Implementation of the MNASNet family of models

torchvision/models/__init__.py

@@ -6,3 +6,4 @@
 from .densenet import *
 from .googlenet import *
 from .mobilenet import *
+from .mnasnet import *

torchvision/models/mnasnet.py

@@ -0,0 +1,176 @@
+import math
+
+import torch
+import torch.nn as nn
+
+
+__all__ = ['MNASNet', 'MNASNet0_5', 'MNASNet0_75', 'MNASNet1_0', 'MNASNet1_3']
+
+# Paper suggests 0.9997 momentum, for TensFlow. Equivalent PyTorch momentum is
+# 1.0 - tensorflow.
+_BN_MOMENTUM = 1 - 0.9997
+
+
+class _InvertedResidual(nn.Module):
+    """ Inverted residual block from MobileNetV2 and MNASNet papers. This can
+    be used to implement MobileNet V2, if ReLU is replaced with ReLU6. """
+
+    def __init__(self, in_ch, out_ch, kernel_size, stride, expansion_factor,
+                 bn_momentum=0.1):
+        super().__init__()
+        assert stride in [1, 2]
+        assert kernel_size in [3, 5]
+        mid_ch = in_ch * expansion_factor
+        self.apply_residual = (in_ch == out_ch and stride == 1)
+        self.layers = nn.Sequential(
+            # Pointwise
+            nn.Conv2d(in_ch, mid_ch, 1, bias=False),
+            nn.BatchNorm2d(mid_ch, momentum=bn_momentum),
+            nn.ReLU(inplace=True),
+            # Depthwise
+            nn.Conv2d(mid_ch, mid_ch, kernel_size, padding=kernel_size // 2,
+                      stride=stride, groups=mid_ch, bias=False),
+            nn.BatchNorm2d(mid_ch, momentum=bn_momentum),
+            nn.ReLU(inplace=True),
+            # Linear pointwise. Note that there's no activation.
+            nn.Conv2d(mid_ch, out_ch, 1, bias=False),
+            nn.BatchNorm2d(out_ch, momentum=bn_momentum))
+
+    def forward(self, input):
+        if self.apply_residual:
+            return self.layers.forward(input) + input
+        else:
+            return self.layers.forward(input)
+
+
+def _stack(in_ch, out_ch, kernel_size, stride, exp_factor, repeats,
+           bn_momentum):
+    """ Creates a stack of inverted residuals as seen in e.g. MobileNetV2 or
+    MNASNet. """
+    assert repeats >= 1
+    # First one has no skip, because feature map size changes.
+    first = _InvertedResidual(in_ch, out_ch, kernel_size, stride, exp_factor,
+                              bn_momentum=bn_momentum)
+    remaining = []
+    for _ in range(1, repeats):
+        remaining.append(
+            _InvertedResidual(out_ch, out_ch, kernel_size, 1, exp_factor,
+                              bn_momentum=bn_momentum))
+    return nn.Sequential(first, *remaining)
+
+
+def _round_to_multiple_of(val, divisor, round_up_bias=0.9):
+    """ Asymmetric rounding to make `val` divisible by `divisor`. With default
+    bias, will round up, unless the number is no more than 10% greater than the
+    smaller divisible value, i.e. (83, 8) -> 80, but (84, 8) -> 88. """
+    assert 0.0 < round_up_bias < 1.0
+    new_val = max(divisor, int(val + divisor / 2) // divisor * divisor)
+    return new_val if new_val >= round_up_bias * val else new_val + divisor
+
+
+def _scale_depths(depths, alpha):
+    """ Scales tensor depths as in reference MobileNet code, prefers rouding up
+    rather than down. """
+    return [_round_to_multiple_of(depth * alpha, 8) for depth in depths]
+
+
+class MNASNet(torch.nn.Module):
+    """ MNASNet, as described in https://arxiv.org/pdf/1807.11626.pdf.
+    >>> model = MNASNet(1000, 1.0)
+    >>> x = torch.rand(1, 3, 224, 224)
+    >>> y = model.forward(x)
+    >>> y.dim()
+    1
+    >>> y.nelement()
+    1000
+    """
+
+    def __init__(self, num_classes, alpha, dropout=0.2):
+        super().__init__()
+        self.alpha = alpha
+        self.num_classes = num_classes
+        depths = _scale_depths([24, 40, 80, 96, 192, 320], alpha)
+        layers = [
+            # First layer: regular conv.
+            nn.Conv2d(3, 32, 3, padding=1, stride=2, bias=False),
+            nn.BatchNorm2d(32, momentum=_BN_MOMENTUM),
+            nn.ReLU(inplace=True),
+            # Depthwise separable, no skip.
+            nn.Conv2d(32, 32, 3, padding=1, stride=1, groups=32, bias=False),
+            nn.BatchNorm2d(32, momentum=_BN_MOMENTUM),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(32, 16, 1, padding=0, stride=1, bias=False),
+            nn.BatchNorm2d(16, momentum=_BN_MOMENTUM),
+            # MNASNet blocks: stacks of inverted residuals.
+            _stack(16, depths[0], 3, 2, 3, 3, _BN_MOMENTUM),
+            _stack(depths[0], depths[1], 5, 2, 3, 3, _BN_MOMENTUM),
+            _stack(depths[1], depths[2], 5, 2, 6, 3, _BN_MOMENTUM),
+            _stack(depths[2], depths[3], 3, 1, 6, 2, _BN_MOMENTUM),
+            _stack(depths[3], depths[4], 5, 2, 6, 4, _BN_MOMENTUM),
+            _stack(depths[4], depths[5], 3, 1, 6, 1, _BN_MOMENTUM),
+            # Final mapping to classifier input.
+            nn.Conv2d(depths[5], 1280, 1, padding=0, stride=1, bias=False),
+            nn.BatchNorm2d(1280, momentum=_BN_MOMENTUM),
+            nn.ReLU(inplace=True),
+        ]
+        self.layers = nn.Sequential(*layers)
+        self.avgpool = nn.AdaptiveAvgPool2d(1)
+        if dropout > 0.0:
+            self.classifier = nn.Sequential(
+                nn.Dropout(inplace=True, p=0.2),
+                nn.Linear(1280, self.num_classes))
+        else:
+            self.classifier = nn.Linear(1280, self.num_classes)
+
+        self._initialize_weights()
+
+    def features(self, x):
+        return self.layers.forward(x)
+
+    def forward(self, x):
+        x = self.features(x)
+        x = self.avgpool(x).squeeze()
+        return self.classifier(x)
+
+    def _initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2.0 / n))
+                if m.bias is not None:
+                    m.bias.data.zero_()
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1.0)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.Linear):
+                n = m.weight.size(1)
+                m.weight.data.normal_(0, 0.01)
+                m.bias.data.zero_()
+
+
+class MNASNet0_5(MNASNet):
+    """ MNASNet with depth multiplier of 0.5. """
+
+    def __init__(self, num_classes):
+        super().__init__(num_classes, 0.5)
+
+
+class MNASNet0_75(MNASNet):
+    """ MNASNet with depth multiplier of 0.75. """
+
+    def __init__(self, num_classes):
+        super().__init__(num_classes, 0.75)
+
+
+class MNASNet1_0(MNASNet):
+    """ MNASNet with depth multiplier of 1.0. """
+
+    def __init__(self, num_classes):
+        super().__init__(num_classes, 1.0)
+
+
+class MNASNet1_3(MNASNet):
+    """ MNASNet with depth multiplier of 1.3. """
+
+    def __init__(self, num_classes):
+        super().__init__(num_classes, 1.3)