Llama-3.1-8B-DALv0.1
/
venv
/lib
/python3.12
/site-packages
/transformers
/models
/vitmatte
/configuration_vitmatte.py
# coding=utf-8 | |
# Copyright 2023 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. | |
"""VitMatte model configuration""" | |
import copy | |
from typing import List | |
from ...configuration_utils import PretrainedConfig | |
from ...utils import logging | |
from ...utils.backbone_utils import verify_backbone_config_arguments | |
from ..auto.configuration_auto import CONFIG_MAPPING | |
logger = logging.get_logger(__name__) | |
class VitMatteConfig(PretrainedConfig): | |
r""" | |
This is the configuration class to store the configuration of [`VitMatteForImageMatting`]. It is used to | |
instantiate a ViTMatte 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 ViTMatte | |
[hustvl/vitmatte-small-composition-1k](https://huggingface.co/hustvl/vitmatte-small-composition-1k) architecture. | |
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
documentation from [`PretrainedConfig`] for more information. | |
Args: | |
backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `VitDetConfig()`): | |
The configuration of the backbone model. | |
backbone (`str`, *optional*): | |
Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this | |
will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone` | |
is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights. | |
use_pretrained_backbone (`bool`, *optional*, defaults to `False`): | |
Whether to use pretrained weights for the backbone. | |
use_timm_backbone (`bool`, *optional*, defaults to `False`): | |
Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers | |
library. | |
backbone_kwargs (`dict`, *optional*): | |
Keyword arguments to be passed to AutoBackbone when loading from a checkpoint | |
e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set. | |
hidden_size (`int`, *optional*, defaults to 384): | |
The number of input channels of the decoder. | |
batch_norm_eps (`float`, *optional*, defaults to 1e-05): | |
The epsilon used by the batch norm layers. | |
initializer_range (`float`, *optional*, defaults to 0.02): | |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
convstream_hidden_sizes (`List[int]`, *optional*, defaults to `[48, 96, 192]`): | |
The output channels of the ConvStream module. | |
fusion_hidden_sizes (`List[int]`, *optional*, defaults to `[256, 128, 64, 32]`): | |
The output channels of the Fusion blocks. | |
Example: | |
```python | |
>>> from transformers import VitMatteConfig, VitMatteForImageMatting | |
>>> # Initializing a ViTMatte hustvl/vitmatte-small-composition-1k style configuration | |
>>> configuration = VitMatteConfig() | |
>>> # Initializing a model (with random weights) from the hustvl/vitmatte-small-composition-1k style configuration | |
>>> model = VitMatteForImageMatting(configuration) | |
>>> # Accessing the model configuration | |
>>> configuration = model.config | |
```""" | |
model_type = "vitmatte" | |
def __init__( | |
self, | |
backbone_config: PretrainedConfig = None, | |
backbone=None, | |
use_pretrained_backbone=False, | |
use_timm_backbone=False, | |
backbone_kwargs=None, | |
hidden_size: int = 384, | |
batch_norm_eps: float = 1e-5, | |
initializer_range: float = 0.02, | |
convstream_hidden_sizes: List[int] = [48, 96, 192], | |
fusion_hidden_sizes: List[int] = [256, 128, 64, 32], | |
**kwargs, | |
): | |
super().__init__(**kwargs) | |
if backbone_config is None and backbone is None: | |
logger.info("`backbone_config` is `None`. Initializing the config with the default `VitDet` backbone.") | |
backbone_config = CONFIG_MAPPING["vitdet"](out_features=["stage4"]) | |
elif isinstance(backbone_config, dict): | |
backbone_model_type = backbone_config.get("model_type") | |
config_class = CONFIG_MAPPING[backbone_model_type] | |
backbone_config = config_class.from_dict(backbone_config) | |
verify_backbone_config_arguments( | |
use_timm_backbone=use_timm_backbone, | |
use_pretrained_backbone=use_pretrained_backbone, | |
backbone=backbone, | |
backbone_config=backbone_config, | |
backbone_kwargs=backbone_kwargs, | |
) | |
self.backbone_config = backbone_config | |
self.backbone = backbone | |
self.use_pretrained_backbone = use_pretrained_backbone | |
self.use_timm_backbone = use_timm_backbone | |
self.backbone_kwargs = backbone_kwargs | |
self.batch_norm_eps = batch_norm_eps | |
self.hidden_size = hidden_size | |
self.initializer_range = initializer_range | |
self.convstream_hidden_sizes = convstream_hidden_sizes | |
self.fusion_hidden_sizes = fusion_hidden_sizes | |
def to_dict(self): | |
""" | |
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns: | |
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, | |
""" | |
output = copy.deepcopy(self.__dict__) | |
output["backbone_config"] = self.backbone_config.to_dict() | |
output["model_type"] = self.__class__.model_type | |
return output | |