add: inference code.

app.py

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+import cv2
+import gradio as gr
+import imutils
+import numpy as np
+import torch
+from pytorchvideo.transforms import (
+    ApplyTransformToKey,
+    Normalize,
+    RandomShortSideScale,
+    RemoveKey,
+    ShortSideScale,
+    UniformTemporalSubsample,
+)
+from torchvision.transforms import (
+    Compose,
+    Lambda,
+    RandomCrop,
+    RandomHorizontalFlip,
+    Resize,
+)
+from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification
+
+MODEL_CKPT = "sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset"
+MODEL = VideoMAEForVideoClassification.from_pretrained(MODEL_CKPT)
+PROCESSOR = VideoMAEFeatureExtractor.from_pretrained(MODEL_CKPT)
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+RESIZE_TO = PROCESSOR.size
+NUM_FRAMES_TO_SAMPLE = MODEL.config.num_frames
+IMAGE_STATS = {"image_mean": [0.485, 0.456, 0.406], "image_std": [0.229, 0.224, 0.225]}
+VAL_TRANSFORMS = Compose(
+    [
+        UniformTemporalSubsample(NUM_FRAMES_TO_SAMPLE),
+        Lambda(lambda x: x / 255.0),
+        Normalize(IMAGE_STATS["image_mean"], IMAGE_STATS["image_std"]),
+        Resize((RESIZE_TO, RESIZE_TO)),
+    ]
+)
+LABELS = list(MODEL.config.label2id.keys())
+
+
+def parse_video(video_file):
+    """A utility to parse the input videos.
+
+    Reference: https://pyimagesearch.com/2018/11/12/yolo-object-detection-with-opencv/
+    """
+    vs = cv2.VideoCapture(video_file)
+
+    # try to determine the total number of frames in the video file
+    try:
+        prop = (
+            cv2.cv.CV_CAP_PROP_FRAME_COUNT
+            if imutils.is_cv2()
+            else cv2.CAP_PROP_FRAME_COUNT
+        )
+        total = int(vs.get(prop))
+        print("[INFO] {} total frames in video".format(total))
+
+    # an error occurred while trying to determine the total
+    # number of frames in the video file
+    except:
+        print("[INFO] could not determine # of frames in video")
+        print("[INFO] no approx. completion time can be provided")
+        total = -1
+
+    frames = []
+
+    # loop over frames from the video file stream
+    while True:
+        # read the next frame from the file
+        (grabbed, frame) = vs.read()
+        if frame is not None:
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frames.append(frame)
+        # if the frame was not grabbed, then we have reached the end
+        # of the stream
+        if not grabbed:
+            break
+
+    return frames
+
+
+def preprocess_video(frames: list):
+    """Utility to apply preprocessing transformations to a video tensor."""
+    # Each frame in the `frames` list has the shape: (height, width, num_channels).
+    # Collated together the `frames` has the the shape: (num_frames, height, width, num_channels).
+    # So, after converting the `frames` list to a torch tensor, we permute the shape
+    # such that it becomes (num_channels, num_frames, height, width) to make
+    # the shape compatible with the preprocessing transformations. After applying the
+    # preprocessing chain, we permute the shape to (num_frames, num_channels, height, width)
+    # to make it compatible with the model. Finally, we add a batch dimension so that our video
+    # classification model can operate on it.
+    video_tensor = torch.tensor(np.array(frames).astype(frames[0].dtype))
+    video_tensor = video_tensor.permute(
+        3, 0, 1, 2
+    )  # (num_channels, num_frames, height, width)
+    video_tensor_pp = VAL_TRANSFORMS(video_tensor)
+    video_tensor_pp = video_tensor_pp.permute(
+        1, 0, 2, 3
+    )  # (num_frames, num_channels, height, width)
+    video_tensor_pp = video_tensor_pp.unsqueeze(0)
+    return video_tensor_pp.to(DEVICE)
+
+
+def infer(video_file):
+    frames = parse_video(video_file)
+    video_tensor = preprocess_video(frames)
+    inputs = {"pixel_values": video_tensor}
+
+    # forward pass
+    with torch.no_grad():
+        outputs = MODEL(**inputs)
+        logits = outputs.logits
+    softmax_scores = torch.nn.functional.softmax(logits, dim=0)
+    confidences = {LABELS[i]: float(softmax_scores[i]) for i in range(len(LABELS))}
+    return confidences
+
+
+gr.Interface(
+    fn=infer,
+    inputs=gr.Video(type="file"),
+    outputs=gr.Label(num_top_classes=3),
+    examples=[
+        ["examples/babycrawling.mp4"],
+        ["examples/baseball.mp4"],
+        ["examples/balancebeam.mp4"],
+    ],
+    title="VideoMAE fine-tuned on a subset of UCF-101",
+    description=(
+        "Gradio demo for VideoMAE for video classification. To use it, simply upload your video or click one of the"
+        " examples to load them. Read more at the links below."
+    ),
+    article=(
+        "<div style='text-align: center;'><a href='https://huggingface.co/docs/transformers/model_doc/videomae' target='_blank'>VideoMAE</a>"
+        " <center><a href='https://huggingface.co/sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset' target='_blank'>Fine-tuned Model</a></center></div>"
+    ),
+    allow_flagging=False,
+    allow_screenshot=False,
+).launch()

requirements.txt

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+opencv-python
+imutils 
+numpy
+torch