hukkai
commented
1 year ago The mmdeploy team is looking at this problem. I believe they will make it convertible soon
Open 1005452649 opened 1 year ago
hukkai
commented
1 year ago The mmdeploy team is looking at this problem. I believe they will make it convertible soon
bewithme
commented
1 year ago The
mmdeployteam is looking at this problem. I believe they will make it convertible soon
have they fixed this bug ? I have the same problem .
bewithme
commented
1 year ago The
mmdeployteam is looking at this problem. I believe they will make it convertible soon
UserWarning: partial object functools.partial(<bound method TwoStageDetector.forward_dummy of FastRCNN(
(backbone): ResNet3dSlowOnly(
(conv1): ConvModule(
(conv): Conv3d(3, 64, kernel_size=(1, 7, 7), stride=(1, 2, 2), padding=(0, 3, 3), bias=False)
(bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(maxpool): MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), dilation=1, ceil_mode=False)
(pool2): MaxPool3d(kernel_size=(2, 1, 1), stride=(2, 1, 1), padding=0, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(64, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(downsample): ConvModule(
(conv): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(1): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(2): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(64, 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
)
(layer2): Sequential(
(0): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(256, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(128, 128, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(downsample): ConvModule(
(conv): Conv3d(256, 512, kernel_size=(1, 1, 1), stride=(1, 2, 2), bias=False)
(bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(1): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(128, 128, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(2): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(128, 128, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(3): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(128, 128, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
)
(layer3): Sequential(
(0): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(512, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(downsample): ConvModule(
(conv): Conv3d(512, 1024, kernel_size=(1, 1, 1), stride=(1, 2, 2), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(1): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(2): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(3): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(4): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(5): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(6): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(7): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(8): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(9): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(10): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(11): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(12): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(13): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(14): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(15): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(16): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(17): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(18): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(19): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(20): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(21): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(22): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(256, 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
)
(layer4): Sequential(
(0): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(1024, 512, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(512, 512, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(512, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(downsample): ConvModule(
(conv): Conv3d(1024, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(1): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(2048, 512, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(512, 512, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(512, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
(2): Bottleneck3d(
(conv1): ConvModule(
(conv): Conv3d(2048, 512, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False)
(bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv2): ConvModule(
(conv): Conv3d(512, 512, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False)
(bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(conv3): ConvModule(
(conv): Conv3d(512, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(bn): BatchNorm3d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): ReLU(inplace=True)
)
)
)
(roi_head): AVARoIHead(
(bbox_roi_extractor): SingleRoIExtractor3D(
(roi_layer): RoIAlign(output_size=(8, 8), spatial_scale=0.0625, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)
(global_pool): AdaptiveAvgPool2d(output_size=8)
)
(bbox_head): BBoxHeadAVA(
(temporal_pool): AdaptiveAvgPool3d(output_size=(1, None, None))
(spatial_pool): AdaptiveMaxPool3d(output_size=(None, 1, 1))
(dropout): Dropout(p=0.5, inplace=False)
(fc_cls): Linear(in_features=2048, out_features=81, bias=True)
)
)
)>, softmax=False) has incorrect arguments, skipping _decide_input_format
warnings.warn("%s, skipping _decide_input_format" % e)
Traceback (most recent call last):
File "/home/ngi/IdeaProjects/mmlab/mmaction2/tools/deployment/pytorch2onnx.py", line 171, in
bewithme
commented
1 year ago Hello, I have one application using Spatio temporal action detection using AVA. So the algorithm has slowfast and ava head. I like to convert to ONNX. Now the algorithm is not listed in onnx convertible. May I know what step/infos/references to look into to convert the "Spatio temporal action detection using AVA" algorithm into ONNX.
have you solved this problem ?
quangtn266
commented
1 year ago Has anyone solved the problem ?
Telephone1024
commented
1 year ago I got the similar error, have u solved the problem?
Hello, I have one application using Spatio temporal action detection using AVA. So the algorithm has slowfast and ava head. I like to convert to ONNX. Now the algorithm is not listed in onnx convertible. May I know what step/infos/references to look into to convert the "Spatio temporal action detection using AVA" algorithm into ONNX.