Add ReXNet w/ remapped weights, feature support

tests/test_models.py

@@ -113,7 +113,7 @@ EXCLUDE_FEAT_FILTERS = [
 ]
 if 'GITHUB_ACTIONS' in os.environ and 'Linux' in platform.system():
     # GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
-    EXCLUDE_FEAT_FILTERS += ['*resnext101_32x32d']
+    EXCLUDE_FEAT_FILTERS += ['*resnext101_32x32d', '*resnext101_32x16d']
 
 
 @pytest.mark.timeout(120)

timm/models/__init__.py

@@ -16,6 +16,7 @@ from .regnet import *
 from .res2net import *
 from .resnest import *
 from .resnet import *
+from .rexnet import *
 from .selecsls import *
 from .senet import *
 from .sknet import *

timm/models/layers/conv_bn_act.py

@@ -14,6 +14,7 @@ class ConvBnAct(nn.Module):
                  drop_block=None, aa_layer=None):
         super(ConvBnAct, self).__init__()
         use_aa = aa_layer is not None
+
         self.conv = create_conv2d(
             in_channels, out_channels, kernel_size, stride=1 if use_aa else stride,
             padding=padding, dilation=dilation, groups=groups, bias=False)
@@ -23,6 +24,14 @@ class ConvBnAct(nn.Module):
         self.bn = norm_act_layer(out_channels, apply_act=apply_act, drop_block=drop_block, **norm_act_args)
         self.aa = aa_layer(channels=out_channels) if stride == 2 and use_aa else None
 
+    @property
+    def in_channels(self):
+        return self.conv.in_channels
+
+    @property
+    def out_channels(self):
+        return self.conv.out_channels
+
     def forward(self, x):
         x = self.conv(x)
         x = self.bn(x)

timm/models/layers/separable_conv.py

@@ -23,6 +23,14 @@ class SeparableConvBnAct(nn.Module):
         norm_act_layer, norm_act_args = convert_norm_act_type(norm_layer, act_layer, norm_kwargs)
         self.bn = norm_act_layer(out_channels, apply_act=apply_act, drop_block=drop_block, **norm_act_args)
 
+    @property
+    def in_channels(self):
+        return self.conv_dw.in_channels
+
+    @property
+    def out_channels(self):
+        return self.conv_pw.out_channels
+
     def forward(self, x):
         x = self.conv_dw(x)
         x = self.conv_pw(x)
@@ -45,6 +53,14 @@ class SeparableConv2d(nn.Module):
         self.conv_pw = create_conv2d(
             int(in_channels * channel_multiplier), out_channels, pw_kernel_size, padding=padding, bias=bias)
 
+    @property
+    def in_channels(self):
+        return self.conv_dw.in_channels
+
+    @property
+    def out_channels(self):
+        return self.conv_pw.out_channels
+
     def forward(self, x):
         x = self.conv_dw(x)
         x = self.conv_pw(x)

timm/models/layers/split_attn.py

@@ -51,6 +51,14 @@ class SplitAttnConv2d(nn.Module):
         self.fc2 = nn.Conv2d(attn_chs, mid_chs, 1, groups=groups)
         self.rsoftmax = RadixSoftmax(radix, groups)
 
+    @property
+    def in_channels(self):
+        return self.conv.in_channels
+
+    @property
+    def out_channels(self):
+        return self.fc1.out_channels
+
     def forward(self, x):
         x = self.conv(x)
         if self.bn0 is not None:

timm/models/rexnet.py

@@ -0,0 +1,257 @@
+""" ReXNet
+
+A PyTorch impl of `ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network` -
+https://arxiv.org/abs/2007.00992
+
+Adapted from original impl at https://github.com/clovaai/rexnet
+Copyright (c) 2020-present NAVER Corp. MIT license
+
+Changes for timm, feature extraction, and rounded channel variant hacked together by Ross Wightman
+"""
+
+import torch.nn as nn
+from math import ceil
+
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from .helpers import build_model_with_cfg
+from .layers import ClassifierHead, create_act_layer, ConvBnAct
+from .registry import register_model
+
+
+def _cfg(url=''):
+    return {
+        'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
+        'crop_pct': 0.875, 'interpolation': 'bicubic',
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
+        'first_conv': 'stem.conv', 'classifier': 'head.fc',
+    }
+
+
+default_cfgs = dict(
+    rexnet_100=_cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_100-1b4dddf4.pth'),
+    rexnet_130=_cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_130-590d768e.pth'),
+    rexnet_150=_cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_150-bd1a6aa8.pth'),
+    rexnet_200=_cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_200-8c0b7f2d.pth'),
+    rexnetr_100=_cfg(
+        url=''),
+    rexnetr_130=_cfg(
+        url=''),
+    rexnetr_150=_cfg(
+        url=''),
+    rexnetr_200=_cfg(
+        url=''),
+)
+
+
+def make_divisible(v, divisor=8, min_value=None):
+    min_value = min_value or divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    return new_v
+
+
+class SEWithNorm(nn.Module):
+
+    def __init__(self, channels, reduction=16, act_layer=nn.ReLU, divisor=1, reduction_channels=None,
+                 gate_layer='sigmoid'):
+        super(SEWithNorm, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        reduction_channels = reduction_channels or make_divisible(channels // reduction, divisor=divisor)
+        print(reduction_channels)
+        self.fc1 = nn.Conv2d(
+            channels, reduction_channels, kernel_size=1, padding=0, bias=True)
+        self.bn = nn.BatchNorm2d(reduction_channels)
+        self.act = act_layer(inplace=True)
+        self.fc2 = nn.Conv2d(
+            reduction_channels, channels, kernel_size=1, padding=0, bias=True)
+        self.gate = create_act_layer(gate_layer)
+
+    def forward(self, x):
+        x_se = self.avg_pool(x)
+        x_se = self.fc1(x_se)
+        x_se = self.bn(x_se)
+        x_se = self.act(x_se)
+        x_se = self.fc2(x_se)
+        return x * self.gate(x_se)
+
+
+class LinearBottleneck(nn.Module):
+    def __init__(self, in_chs, out_chs, stride, exp_ratio=1.0, use_se=True, se_rd=12, ch_div=1):
+        super(LinearBottleneck, self).__init__()
+        self.use_shortcut = stride == 1 and in_chs <= out_chs
+        self.in_channels = in_chs
+        self.out_channels = out_chs
+
+        if exp_ratio != 1.:
+            dw_chs = make_divisible(round(in_chs * exp_ratio), divisor=ch_div)
+            self.conv_exp = ConvBnAct(in_chs, dw_chs, act_layer="swish")
+        else:
+            dw_chs = in_chs
+            self.conv_exp = None
+
+        self.conv_dw = ConvBnAct(dw_chs, dw_chs, 3, stride=stride, groups=dw_chs, apply_act=False)
+        self.se = SEWithNorm(dw_chs, reduction=se_rd, divisor=ch_div) if use_se else None
+        self.act_dw = nn.ReLU6()
+
+        self.conv_pwl = ConvBnAct(dw_chs, out_chs, 1, apply_act=False)
+
+    def feat_channels(self, exp=False):
+        return self.conv_dw.out_channels if exp else self.out_channels
+
+    def forward(self, x):
+        shortcut = x
+        if self.conv_exp is not None:
+            x = self.conv_exp(x)
+        x = self.conv_dw(x)
+        if self.se is not None:
+            x = self.se(x)
+        x = self.act_dw(x)
+        x = self.conv_pwl(x)
+        if self.use_shortcut:
+            x[:, 0:self.in_channels] += shortcut
+        return x
+
+
+def _block_cfg(width_mult=1.0, depth_mult=1.0, initial_chs=16, final_chs=180, use_se=True, ch_div=1):
+    layers = [1, 2, 2, 3, 3, 5]
+    strides = [1, 2, 2, 2, 1, 2]
+    layers = [ceil(element * depth_mult) for element in layers]
+    strides = sum([[element] + [1] * (layers[idx] - 1) for idx, element in enumerate(strides)], [])
+    exp_ratios = [1] * layers[0] + [6] * sum(layers[1:])
+    depth = sum(layers[:]) * 3
+    base_chs = initial_chs / width_mult if width_mult < 1.0 else initial_chs
+
+    # The following channel configuration is a simple instance to make each layer become an expand layer.
+    out_chs_list = []
+    for i in range(depth // 3):
+        out_chs_list.append(make_divisible(round(base_chs * width_mult), divisor=ch_div))
+        base_chs += final_chs / (depth // 3 * 1.0)
+
+    if use_se:
+        use_ses = [False] * (layers[0] + layers[1]) + [True] * sum(layers[2:])
+    else:
+        use_ses = [False] * sum(layers[:])
+
+    return zip(out_chs_list, exp_ratios, strides, use_ses)
+
+
+def _build_blocks(block_cfg, prev_chs, width_mult, se_rd=12, ch_div=1, feature_location='bottleneck'):
+    feat_exp = feature_location == 'expansion'
+    feat_chs = [prev_chs]
+    feature_info = []
+    curr_stride = 2
+    features = []
+    for block_idx, (chs, exp_ratio, stride, se) in enumerate(block_cfg):
+        if stride > 1:
+            fname = 'stem' if block_idx == 0 else f'features.{block_idx - 1}'
+            if block_idx > 0 and feat_exp:
+                fname += '.act_dw'
+            feature_info += [dict(num_chs=feat_chs[-1], reduction=curr_stride, module=fname)]
+            curr_stride *= stride
+        features.append(LinearBottleneck(
+            in_chs=prev_chs, out_chs=chs, exp_ratio=exp_ratio, stride=stride, use_se=se, se_rd=se_rd, ch_div=ch_div))
+        prev_chs = chs
+        feat_chs += [features[-1].feat_channels(feat_exp)]
+    pen_chs = make_divisible(1280 * width_mult, divisor=ch_div)
+    feature_info += [dict(
+        num_chs=pen_chs if feat_exp else feat_chs[-1], reduction=curr_stride,
+        module=f'features.{len(features) - int(not feat_exp)}')]
+    features.append(ConvBnAct(prev_chs, pen_chs, act_layer="swish"))
+    return features, feature_info
+
+
+class ReXNetV1(nn.Module):
+    def __init__(self, in_chans=3, num_classes=1000, global_pool='avg', output_stride=32,
+                 initial_chs=16, final_chs=180, width_mult=1.0, depth_mult=1.0, use_se=True,
+                 se_rd=12, ch_div=1, drop_rate=0.2, feature_location='bottleneck'):
+        super(ReXNetV1, self).__init__()
+
+        assert output_stride == 32  # FIXME support dilation
+        stem_base_chs = 32 / width_mult if width_mult < 1.0 else 32
+        stem_chs = make_divisible(round(stem_base_chs * width_mult), divisor=ch_div)
+        self.stem = ConvBnAct(in_chans, stem_chs, 3, stride=2, act_layer='swish')
+
+        block_cfg = _block_cfg(width_mult, depth_mult, initial_chs, final_chs, use_se, ch_div)
+        features, self.feature_info = _build_blocks(
+            block_cfg, stem_chs, width_mult, se_rd, ch_div, feature_location)
+        self.num_features = features[-1].out_channels
+        self.features = nn.Sequential(*features)
+
+        self.head = ClassifierHead(self.num_features, num_classes, global_pool, drop_rate)
+
+        # FIXME weight init, the original appears to use PyTorch defaults
+
+    def get_classifier(self):
+        return self.head.fc
+
+    def reset_classifier(self, num_classes, global_pool='avg'):
+        self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
+
+    def forward_features(self, x):
+        x = self.stem(x)
+        x = self.features(x)
+        return x
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.head(x)
+        return x
+
+
+def _create_rexnet(variant, pretrained, **kwargs):
+    feature_cfg = dict(flatten_sequential=True)
+    if kwargs.get('feature_location', '') == 'expansion':
+        feature_cfg['feature_cls'] = 'hook'
+    return build_model_with_cfg(
+        ReXNetV1, variant, pretrained, default_cfg=default_cfgs[variant], feature_cfg=feature_cfg, **kwargs)
+
+
+@register_model
+def rexnet_100(pretrained=False, **kwargs):
+    """ReXNet V1 1.0x"""
+    return _create_rexnet('rexnet_100', pretrained, **kwargs)
+
+
+@register_model
+def rexnet_130(pretrained=False, **kwargs):
+    """ReXNet V1 1.3x"""
+    return _create_rexnet('rexnet_130', pretrained, width_mult=1.3, **kwargs)
+
+
+@register_model
+def rexnet_150(pretrained=False, **kwargs):
+    """ReXNet V1 1.5x"""
+    return _create_rexnet('rexnet_150', pretrained, width_mult=1.5, **kwargs)
+
+
+@register_model
+def rexnet_200(pretrained=False, **kwargs):
+    """ReXNet V1 2.0x"""
+    return _create_rexnet('rexnet_200', pretrained, width_mult=2.0, **kwargs)
+
+
+@register_model
+def rexnetr_100(pretrained=False, **kwargs):
+    """ReXNet V1 1.0x w/ rounded (mod 8) channels"""
+    return _create_rexnet('rexnetr_100', pretrained, ch_div=8, **kwargs)
+
+
+@register_model
+def rexnetr_130(pretrained=False, **kwargs):
+    """ReXNet V1 1.3x w/ rounded (mod 8) channels"""
+    return _create_rexnet('rexnetr_130', pretrained, width_mult=1.3, ch_div=8, **kwargs)
+
+
+@register_model
+def rexnetr_150(pretrained=False, **kwargs):
+    """ReXNet V1 1.5x w/ rounded (mod 8) channels"""
+    return _create_rexnet('rexnetr_150', pretrained, width_mult=1.5, ch_div=8, **kwargs)
+
+
+@register_model
+def rexnetr_200(pretrained=False, **kwargs):
+    """ReXNet V1 2.0x w/ rounded (mod 8) channels"""
+    return _create_rexnet('rexnetr_200', pretrained, width_mult=2.0, ch_div=8, **kwargs)