Squash commits (#17981)
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
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@ -90,6 +90,7 @@ Ready-made configurations include the following architectures:
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- XLM
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- XLM-RoBERTa
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- XLM-RoBERTa-XL
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- YOLOS
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In the next two sections, we'll show you how to:
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@ -20,7 +20,7 @@ from typing import TYPE_CHECKING
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from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
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_import_structure = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig"]}
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_import_structure = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}
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try:
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if not is_vision_available():
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@ -45,7 +45,7 @@ else:
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if TYPE_CHECKING:
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from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig
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from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
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try:
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if not is_vision_available():
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@ -14,7 +14,13 @@
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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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@ -151,3 +157,24 @@ class YolosConfig(PretrainedConfig):
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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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@ -452,6 +452,11 @@ class FeaturesManager:
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"question-answering",
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onnx_config_cls="models.xlm_roberta.XLMRobertaOnnxConfig",
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),
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"yolos": supported_features_mapping(
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"default",
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"object-detection",
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onnx_config_cls="models.yolos.YolosOnnxConfig",
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),
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}
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AVAILABLE_FEATURES = sorted(reduce(lambda s1, s2: s1 | s2, (v.keys() for v in _SUPPORTED_MODEL_TYPE.values())))
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@ -202,6 +202,7 @@ PYTORCH_EXPORT_MODELS = {
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("data2vec-text", "facebook/data2vec-text-base"),
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("perceiver", "deepmind/language-perceiver", ("masked-lm", "sequence-classification")),
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("perceiver", "deepmind/vision-perceiver-conv", ("image-classification",)),
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("yolos", "hustvl/yolos-tiny"),
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}
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PYTORCH_EXPORT_WITH_PAST_MODELS = {
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