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import torch
import torch.nn as nn
from PIL import Image
from torchvision.transforms import ToTensor
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class MCNN(nn.Module):
def __init__(self):
super(MCNN, self).__init__()
# Convolution layers
self.conv1 = nn.Conv2d(3, 64, 3, 1, 1)
self.bn1 = nn.BatchNorm2d(64)
self.conv2 = nn.Conv2d(64, 128, 3, 1, 1)
self.bn2 = nn.BatchNorm2d(128)
self.conv3 = nn.Conv2d(128, 256, 3, 1, 1)
self.bn3 = nn.BatchNorm2d(256)
self.conv4 = nn.Conv2d(256, 512, 3, 1, 1) # Added another convolutional layer
self.bn4 = nn.BatchNorm2d(512)
# Pooling layer
self.pool = nn.MaxPool2d(2, 2)
# Fully connected layers
self.fc1 = nn.Linear(100352, 2048)
self.fc2 = nn.Linear(2048, 1024)
self.fc3 = nn.Linear(1024, 512)
self.fc4 = nn.Linear(512, 256)
self.fc5 = nn.Linear(256, 2) # Two classes
# Activation and dropout
self.relu = nn.ReLU()
self.dropout = nn.Dropout(0.2)
def forward(self, pixel_values, labels=None):
x = self.pool(self.relu(self.bn1(self.conv1(pixel_values))))
x = self.pool(self.relu(self.bn2(self.conv2(x))))
x = self.pool(self.relu(self.bn3(self.conv3(x))))
x = self.pool(self.relu(self.bn4(self.conv4(x)))) # Pass through the added conv layer
x = x.view(x.size(0), -1) # flatten
x = self.dropout(self.relu(self.fc1(x)))
x = self.dropout(self.relu(self.fc2(x)))
x = self.dropout(self.relu(self.fc3(x)))
x = self.dropout(self.relu(self.fc4(x)))
logits = self.fc5(x)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(logits.view(-1, 2), labels.view(-1))
if loss is not None:
return logits, loss.item()
else:
return logits, None
def preprocess_image(img, desired_size=224):
im = img
# Resize and pad the image
old_size = im.size
ratio = float(desired_size) / max(old_size)
new_size = tuple([int(x*ratio) for x in old_size])
im = im.resize(new_size)
# Create a new image and paste the resized on it
new_im = Image.new("RGB", (desired_size, desired_size), "white")
new_im.paste(im, ((desired_size-new_size[0])//2,
(desired_size-new_size[1])//2))
return new_im
def predict_image(image, model):
# Ensure model is in eval mode
model.eval()
# Convert image to tensor
transform = ToTensor()
input_tensor = transform(image)
input_batch = input_tensor.unsqueeze(0)
# Move tensors to the right device
input_batch = input_batch.to(device)
# Forward pass of the image through the model
output = model(input_batch)
# Convert model output to probabilities using softmax
probabilities = torch.nn.functional.softmax(output[0], dim=1)
return probabilities.cpu().detach().numpy() |