New CSV Logger (#4148)

* New CSV Logger

* cleanup

* move batch plots into Logger

* rename comment

* Remove total loss from progress bar

* mloss :-1 bug fix

* Update plot_results()

* Update plot_results()

* plot_results bug fix

.gitignore

 !data/*.sh
 
 results*.txt
+results*.csv
 
 # Datasets -------------------------------------------------------------------------------------------------------------
 coco/

train.py

 import time
 from copy import deepcopy
 from pathlib import Path
-from threading import Thread
 
 import math
 import numpy as np
     check_requirements, print_mutation, set_logging, one_cycle, colorstr
 from utils.google_utils import attempt_download
 from utils.loss import ComputeLoss
-from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
+from utils.plots import plot_labels, plot_evolution
 from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, de_parallel
 from utils.loggers.wandb.wandb_utils import check_wandb_resume
 from utils.metrics import fitness
     # Directories
     w = save_dir / 'weights'  # weights dir
     w.mkdir(parents=True, exist_ok=True)  # make dir
-    last, best, results_file = w / 'last.pt', w / 'best.pt', save_dir / 'results.txt'
+    last, best = w / 'last.pt', w / 'best.pt'
 
     # Hyperparameters
     if isinstance(hyp, str):
 
     # Loggers
     if RANK in [-1, 0]:
-        loggers = Loggers(save_dir, results_file, weights, opt, hyp, data_dict, LOGGER).start()  # loggers dict
+        loggers = Loggers(save_dir, weights, opt, hyp, data_dict, LOGGER).start()  # loggers dict
         if loggers.wandb and resume:
             weights, epochs, hyp, data_dict = opt.weights, opt.epochs, opt.hyp, loggers.wandb.data_dict
 
             ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
             ema.updates = ckpt['updates']
 
-        # Results
-        if ckpt.get('training_results') is not None:
-            results_file.write_text(ckpt['training_results'])  # write results.txt
-
         # Epochs
         start_epoch = ckpt['epoch'] + 1
         if resume:
         # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
         # dataset.mosaic_border = [b - imgsz, -b]  # height, width borders
 
-        mloss = torch.zeros(4, device=device)  # mean losses
+        mloss = torch.zeros(3, device=device)  # mean losses
         if RANK != -1:
             train_loader.sampler.set_epoch(epoch)
         pbar = enumerate(train_loader)
-        LOGGER.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size'))
+        LOGGER.info(('\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size'))
         if RANK in [-1, 0]:
             pbar = tqdm(pbar, total=nb)  # progress bar
         optimizer.zero_grad()
                     ema.update(model)
                 last_opt_step = ni
 
-            # Print
+            # Log
             if RANK in [-1, 0]:
                 mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
                 mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G'  # (GB)
-                s = ('%10s' * 2 + '%10.4g' * 6) % (
-                    f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])
-                pbar.set_description(s)
-
-                # Plot
-                if plots:
-                    if ni < 3:
-                        f = save_dir / f'train_batch{ni}.jpg'  # filename
-                        Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
-                    loggers.on_train_batch_end(ni, model, imgs)
+                pbar.set_description(('%10s' * 2 + '%10.4g' * 5) % (
+                    f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
+                loggers.on_train_batch_end(ni, model, imgs, targets, paths, plots)
 
             # end batch ------------------------------------------------------------------------------------------------
 
             fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
             if fi > best_fitness:
                 best_fitness = fi
-            loggers.on_train_val_end(mloss, results, lr, epoch, s, best_fitness, fi)
+            loggers.on_train_val_end(mloss, results, lr, epoch, best_fitness, fi)
 
             # Save model
             if (not nosave) or (final_epoch and not evolve):  # if save
                 ckpt = {'epoch': epoch,
                         'best_fitness': best_fitness,
-                        'training_results': results_file.read_text(),
                         'model': deepcopy(de_parallel(model)).half(),
                         'ema': deepcopy(ema.ema).half(),
                         'updates': ema.updates,
     # end training -----------------------------------------------------------------------------------------------------
     if RANK in [-1, 0]:
         LOGGER.info(f'{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.\n')
-        if plots:
-            plot_results(save_dir=save_dir)  # save as results.png
-
         if not evolve:
             if is_coco:  # COCO dataset
                 for m in [last, best] if best.exists() else [last]:  # speed, mAP tests
                                             save_dir=save_dir,
                                             save_json=True,
                                             plots=False)
-
             # Strip optimizers
             for f in last, best:
                 if f.exists():
                     strip_optimizer(f)  # strip optimizers
-
-        loggers.on_train_end(last, best)
+        loggers.on_train_end(last, best, plots)
 
     torch.cuda.empty_cache()
     return results

utils/loggers/__init__.py

 # YOLOv5 experiment logging utils
 
 import warnings
+from threading import Thread
 
 import torch
 from torch.utils.tensorboard import SummaryWriter
 
 from utils.general import colorstr, emojis
 from utils.loggers.wandb.wandb_utils import WandbLogger
+from utils.plots import plot_images, plot_results
 from utils.torch_utils import de_parallel
 
-LOGGERS = ('txt', 'tb', 'wandb')  # text-file, TensorBoard, Weights & Biases
+LOGGERS = ('csv', 'tb', 'wandb')  # text-file, TensorBoard, Weights & Biases
 
 try:
     import wandb
 
 class Loggers():
     # YOLOv5 Loggers class
-    def __init__(self, save_dir=None, results_file=None, weights=None, opt=None, hyp=None,
-                 data_dict=None, logger=None, include=LOGGERS):
+    def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, data_dict=None, logger=None, include=LOGGERS):
         self.save_dir = save_dir
-        self.results_file = results_file
         self.weights = weights
         self.opt = opt
         self.hyp = hyp
             setattr(self, k, None)  # init empty logger dictionary
 
     def start(self):
-        self.txt = True  # always log to txt
+        self.csv = True  # always log to csv
 
         # Message
         try:
 
         return self
 
-    def on_train_batch_end(self, ni, model, imgs):
+    def on_train_batch_end(self, ni, model, imgs, targets, paths, plots):
         # Callback runs on train batch end
-        if ni == 0:
-            with warnings.catch_warnings():
-                warnings.simplefilter('ignore')  # suppress jit trace warning
-                self.tb.add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), [])
-        if self.wandb and ni == 10:
-            files = sorted(self.save_dir.glob('train*.jpg'))
-            self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
+        if plots:
+            if ni == 0:
+                with warnings.catch_warnings():
+                    warnings.simplefilter('ignore')  # suppress jit trace warning
+                    self.tb.add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), [])
+            if ni < 3:
+                f = self.save_dir / f'train_batch{ni}.jpg'  # filename
+                Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
+            if self.wandb and ni == 10:
+                files = sorted(self.save_dir.glob('train*.jpg'))
+                self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
 
     def on_train_epoch_end(self, epoch):
         # Callback runs on train epoch end
             files = sorted(self.save_dir.glob('val*.jpg'))
             self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]})
 
-    def on_train_val_end(self, mloss, results, lr, epoch, s, best_fitness, fi):
-        # Callback runs on validation end during training
-        vals = list(mloss[:-1]) + list(results) + lr
-        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',
+    def on_train_val_end(self, mloss, results, lr, epoch, best_fitness, fi):
+        # Callback runs on val end during training
+        vals = list(mloss) + list(results) + lr
+        keys = ['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',  # metrics
                 'val/box_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
                 'x/lr0', 'x/lr1', 'x/lr2']  # params
-        if self.txt:
-            with open(self.results_file, 'a') as f:
-                f.write(s + '%10.4g' * 7 % results + '\n')  # append metrics, val_loss
+        x = {k: v for k, v in zip(keys, vals)}  # dict
+
+        if self.csv:
+            file = self.save_dir / 'results.csv'
+            n = len(x) + 1  # number of cols
+            s = '' if file.exists() else (('%20s,' * n % tuple(['epoch'] + keys)).rstrip(',') + '\n')  # add header
+            with open(file, 'a') as f:
+                f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n')
+
         if self.tb:
-            for x, tag in zip(vals, tags):
-                self.tb.add_scalar(tag, x, epoch)  # TensorBoard
+            for k, v in x.items():
+                self.tb.add_scalar(k, v, epoch)  # TensorBoard
+
         if self.wandb:
-            self.wandb.log({k: v for k, v in zip(tags, vals)})
+            self.wandb.log(x)
             self.wandb.end_epoch(best_result=best_fitness == fi)
 
     def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
             if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
                 self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
 
-    def on_train_end(self, last, best):
+    def on_train_end(self, last, best, plots):
         # Callback runs on training end
+        if plots:
+            plot_results(dir=self.save_dir)  # save results.png
         files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
         files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()]  # filter
         if self.wandb:

utils/loss.py

         lcls *= self.hyp['cls']
         bs = tobj.shape[0]  # batch size
 
-        loss = lbox + lobj + lcls
-        return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
+        return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()
 
     def build_targets(self, p, targets):
         # Build targets for compute_loss(), input targets(image,class,x,y,w,h)

utils/plots.py

 # Plotting utils
 
-import glob
-import os
 from copy import copy
 from pathlib import Path
 
     plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)
 
 
-def plot_results_overlay(start=0, stop=0):  # from utils.plots import *; plot_results_overlay()
-    # Plot training 'results*.txt', overlaying train and val losses
-    s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95']  # legends
-    t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1']  # titles
-    for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
-        results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
-        n = results.shape[1]  # number of rows
-        x = range(start, min(stop, n) if stop else n)
-        fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True)
-        ax = ax.ravel()
-        for i in range(5):
-            for j in [i, i + 5]:
-                y = results[j, x]
-                ax[i].plot(x, y, marker='.', label=s[j])
-                # y_smooth = butter_lowpass_filtfilt(y)
-                # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j])
-
-            ax[i].set_title(t[i])
-            ax[i].legend()
-            ax[i].set_ylabel(f) if i == 0 else None  # add filename
-        fig.savefig(f.replace('.txt', '.png'), dpi=200)
-
-
-def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
-    # Plot training 'results*.txt'. from utils.plots import *; plot_results(save_dir='runs/train/exp')
+def plot_results(file='', dir=''):
+    # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
+    save_dir = Path(file).parent if file else Path(dir)
     fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
     ax = ax.ravel()
-    s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall',
-         'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']
-    if bucket:
-        # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]
-        files = ['results%g.txt' % x for x in id]
-        c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/results%g.txt' % (bucket, x) for x in id)
-        os.system(c)
-    else:
-        files = list(Path(save_dir).glob('results*.txt'))
-    assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir)
+    files = list(save_dir.glob('results*.csv'))
+    assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
     for fi, f in enumerate(files):
         try:
-            results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
-            n = results.shape[1]  # number of rows
-            x = range(start, min(stop, n) if stop else n)
-            for i in range(10):
-                y = results[i, x]
-                if i in [0, 1, 2, 5, 6, 7]:
-                    y[y == 0] = np.nan  # don't show zero loss values
-                    # y /= y[0]  # normalize
-                label = labels[fi] if len(labels) else f.stem
-                ax[i].plot(x, y, marker='.', label=label, linewidth=2, markersize=8)
-                ax[i].set_title(s[i])
-                # if i in [5, 6, 7]:  # share train and val loss y axes
+            data = pd.read_csv(f)
+            s = [x.strip() for x in data.columns]
+            x = data.values[:, 0]
+            for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
+                y = data.values[:, j]
+                # y[y == 0] = np.nan  # don't show zero values
+                ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
+                ax[i].set_title(s[j], fontsize=12)
+                # if j in [8, 9, 10]:  # share train and val loss y axes
                 #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
         except Exception as e:
-            print('Warning: Plotting error for %s; %s' % (f, e))
-
+            print(f'Warning: Plotting error for {f}: {e}')
     ax[1].legend()
-    fig.savefig(Path(save_dir) / 'results.png', dpi=200)
+    fig.savefig(save_dir / 'results.png', dpi=200)
 
 
 def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):

val.py

 
         # Compute loss
         if compute_loss:
-            loss += compute_loss([x.float() for x in train_out], targets)[1][:3]  # box, obj, cls
+            loss += compute_loss([x.float() for x in train_out], targets)[1]  # box, obj, cls
 
         # Run NMS
         targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)  # to pixels