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@ -23,6 +23,10 @@ An implementation of EfficienNet that covers variety of related models with effi
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* Single-Path NAS Pixel1
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* Single-Path NAS Pixel1
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- Single-Path NAS: Designing Hardware-Efficient ConvNets - https://arxiv.org/abs/1904.02877
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- Single-Path NAS: Designing Hardware-Efficient ConvNets - https://arxiv.org/abs/1904.02877
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* TinyNet
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- Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets - https://arxiv.org/abs/2010.14819
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- Definitions & weights borrowed from https://github.com/huawei-noah/CV-Backbones/tree/master/tinynet_pytorch
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* And likely more...
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* And likely more...
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The majority of the above models (EfficientNet*, MixNet, MnasNet) and original weights were made available
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The majority of the above models (EfficientNet*, MixNet, MnasNet) and original weights were made available
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@ -407,6 +411,22 @@ default_cfgs = {
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth'),
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth'),
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'tf_mixnet_l': _cfg(
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'tf_mixnet_l': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth'),
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth'),
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"tinynet_a": _cfg(
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input_size=(3, 192, 192), pool_size=(6, 6), # int(224 * 0.86)
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url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_a.pth'),
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"tinynet_b": _cfg(
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input_size=(3, 188, 188), pool_size=(6, 6), # int(224 * 0.84)
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url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_b.pth'),
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"tinynet_c": _cfg(
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input_size=(3, 184, 184), pool_size=(6, 6), # int(224 * 0.825)
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url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_c.pth'),
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"tinynet_d": _cfg(
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input_size=(3, 152, 152), pool_size=(5, 5), # int(224 * 0.68)
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url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_d.pth'),
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"tinynet_e": _cfg(
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input_size=(3, 106, 106), pool_size=(4, 4), # int(224 * 0.475)
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url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_e.pth'),
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}
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}
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@ -1140,6 +1160,31 @@ def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrai
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return model
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return model
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def _gen_tinynet(
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variant, model_width=1.0, depth_multiplier=1.0, pretrained=False, **kwargs
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):
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"""Creates a TinyNet model.
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"""
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arch_def = [
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['ds_r1_k3_s1_e1_c16_se0.25'], ['ir_r2_k3_s2_e6_c24_se0.25'],
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['ir_r2_k5_s2_e6_c40_se0.25'], ['ir_r3_k3_s2_e6_c80_se0.25'],
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['ir_r3_k5_s1_e6_c112_se0.25'], ['ir_r4_k5_s2_e6_c192_se0.25'],
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['ir_r1_k3_s1_e6_c320_se0.25'],
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]
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model_kwargs = dict(
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block_args=decode_arch_def(arch_def, depth_multiplier, depth_trunc='round'),
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num_features=max(1280, round_channels(1280, model_width, 8, None)),
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stem_size=32,
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fix_stem=True,
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round_chs_fn=partial(round_channels, multiplier=model_width),
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act_layer=resolve_act_layer(kwargs, 'swish'),
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norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
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**kwargs,
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)
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model = _create_effnet(variant, pretrained, **model_kwargs)
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return model
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@register_model
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@register_model
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def mnasnet_050(pretrained=False, **kwargs):
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def mnasnet_050(pretrained=False, **kwargs):
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""" MNASNet B1, depth multiplier of 0.5. """
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""" MNASNet B1, depth multiplier of 0.5. """
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@ -2209,3 +2254,33 @@ def tf_mixnet_l(pretrained=False, **kwargs):
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model = _gen_mixnet_m(
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model = _gen_mixnet_m(
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'tf_mixnet_l', channel_multiplier=1.3, pretrained=pretrained, **kwargs)
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'tf_mixnet_l', channel_multiplier=1.3, pretrained=pretrained, **kwargs)
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return model
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return model
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@register_model
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def tinynet_a(pretrained=False, **kwargs):
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model = _gen_tinynet('tinynet_a', 1.0, 1.2, pretrained=pretrained, **kwargs)
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return model
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@register_model
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def tinynet_b(pretrained=False, **kwargs):
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model = _gen_tinynet('tinynet_b', 0.75, 1.1, pretrained=pretrained, **kwargs)
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return model
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@register_model
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def tinynet_c(pretrained=False, **kwargs):
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model = _gen_tinynet('tinynet_c', 0.54, 0.85, pretrained=pretrained, **kwargs)
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return model
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@register_model
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def tinynet_d(pretrained=False, **kwargs):
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model = _gen_tinynet('tinynet_d', 0.54, 0.695, pretrained=pretrained, **kwargs)
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return model
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@register_model
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def tinynet_e(pretrained=False, **kwargs):
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model = _gen_tinynet('tinynet_e', 0.51, 0.6, pretrained=pretrained, **kwargs)
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return model
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