TensorRT SegmentationModel fix (#9465)

* TensorRT SegmentationModel fix

* TensorRT SegmentationModel fix

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* TensorRT SegmentationModel fix

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* TensorRT SegmentationModel fix

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* TensorRT SegmentationModel fix

* TensorRT SegmentationModel fix

* fix

* sort output names

* Update ci-testing.yml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update ci-testing.yml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

export.py

@@ -66,7 +66,7 @@ if platform.system() != 'Windows':
     ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
 from models.experimental import attempt_load
-from models.yolo import ClassificationModel, Detect
+from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel
 from utils.dataloaders import LoadImages
 from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_version,
                            check_yaml, colorstr, file_size, get_default_args, print_args, url2file, yaml_save)
@@ -134,6 +134,15 @@ def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX
     LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
     f = file.with_suffix('.onnx')
 
+    output_names = ['output0', 'output1'] if isinstance(model, SegmentationModel) else ['output0']
+    if dynamic:
+        dynamic = {'images': {0: 'batch', 2: 'height', 3: 'width'}}  # shape(1,3,640,640)
+        if isinstance(model, SegmentationModel):
+            dynamic['output0'] = {0: 'batch', 1: 'anchors'}  # shape(1,25200,85)
+            dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'}  # shape(1,32,160,160)
+        elif isinstance(model, DetectionModel):
+            dynamic['output0'] = {0: 'batch', 1: 'anchors'}  # shape(1,25200,85)
+
     torch.onnx.export(
         model.cpu() if dynamic else model,  # --dynamic only compatible with cpu
         im.cpu() if dynamic else im,
@@ -142,16 +151,8 @@ def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX
         opset_version=opset,
         do_constant_folding=True,
         input_names=['images'],
-        output_names=['output'],
-        dynamic_axes={
-            'images': {
-                0: 'batch',
-                2: 'height',
-                3: 'width'},  # shape(1,3,640,640)
-            'output': {
-                0: 'batch',
-                1: 'anchors'}  # shape(1,25200,85)
-        } if dynamic else None)
+        output_names=output_names,
+        dynamic_axes=dynamic or None)
 
     # Checks
     model_onnx = onnx.load(f)  # load onnx model

models/common.py

@@ -390,18 +390,21 @@ class DetectMultiBackend(nn.Module):
                 model = runtime.deserialize_cuda_engine(f.read())
             context = model.create_execution_context()
             bindings = OrderedDict()
+            output_names = []
             fp16 = False  # default updated below
             dynamic = False
-            for index in range(model.num_bindings):
-                name = model.get_binding_name(index)
-                dtype = trt.nptype(model.get_binding_dtype(index))
-                if model.binding_is_input(index):
-                    if -1 in tuple(model.get_binding_shape(index)):  # dynamic
+            for i in range(model.num_bindings):
+                name = model.get_binding_name(i)
+                dtype = trt.nptype(model.get_binding_dtype(i))
+                if model.binding_is_input(i):
+                    if -1 in tuple(model.get_binding_shape(i)):  # dynamic
                         dynamic = True
-                        context.set_binding_shape(index, tuple(model.get_profile_shape(0, index)[2]))
+                        context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))
                     if dtype == np.float16:
                         fp16 = True
-                shape = tuple(context.get_binding_shape(index))
+                else:  # output
+                    output_names.append(name)
+                shape = tuple(context.get_binding_shape(i))
                 im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
                 bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
             binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
@@ -495,15 +498,17 @@ class DetectMultiBackend(nn.Module):
             y = list(self.executable_network([im]).values())
         elif self.engine:  # TensorRT
             if self.dynamic and im.shape != self.bindings['images'].shape:
-                i_in, i_out = (self.model.get_binding_index(x) for x in ('images', 'output'))
-                self.context.set_binding_shape(i_in, im.shape)  # reshape if dynamic
+                i = self.model.get_binding_index('images')
+                self.context.set_binding_shape(i, im.shape)  # reshape if dynamic
                 self.bindings['images'] = self.bindings['images']._replace(shape=im.shape)
-                self.bindings['output'].data.resize_(tuple(self.context.get_binding_shape(i_out)))
+                for name in self.output_names:
+                    i = self.model.get_binding_index(name)
+                    self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))
             s = self.bindings['images'].shape
             assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}"
             self.binding_addrs['images'] = int(im.data_ptr())
             self.context.execute_v2(list(self.binding_addrs.values()))
-            y = self.bindings['output'].data
+            y = [self.bindings[x].data for x in sorted(self.output_names)]
         elif self.coreml:  # CoreML
             im = im.permute(0, 2, 3, 1).cpu().numpy()  # torch BCHW to numpy BHWC shape(1,320,192,3)
             im = Image.fromarray((im[0] * 255).astype('uint8'))