Duplicate from not-lain/deepfake

Co-authored-by: not-lain <[email protected]>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+__pycache__/pipeline.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+base_model: aaronespasa/deepfake-detection-resnetinceptionv1
+library_name: transformers
+---
+# original model repo : 
+📖 this is a cutomized version of the following model [aaronespasa/deepfake-detection-resnetinceptionv1](https://huggingface.co/aaronespasa/deepfake-detection-resnetinceptionv1)
+# how to use
+```python
+from transformers import pipeline
+pipe = pipeline(model="not-lain/deepfake",trust_remote_code=True)
+pipe.predict("img_path.jpg")
+```
+```python
+>> {"confidences":confidences,"face_with_mask": face_with_mask}
+```
+# dependencies
+to install related dependencies simply use the command
+```
+!wget https://huggingface.co/not-lain/deepfake/resolve/main/requirements.txt && pip install -r requirements.txt
+```

__pycache__/pipeline.cpython-310.pyc

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config.json

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+{
+  "DEVICE": "cpu",
+  "_name_or_path": "not-lain/deepfake",
+  "architectures": [
+    "DeepFakeModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "deepfakeconfig.DeepFakeConfig",
+    "AutoModelForImageClassification": "deepfakemodel.DeepFakeModel"
+  },
+  "custom_pipelines": {
+    "deepfake": {
+      "default": {
+        "model": {
+          "pt": [
+            "not-lain/deepfake",
+            "main"
+          ]
+        }
+      },
+      "impl": "pipeline.DeepFakePipeline",
+      "pt": [
+        "AutoModelForImageClassification"
+      ],
+      "tf": [],
+      "type": "multimodal"
+    }
+  },
+  "model_type": "ResNet",
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.1"
+}

deepfakeconfig.py

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+from transformers import PretrainedConfig
+import torch 
+class DeepFakeConfig(PretrainedConfig):
+    model_type = "ResNet"
+    def __init__(self,**kwargs):
+        super().__init__(**kwargs)
+        self.DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'

deepfakemodel.py

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+from transformers import  PreTrainedModel
+from facenet_pytorch import MTCNN, InceptionResnetV1
+from .deepfakeconfig import DeepFakeConfig
+
+class DeepFakeModel(PreTrainedModel):
+    config_class = DeepFakeConfig
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = InceptionResnetV1(
+            pretrained="vggface2",
+            classify=True,
+            num_classes=1,
+            device=config.DEVICE
+        )
+
+
+DeepFakeConfig.register_for_auto_class()
+DeepFakeModel.register_for_auto_class("AutoModelForImageClassification")

model.safetensors

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+oid sha256:f1300ca17461156f633c87852ca294a257260ab7b965cff81b448f1c295dacd1
+size 94126980

pipeline.py

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+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)

pytorch_model.bin

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requirements.txt

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+Pillow
+facenet-pytorch==2.5.2
+torch==1.11.0
+opencv-python
+grad-cam
+transformers