Classes

class AttentionNet (D_in, H, p)

customized (one hidden layer) fully connected neural network class

Parameters:

D_in: int
    dimension of input track (ignored, can be variable)

H: int
    hidden layer size

p: int
    number of additional covariates (such as lifetime, msd, etc..., to be concatenated to the hidden layer)

Expand source code

class AttentionNet(nn.Module):
    
    """
    customized (one hidden layer) fully connected neural network class
    """

    def __init__(self, D_in, H, p):
        
        """
        Parameters:        
        ==========================================================
            D_in: int
                dimension of input track (ignored, can be variable)
                
            H: int
                hidden layer size
                
            p: int
                number of additional covariates (such as lifetime, msd, etc..., to be concatenated to the hidden layer)            
        """

        super(AttentionNet, self).__init__()
        self.att1 = nn.MultiheadAttention(embed_dim=18, num_heads=3)
        self.ln1 = nn.LayerNorm(D_in)
        self.fc1 = nn.Linear(D_in, 1) 
        self.relu1 = nn.ReLU()
        self.att2 = nn.MultiheadAttention(embed_dim=18, num_heads=3)
        self.ln2 = nn.LayerNorm(D_in)
        self.fc2 = nn.Linear(D_in + p, 1) 
    
    def forward(self, x1, x2):
        print(x1.shape)
        x1 = self.att1(x1, x1)
        x1 = self.ln1(x1)
        x1 = self.fc1(x1)
        x1 = self.relu1(x1)
        x1 = self.att2(x1, x1)
        x1 = self.ln2(x1)
        
        if x2 is not None:
            h1 = torch.cat((h1, x2), 1)
        return self.fc2(h1)

Ancestors

• torch.nn.modules.module.Module

Methods

def forward(self, x1, x2)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the :class:Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Expand source code

def forward(self, x1, x2):
    print(x1.shape)
    x1 = self.att1(x1, x1)
    x1 = self.ln1(x1)
    x1 = self.fc1(x1)
    x1 = self.relu1(x1)
    x1 = self.att2(x1, x1)
    x1 = self.ln2(x1)
    
    if x2 is not None:
        h1 = torch.cat((h1, x2), 1)
    return self.fc2(h1)

class CNN (D_in, H, p)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Parameters:

D_in: int
    dimension of input track (ignored, can be variable)

H: int
    hidden layer size

p: int
    number of additional covariates (such as lifetime, msd, etc..., to be concatenated to the hidden layer)

Expand source code

class CNN(nn.Module):
    def __init__(self, D_in, H, p):
        
        """
        Parameters:        
        ==========================================================
            D_in: int
                dimension of input track (ignored, can be variable)
                
            H: int
                hidden layer size
                
            p: int
                number of additional covariates (such as lifetime, msd, etc..., to be concatenated to the hidden layer)            
        """

        super(CNN, self).__init__()
        self.conv1 = nn.Conv1d(in_channels=1, out_channels=H, kernel_size=7)
        self.maxpool1 = nn.MaxPool1d(kernel_size=2)
        self.conv2 = nn.Conv1d(in_channels=H, out_channels=3, kernel_size=5)
        self.maxpool2 = nn.MaxPool1d(kernel_size=2)
        self.fc = nn.Linear(18 + p, 1) # this is hard-coded
    
    def forward(self, x1, x2):
        x1 = x1.unsqueeze(1) # add channel dim
        x1 = self.conv1(x1)
        x1 = self.maxpool1(x1)
        x1 = self.conv2(x1)
        x1 = self.maxpool2(x1)
        x1 = x1.reshape(x1.shape[0], -1) # flatten channel dim
        
        if x2 is not None:
            x1 = torch.cat((x1, x2), 1)
        return self.fc(x1)

Ancestors

• torch.nn.modules.module.Module

Methods

def forward(self, x1, x2)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the :class:Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Expand source code

def forward(self, x1, x2):
    x1 = x1.unsqueeze(1) # add channel dim
    x1 = self.conv1(x1)
    x1 = self.maxpool1(x1)
    x1 = self.conv2(x1)
    x1 = self.maxpool2(x1)
    x1 = x1.reshape(x1.shape[0], -1) # flatten channel dim
    
    if x2 is not None:
        x1 = torch.cat((x1, x2), 1)
    return self.fc(x1)

class FCNN (D_in, H, p)

customized (one hidden layer) fully connected neural network class

Parameters:

D_in: int
    dimension of input track

H: int
    hidden layer size

p: int
    number of additional covariates (such as lifetime, msd, etc..., to be concatenated to the hidden layer)

Expand source code

class FCNN(nn.Module):
    
    """
    customized (one hidden layer) fully connected neural network class
    """

    def __init__(self, D_in, H, p):
        
        """
        Parameters:        
        ==========================================================
            D_in: int
                dimension of input track
                
            H: int
                hidden layer size
                
            p: int
                number of additional covariates (such as lifetime, msd, etc..., to be concatenated to the hidden layer)            
        """

        super(FCNN, self).__init__()
        self.fc1 = nn.Linear(D_in, H)
        #self.fc2 = nn.Linear(H, H)
        self.bn1 = nn.BatchNorm1d(H)
        self.fc2 = nn.Linear(H + p, 1) 
    
    def forward(self, x1, x2):
        
        z1 = self.fc1(x1)
        z1 = self.bn1(z1)
        h1 = F.relu(z1)
        if x2 is not None:
            h1 = torch.cat((h1, x2), 1)
        z2 = self.fc2(h1)
        #h2 = F.relu(z2)
        #z3 = self.fc3(h2)       
        
        return z2

Ancestors

• torch.nn.modules.module.Module

Methods

def forward(self, x1, x2)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the :class:Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Expand source code

def forward(self, x1, x2):
    
    z1 = self.fc1(x1)
    z1 = self.bn1(z1)
    h1 = F.relu(z1)
    if x2 is not None:
        h1 = torch.cat((h1, x2), 1)
    z2 = self.fc2(h1)
    #h2 = F.relu(z2)
    #z3 = self.fc3(h2)       
    
    return z2

class LSTMNet (D_in, H, p)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Parameters:

D_in: int
    dimension of input track (ignored, can be variable)

H: int
    hidden layer size

p: int
    number of additional covariates (such as lifetime, msd, etc..., to be concatenated to the hidden layer)

Expand source code

class LSTMNet(nn.Module):
    def __init__(self, D_in, H, p):
        
        """
        Parameters:        
        ==========================================================
            D_in: int
                dimension of input track (ignored, can be variable)
                
            H: int
                hidden layer size
                
            p: int
                number of additional covariates (such as lifetime, msd, etc..., to be concatenated to the hidden layer)            
        """

        super(LSTMNet, self).__init__()
        self.lstm = nn.LSTM(input_size=1, hidden_size=H, num_layers=1, batch_first=True)
        self.fc = nn.Linear(H + p, 1) 
    
    def forward(self, x1, x2=None):
        x1 = x1.unsqueeze(2) # add input_size dimension (this is usually for the size of embedding vector)
        outputs, (h1, c1) = self.lstm(x1) # get hidden vec
        h1 = h1.squeeze(0) # remove dimension corresponding to multiple layers / directions
        if x2 is not None:
            h1 = torch.cat((h1, x2), 1)
        return self.fc(h1)

Ancestors

• torch.nn.modules.module.Module

Methods

def forward(self, x1, x2=None)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the :class:Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Expand source code

def forward(self, x1, x2=None):
    x1 = x1.unsqueeze(2) # add input_size dimension (this is usually for the size of embedding vector)
    outputs, (h1, c1) = self.lstm(x1) # get hidden vec
    h1 = h1.squeeze(0) # remove dimension corresponding to multiple layers / directions
    if x2 is not None:
        h1 = torch.cat((h1, x2), 1)
    return self.fc(h1)

class MaxConv (num_units=20, kernel_size=30, nonlin=<function relu>, use_bias=False)

Takes flattened input and predicts it using many conv unit X: batch_size x 1 x num_timepoints OR X: list of size (num_timepoints,)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Expand source code

class MaxConv(nn.Module):
    '''Takes flattened input and predicts it using many conv unit
        X: batch_size x 1 x num_timepoints
            OR
        X: list of size (num_timepoints,)
    '''

    def __init__(self, num_units=20, kernel_size=30, nonlin=F.relu, use_bias=False):
        super(MaxConv, self).__init__()
        self.conv1 = nn.Conv1d(in_channels=1, out_channels=num_units, kernel_size=kernel_size, bias=use_bias)
        #         torch.nn.Conv1d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros')
        self.offset = nn.Parameter(torch.Tensor([0]))

    def forward(self, X, **kwargs):
        if type(X) == list:
            print('list')
            X = torch.tensor(np.array(X).astype(np.float32))
            X = X.unsqueeze(0)
            X = X.unsqueeze(0)
            print(X.shape)
        #         print('in shape', X.shape, X.dtype)
        else:
            X = X.unsqueeze(1)
        X = self.conv1(X)  # .max(dim=-1)
        #         print('out shape', X.shape, X.dtype)
        # max over channels
        X = torch.max(X, dim=1)[0]  # 0 because this returns max, indices

        # max over time step
        X = torch.max(X, dim=1)[0] + self.offset  # 0 because this returns max, indices
        #         print('out2 shape', X.shape, X.dtype)

        X = X.unsqueeze(1)

        #         print('preds', X)
        return X

Ancestors

• torch.nn.modules.module.Module

Methods

def forward(self, X, **kwargs)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the :class:Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Expand source code

def forward(self, X, **kwargs):
    if type(X) == list:
        print('list')
        X = torch.tensor(np.array(X).astype(np.float32))
        X = X.unsqueeze(0)
        X = X.unsqueeze(0)
        print(X.shape)
    #         print('in shape', X.shape, X.dtype)
    else:
        X = X.unsqueeze(1)
    X = self.conv1(X)  # .max(dim=-1)
    #         print('out shape', X.shape, X.dtype)
    # max over channels
    X = torch.max(X, dim=1)[0]  # 0 because this returns max, indices

    # max over time step
    X = torch.max(X, dim=1)[0] + self.offset  # 0 because this returns max, indices
    #         print('out2 shape', X.shape, X.dtype)

    X = X.unsqueeze(1)

    #         print('preds', X)
    return X

class MaxConvLinear (num_timepoints=300, num_linear_filts=1, num_conv_filts=3, patch_size=9, kernel_size=30, nonlin=<function relu>, use_bias=False)

Takes input patch, uses linear filter to convert it to time series, then runs temporal conv, then takes max X: batch_size x H_patch x W_patch x time

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Expand source code

class MaxConvLinear(nn.Module):
    '''Takes input patch, uses linear filter to convert it to time series, then runs temporal conv, then takes max
        X: batch_size x H_patch x W_patch x time
    '''

    def __init__(self, num_timepoints=300, num_linear_filts=1, num_conv_filts=3, patch_size=9,
                 kernel_size=30, nonlin=F.relu, use_bias=False):
        super(MaxConvLinear, self).__init__()
        self.fc1 = nn.Linear(patch_size * patch_size, num_linear_filts, bias=use_bias)
        self.conv1 = nn.Conv1d(in_channels=num_linear_filts, out_channels=num_conv_filts, kernel_size=kernel_size,
                               bias=use_bias)
        self.offset = nn.Parameter(torch.Tensor([0]))

    def forward(self, X, **kwargs):
        s = X.shape  # batch_size x H_patch x W_patch x time
        X = X.reshape(s[0], s[1] * s[2], s[3])
        X = torch.transpose(X, 1, 2)
        #         print('in shape', X.shape, X.dtype)
        X = self.fc1(X)  # .max(dim=-1)
        X = torch.transpose(X, 1, 2)

        X = self.conv1(X)  # .max(dim=-1)
        #         print('out shape', X.shape, X.dtype)
        # max over channels
        X = torch.max(X, dim=1)[0]  # 0 because this returns max, indices

        # max over time step
        X = torch.max(X, dim=1)[0]  # + self.offset # 0 because this returns max, indices
        #         print('out2 shape', X.shape, X.dtype)

        X = X.unsqueeze(1)
        return X

Ancestors

• torch.nn.modules.module.Module

Methods

def forward(self, X, **kwargs)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the :class:Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Expand source code

def forward(self, X, **kwargs):
    s = X.shape  # batch_size x H_patch x W_patch x time
    X = X.reshape(s[0], s[1] * s[2], s[3])
    X = torch.transpose(X, 1, 2)
    #         print('in shape', X.shape, X.dtype)
    X = self.fc1(X)  # .max(dim=-1)
    X = torch.transpose(X, 1, 2)

    X = self.conv1(X)  # .max(dim=-1)
    #         print('out shape', X.shape, X.dtype)
    # max over channels
    X = torch.max(X, dim=1)[0]  # 0 because this returns max, indices

    # max over time step
    X = torch.max(X, dim=1)[0]  # + self.offset # 0 because this returns max, indices
    #         print('out2 shape', X.shape, X.dtype)

    X = X.unsqueeze(1)
    return X

class MaxLinear (input_dim=24300, num_units=20, nonlin=<function relu>, use_bias=False)

Takes flattened input and predicts it using many linear units X: batch_size x num_timepoints

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Expand source code

class MaxLinear(nn.Module):
    '''Takes flattened input and predicts it using many linear units
        X: batch_size x num_timepoints
    '''

    def __init__(self, input_dim=24300, num_units=20, nonlin=F.relu, use_bias=False):
        super(MaxLinear, self).__init__()

        self.fc1 = nn.Linear(input_dim, num_units, bias=use_bias)

    #         self.offset = nn.Parameter(torch.Tensor([0]))

    def forward(self, X, **kwargs):
        #         print('in shape', X.shape, X.dtype)
        X = self.fc1(X)  # .max(dim=-1)
        #         print('out shape', X.shape, X.dtype)
        X = torch.max(X, dim=1)[0]  # 0 because this returns max, indices
        #         print('out2 shape', X.shape, X.dtype)
        return X  # + self.offset

Ancestors

• torch.nn.modules.module.Module

Methods

def forward(self, X, **kwargs)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the :class:Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Expand source code

def forward(self, X, **kwargs):
    #         print('in shape', X.shape, X.dtype)
    X = self.fc1(X)  # .max(dim=-1)
    #         print('out shape', X.shape, X.dtype)
    X = torch.max(X, dim=1)[0]  # 0 because this returns max, indices
    #         print('out2 shape', X.shape, X.dtype)
    return X  # + self.offset

class VideoNet

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Expand source code

class VideoNet(nn.Module):
    def __init__(self):

        super(VideoNet, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=3, kernel_size=5)
        self.relu1 = nn.ReLU()
        self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=1)
        self.conv2 = nn.Conv2d(in_channels=3, out_channels=1, kernel_size=3)
        self.lstm = nn.LSTM(input_size=1, hidden_size=40, num_layers=1, batch_first=True)
        self.fc = nn.Linear(40, 1) 
#         self.conv2 = nn.Conv1d(in_channels=H, out_channels=3, kernel_size=5)
#         self.maxpool2 = nn.MaxPool1d(kernel_size=2)
#         self.fc = nn.Linear(18 + p, 1) # this is hard-coded
    
    def forward(self, x):
        '''
        x: torch.Tensor
            (batch_size, time_steps, height, width)
          = (batch_size, 40, 10, 10)
        '''
#         print('in shape', x.shape)
        # extract features from each time_step separately
        # reshape time_steps and batch into same dim
        batch_size = x.shape[0]
        T = x.shape[1]
        x = x.reshape(batch_size * T, 1, x.shape[2], x.shape[3])
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = torch.max(x, dim=3).values
        x = torch.max(x, dim=2).values
        
        # extract time_steps back out
        # run lstm on result 1D time series
        x = x.reshape(batch_size, T, 1)
        outputs, (h1, c1) = self.lstm(x) # get hidden vec
        h1 = h1.squeeze(0) # remove dimension corresponding to multiple layers / directions
        return self.fc(h1)

Ancestors

• torch.nn.modules.module.Module

Methods

def forward(self, x)

x: torch.Tensor (batch_size, time_steps, height, width) = (batch_size, 40, 10, 10)

Expand source code

    def forward(self, x):
        '''
        x: torch.Tensor
            (batch_size, time_steps, height, width)
          = (batch_size, 40, 10, 10)
        '''
#         print('in shape', x.shape)
        # extract features from each time_step separately
        # reshape time_steps and batch into same dim
        batch_size = x.shape[0]
        T = x.shape[1]
        x = x.reshape(batch_size * T, 1, x.shape[2], x.shape[3])
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = torch.max(x, dim=3).values
        x = torch.max(x, dim=2).values
        
        # extract time_steps back out
        # run lstm on result 1D time series
        x = x.reshape(batch_size, T, 1)
        outputs, (h1, c1) = self.lstm(x) # get hidden vec
        h1 = h1.squeeze(0) # remove dimension corresponding to multiple layers / directions
        return self.fc(h1)