add tinynet models

pull/1037/head
Rahul Somani 3 years ago
parent f7d210d759
commit 31bcd36e46

@ -23,6 +23,10 @@ An implementation of EfficienNet that covers variety of related models with effi
* Single-Path NAS Pixel1
- Single-Path NAS: Designing Hardware-Efficient ConvNets - https://arxiv.org/abs/1904.02877
* TinyNet
- Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets - https://arxiv.org/abs/2010.14819
- Definitions & weights borrowed from https://github.com/huawei-noah/CV-Backbones/tree/master/tinynet_pytorch
* And likely more...
The majority of the above models (EfficientNet*, MixNet, MnasNet) and original weights were made available
@ -407,6 +411,22 @@ default_cfgs = {
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth'),
'tf_mixnet_l': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth'),
"tinynet_a": _cfg(
input_size=(3, 192, 192), # int(224 * 0.86)
url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_a.pth'),
"tinynet_b": _cfg(
input_size=(3, 188, 188), # int(224 * 0.84)
url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_b.pth'),
"tinynet_c": _cfg(
input_size=(3, 184, 184), # int(224 * 0.825)
url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_c.pth'),
"tinynet_d": _cfg(
input_size=(3, 152, 152), # int(224 * 0.68)
url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_d.pth'),
"tinynet_e": _cfg(
input_size=(3, 106, 106), # int(224 * 0.475)
url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_e.pth'),
}
@ -1140,6 +1160,50 @@ def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrai
return model
def _gen_tinynet(
variant, model_width=1.0, depth_multiplier=1.0, pretrained=False, **kwargs
):
"""Creates a TinyNet model.
"""
arch_def = [
['ds_r1_k3_s1_e1_c16_se0.25'], ['ir_r2_k3_s2_e6_c24_se0.25'],
['ir_r2_k5_s2_e6_c40_se0.25'], ['ir_r3_k3_s2_e6_c80_se0.25'],
['ir_r3_k5_s1_e6_c112_se0.25'], ['ir_r4_k5_s2_e6_c192_se0.25'],
['ir_r1_k3_s1_e6_c320_se0.25'],
]
model_kwargs = dict(
block_args = decode_arch_def(arch_def, depth_multiplier, depth_trunc='round'),
num_features = max(1280, round_channels(1280, model_width, 8, None)),
stem_size = 32,
fix_stem = True,
round_chs_fn=partial(round_channels, multiplier=model_width),
act_layer = resolve_act_layer(kwargs, 'swish'),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs,
)
features_only = False
model_cls = EfficientNet
kwargs_filter = None
if kwargs.pop('features_only', False):
features_only = True
# kwargs_filter = ('num_classes', 'num_features', 'head_conv', 'global_pool')
kwargs_filter = ('num_classes', 'num_features', 'conv_head', 'global_pool')
model_cls = EfficientNetFeatures
model = build_model_with_cfg(
model_cls, variant, pretrained,
default_cfg=default_cfgs[variant],
pretrained_strict=not features_only,
kwargs_filter=kwargs_filter,
**model_kwargs)
if features_only:
model.default_cfg = default_cfg_for_features(model.default_cfg)
return model
@register_model
def mnasnet_050(pretrained=False, **kwargs):
""" MNASNet B1, depth multiplier of 0.5. """
@ -2209,3 +2273,33 @@ def tf_mixnet_l(pretrained=False, **kwargs):
model = _gen_mixnet_m(
'tf_mixnet_l', channel_multiplier=1.3, pretrained=pretrained, **kwargs)
return model
@register_model
def tinynet_a(pretrained=False, **kwargs):
model = _gen_tinynet('tinynet_a', 1.0, 1.2, **kwargs)
return model
@register_model
def tinynet_b(pretrained=False, **kwargs):
model = _gen_tinynet('tinynet_b', 0.75, 1.1, pretrained, **kwargs)
return model
@register_model
def tinynet_c(pretrained=False, **kwargs):
model = _gen_tinynet('tinynet_c', 0.54, 0.85, pretrained, **kwargs)
return model
@register_model
def tinynet_d(pretrained=False, **kwargs):
model = _gen_tinynet('tinynet_d', 0.54, 0.695, pretrained, **kwargs)
return model
@register_model
def tinynet_e(pretrained=False, **kwargs):
model = _gen_tinynet('tinynet_e', 0.51, 0.6, pretrained, **kwargs)
return model

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