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This commit is contained in:
Ross Wightman 2020-03-19 10:20:20 -07:00
parent bc998cad91
commit a6a5565de7
1 changed files with 13 additions and 7 deletions
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timm/models/efficientnet.py
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@ -322,6 +322,7 @@ class EfficientNet(nn.Module):
# Stem # Stem
if not fix_stem: if not fix_stem:
stem_size = round_channels(stem_size, channel_multiplier, channel_divisor, channel_min) stem_size = round_channels(stem_size, channel_multiplier, channel_divisor, channel_min)
print(stem_size)
self.conv_stem = create_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type) self.conv_stem = create_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_layer(stem_size, **norm_kwargs) self.bn1 = norm_layer(stem_size, **norm_kwargs)
self.act1 = act_layer(inplace=True) self.act1 = act_layer(inplace=True)
@ -569,7 +570,8 @@ def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwar
return model return model
def _gen_mobilenet_v2(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs): def _gen_mobilenet_v2(
variant, channel_multiplier=1.0, depth_multiplier=1.0, fix_stem_head=False, pretrained=False, **kwargs):
""" Generate MobileNet-V2 network """ Generate MobileNet-V2 network
Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py
Paper: https://arxiv.org/abs/1801.04381 Paper: https://arxiv.org/abs/1801.04381
@ -584,8 +586,10 @@ def _gen_mobilenet_v2(variant, channel_multiplier=1.0, depth_multiplier=1.0, pre
['ir_r1_k3_s1_e6_c320'], ['ir_r1_k3_s1_e6_c320'],
] ]
model_kwargs = dict( model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier), block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier, fix_first_last=fix_stem_head),
num_features=1280 if fix_stem_head else round_channels(1280, channel_multiplier, 8, None),
stem_size=32, stem_size=32,
fix_stem=fix_stem_head,
channel_multiplier=channel_multiplier, channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs), norm_kwargs=resolve_bn_args(kwargs),
act_layer=nn.ReLU6, act_layer=nn.ReLU6,
@ -955,23 +959,25 @@ def mobilenetv2_100(pretrained=False, **kwargs):
@register_model @register_model
def mobilenetv2_100d(pretrained=False, **kwargs): def mobilenetv2_140(pretrained=False, **kwargs):
""" MobileNet V2 """ """ MobileNet V2 """
model = _gen_mobilenet_v2('mobilenetv2_100d', 1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs) model = _gen_mobilenet_v2('mobilenetv2_140', 1.4, pretrained=pretrained, **kwargs)
return model return model
@register_model @register_model
def mobilenetv2_110d(pretrained=False, **kwargs): def mobilenetv2_110d(pretrained=False, **kwargs):
""" MobileNet V2 """ """ MobileNet V2 """
model = _gen_mobilenet_v2('mobilenetv2_110d', 1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs) model = _gen_mobilenet_v2(
'mobilenetv2_100d', 1.1, depth_multiplier=1.2, fix_stem_head=True, pretrained=pretrained, **kwargs)
return model return model
@register_model @register_model
def mobilenetv2_140(pretrained=False, **kwargs): def mobilenetv2_120d(pretrained=False, **kwargs):
""" MobileNet V2 """ """ MobileNet V2 """
model = _gen_mobilenet_v2('mobilenetv2_140', 1.4, pretrained=pretrained, **kwargs) model = _gen_mobilenet_v2(
'mobilenetv2_110d', 1.2, depth_multiplier=1.4, fix_stem_head=True, pretrained=pretrained, **kwargs)
return model return model