From d3f744065088ca9b6b3a0f968c70e90ed37de75b Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Thu, 22 Jul 2021 13:15:24 -0700 Subject: [PATCH] Add EfficientNetV2 XL model defs --- timm/models/efficientnet.py | 70 ++++++++++++++++++++++++++++++++++++- 1 file changed, 69 insertions(+), 1 deletion(-) diff --git a/timm/models/efficientnet.py b/timm/models/efficientnet.py index 848aeddd11..6e14ab6161 100644 --- a/timm/models/efficientnet.py +++ b/timm/models/efficientnet.py @@ -187,6 +187,9 @@ def _cfg(url='', **kwargs): 'efficientnetv2_l': _cfg( url='', input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0), + 'efficientnetv2_xl': _cfg( + url='', + input_size=(3, 384, 384), test_input_size=(3, 512, 512), pool_size=(12, 12), crop_pct=1.0), 'tf_efficientnet_b0': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth', @@ -358,6 +361,10 @@ def _cfg(url='', **kwargs): 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_xl_in21ft1k': _cfg( + url='', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + input_size=(3, 384, 384), test_input_size=(3, 512, 512), pool_size=(12, 12), crop_pct=1.0), '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', @@ -371,6 +378,10 @@ def _cfg(url='', **kwargs): 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), + 'tf_efficientnetv2_xl_in21k': _cfg( + url='', + 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, 512, 512), pool_size=(12, 12), crop_pct=1.0), 'tf_efficientnetv2_b0': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b0-c7cc451f.pth', @@ -408,7 +419,7 @@ class EfficientNet(nn.Module): """ (Generic) EfficientNet A flexible and performant PyTorch implementation of efficient network architectures, including: - * EfficientNet-V2 Small, Medium, Large & B0-B3 + * EfficientNet-V2 Small, Medium, Large, XL & B0-B3 * EfficientNet B0-B8, L2 * EfficientNet-EdgeTPU * EfficientNet-CondConv @@ -1038,6 +1049,36 @@ def _gen_efficientnetv2_l(variant, channel_multiplier=1.0, depth_multiplier=1.0, return model +def _gen_efficientnetv2_xl(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs): + """ Creates an EfficientNet-V2 Xtra-Large model + + Ref impl: https://github.com/google/automl/tree/master/efficientnetv2 + Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298 + """ + + arch_def = [ + ['cn_r4_k3_s1_e1_c32_skip'], + ['er_r8_k3_s2_e4_c64'], + ['er_r8_k3_s2_e4_c96'], + ['ir_r16_k3_s2_e4_c192_se0.25'], + ['ir_r24_k3_s1_e6_c256_se0.25'], + ['ir_r32_k3_s2_e6_c512_se0.25'], + ['ir_r8_k3_s1_e6_c640_se0.25'], + ] + + model_kwargs = dict( + block_args=decode_arch_def(arch_def, depth_multiplier), + num_features=1280, + stem_size=32, + round_chs_fn=partial(round_channels, multiplier=channel_multiplier), + norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)), + act_layer=resolve_act_layer(kwargs, 'silu'), + **kwargs, + ) + model = _create_effnet(variant, pretrained, **model_kwargs) + return model + + def _gen_mixnet_s(variant, channel_multiplier=1.0, pretrained=False, **kwargs): """Creates a MixNet Small model. @@ -1551,6 +1592,13 @@ def efficientnetv2_l(pretrained=False, **kwargs): return model +@register_model +def efficientnetv2_xl(pretrained=False, **kwargs): + """ EfficientNet-V2 Xtra-Large. """ + model = _gen_efficientnetv2_xl('efficientnetv2_xl', pretrained=pretrained, **kwargs) + return model + + @register_model def tf_efficientnet_b0(pretrained=False, **kwargs): """ EfficientNet-B0. Tensorflow compatible variant """ @@ -2019,6 +2067,16 @@ def tf_efficientnetv2_l_in21ft1k(pretrained=False, **kwargs): return model +@register_model +def tf_efficientnetv2_xl_in21ft1k(pretrained=False, **kwargs): + """ EfficientNet-V2 Xtra-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_xl('tf_efficientnetv2_xl_in21ft1k', pretrained=pretrained, **kwargs) + return model + + @register_model def tf_efficientnetv2_s_in21k(pretrained=False, **kwargs): """ EfficientNet-V2 Small w/ ImageNet-21k pretrained weights. Tensorflow compatible variant @@ -2049,6 +2107,16 @@ def tf_efficientnetv2_l_in21k(pretrained=False, **kwargs): return model +@register_model +def tf_efficientnetv2_xl_in21k(pretrained=False, **kwargs): + """ EfficientNet-V2 Xtra-Large w/ ImageNet-21k pretrained weights. Tensorflow compatible variant + """ + kwargs['bn_eps'] = BN_EPS_TF_DEFAULT + kwargs['pad_type'] = 'same' + model = _gen_efficientnetv2_xl('tf_efficientnetv2_xl_in21k', pretrained=pretrained, **kwargs) + return model + + @register_model def tf_efficientnetv2_b0(pretrained=False, **kwargs): """ EfficientNet-V2-B0. Tensorflow compatible variant """