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Unverified Commit 4cbe7140 authored by Vasilis Vryniotis's avatar Vasilis Vryniotis Committed by GitHub
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Refactoring and moving MobileNetV2 to make it reusable (#3177) 

* Moving mobilenet.py to mobilenetv2.py

* Adding mobilenet.py for BC.

* Extending ConvBNReLU for reuse.

* Reduce import scope on mobilenet to only the public and versioned classes and methods.
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torchvision/models/mobilenet.py
View file @ 4cbe7140
from torch import nn from .mobilenetv2 import MobileNetV2, mobilenet_v2
from torch import Tensor
from .utils import load_state_dict_from_url
from typing import Callable, Any, Optional, List
__all__ = ['MobileNetV2', 'mobilenet_v2']
model_urls = {
'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
}
def _make_divisible(v: float, divisor: int, min_value: Optional[int] = None) -> int:
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
:param v:
:param divisor:
:param min_value:
:return:
"""
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v
class ConvBNReLU(nn.Sequential):
def __init__(
self,
in_planes: int,
out_planes: int,
kernel_size: int = 3,
stride: int = 1,
groups: int = 1,
norm_layer: Optional[Callable[..., nn.Module]] = None
) -> None:
padding = (kernel_size - 1) // 2
if norm_layer is None:
norm_layer = nn.BatchNorm2d
super(ConvBNReLU, self).__init__(
nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
norm_layer(out_planes),
nn.ReLU6(inplace=True)
)
class InvertedResidual(nn.Module):
def __init__(
self,
inp: int,
oup: int,
stride: int,
expand_ratio: int,
norm_layer: Optional[Callable[..., nn.Module]] = None
) -> None:
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2]
if norm_layer is None:
norm_layer = nn.BatchNorm2d
hidden_dim = int(round(inp * expand_ratio))
self.use_res_connect = self.stride == 1 and inp == oup
layers: List[nn.Module] = []
if expand_ratio != 1:
# pw
layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer))
layers.extend([
# dw
ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim, norm_layer=norm_layer),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
norm_layer(oup),
])
self.conv = nn.Sequential(*layers)
def forward(self, x: Tensor) -> Tensor:
if self.use_res_connect:
return x + self.conv(x)
else:
return self.conv(x)
class MobileNetV2(nn.Module):
def __init__(
self,
num_classes: int = 1000,
width_mult: float = 1.0,
inverted_residual_setting: Optional[List[List[int]]] = None,
round_nearest: int = 8,
block: Optional[Callable[..., nn.Module]] = None,
norm_layer: Optional[Callable[..., nn.Module]] = None
) -> None:
"""
MobileNet V2 main class
Args:
num_classes (int): Number of classes
width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
inverted_residual_setting: Network structure
round_nearest (int): Round the number of channels in each layer to be a multiple of this number
Set to 1 to turn off rounding
block: Module specifying inverted residual building block for mobilenet
norm_layer: Module specifying the normalization layer to use
"""
super(MobileNetV2, self).__init__()
if block is None:
block = InvertedResidual
if norm_layer is None:
norm_layer = nn.BatchNorm2d
input_channel = 32
last_channel = 1280
if inverted_residual_setting is None:
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],
]
# only check the first element, assuming user knows t,c,n,s are required
if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
raise ValueError("inverted_residual_setting should be non-empty "
"or a 4-element list, got {}".format(inverted_residual_setting))
# building first layer
input_channel = _make_divisible(input_channel * width_mult, round_nearest)
self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
features: List[nn.Module] = [ConvBNReLU(3, input_channel, stride=2, norm_layer=norm_layer)]
# building inverted residual blocks
for t, c, n, s in inverted_residual_setting:
output_channel = _make_divisible(c * width_mult, round_nearest)
for i in range(n):
stride = s if i == 0 else 1
features.append(block(input_channel, output_channel, stride, expand_ratio=t, norm_layer=norm_layer))
input_channel = output_channel
# building last several layers
features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1, norm_layer=norm_layer))
# make it nn.Sequential
self.features = nn.Sequential(*features)
# building classifier
self.classifier = nn.Sequential(
nn.Dropout(0.2),
nn.Linear(self.last_channel, num_classes),
)
# weight initialization
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.zeros_(m.bias)
def _forward_impl(self, x: Tensor) -> Tensor:
# This exists since TorchScript doesn't support inheritance, so the superclass method
# (this one) needs to have a name other than `forward` that can be accessed in a subclass
x = self.features(x)
# Cannot use "squeeze" as batch-size can be 1 => must use reshape with x.shape[0]
x = nn.functional.adaptive_avg_pool2d(x, (1, 1)).reshape(x.shape[0], -1)
x = self.classifier(x)
return x
def forward(self, x: Tensor) -> Tensor:
return self._forward_impl(x)
def mobilenet_v2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MobileNetV2:
"""
Constructs a MobileNetV2 architecture from
`"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.
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 = MobileNetV2(**kwargs)
if pretrained:
state_dict = load_state_dict_from_url(model_urls['mobilenet_v2'],
progress=progress)
model.load_state_dict(state_dict)
return model
torchvision/models/mobilenetv2.py 0 → 100644
View file @ 4cbe7140
from torch import nn
from torch import Tensor
from .utils import load_state_dict_from_url
from typing import Callable, Any, Optional, List
__all__ = ['MobileNetV2', 'mobilenet_v2']
model_urls = {
'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
}
def _make_divisible(v: float, divisor: int, min_value: Optional[int] = None) -> int:
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
:param v:
:param divisor:
:param min_value:
:return:
"""
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v
class ConvBNActivation(nn.Sequential):
def __init__(
self,
in_planes: int,
out_planes: int,
kernel_size: int = 3,
stride: int = 1,
groups: int = 1,
norm_layer: Optional[Callable[..., nn.Module]] = None,
activation_layer: Optional[Callable[..., nn.Module]] = None,
) -> None:
padding = (kernel_size - 1) // 2
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if activation_layer is None:
activation_layer = nn.ReLU6
super(ConvBNReLU, self).__init__(
nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
norm_layer(out_planes),
activation_layer(inplace=True)
)
# necessary for backwards compatibility
ConvBNReLU = ConvBNActivation
class InvertedResidual(nn.Module):
def __init__(
self,
inp: int,
oup: int,
stride: int,
expand_ratio: int,
norm_layer: Optional[Callable[..., nn.Module]] = None
) -> None:
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2]
if norm_layer is None:
norm_layer = nn.BatchNorm2d
hidden_dim = int(round(inp * expand_ratio))
self.use_res_connect = self.stride == 1 and inp == oup
layers: List[nn.Module] = []
if expand_ratio != 1:
# pw
layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer))
layers.extend([
# dw
ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim, norm_layer=norm_layer),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
norm_layer(oup),
])
self.conv = nn.Sequential(*layers)
def forward(self, x: Tensor) -> Tensor:
if self.use_res_connect:
return x + self.conv(x)
else:
return self.conv(x)
class MobileNetV2(nn.Module):
def __init__(
self,
num_classes: int = 1000,
width_mult: float = 1.0,
inverted_residual_setting: Optional[List[List[int]]] = None,
round_nearest: int = 8,
block: Optional[Callable[..., nn.Module]] = None,
norm_layer: Optional[Callable[..., nn.Module]] = None
) -> None:
"""
MobileNet V2 main class
Args:
num_classes (int): Number of classes
width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
inverted_residual_setting: Network structure
round_nearest (int): Round the number of channels in each layer to be a multiple of this number
Set to 1 to turn off rounding
block: Module specifying inverted residual building block for mobilenet
norm_layer: Module specifying the normalization layer to use
"""
super(MobileNetV2, self).__init__()
if block is None:
block = InvertedResidual
if norm_layer is None:
norm_layer = nn.BatchNorm2d
input_channel = 32
last_channel = 1280
if inverted_residual_setting is None:
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],
]
# only check the first element, assuming user knows t,c,n,s are required
if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
raise ValueError("inverted_residual_setting should be non-empty "
"or a 4-element list, got {}".format(inverted_residual_setting))
# building first layer
input_channel = _make_divisible(input_channel * width_mult, round_nearest)
self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
features: List[nn.Module] = [ConvBNReLU(3, input_channel, stride=2, norm_layer=norm_layer)]
# building inverted residual blocks
for t, c, n, s in inverted_residual_setting:
output_channel = _make_divisible(c * width_mult, round_nearest)
for i in range(n):
stride = s if i == 0 else 1
features.append(block(input_channel, output_channel, stride, expand_ratio=t, norm_layer=norm_layer))
input_channel = output_channel
# building last several layers
features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1, norm_layer=norm_layer))
# make it nn.Sequential
self.features = nn.Sequential(*features)
# building classifier
self.classifier = nn.Sequential(
nn.Dropout(0.2),
nn.Linear(self.last_channel, num_classes),
)
# weight initialization
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.zeros_(m.bias)
def _forward_impl(self, x: Tensor) -> Tensor:
# This exists since TorchScript doesn't support inheritance, so the superclass method
# (this one) needs to have a name other than `forward` that can be accessed in a subclass
x = self.features(x)
# Cannot use "squeeze" as batch-size can be 1 => must use reshape with x.shape[0]
x = nn.functional.adaptive_avg_pool2d(x, (1, 1)).reshape(x.shape[0], -1)
x = self.classifier(x)
return x
def forward(self, x: Tensor) -> Tensor:
return self._forward_impl(x)
def mobilenet_v2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MobileNetV2:
"""
Constructs a MobileNetV2 architecture from
`"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.
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 = MobileNetV2(**kwargs)
if pretrained:
state_dict = load_state_dict_from_url(model_urls['mobilenet_v2'],
progress=progress)
model.load_state_dict(state_dict)
return model
torchvision/models/quantization/mobilenet.py
View file @ 4cbe7140
from torch import nn from .mobilenetv2 import MobileNetV2, mobilenet_v2
from torchvision.models.utils import load_state_dict_from_url
from torchvision.models.mobilenet import InvertedResidual, ConvBNReLU, MobileNetV2, model_urls
from torch.quantization import QuantStub, DeQuantStub, fuse_modules
from .utils import _replace_relu, quantize_model
__all__ = ['QuantizableMobileNetV2', 'mobilenet_v2']
quant_model_urls = {
'mobilenet_v2_qnnpack':
'https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth'
}
class QuantizableInvertedResidual(InvertedResidual):
def __init__(self, *args, **kwargs):
super(QuantizableInvertedResidual, self).__init__(*args, **kwargs)
self.skip_add = nn.quantized.FloatFunctional()
def forward(self, x):
if self.use_res_connect:
return self.skip_add.add(x, self.conv(x))
else:
return self.conv(x)
def fuse_model(self):
for idx in range(len(self.conv)):
if type(self.conv[idx]) == nn.Conv2d:
fuse_modules(self.conv, [str(idx), str(idx + 1)], inplace=True)
class QuantizableMobileNetV2(MobileNetV2):
def __init__(self, *args, **kwargs):
"""
MobileNet V2 main class
Args:
Inherits args from floating point MobileNetV2
"""
super(QuantizableMobileNetV2, self).__init__(*args, **kwargs)
self.quant = QuantStub()
self.dequant = DeQuantStub()
def forward(self, x):
x = self.quant(x)
x = self._forward_impl(x)
x = self.dequant(x)
return x
def fuse_model(self):
for m in self.modules():
if type(m) == ConvBNReLU:
fuse_modules(m, ['0', '1', '2'], inplace=True)
if type(m) == QuantizableInvertedResidual:
m.fuse_model()
def mobilenet_v2(pretrained=False, progress=True, quantize=False, **kwargs):
"""
Constructs a MobileNetV2 architecture from
`"MobileNetV2: Inverted Residuals and Linear Bottlenecks"
<https://arxiv.org/abs/1801.04381>`_.
Note that quantize = True returns a quantized model with 8 bit
weights. Quantized models only support inference and run on CPUs.
GPU inference is not yet supported
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
quantize(bool): If True, returns a quantized model, else returns a float model
"""
model = QuantizableMobileNetV2(block=QuantizableInvertedResidual, **kwargs)
_replace_relu(model)
if quantize:
# TODO use pretrained as a string to specify the backend
backend = 'qnnpack'
quantize_model(model, backend)
else:
assert pretrained in [True, False]
if pretrained:
if quantize:
model_url = quant_model_urls['mobilenet_v2_' + backend]
else:
model_url = model_urls['mobilenet_v2']
state_dict = load_state_dict_from_url(model_url,
progress=progress)
model.load_state_dict(state_dict)
return model
torchvision/models/quantization/mobilenetv2.py 0 → 100644
View file @ 4cbe7140
from torch import nn
from torchvision.models.utils import load_state_dict_from_url
from torchvision.models.mobilenetv2 import InvertedResidual, ConvBNReLU, MobileNetV2, model_urls
from torch.quantization import QuantStub, DeQuantStub, fuse_modules
from .utils import _replace_relu, quantize_model
__all__ = ['QuantizableMobileNetV2', 'mobilenet_v2']
quant_model_urls = {
'mobilenet_v2_qnnpack':
'https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth'
}
class QuantizableInvertedResidual(InvertedResidual):
def __init__(self, *args, **kwargs):
super(QuantizableInvertedResidual, self).__init__(*args, **kwargs)
self.skip_add = nn.quantized.FloatFunctional()
def forward(self, x):
if self.use_res_connect:
return self.skip_add.add(x, self.conv(x))
else:
return self.conv(x)
def fuse_model(self):
for idx in range(len(self.conv)):
if type(self.conv[idx]) == nn.Conv2d:
fuse_modules(self.conv, [str(idx), str(idx + 1)], inplace=True)
class QuantizableMobileNetV2(MobileNetV2):
def __init__(self, *args, **kwargs):
"""
MobileNet V2 main class
Args:
Inherits args from floating point MobileNetV2
"""
super(QuantizableMobileNetV2, self).__init__(*args, **kwargs)
self.quant = QuantStub()
self.dequant = DeQuantStub()
def forward(self, x):
x = self.quant(x)
x = self._forward_impl(x)
x = self.dequant(x)
return x
def fuse_model(self):
for m in self.modules():
if type(m) == ConvBNReLU:
fuse_modules(m, ['0', '1', '2'], inplace=True)
if type(m) == QuantizableInvertedResidual:
m.fuse_model()
def mobilenet_v2(pretrained=False, progress=True, quantize=False, **kwargs):
"""
Constructs a MobileNetV2 architecture from
`"MobileNetV2: Inverted Residuals and Linear Bottlenecks"
<https://arxiv.org/abs/1801.04381>`_.
Note that quantize = True returns a quantized model with 8 bit
weights. Quantized models only support inference and run on CPUs.
GPU inference is not yet supported
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
quantize(bool): If True, returns a quantized model, else returns a float model
"""
model = QuantizableMobileNetV2(block=QuantizableInvertedResidual, **kwargs)
_replace_relu(model)
if quantize:
# TODO use pretrained as a string to specify the backend
backend = 'qnnpack'
quantize_model(model, backend)
else:
assert pretrained in [True, False]
if pretrained:
if quantize:
model_url = quant_model_urls['mobilenet_v2_' + backend]
else:
model_url = model_urls['mobilenet_v2']
state_dict = load_state_dict_from_url(model_url,
progress=progress)
model.load_state_dict(state_dict)
return model
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