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ConvNeXt(
  (stem): Sequential(
    (0): Conv2d(3, 128, kernel_size=(4, 4), stride=(4, 4))
    (1): LayerNorm2d((128,), eps=1e-06, elementwise_affine=True)
  )
  (stages): Sequential(
    (0): ConvNeXtStage(
      (downsample): Identity()
      (blocks): Sequential(

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-- Ensure the extension is made
CREATE EXTENSION IF NOT EXISTS vectorscale CASCADE;

-- Create the collection table
CREATE TABLE collection (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  name TEXT NOT NULL
);

-- Create the asset table with a foreign key to the collection table

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-- Dataset Params
SET app.num_collections           TO 30;
SET app.num_assets_per_collection TO 50;
SET app.num_segments_per_asset    TO 300;

-- BUILD DATASET

-- Ensure the extension is made
DROP EXTENSION IF EXISTS vectors;
CREATE EXTENSION vectors;

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import timm
import torch
import torch.distributed as dist
import torch.nn as nn
from upyog.imports import *

from yolox.exp.yolox_base import Exp as DefaultBaseExp
from yolox.models import YOLOPAFPN, YOLOX, YOLOXHead
from yolox.utils import get_local_rank, wait_for_the_master

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import fastcore.all as fastcore
import PIL

@fastcore.patch
def __or__(self: PIL.Image.Image, other: PIL.Image.Image):
    "Horizontally stack two PIL Images"
    assert isinstance(other, PIL.Image.Image)
    widths, heights = zip(*(i.size for i in [self, other]))
    
    new_img = PIL.Image.new("RGB", (sum(widths), max(heights)))

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from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True

img_path = "image.jpg"
lut_path = "lut.cube"

img = Image.open(img_path)  # .convert("RGB")
lut = read_lut(lut_path)

# This returns a PIL Image with the LUT applied

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import torch, torchvision
from onnxruntime import InferenceSession

model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)
x = torch.rand(1, 3, 360, 640)

torch.onnx.export(
    model,
    x,  # ONNX requires fixed input size
    "test-mask-rcnn-export.onnx",

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# !pip install pyav
import av

PATH_VIDEO = "..."

container = av.open(PATH_VIDEO)
video_stream = container.streams.video[0]
video_decoder = container.decode(video_stream)

for frame in video_decoder:

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from icevision.visualize.draw_data import draw_bbox
# import a bunch of other stuff here

cmap_path = "zz_color_map_coco.json"
with open(cmap_path) as f:
    COLOR_MAP_COCO = json.load(f)
    COLOR_MAP_COCO = {
        k: np.array(v).astype(np.float) for k, v in COLOR_MAP_COCO.items()
    }