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30555e7 0190e78 30555e7 0190e78 30555e7 0190e78 30555e7 094f908 0190e78 | 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 | import torch
import torchvision
import torch.nn as nn
from torchvision import transforms
import torchvision.models as models
## 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):
super().__init__()
self.model = models.vgg16(pretrained=True)
for params in self.model.parameters():
params.requires_grad = False
input_shape = self.model.classifier[6].in_features
self.model.classifier[6] = nn.Sequential(nn.Linear(input_shape, 1024),nn.ReLU(), nn.Dropout(0.2),
nn.Linear(1024,256), nn.ReLU(),nn.Dropout(0.2),
nn.Linear(256,7), nn.LogSoftmax(dim=1))
self.model.classifier[6].requires_grad = True
print("New Layers Added:")
for params in self.model.parameters():
if params.requires_grad:
print(params.shape)
def forward(self, x):
return self.model(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([rgb2gray, transforms.Resize((48,48)), transforms.ToTensor()])
trnscm = transforms.Compose([transforms.Resize((100,100)),transforms.ToTensor()])
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