145 lines
6.4 KiB
Python
145 lines
6.4 KiB
Python
# coding=utf-8
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# Copyright 2024 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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""" SegGpt model configuration"""
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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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from ..deprecated._archive_maps import SEGGPT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class SegGptConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`SegGptModel`]. It is used to instantiate a SegGPT
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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 SegGPT
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[BAAI/seggpt-vit-large](https://huggingface.co/BAAI/seggpt-vit-large) 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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hidden_size (`int`, *optional*, defaults to 1024):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 24):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention 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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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-06):
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The epsilon used by the layer normalization layers.
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image_size (`List[int]`, *optional*, defaults to `[896, 448]`):
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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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qkv_bias (`bool`, *optional*, defaults to `True`):
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Whether to add a bias to the queries, keys and values.
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mlp_dim (`int`, *optional*):
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The dimensionality of the MLP layer in the Transformer encoder. If unset, defaults to
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`hidden_size` * 4.
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drop_path_rate (`float`, *optional*, defaults to 0.1):
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The drop path rate for the dropout layers.
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pretrain_image_size (`int`, *optional*, defaults to 224):
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The pretrained size of the absolute position embeddings.
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decoder_hidden_size (`int`, *optional*, defaults to 64):
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Hidden size for decoder.
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use_relative_position_embeddings (`bool`, *optional*, defaults to `True`):
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Whether to use relative position embeddings in the attention layers.
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merge_index (`int`, *optional*, defaults to 2):
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The index of the encoder layer to merge the embeddings.
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intermediate_hidden_state_indices (`List[int]`, *optional*, defaults to `[5, 11, 17, 23]`):
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The indices of the encoder layers which we store as features for the decoder.
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beta (`float`, *optional*, defaults to 0.01):
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Regularization factor for SegGptLoss (smooth-l1 loss).
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Example:
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```python
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>>> from transformers import SegGptConfig, SegGptModel
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>>> # Initializing a SegGPT seggpt-vit-large style configuration
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>>> configuration = SegGptConfig()
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>>> # Initializing a model (with random weights) from the seggpt-vit-large style configuration
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>>> model = SegGptModel(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 = "seggpt"
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def __init__(
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self,
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hidden_size=1024,
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num_hidden_layers=24,
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num_attention_heads=16,
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hidden_act="gelu",
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hidden_dropout_prob=0.0,
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initializer_range=0.02,
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layer_norm_eps=1e-6,
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image_size=[896, 448],
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patch_size=16,
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num_channels=3,
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qkv_bias=True,
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mlp_dim=None,
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drop_path_rate=0.1,
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pretrain_image_size=224,
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decoder_hidden_size=64,
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use_relative_position_embeddings=True,
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merge_index=2,
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intermediate_hidden_state_indices=[5, 11, 17, 23],
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beta=0.01,
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**kwargs,
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):
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super().__init__(**kwargs)
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if merge_index > min(intermediate_hidden_state_indices):
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raise ValueError(
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f"Merge index must be less than the minimum encoder output index, but got {merge_index=} and {intermediate_hidden_state_indices=}"
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)
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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.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_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.qkv_bias = qkv_bias
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self.drop_path_rate = drop_path_rate
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self.pretrain_image_size = pretrain_image_size
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self.decoder_hidden_size = decoder_hidden_size
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self.use_relative_position_embeddings = use_relative_position_embeddings
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self.merge_index = merge_index
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self.intermediate_hidden_state_indices = intermediate_hidden_state_indices
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self.beta = beta
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self.mlp_dim = int(hidden_size * 4) if mlp_dim is None else mlp_dim
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