Utils reorganization (#1392)

* Utils reorganization

* Add new utils files

* cleanup

* simplify

* reduce datasets.py

* remove evolve.sh

* loadWebcam cleanup

train.py

@@ -3,7 +3,6 @@ import logging
 import math
 import os
 import random
-import shutil
 import time
 from pathlib import Path
 from warnings import warn
@@ -23,13 +22,15 @@ from tqdm import tqdm
 
 import test  # import test.py to get mAP after each epoch
 from models.yolo import Model
+from utils.autoanchor import check_anchors
 from utils.datasets import create_dataloader
-from utils.general import (
-    torch_distributed_zero_first, labels_to_class_weights, plot_labels, check_anchors, labels_to_image_weights,
-    compute_loss, plot_images, fitness, strip_optimizer, plot_results, get_latest_run, check_dataset, check_file,
-    check_git_status, check_img_size, increment_path, print_mutation, plot_evolution, set_logging, init_seeds)
+from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
+    fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \
+    print_mutation, set_logging
 from utils.google_utils import attempt_download
-from utils.torch_utils import ModelEMA, select_device, intersect_dicts
+from utils.loss import compute_loss
+from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
+from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first
 
 logger = logging.getLogger(__name__)
 
@@ -209,7 +210,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
 
     # Start training
     t0 = time.time()
-    nw = max(round(hyp['warmup_epochs'] * nb), 1e3)  # number of warmup iterations, max(3 epochs, 1k iterations)
+    nw = max(round(hyp['warmup_epochs'] * nb), 1000)  # number of warmup iterations, max(3 epochs, 1k iterations)
     # nw = min(nw, (epochs - start_epoch) / 2 * nb)  # limit warmup to < 1/2 of training
     maps = np.zeros(nc)  # mAP per class
     results = (0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
@@ -334,9 +335,9 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
                 os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
 
             # Log
-            tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss',  # train loss
+            tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss',  # train loss
                     'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
-                    'val/giou_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
+                    'val/box_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
                     'x/lr0', 'x/lr1', 'x/lr2']  # params
             for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
                 if tb_writer: