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[Step 1/11] Parsing and validating input arguments
[ INFO ] Parsing input parameters
[Step 2/11] Loading OpenVINO Runtime
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
[ INFO ]
[ INFO ] Device info:
[ INFO ] NPU
[ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
[ INFO ]
[ INFO ]
[Step 3/11] Setting device configuration
[ WARNING ] Turn on performance counters for NPU device since report type is detailed_counters.
[Step 4/11] Reading model files
[ INFO ] Loading model files
[ INFO ] Read model took 9.01 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ] input_1 (node: input_1) : f32 / [...] / [1,256,256,1]
[ INFO ] Model outputs:
[ INFO ] group_normalization (node: model/group_normalization/Reshape_4) : f32 / [...] / [1,256,256,32]
[Step 5/11] Resizing model to match image sizes and given batch
[ WARNING ] Input 'input_1' has static shape. Provided data shapes for this input will be ignored.
[ INFO ] Model batch size: 1
[Step 6/11] Configuring input of the model
[ INFO ] Model inputs:
[ INFO ] input_1 (node: input_1) : f32 / [N,H,W,C] / [1,256,256,1]
[ INFO ] Model outputs:
[ INFO ] group_normalization (node: model/group_normalization/Reshape_4) : f32 / [...] / [1,256,256,32]
[Step 7/11] Loading the model to the device
loc(fused["model/group_normalization/batchnorm/add_1", "t_Add"]): error: Reshape has incompatible output shape as clustering: intype = !VPUIP.DistributedBuffer<1x4x8x1xf16, affine_map<(d0, d1, d2, d3) -> (d0, d2, d3, d1)>, @CMX_NN, {mode = "SEGMENTED", num_tiles = [1, 1, 2, 1], num_clusters = 2 : i64}>, out type = !VPUIP.DistributedBuffer<1x1x4x8xf16, affine_map<(d0, d1, d2, d3) -> (d0, d1, d3, d2)>, @CMX_NN, {mode = "SEGMENTED", num_tiles = [1, 1, 2, 1], num_clusters = 2 : i64}>
[ ERROR ] Exception from src\inference\src\cpp\core.cpp:109:
Exception from src\inference\src\dev\plugin.cpp:54:
Exception from src\plugins\intel_npu\src\plugin\src\plugin.cpp:513:
Check 'result == ZE_RESULT_SUCCESS' failed at src\plugins\intel_npu\src\compiler\src\zero_compiler_in_driver.cpp:753:
Failed to compile network. L0 createGraph result: ZE_RESULT_ERROR_INVALID_ARGUMENT, code 0x78000004. Compilation failed
Failed to create executable
Traceback (most recent call last):
File "C:\Users\user\micromamba\envs\opvtest\lib\site-packages\openvino\tools\benchmark\main.py", line 408, in main
compiled_model = benchmark.core.compile_model(model, benchmark.device, device_config)
File "C:\Users\user\micromamba\envs\opvtest\lib\site-packages\openvino\runtime\ie_api.py", line 521, in compile_model
super().compile_model(model, device_name, {} if config is None else config),
RuntimeError: Exception from src\inference\src\cpp\core.cpp:109:
Exception from src\inference\src\dev\plugin.cpp:54:
Exception from src\plugins\intel_npu\src\plugin\src\plugin.cpp:513:
Check 'result == ZE_RESULT_SUCCESS' failed at src\plugins\intel_npu\src\compiler\src\zero_compiler_in_driver.cpp:753:
Failed to compile network. L0 createGraph result: ZE_RESULT_ERROR_INVALID_ARGUMENT, code 0x78000004. Compilation failed
Failed to create executable
[ INFO ] Statistics report is stored to benchmark_report.csv
Issue submission checklist
I'm reporting an issue. It's not a question.
I checked the problem with the documentation, FAQ, open issues, Stack Overflow, etc., and have not found a solution.
There is reproducer code and related data files such as images, videos, models, etc.
The text was updated successfully, but these errors were encountered:
OpenVINO Version
2024.1.0
Operating System
Windows System
Device used for inference
NPU
Framework
Keras (TensorFlow 2)
Model used
Custom
Issue description
Consider a following workflow:
Problem: Adding GroupNormalization layer makes benchmark_app crash on NPU.
Tests were performed on a laptop with Intel Core Ultra 7 155H CPU. Tensorflow version was 2.14.0.
Step-by-step reproduction
Step 1: Model creation in tensorflow
import tensorflow as tf
inp = tf.keras.Input((None, None, 1), dtype=tf.float32)
y = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu')(inp)
y = tf.keras.layers.GroupNormalization(4)(y)
model = tf.keras.Model(inputs=inp, outputs=y)
tf.keras.models.save_model(model, 'test_model')
Step 2: Model conversion to openvino format
mo.exe --saved_model_dir .\test_model --input input_1 --input_shape [1,256,256,1]
Step 3: Performing benchmark on NPU
benchmark_app.exe -m saved_model.xml -hint ctput -data_shape "[1, 256, 256, 1]" -inference_only -report_type detailed_counters -d NPU
Relevant log output
Issue submission checklist
The text was updated successfully, but these errors were encountered: