Pycocotools best.pt after COCO train (#1616)

* Pycocotools best.pt after COCO train

* cleanup

models/hub/yolov3-tiny.yaml

@@ -0,0 +1,41 @@
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+
+# anchors
+anchors:
+  - [10,14, 23,27, 37,58]  # P4/16
+  - [81,82, 135,169, 344,319]  # P5/32
+
+# YOLOv3-tiny backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [16, 3, 1]],  # 0
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 1-P1/2
+   [-1, 1, Conv, [32, 3, 1]],
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 3-P2/4
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 5-P3/8
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 7-P4/16
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 9-P5/32
+   [-1, 1, Conv, [512, 3, 1]],
+   [-1, 1, nn.ZeroPad2d, [0, 1, 0, 1]],  # 11
+   [-1, 1, nn.MaxPool2d, [2, 1, 0]],  # 12
+  ]
+
+# YOLOv3-tiny head
+head:
+  [[-1, 1, Conv, [1024, 3, 1]],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [512, 3, 1]],  # 15 (P5/32-large)
+
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
+   [-1, 1, Conv, [256, 3, 1]],  # 19 (P4/16-medium)
+
+   [[19, 15], 1, Detect, [nc, anchors]],  # Detect(P4, P5)
+  ]

models/hub/yolov3.yaml

@@ -0,0 +1,51 @@
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+
+# anchors
+anchors:
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [116,90, 156,198, 373,326]  # P5/32
+
+# darknet53 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [32, 3, 1]],  # 0
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
+   [-1, 1, Bottleneck, [64]],
+   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4
+   [-1, 2, Bottleneck, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 5-P3/8
+   [-1, 8, Bottleneck, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 7-P4/16
+   [-1, 8, Bottleneck, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P5/32
+   [-1, 4, Bottleneck, [1024]],  # 10
+  ]
+
+# YOLOv3 head
+head:
+  [[-1, 1, Bottleneck, [1024, False]],
+   [-1, 1, Conv, [512, [1, 1]]],
+   [-1, 1, Conv, [1024, 3, 1]],
+   [-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [1024, 3, 1]],  # 15 (P5/32-large)
+
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
+   [-1, 1, Bottleneck, [512, False]],
+   [-1, 1, Bottleneck, [512, False]],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [512, 3, 1]],  # 22 (P4/16-medium)
+
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P3
+   [-1, 1, Bottleneck, [256, False]],
+   [-1, 2, Bottleneck, [256, False]],  # 27 (P3/8-small)
+
+   [[27, 22, 15], 1, Detect, [nc, anchors]],   # Detect(P3, P4, P5)
+  ]

test.py

@@ -1,5 +1,4 @@
 import argparse
-import glob
 import json
 import os
 from pathlib import Path
@@ -246,7 +245,7 @@ def test(data,
     # Save JSON
     if save_json and len(jdict):
         w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weights
-        anno_json = glob.glob('../coco/annotations/instances_val*.json')[0]  # annotations json
+        anno_json = '../coco/annotations/instances_val2017.json'  # annotations json
         pred_json = str(save_dir / f"{w}_predictions.json")  # predictions json
         print('\nEvaluating pycocotools mAP... saving %s...' % pred_json)
         with open(pred_json, 'w') as f:
@@ -266,7 +265,7 @@ def test(data,
             eval.summarize()
             map, map50 = eval.stats[:2]  # update results (mAP@0.5:0.95, mAP@0.5)
         except Exception as e:
-            print('ERROR: pycocotools unable to run: %s' % e)
+            print(f'pycocotools unable to run: {e}')
 
     # Return results
     if not training:

train.py

@@ -22,6 +22,7 @@
 from tqdm import tqdm
 
 import test  # import test.py to get mAP after each epoch
+from models.experimental import attempt_load
 from models.yolo import Model
 from utils.autoanchor import check_anchors
 from utils.datasets import create_dataloader
@@ -193,9 +194,9 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
     # Process 0
     if rank in [-1, 0]:
         ema.updates = start_epoch * nb // accumulate  # set EMA updates
-        testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt,
+        testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt,  # testloader
                                        hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True,
-                                       rank=-1, world_size=opt.world_size, workers=opt.workers)[0]  # testloader
+                                       rank=-1, world_size=opt.world_size, workers=opt.workers, pad=0.5)[0]
 
         if not opt.resume:
             labels = np.concatenate(dataset.labels, 0)
@@ -385,22 +386,32 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
 
     if rank in [-1, 0]:
         # Strip optimizers
-        n = opt.name if opt.name.isnumeric() else ''
-        fresults, flast, fbest = save_dir / f'results{n}.txt', wdir / f'last{n}.pt', wdir / f'best{n}.pt'
-        for f1, f2 in zip([wdir / 'last.pt', wdir / 'best.pt', results_file], [flast, fbest, fresults]):
-            if f1.exists():
-                os.rename(f1, f2)  # rename
-                if str(f2).endswith('.pt'):  # is *.pt
-                    strip_optimizer(f2)  # strip optimizer
-                    os.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket else None  # upload
-        # Finish
+        for f in [last, best]:
+            if f.exists():  # is *.pt
+                strip_optimizer(f)  # strip optimizer
+                os.system('gsutil cp %s gs://%s/weights' % (f, opt.bucket)) if opt.bucket else None  # upload
+
+        # Plots
         if plots:
             plot_results(save_dir=save_dir)  # save as results.png
             if wandb:
                 files = ['results.png', 'precision_recall_curve.png', 'confusion_matrix.png']
                 wandb.log({"Results": [wandb.Image(str(save_dir / f), caption=f) for f in files
                                        if (save_dir / f).exists()]})
         logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
+
+        # Test best.pt
+        if opt.data.endswith('coco.yaml') and nc == 80:  # if COCO
+            results, _, _ = test.test(opt.data,
+                                      batch_size=total_batch_size,
+                                      imgsz=imgsz_test,
+                                      model=attempt_load(best if best.exists() else last, device).half(),
+                                      single_cls=opt.single_cls,
+                                      dataloader=testloader,
+                                      save_dir=save_dir,
+                                      save_json=True,  # use pycocotools
+                                      plots=False)
+
     else:
         dist.destroy_process_group()
 

utils/google_utils.py

@@ -17,7 +17,7 @@ def gsutil_getsize(url=''):
 
 def attempt_download(weights):
     # Attempt to download pretrained weights if not found locally
-    weights = weights.strip().replace("'", '')
+    weights = str(weights).strip().replace("'", '')
     file = Path(weights).name.lower()
 
     msg = weights + ' missing, try downloading from https://github.com/ultralytics/yolov5/releases/'