本文整理汇总了Python中model.MaskRCNN方法的典型用法代码示例。如果您正苦于以下问题：Python model.MaskRCNN方法的具体用法？Python model.MaskRCNN怎么用？Python model.MaskRCNN使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类model的用法示例。
在下文中一共展示了model.MaskRCNN方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: instance_segment_train
# 需要导入模块: import model [as 别名]
# 或者: from model import MaskRCNN [as 别名]
def instance_segment_train(**kwargs):
data_base_dir = kwargs['data_base_dir']
init_with = kwargs['init_with']
outputs_base_dir = 'outputs'
pretrained_model_base_dir = 'pretrained_model'
save_model_dir = os.path.join(outputs_base_dir, 'snapshot')
log_dir = os.path.join(outputs_base_dir, 'log')
coco_model_path = os.path.join(pretrained_model_base_dir, 'mask_rcnn_coco.h5')
imagenet_model_path = os.path.join(pretrained_model_base_dir, 'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5')
os.makedirs(save_model_dir, exist_ok=True)
os.makedirs(log_dir, exist_ok=True)
config = SketchTrainConfig()
config.display()
# Training dataset
dataset_train = SketchDataset(data_base_dir)
dataset_train.load_sketches("train")
dataset_train.prepare()
# Create model in training mode
model = modellib.MaskRCNN(mode="training", config=config,
model_dir=save_model_dir, log_dir=log_dir)
if init_with == "imagenet":
print("Loading weights from ", imagenet_model_path)
model.load_weights(imagenet_model_path, by_name=True)
elif init_with == "coco":
# Load weights trained on MS COCO, but skip layers that
# are different due to the different number of classes
print("Loading weights from ", coco_model_path)
model.load_weights(coco_model_path, by_name=True,
exclude=["mrcnn_class_logits", "mrcnn_bbox_fc",
"mrcnn_bbox", "mrcnn_mask"])
elif init_with == "last":
# Load the last model you trained and continue training
last_model_path = model.find_last()[1]
print("Loading weights from ", last_model_path)
model.load_weights(last_model_path, by_name=True)
else:
print("Training from fresh start.")
# Fine tune all layers
model.train(dataset_train,
learning_rate=config.LEARNING_RATE,
epochs=config.TOTAL_EPOCH,
layers="all")
# Save final weights
save_model_path = os.path.join(save_model_dir, "mask_rcnn_" + config.NAME + "_" + str(config.TOTAL_EPOCH) + ".h5")
model.keras_model.save_weights(save_model_path)