Avoid CUDA round-trip for relevant export formats (#3727)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/nn/autobackend.py

@@ -83,16 +83,23 @@ class AutoBackend(nn.Module):
         nn_module = isinstance(weights, torch.nn.Module)
         pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, ncnn, triton = \
             self._model_type(w)
-        fp16 &= pt or jit or onnx or engine or nn_module or triton  # FP16
+        fp16 &= pt or jit or onnx or xml or engine or nn_module or triton  # FP16
         nhwc = coreml or saved_model or pb or tflite or edgetpu  # BHWC formats (vs torch BCWH)
         stride = 32  # default stride
         model, metadata = None, None
+
+        # Set device
         cuda = torch.cuda.is_available() and device.type != 'cpu'  # use CUDA
+        if cuda and not any([nn_module, pt, jit, engine]):  # GPU dataloader formats
+            device = torch.device('cpu')
+            cuda = False
+
+        # Download if not local
         if not (pt or triton or nn_module):
-            w = attempt_download_asset(w)  # download if not local
+            w = attempt_download_asset(w)
 
-        # NOTE: special case: in-memory pytorch model
-        if nn_module:
+        # Load model
+        if nn_module:  # in-memory PyTorch model
             model = weights.to(device)
             model = model.fuse(verbose=verbose) if fuse else model
             if hasattr(model, 'kpt_shape'):
@@ -269,14 +276,13 @@ class AutoBackend(nn.Module):
             net.load_model(str(w.with_suffix('.bin')))
             metadata = w.parent / 'metadata.yaml'
         elif triton:  # NVIDIA Triton Inference Server
-            LOGGER.info('Triton Inference Server not supported...')
-            '''
-            TODO:
+            """TODO
             check_requirements('tritonclient[all]')
             from utils.triton import TritonRemoteModel
             model = TritonRemoteModel(url=w)
             nhwc = model.runtime.startswith("tensorflow")
-            '''
+            """
+            raise NotImplementedError('Triton Inference Server is not currently supported.')
         else:
             from ultralytics.yolo.engine.exporter import export_formats
             raise TypeError(f"model='{w}' is not a supported model format. "

ultralytics/vit/sam/predict.py

@@ -18,7 +18,9 @@ from .build import build_sam
 
 class Predictor(BasePredictor):
 
-    def __init__(self, cfg=DEFAULT_CFG, overrides={}, _callbacks=None):
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        if overrides is None:
+            overrides = {}
         overrides.update(dict(task='segment', mode='predict', imgsz=1024))
         super().__init__(cfg, overrides, _callbacks)
         # SAM needs retina_masks=True, or the results would be a mess.
@@ -90,7 +92,7 @@ class Predictor(BasePredictor):
                 of masks and H=W=256. These low resolution logits can be passed to
                 a subsequent iteration as mask input.
         """
-        if all([i is None for i in [bboxes, points, masks]]):
+        if all(i is None for i in [bboxes, points, masks]):
             return self.generate(im, *args, **kwargs)
         return self.prompt_inference(im, bboxes, points, labels, masks, multimask_output)
 
@@ -284,7 +286,7 @@ class Predictor(BasePredictor):
 
         return pred_masks, pred_scores, pred_bboxes
 
-    def setup_model(self, model):
+    def setup_model(self, model, verbose=True):
         """Set up YOLO model with specified thresholds and device."""
         device = select_device(self.args.device)
         if model is None:
@@ -306,7 +308,7 @@ class Predictor(BasePredictor):
         # (N, 1, H, W), (N, 1)
         pred_masks, pred_scores = preds[:2]
         pred_bboxes = preds[2] if self.segment_all else None
-        names = dict(enumerate([str(i) for i in range(len(pred_masks))]))
+        names = dict(enumerate(str(i) for i in range(len(pred_masks))))
         results = []
         for i, masks in enumerate([pred_masks]):
             orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs

ultralytics/yolo/engine/predictor.py

@@ -300,17 +300,16 @@ class BasePredictor:
 
     def setup_model(self, model, verbose=True):
         """Initialize YOLO model with given parameters and set it to evaluation mode."""
-        device = select_device(self.args.device, verbose=verbose)
-        model = model or self.args.model
-        self.args.half &= device.type != 'cpu'  # half precision only supported on CUDA
-        self.model = AutoBackend(model,
-                                 device=device,
+        self.model = AutoBackend(model or self.args.model,
+                                 device=select_device(self.args.device, verbose=verbose),
                                  dnn=self.args.dnn,
                                  data=self.args.data,
                                  fp16=self.args.half,
                                  fuse=True,
                                  verbose=verbose)
-        self.device = device
+
+        self.device = self.model.device  # update device
+        self.args.half = self.model.fp16  # update half
         self.model.eval()
 
     def show(self, p):

ultralytics/yolo/engine/validator.py

@@ -109,19 +109,21 @@ class BaseValidator:
             callbacks.add_integration_callbacks(self)
             self.run_callbacks('on_val_start')
             assert model is not None, 'Either trainer or model is needed for validation'
-            self.device = select_device(self.args.device, self.args.batch)
-            self.args.half &= self.device.type != 'cpu'
-            model = AutoBackend(model, device=self.device, dnn=self.args.dnn, data=self.args.data, fp16=self.args.half)
+            model = AutoBackend(model,
+                                device=select_device(self.args.device, self.args.batch),
+                                dnn=self.args.dnn,
+                                data=self.args.data,
+                                fp16=self.args.half)
             self.model = model
+            self.device = model.device  # update device
+            self.args.half = model.fp16  # update half
             stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
             imgsz = check_imgsz(self.args.imgsz, stride=stride)
             if engine:
                 self.args.batch = model.batch_size
-            else:
-                self.device = model.device
-                if not pt and not jit:
-                    self.args.batch = 1  # export.py models default to batch-size 1
-                    LOGGER.info(f'Forcing batch=1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
+            elif not pt and not jit:
+                self.args.batch = 1  # export.py models default to batch-size 1
+                LOGGER.info(f'Forcing batch=1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
 
             if isinstance(self.args.data, str) and self.args.data.endswith('.yaml'):
                 self.data = check_det_dataset(self.args.data)

ultralytics/yolo/utils/checks.py

@@ -213,7 +213,6 @@ def check_requirements(requirements=ROOT.parent / 'requirements.txt', exclude=()
     prefix = colorstr('red', 'bold', 'requirements:')
     check_python()  # check python version
     check_torchvision()  # check torch-torchvision compatibility
-    file = None
     if isinstance(requirements, Path):  # requirements.txt file
         file = requirements.resolve()
         assert file.exists(), f'{prefix} {file} not found, check failed.'
@@ -225,13 +224,13 @@ def check_requirements(requirements=ROOT.parent / 'requirements.txt', exclude=()
     s = ''  # console string
     pkgs = []
     for r in requirements:
-        rmin = r.split('/')[-1].replace('.git', '')  # replace git+https://org/repo.git -> 'repo'
+        r_stripped = r.split('/')[-1].replace('.git', '')  # replace git+https://org/repo.git -> 'repo'
         try:
-            pkg.require(rmin)
+            pkg.require(r_stripped)
         except (pkg.VersionConflict, pkg.DistributionNotFound):  # exception if requirements not met
             try:  # attempt to import (slower but more accurate)
                 import importlib
-                importlib.import_module(next(pkg.parse_requirements(rmin)).name)
+                importlib.import_module(next(pkg.parse_requirements(r_stripped)).name)
             except ImportError:
                 s += f'"{r}" '
                 pkgs.append(r)