Fix efficient head for MobileNetV3

Author: rwightman

models/genmobilenet.py

@@ -290,6 +290,7 @@ def _make_block(self, ba):
         ba['bn_eps'] = self.bn_eps
         ba['folded_bn'] = self.folded_bn
         ba['padding_same'] = self.padding_same
+        # block act fn overrides the model default
         ba['act_fn'] = ba['act_fn'] if ba['act_fn'] is not None else self.act_fn
         assert ba['act_fn'] is not None
         if _DEBUG:
@@ -611,15 +612,14 @@ def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=32, num_f
                  depth_multiplier=1.0, depth_divisor=8, min_depth=None,
                  bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT,
                  drop_rate=0., act_fn=F.relu, se_gate_fn=torch.sigmoid, se_reduce_mid=False,
-                 global_pool='avg', skip_head_conv=False, efficient_head=False,
-                 weight_init='goog', folded_bn=False, padding_same=False):
+                 global_pool='avg', head_conv='default', weight_init='goog',
+                 folded_bn=False, padding_same=False):
         super(GenMobileNet, self).__init__()
         self.num_classes = num_classes
         self.depth_multiplier = depth_multiplier
         self.drop_rate = drop_rate
         self.act_fn = act_fn
         self.num_features = num_features
-        self.efficient_head = efficient_head  # pool before last conv
 
         stem_size = _round_channels(stem_size, depth_multiplier, depth_divisor, min_depth)
         self.conv_stem = sconv2d(
@@ -629,19 +629,22 @@ def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=32, num_f
         in_chs = stem_size
 
         builder = _BlockBuilder(
-            depth_multiplier, depth_divisor, min_depth, act_fn, se_gate_fn, se_reduce_mid,
+            depth_multiplier, depth_divisor, min_depth,
+            act_fn, se_gate_fn, se_reduce_mid,
             bn_momentum, bn_eps, folded_bn, padding_same)
         self.blocks = nn.Sequential(*builder(in_chs, block_args))
         in_chs = builder.in_chs
 
-        if skip_head_conv:
+        if not head_conv or head_conv == 'none':
+            self.efficient_head = False
             self.conv_head = None
             assert in_chs == self.num_features
         else:
+            self.efficient_head = head_conv == 'efficient'
             self.conv_head = sconv2d(
                 in_chs, self.num_features, 1,
-                padding=_padding_arg(0, padding_same), bias=folded_bn and not efficient_head)
-            self.bn2 = None if (folded_bn or efficient_head) else \
+                padding=_padding_arg(0, padding_same), bias=folded_bn and not self.efficient_head)
+            self.bn2 = None if (folded_bn or self.efficient_head) else \
                 nn.BatchNorm2d(self.num_features, momentum=bn_momentum, eps=bn_eps)
 
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
@@ -674,7 +677,7 @@ def forward_features(self, x, pool=True):
         x = self.blocks(x)
         if self.efficient_head:
             # efficient head, currently only mobilenet-v3 performs pool before last 1x1 conv
-            x = self.global_pool(x)  # always need to pool here regardless of bool
+            x = self.global_pool(x)  # always need to pool here regardless of flag
             x = self.conv_head(x)
             # no BN
             x = self.act_fn(x)
@@ -836,7 +839,7 @@ def _gen_mobilenet_v1(depth_multiplier, num_classes=1000, **kwargs):
         bn_momentum=bn_momentum,
         bn_eps=bn_eps,
         act_fn=F.relu6,
-        skip_head_conv=True,
+        head_conv='none',
         **kwargs
         )
     return model
@@ -914,6 +917,7 @@ def _gen_mobilenet_v3(depth_multiplier, num_classes=1000, **kwargs):
         act_fn=hard_swish,
         se_gate_fn=hard_sigmoid,
         se_reduce_mid=True,
+        head_conv='efficient',
         **kwargs
     )
     return model