# Original pytorch implementation by:
# 'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
# - https://arxiv.org/abs/2010.11929
# 'How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers'
# - https://arxiv.org/abs/2106.10270
#
# Model weights are provided by corresponding links and credit to original authors.
# Original code by / Copyright 2020, Ross Wightman.
# Modifications & additions by / Copyright 2021 Zilliz. 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 towhee.models.layers.patch_embed2d import PatchEmbed2D
[docs]def base_configs():
return dict(
img_size=224,
patch_size=16,
in_c=3,
num_classes=1000,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.,
qkv_bias=True,
qk_scale=None,
representation_size=None,
drop_ratio=0,
attn_drop_ratio=0,
drop_path_ratio=0,
embed_layer=PatchEmbed2D,
norm_layer=None,
act_layer=None
)
[docs]def get_configs(model_name):
if model_name == 'vit_base_16x224':
configs = base_configs()
configs.update(dict(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth'
))
return configs
