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TensorRT SegmentationModel fix (#9465)

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* 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>
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Glenn Jocherandpre-commit-ci[bot] authored and GitHub committed 2022-09-18 14:45:08 +02:00
1 parent 95cef1ae6b
commit dc42e6ef22
2 files changed
+28 -22
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models/common.py
+16 -11
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@@ -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'))