|
|
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| import numpy as np |
|
|
| class mapping(nn.Module): |
| def __init__(self, input_dim=1024, hidden_dim = 512, out_dim=1024, layernum=4): |
| ''' |
| ''' |
| super().__init__() |
| self.layernum = layernum |
| if layernum == 4: |
| self.fc1 = nn.Linear(input_dim, hidden_dim) |
| self.fc2 = nn.Linear(hidden_dim, hidden_dim) |
| self.fc3 = nn.Linear(hidden_dim, hidden_dim) |
| self.fc4 = nn.Linear(hidden_dim, out_dim) |
| elif layernum == 2: |
| self.fc1 = nn.Linear(input_dim, hidden_dim) |
| self.fc2 = nn.Linear(hidden_dim, out_dim) |
| self.relu = nn.ReLU(inplace=True) |
|
|
| def forward(self, x): |
| ''' x ''' |
| if self.layernum == 4: |
| x = self.relu(self.fc1(x)) |
| x = self.relu(self.fc2(x)) |
| x = self.relu(self.fc3(x)) |
| x = self.fc4(x) |
| elif self.layernum == 2: |
| x = self.relu(self.fc1(x)) |
| x = self.fc2(x) |
| return x |
|
|
|
|
| class effect_to_weight(nn.Module): |
| def __init__(self, input_dim = 512, hidden_dim = 256, out_dim = 1, layernum=2, hidden_dim2 = 128): |
| ''' |
| ''' |
| super().__init__() |
| |
| self.layernum = layernum |
| if layernum == 2: |
| self.fc1 = nn.Linear(input_dim, hidden_dim) |
| self.fc2 = nn.Linear(hidden_dim, out_dim) |
| elif layernum == 3: |
| self.fc1 = nn.Linear(input_dim, hidden_dim) |
| self.fc2 = nn.Linear(hidden_dim, hidden_dim2) |
| self.fc3 = nn.Linear(hidden_dim2, out_dim) |
| self.relu = nn.ReLU(inplace=True) |
|
|
| def forward(self, x): |
| ''' x ''' |
| if self.layernum == 2: |
| x = self.relu(self.fc1(x)) |
| x = self.fc2(x) |
| else: |
| x = self.relu(self.fc1(x)) |
| x = self.relu(self.fc2(x)) |
| x = self.fc3(x) |
| return x |
|
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|
