def prep_newshapes_train(init_with = "last", FCN_layers= False, batch_sz =5, epoch_steps = 4, folder_name= "mrcnn_newshape_training_logs"):
import mrcnn.new_shapes as new_shapes
MODEL_DIR = os.path.join(MODEL_PATH, folder_name)
# Build configuration object -----------------------------------------------
config = new_shapes.NewShapesConfig()
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 -----------------------------------------------
# Training dataset
dataset_train = new_shapes.NewShapesDataset()
dataset_train.load_shapes(3000, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1])
dataset_train.prepare()
# Validation dataset
dataset_val = new_shapes.NewShapesDataset()
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('MODEL_PATH : ', MODEL_PATH)
print('COCO_MODEL_PATH : ', COCO_MODEL_PATH)
print('RESNET_MODEL_PATH : ', RESNET_MODEL_PATH)
print('MODEL_DIR : ', MODEL_DIR)
print('Last Saved Model : ', model.find_last())
load_model(model, init_with = 'last')
train_generator = data_generator(dataset_train, model.config, shuffle=True,
batch_size=model.config.BATCH_SIZE,
augment = False)
config.display()
return [model, dataset_train, train_generator, config]
def prep_newshapes_test(init_with = 'last', FCN_layers = False, batch_sz = 5, epoch_steps = 4,folder_name= "mrcnn_newshape_test_logs"):
import mrcnn.new_shapes as new_shapes
MODEL_DIR = os.path.join(MODEL_PATH, folder_name)
# Build configuration object -----------------------------------------------
config = new_shapes.NewShapesConfig()
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 -----------------------------------------------
# Training dataset
dataset_test = new_shapes.NewShapesDataset()
dataset_test.load_shapes(3000, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1])
dataset_test.prepare()
# Recreate the model in inference mode
try :
del model
print('delete model is successful')
gc.collect()
except:
pass
KB.clear_session()
model = modellib.MaskRCNN(mode="inference",
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)
test_generator = data_generator(dataset_test, model.config, shuffle=True,
batch_size=model.config.BATCH_SIZE,
augment = False)
model.config.display()
return [model, dataset_test, test_generator, config]
# tfconfig = tf.ConfigProto(
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5),
# device_count = {'GPU': 1}
# )
# tfconfig = tf.ConfigProto()
# tfconfig.gpu_options.allow_growth=True
# tfconfig.gpu_options.visible_device_list = "0"
# tfconfig.gpu_options.per_process_gpu_memory_fraction=0.5
# tf_sess = tf.Session(config=tfconfig)
# set_session(tf_sess)
##------------------------------------------------------------------------------------
##------------------------------------------------------------------------------------
## Build configuration object
##------------------------------------------------------------------------------------
config = shapes.NewShapesConfig()
config.BATCH_SIZE = int(
args.batch_size) # Batch size is 2 (# GPUs * images/GPU).
config.IMAGES_PER_GPU = int(args.batch_size) # Must match BATCH_SIZE
config.STEPS_PER_EPOCH = int(args.steps_in_epoch)
config.LEARNING_RATE = float(args.lr)
config.EPOCHS_TO_RUN = int(args.epochs)
config.FCN_INPUT_SHAPE = config.IMAGE_SHAPE[0:2]
config.LAST_EPOCH_RAN = int(args.last_epoch)
config.display()
##------------------------------------------------------------------------------------
## Build shape dataset
##------------------------------------------------------------------------------------
# Training dataset
def prep_newshapes_train(init_with = "last", FCN_layers= False, batch_sz =5, epoch_steps = 4, training_folder= None):
MODEL_DIR = os.path.join(TRAINING_DIR, training_folder)
# Build configuration object -----------------------------------------------
config = new_shapes.NewShapesConfig()
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 -----------------------------------------------
# Training dataset
dataset_train = new_shapes.NewShapesDataset(config)
dataset_train.load_shapes(10000)
dataset_train.prepare()
# Validation dataset
dataset_val = new_shapes.NewShapesDataset(config)
dataset_val.load_shapes(2500)
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('TRAINING_DIR : ', TRAINING_DIR)
print('COCO_TRAINING_DIR : ', COCO_TRAINING_DIR)
print('RESNET_TRAINING_DIR : ', RESNET_TRAINING_DIR)
print('MODEL_DIR : ', MODEL_DIR)
print('Last Saved Model : ', model.find_last())
# exclude_layers = \
# ['fcn_block1_conv1'
# ,'fcn_block1_conv2'
# ,'fcn_block1_pool'
# ,'fcn_block2_conv1'
# ,'fcn_block2_conv2'
# ,'fcn_block2_pool'
# ,'fcn_block3_conv1'
# ,'fcn_block3_conv2'
# ,'fcn_block3_conv3'
# ,'fcn_block3_pool'
# ,'fcn_block4_conv1'
# ,'fcn_block4_conv2'
# ,'fcn_block4_conv3'
# ,'fcn_block4_pool'
# ,'fcn_block5_conv1'
# ,'fcn_block5_conv2'
# ,'fcn_block5_conv3'
# ,'fcn_block5_pool'
# ,'fcn_fc1'
# ,'dropout_1'
# ,'fcn_fc2'
# ,'dropout_2'
# ,'fcn_classify'
# ,'fcn_bilinear'
# ,'fcn_heatmap_norm'
# ,'fcn_scoring'
# ,'fcn_heatmap'
# ,'fcn_norm_loss']
# load_model(model, init_with = 'last', exclude = exclude_layers)
model.load_model_weights(init_with = init_with)
# print('=====================================')
# print(" Load second weight file ?? ")
# model.keras_model.load_weights('E:/Models/vgg16_weights_tf_dim_ordering_tf_kernels.h5', by_name= True)
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)
config.display()
return [model, dataset_train, dataset_val, train_generator, val_generator, config]
def prep_newshapes_test(init_with = 'last', FCN_layers = False, batch_sz = 5, epoch_steps = 4,training_folder= "mrcnn_newshape_test_logs"):
MODEL_DIR = os.path.join(TRAINING_DIR, training_folder)
# Build configuration object -----------------------------------------------
config = new_shapes.NewShapesConfig()
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]
config.DETECTION_MIN_CONFIDENCE = 0.1
# Build shape dataset -----------------------------------------------
# Training dataset
dataset_test = new_shapes.NewShapesDataset(config)
dataset_test.load_shapes(3000)
dataset_test.prepare()
# Recreate the model in inference mode
try :
del model
print('delete model is successful')
gc.collect()
except:
pass
KB.clear_session()
model = modellib.MaskRCNN(mode="inference",
config=config,
model_dir=MODEL_DIR,
FCN_layers = FCN_layers )
print(' COCO Model Path : ', COCO_TRAINING_DIR)
print(' Checkpoint folder Path: ', MODEL_DIR)
print(' Model Parent Path : ', TRAINING_DIR)
print(' Resent Model Path : ', RESNET_TRAINING_DIR)
# exclude_layers = \
# ['fcn_block1_conv1'
# ,'fcn_block1_conv2'
# ,'fcn_block1_pool'
# ,'fcn_block2_conv1'
# ,'fcn_block2_conv2'
# ,'fcn_block2_pool'
# ,'fcn_block3_conv1'
# ,'fcn_block3_conv2'
# ,'fcn_block3_conv3'
# ,'fcn_block3_pool'
# ,'fcn_block4_conv1'
# ,'fcn_block4_conv2'
# ,'fcn_block4_conv3'
# ,'fcn_block4_pool'
# ,'fcn_block5_conv1'
# ,'fcn_block5_conv2'
# ,'fcn_block5_conv3'
# ,'fcn_block5_pool'
# ,'fcn_fc1'
# ,'dropout_1'
# ,'fcn_fc2'
# ,'dropout_2'
# ,'fcn_classify'
# ,'fcn_bilinear'
# ,'fcn_heatmap_norm'
# ,'fcn_scoring'
# ,'fcn_heatmap'
# ,'fcn_norm_loss']
# load_model(model, init_with = init_with, exclude = exclude_layers)
model.load_model_weights(init_with = init_with)
# print('=====================================')
# print(" Load second weight file ?? ")
# model.keras_model.load_weights('E:/Models/vgg16_weights_tf_dim_ordering_tf_kernels.h5', by_name= True )
test_generator = data_generator(dataset_test, model.config, shuffle=True,
batch_size=model.config.BATCH_SIZE,
augment = False)
model.config.display()
return [model, dataset_test, test_generator, config]