Spaces:
Sleeping
Sleeping
| # from torch import nn as nn | |
| # | |
| # | |
| # class EmotionModel(nn.Module): | |
| # def __init__(self, in_channels=1, num_classes=7): | |
| # super(EmotionModel, self).__init__() | |
| # self.conv1 = nn.Conv2d( | |
| # in_channels=in_channels, out_channels=256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1) | |
| # ) | |
| # self.relu1 = nn.ReLU() | |
| # self.pool1 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) | |
| # self.drop1 = nn.Dropout2d(0.4) | |
| # | |
| # self.conv2 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) | |
| # self.relu2 = nn.ReLU() | |
| # self.pool2 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) | |
| # self.drop2 = nn.Dropout2d(0.4) | |
| # | |
| # self.conv3 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) | |
| # self.relu3 = nn.ReLU() | |
| # self.pool3 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) | |
| # self.drop3 = nn.Dropout2d(0.4) | |
| # | |
| # self.conv4 = nn.Conv2d( | |
| # in_channels=512, out_channels=512 * 4 * 4, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1) | |
| # ) | |
| # self.relu4 = nn.ReLU() | |
| # self.pool4 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) | |
| # self.drop4 = nn.Dropout2d(0.4) | |
| # | |
| # self.fc1 = nn.Linear(in_features=512 * 4 * 4, out_features=512) | |
| # self.relu5 = nn.ReLU() | |
| # self.drop5 = nn.Dropout(0.3) | |
| # self.fc2 = nn.Linear(in_features=512, out_features=256) | |
| # self.relu6 = nn.ReLU() | |
| # self.drop6 = nn.Dropout(0.3) | |
| # self.fc3 = nn.Linear(in_features=256, out_features=num_classes) | |
| # self.softmax = nn.Softmax(dim=1) | |
| # | |
| # def forward(self, x): | |
| # x = self.conv1(x) | |
| # x = self.relu1(x) | |
| # x = self.pool1(x) | |
| # x = self.drop1(x) | |
| # x = self.conv2(x) | |
| # x = self.relu2(x) | |
| # x = self.pool2(x) | |
| # x = self.drop2(x) | |
| # x = self.conv3(x) | |
| # x = self.relu3(x) | |
| # x = self.pool3(x) | |
| # x = self.drop3(x) | |
| # x = self.conv4(x) | |
| # x = self.relu4(x) | |
| # x = self.pool4(x) | |
| # x = self.drop4(x) | |
| # x = x.view(-1, 512 * 4 * 4) | |
| # x = self.fc1(x) | |
| # x = self.relu5(x) | |
| # x = self.drop5(x) | |
| # x = self.fc2(x) | |
| # x = self.relu6(x) | |
| # x = self.drop6(x) | |
| # x = self.fc3(x) | |
| # x = self.softmax(x) | |
| # return x | |
| import torch.nn as nn | |
| class EmotionModel(nn.Module): | |
| def __init__(self, in_channels=1, num_classes=7): | |
| super(EmotionModel, self).__init__() | |
| self.conv1 = nn.Conv2d(in_channels, 256, kernel_size=3, stride=2, padding=1) | |
| self.bn1 = nn.BatchNorm2d(256) | |
| self.relu1 = nn.ReLU() | |
| self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| self.drop1 = nn.Dropout2d(0.4) | |
| self.conv2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1) | |
| self.bn2 = nn.BatchNorm2d(512) | |
| self.relu2 = nn.ReLU() | |
| self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| self.drop2 = nn.Dropout2d(0.4) | |
| self.conv3 = nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1) | |
| self.bn3 = nn.BatchNorm2d(512) | |
| self.relu3 = nn.ReLU() | |
| self.pool3 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| self.drop3 = nn.Dropout2d(0.4) | |
| self.conv4 = nn.Conv2d(512, 512 * 4 * 4, kernel_size=3, stride=2, padding=1) | |
| self.bn4 = nn.BatchNorm2d(512 * 4 * 4) | |
| self.relu4 = nn.ReLU() | |
| self.pool4 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| self.drop4 = nn.Dropout2d(0.4) | |
| self.fc1 = nn.Linear(512 * 4 * 4, 512) | |
| self.bn_fc1 = nn.BatchNorm1d(512) | |
| self.relu5 = nn.ReLU() | |
| self.drop5 = nn.Dropout(0.3) | |
| self.fc2 = nn.Linear(512, 256) | |
| self.bn_fc2 = nn.BatchNorm1d(256) | |
| self.relu6 = nn.ReLU() | |
| self.drop6 = nn.Dropout(0.3) | |
| self.fc3 = nn.Linear(256, num_classes) | |
| self.softmax = nn.Softmax(dim=1) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.bn1(x) | |
| x = self.relu1(x) | |
| x = self.pool1(x) | |
| x = self.drop1(x) | |
| x = self.conv2(x) | |
| x = self.bn2(x) | |
| x = self.relu2(x) | |
| x = self.pool2(x) | |
| x = self.drop2(x) | |
| x = self.conv3(x) | |
| x = self.bn3(x) | |
| x = self.relu3(x) | |
| x = self.pool3(x) | |
| x = self.drop3(x) | |
| x = self.conv4(x) | |
| x = self.bn4(x) | |
| x = self.relu4(x) | |
| x = self.pool4(x) | |
| x = self.drop4(x) | |
| x = x.view(-1, 512 * 4 * 4) | |
| x = self.fc1(x) | |
| x = self.bn_fc1(x) | |
| x = self.relu5(x) | |
| x = self.drop5(x) | |
| x = self.fc2(x) | |
| x = self.bn_fc2(x) | |
| x = self.relu6(x) | |
| x = self.drop6(x) | |
| x = self.fc3(x) | |
| x = self.softmax(x) | |
| return x | |