YOLOv5 Export Benchmarks for GPU (#6963)

* Add benchmarks.py GPU support

* Updates

* Updates

* Updates

* Updates

* Add --half

* Add TRT requirements

* Cleanup

* Add TF to warmup types

* Update export.py

* Update export.py

* Update benchmarks.py

export.py

@@ -75,18 +75,18 @@ from utils.torch_utils import select_device
 
 def export_formats():
     # YOLOv5 export formats
-    x = [['PyTorch', '-', '.pt'],
-         ['TorchScript', 'torchscript', '.torchscript'],
-         ['ONNX', 'onnx', '.onnx'],
-         ['OpenVINO', 'openvino', '_openvino_model'],
-         ['TensorRT', 'engine', '.engine'],
-         ['CoreML', 'coreml', '.mlmodel'],
-         ['TensorFlow SavedModel', 'saved_model', '_saved_model'],
-         ['TensorFlow GraphDef', 'pb', '.pb'],
-         ['TensorFlow Lite', 'tflite', '.tflite'],
-         ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite'],
-         ['TensorFlow.js', 'tfjs', '_web_model']]
-    return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix'])
+    x = [['PyTorch', '-', '.pt', True],
+         ['TorchScript', 'torchscript', '.torchscript', True],
+         ['ONNX', 'onnx', '.onnx', True],
+         ['OpenVINO', 'openvino', '_openvino_model', False],
+         ['TensorRT', 'engine', '.engine', True],
+         ['CoreML', 'coreml', '.mlmodel', False],
+         ['TensorFlow SavedModel', 'saved_model', '_saved_model', True],
+         ['TensorFlow GraphDef', 'pb', '.pb', True],
+         ['TensorFlow Lite', 'tflite', '.tflite', False],
+         ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False],
+         ['TensorFlow.js', 'tfjs', '_web_model', False]]
+    return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'GPU'])
 
 
 def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):

models/common.py

@@ -464,10 +464,11 @@ class DetectMultiBackend(nn.Module):
 
     def warmup(self, imgsz=(1, 3, 640, 640)):
         # Warmup model by running inference once
-        if self.pt or self.jit or self.onnx or self.engine:  # warmup types
-            if isinstance(self.device, torch.device) and self.device.type != 'cpu':  # only warmup GPU models
+        if any((self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb)):  # warmup types
+            if self.device.type != 'cpu':  # only warmup GPU models
                 im = torch.zeros(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device)  # input
-                self.forward(im)  # warmup
+                for _ in range(2 if self.jit else 1):  #
+                    self.forward(im)  # warmup
 
     @staticmethod
     def model_type(p='path/to/model.pt'):

utils/benchmarks.py

@@ -19,6 +19,7 @@ TensorFlow.js               | `tfjs`                        | yolov5s_web_model/
 Requirements:
     $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu  # CPU
     $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow  # GPU
+    $ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com  # TensorRT
 
 Usage:
     $ python utils/benchmarks.py --weights yolov5s.pt --img 640
@@ -41,20 +42,29 @@ import export
 import val
 from utils import notebook_init
 from utils.general import LOGGER, print_args
+from utils.torch_utils import select_device
 
 
 def run(weights=ROOT / 'yolov5s.pt',  # weights path
         imgsz=640,  # inference size (pixels)
         batch_size=1,  # batch size
         data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
+        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+        half=False,  # use FP16 half-precision inference
         ):
     y, t = [], time.time()
     formats = export.export_formats()
-    for i, (name, f, suffix) in formats.iterrows():  # index, (name, file, suffix)
+    device = select_device(device)
+    for i, (name, f, suffix, gpu) in formats.iterrows():  # index, (name, file, suffix, gpu-capable)
         try:
-            w = weights if f == '-' else export.run(weights=weights, imgsz=[imgsz], include=[f], device='cpu')[-1]
+            if device.type != 'cpu':
+                assert gpu, f'{name} inference not supported on GPU'
+            if f == '-':
+                w = weights  # PyTorch format
+            else:
+                w = export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1]  # all others
             assert suffix in str(w), 'export failed'
-            result = val.run(data, w, batch_size, imgsz=imgsz, plots=False, device='cpu', task='benchmark')
+            result = val.run(data, w, batch_size, imgsz, plots=False, device=device, task='benchmark', half=half)
             metrics = result[0]  # metrics (mp, mr, map50, map, *losses(box, obj, cls))
             speeds = result[2]  # times (preprocess, inference, postprocess)
             y.append([name, metrics[3], speeds[1]])  # mAP, t_inference
@@ -78,6 +88,8 @@ def parse_opt():
     parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
     parser.add_argument('--batch-size', type=int, default=1, help='batch size')
     parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
+    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
     opt = parser.parse_args()
     print_args(FILE.stem, opt)
     return opt