92 lines
4.1 KiB
Python
92 lines
4.1 KiB
Python
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# Copyright 2024 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import List
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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SUPERPOINT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"magic-leap-community/superpoint": "https://huggingface.co/magic-leap-community/superpoint/blob/main/config.json"
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}
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class SuperPointConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`SuperPointForKeypointDetection`]. It is used to instantiate a
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SuperPoint model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the SuperPoint
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[magic-leap-community/superpoint](https://huggingface.co/magic-leap-community/superpoint) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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encoder_hidden_sizes (`List`, *optional*, defaults to `[64, 64, 128, 128]`):
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The number of channels in each convolutional layer in the encoder.
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decoder_hidden_size (`int`, *optional*, defaults to 256): The hidden size of the decoder.
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keypoint_decoder_dim (`int`, *optional*, defaults to 65): The output dimension of the keypoint decoder.
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descriptor_decoder_dim (`int`, *optional*, defaults to 256): The output dimension of the descriptor decoder.
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keypoint_threshold (`float`, *optional*, defaults to 0.005):
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The threshold to use for extracting keypoints.
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max_keypoints (`int`, *optional*, defaults to -1):
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The maximum number of keypoints to extract. If `-1`, will extract all keypoints.
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nms_radius (`int`, *optional*, defaults to 4):
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The radius for non-maximum suppression.
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border_removal_distance (`int`, *optional*, defaults to 4):
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The distance from the border to remove keypoints.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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Example:
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```python
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>>> from transformers import SuperPointConfig, SuperPointForKeypointDetection
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>>> # Initializing a SuperPoint superpoint style configuration
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>>> configuration = SuperPointConfig()
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>>> # Initializing a model from the superpoint style configuration
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>>> model = SuperPointForKeypointDetection(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "superpoint"
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def __init__(
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self,
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encoder_hidden_sizes: List[int] = [64, 64, 128, 128],
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decoder_hidden_size: int = 256,
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keypoint_decoder_dim: int = 65,
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descriptor_decoder_dim: int = 256,
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keypoint_threshold: float = 0.005,
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max_keypoints: int = -1,
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nms_radius: int = 4,
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border_removal_distance: int = 4,
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initializer_range=0.02,
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**kwargs,
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):
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self.encoder_hidden_sizes = encoder_hidden_sizes
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self.decoder_hidden_size = decoder_hidden_size
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self.keypoint_decoder_dim = keypoint_decoder_dim
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self.descriptor_decoder_dim = descriptor_decoder_dim
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self.keypoint_threshold = keypoint_threshold
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self.max_keypoints = max_keypoints
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self.nms_radius = nms_radius
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self.border_removal_distance = border_removal_distance
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self.initializer_range = initializer_range
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super().__init__(**kwargs)
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