def __init__(self,
config,
im_list,
model=None,
gpu_id=0): # im_list passed from cityscapes dataset
self.nb_features = config['nb_features']
self.split = config['split']
self.im_list = im_list
workspace.GlobalInit(['caffe2', '--caffe2_log_level=0'])
if not cfg.is_immutable(
): # just in case feature extractor has not been set up already
dset = b'cityscapes_fine_instanceonly_seg_' + self.split
args = Namespace(
cfg_file=MASK_RCNN_CONFIG,
wait=True,
multi_gpu_testing=False,
range=None, #[0, 3],
opts=['OUTPUT_DIR', config['save']])
merge_cfg_from_file(args.cfg_file)
if args.opts is not None:
merge_cfg_from_list(args.opts)
assert_and_infer_cfg()
if model is None or model == False:
self.model = initialize_model_from_cfg(instanciate_head_also=True)
else:
self.model = model
gpu_dev = caffe2_core.DeviceOption(caffe2_pb2.CUDA, gpu_id)
name_scope = 'gpu_{}'.format(gpu_id)
# Subsampler - originally inside the FPN network. But we don't want to predict the subsampled features.
# Instead, we want to predict the features, and then use the same subsampling operator to obtain the subsampled features
with caffe2_core.NameScope(name_scope):
with caffe2_core.DeviceScope(gpu_dev):
self.subsampler = caffe2_core.CreateOperator(
"MaxPool", # operator
["predicted_fpn_res5_2_sum"], #input blobs
["predicted_fpn_res5_2_sum_subsampled_2x"], #output blobs
kernel=1,
pad=0,
stride=2,
deterministic=1)
self.timers = {
k: Timer()
for k in [
'im_detect_bbox', 'im_detect_mask', 'misc_bbox', 'misc_mask',
'im_forward_backbone'
]
}
# For evaluation with respect to the dataset's gt, we save the prediction of the annotated frame for each sequence
self.num_classes = cfg.MODEL.NUM_CLASSES
self.num_images = len(self.im_list)
self.all_boxes_ann_frame, self.all_segms_ann_frame, _ = empty_results(
self.num_classes, self.num_images)
self.id_sequences = []
self.gpu_id = gpu_id
def configure_bbox_reg_weights(model, saved_cfg):
"""Compatibility for old models trained with bounding box regression
mean/std normalization (instead of fixed weights).
"""
if 'MODEL' not in saved_cfg or 'BBOX_REG_WEIGHTS' not in saved_cfg.MODEL:
logger.warning('Model from weights file was trained before config key '
'MODEL.BBOX_REG_WEIGHTS was added. Forcing '
'MODEL.BBOX_REG_WEIGHTS = (1., 1., 1., 1.) to ensure '
'correct **inference** behavior.')
# Generally we don't allow modifying the config, but this is a one-off
# hack to support some very old models
is_immutable = cfg.is_immutable()
cfg.immutable(False)
cfg.MODEL.BBOX_REG_WEIGHTS = (1., 1., 1., 1.)
cfg.immutable(is_immutable)
logger.info('New config:')
logger.info(pprint.pformat(cfg))
assert not model.train, (
'This model was trained with an older version of the code that '
'used bounding box regression mean/std normalization. It can no '
'longer be used for training. To upgrade it to a trainable model '
'please use fb/compat/convert_bbox_reg_normalized_model.py.')
def configure_bbox_reg_weights(model, saved_cfg):
"""Compatibility for old models trained with bounding box regression
mean/std normalization (instead of fixed weights).
"""
if 'MODEL' not in saved_cfg or 'BBOX_REG_WEIGHTS' not in saved_cfg.MODEL:
logger.warning('Model from weights file was trained before config key '
'MODEL.BBOX_REG_WEIGHTS was added. Forcing '
'MODEL.BBOX_REG_WEIGHTS = (1., 1., 1., 1.) to ensure '
'correct **inference** behavior.')
# Generally we don't allow modifying the config, but this is a one-off
# hack to support some very old models
is_immutable = cfg.is_immutable()
cfg.immutable(False)
cfg.MODEL.BBOX_REG_WEIGHTS = (1., 1., 1., 1.)
cfg.immutable(is_immutable)
logger.info('New config:')
logger.info(pprint.pformat(cfg))
assert not model.train, (
'This model was trained with an older version of the code that '
'used bounding box regression mean/std normalization. It can no '
'longer be used for training. To upgrade it to a trainable model '
'please use fb/compat/convert_bbox_reg_normalized_model.py.'
)
def __init__(self,
split,
frame_ss,
nSeq,
features,
savedir,
size=None,
loaded_model=None):
super(CityscapesDatasetAndFeatures, self).__init__()
self.split = split
self.frame_ss = frame_ss
self.nSeq = nSeq or 1
self.features = features
self.FPNfeatures = u'fpn_res5_2_sum' in self.features \
or u'fpn_res4_5_sum' in self.features \
or u'fpn_res3_3_sum' in self.features \
or u'fpn_res2_2_sum' in self.features
self.limitSize = size
# Check which features have been precomputed and load them if they have been found
logger.info('Searching for precomputed features...')
self.precompute_features_dir = PRECOMPUTED_FEATURES_DIR
self.potential_precomputed_feature_types = [
u'fpn_res5_2_sum', u'fpn_res4_5_sum', u'fpn_res3_3_sum',
u'fpn_res2_2_sum'
]
self.load_precomputed = self.check_requested_features_that_could_be_precomputed_were(
)
if self.load_precomputed:
self.precomputed_features_index, self.precomputed_features = \
self.load_requested_precomputed_features()
self.requested_fpn_features = [] + \
([u'fpn_res5_2_sum'] if 'fpn_res5_2_sum' in self.features else []) + \
([u'fpn_res4_5_sum'] if 'fpn_res4_5_sum' in self.features else []) + \
([u'fpn_res3_3_sum'] if 'fpn_res3_3_sum' in self.features else []) + \
([u'fpn_res2_2_sum'] if 'fpn_res2_2_sum' in self.features else [])
import detectron.utils.c2 as c2_utils
c2_utils.import_detectron_ops()
workspace.GlobalInit(['caffe2', '--caffe2_log_level=0'])
dset = b'cityscapes_fine_instanceonly_seg_sequences_' + self.split
if not cfg.is_immutable(
): # just in case feature extractor has not been set up already
# Preparing where to load data from and how using coco api
args = Namespace(cfg_file=MASK_RCNN_CONFIG,
wait=True,
multi_gpu_testing=False,
opts=['OUTPUT_DIR', savedir])
merge_cfg_from_file(args.cfg_file)
if args.opts is not None:
merge_cfg_from_list(args.opts)
assert_and_infer_cfg()
assert os.path.exists(cfg.TEST.WEIGHTS), \
'need path to pretrained instance segmentation model'
assert not cfg.MODEL.RPN_ONLY, 'end to end model required'
dataset = JsonDataset(dset)
self.dataset = dataset
self.im_list = self.dataset.get_roidb()
# Preparing the model from which we obtain the features
if not self.load_precomputed:
if loaded_model is None:
model = initialize_model_from_cfg()
self.model = model
else:
self.model = loaded_model
else:
self.model = False
# Store config for further use in running the Mask RCNN head
logger.info('Cityscapes dataset, size : %d' % len(self))
