Chris-hughes10

from pathlib import Path

import PIL

import numpy as np

class CarsDatasetAdaptor:
    def __init__(self, images_dir_path, annotations_dataframe):
        self.images_dir_path = Path(images_dir_path)
        self.annotations_df = annotations_dataframe

Chris-hughes10

from effdet.config.model_config import efficientdet_model_param_dict
from effdet import get_efficientdet_config, EfficientDet, DetBenchTrain
from effdet.efficientdet import HeadNet
from effdet.config.model_config import efficientdet_model_param_dict

def create_model(num_classes=1, image_size=512, architecture="tf_efficientnetv2_l"):
    efficientdet_model_param_dict['tf_efficientnetv2_l'] = dict(
        name='tf_efficientnetv2_l',
        backbone_name='tf_efficientnetv2_l',
        backbone_args=dict(drop_path_rate=0.2),

Chris-hughes10

import albumentations as A
from albumentations.pytorch.transforms import ToTensorV2


def get_train_transforms(target_img_size=512):
    return A.Compose(
        [
            A.HorizontalFlip(p=0.5),
            A.Resize(height=target_img_size, width=target_img_size, p=1),
            A.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),

Chris-hughes10

from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader

class EfficientDetDataModule(LightningDataModule):
    
    def __init__(self,
                train_dataset_adaptor,
                validation_dataset_adaptor,
                train_transforms=get_train_transforms(target_img_size=512),
                valid_transforms=get_valid_transforms(target_img_size=512),

Chris-hughes10

import torch
from pytorch_lightning import LightningModule
from pytorch_lightning.core.decorators import auto_move_data

class EfficientDetModel(LightningModule):
    def __init__(
        self,
        num_classes=1,
        img_size=512,
        prediction_confidence_threshold=0.2,

Chris-hughes10

    @typedispatch
    def predict(self, images: List):
        """
        For making predictions from images
        Args:
            images: a list of PIL images

        Returns: a tuple of lists containing bboxes, predicted_class_labels, predicted_class_confidences

        """

Chris-hughes10

def _run_inference(self, images_tensor, image_sizes):
        dummy_targets = self._create_dummy_inference_targets(
            num_images=images_tensor.shape[0]
        )

        detections = self.model(images_tensor.to(self.device), dummy_targets)[
            "detections"
        ]
        (
            predicted_bboxes,

Chris-hughes10

from fastcore.basics import patch

@patch
def aggregate_prediction_outputs(self: EfficientDetModel, outputs):

    detections = torch.cat(
        [output["batch_predictions"]["predictions"] for output in outputs]
    )

    image_ids = []

Chris-hughes10

from objdetecteval.metrics.coco_metrics import get_coco_stats

@patch
def validation_epoch_end(self: EfficientDetModel, outputs):
    """Compute and log training loss and accuracy at the epoch level."""

    validation_loss_mean = torch.stack(
        [output["loss"] for output in outputs]
    ).mean()

Chris-hughes10

# this example is taken from 
# https://github.com/Chris-hughes10/pytorch-accelerated/blob/main/examples/train_mnist.py

import os

from torch import nn, optim
from torch.utils.data import random_split
from torchvision import transforms
from torchvision.datasets import MNIST