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import torch
import torchvision
import torch.nn as nn
from torchvision import transforms
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger("transformers")
## Add more imports if required
####################################################################################################################
# Define your model and transform and all necessary helper functions here #
# They will be imported to the exp_recognition.py file #
####################################################################################################################
# Definition of classes as dictionary
classes = {0: 'ANGER', 1: 'DISGUST', 2: 'FEAR', 3: 'HAPPINESS', 4: 'NEUTRAL', 5: 'SADNESS', 6: 'SURPRISE'}
# Example Network
class facExpRec(torch.nn.Module):
def __init__(self, out_features=7):
super().__init__()
self.conv1 = self.convlayer(in_channels=1, out_channels=64, kernel_size=3, max_pool=2)
self.conv2 = self.convlayer(in_channels=64, out_channels=128, kernel_size=3, max_pool=2)
self.conv3 = self.convlayer(in_channels=128, out_channels=512, kernel_size=3, max_pool=2)
self.conv4 = self.convlayer(in_channels=512, out_channels=512, kernel_size=3, max_pool=1)
self.fc1 = self.fclayer(2048, 512)
self.fc2 = nn.Linear(512, 7)
def convlayer(self, in_channels, out_channels, kernel_size, max_pool=2):
return nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(),
# nn.Dropout2d(),
nn.MaxPool2d(kernel_size=max_pool),
)
def fclayer(self, in_features, out_features):
return nn.Sequential(
nn.Linear(in_features, out_features),
nn.BatchNorm1d(out_features),
# nn.Dropout1d(0.4),
nn.ReLU(),
)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = x.view(-1, 2048)
x = self.fc1(x)
x = self.fc2(x)
return x
# Sample Helper function
def rgb2gray(image):
return image.convert('L')
# Sample Transformation function
#YOUR CODE HERE for changing the Transformation values.
trnscm = transforms.Compose([transforms.Resize((48,48)),transforms.Grayscale(), transforms.ToTensor(), transforms.Normalize((0.5), (0.5))]) |