Add the MnasNet-B1 variant weights, add A1/B1 model names as in the stand-alone repo, remove a bit of unused code

README.md

@@ -69,10 +69,11 @@ I've leveraged the training scripts in this repository to train a few of the mod
 | seresnext26_32x4d | 77.104 (22.896) | 93.316 (6.684) | 16.8M | bicubic |
 | efficientnet_b0 | 76.912 (23.088) | 93.210 (6.790) | 5.29M | bicubic |
 | mobilenetv3_100 | 75.634 (24.366) | 92.708 (7.292) | 5.5M | bicubic |
-| semnasnet_100 | 75.448 (24.552) | 92.604 (7.396) | 3.89M | bicubic |
+| mnasnet_a1 | 75.448 (24.552) | 92.604 (7.396) | 3.89M | bicubic |
 | fbnetc_100 | 75.124 (24.876) | 92.386 (7.614) | 5.6M | bilinear |
 | resnet34 | 75.110 (24.890) | 92.284 (7.716) | 22M | bilinear |
 | seresnet34 | 74.808 (25.192) | 92.124 (7.876) | 22M | bilinear |
+| mnasnet_b1 | 74.658 (25.342) | 92.114 (7.886) | 4.38M | bicubic |
 | spnasnet_100 | 74.084 (25.916)  | 91.818 (8.182) | 4.42M | bilinear |
 | seresnet18 | 71.742 (28.258) | 90.334 (9.666) | 11.8M | bicubic |
 

models/gen_efficientnet.py

@@ -28,8 +28,8 @@ from models.conv2d_same import sconv2d
 from data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 
 _models = [
-    'mnasnet_050', 'mnasnet_075', 'mnasnet_100', 'mnasnet_140', 'semnasnet_050', 'semnasnet_075',
+    'mnasnet_050', 'mnasnet_075', 'mnasnet_100', 'mnasnet_b1', 'mnasnet_140', 'semnasnet_050', 'semnasnet_075',
-    'semnasnet_100', 'semnasnet_140', 'mnasnet_small', 'mobilenetv1_100', 'mobilenetv2_100',
+    'semnasnet_100', 'mnasnet_a1', 'semnasnet_140', 'mnasnet_small', 'mobilenetv1_100', 'mobilenetv2_100',
     'mobilenetv3_050', 'mobilenetv3_075', 'mobilenetv3_100', 'chamnetv1_100', 'chamnetv2_100',
     'fbnetc_100', 'spnasnet_100', 'tflite_mnasnet_100', 'tflite_semnasnet_100', 'efficientnet_b0',
     'efficientnet_b1', 'efficientnet_b2', 'efficientnet_b3', 'efficientnet_b4', 'tf_efficientnet_b0',
@@ -50,7 +50,9 @@ def _cfg(url='', **kwargs):
 default_cfgs = {
     'mnasnet_050': _cfg(url=''),
     'mnasnet_075': _cfg(url=''),
-    'mnasnet_100': _cfg(url=''),
+    'mnasnet_100': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth',
+        interpolation='bicubic'),
     'tflite_mnasnet_100': _cfg(
         url='https://www.dropbox.com/s/q55ir3tx8mpeyol/tflite_mnasnet_100-31639cdc.pth?dl=1',
         interpolation='bicubic'),
@@ -161,8 +163,7 @@ def _decode_block_str(block_str, depth_multiplier=1.0):
     is assumed to indicate the block type.
 
     leading string - block type (
-      ir = InvertedResidual, ds = DepthwiseSep, dsa = DeptwhiseSep with pw act,
+      ir = InvertedResidual, ds = DepthwiseSep, dsa = DeptwhiseSep with pw act, cn = ConvBnAct)
-      ca = Cascade3x3, and possibly more)
     r - number of repeat blocks,
     k - kernel size,
     s - strides (1-9),
@@ -227,15 +228,6 @@ def _decode_block_str(block_str, depth_multiplier=1.0):
             block_args['pw_group'] = options['g']
             if options['g'] > 1:
                 block_args['shuffle_type'] = 'mid'
-    elif block_type == 'ca':
-        block_args = dict(
-            block_type=block_type,
-            kernel_size=int(options['k']),
-            out_chs=int(options['c']),
-            stride=int(options['s']),
-            act_fn=act_fn,
-            noskip=noskip,
-        )
     elif block_type == 'ds' or block_type == 'dsa':
         block_args = dict(
             block_type=block_type,
@@ -345,8 +337,6 @@ class _BlockBuilder:
         elif bt == 'ds' or bt == 'dsa':
             ba['drop_connect_rate'] = self.drop_connect_rate
             block = DepthwiseSeparableConv(**ba)
-        elif bt == 'ca':
-            block = CascadeConv(**ba)
         elif bt == 'cn':
             block = ConvBnAct(**ba)
         else:
@@ -565,36 +555,6 @@ class DepthwiseSeparableConv(nn.Module):
         return x
 
 
-class CascadeConv(nn.Sequential):
-    # FIXME haven't used yet
-    def __init__(self, in_chs, out_chs, kernel_size=3, stride=2, act_fn=F.relu, noskip=False,
-                 bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT,
-                 folded_bn=False, padding_same=False):
-        super(CascadeConv, self).__init__()
-        assert stride in [1, 2]
-        self.has_residual = (stride == 1 and in_chs == out_chs) and not noskip
-        self.act_fn = act_fn
-        padding = _padding_arg(1, padding_same)
-
-        self.conv1 = sconv2d(in_chs, in_chs, kernel_size, stride=stride, padding=padding, bias=folded_bn)
-        self.bn1 = None if folded_bn else nn.BatchNorm2d(in_chs, momentum=bn_momentum, eps=bn_eps)
-        self.conv2 = sconv2d(in_chs, out_chs, kernel_size, stride=1, padding=padding, bias=folded_bn)
-        self.bn2 = None if folded_bn else nn.BatchNorm2d(out_chs, momentum=bn_momentum, eps=bn_eps)
-
-    def forward(self, x):
-        residual = x
-        x = self.conv1(x)
-        if self.bn1 is not None:
-            x = self.bn1(x)
-        x = self.act_fn(x)
-        x = self.conv2(x)
-        if self.bn2 is not None:
-            x = self.bn2(x)
-        if self.has_residual:
-            x += residual
-        return x
-
-
 class InvertedResidual(nn.Module):
     """ Inverted residual block w/ optional SE"""
 
@@ -699,7 +659,6 @@ class GenEfficientNet(nn.Module):
         super(GenEfficientNet, self).__init__()
         self.num_classes = num_classes
         self.drop_rate = drop_rate
-        self.drop_connect_rate = drop_connect_rate
         self.act_fn = act_fn
         self.num_features = num_features
 
@@ -730,7 +689,7 @@ class GenEfficientNet(nn.Module):
                 nn.BatchNorm2d(self.num_features, momentum=bn_momentum, eps=bn_eps)
 
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
-        self.classifier = nn.Linear(self.num_features, self.num_classes)
+        self.classifier = nn.Linear(self.num_features * self.global_pool.feat_mult(), self.num_classes)
 
         for m in self.modules():
             if weight_init == 'goog':
@@ -1220,6 +1179,11 @@ def mnasnet_100(num_classes, in_chans=3, pretrained=False, **kwargs):
     return model
 
 
+def mnasnet_b1(num_classes, in_chans=3, pretrained=False, **kwargs):
+    """ MNASNet B1, depth multiplier of 1.0. """
+    return mnasnet_100(num_classes, in_chans, pretrained, **kwargs)
+
+
 def tflite_mnasnet_100(num_classes, in_chans=3, pretrained=False, **kwargs):
     """ MNASNet B1, depth multiplier of 1.0. """
     default_cfg = default_cfgs['tflite_mnasnet_100']
@@ -1273,6 +1237,11 @@ def semnasnet_100(num_classes, in_chans=3, pretrained=False, **kwargs):
     return model
 
 
+def mnasnet_a1(num_classes, in_chans=3, pretrained=False, **kwargs):
+    """ MNASNet A1 (w/ SE), depth multiplier of 1.0. """
+    return semnasnet_100(num_classes, in_chans, pretrained, **kwargs)
+
+
 def tflite_semnasnet_100(num_classes, in_chans=3, pretrained=False, **kwargs):
     """ MNASNet A1, depth multiplier of 1.0. """
     default_cfg = default_cfgs['tflite_semnasnet_100']