Merge pull request #115 from rwightman/mobilenetv2-experiment

MobileNet-V2 experiments

README.md

@@ -2,6 +2,13 @@
 
 ## 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 18, 2020
 * Add EfficientNet-Lite models w/ weights ported from [Tensorflow TPU](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite)
 * Add RandAugment trained ResNeXt-50 32x4d weights with 79.8 top-1. Trained by [Andrew Lavin](https://github.com/andravin) (see Training section for hparams)
@@ -194,10 +201,12 @@ I've leveraged the training scripts in this repository to train a few of the mod
 | seresnext26tn_32x4d | 77.986 (22.014) | 93.746 (6.254) | 16.8M | bicubic | 224 |
 | efficientnet_b0 | 77.698 (22.302) | 93.532 (6.468) | 5.29M | bicubic | 224 |
 | seresnext26d_32x4d | 77.602 (22.398) | 93.608 (6.392) | 16.8M | bicubic | 224 |
+| mobilenetv2_120d | 77.294 (22.706 | 93.502 (6.498) | 5.8M | bicubic | 224 |
 | mixnet_m | 77.256 (22.744) | 93.418 (6.582) | 5.01M | bicubic | 224 |
 | seresnext26_32x4d | 77.104 (22.896) | 93.316 (6.684) | 16.8M | bicubic | 224 |
 | skresnet34 | 76.912 (23.088) | 93.322 (6.678) | 22.2M | bicubic | 224 |
 | resnet26d | 76.68 (23.32) | 93.166 (6.834) | 16M | bicubic | 224 |
+| mobilenetv2_140 | 76.524 (23.476) | 92.990 (7.010) | 6.1M | bicubic | 224 |
 | mixnet_s | 75.988 (24.012) | 92.794 (7.206) | 4.13M | bicubic | 224 |
 | mobilenetv3_large_100 | 75.766 (24.234) | 92.542 (7.458) | 5.5M | bicubic | 224 |
 | mobilenetv3_rw | 75.634 (24.366) | 92.708 (7.292) | 5.5M | bicubic | 224 |
@@ -205,10 +214,12 @@ I've leveraged the training scripts in this repository to train a few of the mod
 | resnet26 | 75.292 (24.708) | 92.57 (7.43) | 16M | bicubic | 224 |
 | fbnetc_100 | 75.124 (24.876) | 92.386 (7.614) | 5.6M | bilinear | 224 |
 | resnet34 | 75.110 (24.890) | 92.284 (7.716) | 22M | bilinear | 224 |
+| mobilenetv2_110d | 75.052 (24.948) | 92.180 (7.820) | 4.5M | bicubic | 224 |
 | seresnet34 | 74.808 (25.192) | 92.124 (7.876) | 22M | bilinear | 224 |
 | mnasnet_b1 | 74.658 (25.342) | 92.114 (7.886) | 4.38M | bicubic | 224 |
 | spnasnet_100 | 74.084 (25.916)  | 91.818 (8.182) | 4.42M | bilinear | 224 |
 | skresnet18 | 73.038 (26.962) | 91.168 (8.832) | 11.9M | bicubic | 224 |
+| mobilenetv2_100 | 72.978 (27.022) | 91.016 (8.984) | 3.5M | bicubic | 224 |
 | seresnet18 | 71.742 (28.258) | 90.334 (9.666) | 11.8M | bicubic | 224 |
 
 ### Ported Weights

timm/models/efficientnet.py

@@ -60,7 +60,15 @@ default_cfgs = {
     'semnasnet_140': _cfg(url=''),
     'mnasnet_small': _cfg(url=''),
 
-    'mobilenetv2_100': _cfg(url=''),
+    'mobilenetv2_100': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_100_ra-b33bc2c4.pth'),
+    'mobilenetv2_110d': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_110d_ra-77090ade.pth'),
+    'mobilenetv2_120d': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_120d_ra-5987e2ed.pth'),
+    'mobilenetv2_140': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_140_ra-21a4e913.pth'),
+
     'fbnetc_100': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth',
         interpolation='bilinear'),
@@ -318,6 +326,7 @@ class EfficientNet(nn.Module):
         # Stem
         if not fix_stem:
             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.bn1 = norm_layer(stem_size, **norm_kwargs)
         self.act1 = act_layer(inplace=True)
@@ -565,7 +574,8 @@ def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwar
     return model
 
 
-def _gen_mobilenet_v2(variant, channel_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
     Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py
     Paper: https://arxiv.org/abs/1801.04381
@@ -580,8 +590,10 @@ def _gen_mobilenet_v2(variant, channel_multiplier=1.0, pretrained=False, **kwarg
         ['ir_r1_k3_s1_e6_c320'],
     ]
     model_kwargs = dict(
-        block_args=decode_arch_def(arch_def),
+        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,
@@ -945,11 +957,34 @@ def mnasnet_small(pretrained=False, **kwargs):
 
 @register_model
 def mobilenetv2_100(pretrained=False, **kwargs):
-    """ MobileNet V2 """
+    """ MobileNet V2 w/ 1.0 channel multiplier """
     model = _gen_mobilenet_v2('mobilenetv2_100', 1.0, pretrained=pretrained, **kwargs)
     return model
 
 
+@register_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
+
+
+@register_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
+
+
+@register_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
+
+
 @register_model
 def fbnetc_100(pretrained=False, **kwargs):
     """ FBNet-C """