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| # coding=utf-8 | |
| # Copyright 2022 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. | |
| """Donut Swin Transformer model configuration""" | |
| from transformers.configuration_utils import PretrainedConfig | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| class UnimerNetConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`UnimerNetModel`]. It is used to instantiate a | |
| Donut 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 Donut | |
| [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) architecture. | |
| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
| documentation from [`PretrainedConfig`] for more information. | |
| Args: | |
| image_size (`int`, *optional*, defaults to 224): | |
| The size (resolution) of each image. | |
| patch_size (`int`, *optional*, defaults to 4): | |
| The size (resolution) of each patch. | |
| num_channels (`int`, *optional*, defaults to 3): | |
| The number of input channels. | |
| embed_dim (`int`, *optional*, defaults to 96): | |
| Dimensionality of patch embedding. | |
| depths (`list(int)`, *optional*, defaults to `[2, 2, 6, 2]`): | |
| Depth of each layer in the Transformer encoder. | |
| num_heads (`list(int)`, *optional*, defaults to `[3, 6, 12, 24]`): | |
| Number of attention heads in each layer of the Transformer encoder. | |
| window_size (`int`, *optional*, defaults to 7): | |
| Size of windows. | |
| mlp_ratio (`float`, *optional*, defaults to 4.0): | |
| Ratio of MLP hidden dimensionality to embedding dimensionality. | |
| qkv_bias (`bool`, *optional*, defaults to `True`): | |
| Whether or not a learnable bias should be added to the queries, keys and values. | |
| hidden_dropout_prob (`float`, *optional*, defaults to 0.0): | |
| The dropout probability for all fully connected layers in the embeddings and encoder. | |
| attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): | |
| The dropout ratio for the attention probabilities. | |
| drop_path_rate (`float`, *optional*, defaults to 0.1): | |
| Stochastic depth rate. | |
| hidden_act (`str` or `function`, *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. | |
| use_absolute_embeddings (`bool`, *optional*, defaults to `False`): | |
| Whether or not to add absolute position embeddings to the patch embeddings. | |
| initializer_range (`float`, *optional*, defaults to 0.02): | |
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
| layer_norm_eps (`float`, *optional*, defaults to 1e-05): | |
| The epsilon used by the layer normalization layers. | |
| Example: | |
| ```python | |
| >>> from transformers import UnimerNetConfig, UnimerNetModel | |
| >>> # Initializing a Donut naver-clova-ix/donut-base style configuration | |
| >>> configuration = UnimerNetConfig() | |
| >>> # Randomly initializing a model from the naver-clova-ix/donut-base style configuration | |
| >>> model = UnimerNetModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "donut-swin" | |
| attribute_map = { | |
| "num_attention_heads": "num_heads", | |
| "num_hidden_layers": "num_layers", | |
| } | |
| def __init__( | |
| self, | |
| image_size=224, | |
| patch_size=4, | |
| num_channels=3, | |
| embed_dim=96, | |
| depths=[2, 2, 6, 2], | |
| num_heads=[3, 6, 12, 24], | |
| window_size=7, | |
| mlp_ratio=4.0, | |
| qkv_bias=True, | |
| hidden_dropout_prob=0.0, | |
| attention_probs_dropout_prob=0.0, | |
| drop_path_rate=0.1, | |
| hidden_act="gelu", | |
| use_absolute_embeddings=False, | |
| initializer_range=0.02, | |
| layer_norm_eps=1e-5, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.image_size = image_size | |
| self.patch_size = patch_size | |
| self.num_channels = num_channels | |
| self.embed_dim = embed_dim | |
| self.depths = depths | |
| self.num_layers = len(depths) | |
| self.num_heads = num_heads | |
| self.window_size = window_size | |
| self.mlp_ratio = mlp_ratio | |
| self.qkv_bias = qkv_bias | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
| self.drop_path_rate = drop_path_rate | |
| self.hidden_act = hidden_act | |
| self.use_absolute_embeddings = use_absolute_embeddings | |
| self.layer_norm_eps = layer_norm_eps | |
| self.initializer_range = initializer_range | |
| # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel | |
| # this indicates the channel dimension after the last stage of the model | |
| self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1)) | |