Throw in some FBNetV3 code I had lying around, some refactoring of SE reduction channel calcs for all EffNet archs.

Author: rwightman

timm/models/byobnet.py

@@ -90,7 +90,7 @@ def _cfg(url='', **kwargs):
     # experimental configs
     'resnet51q': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet51q_ra2-d47dcc76.pth',
-        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8),
+        first_conv='stem.conv1', input_size=(3, 256, 256), pool_size=(8, 8),
         test_input_size=(3, 288, 288), crop_pct=1.0),
     'resnet61q': _cfg(
         first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),

timm/models/efficientnet_blocks.py

@@ -22,18 +22,16 @@ class SqueezeExcite(nn.Module):
         se_ratio (float): ratio of squeeze reduction
         act_layer (nn.Module): activation layer of containing block
         gate_fn (Callable): attention gate function
-        block_in_chs (int): input channels of containing block (for calculating reduction from)
-        reduce_from_block (bool): calculate reduction from block input channels if True
         force_act_layer (nn.Module): override block's activation fn if this is set/bound
-        divisor (int): make reduction channels divisible by this
+        round_chs_fn (Callable): specify a fn to calculate rounding of reduced chs
     """
 
     def __init__(
             self, in_chs, se_ratio=0.25, act_layer=nn.ReLU, gate_fn=sigmoid,
-            block_in_chs=None, reduce_from_block=True, force_act_layer=None, divisor=1):
+            force_act_layer=None, round_chs_fn=None):
         super(SqueezeExcite, self).__init__()
-        reduced_chs = (block_in_chs or in_chs) if reduce_from_block else in_chs
-        reduced_chs = make_divisible(reduced_chs * se_ratio, divisor)
+        round_chs_fn = round_chs_fn or round
+        reduced_chs = round_chs_fn(in_chs * se_ratio)
         act_layer = force_act_layer or act_layer
         self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True)
         self.act1 = create_act_layer(act_layer, inplace=True)
@@ -168,8 +166,7 @@ def __init__(
         self.act2 = act_layer(inplace=True)
 
         # Squeeze-and-excitation
-        self.se = se_layer(
-            mid_chs, se_ratio=se_ratio, act_layer=act_layer, block_in_chs=in_chs) if has_se else nn.Identity()
+        self.se = se_layer(mid_chs, se_ratio=se_ratio, act_layer=act_layer) if has_se else nn.Identity()
 
         # Point-wise linear projection
         self.conv_pwl = create_conv2d(mid_chs, out_chs, pw_kernel_size, padding=pad_type, **conv_kwargs)
@@ -292,8 +289,7 @@ def __init__(
         self.act1 = act_layer(inplace=True)
 
         # Squeeze-and-excitation
-        self.se = SqueezeExcite(
-            mid_chs, se_ratio=se_ratio, act_layer=act_layer, block_in_chs=in_chs) if has_se else nn.Identity()
+        self.se = SqueezeExcite(mid_chs, se_ratio=se_ratio, act_layer=act_layer) if has_se else nn.Identity()
 
         # Point-wise linear projection
         self.conv_pwl = create_conv2d(mid_chs, out_chs, pw_kernel_size, padding=pad_type)

timm/models/efficientnet_builder.py

@@ -265,11 +265,12 @@ class EfficientNetBuilder:
     https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_builder.py
 
     """
-    def __init__(self, output_stride=32, pad_type='', round_chs_fn=round_channels,
+    def __init__(self, output_stride=32, pad_type='', round_chs_fn=round_channels, se_from_exp=False,
                  act_layer=None, norm_layer=None, se_layer=None, drop_path_rate=0., feature_location=''):
         self.output_stride = output_stride
         self.pad_type = pad_type
         self.round_chs_fn = round_chs_fn
+        self.se_from_exp = se_from_exp  # calculate se channel reduction from expanded (mid) chs
         self.act_layer = act_layer
         self.norm_layer = norm_layer
         self.se_layer = se_layer
@@ -301,6 +302,8 @@ def _make_block(self, ba, block_idx, block_count):
         ba['norm_layer'] = self.norm_layer
         if bt != 'cn':
             ba['se_layer'] = self.se_layer
+            if not self.se_from_exp and ba['se_ratio']:
+                ba['se_ratio'] /= ba.get('exp_ratio', 1.0)
             ba['drop_path_rate'] = drop_path_rate
 
         if bt == 'ir':
@@ -418,28 +421,28 @@ def _init_weight_goog(m, n='', fix_group_fanout=True):
         if fix_group_fanout:
             fan_out //= m.groups
         init_weight_fn = get_condconv_initializer(
-            lambda w: w.data.normal_(0, math.sqrt(2.0 / fan_out)), m.num_experts, m.weight_shape)
+            lambda w: nn.init.normal_(w, 0, math.sqrt(2.0 / fan_out)), m.num_experts, m.weight_shape)
         init_weight_fn(m.weight)
         if m.bias is not None:
-            m.bias.data.zero_()
+            nn.init.zeros_(m.bias)
     elif isinstance(m, nn.Conv2d):
         fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
         if fix_group_fanout:
             fan_out //= m.groups
-        m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
+        nn.init.normal_(m.weight, 0, math.sqrt(2.0 / fan_out))
         if m.bias is not None:
-            m.bias.data.zero_()
+            nn.init.zeros_(m.bias)
     elif isinstance(m, nn.BatchNorm2d):
-        m.weight.data.fill_(1.0)
-        m.bias.data.zero_()
+        nn.init.ones_(m.weight)
+        nn.init.zeros_(m.bias)
     elif isinstance(m, nn.Linear):
         fan_out = m.weight.size(0)  # fan-out
         fan_in = 0
         if 'routing_fn' in n:
             fan_in = m.weight.size(1)
         init_range = 1.0 / math.sqrt(fan_in + fan_out)
-        m.weight.data.uniform_(-init_range, init_range)
-        m.bias.data.zero_()
+        nn.init.uniform_(m.weight, -init_range, init_range)
+        nn.init.zeros_(m.bias)
 
 
 def efficientnet_init_weights(model: nn.Module, init_fn=None):

timm/models/ghostnet.py

@@ -40,7 +40,7 @@ def _cfg(url='', **kwargs):
 }
 
 
-_SE_LAYER = partial(SqueezeExcite, gate_fn='hard_sigmoid', divisor=4)
+_SE_LAYER = partial(SqueezeExcite, gate_fn='hard_sigmoid', round_chs_fn=partial(make_divisible, divisor=4))
 
 
 class GhostModule(nn.Module):

timm/models/hardcorenas.py

@@ -4,7 +4,7 @@
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .efficientnet_blocks import SqueezeExcite
-from .efficientnet_builder import decode_arch_def, resolve_act_layer, resolve_bn_args
+from .efficientnet_builder import decode_arch_def, resolve_act_layer, resolve_bn_args, round_channels
 from .helpers import build_model_with_cfg, default_cfg_for_features
 from .layers import get_act_fn
 from .mobilenetv3 import MobileNetV3, MobileNetV3Features
@@ -40,7 +40,7 @@ def _gen_hardcorenas(pretrained, variant, arch_def, **kwargs):
     """
     num_features = 1280
     se_layer = partial(
-        SqueezeExcite, gate_fn=get_act_fn('hard_sigmoid'), force_act_layer=nn.ReLU, reduce_from_block=False, divisor=8)
+        SqueezeExcite, gate_fn=get_act_fn('hard_sigmoid'), force_act_layer=nn.ReLU, round_chs_fn=round_channels)
     model_kwargs = dict(
         block_args=decode_arch_def(arch_def),
         num_features=num_features,

timm/models/layers/helpers.py

@@ -28,4 +28,4 @@ def make_divisible(v, divisor=8, min_value=None, round_limit=.9):
     # Make sure that round down does not go down by more than 10%.
     if new_v < round_limit * v:
         new_v += divisor
-    return new_v
+    return new_v

timm/models/mobilenetv3.py

@@ -72,6 +72,10 @@ def _cfg(url='', **kwargs):
     'tf_mobilenetv3_small_minimal_100': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_minimal_100-922a7843.pth',
         mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
+
+    'fbnetv3_b': _cfg(),
+    'fbnetv3_d': _cfg(),
+    'fbnetv3_g': _cfg(),
 }
 
 
@@ -86,7 +90,7 @@ class MobileNetV3(nn.Module):
     """
 
     def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=16, num_features=1280, head_bias=True,
-                 pad_type='', act_layer=None, norm_layer=None, se_layer=None,
+                 pad_type='', act_layer=None, norm_layer=None, se_layer=None, se_from_exp=True,
                  round_chs_fn=round_channels, drop_rate=0., drop_path_rate=0., global_pool='avg'):
         super(MobileNetV3, self).__init__()
         act_layer = act_layer or nn.ReLU
@@ -104,7 +108,7 @@ def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=16, num_f
 
         # Middle stages (IR/ER/DS Blocks)
         builder = EfficientNetBuilder(
-            output_stride=32, pad_type=pad_type, round_chs_fn=round_chs_fn,
+            output_stride=32, pad_type=pad_type, round_chs_fn=round_chs_fn, se_from_exp=se_from_exp,
             act_layer=act_layer, norm_layer=norm_layer, se_layer=se_layer, drop_path_rate=drop_path_rate)
         self.blocks = nn.Sequential(*builder(stem_size, block_args))
         self.feature_info = builder.features
@@ -161,8 +165,8 @@ class MobileNetV3Features(nn.Module):
     and object detection models.
     """
 
-    def __init__(self, block_args, out_indices=(0, 1, 2, 3, 4), feature_location='bottleneck',
-                 in_chans=3, stem_size=16, output_stride=32, pad_type='', round_chs_fn=round_channels,
+    def __init__(self, block_args, out_indices=(0, 1, 2, 3, 4), feature_location='bottleneck', in_chans=3,
+                 stem_size=16, output_stride=32, pad_type='', round_chs_fn=round_channels, se_from_exp=True,
                  act_layer=None, norm_layer=None, se_layer=None, drop_rate=0., drop_path_rate=0.):
         super(MobileNetV3Features, self).__init__()
         act_layer = act_layer or nn.ReLU
@@ -178,7 +182,7 @@ def __init__(self, block_args, out_indices=(0, 1, 2, 3, 4), feature_location='bo
 
         # Middle stages (IR/ER/DS Blocks)
         builder = EfficientNetBuilder(
-            output_stride=output_stride, pad_type=pad_type, round_chs_fn=round_chs_fn,
+            output_stride=output_stride, pad_type=pad_type, round_chs_fn=round_chs_fn, se_from_exp=se_from_exp,
             act_layer=act_layer, norm_layer=norm_layer, se_layer=se_layer,
             drop_path_rate=drop_path_rate, feature_location=feature_location)
         self.blocks = nn.Sequential(*builder(stem_size, block_args))
@@ -262,7 +266,7 @@ def _gen_mobilenet_v3_rw(variant, channel_multiplier=1.0, pretrained=False, **kw
         round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
         norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
         act_layer=resolve_act_layer(kwargs, 'hard_swish'),
-        se_layer=partial(SqueezeExcite, gate_fn=get_act_fn('hard_sigmoid'), reduce_from_block=False),
+        se_layer=partial(SqueezeExcite, gate_fn=get_act_fn('hard_sigmoid')),
         **kwargs,
     )
     model = _create_mnv3(variant, pretrained, **model_kwargs)
@@ -351,7 +355,7 @@ def _gen_mobilenet_v3(variant, channel_multiplier=1.0, pretrained=False, **kwarg
                 ['cn_r1_k1_s1_c960'],  # hard-swish
             ]
     se_layer = partial(
-        SqueezeExcite, gate_fn=get_act_fn('hard_sigmoid'), force_act_layer=nn.ReLU, reduce_from_block=False, divisor=8)
+        SqueezeExcite, gate_fn=get_act_fn('hard_sigmoid'), force_act_layer=nn.ReLU, round_chs_fn=round_channels)
     model_kwargs = dict(
         block_args=decode_arch_def(arch_def),
         num_features=num_features,
@@ -366,6 +370,86 @@ def _gen_mobilenet_v3(variant, channel_multiplier=1.0, pretrained=False, **kwarg
     return model
 
 
+def _gen_fbnetv3(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
+    """ FBNetV3
+    FIXME untested, this is a preliminary impl of some FBNet-V3 variants.
+    """
+    vl = variant.split('_')[-1]
+    if vl in ('a', 'b'):
+        stem_size = 16
+        arch_def = [
+            # stage 0, 112x112 in
+            ['ds_r2_k3_s1_e1_c16'],
+            # stage 1, 112x112 in
+            ['ir_r1_k5_s2_e4_c24', 'ir_r3_k5_s1_e2_c24'],
+            # stage 2, 56x56 in
+            ['ir_r1_k5_s2_e5_c40_se0.25', 'ir_r4_k5_s1_e3_c40_se0.25'],
+            # stage 3, 28x28 in
+            ['ir_r1_k5_s2_e5_c72', 'ir_r4_k3_s1_e3_c72'],
+            # stage 4, 14x14in
+            ['ir_r1_k3_s1_e5_c120_se0.25', 'ir_r5_k5_s1_e3_c120_se0.25'],
+            # stage 5, 14x14in
+            ['ir_r1_k3_s2_e6_c184_se0.25', 'ir_r5_k5_s1_e4_c184_se0.25', 'ir_r1_k5_s1_e6_c224_se0.25'],
+            # stage 6, 7x7 in
+            ['cn_r1_k1_s1_c1344'],
+        ]
+    elif vl == 'd':
+        stem_size = 24
+        arch_def = [
+            # stage 0, 112x112 in
+            ['ds_r2_k3_s1_e1_c16'],
+            # stage 1, 112x112 in
+            ['ir_r1_k3_s2_e5_c24', 'ir_r5_k3_s1_e2_c24'],
+            # stage 2, 56x56 in
+            ['ir_r1_k5_s2_e4_c40_se0.25', 'ir_r4_k3_s1_e3_c40_se0.25'],
+            # stage 3, 28x28 in
+            ['ir_r1_k3_s2_e5_c72', 'ir_r4_k3_s1_e3_c72'],
+            # stage 4, 14x14in
+            ['ir_r1_k3_s1_e5_c128_se0.25', 'ir_r6_k5_s1_e3_c128_se0.25'],
+            # stage 5, 14x14in
+            ['ir_r1_k3_s2_e6_c208_se0.25', 'ir_r5_k5_s1_e5_c208_se0.25', 'ir_r1_k5_s1_e6_c240_se0.25'],
+            # stage 6, 7x7 in
+            ['cn_r1_k1_s1_c1440'],
+        ]
+    elif vl == 'g':
+        stem_size = 32
+        arch_def = [
+            # stage 0, 112x112 in
+            ['ds_r3_k3_s1_e1_c24'],
+            # stage 1, 112x112 in
+            ['ir_r1_k5_s2_e4_c40', 'ir_r4_k5_s1_e2_c40'],
+            # stage 2, 56x56 in
+            ['ir_r1_k5_s2_e4_c56_se0.25', 'ir_r4_k5_s1_e3_c56_se0.25'],
+            # stage 3, 28x28 in
+            ['ir_r1_k5_s2_e5_c104', 'ir_r4_k3_s1_e3_c104'],
+            # stage 4, 14x14in
+            ['ir_r1_k3_s1_e5_c160_se0.25', 'ir_r8_k5_s1_e3_c160_se0.25'],
+            # stage 5, 14x14in
+            ['ir_r1_k3_s2_e6_c264_se0.25', 'ir_r6_k5_s1_e5_c264_se0.25', 'ir_r2_k5_s1_e6_c288_se0.25'],
+            # stage 6, 7x7 in
+            ['cn_r1_k1_s1_c1728'],  # hard-swish
+        ]
+    else:
+        raise NotImplemented
+    round_chs_fn = partial(round_channels, multiplier=channel_multiplier, round_limit=0.95)
+    se_layer = partial(SqueezeExcite, gate_fn=get_act_fn('hard_sigmoid'), round_chs_fn=round_chs_fn)
+    act_layer = resolve_act_layer(kwargs, 'hard_swish')
+    model_kwargs = dict(
+        block_args=decode_arch_def(arch_def),
+        num_features=1984,
+        head_bias=False,
+        stem_size=stem_size,
+        round_chs_fn=round_chs_fn,
+        se_from_exp=False,
+        norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
+        act_layer=act_layer,
+        se_layer=se_layer,
+        **kwargs,
+    )
+    model = _create_mnv3(variant, pretrained, **model_kwargs)
+    return model
+
+
 @register_model
 def mobilenetv3_large_075(pretrained=False, **kwargs):
     """ MobileNet V3 """
@@ -474,3 +558,24 @@ def tf_mobilenetv3_small_minimal_100(pretrained=False, **kwargs):
     kwargs['pad_type'] = 'same'
     model = _gen_mobilenet_v3('tf_mobilenetv3_small_minimal_100', 1.0, pretrained=pretrained, **kwargs)
     return model
+
+
+@register_model
+def fbnetv3_b(pretrained=False, **kwargs):
+    """ FBNetV3-B """
+    model = _gen_fbnetv3('fbnetv3_b', pretrained=pretrained, **kwargs)
+    return model
+
+
+@register_model
+def fbnetv3_d(pretrained=False, **kwargs):
+    """ FBNetV3-D """
+    model = _gen_fbnetv3('fbnetv3_d', pretrained=pretrained, **kwargs)
+    return model
+
+
+@register_model
+def fbnetv3_g(pretrained=False, **kwargs):
+    """ FBNetV3-G """
+    model = _gen_fbnetv3('fbnetv3_g', pretrained=pretrained, **kwargs)
+    return model