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model_train.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
from torchvision import transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
class ImageMetadataDataset(Dataset):
def __init__(self, csv_file, image_folder, transform=None):
self.metadata = pd.read_csv(csv_file)
self.image_folder = image_folder
self.transform = transform
def __len__(self):
return len(self.metadata)
def __getitem__(self, idx):
img_name = f"{self.image_folder}/{self.metadata.iloc[idx, 0]}"
image = Image.open(img_name)
if self.transform:
image = self.transform(image)
metadata = self.metadata.iloc[idx, 1:].values.astype('float')
sample = {'image': image, 'metadata': torch.tensor(metadata, dtype=torch.float)}
return sample
class MultimodalCNN(nn.Module):
def __init__(self, metadata_size):
super(MultimodalCNN, self).__init__()
self.image_model = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1), # Conv2d-1
nn.BatchNorm2d(64), # BatchNorm2d-2
nn.ReLU(), # ReLU-3
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1), # Conv2d-4
nn.BatchNorm2d(64), # BatchNorm2d-5
nn.ReLU(), # ReLU-6
nn.MaxPool2d(kernel_size=2, stride=2), # MaxPool2d-7
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1), # Conv2d-8
nn.BatchNorm2d(128), # BatchNorm2d-9
nn.ReLU(), # ReLU-10
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),# Conv2d-11
nn.BatchNorm2d(128), # BatchNorm2d-12
nn.ReLU(), # ReLU-13
nn.MaxPool2d(kernel_size=2, stride=2), # MaxPool2d-14
nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),# Conv2d-15
nn.BatchNorm2d(256), # BatchNorm2d-16
nn.ReLU(), # ReLU-17
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),# Conv2d-18
nn.BatchNorm2d(256), # BatchNorm2d-19
nn.ReLU(), # ReLU-20
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),# Conv2d-21
nn.BatchNorm2d(256), # BatchNorm2d-22
nn.ReLU(), # ReLU-23
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),# Conv2d-24
nn.BatchNorm2d(256), # BatchNorm2d-25
nn.ReLU(), # ReLU-26
nn.MaxPool2d(kernel_size=2, stride=2), # MaxPool2d-27
nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),# Conv2d-28
nn.BatchNorm2d(512), # BatchNorm2d-29
nn.ReLU(), # ReLU-30
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),# Conv2d-31
nn.BatchNorm2d(512), # BatchNorm2d-32
nn.ReLU(), # ReLU-33
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),# Conv2d-34
nn.BatchNorm2d(512), # BatchNorm2d-35
nn.ReLU(), # ReLU-36
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),# Conv2d-37
nn.BatchNorm2d(512), # BatchNorm2d-38
nn.ReLU(), # ReLU-39
nn.MaxPool2d(kernel_size=2, stride=2), # MaxPool2d-40
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),# Conv2d-41
nn.BatchNorm2d(512), # BatchNorm2d-42
nn.ReLU(), # ReLU-43
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),# Conv2d-44
nn.BatchNorm2d(512), # BatchNorm2d-45
nn.ReLU(), # ReLU-46
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),# Conv2d-47
nn.BatchNorm2d(512), # BatchNorm2d-48
nn.ReLU(), # ReLU-49
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),# Conv2d-50
nn.BatchNorm2d(512), # BatchNorm2d-51
nn.ReLU(), # ReLU-52
nn.MaxPool2d(kernel_size=2, stride=2), # MaxPool2d-53
nn.AdaptiveAvgPool2d((7, 7)), # AdaptiveAvgPool2d-54
)
self.image_fc = nn.Sequential(
nn.Linear(512 * 7 * 7, 1536), # Linear-55
nn.ReLU(), # ReLU-56
nn.Dropout(0.5), # Dropout-57
nn.Linear(1536, 128) # Linear-58
)
self.metadata_fc = nn.Sequential(
nn.Linear(metadata_size, 128),
nn.ReLU(),
nn.Linear(128, 128),
nn.ReLU(),
nn.Linear(128, 128)
)
self.final_fc = nn.Sequential(
nn.Linear(256, 128),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(128, 1)
)
def forward(self, x_image, x_metadata):
x_image = self.image_model(x_image)
x_image = x_image.view(x_image.size(0), -1)
x_image = self.image_fc(x_image)
x_metadata = self.metadata_fc(x_metadata)
x = torch.cat((x_image, x_metadata), dim=1)
x = self.final_fc(x)
return torch.sigmoid(x)
# Example usage:
transform = transforms.Compose([
transforms.Resize((512, 512)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
dataset = ImageMetadataDataset(csv_file='path_to_csv.csv', image_folder='path_to_images', transform=transform)
dataloader = DataLoader(dataset, batch_size=4, shuffle=True, num_workers=4)
model = MultimodalCNN(metadata_size=dataset.metadata.shape[1] - 1) # -1 to exclude image column
# Define loss and optimizer
criterion = nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# Training loop (simplified)
for epoch in range(10): # Example for 10 epochs
for batch in dataloader:
images, metadata = batch['image'], batch['metadata']
outputs = model(images, metadata)
labels = metadata[:, 0] # Assuming the first column in metadata is the label
loss = criterion(outputs, labels.unsqueeze(1))
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(f"Epoch {epoch + 1}, Loss: {loss.item()}")