diff --git a/models/hub/yolov3-tiny.yaml b/models/hub/yolov3-tiny.yaml
new file mode 100644
index 000000000..85f9fbd49
--- /dev/null
+++ b/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)
+ ]
diff --git a/models/hub/yolov3.yaml b/models/hub/yolov3.yaml
new file mode 100644
index 000000000..f2e761355
--- /dev/null
+++ b/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)
+ ]
diff --git a/test.py b/test.py
index d1c231d4e..cf22218e4 100644
--- a/test.py
+++ b/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:
diff --git a/train.py b/train.py
index 353a19936..b88e44c16 100644
--- a/train.py
+++ b/train.py
@@ -22,6 +22,7 @@ from torch.utils.tensorboard import SummaryWriter
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)
@@ -385,15 +386,12 @@ 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:
@@ -401,6 +399,19 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
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()
diff --git a/utils/google_utils.py b/utils/google_utils.py
index a311efd1b..11d1a6ca7 100644
--- a/utils/google_utils.py
+++ b/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/'