Export ONNX from Veri-Baseline model zoo not working

I download the Veri-Baseline model from model zoo and receive checkpoint error

Run command python onnx_export.py --config-file ../../configs/VeRi/sbs_R50-ibn.yml --name baseline_R50 --output outputs/onnx_model --opts MODEL.WEIGHTS veri_sbs_R50-ibn.pth

""""""CMD Display"""""" [04/04 23:01:43 fastreid.utils.checkpoint]: Loading checkpoint from veri_sbs_R50-ibn.pth [04/04 23:01:43 fastreid.utils.checkpoint]: Some model parameters or buffers are not found in the checkpoint: heads.weight [04/04 23:01:43 fastreid.utils.checkpoint]: The checkpoint state_dict contains keys that are not used by the model: pixel_mean pixel_std heads.bnneck.num_batches_tracked heads.classifier.weight

(backbone): ResNet( (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) (bn1): BatchNorm(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=0, dilation=1, ceil_mode=True) (layer1): Sequential( (0): Bottleneck( (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): IBN( (IN): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) (BN): BatchNorm(32, 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): BatchNorm(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): BatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (se): Identity() (downsample): Sequential( (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm(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): IBN( (IN): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) (BN): BatchNorm(32, 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): BatchNorm(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): BatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (se): Identity() ) (2): Bottleneck( (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): IBN( (IN): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) (BN): BatchNorm(32, 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): BatchNorm(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): BatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (se): Identity() ) ) (layer2): Sequential( (0): Bottleneck( (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): IBN( (IN): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) (BN): BatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn2): BatchNorm(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): BatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (se): Identity() (downsample): Sequential( (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm(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): IBN( (IN): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) (BN): BatchNorm(64, 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): BatchNorm(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): BatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (se): Identity() ) (2): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): IBN( (IN): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) (BN): BatchNorm(64, 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): BatchNorm(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): BatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (se): Identity() ) (3): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): IBN( (IN): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) (BN): BatchNorm(64, 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): BatchNorm(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): BatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (se): Identity() ) ) (layer3): Sequential( (0): Bottleneck( (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): IBN( (IN): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) (BN): BatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn2): BatchNorm(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): BatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (se): Identity() (downsample): Sequential( (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm(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): IBN( (IN): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) (BN): BatchNorm(128, 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): BatchNorm(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): BatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (se): Identity() ) (2): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): IBN( (IN): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) (BN): BatchNorm(128, 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): BatchNorm(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): BatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (se): Identity() ) (3): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): IBN( (IN): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) (BN): BatchNorm(128, 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): BatchNorm(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): BatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (se): Identity() ) (4): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): IBN( (IN): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) (BN): BatchNorm(128, 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): BatchNorm(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): BatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (se): Identity() ) (5): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): IBN( (IN): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) (BN): BatchNorm(128, 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): BatchNorm(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): BatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (se): Identity() ) ) (layer4): Sequential( (0): Bottleneck( (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (se): Identity() (downsample): Sequential( (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (se): Identity() ) (2): Bottleneck( (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (se): Identity() ) ) (NL_1): ModuleList() (NL_2): ModuleList( (0): Non_local( (g): Conv2d(512, 1, kernel_size=(1, 1), stride=(1, 1)) (W): Sequential( (0): Conv2d(1, 512, kernel_size=(1, 1), stride=(1, 1)) (1): BatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (theta): Conv2d(512, 1, kernel_size=(1, 1), stride=(1, 1)) (phi): Conv2d(512, 1, kernel_size=(1, 1), stride=(1, 1)) ) (1): Non_local( (g): Conv2d(512, 1, kernel_size=(1, 1), stride=(1, 1)) (W): Sequential( (0): Conv2d(1, 512, kernel_size=(1, 1), stride=(1, 1)) (1): BatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (theta): Conv2d(512, 1, kernel_size=(1, 1), stride=(1, 1)) (phi): Conv2d(512, 1, kernel_size=(1, 1), stride=(1, 1)) ) ) (NL_3): ModuleList( (0): Non_local( (g): Conv2d(1024, 1, kernel_size=(1, 1), stride=(1, 1)) (W): Sequential( (0): Conv2d(1, 1024, kernel_size=(1, 1), stride=(1, 1)) (1): BatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (theta): Conv2d(1024, 1, kernel_size=(1, 1), stride=(1, 1)) (phi): Conv2d(1024, 1, kernel_size=(1, 1), stride=(1, 1)) ) (1): Non_local( (g): Conv2d(1024, 1, kernel_size=(1, 1), stride=(1, 1)) (W): Sequential( (0): Conv2d(1, 1024, kernel_size=(1, 1), stride=(1, 1)) (1): BatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (theta): Conv2d(1024, 1, kernel_size=(1, 1), stride=(1, 1)) (phi): Conv2d(1024, 1, kernel_size=(1, 1), stride=(1, 1)) ) (2): Non_local( (g): Conv2d(1024, 1, kernel_size=(1, 1), stride=(1, 1)) (W): Sequential( (0): Conv2d(1, 1024, kernel_size=(1, 1), stride=(1, 1)) (1): BatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (theta): Conv2d(1024, 1, kernel_size=(1, 1), stride=(1, 1)) (phi): Conv2d(1024, 1, kernel_size=(1, 1), stride=(1, 1)) ) ) (NL_4): ModuleList() ) (heads): EmbeddingHead( (pool_layer): GeneralizedMeanPoolingP(Parameter containing: tensor([1.9932], device='cuda:0', requires_grad=True), output_size=(1, 1)) (bottleneck): Sequential( (0): BatchNorm(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (cls_layer): CircleSoftmax(num_classes=0, scale=64, margin=0.35) ) ) 04/04 23:01:43 fastreid.onnx_export: Beginning ONNX file converting D:\anaconda\ana\envs\fastreid\lib\site-packages\torch\onnx\utils.py:243: UserWarning: add_node_names' can be set to True only when 'operator_export_type' isONNX. Since 'operator_export_type' is not set to 'ONNX',add_node_namesargument will be ignored. "{}argument will be ignored.".format(arg_name, arg_name)) D:\anaconda\ana\envs\fastreid\lib\site-packages\torch\onnx\utils.py:243: UserWarning:do_constant_folding' can be set to True only when 'operator_export_type' is ONNX. Since 'operator_export_type' is not set to 'ONNX', do_constant_folding argument will be ignored. "{} argument will be ignored.".format(arg_name, arg_name)) [04/04 23:01:47 fastreid.onnx_export]: Completed convert of ONNX model [04/04 23:01:47 fastreid.onnx_export]: Beginning ONNX model path optimization [04/04 23:01:55 fastreid.onnx_export]: Completed ONNX model path optimization Traceback (most recent call last): File "onnx_export.py", line 155, in model_simp, check = simplify(onnx_model) File "D:\anaconda\ana\envs\fastreid\lib\site-packages\onnxsim\onnx_simplifier.py", line 191, in simplify tensor_size_threshold, onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Gather): [TypeInferenceError] Inferred elem type differs from existing elem type: (FLOAT) vs (INT64)

JDAI-CV / fast-reid

Export ONNX from Veri-Baseline model zoo not working #693