def prep_oldshapes_train(init_with = None, FCN_layers = False, batch_sz = 5, epoch_steps = 4, folder_name= "mrcnn_oldshape_training_logs"):
import mrcnn.shapes as shapes
MODEL_DIR = os.path.join(MODEL_PATH, folder_name)
# Build configuration object -----------------------------------------------
config = shapes.ShapesConfig()
config.BATCH_SIZE = batch_sz # Batch size is 2 (# GPUs * images/GPU).
config.IMAGES_PER_GPU = batch_sz # Must match BATCH_SIZE
config.STEPS_PER_EPOCH = epoch_steps
config.FCN_INPUT_SHAPE = config.IMAGE_SHAPE[0:2]
# Build shape dataset -----------------------------------------------
dataset_train = shapes.ShapesDataset()
dataset_train.load_shapes(3000, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1])
dataset_train.prepare()
# Validation dataset
dataset_val = shapes.ShapesDataset()
dataset_val.load_shapes(500, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1])
dataset_val.prepare()
try :
del model
print('delete model is successful')
gc.collect()
except:
pass
KB.clear_session()
model = modellib.MaskRCNN(mode="training", config=config, model_dir=MODEL_DIR, FCN_layers = FCN_layers)
print(' COCO Model Path : ', COCO_MODEL_PATH)
print(' Checkpoint folder Path: ', MODEL_DIR)
print(' Model Parent Path : ', MODEL_PATH)
print(' Resent Model Path : ', RESNET_MODEL_PATH)
load_model(model, init_with = init_with)
train_generator = data_generator(dataset_train, model.config, shuffle=True,
batch_size=model.config.BATCH_SIZE,
augment = False)
val_generator = data_generator(dataset_val, model.config, shuffle=True,
batch_size=model.config.BATCH_SIZE,
augment=False)
model.config.display()
return [model, dataset_train, dataset_val, train_generator, val_generator, config]
# image = dataset_train.load_image(image_id)
# mask, class_ids = dataset_train.load_mask(image_id)
# visualize.display_top_masks(image, mask, class_ids, dataset_train.class_names)
##------------------------------------------------------------------------------------
## Build Model
##------------------------------------------------------------------------------------
try:
del model
gc.collect()
except:
pass
KB.clear_session()
model = modellib.MaskRCNN(mode="training",
config=config,
model_dir=MODEL_DIR,
FCN_layers=True)
print(' COCO Model Path : ', COCO_MODEL_PATH)
print(' Checkpoint folder Path: ', MODEL_DIR)
print(' Model Parent Path : ', MODEL_PATH)
# print(model.find_last())
##----------------------------------------------------------------------------------------------
## Load Model Weight file
##----------------------------------------------------------------------------------------------
load_model(model, init_with=args.model)
config.display()
model.layer_info()
# for image_id in [3]:
# image = dataset_train.load_image(image_id)
# mask, class_ids = dataset_train.load_mask(image_id)
# visualize.display_top_masks(image, mask, class_ids, dataset_train.class_names)
##------------------------------------------------------------------------------------
## Build Model
##------------------------------------------------------------------------------------
try:
del model
gc.collect()
except:
pass
KB.clear_session()
model = modellib.MaskRCNN(mode="training", config=config, model_dir=MODEL_DIR)
#model.keras_model.summary(line_length = 120)
##------------------------------------------------------------------------------------
## Load Model hf5 file
##------------------------------------------------------------------------------------
# KB.set_learning_phase(1)
# ## 2- look for last checkpoint file in a specific folder (not working correctly)
# model.config.LAST_EPOCH_RAN = 5784
# model.model_dir = 'E:\\Models\\mrcnn_logs\\shapes20180428T1819'
# last_model_found = model.find_last()
# print(' last model in MODEL_DIR: ', last_model_found)
# # loc= model.load_weights(model.find_last()[1], by_name=True)
# # print('Load weights complete :', loc)