def load_model_and_weights(args, mode, appcfg):
log.debug("---------------------------->")
from falcon.arch import Model as M
archcfg = M.get_archcfg(appcfg)
log.debug("archcfg: {}".format(archcfg))
cmdcfg = archcfg
# modelcfg_path = os.path.join(appcfg.PATHS.AI_MODEL_CFG_PATH, cmdcfg.model_info)
modelcfg_path = apputil.get_abs_path(appcfg, cmdcfg, 'AI_MODEL_CFG_PATH')
log.info("modelcfg_path: {}".format(modelcfg_path))
modelcfg = M.get_modelcfg(modelcfg_path)
log.info("modelcfg: {}".format(modelcfg))
num_classes_model = apputil.get_num_classes(modelcfg)
name = modelcfg.name
cmdcfg.name = name
cmdcfg.config.NAME = name
cmdcfg.config.NUM_CLASSES = num_classes_model
dnnmod = M.get_module(cmdcfg.dnnarch)
load_model_and_weights = M.get_module_fn(dnnmod, "load_model_and_weights")
model = load_model_and_weights(args, mode, cmdcfg, modelcfg, appcfg)
status = True
return status
def test_load_model(args, mode, appcfg):
log.debug("---------------------------->")
from falcon.arch import Model as M
archcfg = M.get_archcfg(appcfg)
log.debug("archcfg: {}".format(archcfg))
cmdcfg = archcfg
modelcfg_path = os.path.join(appcfg.PATHS.AI_MODEL_CFG_PATH,
cmdcfg.model_info)
log.info("modelcfg_path: {}".format(modelcfg_path))
modelcfg = M.get_modelcfg(modelcfg_path)
class_names = apputil.get_class_names(modelcfg)
log.debug("class_names: {}".format(class_names))
num_classes = len(class_names)
name = modelcfg.name
cmdcfg.name = name
cmdcfg.config.NAME = name
cmdcfg.config.NUM_CLASSES = num_classes
dnnmod = M.get_module(cmdcfg.dnnarch)
weights_path = apputil.get_abs_path(appcfg, modelcfg, 'AI_WEIGHTS_PATH')
cmdcfg['weights_path'] = weights_path
load_model_and_weights = M.get_module_fn(dnnmod, "load_model_and_weights")
model = load_model_and_weights(mode, cmdcfg, appcfg)
log.debug("model: {}".format(model))
# modelsummary_path = os.path.join(appcfg.PATHS.AI_LOGS, name+"-summary.txt")
# log.debug("modelsummary_path: {}".format(modelsummary_path))
# # msummary = model.keras_model.summary()
# # log.debug("model.keras_model.summary(): {}".format(msummary))
# # with open(modelsummary_path, 'w') as fw:
# # fw.write(msummary)
return
def load_and_display(args, mode, appcfg):
log.debug("---------------------------->")
from falcon.arch import Model as M
from importlib import import_module
datacfg = M.get_datacfg(appcfg)
log.debug("datacfg: {}".format(datacfg))
datamod = import_module(datacfg.dataclass)
datamodcls = getattr(datamod, datacfg.dataclass)
dataset = datamodcls(datacfg.name)
subset = args.eval_on
log.debug("subset: {}".format(subset))
# total_img, total_annotation, total_classes = dataset.load_data(appcfg, datacfg, subset)
# log.debug("total_img, total_annotation, total_classes: {}, {}, {}".format(total_img, total_annotation, total_classes))
dataset, num_classes, num_images, class_names, total_stats, total_verify = M.get_dataset_instance(
dataset, appcfg, datacfg, subset)
# colors = viz.random_colors(len(class_names))
# log.debug("len(colors), colors: {},{}".format(len(colors), colors))
log.info("class_names: {}".format(class_names))
log.info("len(class_names): {}".format(len(class_names)))
log.info("num_classes: {}".format(num_classes))
log.info("num_images: {}".format(num_images))
name = dataset.name
log.info("dataset.name: {}".format(name))
# datacfg.name = name
# datacfg.classes = class_names
# datacfg.num_classes = num_classes
# # log.debug("dataset: {}".format(vars(dataset)))
# log.debug("len(dataset.image_info): {}".format(len(dataset.image_info)))
# log.debug("len(dataset.image_ids): {}".format(len(dataset.image_ids)))
return
def setAndLoadModel(args):
print("Inside setAndLoadModel::")
print("args.ORG_NAME - args.ID - args.REL_NUM:: {} {} {}".format(
args.ORG_NAME, args.ID, args.REL_NUM))
# get model details
modelDtls = Model.getDetails(args)
if modelDtls is not None:
app.config["MODEL_DTLS"] = modelDtls
print("setAndLoadModel:Model Details: {}".format(
app.config["MODEL_DTLS"]))
args.DNNARCH = app.config["MODEL_DTLS"]["DNNARCH"]
## TBD Null check doe DNN_MODULE
app.config["DNN_MODULE"] = Util.getDnnModule(
args) ## "pixel.faster_rcnn_end2end"
# load model
app.config["NET"] = Model.load(app.config["DNN_MODULE"],
app.config["MODEL_DTLS"], args)
return True
return False
def detect_in_image(filename):
t1 = time.time()
print('detect_in_image: filename: {}'.format(filename))
all_rows_for_all_classes = Model.exec_prediction(
app.config["DNN_MODULE"], app.config["MODEL_DTLS"], app.config["NET"],
[filename], appcfg.UPLOAD_FOLDER, appcfg.LOG_FOLDER, appcfg)
t2 = time.time()
time_taken = (t2 - t1)
print('Total time taken in detect_in_image: %f seconds' % (time_taken))
return all_rows_for_all_classes
def inspect_annon(args, mode, appcfg):
log.debug("---------------------------->")
from falcon.arch import Model as M
M.inspect_annon(args, mode, appcfg)
def viz_annon(args, mode, appcfg):
log.debug("---------------------------->")
from falcon.arch import Model as M
M.viz_annon(args, mode, appcfg)
def train(args, mode, appcfg):
log.debug("---------------------------->")
from falcon.arch import Model as M
M.train(args, mode, appcfg)