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import torch
# Choose the `slowfast_r50` model
model = torch.hub.load('facebookresearch/pytorchvideo', 'slowfast_r50', pretrained=True)
from typing import Dict
import json
import urllib
from torchvision.transforms import Compose, Lambda
from torchvision.transforms._transforms_video import (
CenterCropVideo,
NormalizeVideo,
)
from pytorchvideo.data.encoded_video import EncodedVideo
from pytorchvideo.transforms import (
ApplyTransformToKey,
ShortSideScale,
UniformTemporalSubsample,
UniformCropVideo
)
import gradio as gr
# 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('"', "")
side_size = 256
mean = [0.45, 0.45, 0.45]
std = [0.225, 0.225, 0.225]
crop_size = 256
num_frames = 32
sampling_rate = 2
frames_per_second = 30
slowfast_alpha = 4
num_clips = 10
num_crops = 3
class PackPathway(torch.nn.Module):
"""
Transform for converting video frames as a list of tensors.
"""
def __init__(self):
super().__init__()
def forward(self, frames: torch.Tensor):
fast_pathway = frames
# Perform temporal sampling from the fast pathway.
slow_pathway = torch.index_select(
frames,
1,
torch.linspace(
0, frames.shape[1] - 1, frames.shape[1] // slowfast_alpha
).long(),
)
frame_list = [slow_pathway, fast_pathway]
return frame_list
transform = ApplyTransformToKey(
key="video",
transform=Compose(
[
UniformTemporalSubsample(num_frames),
Lambda(lambda x: x/255.0),
NormalizeVideo(mean, std),
ShortSideScale(
size=side_size
),
CenterCropVideo(crop_size),
PackPathway()
]
),
)
# The duration of the input clip is also specific to the model.
clip_duration = (num_frames * sampling_rate)/frames_per_second
url_link = "https://dl.fbaipublicfiles.com/pytorchvideo/projects/archery.mp4"
video_path = 'archery.mp4'
try: urllib.URLopener().retrieve(url_link, video_path)
except: urllib.request.urlretrieve(url_link, video_path)
# 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
def inference(in_vid):
start_sec = 0
end_sec = start_sec + clip_duration
# Initialize an EncodedVideo helper class and load the video
video = EncodedVideo.from_path(in_vid)
# 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 = [i.to(device)[None, ...] for i in inputs]
# Pass the input clip through the model
preds = model(inputs)
# 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 = "SLOWFAST"
description = "demo for SLOWFAST, SlowFast 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/1812.03982'>SlowFast Networks for Video Recognition</a> | <a href='https://github.com/facebookresearch/pytorchvideo'>Github Repo</a></p>"
examples = [
['archery.mp4']
]
gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch(debug=True) |