Upload model_architecture.py

model_architecture.py

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+import torch
+import numpy
+from PIL import Image
+from torchvision.transforms import ToTensor
+from transformers import ViTModel, ViTFeatureExtractor
+from transformers.modeling_outputs import SequenceClassifierOutput
+import torch.nn as nn
+
+feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-large-patch32-384')
+class ViTForImageClassification(nn.Module):
+    def __init__(self, num_labels=2):
+      super(ViTForImageClassification, self).__init__()
+      self.vit = ViTModel.from_pretrained('google/vit-large-patch32-384')
+      self.dropout = nn.Dropout(0.1)
+      self.classifier = nn.Linear(self.vit.config.hidden_size, num_labels)
+      self.num_labels = num_labels
+
+    def forward(self, pixel_values, labels=None):
+      outputs = self.vit(pixel_values=pixel_values)
+      output = self.dropout(outputs.last_hidden_state[:,0])
+      logits = self.classifier(output)
+      
+      if labels is not None:
+        loss_fct = nn.CrossEntropyLoss()
+        loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+        )
+      else:
+        return logits
+      
+def preprocess_image(image, desired_size=384):
+    im = image
+
+    # 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, feature_extractor):
+  # Ensure model is in eval mode
+  model.eval()
+
+  # Convert image to tensor
+  transform = ToTensor()
+  input_tensor = transform(image)
+  input_tensor = torch.tensor(numpy.array(feature_extractor(input_tensor)['pixel_values']))
+
+  # Move tensors to the right device
+  input_tensor = input_tensor.cuda()
+
+  # Forward pass of the image through the model
+  output = model(input_tensor)
+
+  # Convert model output to probabilities using softmax
+  probabilities = torch.nn.functional.softmax(output, dim=1)
+
+  return probabilities.cpu().detach().numpy()
+
+
+model = ViTForImageClassification(num_labels=2)
+model.load_state_dict(torch.load("./AID96k_E15_384.pth"))
+model.cuda()
+model.eval()
+img = Image.open("test.png")
+img = preprocess_image(img)
+probs = predict_image(img, model, feature_extractor)
+print(f"AI: {probs[0][0]}")
+print(f"Human: {probs[0][1]}")