384 lines
16 KiB
Python
384 lines
16 KiB
Python
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# coding=utf-8
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# Copyright 2022 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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""" OWL-ViT model configuration"""
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import os
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from collections import OrderedDict
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from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
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if TYPE_CHECKING:
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from ...processing_utils import ProcessorMixin
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from ...utils import TensorType
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from ...configuration_utils import PretrainedConfig
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from ...onnx import OnnxConfig
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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 OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class OwlViTTextConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of an [`OwlViTTextModel`]. It is used to instantiate an
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OwlViT text encoder according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the OwlViT
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[google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) 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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vocab_size (`int`, *optional*, defaults to 49408):
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Vocabulary size of the OWL-ViT text model. Defines the number of different tokens that can be represented
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by the `inputs_ids` passed when calling [`OwlViTTextModel`].
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hidden_size (`int`, *optional*, defaults to 512):
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Dimensionality of the encoder layers and the pooler layer.
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intermediate_size (`int`, *optional*, defaults to 2048):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer encoder.
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max_position_embeddings (`int`, *optional*, defaults to 16):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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hidden_act (`str` or `function`, *optional*, defaults to `"quick_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"` ``"quick_gelu"` are supported.
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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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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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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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initializer_factor (`float`, *optional*, defaults to 1.0):
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A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
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testing).
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pad_token_id (`int`, *optional*, defaults to 0):
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The id of the padding token in the input sequences.
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bos_token_id (`int`, *optional*, defaults to 49406):
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The id of the beginning-of-sequence token in the input sequences.
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eos_token_id (`int`, *optional*, defaults to 49407):
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The id of the end-of-sequence token in the input sequences.
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Example:
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```python
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>>> from transformers import OwlViTTextConfig, OwlViTTextModel
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>>> # Initializing a OwlViTTextModel with google/owlvit-base-patch32 style configuration
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>>> configuration = OwlViTTextConfig()
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>>> # Initializing a OwlViTTextConfig from the google/owlvit-base-patch32 style configuration
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>>> model = OwlViTTextModel(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 = "owlvit_text_model"
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def __init__(
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self,
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vocab_size=49408,
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hidden_size=512,
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intermediate_size=2048,
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num_hidden_layers=12,
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num_attention_heads=8,
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max_position_embeddings=16,
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hidden_act="quick_gelu",
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layer_norm_eps=1e-5,
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attention_dropout=0.0,
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initializer_range=0.02,
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initializer_factor=1.0,
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pad_token_id=0,
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bos_token_id=49406,
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eos_token_id=49407,
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**kwargs,
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):
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_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.max_position_embeddings = max_position_embeddings
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self.hidden_act = hidden_act
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self.layer_norm_eps = layer_norm_eps
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self.attention_dropout = attention_dropout
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self.initializer_range = initializer_range
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self.initializer_factor = initializer_factor
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the text config dict if we are loading from OwlViTConfig
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if config_dict.get("model_type") == "owlvit":
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config_dict = config_dict["text_config"]
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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)
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return cls.from_dict(config_dict, **kwargs)
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class OwlViTVisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of an [`OwlViTVisionModel`]. It is used to instantiate
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an OWL-ViT image encoder according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the OWL-ViT
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[google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) 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 768):
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Dimensionality of the encoder layers and the pooler layer.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_channels (`int`, *optional*, defaults to 3):
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Number of channels in the input images.
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image_size (`int`, *optional*, defaults to 768):
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The size (resolution) of each image.
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patch_size (`int`, *optional*, defaults to 32):
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The size (resolution) of each patch.
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hidden_act (`str` or `function`, *optional*, defaults to `"quick_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"` ``"quick_gelu"` are supported.
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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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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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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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initializer_factor (`float`, *optional*, defaults to 1.0):
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A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
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testing).
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Example:
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```python
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>>> from transformers import OwlViTVisionConfig, OwlViTVisionModel
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>>> # Initializing a OwlViTVisionModel with google/owlvit-base-patch32 style configuration
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>>> configuration = OwlViTVisionConfig()
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>>> # Initializing a OwlViTVisionModel model from the google/owlvit-base-patch32 style configuration
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>>> model = OwlViTVisionModel(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 = "owlvit_vision_model"
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def __init__(
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self,
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hidden_size=768,
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intermediate_size=3072,
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num_hidden_layers=12,
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num_attention_heads=12,
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num_channels=3,
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image_size=768,
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patch_size=32,
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hidden_act="quick_gelu",
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layer_norm_eps=1e-5,
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attention_dropout=0.0,
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initializer_range=0.02,
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initializer_factor=1.0,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_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.num_channels = num_channels
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self.image_size = image_size
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self.patch_size = patch_size
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self.hidden_act = hidden_act
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self.layer_norm_eps = layer_norm_eps
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self.attention_dropout = attention_dropout
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self.initializer_range = initializer_range
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self.initializer_factor = initializer_factor
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the vision config dict if we are loading from OwlViTConfig
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if config_dict.get("model_type") == "owlvit":
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config_dict = config_dict["vision_config"]
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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)
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return cls.from_dict(config_dict, **kwargs)
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class OwlViTConfig(PretrainedConfig):
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r"""
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[`OwlViTConfig`] is the configuration class to store the configuration of an [`OwlViTModel`]. It is used to
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instantiate an OWL-ViT model according to the specified arguments, defining the text model and vision model
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configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the OWL-ViT
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[google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) 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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text_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`OwlViTTextConfig`].
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vision_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`OwlViTVisionConfig`].
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projection_dim (`int`, *optional*, defaults to 512):
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Dimensionality of text and vision projection layers.
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logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
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The inital value of the *logit_scale* parameter. Default is used as per the original OWL-ViT
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implementation.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return a dictionary. If `False`, returns a tuple.
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kwargs (*optional*):
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Dictionary of keyword arguments.
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"""
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model_type = "owlvit"
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def __init__(
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self,
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text_config=None,
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vision_config=None,
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projection_dim=512,
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logit_scale_init_value=2.6592,
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return_dict=True,
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**kwargs,
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):
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super().__init__(**kwargs)
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if text_config is None:
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text_config = {}
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logger.info("text_config is None. Initializing the OwlViTTextConfig with default values.")
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if vision_config is None:
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vision_config = {}
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logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values.")
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self.text_config = OwlViTTextConfig(**text_config)
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self.vision_config = OwlViTVisionConfig(**vision_config)
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self.projection_dim = projection_dim
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self.logit_scale_init_value = logit_scale_init_value
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self.return_dict = return_dict
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self.initializer_factor = 1.0
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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)
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return cls.from_dict(config_dict, **kwargs)
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@classmethod
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def from_text_vision_configs(cls, text_config: Dict, vision_config: Dict, **kwargs):
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r"""
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Instantiate a [`OwlViTConfig`] (or a derived class) from owlvit text model configuration and owlvit vision
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model configuration.
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Returns:
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[`OwlViTConfig`]: An instance of a configuration object
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"""
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config_dict = {}
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config_dict["text_config"] = text_config
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config_dict["vision_config"] = vision_config
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return cls.from_dict(config_dict, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
class OwlViTOnnxConfig(OnnxConfig):
|
||
|
|
@property
|
||
|
|
def inputs(self) -> Mapping[str, Mapping[int, str]]:
|
||
|
|
return OrderedDict(
|
||
|
|
[
|
||
|
|
("input_ids", {0: "batch", 1: "sequence"}),
|
||
|
|
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
|
||
|
|
("attention_mask", {0: "batch", 1: "sequence"}),
|
||
|
|
]
|
||
|
|
)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def outputs(self) -> Mapping[str, Mapping[int, str]]:
|
||
|
|
return OrderedDict(
|
||
|
|
[
|
||
|
|
("logits_per_image", {0: "batch"}),
|
||
|
|
("logits_per_text", {0: "batch"}),
|
||
|
|
("text_embeds", {0: "batch"}),
|
||
|
|
("image_embeds", {0: "batch"}),
|
||
|
|
]
|
||
|
|
)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def atol_for_validation(self) -> float:
|
||
|
|
return 1e-4
|
||
|
|
|
||
|
|
def generate_dummy_inputs(
|
||
|
|
self,
|
||
|
|
processor: "ProcessorMixin",
|
||
|
|
batch_size: int = -1,
|
||
|
|
seq_length: int = -1,
|
||
|
|
framework: Optional["TensorType"] = None,
|
||
|
|
) -> Mapping[str, Any]:
|
||
|
|
text_input_dict = super().generate_dummy_inputs(
|
||
|
|
processor.tokenizer, batch_size=batch_size, seq_length=seq_length, framework=framework
|
||
|
|
)
|
||
|
|
image_input_dict = super().generate_dummy_inputs(
|
||
|
|
processor.image_processor, batch_size=batch_size, framework=framework
|
||
|
|
)
|
||
|
|
return {**text_input_dict, **image_input_dict}
|
||
|
|
|
||
|
|
@property
|
||
|
|
def default_onnx_opset(self) -> int:
|
||
|
|
return 14
|