Add drop_connect impl to try during training, fix a few comments

models/genmobilenet.py

@@ -276,12 +276,13 @@ class _BlockBuilder:
     """
 
     def __init__(self, channel_multiplier=1.0, channel_divisor=8, channel_min=None,
-                 act_fn=None, se_gate_fn=torch.sigmoid, se_reduce_mid=False,
+                 drop_connect_rate=0., act_fn=None, se_gate_fn=torch.sigmoid, se_reduce_mid=False,
                  bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT,
                  folded_bn=False, padding_same=False, verbose=False):
         self.channel_multiplier = channel_multiplier
         self.channel_divisor = channel_divisor
         self.channel_min = channel_min
+        self.drop_connect_rate = drop_connect_rate
         self.act_fn = act_fn
         self.se_gate_fn = se_gate_fn
         self.se_reduce_mid = se_reduce_mid
@@ -310,10 +311,12 @@ class _BlockBuilder:
             print('args:', ba)
         #  could replace this if with lambdas or functools binding if variety increases
         if bt == 'ir':
+            ba['drop_connect_rate'] = self.drop_connect_rate
             ba['se_gate_fn'] = self.se_gate_fn
             ba['se_reduce_mid'] = self.se_reduce_mid
             block = InvertedResidual(**ba)
         elif bt == 'ds' or bt == 'dsa':
+            ba['drop_connect_rate'] = self.drop_connect_rate
             block = DepthwiseSeparableConv(**ba)
         elif bt == 'ca':
             block = CascadeConv(**ba)
@@ -402,6 +405,19 @@ def hard_sigmoid(x):
     return F.relu6(x + 3.) / 6.
 
 
+def drop_connect(inputs, training=False, drop_connect_rate=0.):
+    """Apply drop connect."""
+    if not training:
+        return inputs
+
+    keep_prob = 1 - drop_connect_rate
+    random_tensor = keep_prob + torch.rand(
+        (inputs.size()[0], 1, 1, 1), dtype=inputs.dtype, device=inputs.device)
+    random_tensor.floor_()  # binarize
+    output = inputs.div(keep_prob) * random_tensor
+    return output
+
+
 class ChannelShuffle(nn.Module):
     # FIXME haven't used yet
     def __init__(self, groups):
@@ -474,13 +490,14 @@ class DepthwiseSeparableConv(nn.Module):
                  stride=1, act_fn=F.relu, noskip=False, pw_act=False,
                  se_ratio=0., se_gate_fn=torch.sigmoid,
                  bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT,
-                 folded_bn=False, padding_same=False):
+                 folded_bn=False, padding_same=False, drop_connect_rate=0.):
         super(DepthwiseSeparableConv, self).__init__()
         assert stride in [1, 2]
         self.has_se = se_ratio is not None and se_ratio > 0.
         self.has_residual = (stride == 1 and in_chs == out_chs) and not noskip
         self.has_pw_act = pw_act  # activation after point-wise conv
         self.act_fn = act_fn
+        self.drop_connect_rate = drop_connect_rate
         dw_padding = _padding_arg(kernel_size // 2, padding_same)
         pw_padding = _padding_arg(0, padding_same)
 
@@ -515,7 +532,9 @@ class DepthwiseSeparableConv(nn.Module):
             x = self.act_fn(x)
 
         if self.has_residual:
-            x += residual  # FIXME add drop-connect
+            if self.drop_connect_rate > 0.:
+                x = drop_connect(x, self.training, self.drop_connect_rate)
+            x += residual
         return x
 
 
@@ -557,12 +576,13 @@ class InvertedResidual(nn.Module):
                  se_ratio=0., se_reduce_mid=False, se_gate_fn=torch.sigmoid,
                  shuffle_type=None, pw_group=1,
                  bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT,
-                 folded_bn=False, padding_same=False):
+                 folded_bn=False, padding_same=False, drop_connect_rate=0.):
         super(InvertedResidual, self).__init__()
         mid_chs = int(in_chs * exp_ratio)
         self.has_se = se_ratio is not None and se_ratio > 0.
         self.has_residual = (in_chs == out_chs and stride == 1) and not noskip
         self.act_fn = act_fn
+        self.drop_connect_rate = drop_connect_rate
         dw_padding = _padding_arg(kernel_size // 2, padding_same)
         pw_padding = _padding_arg(0, padding_same)
 
@@ -619,7 +639,9 @@ class InvertedResidual(nn.Module):
             x = self.bn3(x)
 
         if self.has_residual:
-            x += residual  # FIXME add drop-connect
+            if self.drop_connect_rate > 0.:
+                x = drop_connect(x, self.training, self.drop_connect_rate)
+            x += residual
 
         # NOTE maskrcnn_benchmark building blocks have an SE module defined here for some variants
 
@@ -643,12 +665,14 @@ class GenMobileNet(nn.Module):
     def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=32, num_features=1280,
                  channel_multiplier=1.0, channel_divisor=8, channel_min=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,
+                 drop_rate=0., drop_connect_rate=0., act_fn=F.relu,
+                 se_gate_fn=torch.sigmoid, se_reduce_mid=False,
                  global_pool='avg', head_conv='default', weight_init='goog',
-                 folded_bn=False, padding_same=False):
+                 folded_bn=False, padding_same=False,):
         super(GenMobileNet, 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
 
@@ -661,7 +685,7 @@ class GenMobileNet(nn.Module):
 
         builder = _BlockBuilder(
             channel_multiplier, channel_divisor, channel_min,
-            act_fn, se_gate_fn, se_reduce_mid,
+            drop_connect_rate, act_fn, se_gate_fn, se_reduce_mid,
             bn_momentum, bn_eps, folded_bn, padding_same, verbose=_DEBUG)
         self.blocks = nn.Sequential(*builder(in_chs, block_args))
         in_chs = builder.in_chs
@@ -1090,7 +1114,7 @@ def _gen_spnasnet(channel_multiplier, num_classes=1000, **kwargs):
 
 
 def _gen_efficientnet(channel_multiplier=1.0, depth_multiplier=1.0, num_classes=1000, **kwargs):
-    """Creates a MobileNet-V3 model.
+    """Creates an EfficientNet model.
 
     Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
     Paper: https://arxiv.org/abs/1905.11946
@@ -1347,7 +1371,7 @@ def spnasnet_100(num_classes, in_chans=3, pretrained=False, **kwargs):
 def efficientnet_b0(num_classes, in_chans=3, pretrained=False, **kwargs):
     """ EfficientNet """
     default_cfg = default_cfgs['efficientnet_b0']
-    # NOTE dropout should be 0.2 for train
+    # NOTE for train, drop_rate should be 0.2
     model = _gen_efficientnet(
         channel_multiplier=1.0, depth_multiplier=1.0,
         num_classes=num_classes, in_chans=in_chans, **kwargs)
@@ -1360,7 +1384,7 @@ def efficientnet_b0(num_classes, in_chans=3, pretrained=False, **kwargs):
 def efficientnet_b1(num_classes, in_chans=3, pretrained=False, **kwargs):
     """ EfficientNet """
     default_cfg = default_cfgs['efficientnet_b1']
-    # NOTE dropout should be 0.2 for train
+    # NOTE for train, drop_rate should be 0.2
     model = _gen_efficientnet(
         channel_multiplier=1.0, depth_multiplier=1.1,
         num_classes=num_classes, in_chans=in_chans, **kwargs)
@@ -1373,7 +1397,7 @@ def efficientnet_b1(num_classes, in_chans=3, pretrained=False, **kwargs):
 def efficientnet_b2(num_classes, in_chans=3, pretrained=False, **kwargs):
     """ EfficientNet """
     default_cfg = default_cfgs['efficientnet_b2']
-    # NOTE dropout should be 0.3 for train
+    # NOTE for train, drop_rate should be 0.3
     model = _gen_efficientnet(
         channel_multiplier=1.1, depth_multiplier=1.2,
         num_classes=num_classes, in_chans=in_chans, **kwargs)
@@ -1386,7 +1410,7 @@ def efficientnet_b2(num_classes, in_chans=3, pretrained=False, **kwargs):
 def efficientnet_b3(num_classes, in_chans=3, pretrained=False, **kwargs):
     """ EfficientNet """
     default_cfg = default_cfgs['efficientnet_b3']
-    # NOTE dropout should be 0.3 for train
+    # NOTE for train, drop_rate should be 0.3
     model = _gen_efficientnet(
         channel_multiplier=1.2, depth_multiplier=1.4,
         num_classes=num_classes, in_chans=in_chans, **kwargs)
@@ -1399,7 +1423,7 @@ def efficientnet_b3(num_classes, in_chans=3, pretrained=False, **kwargs):
 def efficientnet_b4(num_classes, in_chans=3, pretrained=False, **kwargs):
     """ EfficientNet """
     default_cfg = default_cfgs['efficientnet_b4']
-    # NOTE dropout should be 0.4 for train
+    # NOTE for train, drop_rate should be 0.4
     model = _gen_efficientnet(
         channel_multiplier=1.4, depth_multiplier=1.8,
         num_classes=num_classes, in_chans=in_chans, **kwargs)