def conv3x3(in_planes, out_planes, stride=1, args=None):
"""3x3 convolution with padding"""
# return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
# padding=1, bias=False)
return Conv(kernel_sizes=[3], in_channels=in_planes,
out_channels=[out_planes], strides=[stride],
padding=[1], args=args, is_bias=False).get_conv()
def conv7x7(in_planes, out_planes, stride=2, padding=3, args=None):
return Conv(kernel_sizes=[7],
in_channels=in_planes,
out_channels=[out_planes],
strides=[stride],
padding=[padding],
args=args,
is_bias=False).get_conv()
def conv1x1(in_planes, out_planes, args, stride=1):
"""1x1 convolution"""
# return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
# bias=False)
# It is rather unnecessary to use fft convolution for kernels of size 1x1.
return Conv(kernel_sizes=[1], in_channels=in_planes,
out_channels=[out_planes], strides=[stride],
padding=[0], args=args, is_bias=False).get_conv()
def conv3x3(in_planes, out_planes, compress_rate = args.compress_rate,
stride=1, padding=1, args=args):
"""3x3 convolution with padding"""
# return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
# padding=1, bias=False)
args.compress_rate = compress_rate
return Conv(kernel_sizes=[3], in_channels=in_planes,
out_channels=[out_planes], strides=[stride],
padding=[padding], args=args, is_bias=False).get_conv()
def __init__(self,
args,
kernel_sizes=[8, 5, 3],
out_channels=[128, 256, 128],
strides=[1, 1, 1]):
"""
Create the FCNN model in PyTorch.
:param args: the general arguments (conv type, debug mode, etc).
:param dtype: global - the type of pytorch data/weights.
:param kernel_sizes: the sizes of the kernels in each conv layer.
:param out_channels: the number of filters for each conv layer.
:param strides: the strides for the convolutions.
"""
super(FCNNPytorch, self).__init__()
# input_size: the length (width) of the time series.
self.input_size = args.input_size
# num_classes: number of output classes.
self.num_classes = args.num_classes
# in_channels: number of channels in the input data.
self.in_channels = args.in_channels
self.dtype = args.dtype
self.kernel_sizes = kernel_sizes
self.out_channels = out_channels
self.strides = strides
self.conv_type = args.conv_type
self.is_debug = args.is_debug
self.preserve_energy = args.preserve_energy
self.relu = nn.ReLU(inplace=True)
# For the "same" mode for the convolution, pad the input.
conv_pads = [kernel_size - 1 for kernel_size in kernel_sizes]
conv = Conv(kernel_sizes=kernel_sizes,
in_channels=self.in_channels,
out_channels=out_channels,
strides=strides,
padding=conv_pads,
args=args)
index = 0
self.conv0 = conv.get_conv(param_index=index)
self.bn0 = nn.BatchNorm1d(num_features=out_channels[index])
index = 1
self.conv1 = conv.get_conv(param_index=index)
self.bn1 = nn.BatchNorm1d(num_features=out_channels[index])
index = 2
self.conv2 = conv.get_conv(param_index=index)
self.bn2 = nn.BatchNorm1d(num_features=out_channels[index])
self.lin = nn.Linear(out_channels[index], self.num_classes)
def __init__(self,
input_size,
args,
num_classes=10,
in_channels=1,
kernel_sizes=[5, 5],
out_channels=[10, 20],
strides=[1, 1],
flat_size=320):
"""
:param input_size:
:param args: the general arguments for the program, e.g. conv type.
:param num_classes:
:param in_channels:
:param dtype:
:param kernel_sizes:
:param out_channels:
:param strides:
:param batch_size:
:param flat_size: the size of the flat vector after the conv layers.
"""
super(LeNet, self).__init__()
self.input_size = input_size
self.args = args
if out_channels is None:
self.out_channels = [10, 20]
else:
self.out_channels = out_channels
if flat_size is None:
self.flat_size = 320 # for MNIST dataset
else:
self.flat_size = flat_size
self.relu = nn.ReLU(inplace=True)
# For the "same" mode for the convolution, pad the input.
conv_pads = [0 for _ in kernel_sizes]
conv = Conv(kernel_sizes=kernel_sizes,
in_channels=in_channels,
out_channels=out_channels,
strides=strides,
padding=conv_pads,
args=args)
# self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv1 = conv.get_conv(param_index=0)
# self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2 = conv.get_conv(param_index=1)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(flat_size, 50)
self.fc2 = nn.Linear(50, num_classes)
def get_conv(args,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
bias=True):
return Conv(kernel_sizes=[kernel_size],
in_channels=in_channels,
out_channels=[out_channels],
strides=[stride],
padding=[padding],
args=args,
is_bias=bias).get_conv()