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Introduced the use_batch_norm variable, removed old code

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Hoel Bagard 2021-01-21 16:10:10 +09:00
commit 7a6f5821bd
1 changed files with 16 additions and 25 deletions
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41
layers.py
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@ -7,13 +7,6 @@ import torch.nn.functional as F
from .utils.logger import DummyLogger from .utils.logger import DummyLogger
class LayerInfo():
def __init__(self):
self.memory = 0.0
self.ops = 0.0
self.output = 0.0
class Layer(nn.Module): class Layer(nn.Module):
# Default layer arguments # Default layer arguments
ACTIVATION = F.leaky_relu ACTIVATION = F.leaky_relu
@ -27,14 +20,12 @@ class Layer(nn.Module):
VERBOSE = 0 VERBOSE = 0
LOGGER = DummyLogger() LOGGER = DummyLogger()
def __init__(self, activation, batch_norm): def __init__(self, activation):
super().__init__() super().__init__()
self.name = 'Layer' self.name = 'Layer'
self.info = LayerInfo()
# Preload default # Preload default
self.activation = Layer.ACTIVATION if activation == 0 else activation self.activation = Layer.ACTIVATION if activation == 0 else activation
self.batch_norm = Layer.BATCH_NORM if batch_norm is None else batch_norm
def forward(self, input_data: torch.Tensor) -> torch.Tensor: def forward(self, input_data: torch.Tensor) -> torch.Tensor:
output = input_data output = input_data
@ -46,14 +37,14 @@ class Layer(nn.Module):
class Linear(Layer): class Linear(Layer):
def __init__(self, in_channels: int, out_channels: int, activation=0, batch_norm=None, **kwargs): def __init__(self, in_channels: int, out_channels: int, activation=0, use_batch_norm: bool = False, **kwargs):
super().__init__(activation, batch_norm) super().__init__(activation)
self.fc = nn.Linear(in_channels, out_channels, **kwargs) self.fc = nn.Linear(in_channels, out_channels, **kwargs)
self.batch_norm = nn.BatchNorm1d( self.batch_norm = nn.BatchNorm1d(
out_channels, out_channels,
momentum=Layer.BATCH_NORM_MOMENTUM, momentum=Layer.BATCH_NORM_MOMENTUM,
track_running_stats=Layer.BATCH_NORM_TRAINING) if self.batch_norm else None track_running_stats=Layer.BATCH_NORM_TRAINING) if use_batch_norm else None
def forward(self, input_data: torch.Tensor) -> torch.Tensor: def forward(self, input_data: torch.Tensor) -> torch.Tensor:
return super().forward(self.fc(input_data)) return super().forward(self.fc(input_data))
@ -61,15 +52,15 @@ class Linear(Layer):
class Conv1d(Layer): class Conv1d(Layer):
def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3, def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3,
stride: Union[int, Tuple[int, int]] = 1, activation=0, batch_norm=None, **kwargs): stride: Union[int, Tuple[int, int]] = 1, activation=0, use_batch_norm: bool = False, **kwargs):
super().__init__(activation, batch_norm) super().__init__(activation)
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, stride=stride, self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, stride=stride,
bias=not self.batch_norm, **kwargs) bias=not self.batch_norm, **kwargs)
self.batch_norm = nn.BatchNorm1d( self.batch_norm = nn.BatchNorm1d(
out_channels, out_channels,
momentum=Layer.BATCH_NORM_MOMENTUM, momentum=Layer.BATCH_NORM_MOMENTUM,
track_running_stats=Layer.BATCH_NORM_TRAINING) if self.batch_norm else None track_running_stats=Layer.BATCH_NORM_TRAINING) if use_batch_norm else None
def forward(self, input_data: torch.Tensor) -> torch.Tensor: def forward(self, input_data: torch.Tensor) -> torch.Tensor:
return super().forward(self.conv(input_data)) return super().forward(self.conv(input_data))
@ -77,15 +68,15 @@ class Conv1d(Layer):
class Conv2d(Layer): class Conv2d(Layer):
def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3, def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3,
stride: Union[int, Tuple[int, int]] = 1, activation=0, batch_norm=None, **kwargs): stride: Union[int, Tuple[int, int]] = 1, activation=0, use_batch_norm: bool = False, **kwargs):
super().__init__(activation, batch_norm) super().__init__(activation)
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride,
bias=not self.batch_norm, **kwargs) bias=not self.batch_norm, **kwargs)
self.batch_norm = nn.BatchNorm2d( self.batch_norm = nn.BatchNorm2d(
out_channels, out_channels,
momentum=Layer.BATCH_NORM_MOMENTUM, momentum=Layer.BATCH_NORM_MOMENTUM,
track_running_stats=not Layer.BATCH_NORM_TRAINING) if self.batch_norm else None track_running_stats=not Layer.BATCH_NORM_TRAINING) if use_batch_norm else None
def forward(self, input_data: torch.Tensor) -> torch.Tensor: def forward(self, input_data: torch.Tensor) -> torch.Tensor:
return super().forward(self.conv(input_data)) return super().forward(self.conv(input_data))
@ -93,15 +84,15 @@ class Conv2d(Layer):
class Conv3d(Layer): class Conv3d(Layer):
def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3, def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3,
stride: Union[int, Tuple[int, int]] = 1, activation=0, batch_norm=None, **kwargs): stride: Union[int, Tuple[int, int]] = 1, activation=0, use_batch_norm: bool = False, **kwargs):
super().__init__(activation, batch_norm) super().__init__(activation)
self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=stride, self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=stride,
bias=not self.batch_norm, **kwargs) bias=not self.batch_norm, **kwargs)
self.batch_norm = nn.BatchNorm3d( self.batch_norm = nn.BatchNorm3d(
out_channels, out_channels,
momentum=Layer.BATCH_NORM_MOMENTUM, momentum=Layer.BATCH_NORM_MOMENTUM,
track_running_stats=Layer.BATCH_NORM_TRAINING) if self.batch_norm else None track_running_stats=Layer.BATCH_NORM_TRAINING) if use_batch_norm else None
def forward(self, input_data: torch.Tensor) -> torch.Tensor: def forward(self, input_data: torch.Tensor) -> torch.Tensor:
return super().forward(self.conv(input_data)) return super().forward(self.conv(input_data))
@ -109,8 +100,8 @@ class Conv3d(Layer):
class Deconv2d(Layer): class Deconv2d(Layer):
def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3, def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3,
stride: Union[int, Tuple[int, int]] = 1, activation=0, batch_norm=None, **kwargs): stride: Union[int, Tuple[int, int]] = 1, activation=0, use_batch_norm: bool = False, **kwargs):
super().__init__(activation, batch_norm) super().__init__(activation)
self.deconv = nn.ConvTranspose2d( self.deconv = nn.ConvTranspose2d(
in_channels, out_channels, kernel_size, stride=stride, in_channels, out_channels, kernel_size, stride=stride,
@ -118,7 +109,7 @@ class Deconv2d(Layer):
self.batch_norm = nn.BatchNorm2d( self.batch_norm = nn.BatchNorm2d(
out_channels, out_channels,
momentum=Layer.BATCH_NORM_MOMENTUM, momentum=Layer.BATCH_NORM_MOMENTUM,
track_running_stats=not Layer.BATCH_NORM_TRAINING) if self.batch_norm else None track_running_stats=not Layer.BATCH_NORM_TRAINING) if use_batch_norm else None
def forward(self, input_data: torch.Tensor) -> torch.Tensor: def forward(self, input_data: torch.Tensor) -> torch.Tensor:
return super().forward(self.deconv(input_data)) return super().forward(self.deconv(input_data))