def get_predictor(cls):
''' load trained model'''
with cls.lock:
# check if model is already loaded
if cls.predictor:
return cls.predictor
os.environ['TENSORPACK_FP16'] = 'true'
# create a mask r-cnn model
mask_rcnn_model = ResNetFPNModel(True)
try:
model_dir = os.environ['SM_MODEL_DIR']
except KeyError:
model_dir = '/opt/ml/model'
try:
cls.pretrained_model = os.environ['PRETRAINED_MODEL']
except KeyError:
pass
# file path to previoulsy trained mask r-cnn model
latest_trained_model = ""
model_search_path = os.path.join(model_dir, "model-*.index" )
for model_file in glob.glob(model_search_path):
if model_file > latest_trained_model:
latest_trained_model = model_file
trained_model = latest_trained_model
print(f'Using model: {trained_model}')
# fixed resnet50 backbone weights
cfg.BACKBONE.WEIGHTS = os.path.join(cls.pretrained_model)
cfg.MODE_FPN = True
cfg.MODE_MASK = True
# calling detection dataset gets the number of coco categories
# and saves in the configuration
DetectionDataset()
finalize_configs(is_training=False)
# Create an inference model
# PredictConfig takes a model, input tensors and output tensors
cls.predictor = OfflinePredictor(PredictConfig(
model=mask_rcnn_model,
session_init=get_model_loader(trained_model),
input_names=['images', 'orig_image_dims'],
output_names=[
'generate_{}_proposals_topk_per_image/boxes'.format('fpn' if cfg.MODE_FPN else 'rpn'),
'generate_{}_proposals_topk_per_image/scores'.format('fpn' if cfg.MODE_FPN else 'rpn'),
'fastrcnn_all_scores',
'output/boxes',
'output/scores',
'output/labels',
'output/masks'
]))
return cls.predictor
def get_predictor(cls):
"""load trained model"""
with cls.lock:
# check if model is already loaded
if cls.predictor:
return cls.predictor
os.environ["TENSORPACK_FP16"] = "true"
# create a mask r-cnn model
mask_rcnn_model = ResNetFPNModel(True)
try:
model_dir = os.environ["SM_MODEL_DIR"]
except KeyError:
model_dir = "/opt/ml/model"
try:
resnet_arch = os.environ["RESNET_ARCH"]
except KeyError:
resnet_arch = "resnet50"
# file path to previoulsy trained mask r-cnn model
latest_trained_model = ""
model_search_path = os.path.join(model_dir, "model-*.index")
for model_file in glob.glob(model_search_path):
if model_file > latest_trained_model:
latest_trained_model = model_file
trained_model = latest_trained_model
print(f"Using model: {trained_model}")
# fixed resnet50 backbone weights
cfg.MODE_FPN = True
cfg.MODE_MASK = True
if resnet_arch == "resnet101":
cfg.BACKBONE.RESNET_NUM_BLOCKS = [3, 4, 23, 3]
else:
cfg.BACKBONE.RESNET_NUM_BLOCKS = [3, 4, 6, 3]
cfg_prefix = "CONFIG__"
for key, value in dict(os.environ).items():
if key.startswith(cfg_prefix):
attr_name = key[len(cfg_prefix):]
attr_name = attr_name.replace("__", ".")
value = eval(value)
print(f"update config: {attr_name}={value}")
nested_var = cfg
attr_list = attr_name.split(".")
for attr in attr_list[0:-1]:
nested_var = getattr(nested_var, attr)
setattr(nested_var, attr_list[-1], value)
# calling detection dataset gets the number of coco categories
# and saves in the configuration
DetectionDataset()
finalize_configs(is_training=False)
# Create an inference model
# PredictConfig takes a model, input tensors and output tensors
cls.predictor = OfflinePredictor(
PredictConfig(
model=mask_rcnn_model,
session_init=get_model_loader(trained_model),
input_names=["images", "orig_image_dims"],
output_names=[
"generate_{}_proposals_topk_per_image/boxes".format(
"fpn" if cfg.MODE_FPN else "rpn"),
"generate_{}_proposals_topk_per_image/scores".format(
"fpn" if cfg.MODE_FPN else "rpn"),
"fastrcnn_all_scores",
"output/boxes",
"output/scores",
"output/labels",
"output/masks",
],
))
return cls.predictor
help="Log the full git diff",
action="store_false")
#################################################################################################################
if get_tf_version_tuple() < (1, 6):
# https://github.com/tensorflow/tensorflow/issues/14657
logger.warn(
"TF<1.6 has a bug which may lead to crash in FasterRCNN if you're unlucky."
)
args = parser.parse_args()
if args.config:
cfg.update_args(args.config)
MODEL = ResNetFPNModel(args.fp16)
DetectionDataset(
) # initialize the config with information from our dataset
if args.visualize or args.evaluate or args.predict:
assert tf.test.is_gpu_available()
assert args.load
finalize_configs(is_training=False)
if args.predict or args.visualize:
cfg.TEST.RESULT_SCORE_THRESH = cfg.TEST.RESULT_SCORE_THRESH_VIS
if args.visualize:
do_visualize(MODEL, args.load)
else:
predcfg = PredictConfig(
def get_predictor(cls):
''' load trained model'''
with cls.lock:
# check if model is already loaded
if cls.predictor:
return cls.predictor
os.environ['TENSORPACK_FP16'] = 'true'
# create a mask r-cnn model
mask_rcnn_model = ResNetFPNModel(True)
try:
model_dir = os.environ['SM_MODEL_DIR']
except KeyError:
model_dir = '/opt/ml/model'
try:
cls.pretrained_model = os.environ['PRETRAINED_MODEL']
except KeyError:
pass
try:
div = int(eval(os.environ['divisor']))
except KeyError:
div = 1
pass
rpn_anchor_stride = int(16 / div)
rpn_anchor_sizes = (int(32 / div), int(64 / div), int(128 / div),
int(256 / div), int(512 / div))
try:
rpn_anchor_stride = int(eval(os.environ['rpnanchor_stride']))
except KeyError:
pass
try:
nms_topk = int(eval(os.environ['NMS_TOPK']))
except KeyError:
nms_topk = 2
pass
try:
nms_thresh = eval(os.environ['NMS_THRESH'])
except KeyError:
nms_thresh = 0.7
pass
try:
results_per_img = eval(os.environ['res_perimg'])
except KeyError:
results_per_img = 400
pass
# file path to previoulsy trained mask r-cnn model
latest_trained_model = ""
model_search_path = os.path.join(model_dir, "model-*.index")
for model_file in glob.glob(model_search_path):
if model_file > latest_trained_model:
latest_trained_model = model_file
trained_model = latest_trained_model
print(f'Using model: {trained_model}')
# fixed resnet50 backbone weights
cfg.BACKBONE.WEIGHTS = os.path.join(cls.pretrained_model)
cfg.MODE_FPN = True
cfg.MODE_MASK = True
cfg.RPN.ANCHOR_STRIDE = rpn_anchor_stride
cfg.RPN.ANCHOR_SIZES = rpn_anchor_sizes
cfg.RPN.TEST_PRE_NMS_TOPK = int(6000 * nms_topk)
cfg.RPN.TEST_POST_NMS_TOPK = int(1000 * nms_topk)
cfg.RPN.TEST_PER_LEVEL_NMS_TOPK = int(1000 * nms_topk)
# testing -----------------------
cfg.TEST.FRCNN_NMS_THRESH = nms_thresh
cfg.TEST.RESULT_SCORE_THRESH = 0.05
cfg.TEST.RESULT_SCORE_THRESH_VIS = 0.2 # only visualize confident results
cfg.TEST.RESULTS_PER_IM = results_per_img
# calling detection dataset gets the number of coco categories
# and saves in the configuration
DetectionDataset()
finalize_configs(is_training=False)
# Create an inference model
# PredictConfig takes a model, input tensors and output tensors
cls.predictor = OfflinePredictor(
PredictConfig(
model=mask_rcnn_model,
session_init=get_model_loader(trained_model),
input_names=['images', 'orig_image_dims'],
output_names=[
'generate_{}_proposals_topk_per_image/boxes'.format(
'fpn' if cfg.MODE_FPN else 'rpn'),
'generate_{}_proposals_topk_per_image/scores'.format(
'fpn' if cfg.MODE_FPN else 'rpn'),
'fastrcnn_all_scores', 'output/boxes', 'output/scores',
'output/labels', 'output/masks'
]))
return cls.predictor