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# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Hiera model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
logger = logging.get_logger(__name__)
class HieraConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`HieraModel`]. It is used to instantiate a Hiera
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Hiera
[facebook/hiera-base-224](https://huggingface.co/facebook/hiera-base-224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
embed_dim (`int`, *optional*, defaults to 96):
Dimensionality of patch embedding.
image_size (`list(int)`, *optional*, defaults to `[224, 224]`):
The size (resolution) of input in the format (height, width) for images
and (frames, height, width) for videos.
patch_size (`list(int)`, *optional*, defaults to `[7, 7]`):
The size (resolution) of each patch.
patch_stride (`list(int)`, *optional*, defaults to `[4, 4]`):
The stride of the patch.
patch_padding (`list(int)`, *optional*, defaults to `[3, 3]`):
The padding of the patch.
mlp_ratio (`float`, *optional*, defaults to 4.0):
The ratio of mlp hidden dim to embedding dim.
depths (`list(int)`, *optional*, defaults to `[2, 3, 16, 3]`):
Depth of each layer in the Transformer encoder.
num_heads (`list(int)`, *optional*, defaults to `[1, 2, 4, 8]`):
Number of attention heads in each layer of the Transformer encoder.
embed_dim_multiplier (`float`, *optional*, defaults to 2.0):
The multiplier to the dimensionality of patch embedding in each layer of the Transformer encoder.
num_query_pool (`int`, *optional*, defaults to 3):
The number of query pool stages.
query_stride (`list(int)`, *optional*, defaults to `[2, 2]`):
The stride of the query pool.
masked_unit_size (`list(int)`, *optional*, defaults to `[8, 8]`):
The size of the masked unit.
masked_unit_attention (`list(bool)`, *optional*, defaults to `[True, True, False, False]`):
Whether to use masked unit attention in each layer of the Transformer encoder.
drop_path_rate (`float`, *optional*, defaults to 0.0):
The drop path rate.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
hidden_act (`str`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
`"selu"` and `"gelu_new"` are supported.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices and
the zero_initializer for initializing all bias vectors.
layer_norm_init (`float`, *optional*, defaults to 1.0):
The initial weight value for layer normalization layers.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
decoder_hidden_size (`int`, *optional*):
Dimensionality of decoder embeddings for MAE pretraining.
decoder_depth (`int`, *optional*):
Depth of the decoder for MAE pretraining.
decoder_num_heads (`int`, *optional*):
Number of attention heads in each layer of the decoder for MAE pretraining.
normalize_pixel_loss (`bool`, *optional*, defaults to `True`):
Whether to normalize the pixel loss by the number of pixels.
mask_ratio (`float`, *optional*, defaults to 0.6):
The ratio of masked tokens in the input.
out_features (`List[str]`, *optional*):
If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
(depending on how many stages the model has). If unset and `out_indices` is set, will default to the
corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute.
out_indices (`List[int]`, *optional*):
If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
If unset and `out_features` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute.
Example:
```python
>>> from transformers import HieraConfig, HieraModel
>>> # Initializing a Hiera hiera-base-patch16-224 style configuration
>>> configuration = HieraConfig()
>>> # Initializing a model (with random weights) from the hiera-base-patch16-224 style configuration
>>> model = HieraModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "hiera"
attribute_map = {"num_hidden_layers": "num_layers"}
def __init__(
self,
embed_dim=96,
image_size=[224, 224],
patch_size=[7, 7],
patch_stride=[4, 4],
patch_padding=[3, 3],
mlp_ratio=4.0,
depths=[2, 3, 16, 3],
num_heads=[1, 2, 4, 8],
embed_dim_multiplier=2.0,
num_query_pool=3,
query_stride=[2, 2],
masked_unit_size=[8, 8],
masked_unit_attention=[True, True, False, False],
drop_path_rate=0.0,
num_channels=3,
hidden_act="gelu",
initializer_range=0.02,
layer_norm_init=1.0,
layer_norm_eps=1e-6,
decoder_hidden_size=None,
decoder_depth=None,
decoder_num_heads=None,
normalize_pixel_loss=True,
mask_ratio=0.6,
out_features=None,
out_indices=None,
**kwargs,
):
super().__init__(**kwargs)
if masked_unit_size[0] % query_stride[0] ** (len(depths) - 1) != 0:
raise ValueError(
f"masked_unit_size[0] ({masked_unit_size[0]}) must be divisible by query_stride[0] ({query_stride[0]}) "
f"raised to the power of the number of layers ({len(depths) - 1})"
)
if num_query_pool >= len(depths):
raise ValueError(
f"num_query_pool ({num_query_pool}) must be less than the number of layers ({len(depths)})"
)
self.embed_dim = embed_dim
self.image_size = image_size
self.patch_size = patch_size
self.patch_stride = patch_stride
self.patch_padding = patch_padding
self.mlp_ratio = mlp_ratio
self.depths = depths
self.num_heads = num_heads
self.num_layers = len(depths)
self.embed_dim_multiplier = embed_dim_multiplier
self.num_query_pool = num_query_pool
self.query_stride = query_stride
self.masked_unit_size = masked_unit_size
self.masked_unit_attention = masked_unit_attention
self.drop_path_rate = drop_path_rate
self.num_channels = num_channels
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.layer_norm_init = layer_norm_init
self.layer_norm_eps = layer_norm_eps
self.decoder_hidden_size = decoder_hidden_size
self.decoder_depth = decoder_depth
self.decoder_num_heads = decoder_num_heads
self.normalize_pixel_loss = normalize_pixel_loss
self.mask_ratio = mask_ratio
# we set the hidden_size attribute in order to make Hiera work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
self.hidden_size = int(embed_dim * embed_dim_multiplier ** (len(depths) - 1))
self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
)
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