deleted main.py

app.py

@@ -2,21 +2,14 @@ import gradio as gr
 from typing import List
 import cv2
 import torch
-from torchvision import transforms
 import numpy as np
-from PIL import Image
-from pytorch_grad_cam import GradCAM
-from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
 from pytorch_grad_cam.utils.image import show_cam_on_image
-import io
 from models import YoloV3Lightning
 from utils import load_model_from_checkpoint
 import utils
 import config as cfg
 import matplotlib.pyplot as plt
 import matplotlib.patches as patches
-from dataset import YOLODataset
-from torch.utils.data import Dataset, DataLoader
 from grad_cam import YoloGradCAM
 
 device = torch.device('cpu')

main.py

@@ -1,86 +0,0 @@
-from dataset import *
-from models.YoloV3Lightning import *
-import utils
-
-def init(model, basic_sanity_check=True, find_max_lr=True, train=True, **kwargs):
-    if basic_sanity_check:
-        validate_dataset()
-        sanity_check(model)
-        print("Set basic_sanity_check to False to proceed")
-    else:
-        if find_max_lr:
-            optimizer = kwargs.get('optimizer')
-            criterion = kwargs.get('criterion')
-            train_loader = kwargs.get('train_loader')
-            utils.find_lr(model, optimizer, criterion, train_loader)
-            print("Set find_max_lr to False to proceed further")
-        else:
-
-            train_loader = kwargs.get('train_loader')
-            val_loader = kwargs.get('test_loader')
-
-            if train:
-                trainer = pl.Trainer(
-                    precision=16,
-                    max_epochs=cfg.NUM_EPOCHS,
-                    accelerator='gpu'
-                )
-
-                cargs = {}
-                if cfg.LOAD_MODEL:
-                    cargs = dict(ckpt_path=cfg.CHECKPOINT_FILE)
-
-                trainer.fit(model, train_loader, val_loader, **cargs)
-            else:
-                ckpt_file = kwargs.get('ckpt_file')
-                if ckpt_file:
-                    checkpoint = utils.load_model_from_checkpoint(cfg.DEVICE, file_name=ckpt_file)
-                    model.load_state_dict(checkpoint['model'], strict=False)
-
-            #-- Printing samples
-            model.to(cfg.DEVICE)
-            model.eval()
-            cfg.IMG_DIR = cfg.DATASET + "/images/"
-            cfg.LABEL_DIR = cfg.DATASET + "/labels/"
-            eval_dataset = YOLODataset(
-                cfg.DATASET + "/test.csv",
-                transform=cfg.test_transforms,
-                S=[cfg.IMAGE_SIZE // 32, cfg.IMAGE_SIZE // 16, cfg.IMAGE_SIZE // 8],
-                img_dir=cfg.IMG_DIR,
-                label_dir=cfg.LABEL_DIR,
-                anchors=cfg.ANCHORS,
-                mosaic=False
-            )
-            eval_loader = DataLoader(
-                dataset=eval_dataset,
-                batch_size=cfg.BATCH_SIZE,
-                num_workers=cfg.NUM_WORKERS,
-                pin_memory=cfg.PIN_MEMORY,
-                shuffle=True,
-                drop_last=False,
-            )
-
-            scaled_anchors = (
-                    torch.tensor(cfg.ANCHORS)
-                    * torch.tensor(cfg.S).unsqueeze(1).unsqueeze(1).repeat(1, 3, 2)
-            )
-            scaled_anchors = scaled_anchors.to(cfg.DEVICE)
-
-            utils.plot_examples(model, eval_loader, 0.5, 0.6, scaled_anchors)
-
-            # -- Printing MAP
-            pred_boxes, true_boxes = utils.get_evaluation_bboxes(
-                eval_loader,
-                model,
-                iou_threshold=cfg.NMS_IOU_THRESH,
-                anchors=cfg.ANCHORS,
-                threshold=cfg.CONF_THRESHOLD,
-            )
-            mapval = utils.mean_average_precision(
-                pred_boxes,
-                true_boxes,
-                iou_threshold=cfg.MAP_IOU_THRESH,
-                box_format="midpoint",
-                num_classes=cfg.NUM_CLASSES,
-            )
-            print(f"MAP: {mapval.item()}")