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2972f68 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | # 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
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