Create pipeline.py

pipeline.py

@@ -0,0 +1,72 @@
+from transformers.pipelines import PIPELINE_REGISTRY
+from transformers import Pipeline, AutoModelForImageClassification
+import torch
+from PIL import Image
+import cv2
+from pytorch_grad_cam import GradCAM
+from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
+from pytorch_grad_cam.utils.image import show_cam_on_image
+from facenet_pytorch import MTCNN
+import torch.nn.functional as F
+
+class DeepFakePipeline(Pipeline):
+    def __init__(self,**kwargs):
+        Pipeline.__init__(self,**kwargs)
+    def _sanitize_parameters(self, **kwargs):
+        return {}, {}, {}
+    def preprocess(self, inputs):
+        return inputs
+    def _forward(self,input):
+      return input
+    def postprocess(self,confidences,face_with_mask):
+        out =  {"confidences":confidences,
+        "face_with_mask": face_with_mask}
+        return out
+
+    def predict(self,input_image:str):
+      DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+      mtcnn = MTCNN(
+          select_largest=False,
+          post_process=False,
+          device=DEVICE)
+      mtcnn.to(DEVICE)
+      model = self.model.model
+      model.to(DEVICE)
+
+      input_image = Image.open(input_image)
+      face = mtcnn(input_image)
+      if face is None:
+          raise Exception('No face detected')
+
+      face = face.unsqueeze(0) # add the batch dimension
+      face = F.interpolate(face, size=(256, 256), mode='bilinear', align_corners=False)
+
+      # convert the face into a numpy array to be able to plot it
+      prev_face = face.squeeze(0).permute(1, 2, 0).cpu().detach().int().numpy()
+      prev_face = prev_face.astype('uint8')
+
+      face = face.to(DEVICE)
+      face = face.to(torch.float32)
+      face = face / 255.0
+      face_image_to_plot = face.squeeze(0).permute(1, 2, 0).cpu().detach().int().numpy()
+
+      target_layers=[model.block8.branch1[-1]]
+      cam = GradCAM(model=model, target_layers=target_layers)
+      targets = [ClassifierOutputTarget(0)]
+      grayscale_cam = cam(input_tensor=face, targets=targets,eigen_smooth=True)
+      grayscale_cam = grayscale_cam[0, :]
+      visualization = show_cam_on_image(face_image_to_plot, grayscale_cam, use_rgb=True)
+      face_with_mask = cv2.addWeighted(prev_face, 1, visualization, 0.5, 0)
+
+      with torch.no_grad():
+          output = torch.sigmoid(model(face).squeeze(0))
+          prediction = "real" if output.item() < 0.5 else "fake"
+          
+          real_prediction = 1 - output.item()
+          fake_prediction = output.item()
+          
+          confidences = {
+              'real': real_prediction,
+              'fake': fake_prediction
+          }
+      return self.postprocess(confidences, face_with_mask)