179 lines
7.5 KiB
Python
179 lines
7.5 KiB
Python
# 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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""" YOLOS model configuration"""
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from collections import OrderedDict
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from typing import Mapping
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from packaging import version
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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 YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class YolosConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`YolosModel`]. It is used to instantiate a YOLOS
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the YOLOS
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[hustvl/yolos-base](https://huggingface.co/hustvl/yolos-base) 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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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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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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hidden_act (`str` or `function`, *optional*, defaults to `"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"` are supported.
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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, encoder, and pooler.
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attention_probs_dropout_prob (`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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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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image_size (`List[int]`, *optional*, defaults to `[512, 864]`):
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The size (resolution) of each image.
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patch_size (`int`, *optional*, defaults to 16):
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The size (resolution) of each patch.
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num_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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qkv_bias (`bool`, *optional*, defaults to `True`):
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Whether to add a bias to the queries, keys and values.
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num_detection_tokens (`int`, *optional*, defaults to 100):
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The number of detection tokens.
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use_mid_position_embeddings (`bool`, *optional*, defaults to `True`):
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Whether to use the mid-layer position encodings.
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auxiliary_loss (`bool`, *optional*, defaults to `False`):
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Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
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class_cost (`float`, *optional*, defaults to 1):
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Relative weight of the classification error in the Hungarian matching cost.
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bbox_cost (`float`, *optional*, defaults to 5):
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Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
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giou_cost (`float`, *optional*, defaults to 2):
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Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
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bbox_loss_coefficient (`float`, *optional*, defaults to 5):
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Relative weight of the L1 bounding box loss in the object detection loss.
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giou_loss_coefficient (`float`, *optional*, defaults to 2):
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Relative weight of the generalized IoU loss in the object detection loss.
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eos_coefficient (`float`, *optional*, defaults to 0.1):
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Relative classification weight of the 'no-object' class in the object detection loss.
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Example:
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```python
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>>> from transformers import YolosConfig, YolosModel
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>>> # Initializing a YOLOS hustvl/yolos-base style configuration
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>>> configuration = YolosConfig()
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>>> # Initializing a model (with random weights) from the hustvl/yolos-base style configuration
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>>> model = YolosModel(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 = "yolos"
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def __init__(
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self,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.0,
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attention_probs_dropout_prob=0.0,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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image_size=[512, 864],
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patch_size=16,
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num_channels=3,
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qkv_bias=True,
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num_detection_tokens=100,
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use_mid_position_embeddings=True,
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auxiliary_loss=False,
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class_cost=1,
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bbox_cost=5,
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giou_cost=2,
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bbox_loss_coefficient=5,
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giou_loss_coefficient=2,
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eos_coefficient=0.1,
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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.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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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.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.qkv_bias = qkv_bias
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self.num_detection_tokens = num_detection_tokens
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self.use_mid_position_embeddings = use_mid_position_embeddings
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self.auxiliary_loss = auxiliary_loss
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# Hungarian matcher
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self.class_cost = class_cost
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self.bbox_cost = bbox_cost
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self.giou_cost = giou_cost
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# Loss coefficients
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self.bbox_loss_coefficient = bbox_loss_coefficient
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self.giou_loss_coefficient = giou_loss_coefficient
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self.eos_coefficient = eos_coefficient
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class YolosOnnxConfig(OnnxConfig):
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torch_onnx_minimum_version = version.parse("1.11")
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@property
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def inputs(self) -> Mapping[str, Mapping[int, str]]:
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return OrderedDict(
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[
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("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
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]
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)
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@property
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def atol_for_validation(self) -> float:
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return 1e-4
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@property
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def default_onnx_opset(self) -> int:
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return 12
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