3D ResNet

import torch
# Choose the `slow_r50` model 
model = torch.hub.load('facebookresearch/pytorchvideo', 'slow_r50', pretrained=True)

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
)

# 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 = 8
sampling_rate = 8
frames_per_second = 30

# Note that this transform is specific to the slow_R50 model.
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=(crop_size, crop_size))
        ]
    ),
)

# 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
start_sec = 0
end_sec = start_sec + clip_duration

# Initialize an EncodedVideo helper class and load the video
video = EncodedVideo.from_path(video_path)

# 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]
print("Top 5 predicted labels: %s" % ", ".join(pred_class_names))