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NameError: name 'reloadTRT' is not defined
Hey, I'm trying to run_video.py on my video and use TRT but get this error.
~/TracKit$ python tracking/run_video.py --arch OceanTRT --video videos/1.mp4
===> init Siamese <====
tensorrt toy model: not loading checkpoint
===> load model from TRT <===
===> please ignore the warning information of TRT <===
===> We only provide a toy demo for TensorRT. There are some operations are not supported well.<===
===> If you wang to test on benchmark, please us Pytorch version. <===
===> The tensorrt code will be contingously optimized (with the updating of official TensorRT.)<===
Traceback (most recent call last):
File "tracking/run_video.py", line 231, in
Hey, I'm trying to run_video.py on my video and use TRT but get this error. ~/TracKit$ python tracking/run_video.py --arch OceanTRT --video videos/1.mp4 ===> init Siamese <==== tensorrt toy model: not loading checkpoint ===> load model from TRT <=== ===> please ignore the warning information of TRT <=== ===> We only provide a toy demo for TensorRT. There are some operations are not supported well.<=== ===> If you wang to test on benchmark, please us Pytorch version. <=== ===> The tensorrt code will be contingously optimized (with the updating of official TensorRT.)<=== Traceback (most recent call last): File "tracking/run_video.py", line 231, in main() File "tracking/run_video.py", line 211, in main trtNet = reloadTRT() NameError: name 'reloadTRT' is not defined
The script only supports pytorch version. Pls use test_ocean.py for TRT.
I'm trying now but getting another error.
(TracKit):~/TracKit$ python tracking/test_ocean.py --arch OceanTRT --video videos/1.mp4
Traceback (most recent call last):
File "tracking/test_ocean.py", line 29, in
I'm trying now but getting another error. (TracKit):~/TracKit$ python tracking/test_ocean.py --arch OceanTRT --video videos/1.mp4 Traceback (most recent call last): File "tracking/test_ocean.py", line 29, in from lib.core.eval_otb import eval_auc_tune ModuleNotFoundError: No module named 'lib.core'
The error looks weird, since the path has been imported by _iniy_paths. You could delete this line if only testing on videos.
I've got this one:
(TracKit):~/TracKit$ python tracking/test_ocean.py --arch OceanTRT --video videos/1.mp4
OceanTRT(
(features): ResNet50(
(features): ResNet_plus2(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer3): Sequential(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(5): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
)
)
(neck): AdjustLayer(
(downsample): Sequential(
(0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(connect_model0): MultiDiCorr(
(cls_encode): matrix(
(matrix11_k): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix11_s): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix12_k): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix12_s): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix21_k): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(1, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix21_s): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(1, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(reg_encode): matrix(
(matrix11_k): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix11_s): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix12_k): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix12_s): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix21_k): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(1, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix21_s): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(1, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
)
(connect_model1): box_tower(
(cls_encode): matrix(
(matrix11_k): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix11_s): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix12_k): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix12_s): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix21_k): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(1, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix21_s): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(1, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(reg_encode): matrix(
(matrix11_k): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix11_s): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix12_k): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix12_s): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix21_k): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(1, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(matrix21_s): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(1, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(cls_dw): GroupDW()
(reg_dw): GroupDW()
(bbox_tower): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU()
(9): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(10): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(11): ReLU()
)
(cls_tower): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU()
(9): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(10): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(11): ReLU()
)
(bbox_pred): Conv2d(256, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(cls_pred): Conv2d(256, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(connect_model2): OceanCorr(
(cls_dw): GroupDW()
(reg_dw): GroupDW()
)
)
===> init Siamese <====
tensorrt toy model: not loading checkpoint
===> load model from TRT <===
===> please ignore the warning information of TRT <===
===> We only provide a toy demo for TensorRT. There are some operations are not supported well.<===
===> If you wang to test on benchmark, please us Pytorch version. <===
===> The tensorrt code will be contingously optimized (with the updating of official TensorRT.)<===
[TensorRT] ERROR: INVALID_CONFIG: The engine plan file is generated on an incompatible device, expecting compute 6.1 got compute 7.5, please rebuild.
[TensorRT] ERROR: engine.cpp (1407) - Serialization Error in deserialize: 0 (Core engine deserialization failure)
[TensorRT] ERROR: INVALID_STATE: std::exception
[TensorRT] ERROR: INVALID_CONFIG: Deserialize the cuda engine failed.
Traceback (most recent call last):
File "tracking/test_ocean.py", line 390, in
I've got this one: (TracKit):~/TracKit$ python tracking/test_ocean.py --arch OceanTRT --video videos/1.mp4 OceanTRT( (features): ResNet50( (features): ResNet_plus2( (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) (layer1): Sequential( (0): Bottleneck( (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) (layer2): Sequential( (0): Bottleneck( (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), bias=False) (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (3): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) (layer3): Sequential( (0): Bottleneck( (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (3): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (4): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (5): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) ) ) (neck): AdjustLayer( (downsample): Sequential( (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (connect_model0): MultiDiCorr( (cls_encode): matrix( (matrix11_k): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix11_s): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix12_k): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix12_s): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix21_k): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(1, 2), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix21_s): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(1, 2), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) ) (reg_encode): matrix( (matrix11_k): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix11_s): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix12_k): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix12_s): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix21_k): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(1, 2), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix21_s): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(1, 2), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) ) ) (connect_model1): box_tower( (cls_encode): matrix( (matrix11_k): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix11_s): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix12_k): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix12_s): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix21_k): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(1, 2), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix21_s): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(1, 2), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) ) (reg_encode): matrix( (matrix11_k): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix11_s): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix12_k): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix12_s): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix21_k): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(1, 2), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (matrix21_s): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), dilation=(1, 2), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) ) (cls_dw): GroupDW() (reg_dw): GroupDW() (bbox_tower): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU() (6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (8): ReLU() (9): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (10): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (11): ReLU() ) (cls_tower): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU() (6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (8): ReLU() (9): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (10): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (11): ReLU() ) (bbox_pred): Conv2d(256, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (cls_pred): Conv2d(256, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (connect_model2): OceanCorr( (cls_dw): GroupDW() (reg_dw): GroupDW() ) ) ===> init Siamese <==== tensorrt toy model: not loading checkpoint ===> load model from TRT <=== ===> please ignore the warning information of TRT <=== ===> We only provide a toy demo for TensorRT. There are some operations are not supported well.<=== ===> If you wang to test on benchmark, please us Pytorch version. <=== ===> The tensorrt code will be contingously optimized (with the updating of official TensorRT.)<=== [TensorRT] ERROR: INVALID_CONFIG: The engine plan file is generated on an incompatible device, expecting compute 6.1 got compute 7.5, please rebuild. [TensorRT] ERROR: engine.cpp (1407) - Serialization Error in deserialize: 0 (Core engine deserialization failure) [TensorRT] ERROR: INVALID_STATE: std::exception [TensorRT] ERROR: INVALID_CONFIG: Deserialize the cuda engine failed. Traceback (most recent call last): File "tracking/test_ocean.py", line 390, in main() File "tracking/test_ocean.py", line 218, in main trtNet = reloadTRT() File "tracking/test_ocean.py", line 71, in reloadTRT t_bk.load_state_dict(torch.load(t_bk_path)) File "/home/kfour/anaconda3/envs/TracKit/lib/python3.7/site-packages/torch/nn/modules/module.py", line 815, in load_state_dict load(self) File "/home/kfour/anaconda3/envs/TracKit/lib/python3.7/site-packages/torch/nn/modules/module.py", line 810, in load state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs) File "/home/kfour/anaconda3/envs/TracKit/lib/python3.7/site-packages/torch2trt-0.2.0-py3.7.egg/torch2trt/torch2trt.py", line 443, in _load_from_state_dict AttributeError: 'NoneType' object has no attribute 'create_execution_context'
Is the running environment the same as that in Readme?
I run in environment "TracKit", I didn't create another one like "OceanOnlineTRT"
I run in environment "TracKit", I didn't create another one like "OceanOnlineTRT"
Does the TRT install follow here? Pls check the corresponding version.
(TracKit) kfour@kfour:~/TensorRT-7.0.0.11/python$ pip install tensorrt-7.0.0.11-cp37-none-linux_x86_64.whl
Processing ./tensorrt-7.0.0.11-cp37-none-linux_x86_64.whl tensorrt is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
(TracKit) kfour@kfour:~/TensorRT-7.0.0.11/python$ pip install tensorrt-7.0.0.11-cp37-none-linux_x86_64.whl
Processing ./tensorrt-7.0.0.11-cp37-none-linux_x86_64.whl tensorrt is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
It's hard for me to reproduce the error about TRT, since it works on my machine (2080Ti). Could you google the error about TRT?
I just found this one: https://forums.developer.nvidia.com/t/attributeerror-nonetype-object-has-no-attribute-create-execution-context/110199/5 First row of error: [TensorRT] ERROR: INVALID_CONFIG: The engine plan file is generated on an incompatible device, expecting compute 6.1 got compute 7.5, please rebuild. It looks like I should install cudnn 6.1?
I googled else and understood I need to rebuild the engine for my GPU, do you have any ideas how to do it? I've found that: https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#build_engine_python
I googled else and understood I need to rebuild the engine for my GPU, do you have any ideas how to do it? I've found that: https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#build_engine_python
Have you found the solution?
大佬啊，我也遇到了AttributeError: 'NoneType' object has no attribute 'create_execution_context'这个问题，已经跟install和install_trt里要求的安装成了一模一样了，但是还是报了这个错误，完全不知道哪里错了，只能定位到test_ocean.py里t_bk.load_state_dict(torch.load(t_bk_path))这句开始错了，应该是加载的这个trt文件有问题吧？是不是大佬传上去的trt模型有问题呢？
pls refer to our most recent work https://github.com/researchmm/Stark and https://github.com/researchmm/LightTrack
