Example Usage

Imports

Load the model:

import torch
# Choose the `x3d_s` model 
model_name = 'x3d_s'
model = torch.hub.load('facebookresearch/pytorchvideo', model_name, pretrained=True)

Import remaining functions:

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
)

Setup

Set the model to eval mode and move to desired device.

# Set to GPU or CPU
device = "cpu"
model = model.eval()
model = model.to(device)

Download the id to label mapping for the Kinetics 400 dataset on which the torch hub models were trained. This will be used to get the category label names from the predicted class ids.

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('"', "")

Define input transform

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

Run Inference

Download an example video.

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)

Load the video and transform it to the input format required by the model.

# 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)

Get Predictions

# 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))

Model Description

X3D model architectures are based on [1] pretrained on the Kinetics dataset.

arch depth frame length x sample rate top 1 top 5 Flops (G) Params (M)
X3D XS 4x12 69.12 88.63 0.91 3.79
X3D S 13x6 73.33 91.27 2.96 3.79
X3D M 16x5 75.94 92.72 6.72 3.79

References

[1] Christoph Feichtenhofer, “X3D: Expanding Architectures for Efficient Video Recognition.” https://arxiv.org/abs/2004.04730