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import torch
# Choose the `x3d_s` model
import json
import urllib
from pytorchvideo.data.encoded_video import EncodedVideo
from torchvision.transforms import Compose, Lambda
from torchvision.transforms._transforms_video import (
CenterCropVideo,
NormalizeVideo,
)
from pytorchvideo.transforms import (
ApplyTransformToKey,
ShortSideScale,
UniformTemporalSubsample
)
import gradio as gr
#Video
torch.hub.download_url_to_file('https://dl.fbaipublicfiles.com/pytorchvideo/projects/archery.mp4', 'archery.mp4')
model_name = 'x3d_s'
model = torch.hub.load('facebookresearch/pytorchvideo:main', model_name, pretrained=True)
# Set to GPU or CPU
device = "cpu"
model = model.eval()
model = model.to(device)
json_url = "https://dl.fbaipublicfiles.com/pyslowfast/dataset/class_names/kinetics_classnames.json"
json_filename = "kinetics_classnames.json"
try: urllib.URLopener().retrieve(json_url, json_filename)
except: urllib.request.urlretrieve(json_url, json_filename)
with open(json_filename, "r") as f:
kinetics_classnames = json.load(f)
# Create an id to label name mapping
kinetics_id_to_classname = {}
for k, v in kinetics_classnames.items():
kinetics_id_to_classname[v] = str(k).replace('"', "")
mean = [0.45, 0.45, 0.45]
std = [0.225, 0.225, 0.225]
frames_per_second = 30
model_transform_params = {
"x3d_xs": {
"side_size": 182,
"crop_size": 182,
"num_frames": 4,
"sampling_rate": 12,
},
"x3d_s": {
"side_size": 182,
"crop_size": 182,
"num_frames": 13,
"sampling_rate": 6,
},
"x3d_m": {
"side_size": 256,
"crop_size": 256,
"num_frames": 16,
"sampling_rate": 5,
}
}
# Get transform parameters based on model
transform_params = model_transform_params[model_name]
# Note that this transform is specific to the slow_R50 model.
transform = ApplyTransformToKey(
key="video",
transform=Compose(
[
UniformTemporalSubsample(transform_params["num_frames"]),
Lambda(lambda x: x/255.0),
NormalizeVideo(mean, std),
ShortSideScale(size=transform_params["side_size"]),
CenterCropVideo(
crop_size=(transform_params["crop_size"], transform_params["crop_size"])
)
]
),
)
# The duration of the input clip is also specific to the model.
clip_duration = (transform_params["num_frames"] * transform_params["sampling_rate"])/frames_per_second
def x3dpred(video):
# Select the duration of the clip to load by specifying the start and end duration
# The start_sec should correspond to where the action occurs in the video
start_sec = 0
end_sec = start_sec + clip_duration
# Initialize an EncodedVideo helper class and load the video
video = EncodedVideo.from_path(video)
# Load the desired clip
video_data = video.get_clip(start_sec=start_sec, end_sec=end_sec)
# Apply a transform to normalize the video input
video_data = transform(video_data)
# Move the inputs to the desired device
inputs = video_data["video"]
inputs = inputs.to(device)
# Pass the input clip through the model
preds = model(inputs[None, ...])
# Get the predicted classes
post_act = torch.nn.Softmax(dim=1)
preds = post_act(preds)
pred_classes = preds.topk(k=5).indices[0]
# Map the predicted classes to the label names
pred_class_names = [kinetics_id_to_classname[int(i)] for i in pred_classes]
return "%s" % ", ".join(pred_class_names)
inputs = gr.inputs.Video(label="Input Video")
outputs = gr.outputs.Textbox(label="Top 5 predicted labels")
title = "X3D"
description = "Gradio demo for X3D networks pretrained on the Kinetics 400 dataset. To use it, simply upload your video, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2004.04730' target='_blank'>X3D: Expanding Architectures for Efficient Video Recognition</a> | <a href='https://github.com/facebookresearch/pytorchvideo' target='_blank'>Github Repo</a></p>"
examples = [
['archery.mp4']
]
gr.Interface(x3dpred, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False,).launch(enable_queue=True,cache_examples=True) |