From 7077f16c6a72960dd5a2ca042ed79c8e72eeb765 Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Sat, 15 May 2021 12:42:26 -0700 Subject: [PATCH] Change 21k model naming from _21k to _in21k for consistency with existing 21k models. --- README.md | 4 ++-- timm/models/efficientnet.py | 36 ++++++++++++++++++------------------ 2 files changed, 20 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index cbcfd00f..ca283605 100644 --- a/README.md +++ b/README.md @@ -26,8 +26,8 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor ### May 14, 2021 * Add EfficientNet-V2 official model defs w/ ported weights from official [Tensorflow/Keras](https://github.com/google/automl/tree/master/efficientnetv2) impl. * 1k trained variants: `tf_efficientnetv2_s/m/l` - * 21k trained variants: `tf_efficientnetv2_s/m/l_21k` - * 21k pretrained -> 1k fine-tuned: `tf_efficientnetv2_s/m/l_21ft1k` + * 21k trained variants: `tf_efficientnetv2_s/m/l_in21k` + * 21k pretrained -> 1k fine-tuned: `tf_efficientnetv2_s/m/l_in21ft1k` * v2 models w/ v1 scaling: `tf_efficientnetv2_b0` through `b3` * Rename my prev V2 guess `efficientnet_v2s` -> `efficientnetv2_rw_s` * Some blank `efficientnetv2_*` models in-place for future native PyTorch training diff --git a/timm/models/efficientnet.py b/timm/models/efficientnet.py index 1716e92d..0c0464b5 100644 --- a/timm/models/efficientnet.py +++ b/timm/models/efficientnet.py @@ -332,28 +332,28 @@ default_cfgs = { mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0), - 'tf_efficientnetv2_s_21ft1k': _cfg( + 'tf_efficientnetv2_s_in21ft1k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s_21ft1k-d7dafa41.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0), - 'tf_efficientnetv2_m_21ft1k': _cfg( + 'tf_efficientnetv2_m_in21ft1k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m_21ft1k-bf41664a.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0), - 'tf_efficientnetv2_l_21ft1k': _cfg( + 'tf_efficientnetv2_l_in21ft1k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l_21ft1k-60127a9d.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0), - 'tf_efficientnetv2_s_21k': _cfg( + 'tf_efficientnetv2_s_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s_21k-6337ad01.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843, input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0), - 'tf_efficientnetv2_m_21k': _cfg( + 'tf_efficientnetv2_m_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m_21k-361418a2.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843, input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0), - 'tf_efficientnetv2_l_21k': _cfg( + 'tf_efficientnetv2_l_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l_21k-91a19ec9.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843, input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0), @@ -1929,62 +1929,62 @@ def tf_efficientnetv2_l(pretrained=False, **kwargs): @register_model -def tf_efficientnetv2_s_21ft1k(pretrained=False, **kwargs): +def tf_efficientnetv2_s_in21ft1k(pretrained=False, **kwargs): """ EfficientNet-V2 Small. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_s('tf_efficientnetv2_s_21ft1k', pretrained=pretrained, **kwargs) + model = _gen_efficientnetv2_s('tf_efficientnetv2_s_in21ft1k', pretrained=pretrained, **kwargs) return model @register_model -def tf_efficientnetv2_m_21ft1k(pretrained=False, **kwargs): +def tf_efficientnetv2_m_in21ft1k(pretrained=False, **kwargs): """ EfficientNet-V2 Medium. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_m('tf_efficientnetv2_m_21ft1k', pretrained=pretrained, **kwargs) + model = _gen_efficientnetv2_m('tf_efficientnetv2_m_in21ft1k', pretrained=pretrained, **kwargs) return model @register_model -def tf_efficientnetv2_l_21ft1k(pretrained=False, **kwargs): +def tf_efficientnetv2_l_in21ft1k(pretrained=False, **kwargs): """ EfficientNet-V2 Large. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_l('tf_efficientnetv2_l_21ft1k', pretrained=pretrained, **kwargs) + model = _gen_efficientnetv2_l('tf_efficientnetv2_l_in21ft1k', pretrained=pretrained, **kwargs) return model @register_model -def tf_efficientnetv2_s_21k(pretrained=False, **kwargs): +def tf_efficientnetv2_s_in21k(pretrained=False, **kwargs): """ EfficientNet-V2 Small w/ ImageNet-21k pretrained weights. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_s('tf_efficientnetv2_s_21k', pretrained=pretrained, **kwargs) + model = _gen_efficientnetv2_s('tf_efficientnetv2_s_in21k', pretrained=pretrained, **kwargs) return model @register_model -def tf_efficientnetv2_m_21k(pretrained=False, **kwargs): +def tf_efficientnetv2_m_in21k(pretrained=False, **kwargs): """ EfficientNet-V2 Medium w/ ImageNet-21k pretrained weights. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_m('tf_efficientnetv2_m_21k', pretrained=pretrained, **kwargs) + model = _gen_efficientnetv2_m('tf_efficientnetv2_m_in21k', pretrained=pretrained, **kwargs) return model @register_model -def tf_efficientnetv2_l_21k(pretrained=False, **kwargs): +def tf_efficientnetv2_l_in21k(pretrained=False, **kwargs): """ EfficientNet-V2 Large w/ ImageNet-21k pretrained weights. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_l('tf_efficientnetv2_l_21k', pretrained=pretrained, **kwargs) + model = _gen_efficientnetv2_l('tf_efficientnetv2_l_in21k', pretrained=pretrained, **kwargs) return model