def _get_blobs(im,
rois,
target_scale,
target_max_size,
pose_model=None,
entry=None):
"""Convert an image and RoIs within that image into network inputs."""
blobs = {}
if entry is not None: # add pose to input
blobs['data'], im_scale, blobs['im_info'], pose_blob, pose_line = \
blob_utils.get_image_pose_blob(im, target_scale, target_max_size, entry)
if 'ATR' in cfg.TEST.DATASETS[0]:
blobs['pose_pred_4'], blobs['pose_pred_8'], blobs[
'pose_pred_16'], blobs['pose_pred_32'] = _resize_pose_blob(
pose_blob, 26)
else:
blobs['pose_pred_4'], blobs['pose_pred_8'], blobs[
'pose_pred_16'], blobs['pose_pred_32'] = _resize_pose_blob(
pose_blob, 26)
blobs['pose_line_8'], blobs['pose_line_16'] = _resize_poseline_blob(
pose_line)
else: # no pose input
blobs['data'], im_scale, blobs['im_info'] = \
blob_utils.get_image_blob(im, target_scale, target_max_size)
if rois is not None:
blobs['rois'] = _get_rois_blob(rois, im_scale)
if pose_model is not None:
blobs['pose_pred'] = np.asarray(pose_model.pred_pose_one_img(im),
dtype=np.float32)
return blobs, im_scale
def im_conv_body_only(model, im, target_scale, target_max_size):
"""Runs `model.conv_body_net` on the given image `im`."""
im_blob, im_scale, _im_info = blob_utils.get_image_blob(
im, target_scale, target_max_size)
workspace.FeedBlob(core.ScopedName('data'), im_blob)
workspace.RunNet(model.conv_body_net.Proto().name)
return im_scale
def im_extract_features(model, im, ffpn_levels, timers=None):
"""
Extracts high level features listed in levels from model for the image im.
"""
if timers is None:
timers = defaultdict(Timer)
timers['im_extract_features'].tic()
# Get inputs to the caffe2 model
blobs = {}
blobs['data'], im_scale, blobs['im_info'] = \
blob_utils.get_image_blob(im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE)
for k, v in blobs.items():
workspace.FeedBlob(core.ScopedName(k), v.astype(np.float32,
copy=False))
# Run the net forward
workspace.RunNet(model.backbone.Proto().name)
# Extract the features
features = {}
for feat in ffpn_levels:
features[feat] = workspace.FetchBlob(core.ScopedName(feat))
im_info = blobs['im_info']
return features, im_info, im_scale, im.shape
def _get_blobs(im, rois, target_scale, target_max_size):
"""Convert an image and RoIs within that image into network inputs."""
blobs = {}
blobs['data'], im_scale, blobs['im_info'] = \
blob_utils.get_image_blob(im, target_scale, target_max_size)
if rois is not None:
blobs['rois'] = _get_rois_blob(rois, im_scale)
return blobs, im_scale
def _get_blobs(im, rois, target_scale, target_max_size):
"""Convert an image and RoIs within that image into network inputs."""
blobs = {}
blobs['data'], im_scale, blobs['im_info'] = \
blob_utils.get_image_blob(im, target_scale, target_max_size)
if rois is not None:
blobs['rois'] = _get_rois_blob(rois, im_scale)
return blobs, im_scale
def im_conv_body_only(model, im, target_scale, target_max_size):
"""Runs `model.conv_body_net` on the given image `im`."""
im_blob, im_scale, _im_info = blob_utils.get_image_blob(
im, target_scale, target_max_size
)
workspace.FeedBlob(core.ScopedName('data'), im_blob)
workspace.RunNet(model.conv_body_net.Proto().name)
return im_scale
def im_proposals(model, im):
"""Generate RPN proposals on a single image."""
inputs = {}
inputs['data'], im_scale, inputs['im_info'] = \
blob_utils.get_image_blob(im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE)
for k, v in inputs.items():
workspace.FeedBlob(core.ScopedName(k), v.astype(np.float32,
copy=False))
workspace.RunNet(model.net.Proto().name)
if cfg.FPN.FPN_ON and cfg.FPN.MULTILEVEL_RPN:
k_max = cfg.FPN.RPN_MAX_LEVEL
k_min = cfg.FPN.RPN_MIN_LEVEL
rois_names = [
core.ScopedName('rpn_rois_fpn' + str(l))
for l in range(k_min, k_max + 1)
]
score_names = [
core.ScopedName('rpn_roi_probs_fpn' + str(l))
for l in range(k_min, k_max + 1)
]
blobs = workspace.FetchBlobs(rois_names + score_names)
# Combine predictions across all levels and retain the top scoring
boxes = np.concatenate(blobs[:len(rois_names)])
scores = np.concatenate(blobs[len(rois_names):]).squeeze()
# Discussion: one could do NMS again after combining predictions from
# the different FPN levels. Conceptually, it's probably the right thing
# to do. For arbitrary reasons, the original FPN RPN implementation did
# not do another round of NMS.
inds = np.argsort(-scores)[:cfg.TEST.RPN_POST_NMS_TOP_N]
scores = scores[inds]
boxes = boxes[inds, :]
else:
boxes, scores = workspace.FetchBlobs(
[core.ScopedName('rpn_rois'),
core.ScopedName('rpn_roi_probs')])
scores = scores.squeeze()
# Column 0 is the batch index in the (batch ind, x1, y1, x2, y2) encoding,
# so we remove it since we just want to return boxes
# Scale proposals back to the original input image scale
boxes = boxes[:, 1:] / im_scale
return boxes, scores
def im_proposals(model, im):
"""Generate RPN proposals on a single image."""
inputs = {}
inputs['data'], im_scale, inputs['im_info'] = \
blob_utils.get_image_blob(im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE)
for k, v in inputs.items():
workspace.FeedBlob(core.ScopedName(k), v.astype(np.float32, copy=False))
workspace.RunNet(model.net.Proto().name)
if cfg.FPN.FPN_ON and cfg.FPN.MULTILEVEL_RPN:
k_max = cfg.FPN.RPN_MAX_LEVEL
k_min = cfg.FPN.RPN_MIN_LEVEL
rois_names = [
core.ScopedName('rpn_rois_fpn' + str(l))
for l in range(k_min, k_max + 1)
]
score_names = [
core.ScopedName('rpn_roi_probs_fpn' + str(l))
for l in range(k_min, k_max + 1)
]
blobs = workspace.FetchBlobs(rois_names + score_names)
# Combine predictions across all levels and retain the top scoring
boxes = np.concatenate(blobs[:len(rois_names)])
scores = np.concatenate(blobs[len(rois_names):]).squeeze()
# Discussion: one could do NMS again after combining predictions from
# the different FPN levels. Conceptually, it's probably the right thing
# to do. For arbitrary reasons, the original FPN RPN implementation did
# not do another round of NMS.
inds = np.argsort(-scores)[:cfg.TEST.RPN_POST_NMS_TOP_N]
scores = scores[inds]
boxes = boxes[inds, :]
else:
boxes, scores = workspace.FetchBlobs(
[core.ScopedName('rpn_rois'),
core.ScopedName('rpn_roi_probs')]
)
scores = scores.squeeze()
# Column 0 is the batch index in the (batch ind, x1, y1, x2, y2) encoding,
# so we remove it since we just want to return boxes
# Scale proposals back to the original input image scale
boxes = boxes[:, 1:] / im_scale
return boxes, scores
def im_conv_body_only(model, im, target_scale, target_max_size):
"""Runs `model.conv_body_net` on the given image `im`."""
im_blob, im_scale, _ = blob_utils.get_image_blob(im, target_scale,
target_max_size)
workspace.FeedBlob(core.ScopedName('data'), im_blob)
if os.environ.get('INT8INFO') == "1":
algorithm = AbsmaxCalib()
kind = os.environ.get('INT8CALIB')
if kind == "moving_average":
ema_alpha = 0.5
algorithm = EMACalib(ema_alpha)
elif kind == "kl_divergence":
kl_iter_num_for_range = int(os.environ.get('INT8KLNUM'))
if not kl_iter_num_for_range:
kl_iter_num_for_range = 100
algorithm = KLCalib(kl_iter_num_for_range)
calib = Calibrator(algorithm)
calib.RunCalibIter(workspace, model.conv_body_net.Proto())
else:
workspace.RunNet(model.conv_body_net.Proto().name)
return im_scale
def im_detect_bbox(model, im, timers=None):
"""Generate RetinaNet detections on a single image."""
if timers is None:
timers = defaultdict(Timer)
# Although anchors are input independent and could be precomputed,
# recomputing them per image only brings a small overhead
anchors = _create_cell_anchors()
timers['im_detect_bbox'].tic()
k_max, k_min = cfg.FPN.RPN_MAX_LEVEL, cfg.FPN.RPN_MIN_LEVEL
A = cfg.RETINANET.SCALES_PER_OCTAVE * len(cfg.RETINANET.ASPECT_RATIOS)
inputs = {}
inputs['data'], im_scale, inputs['im_info'] = \
blob_utils.get_image_blob(im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE)
cls_probs, box_preds = [], []
for lvl in range(k_min, k_max + 1):
suffix = 'fpn{}'.format(lvl)
cls_probs.append(core.ScopedName('retnet_cls_prob_{}'.format(suffix)))
box_preds.append(core.ScopedName('retnet_bbox_pred_{}'.format(suffix)))
for k, v in inputs.items():
workspace.FeedBlob(core.ScopedName(k), v.astype(np.float32,
copy=False))
workspace.RunNet(model.net.Proto().name)
cls_probs = workspace.FetchBlobs(cls_probs)
box_preds = workspace.FetchBlobs(box_preds)
# here the boxes_all are [x0, y0, x1, y1, score]
boxes_all = defaultdict(list)
cnt = 0
for lvl in range(k_min, k_max + 1):
# create cell anchors array
stride = 2.**lvl
cell_anchors = anchors[lvl]
# fetch per level probability
cls_prob = cls_probs[cnt]
box_pred = box_preds[cnt]
cls_prob = cls_prob.reshape(
(cls_prob.shape[0], A, int(cls_prob.shape[1] / A),
cls_prob.shape[2], cls_prob.shape[3]))
box_pred = box_pred.reshape(
(box_pred.shape[0], A, 4, box_pred.shape[2], box_pred.shape[3]))
cnt += 1
if cfg.RETINANET.SOFTMAX:
cls_prob = cls_prob[:, :, 1::, :, :]
cls_prob_ravel = cls_prob.ravel()
# In some cases [especially for very small img sizes], it's possible that
# candidate_ind is empty if we impose threshold 0.05 at all levels. This
# will lead to errors since no detections are found for this image. Hence,
# for lvl 7 which has small spatial resolution, we take the threshold 0.0
th = cfg.RETINANET.INFERENCE_TH if lvl < k_max else 0.0
candidate_inds = np.where(cls_prob_ravel > th)[0]
if (len(candidate_inds) == 0):
continue
pre_nms_topn = min(cfg.RETINANET.PRE_NMS_TOP_N, len(candidate_inds))
inds = np.argpartition(cls_prob_ravel[candidate_inds],
-pre_nms_topn)[-pre_nms_topn:]
inds = candidate_inds[inds]
inds_5d = np.array(np.unravel_index(inds, cls_prob.shape)).transpose()
classes = inds_5d[:, 2]
anchor_ids, y, x = inds_5d[:, 1], inds_5d[:, 3], inds_5d[:, 4]
scores = cls_prob[:, anchor_ids, classes, y, x]
boxes = np.column_stack((x, y, x, y)).astype(dtype=np.float32)
boxes *= stride
boxes += cell_anchors[anchor_ids, :]
if not cfg.RETINANET.CLASS_SPECIFIC_BBOX:
box_deltas = box_pred[0, anchor_ids, :, y, x]
else:
box_cls_inds = classes * 4
box_deltas = np.vstack([
box_pred[0, ind:ind + 4, yi, xi]
for ind, yi, xi in zip(box_cls_inds, y, x)
])
pred_boxes = (box_utils.bbox_transform(boxes, box_deltas)
if cfg.TEST.BBOX_REG else boxes)
pred_boxes /= im_scale
pred_boxes = box_utils.clip_tiled_boxes(pred_boxes, im.shape)
box_scores = np.zeros((pred_boxes.shape[0], 5))
box_scores[:, 0:4] = pred_boxes
box_scores[:, 4] = scores
for cls in range(1, cfg.MODEL.NUM_CLASSES):
inds = np.where(classes == cls - 1)[0]
if len(inds) > 0:
boxes_all[cls].extend(box_scores[inds, :])
timers['im_detect_bbox'].toc()
# Combine predictions across all levels and retain the top scoring by class
timers['misc_bbox'].tic()
detections = []
for cls, boxes in boxes_all.items():
cls_dets = np.vstack(boxes).astype(dtype=np.float32)
# do class specific nms here
keep = box_utils.nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
out = np.zeros((len(keep), 6))
out[:, 0:5] = cls_dets
out[:, 5].fill(cls)
detections.append(out)
# detections (N, 6) format:
# detections[:, :4] - boxes
# detections[:, 4] - scores
# detections[:, 5] - classes
detections = np.vstack(detections)
# sort all again
inds = np.argsort(-detections[:, 4])
detections = detections[inds[0:cfg.TEST.DETECTIONS_PER_IM], :]
# Convert the detections to image cls_ format (see core/test_engine.py)
num_classes = cfg.MODEL.NUM_CLASSES
cls_boxes = [[] for _ in range(cfg.MODEL.NUM_CLASSES)]
for c in range(1, num_classes):
inds = np.where(detections[:, 5] == c)[0]
cls_boxes[c] = detections[inds, :5]
timers['misc_bbox'].toc()
return cls_boxes
def im_detect_bbox(model, im, timers=None):
"""Generate RetinaNet detections on a single image."""
if timers is None:
timers = defaultdict(Timer)
# Although anchors are input independent and could be precomputed,
# recomputing them per image only brings a small overhead
anchors = _create_cell_anchors()
timers['im_detect_bbox'].tic()
k_max, k_min = cfg.FPN.RPN_MAX_LEVEL, cfg.FPN.RPN_MIN_LEVEL
A = cfg.RETINANET.SCALES_PER_OCTAVE * len(cfg.RETINANET.ASPECT_RATIOS)
inputs = {}
inputs['data'], im_scale, inputs['im_info'] = \
blob_utils.get_image_blob(im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE)
cls_probs, box_preds = [], []
for lvl in range(k_min, k_max + 1):
suffix = 'fpn{}'.format(lvl)
cls_probs.append(core.ScopedName('retnet_cls_prob_{}'.format(suffix)))
box_preds.append(core.ScopedName('retnet_bbox_pred_{}'.format(suffix)))
for k, v in inputs.items():
workspace.FeedBlob(core.ScopedName(k), v.astype(np.float32, copy=False))
workspace.RunNet(model.net.Proto().name)
cls_probs = workspace.FetchBlobs(cls_probs)
box_preds = workspace.FetchBlobs(box_preds)
# here the boxes_all are [x0, y0, x1, y1, score]
boxes_all = defaultdict(list)
cnt = 0
for lvl in range(k_min, k_max + 1):
# create cell anchors array
stride = 2. ** lvl
cell_anchors = anchors[lvl]
# fetch per level probability
cls_prob = cls_probs[cnt]
box_pred = box_preds[cnt]
cls_prob = cls_prob.reshape((
cls_prob.shape[0], A, int(cls_prob.shape[1] / A),
cls_prob.shape[2], cls_prob.shape[3]))
box_pred = box_pred.reshape((
box_pred.shape[0], A, 4, box_pred.shape[2], box_pred.shape[3]))
cnt += 1
if cfg.RETINANET.SOFTMAX:
cls_prob = cls_prob[:, :, 1::, :, :]
cls_prob_ravel = cls_prob.ravel()
# In some cases [especially for very small img sizes], it's possible that
# candidate_ind is empty if we impose threshold 0.05 at all levels. This
# will lead to errors since no detections are found for this image. Hence,
# for lvl 7 which has small spatial resolution, we take the threshold 0.0
th = cfg.RETINANET.INFERENCE_TH if lvl < k_max else 0.0
candidate_inds = np.where(cls_prob_ravel > th)[0]
if (len(candidate_inds) == 0):
continue
pre_nms_topn = min(cfg.RETINANET.PRE_NMS_TOP_N, len(candidate_inds))
inds = np.argpartition(
cls_prob_ravel[candidate_inds], -pre_nms_topn)[-pre_nms_topn:]
inds = candidate_inds[inds]
inds_5d = np.array(np.unravel_index(inds, cls_prob.shape)).transpose()
classes = inds_5d[:, 2]
anchor_ids, y, x = inds_5d[:, 1], inds_5d[:, 3], inds_5d[:, 4]
scores = cls_prob[:, anchor_ids, classes, y, x]
boxes = np.column_stack((x, y, x, y)).astype(dtype=np.float32)
boxes *= stride
boxes += cell_anchors[anchor_ids, :]
if not cfg.RETINANET.CLASS_SPECIFIC_BBOX:
box_deltas = box_pred[0, anchor_ids, :, y, x]
else:
box_cls_inds = classes * 4
box_deltas = np.vstack(
[box_pred[0, ind:ind + 4, yi, xi]
for ind, yi, xi in zip(box_cls_inds, y, x)]
)
pred_boxes = (
box_utils.bbox_transform(boxes, box_deltas)
if cfg.TEST.BBOX_REG else boxes)
pred_boxes /= im_scale
pred_boxes = box_utils.clip_tiled_boxes(pred_boxes, im.shape)
box_scores = np.zeros((pred_boxes.shape[0], 5))
box_scores[:, 0:4] = pred_boxes
box_scores[:, 4] = scores
for cls in range(1, cfg.MODEL.NUM_CLASSES):
inds = np.where(classes == cls - 1)[0]
if len(inds) > 0:
boxes_all[cls].extend(box_scores[inds, :])
timers['im_detect_bbox'].toc()
# Combine predictions across all levels and retain the top scoring by class
timers['misc_bbox'].tic()
detections = []
for cls, boxes in boxes_all.items():
cls_dets = np.vstack(boxes).astype(dtype=np.float32)
# do class specific nms here
keep = box_utils.nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
out = np.zeros((len(keep), 6))
out[:, 0:5] = cls_dets
out[:, 5].fill(cls)
detections.append(out)
# detections (N, 6) format:
# detections[:, :4] - boxes
# detections[:, 4] - scores
# detections[:, 5] - classes
detections = np.vstack(detections)
# sort all again
inds = np.argsort(-detections[:, 4])
detections = detections[inds[0:cfg.TEST.DETECTIONS_PER_IM], :]
# Convert the detections to image cls_ format (see core/test_engine.py)
num_classes = cfg.MODEL.NUM_CLASSES
cls_boxes = [[] for _ in range(cfg.MODEL.NUM_CLASSES)]
for c in range(1, num_classes):
inds = np.where(detections[:, 5] == c)[0]
cls_boxes[c] = detections[inds, :5]
timers['misc_bbox'].toc()
return cls_boxes
def im_detect_bbox(model, im, timers=None, model1=None):
"""Generate RetinaNet detections on a single image."""
if timers is None:
timers = defaultdict(Timer)
if model1 is None and os.environ.get('COSIM'):
print("cosim must has model1")
fp32_ws_name = "__fp32_ws__"
int8_ws_name = "__int8_ws__"
# Although anchors are input independent and could be precomputed,
# recomputing them per image only brings a small overhead
anchors = _create_cell_anchors()
timers['im_detect_bbox'].tic()
timers['data1'].tic()
k_max, k_min = cfg.FPN.RPN_MAX_LEVEL, cfg.FPN.RPN_MIN_LEVEL
A = cfg.RETINANET.SCALES_PER_OCTAVE * len(cfg.RETINANET.ASPECT_RATIOS)
inputs = {}
inputs['data'], im_scale, inputs['im_info'] = \
blob_utils.get_image_blob(im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE, cfg.TEST.SIZEFIX)
cls_probs, box_preds = [], []
for lvl in range(k_min, k_max + 1):
suffix = 'fpn{}'.format(lvl)
cls_probs.append(core.ScopedName('retnet_cls_prob_{}'.format(suffix)))
box_preds.append(core.ScopedName('retnet_bbox_pred_{}'.format(suffix)))
for k, v in inputs.items():
if os.environ.get('COSIM'):
workspace.SwitchWorkspace(int8_ws_name, True)
workspace.FeedBlob(core.ScopedName(k), v.astype(np.float32,
copy=False))
if os.environ.get('COSIM'):
workspace.SwitchWorkspace(fp32_ws_name, True)
workspace.FeedBlob(core.ScopedName(k),
v.astype(np.float32, copy=False))
timers['data1'].toc()
if os.environ.get('EPOCH2OLD') == "1":
workspace.RunNet(model.net.Proto().name)
timers['run'].tic()
if os.environ.get('INT8INFO') == "1":
algorithm = AbsmaxCalib()
kind = os.environ.get('INT8CALIB')
if kind == "moving_average":
ema_alpha = 0.5
algorithm = EMACalib(ema_alpha)
elif kind == "kl_divergence":
kl_iter_num_for_range = os.environ.get('INT8KLNUM')
if not kl_iter_num_for_range:
kl_iter_num_for_range = 100
kl_iter_num_for_range = int(kl_iter_num_for_range)
algorithm = KLCalib(kl_iter_num_for_range)
calib = Calibrator(algorithm)
calib.RunCalibIter(workspace, model.net.Proto())
else:
if os.environ.get('COSIM'):
with open("int8.txt", "wb") as p:
p.write(str(model.net.Proto()))
with open("fp32.txt", "wb") as p:
p.write(str(model1.net.Proto()))
for i in range(len(model.net.Proto().op)):
workspace.SwitchWorkspace(int8_ws_name)
int8_inputs = []
for inp in model.net.Proto().op[i].input:
int8_inputs.append(workspace.FetchBlob(str(inp)))
logging.warning(" opint8[{0}] is {1}".format(
i,
model.net.Proto().op[i]))
workspace.RunOperatorOnce(model.net.Proto().op[i])
int8_results = []
for res in model.net.Proto().op[i].output:
int8_results.append(workspace.FetchBlob(str(res)))
workspace.SwitchWorkspace(fp32_ws_name)
fp32_inputs = []
for inp1 in model1.net.Proto().op[i].input:
fp32_inputs.append(workspace.FetchBlob(str(inp1)))
logging.warning(" opfp32[{0}] is {1}".format(
i,
model1.net.Proto().op[i]))
workspace.RunOperatorOnce(model1.net.Proto().op[i])
fp32_results = []
for res1 in model1.net.Proto().op[i].output:
fp32_results.append(workspace.FetchBlob(str(res1)))
if len(int8_inputs) != len(fp32_inputs):
logging.error("Wrong number of inputs")
return
if len(int8_results) != len(fp32_results):
logging.error("Wrong number of outputs")
return
logging.warning("begin to check op[{}] {} input".format(
i,
model.net.Proto().op[i].type))
for k in range(len(int8_inputs)):
if model.net.Proto().op[i].input[k][0] == '_':
continue
#assert_allclose(int8_inputs[k], fp32_inputs[k], **tol)
logging.warning("pass checking op[{0}] {1} input".format(
i,
model.net.Proto().op[i].type))
logging.warning("begin to check op[{0}] {1} output".format(
i,
model.net.Proto().op[i].type))
for j, int8_result in enumerate(int8_results):
if model.net.Proto().op[i].output[j][0] == '_':
continue
#logging.warning("int8_outputis {} and fp32 output is {} ".format(int8_results[j], fp32_results[j]))
#if not compare_utils.assert_allclose(int8_results[j], fp32_results[j], **tol):
if not compare_utils.assert_compare(
int8_result, fp32_results[j], 1e-01,
os.environ.get('COSIM')):
for k, int8_input in enumerate(int8_inputs):
logging.warning("int8_input[{}] is {}".format(
k, int8_input))
logging.warning("fp32_input[{}] is {}".format(
k, fp32_inputs[k]))
logging.warning("pass checking op[{0}] {1} output".format(
i,
model.net.Proto().op[i].type))
else:
workspace.RunNet(model.net.Proto().name)
timers['run'].toc()
cls_probs = workspace.FetchBlobs(cls_probs)
box_preds = workspace.FetchBlobs(box_preds)
# here the boxes_all are [x0, y0, x1, y1, score]
boxes_all = defaultdict(list)
batch_size = cls_probs[0].shape[0]
boxes_all_list = [boxes_all] * batch_size
cnt = 0
for lvl in range(k_min, k_max + 1):
# create cell anchors array
stride = 2.**lvl
cell_anchors = anchors[lvl]
# fetch per level probability
cls_prob = cls_probs[cnt]
box_pred = box_preds[cnt]
cls_prob = cls_prob.reshape(
(cls_prob.shape[0], A, int(cls_prob.shape[1] / A),
cls_prob.shape[2], cls_prob.shape[3]))
box_pred = box_pred.reshape(
(box_pred.shape[0], A, 4, box_pred.shape[2], box_pred.shape[3]))
cnt += 1
if cfg.RETINANET.SOFTMAX:
cls_prob = cls_prob[:, :, 1::, :, :]
for i in range(batch_size):
cls_prob_ravel = cls_prob[i, :].ravel()
# In some cases [especially for very small img sizes], it's possible that
# candidate_ind is empty if we impose threshold 0.05 at all levels. This
# will lead to errors since no detections are found for this image. Hence,
# for lvl 7 which has small spatial resolution, we take the threshold 0.0
th = cfg.RETINANET.INFERENCE_TH if lvl < k_max else 0.0
candidate_inds = np.where(cls_prob_ravel > th)[0]
if (len(candidate_inds) == 0):
continue
pre_nms_topn = min(cfg.RETINANET.PRE_NMS_TOP_N,
len(candidate_inds))
inds = np.argpartition(cls_prob_ravel[candidate_inds],
-pre_nms_topn)[-pre_nms_topn:]
inds = candidate_inds[inds]
inds_4d = np.array(np.unravel_index(
inds, (cls_prob[i, :]).shape)).transpose()
classes = inds_4d[:, 1]
anchor_ids, y, x = inds_4d[:, 0], inds_4d[:, 2], inds_4d[:, 3]
scores = cls_prob[i, anchor_ids, classes, y, x]
boxes = np.column_stack((x, y, x, y)).astype(dtype=np.float32)
boxes *= stride
boxes += cell_anchors[anchor_ids, :]
if not cfg.RETINANET.CLASS_SPECIFIC_BBOX:
box_deltas = box_pred[i, anchor_ids, :, y, x]
else:
box_cls_inds = classes * 4
box_deltas = np.vstack([
box_pred[i, ind:ind + 4, yi, xi]
for ind, yi, xi in zip(box_cls_inds, y, x)
])
pred_boxes = (box_utils.bbox_transform(boxes, box_deltas)
if cfg.TEST.BBOX_REG else boxes)
pred_boxes /= im_scale
pred_boxes = box_utils.clip_tiled_boxes(pred_boxes, im[0].shape)
box_scores = np.zeros((pred_boxes.shape[0], 5))
box_scores[:, 0:4] = pred_boxes
box_scores[:, 4] = scores
for cls in range(1, cfg.MODEL.NUM_CLASSES):
inds = np.where(classes == cls - 1)[0]
if len(inds) > 0:
boxes_all_list[i][cls].extend(box_scores[inds, :])
timers['im_detect_bbox'].toc()
cls_boxes_list = []
for i in range(batch_size):
boxes_all = boxes_all_list[i]
# Combine predictions across all levels and retain the top scoring by class
timers['misc_bbox'].tic()
detections = []
for cls, boxes in boxes_all.items():
cls_dets = np.vstack(boxes).astype(dtype=np.float32)
# do class specific nms here
keep = box_utils.nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
out = np.zeros((len(keep), 6))
out[:, 0:5] = cls_dets
out[:, 5].fill(cls)
detections.append(out)
# detections (N, 6) format:
# detections[:, :4] - boxes
# detections[:, 4] - scores
# detections[:, 5] - classes
detections = np.vstack(detections)
# sort all again
inds = np.argsort(-detections[:, 4])
detections = detections[inds[0:cfg.TEST.DETECTIONS_PER_IM], :]
# Convert the detections to image cls_ format (see core/test_engine.py)
num_classes = cfg.MODEL.NUM_CLASSES
cls_boxes = [[] for _ in range(cfg.MODEL.NUM_CLASSES)]
for c in range(1, num_classes):
inds = np.where(detections[:, 5] == c)[0]
cls_boxes[c] = detections[inds, :5]
cls_boxes_list.append(cls_boxes)
timers['misc_bbox'].toc()
return cls_boxes_list