tchaton

from typing import List, Optional, NamedTuple

# use to make model jittable
OptTensor = Optional[Tensor]
ListTensor = List[Tensor]

class TensorBatch(NamedTuple):
    x: Tensor
    edge_index: ListTensor
    edge_attr: OptTensor

tchaton

def get_single_batch(datamodule):
    for batch in datamodule.test_dataloader():
        return datamodule.gather_data(batch, 0)

def run(args):
    datamodule: LightningDataModule = instantiate_datamodule(args)
    model: LightningModule = instantiate_model(args, datamodule)
    print(model)
    model.jittable()
    print(model)

tchaton

{
    // Use IntelliSense to learn about possible attributes.
    // Hover to view descriptions of existing attributes.
    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
    "version": "0.2.0",
    "configurations": [
        {
            "name": "Python: test_params_groups_and_state_are_accessible",
            "type": "python",
            "request": "launch",

tchaton

ssh-rsa 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 thomas@thomass-MacBook-Pro.local

tchaton

model = VideoClassifier(backbone="x3d_xs", num_classes=datamodule.num_classes, serializer=Labels())

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model = VideoClassifier(
  backbone="x3d_xs", 
  num_classes=datamodule.num_classes, 
  serializer=Labels()
)

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predictions = model.predict(os.path.join(flash.PROJECT_ROOT, "data/kinetics/predict"))
print(predictions)
# ['archery', 'bowling', 'flying_kite', 'high_jump', 'marching']

tchaton

import os
from typing import Callable, List

import kornia.augmentation as K
import torch
from pytorchvideo.transforms import ApplyTransformToKey, RandomShortSideScale, UniformTemporalSubsample
from torch.utils.data.sampler import RandomSampler
from torchvision.transforms import CenterCrop, Compose, RandomCrop, RandomHorizontalFlip

import flash

tchaton

# 1. Download a video clip dataset.
download_data("https://pl-flash-data.s3.amazonaws.com/kinetics.zip")

tchaton

# 2. [Optional] Specify transforms to be used during training.
post_tensor_transform = [UniformTemporalSubsample(8), RandomShortSideScale(min_size=256, max_size=320)]

per_batch_transform_on_device = [K.Normalize(torch.tensor([0.45, 0.45, 0.45]), torch.tensor([0.225, 0.225, 0.225]))]

train_post_tensor_transform = post_tensor_transform + [RandomCrop(244), RandomHorizontalFlip(p=0.5)]

val_post_tensor_transform = post_tensor_transform + [CenterCrop(244)]
train_per_batch_transform_on_device = per_batch_transform_on_device