step)
# optimizer
optimizer_params = {'momentum': cfg.TRAIN.momentum,
'wd': cfg.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None}
# train
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback, kvstore=kvstore,
optimizer='sgd', optimizer_params=optimizer_params,
arg_params=arg_params, aux_params=aux_params, begin_epoch=begin_epoch, num_epoch=end_epoch)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr,
lr_step):
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_retina_symbol(config, is_train=True)
feat_sym = []
feat_sym_p4 = sym.get_internals()['box_pred/p4_output']
feat_sym_p5 = sym.get_internals()['box_pred/p5_output']
feat_sym_p6 = sym.get_internals()['box_pred/p6_output']
feat_sym_p7 = sym.get_internals()['box_pred/p7_output']
feat_sym.append(feat_sym_p4)
feat_sym.append(feat_sym_p5)
feat_sym.append(feat_sym_p6)
feat_sym.append(feat_sym_p7)
#######
feat_stride = []
feat_stride.append(config.network.p4_RPN_FEAT_STRIDE)
feat_stride.append(config.network.p5_RPN_FEAT_STRIDE)
feat_stride.append(config.network.p6_RPN_FEAT_STRIDE)
feat_stride.append(config.network.p7_RPN_FEAT_STRIDE)
anchor_scales = []
anchor_scales.append(config.network.p4_ANCHOR_SCALES)
anchor_scales.append(config.network.p5_ANCHOR_SCALES)
anchor_scales.append(config.network.p6_ANCHOR_SCALES)
anchor_scales.append(config.network.p7_ANCHOR_SCALES)
anchor_ratios = []
anchor_ratios.append(config.network.p4_ANCHOR_RATIOS)
anchor_ratios.append(config.network.p5_ANCHOR_RATIOS)
anchor_ratios.append(config.network.p6_ANCHOR_RATIOS)
anchor_ratios.append(config.network.p7_ANCHOR_RATIOS)
#############
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [
load_gt_roidb(config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
flip=config.TRAIN.FLIP) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
# load training data
train_data = AnchorLoader(feat_sym,
feat_stride,
anchor_scales,
anchor_ratios,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
aspect_grouping=config.TRAIN.ASPECT_GROUPING)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
print 'providing maximum shape', max_data_shape, max_label_shape
# infer max shape
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix)
if config.TRAIN.RESUME:
mod._preload_opt_states = '%s-%04d.states' % (prefix, begin_epoch)
# decide training params
# metric
Retina_toal_eval_metric = metric.RetinaToalAccMetric()
Retina_cls_metric = metric.RetinaFocalLossMetric()
Retina_bbox_metric = metric.RetinaL1LossMetric()
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
for child_metric in [
Retina_toal_eval_metric, Retina_cls_metric, Retina_bbox_metric
]:
eval_metrics.add(child_metric)
# callback
batch_end_callback = callback.Speedometer(train_data.batch_size,
frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = [
mx.callback.module_checkpoint(mod,
prefix,
period=1,
save_optimizer_states=True),
callback.do_checkpoint(prefix, means, stds)
]
# decide learning rate
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch
]
lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [
int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff
]
print lr_step.split(',')
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'learning_rate': lr,
'wd': 0.0001,
}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# train
initializer = mx.init.MSRAPrelu(factor_type='out', slope=0)
# adam = mx.optimizer.AdaDelta(rho=0.09, epsilon=1e-14)
#optimizer_params=optimizer_params,
print "-----------------------train--------------------------------"
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='adam',
optimizer_params=optimizer_params,
initializer=initializer,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr,
lr_step):
if config.dataset.dataset != 'JSONList':
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
else:
import datetime
import logging
final_output_path = config.output_path
prefix = prefix + '_' + datetime.datetime.now().strftime(
"%Y-%m-%d_%H_%M_%S")
prefix = os.path.join(final_output_path, prefix)
shutil.copy2(args.cfg, prefix + '.yaml')
log_file = prefix + '.log'
head = '%(asctime)-15s %(message)s'
logging.basicConfig(filename=log_file, format=head)
logger = logging.getLogger()
logger.setLevel(logging.INFO)
logger.info('prefix: %s' % prefix)
print('prefix: %s' % prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [
load_gt_roidb(config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
flip=config.TRAIN.FLIP) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
# load training data
if config.network.MULTI_RPN:
num_layers = len(config.network.MULTI_RPN_STRIDES)
rpn_syms = [
sym.get_internals()['rpn%d_cls_score_output' % l]
for l in range(num_layers)
]
train_data = PyramidAnchorLoader(
rpn_syms,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_strides=config.network.MULTI_RPN_STRIDES,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING,
allowed_border=np.inf)
else:
feat_sym = sym.get_internals()['rpn_cls_score_output']
train_data = AnchorLoader(feat_sym,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_stride=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
print('providing maximum shape', max_data_shape, max_label_shape)
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix)
if config.TRAIN.RESUME:
mod._preload_opt_states = '%s-%04d.states' % (prefix, begin_epoch)
# decide training params
# metric
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
eval_metric = metric.RCNNAccMetric(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
for child_metric in [
rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric,
cls_metric, bbox_metric
]:
eval_metrics.add(child_metric)
if config.network.PREDICT_KEYPOINTS:
kps_cls_acc = metric.KeypointAccMetric(config)
kps_cls_loss = metric.KeypointLogLossMetric(config)
kps_pos_loss = metric.KeypointL1LossMetric(config)
eval_metrics.add(kps_cls_acc)
eval_metrics.add(kps_cls_loss)
eval_metrics.add(kps_pos_loss)
# callback
batch_end_callback = callback.Speedometer(train_data.batch_size,
frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = [
mx.callback.module_checkpoint(mod,
prefix,
period=1,
save_optimizer_states=True),
callback.do_checkpoint(prefix, means, stds)
]
# decide learning rate
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch
]
lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [
int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None
}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr,
lr_step):
mx.random.seed(3)
np.random.seed(3)
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
feat_pyramid_level = np.log2(config.network.RPN_FEAT_STRIDE).astype(int)
feat_sym = [
sym.get_internals()['rpn_cls_score_p' + str(x) + '_output']
for x in feat_pyramid_level
]
print('load symbol END')
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
print('Start load dataset and prepare imdb for training')
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [
load_gt_roidb_poly(config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
flip=config.TRAIN.FLIP) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
print('Start load training data')
# load training data
train_data = PyramidAnchorIterator_poly(
feat_sym,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_strides=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING,
allowed_border=np.inf)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 300, 9)))
print 'providing maximum shape', max_data_shape, max_label_shape
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix)
if config.TRAIN.RESUME:
mod._preload_opt_states = '%s-%04d.states' % (prefix, begin_epoch)
# decide training params
# # metric
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
rpn_fg_metric = metric.RPNFGFraction(config)
eval_fg_metric = metric.RCNNFGAccuracy(config)
eval_metric = metric.RCNNAccMetric(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
# add Rroi loss here
RCNN_proposal_fraction_metric = metric.RCNNFGFraction(config)
Rroi_fg_accuracy = metric.RRoIRCNNFGAccuracy(config)
Rroi_accuracy = metric.RRoIAccMetric(config)
Rroi_cls_metric = metric.RRoIRCNNLogLossMetric(config)
Rroi_bbox_metric = metric.RRoIRCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
for child_metric in [
rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, rpn_fg_metric,
eval_fg_metric, eval_metric, cls_metric, bbox_metric,
RCNN_proposal_fraction_metric, Rroi_fg_accuracy, Rroi_accuracy,
Rroi_cls_metric, Rroi_bbox_metric
]:
eval_metrics.add(child_metric)
# callback
batch_end_callback = callback.Speedometer(train_data.batch_size,
frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
Rroi_means = np.tile(
np.array(config.TRAIN.RRoI_BBOX_MEANS), 2
if config.network.RRoI_CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
Rroi_stds = np.tile(
np.array(config.TRAIN.RRoI_BBOX_STDS), 2
if config.network.RRoI_CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = [
mx.callback.module_checkpoint(mod,
prefix,
period=1,
save_optimizer_states=True),
callback.do_checkpoint_Rroi(prefix, means, stds, Rroi_means, Rroi_stds)
]
# decide learning rate
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch
]
lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [
int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'clip_gradient': None
}
#
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr,
lr_step):
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [
load_gt_roidb(config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
flip=config.TRAIN.FLIP) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
# load training data
train_data = AnchorLoader(feat_sym,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_stride=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
print('providing maximum shape', max_data_shape, max_label_shape)
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
#if config.TRAIN.RESUME:
# print('continue training from ', begin_epoch)
# arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
#else:
# arg_params, aux_params = load_param(pretrained, epoch, convert=True)
# sym_instance.init_weight(config, arg_params, aux_params)
print('transfer learning...')
# Choose the initialization weights (COCO or UADETRAC or pretrained)
#arg_params, aux_params = load_param('/raid10/home_ext/Deformable-ConvNets/output/rfcn_dcn_Shuo_UADTRAC/resnet_v1_101_voc0712_rfcn_dcn_Shuo_UADETRAC/trainlist_full/rfcn_UADTRAC', 5, convert=True)
#arg_params, aux_params = load_param('/raid10/home_ext/Deformable-ConvNets/model/rfcn_dcn_coco', 0, convert=True)
arg_params, aux_params = load_param(
'/raid10/home_ext/Deformable-ConvNets/output/rfcn_dcn_Shuo_AICity/resnet_v1_101_voc0712_rfcn_dcn_Shuo_AICityVOC1080_FreezeCOCO_rpnOnly_all/1080_all/rfcn_AICityVOC1080_FreezeCOCO_rpnOnly_all',
4,
convert=True)
sym_instance.init_weight_Shuo(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix)
#freeze parameters using fixed_param_names:list of str
para_file = open(
'/raid10/home_ext/Deformable-ConvNets/rfcn/symbols/arg_params.txt')
para_list = [line.split('<')[0] for line in para_file.readlines()]
# para_list.remove('rfcn_cls_weight')
# para_list.remove('rfcn_cls_bias')
# para_list.remove('rfcn_cls_offset_t_weight')
# para_list.remove('rfcn_cls_offset_t_bias')
#
para_list.remove('res5a_branch2b_offset_weight')
para_list.remove('res5a_branch2b_offset_bias')
para_list.remove('res5b_branch2b_offset_weight')
para_list.remove('res5b_branch2b_offset_bias')
para_list.remove('res5c_branch2b_offset_weight')
para_list.remove('res5c_branch2b_offset_bias')
para_list.remove('conv_new_1_weight')
para_list.remove('conv_new_1_bias')
para_list.remove('rfcn_bbox_weight')
para_list.remove('rfcn_bbox_bias')
para_list.remove('rfcn_bbox_offset_t_weight')
para_list.remove('rfcn_bbox_offset_t_bias')
mod = MutableModule_Shuo(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix,
fixed_param_names=para_list)
if config.TRAIN.RESUME:
mod._preload_opt_states = '%s-%04d.states' % (prefix, begin_epoch)
# decide training params
# metric
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
eval_metric = metric.RCNNAccMetric(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
for child_metric in [
rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric,
cls_metric, bbox_metric
]:
eval_metrics.add(child_metric)
# callback
batch_end_callback = callback.Speedometer(train_data.batch_size,
frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = [
mx.callback.module_checkpoint(mod,
prefix,
period=1,
save_optimizer_states=True),
callback.do_checkpoint(prefix, means, stds)
]
# decide learning rate
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch
]
lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [
int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None
}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
def pred_eval(predictor,
test_data,
imdb,
cfg,
vis=False,
thresh=1e-3,
logger=None,
ignore_cache=True):
"""
wrapper for calculating offline validation for faster data analysis
in this example, all threshold are set by hand
:param predictor: Predictor
:param test_data: data iterator, must be non-shuffle
:param imdb: image database
:param vis: controls visualization
:param thresh: valid detection threshold
:return:
"""
det_file = os.path.join(imdb.result_path, imdb.name + '_detections.pkl')
if os.path.exists(det_file) and not ignore_cache:
with open(det_file, 'rb') as fid:
all_boxes = pickle.load(fid)
info_str = imdb.evaluate_detections(all_boxes)
if logger:
logger.info('evaluate detections: \n{}'.format(info_str))
return
assert vis or not test_data.shuffle
data_names = [k[0] for k in test_data.provide_data[0]]
if not isinstance(test_data, PrefetchingIter):
test_data = PrefetchingIter(test_data)
nms = py_nms_wrapper(cfg.TEST.NMS)
# limit detections to max_per_image over all classes
max_per_image = cfg.TEST.max_per_image
num_images = imdb.num_images
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
rois = []
idx = 0
data_time, net_time, post_time = 0.0, 0.0, 0.0
t = time.time()
for im_info, data_batch in test_data:
t1 = time.time() - t
t = time.time()
scales = [iim_info[0, 2] for iim_info in im_info]
scores_all, boxes_all, roi_score_all, rois_all, roi_feat_all, data_dict_all = im_detect(
predictor, data_batch, data_names, scales, cfg)
assert len(roi_score_all) == len(rois_all) == len(roi_feat_all) == 1
nms_input = np.hstack((rois_all[0], roi_score_all[0]))
roi_nms = psoft(nms_input, -1.0)
roi_bbox = roi_nms[:, :4]
roi_score = roi_nms[:, 4]
roi_feat = np.zeros((roi_nms.shape[0], roi_feat_all[0].shape[1]),
dtype=roi_feat_all[0].dtype)
for i in range(roi_nms.shape[0]):
for j in range(nms_input.shape[0]):
if np.all(roi_nms[i] == nms_input[j]):
roi_feat[i] = roi_feat_all[0][j]
rois.append({'score': roi_score, 'bbox': roi_bbox, 'feat': roi_feat})
t2 = time.time() - t
t = time.time()
for delta, (scores, boxes, data_dict) in enumerate(
zip(scores_all, boxes_all, data_dict_all)):
# scores: 300 x 81 numpy.array
nms_dets = []
for j in range(1, imdb.num_classes):
indexes = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[indexes, j, np.newaxis]
cls_boxes = boxes[indexes,
4:8] if cfg.CLASS_AGNOSTIC else boxes[
indexes, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores))
nms_dets.append(psoft(cls_dets, thresh))
for j in range(1, imdb.num_classes):
all_boxes[j][idx + delta] = nms_dets[j - 1]
if max_per_image > 0:
image_scores = np.hstack([
all_boxes[j][idx + delta][:, -1]
for j in range(1, imdb.num_classes)
])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in range(1, imdb.num_classes):
keep = np.where(
all_boxes[j][idx + delta][:,
-1] >= image_thresh)[0]
all_boxes[j][idx +
delta] = all_boxes[j][idx +
delta][keep, :]
if vis:
boxes_this_image = [[]] + [
all_boxes[j][idx + delta]
for j in range(1, imdb.num_classes)
]
vis_all_detection(data_dict['data'].asnumpy(),
boxes_this_image, imdb.classes,
scales[delta], cfg)
idx += test_data.batch_size
t3 = time.time() - t
t = time.time()
data_time += t1
net_time += t2
post_time += t3
print('testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, imdb.num_images, data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size))
if logger:
logger.info(
'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, imdb.num_images,
data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size))
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, protocol=pickle.HIGHEST_PROTOCOL)
info_str = imdb.evaluate_detections(all_boxes, rois)
if logger:
logger.info('evaluate detections: \n{}'.format(info_str))
def pred_eval(predictor,
test_data,
imdb,
cfg,
vis=False,
thresh=1e-3,
logger=None,
ignore_cache=True):
"""
wrapper for calculating offline validation for faster data analysis
in this example, all threshold are set by hand
:param predictor: Predictor
:param test_data: data iterator, must be non-shuffle
:param imdb: image database
:param vis: controls visualization
:param thresh: valid detection threshold
:return:
"""
det_file = os.path.join(imdb.result_path, imdb.name + '_detections.pkl')
if os.path.exists(det_file) and not ignore_cache:
with open(det_file, 'rb') as fid:
all_boxes = cPickle.load(fid)
info_str = imdb.evaluate_detections(all_boxes)
if logger:
logger.info('evaluate detections: \n{}'.format(info_str))
return
assert vis or not test_data.shuffle
data_names = [k[0] for k in test_data.provide_data[0]]
if not isinstance(test_data, PrefetchingIter):
test_data = PrefetchingIter(test_data)
#if cfg.TEST.SOFTNMS:
# nms = py_softnms_wrapper(cfg.TEST.NMS)
#else:
# nms = py_nms_wrapper(cfg.TEST.NMS)
if cfg.TEST.SOFTNMS:
nms = py_softnms_wrapper(cfg.TEST.NMS)
else:
nms = py_nms_wrapper(cfg.TEST.NMS)
# limit detections to max_per_image over all classes
max_per_image = cfg.TEST.max_per_image
num_images = imdb.num_images
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
class_lut = [[] for _ in range(imdb.num_classes)]
valid_tally = 0
valid_sum = 0
idx = 0
t = time.time()
inference_count = 0
all_inference_time = []
post_processing_time = []
for im_info, data_batch in test_data:
t1 = time.time() - t
t = time.time()
scales = [iim_info[0, 2] for iim_info in im_info]
scores_all, boxes_all, data_dict_all = im_detect(
predictor, data_batch, data_names, scales, cfg)
t2 = time.time() - t
t = time.time()
for delta, (scores, boxes, data_dict) in enumerate(
zip(scores_all, boxes_all, data_dict_all)):
if cfg.TEST.LEARN_NMS:
for j in range(1, imdb.num_classes):
indexes = np.where(scores[:, j - 1] > thresh)[0]
cls_scores = scores[indexes, j - 1:j]
cls_boxes = boxes[indexes, j - 1, :]
cls_dets = np.hstack((cls_boxes, cls_scores))
# count the valid ground truth
if len(cls_scores) > 0:
class_lut[j].append(idx + delta)
valid_tally += len(cls_scores)
valid_sum += len(scores)
all_boxes[j][idx + delta] = cls_dets
else:
for j in range(1, imdb.num_classes):
indexes = np.where(scores[:, j] > thresh)[0]
if cfg.TEST.FIRST_N > 0:
# todo: check whether the order affects the result
sort_indices = np.argsort(
scores[:, j])[-cfg.TEST.FIRST_N:]
# sort_indices = np.argsort(-scores[:, j])[0:cfg.TEST.FIRST_N]
indexes = np.intersect1d(sort_indices, indexes)
cls_scores = scores[indexes, j, np.newaxis]
cls_boxes = boxes[indexes,
4:8] if cfg.CLASS_AGNOSTIC else boxes[
indexes, j * 4:(j + 1) * 4]
# count the valid ground truth
if len(cls_scores) > 0:
class_lut[j].append(idx + delta)
valid_tally += len(cls_scores)
valid_sum += len(scores)
# print np.min(cls_scores), valid_tally, valid_sum
# cls_scores = scores[:, j, np.newaxis]
# cls_scores[cls_scores <= thresh] = thresh
# cls_boxes = boxes[:, 4:8] if cfg.CLASS_AGNOSTIC else boxes[:, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores))
if cfg.TEST.SOFTNMS:
all_boxes[j][idx + delta] = nms(cls_dets)
else:
keep = nms(cls_dets)
all_boxes[j][idx + delta] = cls_dets[keep, :]
if max_per_image > 0:
image_scores = np.hstack([
all_boxes[j][idx + delta][:, -1]
for j in range(1, imdb.num_classes)
])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in range(1, imdb.num_classes):
keep = np.where(
all_boxes[j][idx + delta][:,
-1] >= image_thresh)[0]
all_boxes[j][idx +
delta] = all_boxes[j][idx +
delta][keep, :]
if vis:
boxes_this_image = [[]] + [
all_boxes[j][idx + delta]
for j in range(1, imdb.num_classes)
]
vis_all_detection(data_dict['data'].asnumpy(),
boxes_this_image, imdb.classes,
scales[delta], cfg)
idx += test_data.batch_size
t3 = time.time() - t
t = time.time()
post_processing_time.append(t3)
all_inference_time.append(t1 + t2 + t3)
inference_count += 1
if inference_count % 200 == 0:
valid_count = 500 if inference_count > 500 else inference_count
print("--->> running-average inference time per batch: {}".format(
float(sum(all_inference_time[-valid_count:])) / valid_count))
print("--->> running-average post processing time per batch: {}".
format(
float(sum(post_processing_time[-valid_count:])) /
valid_count))
print 'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, imdb.num_images, t1, t2, t3)
if logger:
logger.info(
'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, imdb.num_images, t1, t2, t3))
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, protocol=cPickle.HIGHEST_PROTOCOL)
# np.save('class_lut.npy', class_lut)
info_str = imdb.evaluate_detections(all_boxes)
if logger:
logger.info('evaluate detections: \n{}'.format(info_str))
num_valid_classes = [len(x) for x in class_lut]
logger.info('valid class ratio:{}'.format(
np.sum(num_valid_classes) / float(num_images)))
logger.info('valid score ratio:{}'.format(
float(valid_tally) / float(valid_sum + 0.01)))
def pred_eval(predictor, test_data, imdb, cfg, vis=False, thresh=1e-3, logger=None, ignore_cache=True):
#def pred_eval(predictor, test_data, imdb, cfg, vis=False, thresh=0.7, logger=None, ignore_cache=True):
"""
wrapper for calculating offline validation for faster data analysis
in this example, all threshold are set by hand
:param predictor: Predictor
:param test_data: data iterator, must be non-shuffle
:param imdb: image database
:param vis: controls visualization
:param thresh: valid detection threshold
:return:
"""
co_occur_matrix = np.load('/home/user/Deformable-ConvNets2/tmp/co_occur_matrix.npy')
nor_co_occur_matrix = np.zeros((90,90))
row_max = np.zeros(90)
co_occur_matrix = co_occur_matrix.astype(int)
for ind, val in enumerate(co_occur_matrix):
row_sum = np.sum(co_occur_matrix[:,ind])
if not row_sum == 0:
nor_co_occur_matrix[:,ind] = co_occur_matrix[:,ind]/row_sum
row_max[ind] = np.amax(nor_co_occur_matrix[:,ind])
assert vis or not test_data.shuffle
data_names = [k[0] for k in test_data.provide_data[0]]
roidb = test_data.roidb
if not isinstance(test_data, PrefetchingIter):
test_data = PrefetchingIter(test_data)
soft_nms = py_softnms_wrapper(cfg.TEST.NMS)
# limit detections to max_per_image over all classes
max_per_image = cfg.TEST.max_per_image
num_images = imdb.num_images
# all detections are collected into:
all_boxes = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
idx = 0
data_time, net_time, post_time = 0.0, 0.0, 0.0
t = time.time()
#pl = Pool(8)
annotation_file = '/home/user/Deformable-ConvNets-test/data/coco/annotations/kinstances_unlabeled2017.json'
dataset = json.load(open(annotation_file, 'r'))
annotations = []
id_count = 1
img_count = 1
for im_info, data_batch in test_data:
t1 = time.time() - t
t = time.time()
scales = [iim_info[0, 2] for iim_info in im_info]
scores_all, boxes_all, data_dict_all = im_detect(predictor, data_batch, data_names, scales, cfg)
t2 = time.time() - t
t = time.time()
for delta, (scores, boxes, data_dict) in enumerate(zip(scores_all, boxes_all, data_dict_all)):
for j in range(1, imdb.num_classes):
indexes = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[indexes, j, np.newaxis]
cls_boxes = boxes[indexes, 4:8] if cfg.CLASS_AGNOSTIC else boxes[indexes, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores))
keep = soft_nms(cls_dets)
keep = keep.tolist()
all_boxes[j][idx+delta] = cls_dets[keep, :]
if max_per_image > 0:
image_scores = np.hstack([all_boxes[j][idx+delta][:, -1]
for j in range(1, imdb.num_classes)])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in range(1, imdb.num_classes):
keep = np.where(all_boxes[j][idx+delta][:, -1] >= image_thresh)[0]
all_boxes[j][idx+delta] = all_boxes[j][idx+delta][keep, :]
if vis:
boxes_this_image = [[]] + [all_boxes[j][idx+delta] for j in range(1, imdb.num_classes)]
im_name = roidb[idx]['image']
im_name = im_name.rsplit("/", 1)
im_name = im_name[-1]
result = draw_all_detection(data_dict['data'].asnumpy(), boxes_this_image, imdb.classes,
scales[delta], cfg, im_name, annotations, id_count,
nor_co_occur_matrix, row_max)
annotations = result['ann']
id_count = result['id_count']
idx += test_data.batch_size
t3 = time.time() - t
t = time.time()
data_time += t1
net_time += t2
post_time += t3
print 'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(idx, imdb.num_images, data_time / idx * test_data.batch_size, net_time / idx * test_data.batch_size, post_time / idx * test_data.batch_size)
if logger:
logger.info('testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(idx, imdb.num_images, data_time / idx * test_data.batch_size, net_time / idx * test_data.batch_size, post_time / idx * test_data.batch_size))
dataset.update({'annotations':annotations})
save_annotation_file = '/home/user/Deformable-ConvNets-test/data/coco/annotations/instances_unlabeled2017_ssl.json'
with open(save_annotation_file, 'w') as f:
json.dump(dataset, f)
print "Finish generate pseudo ground truth!"
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr, lr_step):
np.random.seed(0)
mx.random.seed(0)
logger, final_output_path = create_logger(config.output_path, args.cfg, config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'), final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [load_gt_roidb(config.dataset.dataset, image_set, config.dataset.root_path, config.dataset.dataset_path,
flip=config.TRAIN.FLIP)
for image_set in image_sets]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
# load training data
train_data = AnchorLoader(feat_sym, roidb, config, batch_size=input_batch_size, shuffle=config.TRAIN.SHUFFLE, ctx=ctx,
feat_stride=config.network.RPN_FEAT_STRIDE, anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS, aspect_grouping=config.TRAIN.ASPECT_GROUPING)
# infer max shape
# max_dats_shape=['data', (1,3,600,1000)]
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))]
# max_data_shape=[], max_lable_shape=[]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
logger.info('providing maximum shape'+str(max_data_shape)+" "+str(max_label_shape))
data_shape_dict = dict(train_data.provide_data_single + train_data.provide_label_single)
# add by chaojie
logger.info("data_sahpe_dict:\n{}".format(pprint.pformat(data_shape_dict)))
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
pprint.pprint(sym_instance.arg_shape_dict)
logger.info("sym_instance.arg_shape_dict\n")
logging.info(pprint.pformat(sym_instance.arg_shape_dict))
#dot = mx.viz.plot_network(sym, node_attrs={'shape': 'rect', 'fixedsize': 'false'})
#dot.render(os.path.join('./output/rcnn/network_vis', config.symbol + '_rcnn'))
# load and initialize params
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params, data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(sym, data_names=data_names, label_names=label_names,
logger=logger, context=ctx, max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)], fixed_param_prefix=fixed_param_prefix)
if config.TRAIN.RESUME:
mod._preload_opt_states = '%s-%04d.states'%(prefix, begin_epoch)
# decide training params
# metric
eval_metric = metric.RCNNAccMetric(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
if config.TRAIN.JOINT_TRAINING or (not config.TRAIN.LEARN_NMS):
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
for child_metric in [rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric]:
eval_metrics.add(child_metric)
for child_metric in [eval_metric, cls_metric, bbox_metric]:
eval_metrics.add(child_metric)
if config.TRAIN.LEARN_NMS:
eval_metrics.add(metric.NMSLossMetric(config, 'pos'))
eval_metrics.add(metric.NMSLossMetric(config, 'neg'))
eval_metrics.add(metric.NMSAccMetric(config))
# callback
batch_end_callback = callback.Speedometer(train_data.batch_size, frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS), 2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS), 2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = [mx.callback.module_checkpoint(mod, prefix, period=1, save_optimizer_states=True),
callback.do_checkpoint(prefix, means, stds)]
# decide learning rate
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch]
lr = base_lr * (lr_factor ** (len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor, config.TRAIN.warmup, config.TRAIN.warmup_lr, config.TRAIN.warmup_step)
# optimizer
optimizer_params = {'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# train
mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback, kvstore=config.default.kvstore,
optimizer='sgd', optimizer_params=optimizer_params,
arg_params=arg_params, aux_params=aux_params, begin_epoch=begin_epoch, num_epoch=end_epoch)
def pred_eval(predictor,
test_data,
imdb,
cfg,
vis=False,
thresh=1e-3,
logger=None,
ignore_cache=True):
"""
wrapper for calculating offline validation for faster data analysis
in this example, all threshold are set by hand
:param predictor: Predictor
:param test_data: data iterator, must be non-shuffle
:param imdb: image database
:param vis: controls visualization
:param thresh: valid detection threshold
:return:
"""
det_file = os.path.join(imdb.result_path, imdb.name + '_detections.pkl')
if os.path.exists(det_file) and not ignore_cache:
with open(det_file, 'rb') as fid:
cache_res = cPickle.load(fid)
all_boxes = cache_res['all_boxes']
all_keypoints = cache_res.get('all_keypoints')
info_str = imdb.evaluate_detections(all_boxes,
all_keypoints=all_keypoints)
if logger:
logger.info('evaluate detections: \n{}'.format(info_str))
return
assert vis or not test_data.shuffle
data_names = [k[0] for k in test_data.provide_data[0]]
if not isinstance(test_data, PrefetchingIter):
test_data = PrefetchingIter(test_data)
nms = py_nms_wrapper(cfg.TEST.NMS)
# limit detections to max_per_image over all classes
max_per_image = cfg.TEST.max_per_image
num_images = imdb.num_images
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[np.array([]) for _ in range(num_images)]
for _ in range(imdb.num_classes)]
all_keypoints = None
if cfg.network.PREDICT_KEYPOINTS:
all_keypoints = [[np.array([]) for _ in range(num_images)]
for _ in range(imdb.num_classes)]
idx = 0
data_time, net_time, post_time = 0.0, 0.0, 0.0
t = time.time()
for im_info, data_batch in test_data:
t1 = time.time() - t
t = time.time()
scales = [iim_info[0, 2] for iim_info in im_info]
rets = im_detect(predictor, data_batch, data_names, scales, cfg)
scores_all = rets[0]
boxes_all = rets[1]
data_dict_all = rets[-1]
if cfg.network.PREDICT_KEYPOINTS:
pred_kps_all = rets[2]
t2 = time.time() - t
t = time.time()
for delta, (scores, boxes, data_dict) in enumerate(
zip(scores_all, boxes_all, data_dict_all)):
for j in range(1, imdb.num_classes):
indexes = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[indexes, j, np.newaxis]
cls_boxes = boxes[indexes,
4:8] if cfg.CLASS_AGNOSTIC else boxes[
indexes, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores))
keep = nms(cls_dets)
all_boxes[j][idx + delta] = cls_dets[keep, :]
if cfg.network.PREDICT_KEYPOINTS:
all_keypoints[j][idx + delta] = pred_kps_all[delta][
indexes, :][keep, :]
if max_per_image > 0:
image_scores = np.hstack([
all_boxes[j][idx + delta][:, -1]
for j in range(1, imdb.num_classes)
])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in range(1, imdb.num_classes):
keep = np.where(
all_boxes[j][idx + delta][:,
-1] >= image_thresh)[0]
all_boxes[j][idx +
delta] = all_boxes[j][idx +
delta][keep, :]
if cfg.network.PREDICT_KEYPOINTS:
all_keypoints[j][idx + delta] = all_keypoints[j][
idx + delta][keep, :]
if vis:
boxes_this_image = [[]] + [
all_boxes[j][idx + delta]
for j in range(1, imdb.num_classes)
]
vis_all_detection(data_dict['data'].asnumpy(),
boxes_this_image, imdb.classes,
scales[delta], cfg)
idx += test_data.batch_size
t3 = time.time() - t
t = time.time()
data_time += t1
net_time += t2
post_time += t3
print 'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, imdb.num_images, data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size)
if logger:
logger.info(
'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, imdb.num_images,
data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size))
with open(det_file, 'wb') as f:
cPickle.dump({
'all_boxes': all_boxes,
'all_keypoints': all_keypoints
},
f,
protocol=cPickle.HIGHEST_PROTOCOL)
info_str = imdb.evaluate_detections(all_boxes, all_keypoints=all_keypoints)
if logger:
logger.info('evaluate detections: \n{}'.format(info_str))
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr,
lr_step):
mx.random.seed(3)
np.random.seed(3)
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
config['final_output_path'] = final_output_path
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
feat_pyramid_level = np.log2(config.network.RPN_FEAT_STRIDE).astype(int)
feat_sym = [
sym.get_internals()['rpn_cls_score_p' + str(x) + '_output']
for x in feat_pyramid_level
]
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
#leonid: adding semicolumn ";" support to allow several different datasets to be merged
datasets = config.dataset.dataset.split(';')
image_sets = config.dataset.image_set.split(';')
data_paths = config.dataset.dataset_path.split(';')
if type(config.dataset.per_category_epoch_max) is str:
per_category_epoch_max = [
float(x) for x in config.dataset.per_category_epoch_max.split(';')
]
else:
per_category_epoch_max = [float(config.dataset.per_category_epoch_max)]
roidbs = []
categ_index_offs = 0
if 'classes_list_fname' not in config.dataset:
classes_list_fname = ''
else:
classes_list_fname = config.dataset.classes_list_fname
if 'num_ex_per_class' not in config.dataset:
num_ex_per_class = ''
else:
num_ex_per_class = config.dataset.num_ex_per_class
for iD, dataset in enumerate(datasets):
# load dataset and prepare imdb for training
image_sets_cur = [iset for iset in image_sets[iD].split('+')]
for image_set in image_sets_cur:
cur_roidb, cur_num_classes = load_gt_roidb(
dataset,
image_set,
config.dataset.root_path,
data_paths[iD],
flip=config.TRAIN.FLIP,
per_category_epoch_max=per_category_epoch_max[iD],
return_num_classes=True,
categ_index_offs=categ_index_offs,
classes_list_fname=classes_list_fname,
num_ex_per_class=num_ex_per_class)
roidbs.append(cur_roidb)
categ_index_offs += cur_num_classes
# roidbs.extend([
# load_gt_roidb(
# dataset,
# image_set,
# config.dataset.root_path,
# data_paths[iD],
# flip=config.TRAIN.FLIP,
# per_category_epoch_max=per_category_epoch_max[iD])
# for image_set in image_sets])
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
# load training data
train_data = PyramidAnchorIterator(
feat_sym,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_strides=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING,
allowed_border=np.inf)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
print 'providing maximum shape', max_data_shape, max_label_shape
if not config.network.base_net_lock:
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
else:
data_shape_dict = dict(train_data.provide_data_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight(config, arg_params, aux_params)
if config.TRAIN.LOAD_EMBEDDING:
import cPickle
with open(config.TRAIN.EMBEDDING_FNAME, 'rb') as fid:
model_data = cPickle.load(fid)
for fcn in ['1', '2', '3']:
layer = model_data['dense_' + fcn]
weight = ListList2ndarray(layer[0])
bias = mx.nd.array(layer[1])
arg_params['embed_dense_' + fcn + '_weight'] = weight
arg_params['embed_dense_' + fcn + '_bias'] = bias
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
alt_fixed_param_prefix = config.network.ALT_FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
if not config.network.base_net_lock:
label_names = [k[0] for k in train_data.provide_label_single]
else:
label_names = []
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix,
alt_fixed_param_prefix=alt_fixed_param_prefix)
# Leonid: Comment out the following two lines if switching to smaller number of GPUs and resuming training, then after it starts running un-comment back
# if config.TRAIN.RESUME:
# mod._preload_opt_states = '%s-%04d.states'%(prefix, begin_epoch)
#TODO: release this.
# decide training params
# metric
if not config.network.base_net_lock:
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
rpn_fg_metric = metric.RPNFGFraction(config)
eval_metric = metric.RCNNAccMetric(config)
eval_fg_metric = metric.RCNNFGAccuracy(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
if not config.network.base_net_lock:
all_child_metrics = [
rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, rpn_fg_metric,
eval_fg_metric, eval_metric, cls_metric, bbox_metric
]
else:
all_child_metrics = [
rpn_fg_metric, eval_fg_metric, eval_metric, cls_metric, bbox_metric
]
# all_child_metrics = [rpn_eval_metric, rpn_bbox_metric, rpn_fg_metric, eval_fg_metric, eval_metric, cls_metric, bbox_metric]
################################################
### added / updated by Leonid to support oneshot
################################################
if config.network.EMBEDDING_DIM != 0:
if config.network.EMBED_LOSS_ENABLED:
all_child_metrics += [
metric.RepresentativesMetric(config, final_output_path)
] # moved from above. JS.
all_child_metrics += [metric.EmbedMetric(config)]
if config.network.BG_REPS:
all_child_metrics += [metric.BGModelMetric(config)]
if config.network.REPS_CLS_LOSS:
all_child_metrics += [metric.RepsCLSMetric(config)]
if config.network.ADDITIONAL_LINEAR_CLS_LOSS:
all_child_metrics += [metric.RCNNLinLogLossMetric(config)]
if config.network.VAL_FILTER_REGRESS:
all_child_metrics += [metric.ValRegMetric(config)]
if config.network.SCORE_HIST_REGRESS:
all_child_metrics += [metric.ScoreHistMetric(config)]
################################################
for child_metric in all_child_metrics:
eval_metrics.add(child_metric)
# callback
batch_end_callback = callback.Speedometer(train_data.batch_size,
frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = [
mx.callback.module_checkpoint(mod,
prefix,
period=1,
save_optimizer_states=True),
callback.do_checkpoint(prefix, means, stds)
]
# decide learning rate
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch
]
lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [
int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'clip_gradient': None
}
#
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
if args.debug == 1:
import copy
arg_params_ = copy.deepcopy(arg_params)
aux_params_ = copy.deepcopy(aux_params)
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch,
config=config)
if args.debug == 1:
t = dictCompare(aux_params_, aux_params)
t = dictCompare(arg_params_, arg_params)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr,
lr_step):
mx.random.seed(3)
np.random.seed(3)
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
dot = mx.viz.plot_network(sym)
dot.render('graph/nn.gv', view=False)
all_layers = sym.get_internals().list_outputs()
node_file = 'graph/nodes.txt'
if os.path.exists(node_file):
os.remove(node_file)
with open(node_file, 'a+') as f:
for layer in all_layers:
f.write(layer + '\n')
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
sdsdbs = []
for image_set in image_sets:
if image_set == 'train2014':
gt_sdsdb_file = os.path.join(config.dataset.root_path, 'cache',
'COCOMask', 'train2014',
'gt_sdsdb.pkl')
if os.path.exists(gt_sdsdb_file):
with open(gt_sdsdb_file, 'rb') as f:
sdsdbs.append(pkl.load(f))
else:
train2014_sdsdb = load_gt_sdsdb(
config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
mask_size=config.MASK_SIZE,
binary_thresh=config.BINARY_THRESH,
result_path=final_output_path,
flip=config.TRAIN.FLIP)
with open(gt_sdsdb_file, 'wb') as f:
pkl.dump(train2014_sdsdb, f, protocol=pkl.HIGHEST_PROTOCOL)
sdsdbs.append(train2014_sdsdb)
elif image_set == 'valminusminival2014':
gt_sdsdb_file = os.path.join(config.dataset.root_path, 'cache',
'COCOMask', 'val2014', 'gt_sdsdb.pkl')
if os.path.exists(gt_sdsdb_file):
with open(gt_sdsdb_file, 'rb') as f:
sdsdbs.append(pkl.load(f))
else:
val2014_sdsdb = load_gt_sdsdb(
config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
mask_size=config.MASK_SIZE,
binary_thresh=config.BINARY_THRESH,
result_path=final_output_path,
flip=config.TRAIN.FLIP)
with open(gt_sdsdb_file, 'wb') as f:
pkl.dump(val2014_sdsdb, f, protocol=pkl.HIGHEST_PROTOCOL)
sdsdbs.append(val2014_sdsdb)
sdsdb = merge_roidb(sdsdbs)
sdsdb = filter_roidb(sdsdb, config)
# load training data
train_data = AnchorLoader(feat_sym,
sdsdb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_stride=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING,
allowed_border=config.TRAIN.RPN_ALLOWED_BORDER)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max(v[1] for v in config.SCALES)))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
max_data_shape.append(('gt_masks', (config.TRAIN.BATCH_IMAGES, 100,
max([v[0] for v in config.SCALES]),
max(v[1] for v in config.SCALES))))
print 'providing maximum shape', max_data_shape, max_label_shape
# infer shape
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
print 'data shape:'
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if config.TRAIN.RESUME:
print 'continue training from ', begin_epoch
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in xrange(batch_size)],
max_label_shapes=[max_label_shape for _ in xrange(batch_size)],
fixed_param_prefix=fixed_param_prefix)
# decide training metric
# RPN, classification accuracy/loss, regression loss
rpn_acc = metric.RPNAccMetric()
rpn_cls_loss = metric.RPNLogLossMetric()
rpn_bbox_loss = metric.RPNL1LossMetric()
fcis_acc = metric.FCISAccMetric(config)
fcis_acc_fg = metric.FCISAccFGMetric(config)
fcis_cls_loss = metric.FCISLogLossMetric(config)
fcis_bbox_loss = metric.FCISL1LossMetric(config)
fcis_mask_loss = metric.FCISMaskLossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
for child_metric in [
rpn_acc, rpn_cls_loss, rpn_bbox_loss, fcis_acc, fcis_acc_fg,
fcis_cls_loss, fcis_bbox_loss, fcis_mask_loss
]:
eval_metrics.add(child_metric)
batch_end_callback = callback.Speedometer(train_data.batch_size,
frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = callback.do_checkpoint(prefix, means, stds)
# print epoch, begin_epoch, end_epoch, lr_step
base_lr = lr
lr_factor = 0.1
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch
]
lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [
int(epoch * len(sdsdb) / batch_size) for epoch in lr_epoch_diff
]
print 'lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None
}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# del sdsdb
# a = mx.viz.plot_network(sym)
# a.render('../example', view=True)
# print 'prepare sds finished'
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch,
batches_checkpoint=epoch_end_callback,
num_batches_save_ckpt=2000)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr,
lr_step):
mx.random.seed(3)
np.random.seed(3)
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
feat_pyramid_level = np.log2(config.network.RPN_FEAT_STRIDE).astype(int)
feat_sym = [
sym.get_internals()['rpn_cls_score_p' + str(x) + '_output']
for x in feat_pyramid_level
]
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [
load_gt_roidb(config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
flip=config.TRAIN.FLIP) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
# load training data
train_data = PyramidAnchorIterator(
feat_sym,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_strides=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING,
allowed_border=np.inf)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
print 'providing maximum shape', max_data_shape, max_label_shape
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
# sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
# sym_instance.check_parameter_shapes(arg_params, aux_params, data_shape_dict)
# decide training params
# metric
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
rpn_fg_metric = metric.RPNFGFraction(config)
eval_metric = metric.RCNNAccMetric(config)
eval_fg_metric = metric.RCNNFGAccuracy(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
for child_metric in [
rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, rpn_fg_metric,
eval_fg_metric, eval_metric, cls_metric, bbox_metric
]:
eval_metrics.add(child_metric)
# callback
# batch_end_callback = callback.Speedometer(train_data.batch_size, frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
# epoch_end_callback = [mx.callback.module_checkpoint(mod, prefix, period=1,
# save_optimizer_states=True), callback.do_checkpoint(prefix, means, stds)]
# decide learning rate
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch
]
lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [
int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'clip_gradient': None
}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
net = FPNNet(sym, args_pretrained=arg_params, auxes_pretrained=aux_params)
# create multi-threaded DataParallel Model.
net_parallel = DataParallelModel(net, ctx_list=ctx)
# create trainer,
# !Important: A trainer can be only created after the function `resnet_ctx` is called.
# Please Note that DataParallelModel will call reset_ctx to initialize parameters on gpus.
trainer = mx.gluon.Trainer(net.collect_params(), 'sgd', optimizer_params)
for epoch in range(begin_epoch, config.TRAIN.end_epoch):
train_data.reset()
net.hybridize(static_alloc=True, static_shape=False)
progress_bar = tqdm.tqdm(total=len(roidb))
for nbatch, data_batch in enumerate(train_data):
inputs = [[
x.astype('f').as_in_context(c) for x in d + l
] for c, d, l in zip(ctx, data_batch.data, data_batch.label)]
with ag.record():
outputs = net_parallel(*inputs)
ag.backward(sum(outputs, ()))
trainer.step(1)
eval_metrics.update(data_batch.label[0], outputs[0])
if nbatch % 100 == 0:
msg = ','.join([
'{}={:.3f}'.format(w, v)
for w, v in zip(*eval_metrics.get())
])
msg += ",lr={}".format(trainer.learning_rate)
logger.info(msg)
print(msg)
eval_metrics.reset()
progress_bar.update(len(inputs))
progress_bar.close()
net.hybridize(static_alloc=True, static_shape=False)
re = ("mAP", 0.0)
logger.info(re)
save_path = "{}-{}-{}.params".format(prefix, epoch, re[1])
net.collect_params().save(save_path)
logger.info("Saved checkpoint to {}.".format(save_path))
def pred_double_eval(predictor,
test_data,
imdb,
cfg,
vis=False,
thresh=1e-3,
logger=None,
ignore_cache=True,
show_gt=False):
"""
wrapper for calculating offline validation for faster data analysis
in this example, all threshold are set by hand
:param predictor: Predictor
:param test_data: data iterator, must be non-shuffle
:param imdb: image database
:param vis: controls visualization
:param thresh: valid detection threshold
:return:
"""
det_file = os.path.join(imdb.result_path, imdb.name + '_detections.pkl')
if os.path.exists(det_file) and not ignore_cache:
with open(det_file, 'rb') as fid:
all_boxes = cPickle.load(fid)
info_str = imdb.evaluate_detections(all_boxes)
if logger:
logger.info('evaluate detections: \n{}'.format(info_str))
return
assert vis or not test_data.shuffle
data_names = [k[0] for k in test_data.provide_data[0]]
num_images = test_data.size
if not isinstance(test_data, PrefetchingIter):
test_data = PrefetchingIter(test_data)
#if cfg.TEST.SOFTNMS:
# nms = py_softnms_wrapper(cfg.TEST.NMS)
#else:
# nms = py_nms_wrapper(cfg.TEST.NMS)
if cfg.TEST.SOFTNMS:
nms = py_softnms_wrapper(cfg.TEST.NMS)
else:
nms = py_nms_wrapper(cfg.TEST.NMS)
# limit detections to max_per_image over all classes
max_per_image = cfg.TEST.max_per_image
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
ref_all_boxes = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
# class_lut = [[] for _ in range(imdb.num_classes)]
valid_tally = 0
valid_sum = 0
idx = 0
t = time.time()
inference_count = 0
all_inference_time = []
post_processing_time = []
nms_full_count = []
nms_pos_count = []
is_max_count = []
all_count = []
for im_info, data_batch in test_data:
t1 = time.time() - t
t = time.time()
scales = [iim_info[0, 2] for iim_info in im_info]
scores_all, boxes_all, ref_scores_all, ref_boxes_all, data_dict_all = im_double_detect(
predictor, data_batch, data_names, scales, cfg)
t2 = time.time() - t
t = time.time()
# for delta, (scores, boxes, data_dict) in enumerate(zip(scores_all, boxes_all, data_dict_all)):
nms_full_count_per_batch = 0
nms_pos_count_per_batch = 0
global num_of_is_full_max
is_max_count_per_batch = num_of_is_full_max[0]
all_count_per_batch = 0
for delta, (scores, boxes, ref_scores, ref_boxes,
data_dict) in enumerate(
zip(scores_all, boxes_all, ref_scores_all,
ref_boxes_all, data_dict_all)):
if cfg.TEST.LEARN_NMS:
for j in range(1, imdb.num_classes):
indexes = np.where(scores[:, j - 1, 0] > thresh)[0]
cls_scores = scores[indexes, j - 1, :]
cls_boxes = boxes[indexes, j - 1, :]
cls_dets = np.hstack((cls_boxes, cls_scores))
# count the valid ground truth
if len(cls_scores) > 0:
# class_lut[j].append(idx + delta)
valid_tally += len(cls_scores)
valid_sum += len(scores)
all_boxes[j][idx + delta] = cls_dets
if DEBUG:
keep = nms(cls_dets)
nms_cls_dets = cls_dets[keep, :]
target = data_dict['nms_multi_target']
target_indices = np.where(target[:, 4] == j - 1)
target = target[target_indices]
nms_full_count_per_batch += bbox_equal_count(
nms_cls_dets, target)
gt_boxes = data_dict['gt_boxes'][0].asnumpy()
gt_boxes = gt_boxes[np.where(gt_boxes[:,
4] == j)[0], :4]
gt_boxes /= scales[delta]
if len(cls_boxes) != 0 and len(gt_boxes) != 0:
overlap_mat = bbox_overlaps(
cls_boxes.astype(np.float),
gt_boxes.astype(np.float))
keep = nms(
cls_dets[np.where(overlap_mat > 0.5)[0]])
nms_cls_dets = cls_dets[np.where(
overlap_mat > 0.5)[0]][keep]
nms_pos_count_per_batch += bbox_equal_count(
nms_cls_dets, target)
all_count_per_batch += len(target)
else:
for j in range(1, imdb.num_classes):
indexes = np.where(scores[:, j] > thresh)[0]
if cfg.TEST.FIRST_N > 0:
# todo: check whether the order affects the result
sort_indices = np.argsort(
scores[:, j])[-cfg.TEST.FIRST_N:]
# sort_indices = np.argsort(-scores[:, j])[0:cfg.TEST.FIRST_N]
indexes = np.intersect1d(sort_indices, indexes)
cls_scores = scores[indexes, j, np.newaxis]
cls_boxes = boxes[indexes,
4:8] if cfg.CLASS_AGNOSTIC else boxes[
indexes, j * 4:(j + 1) * 4]
# count the valid ground truth
if len(cls_scores) > 0:
# class_lut[j].append(idx+delta)
valid_tally += len(cls_scores)
valid_sum += len(scores)
# print np.min(cls_scores), valid_tally, valid_sum
# cls_scores = scores[:, j, np.newaxis]
# cls_scores[cls_scores <= thresh] = thresh
# cls_boxes = boxes[:, 4:8] if cfg.CLASS_AGNOSTIC else boxes[:, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores))
if cfg.TEST.SOFTNMS:
all_boxes[j][idx + delta] = nms(cls_dets)
else:
keep = nms(cls_dets)
all_boxes[j][idx + delta] = cls_dets[keep, :]
# all_boxes[j][idx + delta] = cls_dets
if max_per_image > 0:
image_scores = np.hstack([
all_boxes[j][idx + delta][:, -1]
for j in range(1, imdb.num_classes)
])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in range(1, imdb.num_classes):
keep = np.where(
all_boxes[j][idx + delta][:,
-1] >= image_thresh)[0]
all_boxes[j][idx +
delta] = all_boxes[j][idx +
delta][keep, :]
if vis:
boxes_this_image = [[]] + [
all_boxes[j][idx + delta]
for j in range(1, imdb.num_classes)
]
if show_gt:
gt_boxes = data_dict['gt_boxes'][0]
for gt_box in gt_boxes:
gt_box = gt_box.asnumpy()
gt_cls = int(gt_box[4])
gt_box = gt_box / scales[delta]
gt_box[4] = 1
if cfg.TEST.LEARN_NMS:
gt_box = np.append(gt_box, 1)
boxes_this_image[gt_cls] = np.vstack(
(boxes_this_image[gt_cls], gt_box))
if cfg.TEST.LEARN_NMS:
target_boxes = data_dict['nms_multi_target']
for target_box in target_boxes:
print("cur", target_box * scales[delta])
target_cls = int(target_box[4]) + 1
target_box[4] = 2 + target_box[5]
target_box[5] = target_box[6]
target_box = target_box[:6]
boxes_this_image[target_cls] = np.vstack(
(boxes_this_image[target_cls], target_box))
# vis_all_detection(data_dict['ref_data'].asnumpy(), boxes_this_image, imdb.classes, scales[delta], cfg)
# vis_double_all_detection(data_dict['data'].asnumpy(), boxes_this_image, data_dict['ref_data'].asnumpy(), ref_boxes_this_image, imdb.classes, scales[delta], cfg)
if cfg.TEST.LEARN_NMS:
for j in range(1, imdb.num_classes):
indexes = np.where(ref_scores[:, j - 1, 0] > thresh)[0]
cls_scores = ref_scores[indexes, j - 1, :]
cls_boxes = ref_boxes[indexes, j - 1, :]
cls_dets = np.hstack((cls_boxes, cls_scores))
# count the valid ground truth
if len(cls_scores) > 0:
# class_lut[j].append(idx + delta)
valid_tally += len(cls_scores)
valid_sum += len(ref_scores)
ref_all_boxes[j][idx + delta] = cls_dets
if DEBUG:
pass
keep = nms(cls_dets)
nms_cls_dets = cls_dets[keep, :]
target = data_dict['ref_nms_multi_target']
target_indices = np.where(target[:, 4] == j - 1)
target = target[target_indices]
nms_full_count_per_batch += bbox_equal_count(
nms_cls_dets, target)
gt_boxes = data_dict['ref_gt_boxes'][0].asnumpy()
gt_boxes = gt_boxes[np.where(gt_boxes[:,
4] == j)[0], :4]
gt_boxes /= scales[delta]
if len(cls_boxes) != 0 and len(gt_boxes) != 0:
overlap_mat = bbox_overlaps(
cls_boxes.astype(np.float),
gt_boxes.astype(np.float))
keep = nms(
cls_dets[np.where(overlap_mat > 0.5)[0]])
nms_cls_dets = cls_dets[np.where(
overlap_mat > 0.5)[0]][keep]
nms_pos_count_per_batch += bbox_equal_count(
nms_cls_dets, target)
all_count_per_batch += len(target)
else:
for j in range(1, imdb.num_classes):
indexes = np.where(ref_scores[:, j] > thresh)[0]
if cfg.TEST.FIRST_N > 0:
# todo: check whether the order affects the result
sort_indices = np.argsort(
ref_scores[:, j])[-cfg.TEST.FIRST_N:]
# sort_indices = np.argsort(-scores[:, j])[0:cfg.TEST.FIRST_N]
indexes = np.intersect1d(sort_indices, indexes)
cls_scores = ref_scores[indexes, j, np.newaxis]
cls_boxes = ref_boxes[
indexes,
4:8] if cfg.CLASS_AGNOSTIC else ref_boxes[indexes, j *
4:(j + 1) *
4]
# count the valid ground truth
if len(cls_scores) > 0:
# class_lut[j].append(idx+delta)
valid_tally += len(cls_scores)
valid_sum += len(ref_scores)
# print np.min(cls_scores), valid_tally, valid_sum
# cls_scores = scores[:, j, np.newaxis]
# cls_scores[cls_scores <= thresh] = thresh
# cls_boxes = boxes[:, 4:8] if cfg.CLASS_AGNOSTIC else boxes[:, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores))
if cfg.TEST.SOFTNMS:
ref_all_boxes[j][idx + delta] = nms(cls_dets)
else:
keep = nms(cls_dets)
ref_all_boxes[j][idx + delta] = cls_dets[keep, :]
if max_per_image > 0:
image_scores = np.hstack([
ref_all_boxes[j][idx + delta][:, -1]
for j in range(1, imdb.num_classes)
])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in range(1, imdb.num_classes):
keep = np.where(
ref_all_boxes[j][idx +
delta][:, -1] >= image_thresh)[0]
ref_all_boxes[j][idx + delta] = ref_all_boxes[j][
idx + delta][keep, :]
if vis:
ref_boxes_this_image = [[]] + [
ref_all_boxes[j][idx + delta]
for j in range(1, imdb.num_classes)
]
if show_gt:
gt_boxes = data_dict['ref_gt_boxes'][0]
for gt_box in gt_boxes:
gt_box = gt_box.asnumpy()
gt_cls = int(gt_box[4])
gt_box = gt_box / scales[delta]
gt_box[4] = 1
if cfg.TEST.LEARN_NMS:
gt_box = np.append(gt_box, 1)
ref_boxes_this_image[gt_cls] = np.vstack(
(ref_boxes_this_image[gt_cls], gt_box))
if cfg.TEST.LEARN_NMS:
target_boxes = data_dict['ref_nms_multi_target']
for target_box in target_boxes:
print("ref", target_box * scales[delta])
target_cls = int(target_box[4]) + 1
target_box[4] = 2 + target_box[5]
target_box[5] = target_box[6]
target_box = target_box[:6]
ref_boxes_this_image[target_cls] = np.vstack(
(ref_boxes_this_image[target_cls], target_box))
vis_double_all_detection(data_dict['data'][0:1].asnumpy(),
boxes_this_image,
data_dict['data'][1:2].asnumpy(),
ref_boxes_this_image, imdb.classes,
scales[delta], cfg)
# vis_all_detection(data_dict['ref_data'].asnumpy(), ref_boxes_this_image, imdb.classes, scales[delta], cfg)
if DEBUG:
nms_full_count.append(nms_full_count_per_batch)
nms_pos_count.append(nms_pos_count_per_batch)
is_max_count.append(is_max_count_per_batch)
all_count.append(all_count_per_batch)
print("full:{} pos:{} max:{}".format(
1.0 * sum(nms_full_count) / sum(all_count),
1.0 * sum(nms_pos_count) / sum(all_count),
1.0 * sum(is_max_count) / sum(all_count)))
idx += test_data.batch_size
t3 = time.time() - t
t = time.time()
post_processing_time.append(t3)
all_inference_time.append(t1 + t2 + t3)
inference_count += 1
if inference_count % 200 == 0:
valid_count = 500 if inference_count > 500 else inference_count
print("--->> running-average inference time per batch: {}".format(
float(sum(all_inference_time[-valid_count:])) / valid_count))
print("--->> running-average post processing time per batch: {}".
format(
float(sum(post_processing_time[-valid_count:])) /
valid_count))
print 'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, num_images, t1, t2, t3)
if logger:
logger.info(
'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, num_images, t1, t2, t3))
def train_net(args, ctx, pretrained_dir, pretrained_resnet, epoch, prefix,
begin_epoch, end_epoch, lr, lr_step):
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=True)
feat_sym = sym.get_internals()['rpn_cls_score_output']
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
git_commit_id = commands.getoutput('git rev-parse HEAD')
print("Git commit id:", git_commit_id)
logger.info('Git commit id: {}'.format(git_commit_id))
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [
load_gt_roidb(config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
motion_iou_path=config.dataset.motion_iou_path,
flip=config.TRAIN.FLIP,
use_philly=args.usePhilly) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
# load training data
train_data = AnchorLoader(feat_sym,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_stride=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING,
normalize_target=config.network.NORMALIZE_RPN,
bbox_mean=config.network.ANCHOR_MEANS,
bbox_std=config.network.ANCHOR_STDS)
# infer max shape
max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
print('providing maximum shape', max_data_shape, max_label_shape)
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix)
# load and initialize params
params_loaded = False
if config.TRAIN.RESUME:
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
mod._preload_opt_states = '%s-%04d.states' % (prefix, begin_epoch)
print('continue training from ', begin_epoch)
logger.info('continue training from ', begin_epoch)
params_loaded = True
elif config.TRAIN.AUTO_RESUME:
for cur_epoch in range(end_epoch - 1, begin_epoch, -1):
params_filename = '{}-{:04d}.params'.format(prefix, cur_epoch)
states_filename = '{}-{:04d}.states'.format(prefix, cur_epoch)
if os.path.exists(params_filename) and os.path.exists(
states_filename):
begin_epoch = cur_epoch
arg_params, aux_params = load_param(prefix,
cur_epoch,
convert=True)
mod._preload_opt_states = states_filename
print('auto continue training from {}, {}'.format(
params_filename, states_filename))
logger.info('auto continue training from {}, {}'.format(
params_filename, states_filename))
params_loaded = True
break
if not params_loaded:
arg_params, aux_params = load_param(os.path.join(
pretrained_dir, pretrained_resnet),
epoch,
convert=True)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# decide training params
# metric
eval_metric = metric.RCNNAccMetric(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
for child_metric in [eval_metric, cls_metric, bbox_metric]:
eval_metrics.add(child_metric)
if config.TRAIN.JOINT_TRAINING or (not config.TRAIN.LEARN_NMS):
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
for child_metric in [rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric]:
eval_metrics.add(child_metric)
if config.TRAIN.LEARN_NMS:
eval_metrics.add(metric.NMSLossMetric(config, 'pos'))
eval_metrics.add(metric.NMSLossMetric(config, 'neg'))
eval_metrics.add(metric.NMSAccMetric(config))
# callback
batch_end_callback = [
callback.Speedometer(train_data.batch_size, frequent=args.frequent)
]
if config.USE_PHILLY:
total_iter = (end_epoch - begin_epoch) * len(roidb) / input_batch_size
progress_frequent = min(args.frequent * 10, 100)
batch_end_callback.append(
callback.PhillyProgressCallback(total_iter, progress_frequent))
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = [
mx.callback.module_checkpoint(mod,
prefix,
period=1,
save_optimizer_states=True),
callback.do_checkpoint(prefix, means, stds)
]
# decide learning rate
# base_lr = lr * len(ctx) * config.TRAIN.BATCH_IMAGES
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch
]
lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [
int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None
}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
def pred_eval_impression_offline_seq_nms(gpu_id, first_predictor, cur_predictor, key_predictor, test_data, imdb, cfg, vis=False, thresh=1e-4, logger=None, ignore_cache=True):
"""
wrapper for calculating offline validation for faster data analysis
in this example, all threshold are set by hand
:param predictor: Predictor
:param test_data: data iterator, must be non-shuffle
:param imdb: image database
:param vis: controls visualization
:param thresh: valid detection threshold
:return:
"""
det_file = os.path.join(imdb.result_path, imdb.name + '_' + str(gpu_id))
print 'det_file=', det_file
if os.path.exists(det_file) and not ignore_cache:
with open(det_file, 'rb') as fid:
all_boxes, frame_ids = cPickle.load(fid)
return all_boxes, frame_ids
assert vis or not test_data.shuffle
vis = False
data_names = [k[0] for k in test_data.provide_data[0]]
num_images = test_data.size
roidb_frame_ids = [x['frame_id'] for x in test_data.roidb]
if not isinstance(test_data, PrefetchingIter):
test_data = PrefetchingIter(test_data)
# limit detections to max_per_image over all classes
# max_per_image = cfg.TEST.max_per_image
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
frame_ids = np.zeros(num_images, dtype=np.int)
roidb_idx = -1
roidb_offset = -1
idx = 0
data_time, net_time, post_time = 0.0, 0.0, 0.0
t = time.time()
for im_info, key_frame_flag, data_batch in test_data:
t1 = time.time() - t
t= time.time()
scales = [iim_info[0, 2] for iim_info in im_info]
if key_frame_flag == 0: # current frame is the first frame of the video
key_scores_all, key_boxes_all, key_data_dict_all, conv_feat, _, _ = im_detect_impression_offline(first_predictor, data_batch, data_names, scales, cfg)
feat_task = conv_feat
impression = conv_feat
data_batch.data[0][-1] = feat_task
data_batch.provide_data[0][-1] = ('key_feat_task', feat_task.shape)
scores_all, pred_boxes_all, data_dict_all, _, _, _ = im_detect_impression_offline(cur_predictor, data_batch, data_names, scales, cfg)
elif key_frame_flag == 1: #current frame is the keyframe
scores_all = key_scores_all
pred_boxes_all = key_boxes_all
data_dict_all = key_data_dict_all
elif key_frame_flag == 2: #first frame of the new segment
data_batch.data[0][-2] = impression
data_batch.provide_data[0][-2] = ('impression', impression.shape)
key_scores_all, key_boxes_all, key_data_dict_all, _, impression, feat_task = im_detect_impression_offline(key_predictor, data_batch, data_names, scales, cfg)
data_batch.data[0][-1] = feat_task
data_batch.provide_data[0][-1] = ('key_feat_task', feat_task.shape)
scores_all, pred_boxes_all, data_dict_all, _, _, _ = im_detect_impression_offline(cur_predictor, data_batch, data_names, scales, cfg)
else:
data_batch.data[0][-1] = feat_task
data_batch.provide_data[0][-1] = ('key_feat_task', feat_task.shape)
scores_all, pred_boxes_all, data_dict_all, _, _, _ = im_detect_impression_offline(cur_predictor, data_batch, data_names, scales, cfg)
if key_frame_flag == 0:
roidb_idx += 1
roidb_offset = 0
else:
roidb_offset += 1
frame_ids[idx] = roidb_frame_ids[roidb_idx] + roidb_offset
t2 = time.time() - t
t = time.time()
for delta, (scores, boxes, data_dict) in enumerate(zip(scores_all, pred_boxes_all, data_dict_all)):
for j in range(1, imdb.num_classes):
indexes = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[indexes, j, np.newaxis]
cls_boxes = boxes[indexes, 4:8] if cfg.CLASS_AGNOSTIC else boxes[indexes, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores))
all_boxes[j][idx + delta] = cls_dets
if vis:
show_boxes_with_nms(data_dict['data_cur'].asnumpy(), scores, boxes, imdb.classes, scales[delta], cfg)
idx += test_data.batch_size
t3 = time.time() - t
t = time.time()
data_time += t1
net_time += t2
post_time += t3
print 'seq nms testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(idx, num_images,
data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size)
if logger:
logger.info('seq nms testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(idx, num_images,
data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size))
with open(det_file, 'wb') as f:
cPickle.dump((all_boxes, frame_ids), f, protocol=cPickle.HIGHEST_PROTOCOL)
return all_boxes, frame_ids
def pred_eval(gpu_id,
key_predictor,
cur_predictor,
test_data,
imdb,
cfg,
vis=False,
thresh=1e-4,
logger=None,
ignore_cache=True):
"""
wrapper for calculating offline validation for faster data analysis
in this example, all threshold are set by hand
:param predictor: Predictor
:param test_data: data iterator, must be non-shuffle
:param imdb: image database
:param vis: controls visualization
:param thresh: valid detection threshold
:return:
"""
det_file = os.path.join(imdb.result_path,
imdb.name + '_' + str(gpu_id) + '_detections.pkl')
if os.path.exists(det_file) and not ignore_cache:
with open(det_file, 'rb') as fid:
all_boxes, frame_ids = cPickle.load(fid)
return all_boxes, frame_ids
assert vis or not test_data.shuffle
data_names = [k[0] for k in test_data.provide_data[0]]
num_images = test_data.size
roidb_frame_ids = [x['frame_id'] for x in test_data.roidb]
if not isinstance(test_data, PrefetchingIter):
test_data = PrefetchingIter(test_data)
nms = py_nms_wrapper(cfg.TEST.NMS)
# limit detections to max_per_image over all classes
max_per_image = cfg.TEST.max_per_image
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
frame_ids = np.zeros(num_images, dtype=np.int)
roidb_idx = -1
roidb_offset = -1
idx = 0
data_time, net_time, post_time = 0.0, 0.0, 0.0
t = time.time()
for im_info, key_frame_flag, data_batch in test_data:
t1 = time.time() - t
t = time.time()
scales = [iim_info[0, 2] for iim_info in im_info]
if key_frame_flag != 2:
scores_all, boxes_all, data_dict_all, feat = im_detect(
key_predictor, data_batch, data_names, scales, cfg)
else:
data_batch.data[0][-1] = feat
data_batch.provide_data[0][-1] = ('feat_key', feat.shape)
scores_all, boxes_all, data_dict_all, _ = im_detect(
cur_predictor, data_batch, data_names, scales, cfg)
if key_frame_flag == 0:
roidb_idx += 1
roidb_offset = 0
else:
roidb_offset += 1
frame_ids[idx] = roidb_frame_ids[roidb_idx] + roidb_offset
t2 = time.time() - t
t = time.time()
for delta, (scores, boxes, data_dict) in enumerate(
zip(scores_all, boxes_all, data_dict_all)):
for j in range(1, imdb.num_classes):
indexes = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[indexes, j, np.newaxis]
cls_boxes = boxes[indexes,
4:8] if cfg.CLASS_AGNOSTIC else boxes[
indexes, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores))
keep = nms(cls_dets)
all_boxes[j][idx + delta] = cls_dets[keep, :]
if max_per_image > 0:
image_scores = np.hstack([
all_boxes[j][idx + delta][:, -1]
for j in range(1, imdb.num_classes)
])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in range(1, imdb.num_classes):
keep = np.where(
all_boxes[j][idx + delta][:,
-1] >= image_thresh)[0]
all_boxes[j][idx +
delta] = all_boxes[j][idx +
delta][keep, :]
if vis:
boxes_this_image = [[]] + [
all_boxes[j][idx + delta]
for j in range(1, imdb.num_classes)
]
vis_all_detection(data_dict['data'].asnumpy(),
boxes_this_image, imdb.classes,
scales[delta], cfg)
idx += test_data.batch_size
t3 = time.time() - t
t = time.time()
data_time += t1
net_time += t2
post_time += t3
print 'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, num_images, data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size)
if logger:
logger.info(
'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, num_images, data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size))
with open(det_file, 'wb') as f:
cPickle.dump((all_boxes, frame_ids),
f,
protocol=cPickle.HIGHEST_PROTOCOL)
return all_boxes, frame_ids
def pred_eval(predictor,
test_data,
imdb,
vis=False,
ignore_cache=None,
logger=None):
"""
wrapper for calculating offline validation for faster data analysis
in this example, all threshold are set by hand
:param predictor: Predictor
:param test_data: data iterator, must be non-shuffle
:param imdb: image database
:param vis: controls visualization
:param ignore_cache: ignore the saved cache file
:param logger: the logger instance
:return:
"""
res_file = os.path.join(imdb.result_path, imdb.name + '_segmentations.pkl')
if os.path.exists(res_file) and not ignore_cache:
with open(res_file, 'rb') as fid:
evaluation_results = cPickle.load(fid)
print 'evaluate segmentation: \n'
if logger:
logger.info('evaluate segmentation: \n')
meanIU = evaluation_results['meanIU']
IU_array = evaluation_results['IU_array']
print 'IU_array:\n'
if logger:
logger.info('IU_array:\n')
for i in range(len(IU_array)):
print '%.5f' % IU_array[i]
if logger:
logger.info('%.5f' % IU_array[i])
print 'meanIU:%.5f' % meanIU
if logger:
logger.info('meanIU:%.5f' % meanIU)
return
assert vis or not test_data.shuffle
if not isinstance(test_data, PrefetchingIter):
test_data = PrefetchingIter(test_data)
num_images = imdb.num_images
all_segmentation_result = [[] for _ in xrange(num_images)]
idx = 0
data_time, net_time, post_time = 0.0, 0.0, 0.0
t = time.time()
for data_batch in test_data:
t1 = time.time() - t
t = time.time()
output_all = predictor.predict(data_batch)
output_all = [
mx.ndarray.argmax(output['softmax_output'], axis=1).asnumpy()
for output in output_all
]
t2 = time.time() - t
t = time.time()
all_segmentation_result[idx:idx + test_data.batch_size] = [
output.astype('int8') for output in output_all
]
idx += test_data.batch_size
t3 = time.time() - t
t = time.time()
data_time += t1
net_time += t2
post_time += t3
print 'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, imdb.num_images, data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size)
if logger:
logger.info(
'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(
idx, imdb.num_images,
data_time / idx * test_data.batch_size,
net_time / idx * test_data.batch_size,
post_time / idx * test_data.batch_size))
evaluation_results = imdb.evaluate_segmentations(all_segmentation_result)
if not os.path.exists(res_file) or ignore_cache:
with open(res_file, 'wb') as f:
cPickle.dump(evaluation_results,
f,
protocol=cPickle.HIGHEST_PROTOCOL)
print 'evaluate segmentation: \n'
if logger:
logger.info('evaluate segmentation: \n')
meanIU = evaluation_results['meanIU']
IU_array = evaluation_results['IU_array']
print 'IU_array:\n'
if logger:
logger.info('IU_array:\n')
for i in range(len(IU_array)):
print '%.5f' % IU_array[i]
if logger:
logger.info('%.5f' % IU_array[i])
print 'meanIU:%.5f' % meanIU
if logger:
logger.info('meanIU:%.5f' % meanIU)
def train_net(args, ctx, pretrained, pretrained_flow, epoch, prefix,
begin_epoch, end_epoch, lr, lr_step):
logger, final_output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
prefix = os.path.join(final_output_path, prefix)
# load symbol
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
final_output_path)
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_train_symbol(config)
feat_sym = sym.get_internals()['rpn_cls_score_output']
feat_conv_3x3_relu = sym.get_internals()['feat_conv_3x3_relu_output']
# setup multi-gpu
batch_size = len(ctx)
input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size
# print config
pprint.pprint(config)
logger.info('training config:{}\n'.format(pprint.pformat(config)))
# load dataset and prepare imdb for training
image_sets = [iset for iset in config.dataset.image_set.split('+')]
roidbs = [
load_gt_roidb(config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
flip=config.TRAIN.FLIP) for image_set in image_sets
]
roidb = merge_roidb(roidbs)
roidb = filter_roidb(roidb, config)
# load training data
train_data = AnchorLoader(feat_sym,
feat_conv_3x3_relu,
roidb,
config,
batch_size=input_batch_size,
shuffle=config.TRAIN.SHUFFLE,
ctx=ctx,
feat_stride=config.network.RPN_FEAT_STRIDE,
anchor_scales=config.network.ANCHOR_SCALES,
anchor_ratios=config.network.ANCHOR_RATIOS,
aspect_grouping=config.TRAIN.ASPECT_GROUPING,
normalize_target=config.network.NORMALIZE_RPN,
bbox_mean=config.network.ANCHOR_MEANS,
bbox_std=config.network.ANCHOR_STDS)
# infer max shape
#max_data_shape = [('data', (config.TRAIN.BATCH_IMAGES, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES]))),
# ('data_ref', (config.TRAIN.BATCH_IMAGES, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES]))),
# ('eq_flag', (1,))]
data_shape1 = {
'data_ref':
(config.TRAIN.BATCH_IMAGES, 3, max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES])),
}
_, feat_shape111, _ = feat_conv_3x3_relu.infer_shape(**data_shape1)
max_data_shape = [('data_ref', (config.TRAIN.BATCH_IMAGES, 3,
max([v[0] for v in config.SCALES]),
max([v[1] for v in config.SCALES]))),
('eq_flag', (1, )),
('motion_vector', (config.TRAIN.BATCH_IMAGES, 2,
int(feat_shape111[0][2]),
int(feat_shape111[0][3])))]
max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape)
max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_IMAGES, 100, 5)))
print 'providing maximum shape', max_data_shape, max_label_shape
data_shape_dict = dict(train_data.provide_data_single +
train_data.provide_label_single)
pprint.pprint(data_shape_dict)
sym_instance.infer_shape(data_shape_dict)
# load and initialize params
if config.TRAIN.RESUME:
print('continue training from ', begin_epoch)
arg_params, aux_params = load_param(prefix, begin_epoch, convert=True)
else:
arg_params, aux_params = load_param(pretrained, epoch, convert=True)
#arg_params_flow, aux_params_flow = load_param(pretrained_flow, epoch, convert=True)
#arg_params.update(arg_params_flow)
#aux_params.update(aux_params_flow)
sym_instance.init_weight(config, arg_params, aux_params)
# check parameter shapes
sym_instance.check_parameter_shapes(arg_params, aux_params,
data_shape_dict)
# create solver
fixed_param_prefix = config.network.FIXED_PARAMS
data_names = [k[0] for k in train_data.provide_data_single]
label_names = [k[0] for k in train_data.provide_label_single]
mod = MutableModule(
sym,
data_names=data_names,
label_names=label_names,
logger=logger,
context=ctx,
max_data_shapes=[max_data_shape for _ in range(batch_size)],
max_label_shapes=[max_label_shape for _ in range(batch_size)],
fixed_param_prefix=fixed_param_prefix)
if config.TRAIN.RESUME:
mod._preload_opt_states = '%s-%04d.states' % (prefix, begin_epoch)
# decide training params
# metric
rpn_eval_metric = metric.RPNAccMetric()
rpn_cls_metric = metric.RPNLogLossMetric()
rpn_bbox_metric = metric.RPNL1LossMetric()
eval_metric = metric.RCNNAccMetric(config)
cls_metric = metric.RCNNLogLossMetric(config)
bbox_metric = metric.RCNNL1LossMetric(config)
eval_metrics = mx.metric.CompositeEvalMetric()
# rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric
for child_metric in [
rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric,
cls_metric, bbox_metric
]:
eval_metrics.add(child_metric)
# callback
batch_end_callback = callback.Speedometer(train_data.batch_size,
frequent=args.frequent)
means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
2 if config.CLASS_AGNOSTIC else config.dataset.NUM_CLASSES)
epoch_end_callback = [
mx.callback.module_checkpoint(mod,
prefix,
period=1,
save_optimizer_states=True),
callback.do_checkpoint(prefix, means, stds)
]
# decide learning rate
base_lr = lr
lr_factor = config.TRAIN.lr_factor
lr_epoch = [float(epoch) for epoch in lr_step.split(',')]
lr_epoch_diff = [
epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch
]
lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff)))
lr_iters = [
int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff
]
print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters)
lr_scheduler = WarmupMultiFactorScheduler(lr_iters, lr_factor,
config.TRAIN.warmup,
config.TRAIN.warmup_lr,
config.TRAIN.warmup_step)
# optimizer
optimizer_params = {
'momentum': config.TRAIN.momentum,
'wd': config.TRAIN.wd,
'learning_rate': lr,
'lr_scheduler': lr_scheduler,
'rescale_grad': 1.0,
'clip_gradient': None
}
if not isinstance(train_data, PrefetchingIter):
train_data = PrefetchingIter(train_data)
print('Start to train model')
# train
mod.fit(train_data,
eval_metric=eval_metrics,
epoch_end_callback=epoch_end_callback,
batch_end_callback=batch_end_callback,
kvstore=config.default.kvstore,
optimizer='sgd',
optimizer_params=optimizer_params,
arg_params=arg_params,
aux_params=aux_params,
begin_epoch=begin_epoch,
num_epoch=end_epoch)
def pred_eval(predictor, test_data, imdb, cfg, vis=False, thresh=1e-3, logger=None, ignore_cache=True):
"""
wrapper for calculating offline validation for faster data analysis
in this example, all threshold are set by hand
:param predictor: Predictor
:param test_data: data iterator, must be non-shuffle
:param imdb: image database
:param vis: controls visualization
:param thresh: valid detection threshold
:return:
"""
det_file = os.path.join(imdb.result_path, imdb.name + '_detections.pkl')
if os.path.exists(det_file) and not ignore_cache:
with open(det_file, 'rb') as fid:
all_boxes = cPickle.load(fid)
info_str = imdb.evaluate_detections(all_boxes)
if logger:
logger.info('evaluate detections: \n{}'.format(info_str))
return
assert vis or not test_data.shuffle
data_names = [k[0] for k in test_data.provide_data[0]]
if not isinstance(test_data, PrefetchingIter):
test_data = PrefetchingIter(test_data)
# limit detections to max_per_image over all classes
max_per_image = cfg.TEST.max_per_image
num_images = imdb.num_images
for test_scale_index, test_scale in enumerate(cfg.TEST_SCALES):
det_file_single_scale = os.path.join(imdb.result_path, imdb.name + '_detections_' + str(test_scale_index) + '.pkl')
# if os.path.exists(det_file_single_scale):
# continue
cfg.SCALES = [test_scale]
test_data.reset()
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes_single_scale = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
detect_at_single_scale(predictor, data_names, imdb, test_data, cfg, thresh, vis, all_boxes_single_scale, logger)
with open(det_file_single_scale, 'wb') as f:
cPickle.dump(all_boxes_single_scale, f, protocol=cPickle.HIGHEST_PROTOCOL)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)] for _ in range(imdb.num_classes)]
for test_scale_index, test_scale in enumerate(cfg.TEST_SCALES):
det_file_single_scale = os.path.join(imdb.result_path, imdb.name + '_detections_' + str(test_scale_index) + '.pkl')
if os.path.exists(det_file_single_scale):
with open(det_file_single_scale, 'rb') as fid:
all_boxes_single_scale = cPickle.load(fid)
for idx_class in range(1, imdb.num_classes):
for idx_im in range(0, num_images):
if len(all_boxes[idx_class][idx_im]) == 0:
all_boxes[idx_class][idx_im] = all_boxes_single_scale[idx_class][idx_im]
else:
all_boxes[idx_class][idx_im] = np.vstack((all_boxes[idx_class][idx_im], all_boxes_single_scale[idx_class][idx_im]))
for idx_class in range(1, imdb.num_classes):
for idx_im in range(0, num_images):
if cfg.TEST.USE_SOFTNMS:
soft_nms = py_softnms_wrapper(cfg.TEST.SOFTNMS_THRESH, max_dets=max_per_image)
all_boxes[idx_class][idx_im] = soft_nms(all_boxes[idx_class][idx_im])
else:
nms = py_nms_wrapper(cfg.TEST.NMS)
keep = nms(all_boxes[idx_class][idx_im])
all_boxes[idx_class][idx_im] = all_boxes[idx_class][idx_im][keep, :]
if max_per_image > 0:
for idx_im in range(0, num_images):
image_scores = np.hstack([all_boxes[j][idx_im][:, -1]
for j in range(1, imdb.num_classes)])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in range(1, imdb.num_classes):
keep = np.where(all_boxes[j][idx_im][:, -1] >= image_thresh)[0]
all_boxes[j][idx_im] = all_boxes[j][idx_im][keep, :]
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, protocol=cPickle.HIGHEST_PROTOCOL)
info_str = imdb.evaluate_detections(all_boxes)
if logger:
logger.info('evaluate detections: \n{}'.format(info_str))
