FM / dpt

/usr/local/lib/python3.6/site-packages/transformers/models/dpt/

/usr/local/lib/python3.6/site-packages/transformers/models/dpt

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
configuration_dpt.py	8415	0644	edit dl rm
convert_dpt_to_pytorch.py	11785	0644	edit dl rm
feature_extraction_dpt.py	8322	0644	edit dl rm
modeling_dpt.py	43274	0644	edit dl rm
__init__.py	1944	0644	edit dl rm

Edit: /usr/local/lib/python3.6/site-packages/transformers/models/dpt/__init__.py (1944B)

# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.

# Copyright 2022 The HuggingFace 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.
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available

_import_structure = {
    "configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"],
}

if is_vision_available():
    _import_structure["feature_extraction_dpt"] = ["DPTFeatureExtractor"]

if is_torch_available():
    _import_structure["modeling_dpt"] = [
        "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DPTForDepthEstimation",
        "DPTForSemanticSegmentation",
        "DPTModel",
        "DPTPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

if is_vision_available():
        from .feature_extraction_dpt import DPTFeatureExtractor

if is_torch_available():
        from .modeling_dpt import (
            DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DPTForDepthEstimation,
            DPTForSemanticSegmentation,
            DPTModel,
            DPTPreTrainedModel,
        )

else:
    import sys

sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace 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. from typing import TYPE_CHECKING from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available _import_structure = { "configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"], } if is_vision_available(): _import_structure["feature_extraction_dpt"] = ["DPTFeatureExtractor"] if is_torch_available(): _import_structure["modeling_dpt"] = [ "DPT_PRETRAINED_MODEL_ARCHIVE_LIST", "DPTForDepthEstimation", "DPTForSemanticSegmentation", "DPTModel", "DPTPreTrainedModel", ] if TYPE_CHECKING: from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig if is_vision_available(): from .feature_extraction_dpt import DPTFeatureExtractor if is_torch_available(): from .modeling_dpt import ( DPT_PRETRAINED_MODEL_ARCHIVE_LIST, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel, DPTPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)