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import torch
import torch.nn as nn
from torchvision.transforms._transforms_video import (CenterCropVideo,NormalizeVideo)
from torchvision.transforms import (Compose,Lambda,RandomCrop,RandomHorizontalFlip,Resize)
from pytorchvideo.transforms import (ApplyTransformToKey,Normalize,RandomShortSideScale,UniformTemporalSubsample,Permute)
import numpy as np
import gradio as gr
import spaces
# torch.save(model.state_dict(), 'model.pth')
video_transform = Compose([
ApplyTransformToKey(key = 'video',
transform = Compose([
UniformTemporalSubsample(20),
Lambda(lambda x:x/255),
Normalize((0.45,0.45,0.45),(0.225,0.225,0.225)),
RandomShortSideScale(min_size = 248, max_size = 256),
CenterCropVideo(224),
RandomHorizontalFlip(p=0.5),
]),
),
])
#============================================================
class mymodel_test(nn.Module):
def __init__(self):
super(mymodel_test,self).__init__()
self.video_model = torch.hub.load('facebookresearch/pytorchvideo','efficient_x3d_xs', pretrained=False)
self.relu = nn.ReLU()
self.Linear = nn.Linear(400,1)
def forward(self,x):
x = self.relu(self.video_model(x))
x = self.Linear(x)
return x
#============================================================
model_test = mymodel_test()
model_test.load_state_dict(torch.load('model.pth'))
from pytorchvideo.data.encoded_video import EncodedVideo
@spaces.GPU
def interface_video(video_path):
video = EncodedVideo.from_path(video_path)
video_data = video.get_clip(0,2)
video_data = video_transform(video_data)
video_data['video'].shape
model = model_test
inputs = video_data['video']
inputs = torch.unsqueeze(inputs, 0 )
inputs.shape
preds = model(inputs)
preds = preds.detach().cpu().numpy()
preds = np.where(preds>0.5,1,0)
if(preds==0):
return 'non violence'
else:
return 'violence'
# return preds
demo = gr.Blocks()
with demo:
with gr.Row():
with gr.Column():
with gr.Row():
input_video = gr.Video(label='Input Video', height=360)
# input_video = load_video(input_video)
with gr.Row():
submit_video_button = gr.Button('Submit')
with gr.Column():
label_video = gr.Label()
submit_video_button.click(fn=iinterface_video, inputs=input_video, outputs=label_video)
demo.launch() |