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- <main>
- <article id="content">
- <header>
- <h1 class="title">Module <code>src.models</code></h1>
- </header>
- <section id="section-intro">
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">import numpy as np
- from torch import nn
- import torch.nn.functional as F
- import torch
- 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)
- 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
-
-
- 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)
-
- 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)
-
- 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)
- 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
- 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
- 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</code></pre>
- </details>
- </section>
- <section>
- </section>
- <section>
- </section>
- <section>
- </section>
- <section>
- <h2 class="section-title" id="header-classes">Classes</h2>
- <dl>
- <dt id="src.models.AttentionNet"><code class="flex name class">
- <span>class <span class="ident">AttentionNet</span></span>
- <span>(</span><span>D_in, H, p)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>customized (one hidden layer) fully connected neural network class</p>
- <h1 id="parameters">Parameters:</h1>
- <pre><code>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)
- </code></pre></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">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)</code></pre>
- </details>
- <h3>Ancestors</h3>
- <ul class="hlist">
- <li>torch.nn.modules.module.Module</li>
- </ul>
- <h3>Methods</h3>
- <dl>
- <dt id="src.models.AttentionNet.forward"><code class="name flex">
- <span>def <span class="ident">forward</span></span>(<span>self, x1, x2)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Defines the computation performed at every call.</p>
- <p>Should be overridden by all subclasses.</p>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>Although the recipe for forward pass needs to be defined within
- this function, one should call the :class:<code>Module</code> instance afterwards
- instead of this since the former takes care of running the
- registered hooks while the latter silently ignores them.</p>
- </div></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">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)</code></pre>
- </details>
- </dd>
- </dl>
- </dd>
- <dt id="src.models.CNN"><code class="flex name class">
- <span>class <span class="ident">CNN</span></span>
- <span>(</span><span>D_in, H, p)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Base class for all neural network modules.</p>
- <p>Your models should also subclass this class.</p>
- <p>Modules can also contain other Modules, allowing to nest them in
- a tree structure. You can assign the submodules as regular attributes::</p>
- <pre><code>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))
- </code></pre>
- <p>Submodules assigned in this way will be registered, and will have their
- parameters converted too when you call :meth:<code>to</code>, etc.</p>
- <p>:ivar training: Boolean represents whether this module is in training or
- evaluation mode.
- :vartype training: bool</p>
- <h1 id="parameters">Parameters:</h1>
- <pre><code>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)
- </code></pre></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">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)</code></pre>
- </details>
- <h3>Ancestors</h3>
- <ul class="hlist">
- <li>torch.nn.modules.module.Module</li>
- </ul>
- <h3>Methods</h3>
- <dl>
- <dt id="src.models.CNN.forward"><code class="name flex">
- <span>def <span class="ident">forward</span></span>(<span>self, x1, x2)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Defines the computation performed at every call.</p>
- <p>Should be overridden by all subclasses.</p>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>Although the recipe for forward pass needs to be defined within
- this function, one should call the :class:<code>Module</code> instance afterwards
- instead of this since the former takes care of running the
- registered hooks while the latter silently ignores them.</p>
- </div></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">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)</code></pre>
- </details>
- </dd>
- </dl>
- </dd>
- <dt id="src.models.FCNN"><code class="flex name class">
- <span>class <span class="ident">FCNN</span></span>
- <span>(</span><span>D_in, H, p)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>customized (one hidden layer) fully connected neural network class</p>
- <h1 id="parameters">Parameters:</h1>
- <pre><code>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)
- </code></pre></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">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</code></pre>
- </details>
- <h3>Ancestors</h3>
- <ul class="hlist">
- <li>torch.nn.modules.module.Module</li>
- </ul>
- <h3>Methods</h3>
- <dl>
- <dt id="src.models.FCNN.forward"><code class="name flex">
- <span>def <span class="ident">forward</span></span>(<span>self, x1, x2)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Defines the computation performed at every call.</p>
- <p>Should be overridden by all subclasses.</p>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>Although the recipe for forward pass needs to be defined within
- this function, one should call the :class:<code>Module</code> instance afterwards
- instead of this since the former takes care of running the
- registered hooks while the latter silently ignores them.</p>
- </div></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">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</code></pre>
- </details>
- </dd>
- </dl>
- </dd>
- <dt id="src.models.LSTMNet"><code class="flex name class">
- <span>class <span class="ident">LSTMNet</span></span>
- <span>(</span><span>D_in, H, p)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Base class for all neural network modules.</p>
- <p>Your models should also subclass this class.</p>
- <p>Modules can also contain other Modules, allowing to nest them in
- a tree structure. You can assign the submodules as regular attributes::</p>
- <pre><code>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))
- </code></pre>
- <p>Submodules assigned in this way will be registered, and will have their
- parameters converted too when you call :meth:<code>to</code>, etc.</p>
- <p>:ivar training: Boolean represents whether this module is in training or
- evaluation mode.
- :vartype training: bool</p>
- <h1 id="parameters">Parameters:</h1>
- <pre><code>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)
- </code></pre></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">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)</code></pre>
- </details>
- <h3>Ancestors</h3>
- <ul class="hlist">
- <li>torch.nn.modules.module.Module</li>
- </ul>
- <h3>Methods</h3>
- <dl>
- <dt id="src.models.LSTMNet.forward"><code class="name flex">
- <span>def <span class="ident">forward</span></span>(<span>self, x1, x2=None)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Defines the computation performed at every call.</p>
- <p>Should be overridden by all subclasses.</p>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>Although the recipe for forward pass needs to be defined within
- this function, one should call the :class:<code>Module</code> instance afterwards
- instead of this since the former takes care of running the
- registered hooks while the latter silently ignores them.</p>
- </div></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">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)</code></pre>
- </details>
- </dd>
- </dl>
- </dd>
- <dt id="src.models.MaxConv"><code class="flex name class">
- <span>class <span class="ident">MaxConv</span></span>
- <span>(</span><span>num_units=20, kernel_size=30, nonlin=<function relu>, use_bias=False)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>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,)</p>
- <p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">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</code></pre>
- </details>
- <h3>Ancestors</h3>
- <ul class="hlist">
- <li>torch.nn.modules.module.Module</li>
- </ul>
- <h3>Methods</h3>
- <dl>
- <dt id="src.models.MaxConv.forward"><code class="name flex">
- <span>def <span class="ident">forward</span></span>(<span>self, X, **kwargs)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Defines the computation performed at every call.</p>
- <p>Should be overridden by all subclasses.</p>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>Although the recipe for forward pass needs to be defined within
- this function, one should call the :class:<code>Module</code> instance afterwards
- instead of this since the former takes care of running the
- registered hooks while the latter silently ignores them.</p>
- </div></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">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</code></pre>
- </details>
- </dd>
- </dl>
- </dd>
- <dt id="src.models.MaxConvLinear"><code class="flex name class">
- <span>class <span class="ident">MaxConvLinear</span></span>
- <span>(</span><span>num_timepoints=300, num_linear_filts=1, num_conv_filts=3, patch_size=9, kernel_size=30, nonlin=<function relu>, use_bias=False)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>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</p>
- <p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">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</code></pre>
- </details>
- <h3>Ancestors</h3>
- <ul class="hlist">
- <li>torch.nn.modules.module.Module</li>
- </ul>
- <h3>Methods</h3>
- <dl>
- <dt id="src.models.MaxConvLinear.forward"><code class="name flex">
- <span>def <span class="ident">forward</span></span>(<span>self, X, **kwargs)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Defines the computation performed at every call.</p>
- <p>Should be overridden by all subclasses.</p>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>Although the recipe for forward pass needs to be defined within
- this function, one should call the :class:<code>Module</code> instance afterwards
- instead of this since the former takes care of running the
- registered hooks while the latter silently ignores them.</p>
- </div></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">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</code></pre>
- </details>
- </dd>
- </dl>
- </dd>
- <dt id="src.models.MaxLinear"><code class="flex name class">
- <span>class <span class="ident">MaxLinear</span></span>
- <span>(</span><span>input_dim=24300, num_units=20, nonlin=<function relu>, use_bias=False)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Takes flattened input and predicts it using many linear units
- X: batch_size x num_timepoints</p>
- <p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">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</code></pre>
- </details>
- <h3>Ancestors</h3>
- <ul class="hlist">
- <li>torch.nn.modules.module.Module</li>
- </ul>
- <h3>Methods</h3>
- <dl>
- <dt id="src.models.MaxLinear.forward"><code class="name flex">
- <span>def <span class="ident">forward</span></span>(<span>self, X, **kwargs)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Defines the computation performed at every call.</p>
- <p>Should be overridden by all subclasses.</p>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>Although the recipe for forward pass needs to be defined within
- this function, one should call the :class:<code>Module</code> instance afterwards
- instead of this since the former takes care of running the
- registered hooks while the latter silently ignores them.</p>
- </div></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">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</code></pre>
- </details>
- </dd>
- </dl>
- </dd>
- <dt id="src.models.VideoNet"><code class="flex name class">
- <span>class <span class="ident">VideoNet</span></span>
- </code></dt>
- <dd>
- <section class="desc"><p>Base class for all neural network modules.</p>
- <p>Your models should also subclass this class.</p>
- <p>Modules can also contain other Modules, allowing to nest them in
- a tree structure. You can assign the submodules as regular attributes::</p>
- <pre><code>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))
- </code></pre>
- <p>Submodules assigned in this way will be registered, and will have their
- parameters converted too when you call :meth:<code>to</code>, etc.</p>
- <p>:ivar training: Boolean represents whether this module is in training or
- evaluation mode.
- :vartype training: bool</p>
- <p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">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)</code></pre>
- </details>
- <h3>Ancestors</h3>
- <ul class="hlist">
- <li>torch.nn.modules.module.Module</li>
- </ul>
- <h3>Methods</h3>
- <dl>
- <dt id="src.models.VideoNet.forward"><code class="name flex">
- <span>def <span class="ident">forward</span></span>(<span>self, x)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>x: torch.Tensor
- (batch_size, time_steps, height, width)
- = (batch_size, 40, 10, 10)</p></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python"> 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)</code></pre>
- </details>
- </dd>
- </dl>
- </dd>
- </dl>
- </section>
- </article>
- <nav id="sidebar">
- <h1>Index</h1>
- <div class="toc">
- <ul></ul>
- </div>
- <ul id="index">
- <li><h3>Super-module</h3>
- <ul>
- <li><code><a title="src" href="index.html">src</a></code></li>
- </ul>
- </li>
- <li><h3><a href="#header-classes">Classes</a></h3>
- <ul>
- <li>
- <h4><code><a title="src.models.AttentionNet" href="#src.models.AttentionNet">AttentionNet</a></code></h4>
- <ul class="">
- <li><code><a title="src.models.AttentionNet.forward" href="#src.models.AttentionNet.forward">forward</a></code></li>
- </ul>
- </li>
- <li>
- <h4><code><a title="src.models.CNN" href="#src.models.CNN">CNN</a></code></h4>
- <ul class="">
- <li><code><a title="src.models.CNN.forward" href="#src.models.CNN.forward">forward</a></code></li>
- </ul>
- </li>
- <li>
- <h4><code><a title="src.models.FCNN" href="#src.models.FCNN">FCNN</a></code></h4>
- <ul class="">
- <li><code><a title="src.models.FCNN.forward" href="#src.models.FCNN.forward">forward</a></code></li>
- </ul>
- </li>
- <li>
- <h4><code><a title="src.models.LSTMNet" href="#src.models.LSTMNet">LSTMNet</a></code></h4>
- <ul class="">
- <li><code><a title="src.models.LSTMNet.forward" href="#src.models.LSTMNet.forward">forward</a></code></li>
- </ul>
- </li>
- <li>
- <h4><code><a title="src.models.MaxConv" href="#src.models.MaxConv">MaxConv</a></code></h4>
- <ul class="">
- <li><code><a title="src.models.MaxConv.forward" href="#src.models.MaxConv.forward">forward</a></code></li>
- </ul>
- </li>
- <li>
- <h4><code><a title="src.models.MaxConvLinear" href="#src.models.MaxConvLinear">MaxConvLinear</a></code></h4>
- <ul class="">
- <li><code><a title="src.models.MaxConvLinear.forward" href="#src.models.MaxConvLinear.forward">forward</a></code></li>
- </ul>
- </li>
- <li>
- <h4><code><a title="src.models.MaxLinear" href="#src.models.MaxLinear">MaxLinear</a></code></h4>
- <ul class="">
- <li><code><a title="src.models.MaxLinear.forward" href="#src.models.MaxLinear.forward">forward</a></code></li>
- </ul>
- </li>
- <li>
- <h4><code><a title="src.models.VideoNet" href="#src.models.VideoNet">VideoNet</a></code></h4>
- <ul class="">
- <li><code><a title="src.models.VideoNet.forward" href="#src.models.VideoNet.forward">forward</a></code></li>
- </ul>
- </li>
- </ul>
- </li>
- </ul>
- </nav>
- </main>
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- <p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.2</a>.</p>
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