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equal_odds.py
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import numpy as np
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import torch.nn.functional as F
from PIL import Image
import glob
print("Loading Images")
#bald_males = ["5imgs of bald males"]
#bald_females = ["5imgs of bald females"]
labels = torch.tensor([0, 0, 0, 0, 0])
bald_males = glob.glob('./Bald_Males/*.jpg')
bald_females = glob.glob('./Bald_Females/*.jpg')
#print(len(bald_males))
#print(len(bald_females))
transform = transforms.Compose(
[transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
"""
image_set_bald_males = []
for i in range(len(bald_males)):
image_set_bald_males.append(Image.open(bald_males[i]))
image_tensors = []
for i in range(len(image_set_bald_males)):
image_tensors.append(transform(image_set_bald_males[i]))
for i in range(len(image_tensors)):
image_tensors[i] = torch.unsqueeze(image_tensors[i], 0)
#print(len(image_set_bald_males))
#print(image_set_bald_males)
#print(len(image_tensors))
print("Images Loaded Sucessfully")
# Load Model
print("Loading Model Architecture")
model = torchvision.models.resnet18(weights='IMAGENET1K_V1')
in_features = model.fc.in_features
out_features = 2
class SoftmaxClassifier(nn.Module):
def __init__(self, in_features, out_features):
super(SoftmaxClassifier, self).__init__()
self.fc = nn.Linear(in_features, out_features)
def forward(self, x):
x = self.fc(x)
x = F.softmax(x, dim=1)
return x
model.fc = SoftmaxClassifier(in_features=in_features, out_features=out_features)
#print(model)
print("Model Architecture Loaded")
# Load model checkpoints
print("Loading Model Checkpoints")
checkpoint_path = 'model_bald_checkpoint.pth'
checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu'))
# Load model state dict
model.load_state_dict(checkpoint['model_state_dict'])
print("Model Checkpoints Loaded Successfully")
model.eval()
correct = 0
for i in range(len(image_tensors)):
output = model(image_tensors[i])
_, predicted = torch.max(output, 1)
#break
correct += (predicted == labels[i]).sum().item()
#print(predicted == labels[i])
#print(correct)
#break
acc = correct/(len(image_tensors))
print("Accuracy:", acc*100)
#print(output)
#print(predicted)
"""
image_set_bald_females = []
for i in range(len(bald_females)):
image_set_bald_females.append(Image.open(bald_females[i]))
image_tensors = []
for i in range(len(image_set_bald_females)):
image_tensors.append(transform(image_set_bald_females[i]))
for i in range(len(image_tensors)):
image_tensors[i] = torch.unsqueeze(image_tensors[i], 0)
#print(len(image_set_bald_males))
#print(image_set_bald_males)
#print(len(image_tensors))
print("Images Loaded Sucessfully")
# Load Model
print("Loading Model Architecture")
model = torchvision.models.resnet18(weights='IMAGENET1K_V1')
in_features = model.fc.in_features
out_features = 2
class SoftmaxClassifier(nn.Module):
def __init__(self, in_features, out_features):
super(SoftmaxClassifier, self).__init__()
self.fc = nn.Linear(in_features, out_features)
def forward(self, x):
x = self.fc(x)
x = F.softmax(x, dim=1)
return x
model.fc = SoftmaxClassifier(in_features=in_features, out_features=out_features)
#print(model)
print("Model Architecture Loaded")
# Load model checkpoints
print("Loading Model Checkpoints")
checkpoint_path = 'model_bald_checkpoint.pth'
checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu'))
# Load model state dict
model.load_state_dict(checkpoint['model_state_dict'])
print("Model Checkpoints Loaded Successfully")
model.eval()
correct = 0
for i in range(len(image_tensors)):
output = model(image_tensors[i])
_, predicted = torch.max(output, 1)
#break
correct += (predicted == labels[i]).sum().item()
#print(predicted == labels[i])
#print(correct)
#break
acc = correct/(len(image_tensors))
print("Accuracy:", acc*100)
#print(output)
#print(predicted)