EMA FP32 assert classification bug fix (#9016)

* Return EMA float on classification val

* verbose val fix

* EMA check

classify/val.py

@@ -116,7 +116,7 @@ def run(
     if verbose:  # all classes
         LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}")
         LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}")
-        for i, c in enumerate(model.names):
+        for i, c in model.names.items():
             aci = acc[targets == i]
             top1i, top5i = aci.mean(0).tolist()
             LOGGER.info(f"{c:>24}{aci.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")
@@ -127,6 +127,7 @@ def run(
         LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}' % t)
         LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
 
+    model.float()  # for training
     return top1, top5, loss
 
 

export.py

@@ -599,7 +599,7 @@ def parse_opt():
     parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold')
     parser.add_argument('--include',
                         nargs='+',
-                        default=['torchscript', 'onnx'],
+                        default=['torchscript'],
                         help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs')
     opt = parser.parse_args()
     print_args(vars(opt))

models/experimental.py

@@ -8,7 +8,6 @@
 import torch
 import torch.nn as nn
 
-from models.common import Conv
 from utils.downloads import attempt_download
 
 
@@ -79,11 +78,16 @@ def attempt_load(weights, device=None, inplace=True, fuse=True):
     for w in weights if isinstance(weights, list) else [weights]:
         ckpt = torch.load(attempt_download(w), map_location='cpu')  # load
         ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float()  # FP32 model
+
+        # Model compatibility updates
         if not hasattr(ckpt, 'stride'):
-            ckpt.stride = torch.tensor([32.])  # compatibility update for ResNet etc.
+            ckpt.stride = torch.tensor([32.])
+        if hasattr(ckpt, 'names') and isinstance(ckpt.names, (list, tuple)):
+            ckpt.names = dict(enumerate(ckpt.names))  # convert to dict
+
         model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval())  # model in eval mode
 
-    # Compatibility updates
+    # Module compatibility updates
     for m in model.modules():
         t = type(m)
         if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):

train.py

@@ -107,8 +107,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
         data_dict = data_dict or check_dataset(data)  # check if None
     train_path, val_path = data_dict['train'], data_dict['val']
     nc = 1 if single_cls else int(data_dict['nc'])  # number of classes
-    names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
-    assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}'  # check
+    names = {0: 'item'} if single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
     is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt')  # COCO dataset
 
     # Model

utils/torch_utils.py

@@ -408,8 +408,6 @@ class ModelEMA:
     def __init__(self, model, decay=0.9999, tau=2000, updates=0):
         # Create EMA
         self.ema = deepcopy(de_parallel(model)).eval()  # FP32 EMA
-        # if next(model.parameters()).device.type != 'cpu':
-        #     self.ema.half()  # FP16 EMA
         self.updates = updates  # number of EMA updates
         self.decay = lambda x: decay * (1 - math.exp(-x / tau))  # decay exponential ramp (to help early epochs)
         for p in self.ema.parameters():
@@ -423,9 +421,10 @@ def update(self, model):
 
         msd = de_parallel(model).state_dict()  # model state_dict
         for k, v in self.ema.state_dict().items():
-            if v.dtype.is_floating_point:
+            if v.dtype.is_floating_point:  # true for FP16 and FP32
                 v *= d
-                v += (1 - d) * msd[k].detach()
+                v += (1 - d) * msd[k]
+        assert v.dtype == msd[k].dtype == torch.float32, f'EMA {v.dtype} and model {msd[k]} must be updated in FP32'
 
     def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
         # Update EMA attributes