Add MobileNetV2

exps/experimental/test-flops.py

@@ -0,0 +1,24 @@
+import sys, time, random, argparse
+from copy import deepcopy
+import torchvision.models as models
+from pathlib import Path
+lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
+if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
+
+from utils import get_model_infos
+#from models.ImageNet_MobileNetV2 import MobileNetV2
+from torchvision.models.mobilenet import MobileNetV2
+
+def main(width_mult):
+  # model = MobileNetV2(1001, width_mult, 32, 1280, 'InvertedResidual', 0.2)
+  model = MobileNetV2(width_mult=width_mult)
+  print(model)
+  flops, params = get_model_infos(model, (2, 3, 224, 224))
+  print('FLOPs : {:}'.format(flops))
+  print('Params : {:}'.format(params))
+  print('-'*50)
+
+
+if __name__ == '__main__':
+  main(1.0)
+  main(1.4)

lib/models/ImageNet_MobileNetV2.py

@@ -0,0 +1,101 @@
+# MobileNetV2: Inverted Residuals and Linear Bottlenecks, CVPR 2018
+from torch import nn
+from .initialization import initialize_resnet
+
+
+class ConvBNReLU(nn.Module):
+  def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
+    super(ConvBNReLU, self).__init__()
+    padding = (kernel_size - 1) // 2
+    self.conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False)
+    self.bn   = nn.BatchNorm2d(out_planes)
+    self.relu = nn.ReLU6(inplace=True)
+  
+  def forward(self, x):
+    out = self.conv( x )
+    out = self.bn  ( out )
+    out = self.relu( out )
+    return out
+
+
+class InvertedResidual(nn.Module):
+  def __init__(self, inp, oup, stride, expand_ratio):
+    super(InvertedResidual, self).__init__()
+    self.stride = stride
+    assert stride in [1, 2]
+
+    hidden_dim = int(round(inp * expand_ratio))
+    self.use_res_connect = self.stride == 1 and inp == oup
+
+    layers = []
+    if expand_ratio != 1:
+      # pw
+      layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
+    layers.extend([
+      # dw
+      ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
+      # pw-linear
+      nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+      nn.BatchNorm2d(oup),
+    ])
+    self.conv = nn.Sequential(*layers)
+
+  def forward(self, x):
+    if self.use_res_connect:
+      return x + self.conv(x)
+    else:
+      return self.conv(x)
+
+
+class MobileNetV2(nn.Module):
+  def __init__(self, num_classes, width_mult, input_channel, last_channel, block_name, dropout):
+    super(MobileNetV2, self).__init__()
+    if block_name == 'InvertedResidual':
+      block = InvertedResidual
+    else:
+      raise ValueError('invalid block name : {:}'.format(block_name))
+    inverted_residual_setting = [
+      # t, c,  n, s
+      [1, 16 , 1, 1],
+      [6, 24 , 2, 2],
+      [6, 32 , 3, 2],
+      [6, 64 , 4, 2],
+      [6, 96 , 3, 1],
+      [6, 160, 3, 2],
+      [6, 320, 1, 1],
+    ]
+
+    # building first layer
+    input_channel = int(input_channel * width_mult)
+    self.last_channel = int(last_channel * max(1.0, width_mult))
+    features = [ConvBNReLU(3, input_channel, stride=2)]
+    # building inverted residual blocks
+    for t, c, n, s in inverted_residual_setting:
+      output_channel = int(c * width_mult)
+      for i in range(n):
+        stride = s if i == 0 else 1
+        features.append(block(input_channel, output_channel, stride, expand_ratio=t))
+        input_channel = output_channel
+    # building last several layers
+    features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1))
+    # make it nn.Sequential
+    self.features = nn.Sequential(*features)
+
+    # building classifier
+    self.classifier = nn.Sequential(
+      nn.Dropout(dropout),
+      nn.Linear(self.last_channel, num_classes),
+    )
+    self.message = 'MobileNetV2 : width_mult={:}, in-C={:}, last-C={:}, block={:}, dropout={:}'.format(width_mult, input_channel, last_channel, block_name, dropout)
+
+    # weight initialization
+    self.apply( initialize_resnet )
+
+  def get_message(self):
+    return self.message
+
+  def forward(self, inputs):
+    features = self.features(inputs)
+    vectors  = features.mean([2, 3])
+    predicts = self.classifier(vectors)
+    return features, predicts

lib/models/__init__.py

@@ -110,8 +110,11 @@ def get_imagenet_models(config):
   super_type = getattr(config, 'super_type', 'basic')
   if super_type == 'basic':
     from .ImagenetResNet import ResNet
+    from .ImageNet_MobileNetV2 import MobileNetV2
     if config.arch == 'resnet':
       return ResNet(config.block_name, config.layers, config.deep_stem, config.class_num, config.zero_init_residual, config.groups, config.width_per_group)
+    elif config.arch == 'mobilenet_v2':
+      return MobileNetV2(config.class_num, config.width_multi, config.input_channel, config.last_channel, 'InvertedResidual', config.dropout)
     else:
       raise ValueError('invalid arch : {:}'.format( config.arch ))
   elif super_type.startswith('infer'): # NAS searched architecture