def compute_test_set_aps(eval_model, cfg):
num_test_images = cfg["DATA"].NUM_TEST_IMAGES
classes = cfg["DATA"].CLASSES
image_input = input_variable(shape=(cfg.NUM_CHANNELS, cfg.IMAGE_HEIGHT,
cfg.IMAGE_WIDTH),
dynamic_axes=[Axis.default_batch_axis()],
name=cfg["MODEL"].FEATURE_NODE_NAME)
roi_input = input_variable((cfg.INPUT_ROIS_PER_IMAGE, 5),
dynamic_axes=[Axis.default_batch_axis()])
dims_input = input_variable((6), dynamic_axes=[Axis.default_batch_axis()])
frcn_eval = eval_model(image_input, dims_input)
# Create the minibatch source
minibatch_source = ObjectDetectionMinibatchSource(
cfg["DATA"].TEST_MAP_FILE,
cfg["DATA"].TEST_ROI_FILE,
max_annotations_per_image=cfg.INPUT_ROIS_PER_IMAGE,
pad_width=cfg.IMAGE_WIDTH,
pad_height=cfg.IMAGE_HEIGHT,
pad_value=cfg["MODEL"].IMG_PAD_COLOR,
randomize=False,
use_flipping=False,
max_images=cfg["DATA"].NUM_TEST_IMAGES,
num_classes=cfg["DATA"].NUM_CLASSES,
proposal_provider=None)
# define mapping from reader streams to network inputs
input_map = {
minibatch_source.image_si: image_input,
minibatch_source.roi_si: roi_input,
minibatch_source.dims_si: dims_input
}
# 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_test_images)]
for _ in range(cfg["DATA"].NUM_CLASSES)]
# evaluate test images and write netwrok output to file
print("Evaluating Faster R-CNN model for %s images." % num_test_images)
all_gt_infos = {key: [] for key in classes}
for img_i in range(0, num_test_images):
mb_data = minibatch_source.next_minibatch(1, input_map=input_map)
gt_row = mb_data[roi_input].asarray()
gt_row = gt_row.reshape((cfg.INPUT_ROIS_PER_IMAGE, 5))
all_gt_boxes = gt_row[np.where(gt_row[:, -1] > 0)]
for cls_index, cls_name in enumerate(classes):
if cls_index == 0: continue
cls_gt_boxes = all_gt_boxes[np.where(
all_gt_boxes[:, -1] == cls_index)]
all_gt_infos[cls_name].append({
'bbox':
np.array(cls_gt_boxes),
'difficult': [False] * len(cls_gt_boxes),
'det': [False] * len(cls_gt_boxes)
})
output = frcn_eval.eval({
image_input: mb_data[image_input],
dims_input: mb_data[dims_input]
})
out_dict = dict([(k.name, k) for k in output])
out_cls_pred = output[out_dict['cls_pred']][0]
out_rpn_rois = output[out_dict['rpn_rois']][0]
out_bbox_regr = output[out_dict['bbox_regr']][0]
labels = out_cls_pred.argmax(axis=1)
scores = out_cls_pred.max(axis=1)
regressed_rois = regress_rois(out_rpn_rois, out_bbox_regr, labels,
mb_data[dims_input].asarray())
labels.shape = labels.shape + (1, )
scores.shape = scores.shape + (1, )
coords_score_label = np.hstack((regressed_rois, scores, labels))
# shape of all_boxes: e.g. 21 classes x 4952 images x 58 rois x 5 coords+score
for cls_j in range(1, cfg["DATA"].NUM_CLASSES):
coords_score_label_for_cls = coords_score_label[np.where(
coords_score_label[:, -1] == cls_j)]
all_boxes[cls_j][
img_i] = coords_score_label_for_cls[:, :-1].astype(np.float32,
copy=False)
if (img_i + 1) % 100 == 0:
print("Processed {} samples".format(img_i + 1))
# calculate mAP
aps = evaluate_detections(all_boxes,
all_gt_infos,
classes,
use_gpu_nms=cfg.USE_GPU_NMS,
device_id=cfg.GPU_ID,
nms_threshold=cfg.RESULTS_NMS_THRESHOLD,
conf_threshold=cfg.RESULTS_NMS_CONF_THRESHOLD)
return aps
def compute_test_set_aps(eval_model, cfg):
num_test_images = cfg["DATA"].NUM_TEST_IMAGES
classes = cfg["DATA"].CLASSES
image_input = input_variable(shape=(cfg.NUM_CHANNELS, cfg.IMAGE_HEIGHT, cfg.IMAGE_WIDTH),
dynamic_axes=[Axis.default_batch_axis()],
name=cfg["MODEL"].FEATURE_NODE_NAME)
roi_input = input_variable((cfg.INPUT_ROIS_PER_IMAGE, 5), dynamic_axes=[Axis.default_batch_axis()])
roi_proposals = input_variable((cfg.NUM_ROI_PROPOSALS, 4), dynamic_axes=[Axis.default_batch_axis()], name="roi_proposals")
dims_input = input_variable((6), dynamic_axes=[Axis.default_batch_axis()])
frcn_eval = eval_model(image_input, roi_proposals)
# Create the minibatch source
if cfg.USE_PRECOMPUTED_PROPOSALS:
try:
cfg["DATA"].TEST_PRECOMPUTED_PROPOSALS_FILE = os.path.join(cfg["DATA"].MAP_FILE_PATH, cfg["DATA"].TEST_PRECOMPUTED_PROPOSALS_FILE)
proposal_provider = ProposalProvider.fromfile(cfg["DATA"].TEST_PRECOMPUTED_PROPOSALS_FILE, cfg.NUM_ROI_PROPOSALS)
except:
print("To use precomputed proposals please specify the following parameters in your configuration:\n"
"__C.DATA.TRAIN_PRECOMPUTED_PROPOSALS_FILE\n"
"__C.DATA.TEST_PRECOMPUTED_PROPOSALS_FILE")
exit(-1)
else:
proposal_provider = ProposalProvider.fromconfig(cfg)
minibatch_source = ObjectDetectionMinibatchSource(
cfg["DATA"].TEST_MAP_FILE,
cfg["DATA"].TEST_ROI_FILE,
max_annotations_per_image=cfg.INPUT_ROIS_PER_IMAGE,
pad_width=cfg.IMAGE_WIDTH,
pad_height=cfg.IMAGE_HEIGHT,
pad_value=cfg["MODEL"].IMG_PAD_COLOR,
randomize=False, use_flipping=False,
max_images=cfg["DATA"].NUM_TEST_IMAGES,
num_classes=cfg["DATA"].NUM_CLASSES,
proposal_provider=proposal_provider,
provide_targets=False)
# define mapping from reader streams to network inputs
input_map = {
minibatch_source.image_si: image_input,
minibatch_source.roi_si: roi_input,
minibatch_source.proposals_si: roi_proposals,
minibatch_source.dims_si: dims_input
}
# 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_test_images)] for _ in range(cfg["DATA"].NUM_CLASSES)]
# evaluate test images and write netwrok output to file
print("Evaluating Fast R-CNN model for %s images." % num_test_images)
all_gt_infos = {key: [] for key in classes}
for img_i in range(0, num_test_images):
mb_data = minibatch_source.next_minibatch(1, input_map=input_map)
gt_row = mb_data[roi_input].asarray()
gt_row = gt_row.reshape((cfg.INPUT_ROIS_PER_IMAGE, 5))
all_gt_boxes = gt_row[np.where(gt_row[:,-1] > 0)]
for cls_index, cls_name in enumerate(classes):
if cls_index == 0: continue
cls_gt_boxes = all_gt_boxes[np.where(all_gt_boxes[:,-1] == cls_index)]
all_gt_infos[cls_name].append({'bbox': np.array(cls_gt_boxes),
'difficult': [False] * len(cls_gt_boxes),
'det': [False] * len(cls_gt_boxes)})
output = frcn_eval.eval({image_input: mb_data[image_input], roi_proposals: mb_data[roi_proposals]})
out_dict = dict([(k.name, k) for k in output])
out_cls_pred = output[out_dict['cls_pred']][0]
out_rpn_rois = mb_data[roi_proposals].data.asarray()
out_bbox_regr = output[out_dict['bbox_regr']][0]
labels = out_cls_pred.argmax(axis=1)
scores = out_cls_pred.max(axis=1)
regressed_rois = regress_rois(out_rpn_rois, out_bbox_regr, labels, mb_data[dims_input].asarray())
labels.shape = labels.shape + (1,)
scores.shape = scores.shape + (1,)
coords_score_label = np.hstack((regressed_rois, scores, labels))
# shape of all_boxes: e.g. 21 classes x 4952 images x 58 rois x 5 coords+score
for cls_j in range(1, cfg["DATA"].NUM_CLASSES):
coords_score_label_for_cls = coords_score_label[np.where(coords_score_label[:,-1] == cls_j)]
all_boxes[cls_j][img_i] = coords_score_label_for_cls[:,:-1].astype(np.float32, copy=False)
if (img_i+1) % 100 == 0:
print("Processed {} samples".format(img_i+1))
# calculate mAP
aps = evaluate_detections(all_boxes, all_gt_infos, classes,
use_gpu_nms = cfg.USE_GPU_NMS,
device_id = cfg.GPU_ID,
nms_threshold=cfg.RESULTS_NMS_THRESHOLD,
conf_threshold = cfg.RESULTS_NMS_CONF_THRESHOLD)
return aps