223 lines
11 KiB
Python
223 lines
11 KiB
Python
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# coding=utf-8
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# Copyright Microsoft Research and The HuggingFace Inc. 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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""" LayoutLMv2 model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import is_detectron2_available, logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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# soft dependency
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if is_detectron2_available():
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import detectron2
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class LayoutLMv2Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`LayoutLMv2Model`]. It is used to instantiate an
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LayoutLMv2 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 LayoutLMv2
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[microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) 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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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the LayoutLMv2 model. Defines the number of different tokens that can be represented by
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the `inputs_ids` passed when calling [`LayoutLMv2Model`] or [`TFLayoutLMv2Model`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimension of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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type_vocab_size (`int`, *optional*, defaults to 2):
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The vocabulary size of the `token_type_ids` passed when calling [`LayoutLMv2Model`] or
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[`TFLayoutLMv2Model`].
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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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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
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The maximum value that the 2D position embedding might ever be used with. Typically set this to something
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large just in case (e.g., 1024).
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max_rel_pos (`int`, *optional*, defaults to 128):
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The maximum number of relative positions to be used in the self-attention mechanism.
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rel_pos_bins (`int`, *optional*, defaults to 32):
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The number of relative position bins to be used in the self-attention mechanism.
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fast_qkv (`bool`, *optional*, defaults to `True`):
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Whether or not to use a single matrix for the queries, keys, values in the self-attention layers.
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max_rel_2d_pos (`int`, *optional*, defaults to 256):
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The maximum number of relative 2D positions in the self-attention mechanism.
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rel_2d_pos_bins (`int`, *optional*, defaults to 64):
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The number of 2D relative position bins in the self-attention mechanism.
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image_feature_pool_shape (`List[int]`, *optional*, defaults to [7, 7, 256]):
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The shape of the average-pooled feature map.
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coordinate_size (`int`, *optional*, defaults to 128):
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Dimension of the coordinate embeddings.
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shape_size (`int`, *optional*, defaults to 128):
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Dimension of the width and height embeddings.
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has_relative_attention_bias (`bool`, *optional*, defaults to `True`):
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Whether or not to use a relative attention bias in the self-attention mechanism.
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has_spatial_attention_bias (`bool`, *optional*, defaults to `True`):
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Whether or not to use a spatial attention bias in the self-attention mechanism.
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has_visual_segment_embedding (`bool`, *optional*, defaults to `False`):
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Whether or not to add visual segment embeddings.
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detectron2_config_args (`dict`, *optional*):
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Dictionary containing the configuration arguments of the Detectron2 visual backbone. Refer to [this
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file](https://github.com/microsoft/unilm/blob/master/layoutlmft/layoutlmft/models/layoutlmv2/detectron2_config.py)
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for details regarding default values.
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Example:
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```python
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>>> from transformers import LayoutLMv2Config, LayoutLMv2Model
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>>> # Initializing a LayoutLMv2 microsoft/layoutlmv2-base-uncased style configuration
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>>> configuration = LayoutLMv2Config()
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>>> # Initializing a model (with random weights) from the microsoft/layoutlmv2-base-uncased style configuration
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>>> model = LayoutLMv2Model(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 = "layoutlmv2"
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def __init__(
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self,
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vocab_size=30522,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=2,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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pad_token_id=0,
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max_2d_position_embeddings=1024,
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max_rel_pos=128,
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rel_pos_bins=32,
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fast_qkv=True,
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max_rel_2d_pos=256,
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rel_2d_pos_bins=64,
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convert_sync_batchnorm=True,
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image_feature_pool_shape=[7, 7, 256],
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coordinate_size=128,
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shape_size=128,
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has_relative_attention_bias=True,
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has_spatial_attention_bias=True,
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has_visual_segment_embedding=False,
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detectron2_config_args=None,
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**kwargs,
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):
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super().__init__(
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vocab_size=vocab_size,
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hidden_size=hidden_size,
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num_hidden_layers=num_hidden_layers,
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num_attention_heads=num_attention_heads,
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intermediate_size=intermediate_size,
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hidden_act=hidden_act,
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hidden_dropout_prob=hidden_dropout_prob,
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attention_probs_dropout_prob=attention_probs_dropout_prob,
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max_position_embeddings=max_position_embeddings,
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type_vocab_size=type_vocab_size,
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initializer_range=initializer_range,
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layer_norm_eps=layer_norm_eps,
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pad_token_id=pad_token_id,
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**kwargs,
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)
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self.max_2d_position_embeddings = max_2d_position_embeddings
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self.max_rel_pos = max_rel_pos
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self.rel_pos_bins = rel_pos_bins
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self.fast_qkv = fast_qkv
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self.max_rel_2d_pos = max_rel_2d_pos
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self.rel_2d_pos_bins = rel_2d_pos_bins
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self.convert_sync_batchnorm = convert_sync_batchnorm
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self.image_feature_pool_shape = image_feature_pool_shape
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self.coordinate_size = coordinate_size
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self.shape_size = shape_size
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self.has_relative_attention_bias = has_relative_attention_bias
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self.has_spatial_attention_bias = has_spatial_attention_bias
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self.has_visual_segment_embedding = has_visual_segment_embedding
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self.detectron2_config_args = (
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detectron2_config_args if detectron2_config_args is not None else self.get_default_detectron2_config()
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)
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@classmethod
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def get_default_detectron2_config(self):
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return {
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"MODEL.MASK_ON": True,
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"MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
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"MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
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"MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
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"MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
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"MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
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"MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
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"MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
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"MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
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"MODEL.POST_NMS_TOPK_TEST": 1000,
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"MODEL.ROI_HEADS.NAME": "StandardROIHeads",
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"MODEL.ROI_HEADS.NUM_CLASSES": 5,
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"MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
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"MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
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"MODEL.ROI_BOX_HEAD.NUM_FC": 2,
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"MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
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"MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
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"MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
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"MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
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"MODEL.RESNETS.DEPTH": 101,
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"MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
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"MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
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"MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
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"MODEL.RESNETS.NUM_GROUPS": 32,
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"MODEL.RESNETS.WIDTH_PER_GROUP": 8,
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"MODEL.RESNETS.STRIDE_IN_1X1": False,
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}
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def get_detectron2_config(self):
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detectron2_config = detectron2.config.get_cfg()
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for k, v in self.detectron2_config_args.items():
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attributes = k.split(".")
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to_set = detectron2_config
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for attribute in attributes[:-1]:
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to_set = getattr(to_set, attribute)
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setattr(to_set, attributes[-1], v)
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return detectron2_config
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