123871 - rpi4b aidge
123871 - rpi4b aidge
Summary: Pipeline failed, but error.log is filled.
Model Details
- model name : ssd_mobilenet_v1_12-int8.onnx
- model url : Download here
Logs Details
user.log
[[94mNOTICE[0m] - Loaded node [[1m[3mcopy__21_Preprocessor_map_while_Less[0m] of type [[1m[3mLess[0m] as a GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mgeneric_loop_Loop__42[0m] of type [[1m[3mLoop[0m] as a GenericOperator.
[[94mNOTICE[0m] * body : node {
[[94mNOTICE[0m] input: "Preprocessor/map/while/Identity:0"
[[94mNOTICE[0m] output: "Unsqueeze__3...
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5257_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [0, 0, 1, 1]
[[94mNOTICE[0m] * strides : [2, 2]
[[33mWARNING[0m] - Trying to load node named
[[33mWARNING[0m] [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_1_depthwise_depthwise_quant[0m] of type
[[33mWARNING[0m] [[1m[3mQLinearConv[0m].
[[33mWARNING[0m] Loading node using a [[1m[3mGenericOperator[0m].
[[33mWARNING[0m] Please report this issue at https://gitlab.eclipse.org/eclipse/aidge/aidge_onnx by
[[33mWARNING[0m] providing your ONNX model and the following error:
[[33mWARNING[0m] "ONNX_NODE_CONVERTER_ returned: list index out of range"
[[94mNOTICE[0m] - Loaded node [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_1_depthwise_depthwise_quant[0m]
[[94mNOTICE[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 32
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [1, 1, 1, 1]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5268_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Trying to load node named
[[33mWARNING[0m] [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_2_depthwise_depthwise_quant[0m] of type
[[33mWARNING[0m] [[1m[3mQLinearConv[0m].
[[33mWARNING[0m] Loading node using a [[1m[3mGenericOperator[0m].
[[33mWARNING[0m] Please report this issue at https://gitlab.eclipse.org/eclipse/aidge/aidge_onnx by
[[33mWARNING[0m] providing your ONNX model and the following error:
[[33mWARNING[0m] "ONNX_NODE_CONVERTER_ returned: list index out of range"
[[94mNOTICE[0m] - Loaded node [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_2_depthwise_depthwise_quant[0m]
[[94mNOTICE[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 64
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [0, 0, 1, 1]
[[94mNOTICE[0m] * strides : [2, 2]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5278_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Trying to load node named
[[33mWARNING[0m] [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_3_depthwise_depthwise_quant[0m] of type
[[33mWARNING[0m] [[1m[3mQLinearConv[0m].
[[33mWARNING[0m] Loading node using a [[1m[3mGenericOperator[0m].
[[33mWARNING[0m] Please report this issue at https://gitlab.eclipse.org/eclipse/aidge/aidge_onnx by
[[33mWARNING[0m] providing your ONNX model and the following error:
[[33mWARNING[0m] "ONNX_NODE_CONVERTER_ returned: list index out of range"
[[94mNOTICE[0m] - Loaded node [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_3_depthwise_depthwise_quant[0m]
[[94mNOTICE[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 128
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [1, 1, 1, 1]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5288_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Trying to load node named
[[33mWARNING[0m] [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_4_depthwise_depthwise_quant[0m] of type
[[33mWARNING[0m] [[1m[3mQLinearConv[0m].
[[33mWARNING[0m] Loading node using a [[1m[3mGenericOperator[0m].
[[33mWARNING[0m] Please report this issue at https://gitlab.eclipse.org/eclipse/aidge/aidge_onnx by
[[33mWARNING[0m] providing your ONNX model and the following error:
[[33mWARNING[0m] "ONNX_NODE_CONVERTER_ returned: list index out of range"
[[94mNOTICE[0m] - Loaded node [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_4_depthwise_depthwise_quant[0m]
[[94mNOTICE[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 128
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [1, 1, 1, 1]
[[94mNOTICE[0m] * strides : [2, 2]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5298_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Trying to load node named
[[33mWARNING[0m] [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_5_depthwise_depthwise_quant[0m] of type
[[33mWARNING[0m] [[1m[3mQLinearConv[0m].
[[33mWARNING[0m] Loading node using a [[1m[3mGenericOperator[0m].
[[33mWARNING[0m] Please report this issue at https://gitlab.eclipse.org/eclipse/aidge/aidge_onnx by
[[33mWARNING[0m] providing your ONNX model and the following error:
[[33mWARNING[0m] "ONNX_NODE_CONVERTER_ returned: list index out of range"
[[94mNOTICE[0m] - Loaded node [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_5_depthwise_depthwise_quant[0m]
[[94mNOTICE[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 256
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [1, 1, 1, 1]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5308_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Trying to load node named
[[33mWARNING[0m] [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_6_depthwise_depthwise_quant[0m] of type
[[33mWARNING[0m] [[1m[3mQLinearConv[0m].
[[33mWARNING[0m] Loading node using a [[1m[3mGenericOperator[0m].
[[33mWARNING[0m] Please report this issue at https://gitlab.eclipse.org/eclipse/aidge/aidge_onnx by
[[33mWARNING[0m] providing your ONNX model and the following error:
[[33mWARNING[0m] "ONNX_NODE_CONVERTER_ returned: list index out of range"
[[94mNOTICE[0m] - Loaded node [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_6_depthwise_depthwise_quant[0m]
[[94mNOTICE[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 256
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [0, 0, 1, 1]
[[94mNOTICE[0m] * strides : [2, 2]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5318_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Trying to load node named
[[33mWARNING[0m] [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_7_depthwise_depthwise_quant[0m] of type
[[33mWARNING[0m] [[1m[3mQLinearConv[0m].
[[33mWARNING[0m] Loading node using a [[1m[3mGenericOperator[0m].
[[33mWARNING[0m] Please report this issue at https://gitlab.eclipse.org/eclipse/aidge/aidge_onnx by
[[33mWARNING[0m] providing your ONNX model and the following error:
[[33mWARNING[0m] "ONNX_NODE_CONVERTER_ returned: list index out of range"
[[94mNOTICE[0m] - Loaded node [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_7_depthwise_depthwise_quant[0m]
[[94mNOTICE[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 512
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [1, 1, 1, 1]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5328_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Trying to load node named
[[33mWARNING[0m] [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_8_depthwise_depthwise_quant[0m] of type
[[33mWARNING[0m] [[1m[3mQLinearConv[0m].
[[33mWARNING[0m] Loading node using a [[1m[3mGenericOperator[0m].
[[33mWARNING[0m] Please report this issue at https://gitlab.eclipse.org/eclipse/aidge/aidge_onnx by
[[33mWARNING[0m] providing your ONNX model and the following error:
[[33mWARNING[0m] "ONNX_NODE_CONVERTER_ returned: list index out of range"
[[94mNOTICE[0m] - Loaded node [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_8_depthwise_depthwise_quant[0m]
[[94mNOTICE[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 512
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [1, 1, 1, 1]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5338_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Trying to load node named
[[33mWARNING[0m] [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_9_depthwise_depthwise_quant[0m] of type
[[33mWARNING[0m] [[1m[3mQLinearConv[0m].
[[33mWARNING[0m] Loading node using a [[1m[3mGenericOperator[0m].
[[33mWARNING[0m] Please report this issue at https://gitlab.eclipse.org/eclipse/aidge/aidge_onnx by
[[33mWARNING[0m] providing your ONNX model and the following error:
[[33mWARNING[0m] "ONNX_NODE_CONVERTER_ returned: list index out of range"
[[94mNOTICE[0m] - Loaded node [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_9_depthwise_depthwise_quant[0m]
[[94mNOTICE[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 512
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [1, 1, 1, 1]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5348_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Trying to load node named
[[33mWARNING[0m] [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_10_depthwise_depthwise_quant[0m] of type
[[33mWARNING[0m] [[1m[3mQLinearConv[0m].
[[33mWARNING[0m] Loading node using a [[1m[3mGenericOperator[0m].
[[33mWARNING[0m] Please report this issue at https://gitlab.eclipse.org/eclipse/aidge/aidge_onnx by
[[33mWARNING[0m] providing your ONNX model and the following error:
[[33mWARNING[0m] "ONNX_NODE_CONVERTER_ returned: list index out of range"
[[94mNOTICE[0m] - Loaded node
[[94mNOTICE[0m] [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_10_depthwise_depthwise_quant[0m] of type
[[94mNOTICE[0m] [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 512
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [1, 1, 1, 1]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5358_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Trying to load node named
[[33mWARNING[0m] [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_11_depthwise_depthwise_quant[0m] of type
[[33mWARNING[0m] [[1m[3mQLinearConv[0m].
[[33mWARNING[0m] Loading node using a [[1m[3mGenericOperator[0m].
[[33mWARNING[0m] Please report this issue at https://gitlab.eclipse.org/eclipse/aidge/aidge_onnx by
[[33mWARNING[0m] providing your ONNX model and the following error:
[[33mWARNING[0m] "ONNX_NODE_CONVERTER_ returned: list index out of range"
[[94mNOTICE[0m] - Loaded node
[[94mNOTICE[0m] [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_11_depthwise_depthwise_quant[0m] of type
[[94mNOTICE[0m] [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 512
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [1, 1, 1, 1]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5368_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Trying to load node named
[[33mWARNING[0m] [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_12_depthwise_depthwise_quant[0m] of type
[[33mWARNING[0m] [[1m[3mQLinearConv[0m].
[[33mWARNING[0m] Loading node using a [[1m[3mGenericOperator[0m].
[[33mWARNING[0m] Please report this issue at https://gitlab.eclipse.org/eclipse/aidge/aidge_onnx by
[[33mWARNING[0m] providing your ONNX model and the following error:
[[33mWARNING[0m] "ONNX_NODE_CONVERTER_ returned: list index out of range"
[[94mNOTICE[0m] - Loaded node
[[94mNOTICE[0m] [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_12_depthwise_depthwise_quant[0m] of type
[[94mNOTICE[0m] [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 512
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [1, 1, 1, 1]
[[94mNOTICE[0m] * strides : [2, 2]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5386_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Trying to load node named
[[33mWARNING[0m] [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_13_depthwise_depthwise_quant[0m] of type
[[33mWARNING[0m] [[1m[3mQLinearConv[0m].
[[33mWARNING[0m] Loading node using a [[1m[3mGenericOperator[0m].
[[33mWARNING[0m] Please report this issue at https://gitlab.eclipse.org/eclipse/aidge/aidge_onnx by
[[33mWARNING[0m] providing your ONNX model and the following error:
[[33mWARNING[0m] "ONNX_NODE_CONVERTER_ returned: list index out of range"
[[94mNOTICE[0m] - Loaded node
[[94mNOTICE[0m] [[1m[3mFeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_13_depthwise_depthwise_quant[0m] of type
[[94mNOTICE[0m] [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1024
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [1, 1, 1, 1]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5396_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5406_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5408_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [0, 0, 1, 1]
[[94mNOTICE[0m] * strides : [2, 2]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5418_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5420_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [1, 1, 1, 1]
[[94mNOTICE[0m] * strides : [2, 2]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5430_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5432_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [1, 1, 1, 1]
[[94mNOTICE[0m] * strides : [2, 2]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5442_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mConv__5444_quant[0m] of type [[1m[3mQLinearConv[0m] as a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [3, 3]
[[94mNOTICE[0m] * pads : [0, 0, 1, 1]
[[94mNOTICE[0m] * strides : [2, 2]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mBoxPredictor_4_ClassPredictor_BiasAdd_quant[0m] of type [[1m[3mQLinearConv[0m] as a
[[94mNOTICE[0m] GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mBoxPredictor_3_ClassPredictor_BiasAdd_quant[0m] of type [[1m[3mQLinearConv[0m] as a
[[94mNOTICE[0m] GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mBoxPredictor_2_ClassPredictor_BiasAdd_quant[0m] of type [[1m[3mQLinearConv[0m] as a
[[94mNOTICE[0m] GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mBoxPredictor_1_ClassPredictor_BiasAdd_quant[0m] of type [[1m[3mQLinearConv[0m] as a
[[94mNOTICE[0m] GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mBoxPredictor_0_ClassPredictor_BiasAdd_quant[0m] of type [[1m[3mQLinearConv[0m] as a
[[94mNOTICE[0m] GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mBoxPredictor_5_ClassPredictor_BiasAdd_quant[0m] of type [[1m[3mQLinearConv[0m] as a
[[94mNOTICE[0m] GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mBoxPredictor_4_BoxEncodingPredictor_BiasAdd_quant[0m] of type [[1m[3mQLinearConv[0m] as
[[94mNOTICE[0m] a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mBoxPredictor_3_BoxEncodingPredictor_BiasAdd_quant[0m] of type [[1m[3mQLinearConv[0m] as
[[94mNOTICE[0m] a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mBoxPredictor_2_BoxEncodingPredictor_BiasAdd_quant[0m] of type [[1m[3mQLinearConv[0m] as
[[94mNOTICE[0m] a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mBoxPredictor_1_BoxEncodingPredictor_BiasAdd_quant[0m] of type [[1m[3mQLinearConv[0m] as
[[94mNOTICE[0m] a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currenMODEL : ssd_mobilenet_v1_12int8.onnx
===============
ONNX Graph
===============
inputs ['unk__6578', 'unk__6579', 'unk__6580', 3]
===============
Aidge Graph
===============
Node(name='BoxPredictor_5_ClassPredictor_BiasAdd_0_BoxPredictor_5_ClassPredictor_BiasAdd_DequantizeLinear', optype='Dequantizer', parents: [1], children: [[1]])
Node(name='Preprocessor_sub_0_scale', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_6_pointwise_BatchNorm_batchnorm_sub_0_quantized', optype='Producer', children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5613_quantized', optype='Producer', children: [[1]])
Node(name='ConstantFolding_Postprocessor_Decode_div_recip_0', optype='Producer', children: [[1, 1]])
Node(name='Conv__5318_0_scale', optype='Producer', children: [[1, 1]])
Node(name='BoxPredictor_3_BoxEncodingPredictor_biases_read_0_quantized', optype='Producer', children: [[1]])
Node(name='Conv__5318_0_zero_point', optype='Producer', children: [[1, 1]])
Node(name='Postprocessor_ToFloat', optype='Cast', parents: [1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_6_pointwise_Conv2D_merged_input_0_zero_point', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_7_depthwise_depthwise_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_6_pointwise_Conv2D_merged_input_0_scale', optype='Producer', children: [[1]])
Node(name='zero__150', optype='Producer', children: [[1, 1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_6_depthwise_BatchNorm_batchnorm_add_1_0_zero_point', optype='Producer', children: [[1, 1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_6_depthwise_BatchNorm_batchnorm_add_1_0_scale', optype='Producer', children: [[1, 1]])
Node(name='BoxPredictor_3_BoxEncodingPredictor_BiasAdd_0_scale', optype='Producer', children: [[1]])
Node(name='copy__21_Preprocessor_map_while_Less__126', optype='Cast', parents: [1], children: [[1]])
Node(name='ConvAddFusion_Add_B_const_fold_opt__5599_quantized', optype='Producer', children: [[1]])
Node(name='Conv__5278_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_6_pointwise_Conv2D_merged_input_0_quantized', optype='Producer', children: [[1]])
Node(name='Conv__5288_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='Preprocessor_map_Shape__120', optype='Cast', parents: [1], children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5615_zero_point', optype='Producer', children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5615_quantized', optype='Producer', children: [[1]])
Node(name='ToFloat', optype='Cast', parents: [0], children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5615_scale', optype='Producer', children: [[1]])
Node(name='BoxPredictor_3_BoxEncodingPredictor_BiasAdd_0_zero_point', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_5_pointwise_BatchNorm_batchnorm_sub_0_quantized', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_6_depthwise_depthwise_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='trip_count__73', optype='Producer', children: [[1, 1]])
Node(name='Conv__5308_0_scale', optype='Producer', children: [[1, 1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_8_depthwise_depthwise_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_4_pointwise_BatchNorm_batchnorm_sub_0_quantized', optype='Producer', children: [[1]])
Node(name='add', optype='Add', parents: [1, 1], children: [[]])
Node(name='Conv__5298_0_scale', optype='Producer', children: [[1, 1]])
Node(name='Conv__5298_0_zero_point', optype='Producer', children: [[1, 1]])
Node(name='BoxPredictor_3_BoxEncodingPredictor_weights_read_0_quantized', optype='Producer', children: [[1]])
Node(name='Postprocessor_ToFloat_3', optype='Cast', parents: [1], children: [[]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_4_pointwise_Conv2D_merged_input_0_zero_point', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_10_depthwise_depthwise_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_4_pointwise_Conv2D_merged_input_0_scale', optype='Producer', children: [[1]])
Node(name='generic_loop_Loop__74', optype='Loop', parents: [1, 1, 1], children: [[], [1], [], [], [1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_4_pointwise_Conv2D_merged_input_0_quantized', optype='Producer', children: [[1]])
Node(name='Conv__5308_0_zero_point', optype='Producer', children: [[1, 1]])
Node(name='copy__43_Postprocessor_BatchMultiClassNonMaxSuppression_map_while_Less', optype='Less', parents: [1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_5_pointwise_Conv2D_merged_input_0_zero_point', optype='Producer', children: [[1]])
Node(name='BoxPredictor_3_BoxEncodingPredictor_weights_read_0_zero_point', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_5_pointwise_Conv2D_merged_input_0_scale', optype='Producer', children: [[1]])
Node(name='Conv__5328_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='Postprocessor_BatchMultiClassNonMaxSuppression_ones', optype='Expand', parents: [1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_5_pointwise_Conv2D_merged_input_0_quantized', optype='Producer', children: [[1]])
Node(name='Conv__5298_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='ConvAddFusion_Add_B_const_fold_opt__5628_quantized', optype='Producer', children: [[1]])
Node(name='copy__43_Postprocessor_BatchMultiClassNonMaxSuppression_map_while_Less__642', optype='Cast', parents: [1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_5_depthwise_BatchNorm_batchnorm_add_1_0_scale', optype='Producer', children: [[1, 1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_5_depthwise_BatchNorm_batchnorm_add_1_0_zero_point', optype='Producer', children: [[1, 1]])
Node(name='BoxPredictor_3_BoxEncodingPredictor_weights_read_0_scale', optype='Producer', children: [[1]])
Node(name='Postprocessor_BatchMultiClassNonMaxSuppression_mul', optype='Mul', parents: [1, 1], children: [[]])
Node(name='ConvMulFusion_W_const_fold_opt__5617_zero_point', optype='Producer', children: [[1]])
Node(name='Conv__5338_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5617_scale', optype='Producer', children: [[1]])
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Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_0_BatchNorm_batchnorm_sub_0_quantized', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_Conv2d_13_pointwise_1_Conv2d_3_1x1_128_Conv2D_merged_input_0_scale', optype='Producer', children: [[1]])
Node(name='BoxPredictor_4_BoxEncodingPredictor_BiasAdd_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_9_pointwise_BatchNorm_batchnorm_sub_0_quantized', optype='Producer', children: [[1]])
Node(name='ConvAddFusion_Add_B_const_fold_opt__5624_quantized', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512_BatchNorm_batchnorm_sub_0_quantized', optype='Producer', children: [[1]])
Node(name='BoxPredictor_2_ClassPredictor_weights_read_0_quantized', optype='Producer', children: [[1]])
Node(name='BoxPredictor_4_BoxEncodingPredictor_BiasAdd_0_BoxPredictor_4_BoxEncodingPredictor_BiasAdd_DequantizeLinear', optype='Dequantizer', parents: [1], children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5626_quantized', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_3_depthwise_BatchNorm_batchnorm_add_1_0_scale', optype='Producer', children: [[1, 1]])
Node(name='FeatureExtractor_MobilenetV1_Conv2d_13_pointwise_1_Conv2d_3_1x1_128_Conv2D_merged_input_0_quantized', optype='Producer', children: [[1]])
Node(name='BoxPredictor_5_stack_Concat__472', optype='Concat', parents: [1, 1, 1, 1], children: [[1]])
Node(name='Conv__5348_0_scale', optype='Producer', children: [[1, 1]])
Node(name='const_ends__533', optype='Producer', children: [[1, 1, 1, 1, 1, 1, 1, 1, 1]])
Node(name='Conv__5408_0_scale', optype='Producer', children: [[1, 1, 1, 1]])
Node(name='BoxPredictor_5_Reshape__482', optype='Cast', parents: [1], children: [[1]])
Node(name='Conv__5348_0_zero_point', optype='Producer', children: [[1, 1]])
Node(name='BoxPredictor_3_ClassPredictor_BiasAdd_0_scale', optype='Producer', children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5552_zero_point', optype='Producer', children: [[1]])
Node(name='Conv__5408_0_zero_point', optype='Producer', children: [[1, 1, 1, 1]])
Node(name='BoxPredictor_0_stack_Concat__592', optype='Concat', parents: [1, 1, 1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_9_pointwise_Conv2D_merged_input_0_zero_point', optype='Producer', children: [[1]])
Node(name='BoxPredictor_4_strided_slice', optype='Slice', parents: [1, 1, 1, 1, 0], children: [[1, 1]])
Node(name='Const__5454', optype='Producer', children: [[1, 1, 1, 1, 1, 1]])
Node(name='FeatureExtractor_MobilenetV1_Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512_Conv2D_merged_input_0_zero_point', optype='Producer', children: [[1]])
Node(name='BoxPredictor_1_stack_Concat__568', optype='Concat', parents: [1, 1, 1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_9_pointwise_Conv2D_merged_input_0_scale', optype='Producer', children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5552_quantized', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512_Conv2D_merged_input_0_scale', optype='Producer', children: [[1]])
Node(name='BoxPredictor_3_ClassPredictor_BiasAdd_0_zero_point', optype='Producer', children: [[1]])
Node(name='BoxPredictor_2_stack_Concat__544', optype='Concat', parents: [1, 1, 1, 1], children: [[1]])
Node(name='ConvAddFusion_Add_B_const_fold_opt__5623_quantized', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_2_pointwise_BatchNorm_batchnorm_sub_0_quantized', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512_Conv2D_merged_input_0_quantized', optype='Producer', children: [[1]])
Node(name='BoxPredictor_3_stack_Concat__520', optype='Concat', parents: [1, 1, 1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_9_pointwise_Conv2D_merged_input_0_quantized', optype='Producer', children: [[1]])
Node(name='Postprocessor_BatchMultiClassNonMaxSuppression_map_while_MultiClassNonMaxSuppression_ClipToWindow_77_Greater_y_0', optype='Producer', children: [[1, 1]])
Node(name='FeatureExtractor_MobilenetV1_Conv2d_13_pointwise_1_Conv2d_2_1x1_256_BatchNorm_batchnorm_sub_0_quantized', optype='Producer', children: [[1]])
Node(name='Postprocessor_Slice', optype='Slice', parents: [1, 1, 1, 0, 0], children: [[]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_9_depthwise_BatchNorm_batchnorm_add_1_0_scale', optype='Producer', children: [[1, 1]])
Node(name='BoxPredictor_3_ClassPredictor_weights_read_0_zero_point', optype='Producer', children: [[1]])
Node(name='Postprocessor_BatchMultiClassNonMaxSuppression_map_while_MultiClassNonMaxSuppression_add_1_y_0', optype='Producer', children: [[1]])
Node(name='copy__21_Preprocessor_map_while_Less', optype='Less', parents: [1, 1], children: [[1]])
Node(name='BoxPredictor_2_BoxEncodingPredictor_weights_read_0_scale', optype='Producer', children: [[1]])
Node(name='Postprocessor_convert_scores', optype='Sigmoid', parents: [1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_9_depthwise_BatchNorm_batchnorm_add_1_0_zero_point', optype='Producer', children: [[1, 1]])
Node(name='ConvMulFusion_W_const_fold_opt__5537_scale', optype='Producer', children: [[1]])
Node(name='Preprocessor_map_strided_slice__124_0', optype='Squeeze', parents: [1, 0], children: [[1]])
Node(name='concat_1', optype='Concat', parents: [1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5610_zero_point', optype='Producer', children: [[1]])
Node(name='Conv__5257_0_scale', optype='Producer', children: [[1, 1]])
Node(name='Preprocessor_map_Shape_0', optype='Shape', parents: [1], children: [[1]])
Node(name='BoxPredictor_0_ClassPredictor_weights_read_0_quantized', optype='Producer', children: [[1]])
Node(name='BoxPredictor_0_Reshape_1', optype='Reshape', parents: [1, 1], children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5610_scale', optype='Producer', children: [[1]])
Node(name='Conv__5257_0_zero_point', optype='Producer', children: [[1, 1]])
Node(name='Preprocessor_map_strided_slice', optype='Slice', parents: [1, 1, 1, 1, 0], children: [[1]])
Node(name='BoxPredictor_0_Reshape_1__597', optype='Cast', parents: [1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_8_pointwise_BatchNorm_batchnorm_sub_0_quantized', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_0_Conv2D_merged_input_0_zero_point', optype='Producer', children: [[1]])
Node(name='BoxPredictor_5_BoxEncodingPredictor_biases_read_0_quantized', optype='Producer', children: [[1]])
Node(name='BoxPredictor_0_strided_slice', optype='Slice', parents: [1, 1, 1, 1, 0], children: [[1, 1]])
Node(name='ConvMulFusion_W_const_fold_opt__5610_quantized', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_5_depthwise_depthwise_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='const_axes__123', optype='Producer', children: [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
Node(name='BoxPredictor_5_BoxEncodingPredictor_BiasAdd_0_scale', optype='Producer', children: [[1]])
Node(name='BoxPredictor_1_Reshape_1', optype='Reshape', parents: [1, 1], children: [[1]])
Node(name='Conv__5338_0_scale', optype='Producer', children: [[1, 1]])
Node(name='BoxPredictor_5_Reshape_1', optype='Reshape', parents: [1, 1], children: [[1]])
Node(name='const_fold_opt__5584', optype='Producer', children: [[1, 1]])
Node(name='BoxPredictor_5_BoxEncodingPredictor_BiasAdd_0_zero_point', optype='Producer', children: [[1]])
Node(name='BoxPredictor_0_stack_1_Concat__587', optype='Concat', parents: [1, 1, 1], children: [[1]])
Node(name='Conv__5338_0_zero_point', optype='Producer', children: [[1, 1]])
Node(name='const_fold_opt__6209', optype='Producer', children: [[1]])
Node(name='BoxPredictor_5_BoxEncodingPredictor_weights_read_0_zero_point', optype='Producer', children: [[1]])
Node(name='Conv__5444_0_Conv__5444_DequantizeLinear', optype='Dequantizer', parents: [1], children: [[1]])
Node(name='BoxPredictor_1_stack_1_Concat__563', optype='Concat', parents: [1, 1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_8_pointwise_Conv2D_merged_input_0_zero_point', optype='Producer', children: [[1]])
Node(name='BoxPredictor_2_BoxEncodingPredictor_weights_read_0_zero_point', optype='Producer', children: [[1]])
Node(name='const_fold_opt__5547', optype='Producer', children: [[1, 1, 1, 1, 1, 1, 1]])
Node(name='BoxPredictor_5_BoxEncodingPredictor_weights_read_0_quantized', optype='Producer', children: [[1]])
Node(name='BoxPredictor_4_Reshape_1', optype='Reshape', parents: [1, 1], children: [[1]])
Node(name='BoxPredictor_1_Reshape_1__573', optype='Cast', parents: [1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_8_pointwise_Conv2D_merged_input_0_scale', optype='Producer', children: [[1]])
Node(name='const_fold_opt__5558', optype='Producer', children: [[1, 1]])
Node(name='BoxPredictor_5_BoxEncodingPredictor_weights_read_0_scale', optype='Producer', children: [[1]])
Node(name='BoxPredictor_1_strided_slice', optype='Slice', parents: [1, 1, 1, 1, 0], children: [[1, 1]])
Node(name='ConvAddFusion_Add_B_const_fold_opt__5634_quantized', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_3_depthwise_depthwise_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='const_fold_opt__5553', optype='Producer', children: [[1]])
Node(name='BoxPredictor_0_BoxEncodingPredictor_biases_read_0_quantized', optype='Producer', children: [[1]])
Node(name='BoxPredictor_2_Reshape_1', optype='Reshape', parents: [1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_8_pointwise_Conv2D_merged_input_0_quantized', optype='Producer', children: [[1]])
Node(name='Conv__5268_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='const_ends__599', optype='Producer', children: [[1]])
Node(name='BoxPredictor_0_BoxEncodingPredictor_BiasAdd_0_scale', optype='Producer', children: [[1]])
Node(name='BoxPredictor_2_strided_slice', optype='Slice', parents: [1, 1, 1, 1, 0], children: [[1, 1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_8_depthwise_BatchNorm_batchnorm_add_1_0_scale', optype='Producer', children: [[1, 1]])
Node(name='MultipleGridAnchorGenerator_mul_19_x_0', optype='Producer', children: [[1, 1, 1, 1]])
Node(name='BoxPredictor_0_BoxEncodingPredictor_weights_read_0_scale', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_2_depthwise_depthwise_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='BoxPredictor_3_Reshape_1', optype='Reshape', parents: [1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_8_depthwise_BatchNorm_batchnorm_add_1_0_zero_point', optype='Producer', children: [[1, 1]])
Node(name='ConstantFolding_Postprocessor_div_1_recip_0', optype='Producer', children: [[1, 1]])
Node(name='BoxPredictor_0_BoxEncodingPredictor_weights_read_0_quantized', optype='Producer', children: [[1]])
Node(name='BoxPredictor_3_strided_slice', optype='Slice', parents: [1, 1, 1, 1, 0], children: [[1, 1]])
Node(name='ConvMulFusion_W_const_fold_opt__5612_scale', optype='Producer', children: [[1]])
Node(name='BoxPredictor_0_BoxEncodingPredictor_BiasAdd_0_zero_point', optype='Producer', children: [[1]])
Node(name='BoxPredictor_5_ClassPredictor_BiasAdd_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5612_zero_point', optype='Producer', children: [[1]])
Node(name='Conv__5257_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_0_Conv2D_merged_input_0_scale', optype='Producer', children: [[1]])
Node(name='BoxPredictor_0_BoxEncodingPredictor_weights_read_0_zero_point', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_7_pointwise_BatchNorm_batchnorm_sub_0_quantized', optype='Producer', children: [[1]])
===============
Supported nodes
===============
Native operators: 496 (20 types)
- Add: 7
- Cast: 33
- Concat: 18
- Dequantizer: 18
- Exp: 2
- Expand: 1
- Gather: 6
- Mul: 18
- Producer: 314
- Quantizer: 1
- Reshape: 15
- Shape: 9
- Sigmoid: 1
- Slice: 10
- Split: 3
- Squeeze: 13
- Sub: 5
- Tile: 1
- Transpose: 11
- Unsqueeze: 10
Generic operators: 51 (3 types)
- Less: 2
- Loop: 2
- QLinearConv: 47
Native types coverage: 87.0% (20/23)
Native operators coverage: 90.7% (496/547)
(defaultdict(<class 'int'>, {'Reshape': 15, 'Producer': 314, 'Slice': 10, 'Concat': 18, 'Cast': 33, 'Gather': 6, 'Dequantizer': 18, 'Shape': 9, 'Transpose': 11, 'Add': 7, 'Expand': 1, 'Mul': 18, 'Unsqueeze': 10, 'Squeeze': 13, 'Quantizer': 1, 'Sub': 5, 'Split': 3, 'Exp': 2, 'Tile': 1, 'Sigmoid': 1}), defaultdict(<class 'int'>, {'QLinearConv': 47, 'Loop': 2, 'Less': 2}))
===============\Graph manipulation
===============
Remove flatten
Fuse batchnorm
Expand metaop
Fuse to metaop
===============
New Aidge Graph
===============
Node(name='BoxPredictor_4_ClassPredictor_BiasAdd_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_1_pointwise_Conv2D_merged_input_0_quantized', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_0_BatchNorm_batchnorm_sub_0_quantized', optype='Producer', children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5537_zero_point', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_1_depthwise_BatchNorm_batchnorm_add_1_0_zero_point', optype='Producer', children: [[1, 1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_1_depthwise_BatchNorm_batchnorm_add_1_0_scale', optype='Producer', children: [[1, 1]])
Node(name='ConvAddFusion_Add_B_const_fold_opt__5630_quantized', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_1_pointwise_Conv2D_merged_input_0_scale', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_0_Conv2D_merged_input_0_quantized', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_1_pointwise_Conv2D_merged_input_0_zero_point', optype='Producer', children: [[1]])
Node(name='Postprocessor_BatchMultiClassNonMaxSuppression_map_while_add_y_0', optype='Producer', children: [[1]])
Node(name='Gather__5467', optype='Gather', parents: [1, 1], children: [[1]])
Node(name='Postprocessor_Decode_add', optype='Add', parents: [1, 1], children: [[1, 1]])
Node(name='ConvMulFusion_W_const_fold_opt__5537_quantized', optype='Producer', children: [[1]])
Node(name='Postprocessor_BatchMultiClassNonMaxSuppression_mul_y_0', optype='Producer', children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5617_quantized', optype='Producer', children: [[1]])
Node(name='BoxPredictor_4_ClassPredictor_BiasAdd_0_BoxPredictor_4_ClassPredictor_BiasAdd_DequantizeLinear_MulDeQuant', optype='Mul', parents: [1, 1], children: [[1]])
Node(name='const_ends__533', optype='Producer', children: [[1, 1, 1, 1, 1, 1, 1, 1, 1]])
Node(name='BoxPredictor_4_ClassPredictor_BiasAdd_0_BoxPredictor_4_ClassPredictor_BiasAdd_DequantizeLinear_CastIn', optype='Cast', parents: [1], children: [[1]])
Node(name='Postprocessor_Decode_add_1', optype='Add', parents: [1, 1], children: [[1, 1]])
Node(name='ConvMulFusion_W_const_fold_opt__5626_zero_point', optype='Producer', children: [[1]])
Node(name='ScalingFactor', optype='Producer', children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5552_zero_point', optype='Producer', children: [[1]])
Node(name='ZeroPoint', optype='Producer', children: [[1]])
Node(name='Postprocessor_Decode_get_center_coordinates_and_sizes_add', optype='Add', parents: [1, 1], children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5626_scale', optype='Producer', children: [[1]])
Node(name='BoxPredictor_4_ClassPredictor_BiasAdd__499', optype='Transpose', parents: [1], children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5552_quantized', optype='Producer', children: [[1]])
Node(name='Conv__5432_0_Conv__5432_DequantizeLinear_SubDeQuant', optype='Sub', parents: [1, 1], children: [[1]])
Node(name='Postprocessor_Decode_div_7', optype='Mul', parents: [1, 1], children: [[1, 1]])
Node(name='Shape__5468_0', optype='Shape', parents: [1], children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5552_scale', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_9_pointwise_BatchNorm_batchnorm_sub_0_quantized', optype='Producer', children: [[1]])
Node(name='Shape__5472_0', optype='Shape', parents: [1], children: [[1]])
Node(name='Postprocessor_Decode_mul_2', optype='Mul', parents: [1, 1], children: [[1]])
Node(name='ConvMulFusion_W_const_fold_opt__5626_quantized', optype='Producer', children: [[1]])
Node(name='ScalingFactor_1', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_3_depthwise_BatchNorm_batchnorm_add_1_0_scale', optype='Producer', children: [[1, 1]])
Node(name='Conv__5348_0_scale', optype='Producer', children: [[1, 1]])
Node(name='Conv__5442_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='Postprocessor_Decode_mul_1', optype='Mul', parents: [1, 1], children: [[1]])
Node(name='Gather__5475', optype='Gather', parents: [1, 1], children: [[1]])
Node(name='Postprocessor_BatchMultiClassNonMaxSuppression_map_while_MultiClassNonMaxSuppression_add_1_y_0', optype='Producer', children: [[1]])
Node(name='Conv__5444_0_Conv__5444_DequantizeLinear_MulDeQuant', optype='Mul', parents: [1, 1], children: [[1]])
Node(name='Postprocessor_Decode_mul', optype='Mul', parents: [1, 1], children: [[1]])
Node(name='Conv__5348_0_zero_point', optype='Producer', children: [[1, 1]])
Node(name='Conv__5444_0_Conv__5444_DequantizeLinear_SubDeQuant', optype='Sub', parents: [1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_2_pointwise_BatchNorm_batchnorm_sub_0_quantized', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_9_pointwise_Conv2D_merged_input_0_zero_point', optype='Producer', children: [[1]])
Node(name='Conv__5444_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1, 1, 1]])
Node(name='Postprocessor_Decode_mul_3', optype='Mul', parents: [1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_9_pointwise_Conv2D_merged_input_0_scale', optype='Producer', children: [[1]])
Node(name='Conv__5430_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1]])
Node(name='Postprocessor_BatchMultiClassNonMaxSuppression_map_while_MultiClassNonMaxSuppression_ClipToWindow_77_Greater_y_0', optype='Producer', children: [[1, 1]])
Node(name='Conv__5444_0_Conv__5444_DequantizeLinear_CastIn', optype='Cast', parents: [1], children: [[1]])
Node(name='Postprocessor_Decode_get_center_coordinates_and_sizes_add_1', optype='Add', parents: [1, 1], children: [[1]])
Node(name='Conv__5432_quant', optype='QLinearConv', parents: [1, 1, 1, 1, 1, 1, 1, 1, 1], children: [[1, 1, 1, 1]])
Node(name='Conv__5257_0_scale', optype='Producer', children: [[1, 1]])
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Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_10_pointwise_Conv2D_merged_input_0_zero_point', optype='Producer', children: [[1]])
Node(name='ZeroPoint', optype='Producer', children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_10_pointwise_Conv2D_merged_input_0_scale', optype='Producer', children: [[1]])
Node(name='BoxPredictor_2_ClassPredictor_BiasAdd__547', optype='Transpose', parents: [1], children: [[1]])
Node(name='ConvAddFusion_Add_B_const_fold_opt__5627_quantized', optype='Producer', children: [[1]])
Node(name='const_fold_opt__5607', optype='Producer', children: [[1, 1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_10_pointwise_Conv2D_merged_input_0_quantized', optype='Producer', children: [[1]])
Node(name='BoxPredictor_2_ClassPredictor_BiasAdd_0_BoxPredictor_2_ClassPredictor_BiasAdd_DequantizeLinear_SubDeQuant', optype='Sub', parents: [1, 1], children: [[1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_10_depthwise_BatchNorm_batchnorm_add_1_0_scale', optype='Producer', children: [[1, 1]])
Node(name='Gather__5463', optype='Gather', parents: [1, 1], children: [[1]])
Node(name='Postprocessor_BatchMultiClassNonMaxSuppression_map_while_MultiClassNonMaxSuppression_range_start_0', optype='Producer', children: [[1, 1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_10_depthwise_BatchNorm_batchnorm_add_1_0_zero_point', optype='Producer', children: [[1, 1]])
Node(name='FeatureExtractor_MobilenetV1_MobilenetV1_Conv2d_7_pointwise_BatchNorm_batchnorm_sub_0_quantized', optype='Producer', children: [[1]])
===============
Supported nodes 2
===============
Native operators: 577 (20 types)
- Add: 8
- Cast: 53
- Clip: 1
- Concat: 18
- Exp: 2
- Expand: 1
- Gather: 6
- Mul: 37
- Producer: 354
- Reshape: 15
- Round: 1
- Shape: 9
- Sigmoid: 1
- Slice: 10
- Split: 3
- Squeeze: 13
- Sub: 23
- Tile: 1
- Transpose: 11
- Unsqueeze: 10
Generic operators: 51 (3 types)
- Less: 2
- Loop: 2
- QLinearConv: 47
Native types coverage: 87.0% (20/23)
Native operators coverage: 91.9% (577/628)
(defaultdict(<class 'int'>, {'Slice': 10, 'Squeeze': 13, 'Split': 3, 'Cast': 53, 'Shape': 9, 'Reshape': 15, 'Concat': 18, 'Clip': 1, 'Producer': 354, 'Add': 8, 'Transpose': 11, 'Mul': 37, 'Sub': 23, 'Unsqueeze': 10, 'Sigmoid': 1, 'Gather': 6, 'Expand': 1, 'Exp': 2, 'Round': 1, 'Tile': 1}), defaultdict(<class 'int'>, {'QLinearConv': 47, 'Loop': 2, 'Less': 2}))
===============
Supported nodes
===============
Native operators: 577 (20 types)
- Add: 8
- Cast: 53
- Clip: 1
- Concat: 18
- Exp: 2
- Expand: 1
- Gather: 6
- Mul: 37
- Producer: 354
- Reshape: 15
- Round: 1
- Shape: 9
- Sigmoid: 1
- Slice: 10
- Split: 3
- Squeeze: 13
- Sub: 23
- Tile: 1
- Transpose: 11
- Unsqueeze: 10
Generic operators: 51 (3 types)
- Less: 2
- Loop: 2
- QLinearConv: 47
Native types coverage: 87.0% (20/23)
Native operators coverage: 91.9% (577/628)
(defaultdict(<class 'int'>, {'Producer': 354, 'Mul': 37, 'Cast': 53, 'Transpose': 11, 'Sub': 23, 'Shape': 9, 'Gather': 6, 'Add': 8, 'Expand': 1, 'Unsqueeze': 10, 'Squeeze': 13, 'Slice': 10, 'Reshape': 15, 'Concat': 18, 'Split': 3, 'Exp': 2, 'Tile': 1, 'Round': 1, 'Clip': 1, 'Sigmoid': 1}), defaultdict(<class 'int'>, {'QLinearConv': 47, 'Loop': 2, 'Less': 2}))
===============
Compile
===============
tly only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mBoxPredictor_0_BoxEncodingPredictor_BiasAdd_quant[0m] of type [[1m[3mQLinearConv[0m] as
[[94mNOTICE[0m] a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[33mWARNING[0m] - Aidge currently only supports layerwise scaling and not channelwise for
[[33mWARNING[0m] QLinearConv[Weight DequantizeLinear] node. This node will be filled by a
[[33mWARNING[0m] GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mBoxPredictor_5_BoxEncodingPredictor_BiasAdd_quant[0m] of type [[1m[3mQLinearConv[0m] as
[[94mNOTICE[0m] a GenericOperator.
[[94mNOTICE[0m] * auto_pad : b'NOTSET'
[[94mNOTICE[0m] * dilations : [1, 1]
[[94mNOTICE[0m] * group : 1
[[94mNOTICE[0m] * kernel_shape : [1, 1]
[[94mNOTICE[0m] * pads : [0, 0, 0, 0]
[[94mNOTICE[0m] * strides : [1, 1]
[[94mNOTICE[0m] - - Postprocessor_Tile (Tile)
[[94mNOTICE[0m] - - Postprocessor_BatchMultiClassNonMaxSuppression_ones (Expand)
[[94mNOTICE[0m] - Loaded node [[1m[3mcopy__43_Postprocessor_BatchMultiClassNonMaxSuppression_map_while_Less[0m] of
[[94mNOTICE[0m] type [[1m[3mLess[0m] as a GenericOperator.
[[94mNOTICE[0m] - Loaded node [[1m[3mgeneric_loop_Loop__74[0m] of type [[1m[3mLoop[0m] as a GenericOperator.
[[94mNOTICE[0m] * body : node {
[[94mNOTICE[0m] input: "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/I...
[[94mNOTICE[0m] - Node name "ZeroPoint" is a duplicate, renaming it to ZeroPoint_1.
[[94mNOTICE[0m] - Node name "ScalingFactor" is a duplicate, renaming it to ScalingFactor_1.
[[94mNOTICE[0m] - Node name "ZeroPoint" is a duplicate, renaming it to ZeroPoint_2.
[[94mNOTICE[0m] - Node name "ScalingFactor" is a duplicate, renaming it to ScalingFactor_2.
[[94mNOTICE[0m] - Node name "ZeroPoint" is a duplicate, renaming it to ZeroPoint_3.
[[94mNOTICE[0m] - Node name "ScalingFactor" is a duplicate, renaming it to ScalingFactor_3.
[[94mNOTICE[0m] - Node name "ZeroPoint" is a duplicate, renaming it to ZeroPoint_4.
[[94mNOTICE[0m] - Node name "ScalingFactor" is a duplicate, renaming it to ScalingFactor_4.
[[94mNOTICE[0m] - Node name "ZeroPoint" is a duplicate, renaming it to ZeroPoint_5.
[[94mNOTICE[0m] - Node name "ScalingFactor" is a duplicate, renaming it to ScalingFactor_5.
[[94mNOTICE[0m] - Node name "ZeroPoint" is a duplicate, renaming it to ZeroPoint_6.
[[94mNOTICE[0m] - Node name "ScalingFactor" is a duplicate, renaming it to ScalingFactor_6.
[[94mNOTICE[0m] - Node name "ZeroPoint" is a duplicate, renaming it to ZeroPoint_7.
[[94mNOTICE[0m] - Node name "ScalingFactor" is a duplicate, renaming it to ScalingFactor_7.
[[94mNOTICE[0m] - Node name "ZeroPoint" is a duplicate, renaming it to ZeroPoint_8.
[[94mNOTICE[0m] - Node name "ScalingFactor" is a duplicate, renaming it to ScalingFactor_8.
[[94mNOTICE[0m] - Node name "ZeroPoint" is a duplicate, renaming it to ZeroPoint_9.
[[94mNOTICE[0m] - Node name "ScalingFactor" is a duplicate, renaming it to ScalingFactor_9.
Traceback (most recent call last):
File "/app/AI_Project/01_generate_cpp.py", line 99, in <module>
model_aidge.compile("cpu", aidge_core.dtype.float32, dims=[dims])
TypeError: compile(): incompatible function arguments. The following argument types are supported:
1. (self: aidge_core.aidge_core.GraphView, backend: str, datatype: aidge_core.aidge_core.dtype, device: typing.SupportsInt | typing.SupportsIndex = 0, dims: collections.abc.Sequence[collections.abc.Sequence[typing.SupportsInt | typing.SupportsIndex]] = []) -> None
Invoked with: GraphView(name='', Nodes: 628 (inputs: 38, outputs: 4)), 'cpu', <dtype.float32: 1>; kwargs: dims=[[1, 'unk__6579', 'unk__6580', 3]]
Error: export_model/data directory does not exist.error.log
sed: can't read export_model/Makefile: No such file or directory
sed: can't read export_model/Makefile: No such file or directory
sed: can't read export_model/Makefile: No such file or directory
sed: can't read export_model/Makefile: No such file or directory
./AI_Project/03_build.sh: line 17: cd: export_model: No such file or directory
make: AI_Build AI_Deploy AI_Manager AI_Project AI_Support ConvNet.onnx MLP_MNIST.onnx MobileNet-v2.onnx README.md __pycache__ config.json docker examples exit_functions.sh mnist.onnx mnist_test_input_type2.bin model_1D_classifier.onnx model_type2.onnx print_raw_output.py ssd_mobilenet_v1_12int8.onnx type1_test.sh type2_test.sh type3_test.sh No targets specified and no makefile found. Stop.Report Details
report.json
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