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model.py
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import torch.nn as F
import torch
class _ResBlock1d(F.Module):
def __init__(self, in_channels:int, out_channels:int, kernel_size:int, stride:int=2, padding:int=None, downsample:bool=False):
super().__init__()
if padding is None:
padding = int(kernel_size/2)
if downsample:
self.conv1 = F.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
self.shortcut = F.Sequential(
F.Conv1d(in_channels, out_channels, kernel_size=1, stride=stride),
F.BatchNorm1d(out_channels)
)
else:
self.conv1 = F.Conv1d(in_channels, out_channels, kernel_size=kernel_size, padding=padding)
self.shortcut = F.Sequential()
self.conv2 = F.Conv1d(out_channels, out_channels, kernel_size=kernel_size, padding=padding)
self.bn1 = F.BatchNorm1d(out_channels)
self.bn2 = F.BatchNorm1d(out_channels)
def forward(self, input:torch.Tensor):
shortcut = self.shortcut(input)
input = F.ReLU()(self.bn1(self.conv1(input)))
input = F.ReLU()(self.bn2(self.conv2(input)))
input = input + shortcut
return F.ReLU()(input)
class ResNet1d18(F.Module):
def __init__(self, in_channels:int, resblock:F.Module=_ResBlock1d, kernel_size:int=3, outputs:int=1000, last_activation:str=None):
super().__init__()
self.layer0 = F.Sequential(
F.Conv1d(in_channels, 32, kernel_size=15, stride=2, padding=7),
F.MaxPool1d(kernel_size=3, stride=2, padding=1),
F.BatchNorm1d(32),
F.ReLU()
)
self.layer2 = F.Sequential(
resblock(32, 64, kernel_size=kernel_size, downsample=True),
resblock(64, 64, kernel_size=kernel_size, downsample=False)
)
self.layer3 = F.Sequential(
resblock(64, 128, kernel_size=kernel_size, downsample=True),
resblock(128, 128, kernel_size=kernel_size, downsample=False)
)
self.layer4 = F.Sequential(
resblock(128, 256, kernel_size=kernel_size, downsample=True),
resblock(256, 256, kernel_size=kernel_size, downsample=False)
)
self.dense1 = F.Linear(24*256, 512)
self.dense2 = F.Linear(512, outputs)
self.flatten = F.Flatten()
self.dropout = F.Dropout(0.2)
if last_activation == "Sigmoid":
self.last_activation = torch.nn.Sigmoid()
else:
self.last_activation = torch.nn.Identity()
def forward(self, input):
input = self.dropout(self.layer0(input))
input = self.dropout(self.layer2(input))
input = self.dropout(self.layer3(input))
input = self.dropout(self.layer4(input))
input = self.dropout(self.flatten(input))
input = F.ReLU()(self.dense1(input))
input = (self.dense2(input))
output = self.last_activation(input)
return output
class ResnetImgModel(F.Module):
def __init__(self):
super(ResnetImgModel, self).__init__()
self.layers = []
self.conv1 = F.Conv2d(1, 32, kernel_size=7, padding=1)
self.layers.append(self.conv1)
self.batch_norm1 = F.BatchNorm2d(32)
self.layers.append(self.batch_norm1)
self.conv2 = F.Conv2d(32, 32, kernel_size=3, padding=1)
self.layers.append(self.conv2)
self.batch_norm2 = F.BatchNorm2d(32)
self.layers.append(self.batch_norm2)
self.conv3 = F.Conv2d(32, 64, kernel_size=5, stride=2, padding=1)
self.layers.append(self.conv3)
self.batch_norm3 = F.BatchNorm2d(64)
self.layers.append(self.batch_norm3)
self.dense1 = F.Linear(64*8*8, 512)
self.layers.append(self.dense1)
self.dense2 = F.Linear(512, 1)
self.layers.append(self.dense2)
self.dropout = F.Dropout(0.5)
self.layers.append(self.dropout)
self.activation = F.ReLU()
self.layers.append(self.activation)
self.maxpool = F.MaxPool2d(3, stride=2)
self.layers.append(self.maxpool)
self.flatten = F.Flatten()
self.sigmoid = F.Sigmoid()
def forward(self, x):
x = self.maxpool(self.conv1(x))
x = self.dropout(self.activation(self.batch_norm1(x)))
x = self.dropout(self.activation(self.activation(self.batch_norm2(self.conv2(x))) + x))
x = self.dropout(self.activation(self.batch_norm3(self.conv3(x))))
x = self.flatten(x)
x = self.activation(self.dense1(x))
x = self.sigmoid(self.dense2(x))
return x
def save_txt(self, filename:str):
"""Save the layers in a txt
Args:
filename (str): path to the txt file
"""
with open(filename, 'w') as f:
for layer in self.layers:
f.write(str(layer._get_name) + "\n")
f.close()
class SimpleSignalModel(F.Module):
def __init__(self, last_activation:str=None):
super(SimpleSignalModel, self).__init__()
self.layers = []
self.conv1 = F.Conv1d(1, 64, kernel_size=15, padding=7)
self.layers.append(self.conv1)
self.batch_norm1 = F.BatchNorm1d(64)
self.layers.append(self.batch_norm1)
self.conv2 = F.Conv1d(64, 64, kernel_size=7, padding=3)
self.layers.append(self.conv2)
self.batch_norm2 = F.BatchNorm1d(64)
self.layers.append(self.batch_norm2)
self.conv3 = F.Conv1d(64, 64, kernel_size=7, padding=3)
self.layers.append(self.conv3)
self.batch_norm3 = F.BatchNorm1d(64)
self.layers.append(self.batch_norm3)
self.conv4 = F.Conv1d(64, 64, kernel_size=7, padding=3)
self.layers.append(self.conv3)
self.batch_norm4 = F.BatchNorm1d(64)
self.layers.append(self.batch_norm4)
self.conv5 = F.Conv1d(64, 64, kernel_size=7, padding=3)
self.layers.append(self.conv3)
self.batch_norm5 = F.BatchNorm1d(64)
self.layers.append(self.batch_norm4)
self.dense1 = F.Linear(12*64, 512)
self.layers.append(self.dense1)
self.dense2 = F.Linear(512, 1)
self.layers.append(self.dense2)
self.dropout = F.Dropout(0.1)
self.layers.append(self.dropout)
self.activation = F.ReLU()
self.layers.append(self.activation)
self.maxpool = F.MaxPool1d(15, stride=4, padding=7)
self.layers.append(self.maxpool)
self.flatten = F.Flatten()
if last_activation == "Sigmoid":
self.last_activation = torch.nn.Sigmoid()
else:
self.last_activation = torch.nn.Identity()
def forward(self, x):
x = self.maxpool(self.conv1(x))
x = self.dropout(self.activation(self.batch_norm1(x)))
x = self.dropout(self.activation(self.batch_norm2(self.conv2(x))))
x = self.dropout(self.activation(self.activation(self.batch_norm3(self.conv3(x))) + x))
x = self.maxpool(x)
x = self.dropout(self.activation(self.activation(self.batch_norm4(self.conv4(x))) + x))
x = self.maxpool(x)
x = self.dropout(self.activation(self.activation(self.batch_norm5(self.conv5(x))) + x))
#x = self.maxpool(x)
x = self.flatten(x)
x = self.activation(self.dense1(x))
x = self.last_activation(self.dense2(x))
return x
def save_txt(self, filename:str):
"""Save the layers in a txt
Args:
filename (str): path to the txt file
"""
with open(filename, 'w') as f:
for layer in self.layers:
f.write(str(layer._get_name) + "\n")
f.close()
class SimpleMnistModel(F.Module):
def __init__(self):
super(SimpleMnistModel, self).__init__()
self.layers = []
self.conv1 = F.Conv2d(1, 8, kernel_size=5, stride=2)
self.layers.append(self.conv1)
self.batch_norm1 = F.BatchNorm2d(8)
self.layers.append(self.batch_norm1)
self.conv2 = F.Conv2d(8, 16, kernel_size=3)
self.layers.append(self.conv2)
self.batch_norm2 = F.BatchNorm2d(16)
self.layers.append(self.batch_norm2)
self.conv3 = F.Conv2d(16, 32, kernel_size=3)
self.layers.append(self.conv3)
self.batch_norm3 = F.BatchNorm2d(32)
self.layers.append(self.batch_norm3)
self.dense1 = F.Linear(32*64, 512)
self.layers.append(self.dense1)
self.dense2 = F.Linear(512, 1)
self.layers.append(self.dense2)
self.dropout = F.Dropout(0)
self.layers.append(self.dropout)
self.activation = F.ReLU()
self.layers.append(self.activation)
self.flatten = F.Flatten()
self.sigmoid = F.Sigmoid()
def forward(self, x):
x = self.conv1(x)
x = self.dropout(self.activation(self.batch_norm1(x)))
x = self.dropout(self.activation(self.batch_norm2(self.conv2(x))))
x = self.dropout(self.activation(self.batch_norm3(self.conv3(x))))
x = self.flatten(x)
x = self.activation(self.dense1(x))
x = self.sigmoid(self.dense2(x))
return x
def save_txt(self, filename:str):
"""Save the layers in a txt
Args:
filename (str): path to the txt file
"""
with open(filename, 'w') as f:
for layer in self.layers:
f.write(str(layer._get_name) + "\n")
f.close()
class SimpleCifarModel(F.Module):
def __init__(self):
super(SimpleCifarModel, self).__init__()
self.layers = []
self.conv1 = F.Conv2d(3, 64, kernel_size=7, padding=3, stride=2)
self.layers.append(self.conv1)
self.batch_norm1 = F.BatchNorm2d(64)
self.layers.append(self.batch_norm1)
self.conv2 = F.Conv2d(64, 128, kernel_size=5, padding=1, stride=2)
self.layers.append(self.conv2)
self.batch_norm2 = F.BatchNorm2d(128)
self.layers.append(self.batch_norm2)
self.dense1 = F.Linear(49*128, 2048)
self.layers.append(self.dense1)
self.dense2 = F.Linear(2048, 1)
self.layers.append(self.dense2)
self.dropout = F.Dropout(0.1)
self.layers.append(self.dropout)
self.activation = F.ReLU()
self.layers.append(self.activation)
self.flatten = F.Flatten()
self.sigmoid = F.Sigmoid()
def forward(self, x):
x = self.dropout(self.activation(self.batch_norm1(self.conv1(x))))
x = self.dropout(self.activation(self.batch_norm2(self.conv2(x))))
x = self.flatten(x)
x = self.activation(self.dense1(x))
x = self.sigmoid(self.dense2(x))
return x
def save_txt(self, filename:str):
"""Save the layers in a txt
Args:
filename (str): path to the txt file
"""
with open(filename, 'w') as f:
for layer in self.layers:
f.write(str(layer._get_name) + "\n")
f.close()
class ResnetCifarModel(F.Module):
def __init__(self):
super(ResnetCifarModel, self).__init__()
self.layers = []
self.conv1 = F.Conv2d(3, 32, kernel_size=7, padding=3)
self.layers.append(self.conv1)
self.batch_norm1 = F.BatchNorm2d(32)
self.layers.append(self.batch_norm1)
self.conv2 = F.Conv2d(32, 32, kernel_size=3, padding=1)
self.layers.append(self.conv2)
self.batch_norm2 = F.BatchNorm2d(32)
self.layers.append(self.batch_norm2)
self.conv3 = F.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)
self.layers.append(self.conv3)
self.batch_norm3 = F.BatchNorm2d(64)
self.layers.append(self.batch_norm3)
self.conv4 = F.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)
self.layers.append(self.conv4)
self.batch_norm4 = F.BatchNorm2d(64)
self.layers.append(self.batch_norm4)
self.dense1 = F.Linear(64*64, 1024)
self.layers.append(self.dense1)
self.dense2 = F.Linear(1024, 1)
self.layers.append(self.dense2)
self.dropout = F.Dropout(0.2)
self.layers.append(self.dropout)
self.activation = F.ReLU()
self.layers.append(self.activation)
self.maxpool = F.MaxPool2d(3, stride=2)
self.layers.append(self.maxpool)
self.flatten = F.Flatten()
self.sigmoid = F.Sigmoid()
def forward(self, x):
x = self.conv1(x)
x = self.maxpool(x)
x_copy = self.dropout(self.activation(self.batch_norm1(x)))
x = self.dropout(self.activation(self.batch_norm2(self.conv2(x_copy))))
x = self.dropout(self.activation(self.batch_norm3(self.conv3(x))))
x_copy = self.dropout(self.activation(self.batch_norm4(self.conv4(x_copy))))
x = self.activation(x + x_copy) #Inutile pour relu mais on sait jamais
x = self.flatten(x)
x = self.activation(self.dense1(x))
x = self.sigmoid(self.dense2(x))
return x
def save_txt(self, filename:str):
"""Save the layers in a txt
Args:
filename (str): path to the txt file
"""
with open(filename, 'w') as f:
for layer in self.layers:
f.write(str(layer._get_name) + "\n")
f.close()
dict_models = {
"ResNet1d18": ResNet1d18,
"ResnetImgModel": ResnetImgModel,
"SimpleSignalModel": SimpleSignalModel,
"SimpleMnistModel": SimpleMnistModel,
"SimpleCifarModel": SimpleCifarModel,
"ResnetCifarModel": ResnetCifarModel,
}
def model_by_name(model_name:str) -> F.Module:
"""Return model class using the given name
Args:
model_name (str): the model name
Returns:
F.Module: the class corresponding to the name
"""
if model_name in dict_models:
return dict_models[model_name]
raise ValueError("There is no model with the given name")