Spaces:
Running
Running
File size: 2,383 Bytes
c6185a5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
import gradio as gr
import torch
import torch.nn.functional as F
from facenet_pytorch import MTCNN, InceptionResnetV1
import os
import numpy as np
from PIL import Image
import zipfile
with zipfile.ZipFile("examples.zip","r") as zip_ref:
zip_ref.extractall(".")
DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'
mtcnn = MTCNN(
select_largest=False,
post_process=False,
device=DEVICE
).to(DEVICE).eval()
model = InceptionResnetV1(
pretrained="vggface2",
classify=True,
num_classes=1,
device=DEVICE
)
checkpoint = torch.load("resnetinceptionv1_epoch_32.pth")
model.load_state_dict(checkpoint['model_state_dict'])
model.to(DEVICE)
model.eval()
EXAMPLES_FOLDER = 'examples'
examples_names = os.listdir(EXAMPLES_FOLDER)
examples = []
for example_name in examples_names:
example_path = os.path.join(EXAMPLES_FOLDER, example_name)
label = example_name.split('_')[0]
example = {
'path': example_path,
'label': label
}
examples.append(example)
np.random.shuffle(examples) # shuffle
def predict(input_image:Image.Image, true_label:str):
"""Predict the label of the 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
face_image_to_plot = face.squeeze(0).permute(1, 2, 0).cpu().detach().int().numpy()
face = face.to(DEVICE)
face = face.to(torch.float32)
face = face / 255.0
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 confidences, true_label, face_image_to_plot
interface = gr.Interface(
fn=predict,
inputs=[
gr.inputs.Image(label="Input Image", type="pil"),
"text"
],
outputs=[
gr.outputs.Label(label="Class"),
"text",
gr.outputs.Image(label="Face")
],
examples=[[examples[i]["path"], examples[i]["label"]] for i in range(10)]
).launch() |