8.1.1.7.1.3.1.1. blueoil.networks.object_detection.lm_fyolo
¶
8.1.1.7.1.3.1.1.1. Module Contents¶
8.1.1.7.1.3.1.1.1.1. Classes¶
LM original object detection network based on Yolov2 and F-Yolo. |
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LM original object detection network based on Yolov2 and F-Yolo. |
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class
blueoil.networks.object_detection.lm_fyolo.
LMFYolo
¶ Bases:
blueoil.networks.object_detection.yolo_v2.YoloV2
LM original object detection network based on Yolov2 and F-Yolo.
- Ref:
F-Yolo https://arxiv.org/abs/1805.06361 YoloV2 https://arxiv.org/abs/1612.08242
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train
(self, loss, optimizer, var_list=[])¶
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base
(self, images, is_training)¶ Base network. Returns: Output. output shape depends on parameter.
When data_format is NHWC shape is [
batch_size, num_cell[0], num_cell[1], (num_classes + 5(x, y ,w, h, confidence)) * boxes_per_cell(length of anchors),
] When data_format is NCHW shape is [
batch_size, (num_classes + 5(x, y ,w, h, confidence)) * boxes_per_cell(length of anchors), num_cell[0], num_cell[1],
]
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class
blueoil.networks.object_detection.lm_fyolo.
LMFYoloQuantize
(quantize_first_convolution=True, quantize_last_convolution=True, activation_quantizer=None, activation_quantizer_kwargs={}, weight_quantizer=None, weight_quantizer_kwargs={}, *args, **kwargs)¶ Bases:
blueoil.networks.object_detection.lm_fyolo.LMFYolo
LM original object detection network based on Yolov2 and F-Yolo.
- Ref:
F-Yolo https://arxiv.org/abs/1805.06361 YoloV2 https://arxiv.org/abs/1612.08242
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static
_quantized_variable_getter
(weight_quantization, quantize_first_convolution, quantize_last_convolution, getter, name, *args, **kwargs)¶ Get the quantized variables.
Use if to choose or skip the target should be quantized.
- Parameters
getter – Default from tensorflow.
name – Default from tensorflow.
weight_quantization – Callable object which quantize variable.
args – Args.
kwargs – Kwargs.
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base
(self, images, is_training)¶ Base network. Returns: Output. output shape depends on parameter.
When data_format is NHWC shape is [
batch_size, num_cell[0], num_cell[1], (num_classes + 5(x, y ,w, h, confidence)) * boxes_per_cell(length of anchors),
] When data_format is NCHW shape is [
batch_size, (num_classes + 5(x, y ,w, h, confidence)) * boxes_per_cell(length of anchors), num_cell[0], num_cell[1],
]