PyTorch Hub results.render() (#1897)

models/common.py

@@ -1,6 +1,7 @@
 # This file contains modules common to various models
 
 import math
+
 import numpy as np
 import requests
 import torch
@@ -240,7 +241,7 @@ class Detections:
         self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
         self.n = len(self.pred)
 
-    def display(self, pprint=False, show=False, save=False):
+    def display(self, pprint=False, show=False, save=False, render=False):
         colors = color_list()
         for i, (img, pred) in enumerate(zip(self.imgs, self.pred)):
             str = f'Image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} '
@@ -248,19 +249,21 @@ class Detections:
                 for c in pred[:, -1].unique():
                     n = (pred[:, -1] == c).sum()  # detections per class
                     str += f'{n} {self.names[int(c)]}s, '  # add to string
-                if show or save:
+                if show or save or render:
                     img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img  # from np
                     for *box, conf, cls in pred:  # xyxy, confidence, class
                         # str += '%s %.2f, ' % (names[int(cls)], conf)  # label
                         ImageDraw.Draw(img).rectangle(box, width=4, outline=colors[int(cls) % 10])  # plot
+            if pprint:
+                print(str)
+            if show:
+                img.show(f'Image {i}')  # show
             if save:
                 f = f'results{i}.jpg'
                 str += f"saved to '{f}'"
                 img.save(f)  # save
-            if show:
-                img.show(f'Image {i}')  # show
-            if pprint:
-                print(str)
+            if render:
+                self.imgs[i] = np.asarray(img)
 
     def print(self):
         self.display(pprint=True)  # print results
@@ -271,6 +274,10 @@ class Detections:
     def save(self):
         self.display(save=True)  # save results
 
+    def render(self):
+        self.display(render=True)  # render results
+        return self.imgs
+
     def __len__(self):
         return self.n
 

train.py

@@ -28,7 +28,7 @@ from utils.autoanchor import check_anchors
 from utils.datasets import create_dataloader
 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, \
-    check_requirements, print_mutation, set_logging, one_cycle
+    check_requirements, print_mutation, set_logging, one_cycle, colorstr
 from utils.google_utils import attempt_download
 from utils.loss import compute_loss
 from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
@@ -44,7 +44,7 @@ except ImportError:
 
 
 def train(hyp, opt, device, tb_writer=None, wandb=None):
-    logger.info(f'Hyperparameters {hyp}')
+    logger.info(colorstr('blue', 'bold', 'Hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
     save_dir, epochs, batch_size, total_batch_size, weights, rank = \
         Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
 
@@ -233,9 +233,10 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
     results = (0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
     scheduler.last_epoch = start_epoch - 1  # do not move
     scaler = amp.GradScaler(enabled=cuda)
-    logger.info('Image sizes %g train, %g test\n'
-                'Using %g dataloader workers\nLogging results to %s\n'
-                'Starting training for %g epochs...' % (imgsz, imgsz_test, dataloader.num_workers, save_dir, epochs))
+    logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n'
+                f'Using {dataloader.num_workers} dataloader workers\n'
+                f'Logging results to {save_dir}\n'
+                f'Starting training for {epochs} epochs...')
     for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
         model.train()
 

utils/general.py

@@ -25,6 +25,7 @@ from utils.torch_utils import init_torch_seeds
 torch.set_printoptions(linewidth=320, precision=5, profile='long')
 np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format})  # format short g, %precision=5
 cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
+os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8))  # NumExpr max threads
 
 
 def set_logging(rank=-1):
@@ -117,7 +118,7 @@ def one_cycle(y1=0.0, y2=1.0, steps=100):
 
 def colorstr(*input):
     # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e.  colorstr('blue', 'hello world')
-    *prefix, str = input  # color arguments, string
+    *prefix, string = input  # color arguments, string
     colors = {'black': '\033[30m',  # basic colors
               'red': '\033[31m',
               'green': '\033[32m',
@@ -136,9 +137,9 @@ def colorstr(*input):
               'bright_white': '\033[97m',
               'end': '\033[0m',  # misc
               'bold': '\033[1m',
-              'undelrine': '\033[4m'}
+              'underline': '\033[4m'}
 
-    return ''.join(colors[x] for x in prefix) + str + colors['end']
+    return ''.join(colors[x] for x in prefix) + f'{string}' + colors['end']
 
 
 def labels_to_class_weights(labels, nc=80):