232 lines
11 KiB
Python
232 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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""" BEiT model configuration"""
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from collections import OrderedDict
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from typing import Mapping
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from packaging import version
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from ...configuration_utils import PretrainedConfig
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from ...onnx import OnnxConfig
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from ...utils import logging
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from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class BeitConfig(BackboneConfigMixin, PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`BeitModel`]. It is used to instantiate an BEiT
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the BEiT
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[microsoft/beit-base-patch16-224-pt22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k) architecture.
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Args:
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vocab_size (`int`, *optional*, defaults to 8192):
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Vocabulary size of the BEiT model. Defines the number of different image tokens that can be used during
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pre-training.
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality 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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Dimensionality 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 0.0):
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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 0.0):
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The dropout ratio for the attention probabilities.
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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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image_size (`int`, *optional*, defaults to 224):
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The size (resolution) of each image.
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patch_size (`int`, *optional*, defaults to 16):
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The size (resolution) of each patch.
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num_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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use_mask_token (`bool`, *optional*, defaults to `False`):
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Whether to use a mask token for masked image modeling.
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use_absolute_position_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to use BERT-style absolute position embeddings.
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use_relative_position_bias (`bool`, *optional*, defaults to `False`):
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Whether to use T5-style relative position embeddings in the self-attention layers.
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use_shared_relative_position_bias (`bool`, *optional*, defaults to `False`):
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Whether to use the same relative position embeddings across all self-attention layers of the Transformer.
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layer_scale_init_value (`float`, *optional*, defaults to 0.1):
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Scale to use in the self-attention layers. 0.1 for base, 1e-5 for large. Set 0 to disable layer scale.
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drop_path_rate (`float`, *optional*, defaults to 0.1):
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Stochastic depth rate per sample (when applied in the main path of residual layers).
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use_mean_pooling (`bool`, *optional*, defaults to `True`):
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Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the
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CLS token, before applying the classification head.
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pool_scales (`Tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`):
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Pooling scales used in Pooling Pyramid Module applied on the last feature map.
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use_auxiliary_head (`bool`, *optional*, defaults to `True`):
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Whether to use an auxiliary head during training.
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auxiliary_loss_weight (`float`, *optional*, defaults to 0.4):
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Weight of the cross-entropy loss of the auxiliary head.
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auxiliary_channels (`int`, *optional*, defaults to 256):
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Number of channels to use in the auxiliary head.
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auxiliary_num_convs (`int`, *optional*, defaults to 1):
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Number of convolutional layers to use in the auxiliary head.
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auxiliary_concat_input (`bool`, *optional*, defaults to `False`):
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Whether to concatenate the output of the auxiliary head with the input before the classification layer.
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semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
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The index that is ignored by the loss function of the semantic segmentation model.
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out_features (`List[str]`, *optional*):
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If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
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(depending on how many stages the model has). If unset and `out_indices` is set, will default to the
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corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
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same order as defined in the `stage_names` attribute.
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out_indices (`List[int]`, *optional*):
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If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
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many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
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If unset and `out_features` is unset, will default to the last stage. Must be in the
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same order as defined in the `stage_names` attribute.
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add_fpn (`bool`, *optional*, defaults to `False`):
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Whether to add a FPN as part of the backbone. Only relevant for [`BeitBackbone`].
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reshape_hidden_states (`bool`, *optional*, defaults to `True`):
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Whether to reshape the feature maps to 4D tensors of shape `(batch_size, hidden_size, height, width)` in
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case the model is used as backbone. If `False`, the feature maps will be 3D tensors of shape `(batch_size,
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seq_len, hidden_size)`. Only relevant for [`BeitBackbone`].
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Example:
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```python
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>>> from transformers import BeitConfig, BeitModel
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>>> # Initializing a BEiT beit-base-patch16-224-pt22k style configuration
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>>> configuration = BeitConfig()
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>>> # Initializing a model (with random weights) from the beit-base-patch16-224-pt22k style configuration
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>>> model = BeitModel(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 = "beit"
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def __init__(
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self,
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vocab_size=8192,
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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=0.0,
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attention_probs_dropout_prob=0.0,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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image_size=224,
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patch_size=16,
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num_channels=3,
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use_mask_token=False,
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use_absolute_position_embeddings=False,
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use_relative_position_bias=False,
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use_shared_relative_position_bias=False,
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layer_scale_init_value=0.1,
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drop_path_rate=0.1,
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use_mean_pooling=True,
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pool_scales=[1, 2, 3, 6],
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use_auxiliary_head=True,
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auxiliary_loss_weight=0.4,
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auxiliary_channels=256,
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auxiliary_num_convs=1,
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auxiliary_concat_input=False,
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semantic_loss_ignore_index=255,
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out_features=None,
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out_indices=None,
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add_fpn=False,
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reshape_hidden_states=True,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.use_mask_token = use_mask_token
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self.use_absolute_position_embeddings = use_absolute_position_embeddings
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self.use_relative_position_bias = use_relative_position_bias
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self.use_shared_relative_position_bias = use_shared_relative_position_bias
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self.layer_scale_init_value = layer_scale_init_value
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self.drop_path_rate = drop_path_rate
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self.use_mean_pooling = use_mean_pooling
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# decode head attributes (semantic segmentation)
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self.pool_scales = pool_scales
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# auxiliary head attributes (semantic segmentation)
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self.use_auxiliary_head = use_auxiliary_head
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self.auxiliary_loss_weight = auxiliary_loss_weight
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self.auxiliary_channels = auxiliary_channels
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self.auxiliary_num_convs = auxiliary_num_convs
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self.auxiliary_concat_input = auxiliary_concat_input
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self.semantic_loss_ignore_index = semantic_loss_ignore_index
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# handle backwards compatibility
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if "segmentation_indices" in kwargs:
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logger.warning(
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"The `segmentation_indices` argument is deprecated and will be removed in a future version, use `out_indices` instead.",
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FutureWarning,
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)
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out_indices = kwargs.pop("segmentation_indices")
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# backbone attributes
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self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, self.num_hidden_layers + 1)]
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self._out_features, self._out_indices = get_aligned_output_features_output_indices(
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out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
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)
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self.add_fpn = add_fpn
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self.reshape_hidden_states = reshape_hidden_states
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# Copied from transformers.models.vit.configuration_vit.ViTOnnxConfig
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class BeitOnnxConfig(OnnxConfig):
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torch_onnx_minimum_version = version.parse("1.11")
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@property
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def inputs(self) -> Mapping[str, Mapping[int, str]]:
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return OrderedDict(
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[
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("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
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]
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)
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@property
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def atol_for_validation(self) -> float:
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return 1e-4
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