120 lines
5.3 KiB
Python
120 lines
5.3 KiB
Python
# 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 TYPE_CHECKING, Optional
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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from ..auto import CONFIG_MAPPING
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if TYPE_CHECKING:
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from ..superpoint import SuperPointConfig
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logger = logging.get_logger(__name__)
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class SuperGlueConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`SuperGlueModel`]. It is used to instantiate a
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SuperGlue 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 SuperGlue
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[magic-leap-community/superglue_indoor](https://huggingface.co/magic-leap-community/superglue_indoor) 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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keypoint_detector_config (`Union[AutoConfig, dict]`, *optional*, defaults to `SuperPointConfig`):
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The config object or dictionary of the keypoint detector.
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hidden_size (`int`, *optional*, defaults to 256):
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The dimension of the descriptors.
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keypoint_encoder_sizes (`list[int]`, *optional*, defaults to `[32, 64, 128, 256]`):
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The sizes of the keypoint encoder layers.
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gnn_layers_types (`list[str]`, *optional*, defaults to `['self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross']`):
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The types of the GNN layers. Must be either 'self' or 'cross'.
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num_attention_heads (`int`, *optional*, defaults to 4):
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The number of heads in the GNN layers.
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sinkhorn_iterations (`int`, *optional*, defaults to 100):
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The number of Sinkhorn iterations.
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matching_threshold (`float`, *optional*, defaults to 0.0):
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The matching threshold.
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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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Examples:
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```python
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>>> from transformers import SuperGlueConfig, SuperGlueModel
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>>> # Initializing a SuperGlue superglue style configuration
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>>> configuration = SuperGlueConfig()
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>>> # Initializing a model from the superglue style configuration
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>>> model = SuperGlueModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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"""
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model_type = "superglue"
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def __init__(
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self,
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keypoint_detector_config: "SuperPointConfig" = None,
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hidden_size: int = 256,
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keypoint_encoder_sizes: Optional[list[int]] = None,
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gnn_layers_types: Optional[list[str]] = None,
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num_attention_heads: int = 4,
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sinkhorn_iterations: int = 100,
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matching_threshold: float = 0.0,
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initializer_range: float = 0.02,
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**kwargs,
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):
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self.gnn_layers_types = gnn_layers_types if gnn_layers_types is not None else ["self", "cross"] * 9
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# Check whether all gnn_layers_types are either 'self' or 'cross'
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if not all(layer_type in ["self", "cross"] for layer_type in self.gnn_layers_types):
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raise ValueError("All gnn_layers_types must be either 'self' or 'cross'")
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if hidden_size % num_attention_heads != 0:
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raise ValueError("hidden_size % num_attention_heads is different from zero")
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self.keypoint_encoder_sizes = (
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keypoint_encoder_sizes if keypoint_encoder_sizes is not None else [32, 64, 128, 256]
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)
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self.hidden_size = hidden_size
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self.keypoint_encoder_sizes = keypoint_encoder_sizes
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self.gnn_layers_types = gnn_layers_types
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self.num_attention_heads = num_attention_heads
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self.sinkhorn_iterations = sinkhorn_iterations
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self.matching_threshold = matching_threshold
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if isinstance(keypoint_detector_config, dict):
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keypoint_detector_config["model_type"] = (
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keypoint_detector_config["model_type"] if "model_type" in keypoint_detector_config else "superpoint"
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)
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keypoint_detector_config = CONFIG_MAPPING[keypoint_detector_config["model_type"]](
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**keypoint_detector_config
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)
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if keypoint_detector_config is None:
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keypoint_detector_config = CONFIG_MAPPING["superpoint"]()
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self.keypoint_detector_config = keypoint_detector_config
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self.initializer_range = initializer_range
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self.attention_probs_dropout_prob = 0
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self.is_decoder = False
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super().__init__(**kwargs)
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__all__ = ["SuperGlueConfig"]
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