Model parsing error: false depthwise convolution layer
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zaytsevk commented
I have created a simple model with single convolution layer, one input and one output channel:
import onnx
import onnx.helper as helper
import onnx.numpy_helper as numpy_helper
import numpy as np
# Create a tensor for the weights of the convolutional layer
weights = np.random.randn(1, 1, 9, 1).astype(np.float32)
weights_tensor = numpy_helper.from_array(weights, name='conv1_weight')
# Create the input tensor value info
input_tensor = helper.make_tensor_value_info('input', onnx.TensorProto.FLOAT, [1, 1, 5, 5])
# Create the output tensor value info
output_tensor = helper.make_tensor_value_info('output', onnx.TensorProto.FLOAT, [1, 1, 5, 5])
# Create the node (layer)
conv_node = helper.make_node(
'Conv',
inputs=['input', 'conv1_weight'],
outputs=['output'],
kernel_shape=[9, 1],
pads=[4, 0, 4, 0],
strides=[1, 1]
)
# Create the graph
graph = helper.make_graph(
[conv_node],
'conv_graph',
[input_tensor],
[output_tensor],
[weights_tensor]
)
# Create the model
model = helper.make_model(graph, producer_name='onnx-example')
# Save the model to a file
onnx.save(model, 'single_conv_layer.onnx')
print("ONNX model for a single convolutional layer has been created and saved as 'single_conv_layer.onnx'")During the model compilation I have received the following message from the parser while there is no depthwise convolution layer in the model:
[TIDL Import] UNSUPPORTED: Allowlisting : Layer name - output : Depthwise convolution layer with Kernel 9x1 and Stride 1x1 is not supported -- [tidlAllowlistingConstraints/tidl_constraint.cpp, 85]
[TIDL Import] [PARSER] UNSUPPORTED: Layer is not supported by TIDL --- layer type - Conv, Node name - -- [tidl_onnxRtImport_core.cpp, 519]Thus, the parser reports a false depthwise convolution layer since the number of groups equals to 1 and all inputs are convolved to all outputs. Here is the Python code used for model compilation:
import numpy as np
import onnxruntime as rt
##########################################
### Script parameters ###
##########################################
model_path = 'single_conv_layer.onnx'
EP_list = ['TIDLCompilationProvider','CPUExecutionProvider']
options = {}
options['artifacts_folder'] = './model-artifacts-dir/'
options['tidl_tools_path'] = '/home/root/tidl_tools'
options['debug_level'] = 1
##########################################
so = rt.SessionOptions()
# Load the ONNX model
session = rt.InferenceSession(model_path, providers=EP_list, provider_options=[options, {}], sess_options=so)
# Create a random input tensor with the same shape as the input tensor defined in the model
input_data = np.random.rand(1, 1, 5, 5).astype(np.float32)
# Run the model
outputs = session.run(None, {'input': input_data})
# Print the output
print("Model output:", outputs[0])You can find single_conv_layer.onnx file in the archive single_conv_layer.zip. Below is complete console log for model compilation with debug_level=3:
tidl_tools_path = /home/root/tidl_tools
artifacts_folder = ./model-artifacts-dir/
tidl_tensor_bits = 8
debug_level = 3
num_tidl_subgraphs = 16
tidl_denylist =
tidl_denylist_layer_name =
tidl_denylist_layer_type =
tidl_allowlist_layer_name =
model_type =
tidl_calibration_accuracy_level = 7
tidl_calibration_options:num_frames_calibration = 20
tidl_calibration_options:bias_calibration_iterations = 50
mixed_precision_factor = -1.000000
model_group_id = 0
power_of_2_quantization = 2
ONNX QDQ Enabled = 0
enable_high_resolution_optimization = 0
pre_batchnorm_fold = 1
add_data_convert_ops = 0
output_feature_16bit_names_list =
m_params_16bit_names_list =
m_single_core_layers_names_list =
Inference mode = 0
Number of cores = 1
reserved_compile_constraints_flag = 1601
partial_init_during_compile = 0
ti_internal_reserved_1 =
========================= [Model Compilation Started] =========================
Model compilation will perform the following stages:
1. Parsing
2. Graph Optimization
3. Quantization & Calibration
4. Memory Planning
============================== [Version Summary] ==============================
-------------------------------------------------------------------------------
| TIDL Tools Version | 10_00_04_00 |
-------------------------------------------------------------------------------
| C7x Firmware Version | 10_00_02_00 |
-------------------------------------------------------------------------------
| Runtime Version | 1.14.0+10000005 |
-------------------------------------------------------------------------------
| Model Opset Version | 18 |
-------------------------------------------------------------------------------
NOTE: The runtime version here specifies ONNXRT_VERSION+TIDL_VERSION
Ex: 1.14.0+1000XXXX -> ONNXRT 1.14.0 and a TIDL_VERSION 10.00.XX.XX
============================== [Parsing Started] ==============================
[TIDL Import] [PARSER] WARNING: Network not identified as Object Detection network : (1) Ignore if network is not Object Detection network (2) If network is Object Detection network, please specify "model_type":"OD" as part of OSRT compilation options
[TIDL Import] UNSUPPORTED: Allowlisting : Layer name - output : Depthwise convolution layer with Kernel 9x1 and Stride 1x1 is not supported -- [tidlAllowlistingConstraints/tidl_constraint.cpp, 85]
[TIDL Import] [PARSER] UNSUPPORTED: Layer is not supported by TIDL --- layer type - Conv, Node name - -- [tidl_onnxRtImport_core.cpp, 519]
------------------------- Subgraph Information Summary -------------------------
-------------------------------------------------------------------------------
| Core | No. of Nodes | Number of Subgraphs |
-------------------------------------------------------------------------------
| C7x | 0 | 0 |
| CPU | 1 | x |
-------------------------------------------------------------------------------
--------------------------------------------------------------------------------------------------
| Node | Node Name | Reason |
--------------------------------------------------------------------------------------------------
| Conv | output | Depthwise convolution layer with Kernel 9x1 and Stride 1x1 is not supported |
--------------------------------------------------------------------------------------------------
Running Runtimes GraphViz - /home/root/tidl_tools/tidl_graphVisualiser_runtimes.out ./model-artifacts-dir//allowedNode.txt ./model-artifacts-dir//tempDir/graphvizInfo.txt ./model-artifacts-dir//tempDir/runtimes_visualization.svg
============================= [Parsing Completed] =============================
Model output: [[[[ 0.24346259 0.7302017 -0.3997852 -0.09163873 0.1756232 ]
[ 0.5447106 0.14750001 0.44192222 0.50972265 0.668015 ]
[ 0.25353688 1.1781881 0.46983877 0.47023544 0.4859341 ]
[-0.5246689 -0.97710145 -0.54835 0.01153768 -0.42153066]
[-0.13848214 -0.18936723 -0.2901531 -0.37395775 0.07250781]]]]zaytsevk commented
The issue was reported to TI's E2E Processors forum: https://e2e.ti.com/support/processors-group/processors/f/processors-forum/1413779/am69a-parsing-error-false-depthwise-convolution-layer
I will close it here to avoid duplication.