def add_fast_rcnn_blobs(blobs, im_scales, roidb):
"""Add blobs needed for training Fast R-CNN style models."""
global label_code
if label_code == None:
f = open('/data/dataset/AVA/preproc/annotations/ava_label_codes.json',
'r')
label_code = json.load(f)
# Sample training RoIs from each image and append them to the blob lists
for im_i, entry in enumerate(roidb):
frcn_blobs = _sample_rois(entry, im_scales[im_i], im_i, label_code)
for k, v in frcn_blobs.items():
blobs[k].append(v)
# Concat the training blob lists into tensors
for k, v in blobs.items():
if isinstance(v, list) and len(v) > 0:
blobs[k] = np.concatenate(v)
# Add FPN multilevel training RoIs, if configured
if cfg.FPN.FPN_ON and cfg.FPN.MULTILEVEL_ROIS:
_add_multilevel_rois(blobs)
# Perform any final work and validity checks after the collating blobs for
# all minibatch images
valid = True
if cfg.MODEL.KEYPOINTS_ON:
valid = keypoint_rcnn_roi_data.finalize_keypoint_minibatch(
blobs, valid)
return valid
def add_fast_rcnn_blobs(blobs, im_scales, roidb):
"""Add blobs needed for training Fast R-CNN style models."""
# Sample training RoIs from each image and append them to the blob lists
blobs['fg_num'] = [0.]
for im_i, entry in enumerate(roidb):
frcn_blobs = _sample_rois(entry, im_scales[im_i], im_i)
for k, v in frcn_blobs.items():
if k == 'fg_num':
blobs[k] += v
else:
blobs[k].append(v)
# Concat the training blob lists into tensors
#
for k, v in blobs.items():
if isinstance(v, list) and len(v) > 0:
blobs[k] = np.concatenate(v)
# Add FPN multilevel training RoIs, if configured
if cfg.FPN.FPN_ON and cfg.FPN.MULTILEVEL_ROIS:
_add_multilevel_rois(blobs)
# Perform any final work and validity checks after the collating blobs for
# all minibatch images
valid = True
if cfg.MODEL.KEYPOINTS_ON:
valid = keypoint_rcnn_roi_data.finalize_keypoint_minibatch(blobs, valid)
return valid
def add_cascade_rcnn_blobs(blobs, im_scales, roidb, stage):
"""Add blobs needed for training Cascade R-CNN style models."""
# Sample training RoIs from each image and append them to the blob lists
for im_i, entry in enumerate(roidb):
frcn_blobs = _sample_rois(entry, im_scales[im_i], im_i, stage)
for k, v in frcn_blobs.items():
if 'mask' not in k and 'keypoint' not in k:
k += "_{}".format(stage)
blobs[k].append(v)
# Concat the training blob lists into tensors
for k, v in blobs.items():
if isinstance(v, list) and len(v) > 0:
blobs[k] = np.concatenate(v)
# Add FPN multilevel training RoIs, if configured
if cfg.FPN.FPN_ON and cfg.FPN.MULTILEVEL_ROIS:
_add_multilevel_rois(blobs, stage)
# Perform any final work and validity checks after the collating blobs for
# all minibatch images
valid = True
if cfg.MODEL.KEYPOINTS_ON and cfg.KRCNN.AT_STAGE == stage:
valid = keypoint_rcnn_roi_data.finalize_keypoint_minibatch(
blobs, valid)
return valid
def add_fast_rcnn_blobs(blobs, im_scales, roidb):
"""Add blobs needed for training Fast R-CNN style models."""
# Sample training RoIs from each image and append them to the blob lists
# # by Jerome: make sure the roi batch is equally divisible by IMGS_PER_BATCH by decreasing the rois per img to
# # the amount of the image in the batch with the least of them
# min_rois_per_img = min(len(entry['gt_classes']) for entry in roidb)
# if min_rois_per_img < int(cfg.TRAIN.BATCH_SIZE_PER_IM):
# print('rois per img pruned to', min_rois_per_img)
for im_i, entry in enumerate(roidb):
if True or (cfg.TRAIN.DOMAIN_ADAPTATION
and entry['is_source']) or not cfg.TRAIN.DOMAIN_ADAPTATION:
frcn_blobs = _sample_rois(entry, im_scales[im_i],
im_i) #, max_rois=min_rois_per_img)
for k, v in frcn_blobs.items():
blobs[k].append(v)
if cfg.TRAIN.DOMAIN_ADAPTATION:
da_blobs = _sample_da_rois(entry, im_scales[im_i], im_i)
for k, v in da_blobs.items():
blobs[k].append(v)
# Concat the training blob lists into tensors
for k, v in blobs.items():
if isinstance(v, list) and len(v) > 0:
blobs[k] = np.concatenate(v)
# Add FPN multilevel training RoIs, if configured
if cfg.FPN.FPN_ON and cfg.FPN.MULTILEVEL_ROIS:
_add_multilevel_rois(blobs)
if cfg.TRAIN.PADA:
# Cancel out the large gradients from loss-averaging over small roi proposal batches.
# loss-averaging for regular batches means dividing the instance losses by the regular batch size.
# we correct small batch loss-averaging by multiplying by the small batch size, and dividing by the regular batch size.
regular_rois_batch_size = cfg.TRAIN.IMS_PER_BATCH * cfg.TRAIN.BATCH_SIZE_PER_IM
pada_weights = blobs['pada_roi_weights']
rois = len(pada_weights)
pada_weights *= rois / regular_rois_batch_size # lower pada_weights means lower learning rate.
blobs['pada_roi_weights'] = pada_weights
# Perform any final work and validity checks after the collating blobs for
# all minibatch images
valid = True
if cfg.MODEL.KEYPOINTS_ON:
valid = keypoint_rcnn_roi_data.finalize_keypoint_minibatch(
blobs, valid)
return valid
def add_fast_rcnn_blobs(blobs, im_scales, roidb):
"""Add blobs needed for training Fast R-CNN style models."""
# Sample training RoIs from each image and append them to the blob lists
for im_i, entry in enumerate(roidb):
frcn_blobs = _sample_rois(entry, im_scales[im_i], im_i)
for k, v in frcn_blobs.items():
blobs[k].append(v)
# Concat the training blob lists into tensors
for k, v in blobs.items():
if isinstance(v, list) and len(v) > 0:
blobs[k] = np.concatenate(v)
# Add FPN multilevel training RoIs, if configured
if cfg.FPN.FPN_ON and cfg.FPN.MULTILEVEL_ROIS:
_add_multilevel_rois(blobs)
# Perform any final work and validity checks after the collating blobs for
# all minibatch images
valid = True
if cfg.MODEL.KEYPOINTS_ON:
valid = keypoint_rcnn_roi_data.finalize_keypoint_minibatch(blobs, valid)
return valid
