Chris-hughes10

Chris-hughes10

#!/bin/bash
# =============================================================================
# Install AzCopy on Linux
# https://docs.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10
# https://github.com/Azure/azure-storage-azcopy
# -----------------------------------------------------------------------------
# Developer.......: Andre Essing (https://www.andre-essing.de/)
#                                (https://github.com/aessing)
#                                (https://twitter.com/aessing)
#                                (https://www.linkedin.com/in/aessing/)

Chris-hughes10

Chris-hughes10

import argparse
from pathlib import Path

import timm
import timm.data
import timm.loss
import timm.optim
import timm.utils
import torch
import torchmetrics

Chris-hughes10

from pathlib import Path

from timm.data.parsers.parser import Parser


class ParserImageName(Parser):
    def __init__(self, root, class_to_idx=None):
        super().__init__()

        self.root = Path(root)

Chris-hughes10

Chris-hughes10

from pytorch_accelerated.callbacks import TrainerCallback
import torchmetrics

class RecommenderMetricsCallback(TrainerCallback):
    def __init__(self):
        self.metrics = torchmetrics.MetricCollection(
            {
                "mse": torchmetrics.MeanSquaredError(),
                "mae": torchmetrics.MeanAbsoluteError(),
            }

Chris-hughes10

import torch
from torch import nn

class MfDotBias(nn.Module):

    def __init__(
        self, n_factors, n_users, n_items, ratings_range=None, use_biases=True
    ):
        super().__init__()
        self.bias = use_biases

Chris-hughes10

# https://github.com/Chris-hughes10/pytorch-accelerated/blob/main/examples/metrics/train_with_metrics_in_callback.py

import os

from torch import nn, optim
from torch.utils.data import random_split
from torchmetrics import MetricCollection, Accuracy, Precision, Recall
from torchvision import transforms
from torchvision.datasets import MNIST

Chris-hughes10

from torchmetrics import MetricCollection, Accuracy, Precision, Recall

from pytorch_accelerated.callbacks import TrainerCallback


class ClassificationMetricsCallback(TrainerCallback):
    def __init__(self, num_classes):
        self.metrics = MetricCollection(
            {
                "accuracy": Accuracy(num_classes=num_classes),