def compute_rpn_proposals(rpn_model, image_input, roi_input, dims_input, cfg):
num_images = cfg["DATA"].NUM_TRAIN_IMAGES
# Create the minibatch source
od_minibatch_source = ObjectDetectionMinibatchSource(
cfg["DATA"].TRAIN_MAP_FILE, cfg["DATA"].TRAIN_ROI_FILE,
num_classes=cfg["DATA"].NUM_CLASSES,
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,
max_images=num_images,
randomize=False, use_flipping=False,
proposal_provider=None)
# define mapping from reader streams to network inputs
input_map = {
od_minibatch_source.image_si: image_input,
od_minibatch_source.roi_si: roi_input,
od_minibatch_source.dims_si: dims_input
}
buffered_proposals = [None for _ in range(num_images)]
sample_count = 0
while sample_count < num_images:
data = od_minibatch_source.next_minibatch(1, input_map=input_map)
output = rpn_model.eval(data)
out_dict = dict([(k.name, k) for k in output])
out_rpn_rois = output[out_dict['rpn_rois']][0]
buffered_proposals[sample_count] = np.round(out_rpn_rois).astype(np.int16)
sample_count += 1
if sample_count % 500 == 0:
print("Buffered proposals for {} samples".format(sample_count))
return buffered_proposals
def compute_rpn_proposals(rpn_model, image_input, roi_input, dims_input, cfg):
num_images = cfg["DATA"].NUM_TRAIN_IMAGES
# Create the minibatch source
od_minibatch_source = ObjectDetectionMinibatchSource(
cfg["DATA"].TRAIN_MAP_FILE, cfg["DATA"].TRAIN_ROI_FILE,
num_classes=cfg["DATA"].NUM_CLASSES,
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,
max_images=num_images,
randomize=False, use_flipping=False,
proposal_provider=None)
# define mapping from reader streams to network inputs
input_map = {
od_minibatch_source.image_si: image_input,
od_minibatch_source.roi_si: roi_input,
od_minibatch_source.dims_si: dims_input
}
buffered_proposals = [None for _ in range(num_images)]
sample_count = 0
while sample_count < num_images:
data = od_minibatch_source.next_minibatch(1, input_map=input_map)
output = rpn_model.eval(data)
out_dict = dict([(k.name, k) for k in output])
out_rpn_rois = output[out_dict['rpn_rois']][0]
buffered_proposals[sample_count] = np.round(out_rpn_rois).astype(np.int16)
sample_count += 1
if sample_count % 500 == 0:
print("Buffered proposals for {} samples".format(sample_count))
return buffered_proposals
def create_train_reader(cfg):
return ObjectDetectionMinibatchSource(
cfg["DATA"].TRAIN_MAP_FILE,
cfg["DATA"].TRAIN_ROI_FILE,
num_classes=cfg["DATA"].NUM_CLASSES,
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=True,
use_flipping=cfg["TRAIN"].USE_FLIPPED,
max_images=cfg["DATA"].NUM_TRAIN_IMAGES,
proposal_provider=None)
def train_fast_rcnn(cfg):
# Train only if no model exists yet
model_path = cfg['MODEL_PATH']
if os.path.exists(model_path) and cfg["CNTK"].MAKE_MODE:
print("Loading existing model from %s" % model_path)
return load_model(model_path)
else:
# Input variables denoting features and labeled ground truth rois (as 5-tuples per roi)
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_proposals = input_variable(
(cfg.NUM_ROI_PROPOSALS, 4),
dynamic_axes=[Axis.default_batch_axis()],
name="roi_proposals")
label_targets = input_variable(
(cfg.NUM_ROI_PROPOSALS, cfg["DATA"].NUM_CLASSES),
dynamic_axes=[Axis.default_batch_axis()])
bbox_targets = input_variable(
(cfg.NUM_ROI_PROPOSALS, 4 * cfg["DATA"].NUM_CLASSES),
dynamic_axes=[Axis.default_batch_axis()])
bbox_inside_weights = input_variable(
(cfg.NUM_ROI_PROPOSALS, 4 * cfg["DATA"].NUM_CLASSES),
dynamic_axes=[Axis.default_batch_axis()])
# Instantiate the Fast R-CNN prediction model and loss function
loss, pred_error = create_fast_rcnn_model(image_input, roi_proposals,
label_targets, bbox_targets,
bbox_inside_weights, cfg)
if isinstance(loss, cntk.Variable):
loss = combine([loss])
if cfg["CNTK"].DEBUG_OUTPUT:
print("Storing graphs and models to %s." % cfg.OUTPUT_PATH)
plot(
loss,
os.path.join(cfg.OUTPUT_PATH,
"graph_frcn_train." + cfg["CNTK"].GRAPH_TYPE))
# Set learning parameters
lr_factor = cfg["CNTK"].LR_FACTOR
lr_per_sample_scaled = [
x * lr_factor for x in cfg["CNTK"].LR_PER_SAMPLE
]
mm_schedule = momentum_schedule(cfg["CNTK"].MOMENTUM_PER_MB)
l2_reg_weight = cfg["CNTK"].L2_REG_WEIGHT
epochs_to_train = cfg["CNTK"].MAX_EPOCHS
print("Using base model: {}".format(cfg["MODEL"].BASE_MODEL))
print("lr_per_sample: {}".format(lr_per_sample_scaled))
# --- train ---
# Instantiate the learners and the trainer object
params = loss.parameters
biases = [p for p in params if '.b' in p.name or 'b' == p.name]
others = [p for p in params if not p in biases]
bias_lr_mult = cfg["CNTK"].BIAS_LR_MULT
lr_schedule = learning_parameter_schedule_per_sample(
lr_per_sample_scaled)
learner = momentum_sgd(others,
lr_schedule,
mm_schedule,
l2_regularization_weight=l2_reg_weight,
unit_gain=False,
use_mean_gradient=True)
bias_lr_per_sample = [
v * bias_lr_mult for v in cfg["CNTK"].LR_PER_SAMPLE
]
bias_lr_schedule = learning_parameter_schedule_per_sample(
bias_lr_per_sample)
bias_learner = momentum_sgd(biases,
bias_lr_schedule,
mm_schedule,
l2_regularization_weight=l2_reg_weight,
unit_gain=False,
use_mean_gradient=True)
trainer = Trainer(None, (loss, pred_error), [learner, bias_learner])
# Get minibatches of images and perform model training
print("Training model for %s epochs." % epochs_to_train)
log_number_of_parameters(loss)
# Create the minibatch source
if cfg.USE_PRECOMPUTED_PROPOSALS:
proposal_provider = ProposalProvider.fromfile(
cfg["DATA"].TRAIN_PRECOMPUTED_PROPOSALS_FILE,
cfg.NUM_ROI_PROPOSALS)
else:
proposal_provider = ProposalProvider.fromconfig(cfg)
od_minibatch_source = ObjectDetectionMinibatchSource(
cfg["DATA"].TRAIN_MAP_FILE,
cfg["DATA"].TRAIN_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=True,
use_flipping=cfg["TRAIN"].USE_FLIPPED,
max_images=cfg["DATA"].NUM_TRAIN_IMAGES,
num_classes=cfg["DATA"].NUM_CLASSES,
proposal_provider=proposal_provider,
provide_targets=True,
proposal_iou_threshold=cfg.BBOX_THRESH,
normalize_means=None
if not cfg.BBOX_NORMALIZE_TARGETS else cfg.BBOX_NORMALIZE_MEANS,
normalize_stds=None
if not cfg.BBOX_NORMALIZE_TARGETS else cfg.BBOX_NORMALIZE_STDS)
# define mapping from reader streams to network inputs
input_map = {
od_minibatch_source.image_si: image_input,
od_minibatch_source.proposals_si: roi_proposals,
od_minibatch_source.label_targets_si: label_targets,
od_minibatch_source.bbox_targets_si: bbox_targets,
od_minibatch_source.bbiw_si: bbox_inside_weights
}
progress_printer = ProgressPrinter(tag='Training',
num_epochs=epochs_to_train,
gen_heartbeat=True)
for epoch in range(epochs_to_train): # loop over epochs
sample_count = 0
while sample_count < cfg[
"DATA"].NUM_TRAIN_IMAGES: # loop over minibatches in the epoch
data = od_minibatch_source.next_minibatch(min(
cfg.MB_SIZE, cfg["DATA"].NUM_TRAIN_IMAGES - sample_count),
input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
progress_printer.update_with_trainer(
trainer, with_metric=True) # log progress
if sample_count % 100 == 0:
continue
#print("Processed {} samples".format(sample_count))
progress_printer.epoch_summary(with_metric=True)
eval_model = create_fast_rcnn_eval_model(loss, image_input,
roi_proposals, cfg)
eval_model.save(cfg['MODEL_PATH'])
return eval_model
def train_model(image_input, roi_input, dims_input, loss, pred_error,
lr_per_sample, mm_schedule, l2_reg_weight, epochs_to_train, cfg,
rpn_rois_input=None, buffered_rpn_proposals=None):
if isinstance(loss, cntk.Variable):
loss = combine([loss])
params = loss.parameters
biases = [p for p in params if '.b' in p.name or 'b' == p.name]
others = [p for p in params if not p in biases]
bias_lr_mult = cfg["CNTK"].BIAS_LR_MULT
if cfg["CNTK"].DEBUG_OUTPUT:
print("biases")
for p in biases: print(p)
print("others")
for p in others: print(p)
print("bias_lr_mult: {}".format(bias_lr_mult))
# Instantiate the learners and the trainer object
lr_schedule = learning_parameter_schedule_per_sample(lr_per_sample)
learner = momentum_sgd(others, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight,
unit_gain=False, use_mean_gradient=True)
bias_lr_per_sample = [v * bias_lr_mult for v in lr_per_sample]
bias_lr_schedule = learning_parameter_schedule_per_sample(bias_lr_per_sample)
bias_learner = momentum_sgd(biases, bias_lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight,
unit_gain=False, use_mean_gradient=True)
trainer = Trainer(None, (loss, pred_error), [learner, bias_learner])
# Get minibatches of images and perform model training
print("Training model for %s epochs." % epochs_to_train)
log_number_of_parameters(loss)
# Create the minibatch source
if buffered_rpn_proposals is not None:
proposal_provider = ProposalProvider.fromlist(buffered_rpn_proposals, requires_scaling=False)
else:
proposal_provider = None
od_minibatch_source = ObjectDetectionMinibatchSource(
cfg["DATA"].TRAIN_MAP_FILE, cfg["DATA"].TRAIN_ROI_FILE,
num_classes=cfg["DATA"].NUM_CLASSES,
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=True,
use_flipping=cfg["TRAIN"].USE_FLIPPED,
max_images=cfg["DATA"].NUM_TRAIN_IMAGES,
proposal_provider=proposal_provider)
# define mapping from reader streams to network inputs
input_map = {
od_minibatch_source.image_si: image_input,
od_minibatch_source.roi_si: roi_input,
}
if buffered_rpn_proposals is not None:
input_map[od_minibatch_source.proposals_si] = rpn_rois_input
else:
input_map[od_minibatch_source.dims_si] = dims_input
progress_printer = ProgressPrinter(tag='Training', num_epochs=epochs_to_train, gen_heartbeat=True)
for epoch in range(epochs_to_train): # loop over epochs
sample_count = 0
while sample_count < cfg["DATA"].NUM_TRAIN_IMAGES: # loop over minibatches in the epoch
data = od_minibatch_source.next_minibatch(min(cfg.MB_SIZE, cfg["DATA"].NUM_TRAIN_IMAGES-sample_count), input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
#progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
if sample_count % 100 == 0:
continue
#print("Processed {} samples".format(sample_count))
progress_printer.epoch_summary(with_metric=True)
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 train_fast_rcnn(cfg):
# Train only if no model exists yet
model_path = cfg['MODEL_PATH']
if os.path.exists(model_path) and cfg["CNTK"].MAKE_MODE:
print("Loading existing model from %s" % model_path)
return load_model(model_path)
else:
# Input variables denoting features and labeled ground truth rois (as 5-tuples per roi)
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_proposals = input_variable((cfg.NUM_ROI_PROPOSALS, 4), dynamic_axes=[Axis.default_batch_axis()], name = "roi_proposals")
label_targets = input_variable((cfg.NUM_ROI_PROPOSALS, cfg["DATA"].NUM_CLASSES), dynamic_axes=[Axis.default_batch_axis()])
bbox_targets = input_variable((cfg.NUM_ROI_PROPOSALS, 4*cfg["DATA"].NUM_CLASSES), dynamic_axes=[Axis.default_batch_axis()])
bbox_inside_weights = input_variable((cfg.NUM_ROI_PROPOSALS, 4*cfg["DATA"].NUM_CLASSES), dynamic_axes=[Axis.default_batch_axis()])
# Instantiate the Fast R-CNN prediction model and loss function
loss, pred_error = create_fast_rcnn_model(image_input, roi_proposals, label_targets, bbox_targets, bbox_inside_weights, cfg)
if isinstance(loss, cntk.Variable):
loss = combine([loss])
if cfg["CNTK"].DEBUG_OUTPUT:
print("Storing graphs and models to %s." % cfg.OUTPUT_PATH)
plot(loss, os.path.join(cfg.OUTPUT_PATH, "graph_frcn_train." + cfg["CNTK"].GRAPH_TYPE))
# Set learning parameters
lr_factor = cfg["CNTK"].LR_FACTOR
lr_per_sample_scaled = [x * lr_factor for x in cfg["CNTK"].LR_PER_SAMPLE]
mm_schedule = momentum_schedule(cfg["CNTK"].MOMENTUM_PER_MB)
l2_reg_weight = cfg["CNTK"].L2_REG_WEIGHT
epochs_to_train = cfg["CNTK"].MAX_EPOCHS
print("Using base model: {}".format(cfg["MODEL"].BASE_MODEL))
print("lr_per_sample: {}".format(lr_per_sample_scaled))
# --- train ---
# Instantiate the learners and the trainer object
params = loss.parameters
biases = [p for p in params if '.b' in p.name or 'b' == p.name]
others = [p for p in params if not p in biases]
bias_lr_mult = cfg["CNTK"].BIAS_LR_MULT
lr_schedule = learning_rate_schedule(lr_per_sample_scaled, unit=UnitType.sample)
learner = momentum_sgd(others, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight, unit_gain=False, use_mean_gradient=True)
bias_lr_per_sample = [v * bias_lr_mult for v in cfg["CNTK"].LR_PER_SAMPLE]
bias_lr_schedule = learning_rate_schedule(bias_lr_per_sample, unit=UnitType.sample)
bias_learner = momentum_sgd(biases, bias_lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight, unit_gain=False, use_mean_gradient=True)
trainer = Trainer(None, (loss, pred_error), [learner, bias_learner])
# Get minibatches of images and perform model training
print("Training model for %s epochs." % epochs_to_train)
log_number_of_parameters(loss)
# Create the minibatch source
if cfg.USE_PRECOMPUTED_PROPOSALS:
proposal_provider = ProposalProvider.fromfile(cfg["DATA"].TRAIN_PRECOMPUTED_PROPOSALS_FILE, cfg.NUM_ROI_PROPOSALS)
else:
proposal_provider = ProposalProvider.fromconfig(cfg)
od_minibatch_source = ObjectDetectionMinibatchSource(
cfg["DATA"].TRAIN_MAP_FILE, cfg["DATA"].TRAIN_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=True,
use_flipping=cfg["TRAIN"].USE_FLIPPED,
max_images=cfg["DATA"].NUM_TRAIN_IMAGES,
num_classes=cfg["DATA"].NUM_CLASSES,
proposal_provider=proposal_provider,
provide_targets=True,
proposal_iou_threshold = cfg.BBOX_THRESH,
normalize_means = None if not cfg.BBOX_NORMALIZE_TARGETS else cfg.BBOX_NORMALIZE_MEANS,
normalize_stds = None if not cfg.BBOX_NORMALIZE_TARGETS else cfg.BBOX_NORMALIZE_STDS)
# define mapping from reader streams to network inputs
input_map = {
od_minibatch_source.image_si: image_input,
od_minibatch_source.proposals_si: roi_proposals,
od_minibatch_source.label_targets_si: label_targets,
od_minibatch_source.bbox_targets_si: bbox_targets,
od_minibatch_source.bbiw_si: bbox_inside_weights
}
progress_printer = ProgressPrinter(tag='Training', num_epochs=epochs_to_train, gen_heartbeat=True)
for epoch in range(epochs_to_train): # loop over epochs
sample_count = 0
while sample_count < cfg["DATA"].NUM_TRAIN_IMAGES: # loop over minibatches in the epoch
data = od_minibatch_source.next_minibatch(min(cfg.MB_SIZE, cfg["DATA"].NUM_TRAIN_IMAGES - sample_count), input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
if sample_count % 100 == 0:
print("Processed {} samples".format(sample_count))
progress_printer.epoch_summary(with_metric=True)
eval_model = create_fast_rcnn_eval_model(loss, image_input, roi_proposals, cfg)
eval_model.save(cfg['MODEL_PATH'])
return eval_model
def train_model(image_input, roi_input, dims_input, loss, pred_error,
lr_per_sample, mm_schedule, l2_reg_weight, epochs_to_train, cfg,
rpn_rois_input=None, buffered_rpn_proposals=None):
if isinstance(loss, cntk.Variable):
loss = combine([loss])
params = loss.parameters
biases = [p for p in params if '.b' in p.name or 'b' == p.name]
others = [p for p in params if not p in biases]
bias_lr_mult = cfg["CNTK"].BIAS_LR_MULT
if cfg["CNTK"].DEBUG_OUTPUT:
print("biases")
for p in biases: print(p)
print("others")
for p in others: print(p)
print("bias_lr_mult: {}".format(bias_lr_mult))
# Instantiate the learners and the trainer object
lr_schedule = learning_parameter_schedule_per_sample(lr_per_sample)
learner = momentum_sgd(others, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight,
unit_gain=False, use_mean_gradient=True)
bias_lr_per_sample = [v * bias_lr_mult for v in lr_per_sample]
bias_lr_schedule = learning_parameter_schedule_per_sample(bias_lr_per_sample)
bias_learner = momentum_sgd(biases, bias_lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight,
unit_gain=False, use_mean_gradient=True)
trainer = Trainer(None, (loss, pred_error), [learner, bias_learner])
# Get minibatches of images and perform model training
print("Training model for %s epochs." % epochs_to_train)
log_number_of_parameters(loss)
# Create the minibatch source
if buffered_rpn_proposals is not None:
proposal_provider = ProposalProvider.fromlist(buffered_rpn_proposals, requires_scaling=False)
else:
proposal_provider = None
od_minibatch_source = ObjectDetectionMinibatchSource(
cfg["DATA"].TRAIN_MAP_FILE, cfg["DATA"].TRAIN_ROI_FILE,
num_classes=cfg["DATA"].NUM_CLASSES,
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=True,
use_flipping=cfg["TRAIN"].USE_FLIPPED,
max_images=cfg["DATA"].NUM_TRAIN_IMAGES,
proposal_provider=proposal_provider)
# define mapping from reader streams to network inputs
input_map = {
od_minibatch_source.image_si: image_input,
od_minibatch_source.roi_si: roi_input,
}
if buffered_rpn_proposals is not None:
input_map[od_minibatch_source.proposals_si] = rpn_rois_input
else:
input_map[od_minibatch_source.dims_si] = dims_input
progress_printer = ProgressPrinter(tag='Training', num_epochs=epochs_to_train, gen_heartbeat=True)
for epoch in range(epochs_to_train): # loop over epochs
sample_count = 0
while sample_count < cfg["DATA"].NUM_TRAIN_IMAGES: # loop over minibatches in the epoch
data = od_minibatch_source.next_minibatch(min(cfg.MB_SIZE, cfg["DATA"].NUM_TRAIN_IMAGES-sample_count), input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
if sample_count % 100 == 0:
print("Processed {} samples".format(sample_count))
progress_printer.epoch_summary(with_metric=True)
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