165 lines
7.9 KiB
Python
165 lines
7.9 KiB
Python
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# coding=utf-8
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# Copyright 2023 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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""" FocalNet model configuration"""
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from ...configuration_utils import PretrainedConfig
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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 FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class FocalNetConfig(BackboneConfigMixin, PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`FocalNetModel`]. It is used to instantiate a
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FocalNet model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the FocalNet
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[microsoft/focalnet-tiny](https://huggingface.co/microsoft/focalnet-tiny) 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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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 4):
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The size (resolution) of each patch in the embeddings layer.
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num_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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embed_dim (`int`, *optional*, defaults to 96):
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Dimensionality of patch embedding.
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use_conv_embed (`bool`, *optional*, defaults to `False`):
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Whether to use convolutional embedding. The authors noted that using convolutional embedding usually
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improve the performance, but it's not used by default.
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hidden_sizes (`List[int]`, *optional*, defaults to `[192, 384, 768, 768]`):
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Dimensionality (hidden size) at each stage.
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depths (`list(int)`, *optional*, defaults to `[2, 2, 6, 2]`):
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Depth (number of layers) of each stage in the encoder.
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focal_levels (`list(int)`, *optional*, defaults to `[2, 2, 2, 2]`):
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Number of focal levels in each layer of the respective stages in the encoder.
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focal_windows (`list(int)`, *optional*, defaults to `[3, 3, 3, 3]`):
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Focal window size in each layer of the respective stages in the 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. If string, `"gelu"`, `"relu"`,
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`"selu"` and `"gelu_new"` are supported.
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mlp_ratio (`float`, *optional*, defaults to 4.0):
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Ratio of MLP hidden dimensionality to embedding dimensionality.
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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 and encoder.
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drop_path_rate (`float`, *optional*, defaults to 0.1):
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Stochastic depth rate.
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use_layerscale (`bool`, *optional*, defaults to `False`):
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Whether to use layer scale in the encoder.
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layerscale_value (`float`, *optional*, defaults to 0.0001):
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The initial value of the layer scale.
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use_post_layernorm (`bool`, *optional*, defaults to `False`):
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Whether to use post layer normalization in the encoder.
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use_post_layernorm_in_modulation (`bool`, *optional*, defaults to `False`):
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Whether to use post layer normalization in the modulation layer.
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normalize_modulator (`bool`, *optional*, defaults to `False`):
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Whether to normalize the modulator.
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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-05):
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The epsilon used by the layer normalization layers.
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encoder_stride (`int`, *optional*, defaults to 32):
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Factor to increase the spatial resolution by in the decoder head for masked image modeling.
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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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Example:
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```python
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>>> from transformers import FocalNetConfig, FocalNetModel
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>>> # Initializing a FocalNet microsoft/focalnet-tiny style configuration
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>>> configuration = FocalNetConfig()
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>>> # Initializing a model (with random weights) from the microsoft/focalnet-tiny style configuration
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>>> model = FocalNetModel(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 = "focalnet"
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def __init__(
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self,
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image_size=224,
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patch_size=4,
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num_channels=3,
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embed_dim=96,
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use_conv_embed=False,
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hidden_sizes=[192, 384, 768, 768],
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depths=[2, 2, 6, 2],
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focal_levels=[2, 2, 2, 2],
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focal_windows=[3, 3, 3, 3],
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hidden_act="gelu",
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mlp_ratio=4.0,
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hidden_dropout_prob=0.0,
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drop_path_rate=0.1,
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use_layerscale=False,
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layerscale_value=1e-4,
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use_post_layernorm=False,
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use_post_layernorm_in_modulation=False,
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normalize_modulator=False,
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initializer_range=0.02,
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layer_norm_eps=1e-5,
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encoder_stride=32,
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out_features=None,
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out_indices=None,
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**kwargs,
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):
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super().__init__(**kwargs)
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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.embed_dim = embed_dim
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self.use_conv_embed = use_conv_embed
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self.hidden_sizes = hidden_sizes
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self.depths = depths
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self.focal_levels = focal_levels
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self.focal_windows = focal_windows
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self.hidden_act = hidden_act
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self.mlp_ratio = mlp_ratio
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self.hidden_dropout_prob = hidden_dropout_prob
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self.drop_path_rate = drop_path_rate
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self.use_layerscale = use_layerscale
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self.layerscale_value = layerscale_value
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self.use_post_layernorm = use_post_layernorm
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self.use_post_layernorm_in_modulation = use_post_layernorm_in_modulation
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self.normalize_modulator = normalize_modulator
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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.encoder_stride = encoder_stride
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self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(self.depths) + 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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