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import gradio as gr
import torch
from pytorchvideo.data.encoded_video import EncodedVideo
from pytorchvideo.transforms import UniformTemporalSubsample
from transformers import VideoMAEForVideoClassification
import torch.nn.functional as F
import torchvision.transforms.functional as F_t # Changed import
# Check device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Load pre-trained model
model_path = "model"
loaded_model = VideoMAEForVideoClassification.from_pretrained(model_path)
loaded_model = loaded_model.to(device)
loaded_model.eval()
# Label names for prediction
label_names = [
'Archery', 'BalanceBeam', 'BenchPress', 'ApplyEyeMakeup', 'BasketballDunk',
'BandMarching', 'BabyCrawling', 'ApplyLipstick', 'BaseballPitch', 'Basketball'
]
def load_video(video_path):
try:
video = EncodedVideo.from_path(video_path)
video_data = video.get_clip(start_sec=0, end_sec=video.duration)
return video_data['video']
except Exception as e:
raise ValueError(f"Error loading video: {str(e)}")
def preprocess_video(video_frames):
try:
# Temporal subsampling
transform_temporal = UniformTemporalSubsample(16)
video_frames = transform_temporal(video_frames)
# Convert to float and normalize to [0, 1]
video_frames = video_frames.float() / 255.0
# Ensure channel order is [T, C, H, W]
if video_frames.shape[0] == 3:
video_frames = video_frames.permute(1, 0, 2, 3)
# Normalize using torchvision's functional transform
mean = torch.tensor([0.485, 0.456, 0.406])
std = torch.tensor([0.229, 0.224, 0.225])
for t in range(video_frames.shape[0]):
video_frames[t] = F_t.normalize(video_frames[t], mean, std)
# Resize frames
video_frames = torch.stack([
F_t.resize(frame, [224, 224], antialias=True)
for frame in video_frames
])
# Add batch dimension
video_frames = video_frames.unsqueeze(0)
return video_frames
except Exception as e:
raise ValueError(f"Error preprocessing video: {str(e)}")
def predict_video(video):
try:
# Load and preprocess video
video_data = load_video(video)
processed_video = preprocess_video(video_data)
processed_video = processed_video.to(device)
# Make predictions
with torch.no_grad():
outputs = loaded_model(processed_video)
logits = outputs.logits
probabilities = F.softmax(logits, dim=-1)[0]
top_3 = torch.topk(probabilities, 3)
# Format results
results = [
f"{label_names[idx.item()]}: {prob.item():.2%}"
for idx, prob in zip(top_3.indices, top_3.values)
]
return "\n".join(results)
except Exception as e:
return f"Error processing video: {str(e)}"
# Gradio interface
iface = gr.Interface(
fn=predict_video,
inputs=gr.Video(label="Upload Video"),
outputs=gr.Textbox(label="Top 3 Predictions"),
title="Video Action Recognition",
description="Upload a video to classify the action being performed. The model will return the top 3 predictions.",
examples=[
["test_video_1.avi"],
["test_video_2.avi"],
["test_video_3.avi"]
]
)
if __name__ == "__main__":
iface.launch(debug=True, share=True) |