Source code for torchvision.models.mnasnet
import warnings
import torch
from torch import Tensor
import torch.nn as nn
from .._internally_replaced_utils import load_state_dict_from_url
from typing import Any, Dict, List
__all__ = ['MNASNet', 'mnasnet0_5', 'mnasnet0_75', 'mnasnet1_0', 'mnasnet1_3']
_MODEL_URLS = {
"mnasnet0_5":
"https://download.pytorch.org/models/mnasnet0.5_top1_67.823-3ffadce67e.pth",
"mnasnet0_75": None,
"mnasnet1_0":
"https://download.pytorch.org/models/mnasnet1.0_top1_73.512-f206786ef8.pth",
"mnasnet1_3": None
}
# Paper suggests 0.9997 momentum, for TensorFlow. Equivalent PyTorch momentum is
# 1.0 - tensorflow.
_BN_MOMENTUM = 1 - 0.9997
class _InvertedResidual(nn.Module):
def __init__(
self,
in_ch: int,
out_ch: int,
kernel_size: int,
stride: int,
expansion_factor: int,
bn_momentum: float = 0.1
) -> None:
super(_InvertedResidual, self).__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: Tensor) -> Tensor:
if self.apply_residual:
return self.layers(input) + input
else:
return self.layers(input)
def _stack(in_ch: int, out_ch: int, kernel_size: int, stride: int, exp_factor: int, repeats: int,
bn_momentum: float) -> nn.Sequential:
""" Creates a stack of inverted residuals. """
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: float, divisor: int, round_up_bias: float = 0.9) -> int:
""" 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 _get_depths(alpha: float) -> List[int]:
""" Scales tensor depths as in reference MobileNet code, prefers rouding up
rather than down. """
depths = [32, 16, 24, 40, 80, 96, 192, 320]
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. This
implements the B1 variant of the model.
>>> model = MNASNet(1.0, num_classes=1000)
>>> x = torch.rand(1, 3, 224, 224)
>>> y = model(x)
>>> y.dim()
2
>>> y.nelement()
1000
"""
# Version 2 adds depth scaling in the initial stages of the network.
_version = 2
def __init__(
self,
alpha: float,
num_classes: int = 1000,
dropout: float = 0.2
) -> None:
super(MNASNet, self).__init__()
assert alpha > 0.0
self.alpha = alpha
self.num_classes = num_classes
depths = _get_depths(alpha)
layers = [
# First layer: regular conv.
nn.Conv2d(3, depths[0], 3, padding=1, stride=2, bias=False),
nn.BatchNorm2d(depths[0], momentum=_BN_MOMENTUM),
nn.ReLU(inplace=True),
# Depthwise separable, no skip.
nn.Conv2d(depths[0], depths[0], 3, padding=1, stride=1,
groups=depths[0], bias=False),
nn.BatchNorm2d(depths[0], momentum=_BN_MOMENTUM),
nn.ReLU(inplace=True),
nn.Conv2d(depths[0], depths[1], 1, padding=0, stride=1, bias=False),
nn.BatchNorm2d(depths[1], momentum=_BN_MOMENTUM),
# MNASNet blocks: stacks of inverted residuals.
_stack(depths[1], depths[2], 3, 2, 3, 3, _BN_MOMENTUM),
_stack(depths[2], depths[3], 5, 2, 3, 3, _BN_MOMENTUM),
_stack(depths[3], depths[4], 5, 2, 6, 3, _BN_MOMENTUM),
_stack(depths[4], depths[5], 3, 1, 6, 2, _BN_MOMENTUM),
_stack(depths[5], depths[6], 5, 2, 6, 4, _BN_MOMENTUM),
_stack(depths[6], depths[7], 3, 1, 6, 1, _BN_MOMENTUM),
# Final mapping to classifier input.
nn.Conv2d(depths[7], 1280, 1, padding=0, stride=1, bias=False),
nn.BatchNorm2d(1280, momentum=_BN_MOMENTUM),
nn.ReLU(inplace=True),
]
self.layers = nn.Sequential(*layers)
self.classifier = nn.Sequential(nn.Dropout(p=dropout, inplace=True),
nn.Linear(1280, num_classes))
self._initialize_weights()
def forward(self, x: Tensor) -> Tensor:
x = self.layers(x)
# Equivalent to global avgpool and removing H and W dimensions.
x = x.mean([2, 3])
return self.classifier(x)
def _initialize_weights(self) -> None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode="fan_out",
nonlinearity="relu")
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.BatchNorm2d):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.kaiming_uniform_(m.weight, mode="fan_out",
nonlinearity="sigmoid")
nn.init.zeros_(m.bias)
def _load_from_state_dict(self, state_dict: Dict, prefix: str, local_metadata: Dict, strict: bool,
missing_keys: List[str], unexpected_keys: List[str], error_msgs: List[str]) -> None:
version = local_metadata.get("version", None)
assert version in [1, 2]
if version == 1 and not self.alpha == 1.0:
# In the initial version of the model (v1), stem was fixed-size.
# All other layer configurations were the same. This will patch
# the model so that it's identical to v1. Model with alpha 1.0 is
# unaffected.
depths = _get_depths(self.alpha)
v1_stem = [
nn.Conv2d(3, 32, 3, padding=1, stride=2, bias=False),
nn.BatchNorm2d(32, momentum=_BN_MOMENTUM),
nn.ReLU(inplace=True),
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),
_stack(16, depths[2], 3, 2, 3, 3, _BN_MOMENTUM),
]
for idx, layer in enumerate(v1_stem):
self.layers[idx] = layer
# The model is now identical to v1, and must be saved as such.
self._version = 1
warnings.warn(
"A new version of MNASNet model has been implemented. "
"Your checkpoint was saved using the previous version. "
"This checkpoint will load and work as before, but "
"you may want to upgrade by training a newer model or "
"transfer learning from an updated ImageNet checkpoint.",
UserWarning)
super(MNASNet, self)._load_from_state_dict(
state_dict, prefix, local_metadata, strict, missing_keys,
unexpected_keys, error_msgs)
def _load_pretrained(model_name: str, model: nn.Module, progress: bool) -> None:
if model_name not in _MODEL_URLS or _MODEL_URLS[model_name] is None:
raise ValueError(
"No checkpoint is available for model type {}".format(model_name))
checkpoint_url = _MODEL_URLS[model_name]
model.load_state_dict(
load_state_dict_from_url(checkpoint_url, progress=progress))
[docs]def mnasnet0_5(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MNASNet:
r"""MNASNet with depth multiplier of 0.5 from
`"MnasNet: Platform-Aware Neural Architecture Search for Mobile"
<https://arxiv.org/pdf/1807.11626.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
model = MNASNet(0.5, **kwargs)
if pretrained:
_load_pretrained("mnasnet0_5", model, progress)
return model
[docs]def mnasnet0_75(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MNASNet:
r"""MNASNet with depth multiplier of 0.75 from
`"MnasNet: Platform-Aware Neural Architecture Search for Mobile"
<https://arxiv.org/pdf/1807.11626.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
model = MNASNet(0.75, **kwargs)
if pretrained:
_load_pretrained("mnasnet0_75", model, progress)
return model
[docs]def mnasnet1_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MNASNet:
r"""MNASNet with depth multiplier of 1.0 from
`"MnasNet: Platform-Aware Neural Architecture Search for Mobile"
<https://arxiv.org/pdf/1807.11626.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
model = MNASNet(1.0, **kwargs)
if pretrained:
_load_pretrained("mnasnet1_0", model, progress)
return model
[docs]def mnasnet1_3(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MNASNet:
r"""MNASNet with depth multiplier of 1.3 from
`"MnasNet: Platform-Aware Neural Architecture Search for Mobile"
<https://arxiv.org/pdf/1807.11626.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
model = MNASNet(1.3, **kwargs)
if pretrained:
_load_pretrained("mnasnet1_3", model, progress)
return model
