Add newly trained MobileNet-V2 weights w/ model defs

Author: rwightman

README.md

@@ -6,6 +6,13 @@ All models are implemented by GenEfficientNet or MobileNetV3 classes, with strin
 
 ## What's New
 
+### April 5, 2020
+* Add some newly trained MobileNet-V2 models trained with latest h-params, rand augment. They compare quite favourably to EfficientNet-Lite
+  * 3.5M param MobileNet-V2 100 @ 73%
+  * 4.5M param MobileNet-V2 110d @ 75%
+  * 6.1M param MobileNet-V2 140 @ 76.5%
+  * 5.8M param MobileNet-V2 120d @ 77.3%
+  
 ### March 23, 2020
  * Add EfficientNet-Lite models w/ weights ported from [Tensorflow TPU](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite)
  * Add PyTorch trained MobileNet-V3 Large weights with 75.77% top-1
@@ -80,14 +87,18 @@ I've managed to train several of the models to accuracies close to or above the
 | efficientnet_b1 | 78.692 (21.308) | 94.086 (5.914) | 7.8 | 694 | bicubic | 240 | 0.882 |
 | efficientnet_es | 78.066 (21.934) | 93.926 (6.074) | 5.44 | TBD | bicubic | 224 | 0.875 |
 | efficientnet_b0 | 77.698 (22.302) | 93.532 (6.468) | 5.3 | 390 | bicubic | 224 | 0.875 |
+| mobilenetv2_120d | 77.294 (22.706 | 93.502 (6.498) | 5.8 | TBD | bicubic | 224 | 0.875 |
 | mixnet_m | 77.256 (22.744) | 93.418 (6.582) | 5.01 | 353 | bicubic | 224 | 0.875 |
+| mobilenetv2_140 | 76.524 (23.476) | 92.990 (7.010) | 6.1 | TBD | bicubic | 224 | 0.875 |
 | mixnet_s | 75.988 (24.012) | 92.794 (7.206) | 4.13 | TBD | bicubic | 224 | 0.875 |
-| mobilenetv3_large_100 | 75.766 (24.234) | 92.542 (7.458) | 5.5M | bicubic | 224 | 0.875 |
+| mobilenetv3_large_100 | 75.766 (24.234) | 92.542 (7.458) | 5.5 | TBD | bicubic | 224 | 0.875 |
 | mobilenetv3_rw | 75.634 (24.366) | 92.708 (7.292) | 5.5 | 219 | bicubic | 224 | 0.875 |
 | mnasnet_a1 | 75.448 (24.552) | 92.604 (7.396) | 3.9 | 312 | bicubic | 224 | 0.875 |
 | fbnetc_100 | 75.124 (24.876) | 92.386 (7.614) | 5.6 | 385 | bilinear | 224 | 0.875 |
+| mobilenetv2_110d | 75.052 (24.948) | 92.180 (7.820) | 4.5 | TBD | bicubic | 224 | 0.875 |
 | mnasnet_b1 | 74.658 (25.342) | 92.114 (7.886) | 4.4 | 315 | bicubic | 224 | 0.875 |
 | spnasnet_100 | 74.084 (25.916)  | 91.818 (8.182) | 4.4 | TBD | bilinear | 224 | 0.875 |
+| mobilenetv2_100 | 72.978 (27.022) | 91.016 (8.984) | 3.5 | TBD | bicubic | 224 | 0.875 |
 
 
 More pretrained models to come...

geffnet/gen_efficientnet.py

@@ -34,6 +34,7 @@
 
 __all__ = ['GenEfficientNet', 'mnasnet_050', 'mnasnet_075', 'mnasnet_100', 'mnasnet_b1', 'mnasnet_140',
            'semnasnet_050', 'semnasnet_075', 'semnasnet_100', 'mnasnet_a1', 'semnasnet_140', 'mnasnet_small',
+           'mobilenetv2_100', 'mobilenetv2_140', 'mobilenetv2_110d', 'mobilenetv2_120d',
            'fbnetc_100', 'spnasnet_100', 'efficientnet_b0', 'efficientnet_b1', 'efficientnet_b2',  'efficientnet_b3',
            'efficientnet_b4', 'efficientnet_b5', 'efficientnet_b6', 'efficientnet_b7', 'efficientnet_b8',
            'efficientnet_l2', 'efficientnet_es', 'efficientnet_em', 'efficientnet_el',
@@ -67,6 +68,15 @@
         'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth',
     'semnasnet_140': None,
 
+    'mobilenetv2_100':
+        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_100_ra-b33bc2c4.pth',
+    'mobilenetv2_110d':
+        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_110d_ra-77090ade.pth',
+    'mobilenetv2_120d':
+        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_120d_ra-5987e2ed.pth',
+    'mobilenetv2_140':
+        'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_140_ra-21a4e913.pth',
+
     'fbnetc_100':
         'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth',
     'spnasnet_100':
@@ -385,6 +395,35 @@ def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwar
     return model
 
 
+def _gen_mobilenet_v2(
+        variant, channel_multiplier=1.0, depth_multiplier=1.0, fix_stem_head=False, pretrained=False, **kwargs):
+    """ Generate MobileNet-V2 network
+    Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py
+    Paper: https://arxiv.org/abs/1801.04381
+    """
+    arch_def = [
+        ['ds_r1_k3_s1_c16'],
+        ['ir_r2_k3_s2_e6_c24'],
+        ['ir_r3_k3_s2_e6_c32'],
+        ['ir_r4_k3_s2_e6_c64'],
+        ['ir_r3_k3_s1_e6_c96'],
+        ['ir_r3_k3_s2_e6_c160'],
+        ['ir_r1_k3_s1_e6_c320'],
+    ]
+    model_kwargs = dict(
+        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,
+        fix_stem=fix_stem_head,
+        channel_multiplier=channel_multiplier,
+        norm_kwargs=resolve_bn_args(kwargs),
+        act_layer=nn.ReLU6,
+        **kwargs
+    )
+    model = _create_model(model_kwargs, variant, pretrained)
+    return model
+
+
 def _gen_fbnetc(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
     """ FBNet-C
 
@@ -719,6 +758,32 @@ def mnasnet_small(pretrained=False, **kwargs):
     return model
 
 
+def mobilenetv2_100(pretrained=False, **kwargs):
+    """ MobileNet V2 w/ 1.0 channel multiplier """
+    model = _gen_mobilenet_v2('mobilenetv2_100', 1.0, pretrained=pretrained, **kwargs)
+    return model
+
+
+def mobilenetv2_140(pretrained=False, **kwargs):
+    """ MobileNet V2 w/ 1.4 channel multiplier """
+    model = _gen_mobilenet_v2('mobilenetv2_140', 1.4, pretrained=pretrained, **kwargs)
+    return model
+
+
+def mobilenetv2_110d(pretrained=False, **kwargs):
+    """ MobileNet V2 w/ 1.1 channel, 1.2 depth multipliers"""
+    model = _gen_mobilenet_v2(
+        'mobilenetv2_110d', 1.1, depth_multiplier=1.2, fix_stem_head=True, pretrained=pretrained, **kwargs)
+    return model
+
+
+def mobilenetv2_120d(pretrained=False, **kwargs):
+    """ MobileNet V2 w/ 1.2 channel, 1.4 depth multipliers """
+    model = _gen_mobilenet_v2(
+        'mobilenetv2_120d', 1.2, depth_multiplier=1.4, fix_stem_head=True, pretrained=pretrained, **kwargs)
+    return model
+
+
 def fbnetc_100(pretrained=False, **kwargs):
     """ FBNet-C """
     if pretrained:

geffnet/version.py

@@ -1 +1 @@
-__version__ = '0.9.8'
+__version__ = '0.9.9'

hubconf.py

@@ -12,6 +12,11 @@
 from geffnet import mixnet_l
 from geffnet import mixnet_xl
 
+from geffnet import mobilenetv2_100
+from geffnet import mobilenetv2_110d
+from geffnet import mobilenetv2_120d
+from geffnet import mobilenetv2_140
+
 from geffnet import mobilenetv3_large_100
 from geffnet import mobilenetv3_rw
 from geffnet import mnasnet_a1