PINTO0309
commented
7 months ago I don't know what tfmot.quantization.keras.quantize_mode is doing internally, but it appears that there is no 5D Pad in the keras_model.summary() result. The error Shape must be rank 4 but is rank 7 is unintelligible.
import onnx2tf
"""
onnxsim model.onnx model.onnx \
--overwrite-input-shape "input:1,3,1024,608"
pip install -U \
tensorflow_model_optimization==0.8.0 \
onnx2tf==1.20.0 \
tensorflow==2.16.1 \
tf-keras~=2.16
"""
keras_model = \
onnx2tf.convert(
input_onnx_file_path='model.onnx',
disable_model_save=True
)
print('******************************************************')
keras_model.summary()
import tensorflow_model_optimization as tfmot
quant_aware_model = \
tfmot.quantization.keras.quantize_model(
to_quantize=keras_model,
quantized_layer_name_prefix='quant_'
)
print('******************************************************')
quant_aware_model.summary() :
:
INFO: 77 / 77
INFO: onnx_op_type: Add onnx_op_name: /Add
INFO: input_name.1: /Mul_2_output_0 shape: [1, 1, 1024, 608] dtype: float32
INFO: input_name.2: /Mul_3_output_0 shape: [1, 1, 1024, 608] dtype: float32
INFO: output_name.1: output shape: [1, 1, 1024, 608] dtype: float32
INFO: tf_op_type: add
INFO: input.1.x: name: tf.math.multiply_8/Mul:0 shape: (1, 1024, 608, 1) dtype: <dtype: 'float32'>
INFO: input.2.y: name: tf.math.multiply_13/Mul:0 shape: (1, 1024, 608, 1) dtype: <dtype: 'float32'>
INFO: output.1.output: name: tf.math.add_22/Add:0 shape: (1, 1024, 608, 1) dtype: <dtype: 'float32'>
******************************************************
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input (InputLayer) [(1, 1024, 608, 3)] 0 []
tf.math.subtract (TFOpLamb (1, 1024, 608, 3) 0 ['input[0][0]']
da)
tf.math.divide (TFOpLambda (1, 1024, 608, 3) 0 ['tf.math.subtract[0][0]']
)
tf.compat.v1.pad (TFOpLamb (1, 1026, 610, 3) 0 ['tf.math.divide[0][0]']
da)
tf.nn.convolution_2 (TFOpL (1, 512, 304, 32) 0 ['tf.compat.v1.pad[0][0]']
ambda)
tf.math.add_2 (TFOpLambda) (1, 512, 304, 32) 0 ['tf.nn.convolution_2[0][0]']
tf.nn.relu_2 (TFOpLambda) (1, 512, 304, 32) 0 ['tf.math.add_2[0][0]']
tf.compat.v1.pad_1 (TFOpLa (1, 514, 306, 32) 0 ['tf.nn.relu_2[0][0]']
mbda)
tf.nn.convolution_3 (TFOpL (1, 256, 152, 32) 0 ['tf.compat.v1.pad_1[0][0]']
ambda)
tf.math.add_3 (TFOpLambda) (1, 256, 152, 32) 0 ['tf.nn.convolution_3[0][0]']
tf.nn.relu_3 (TFOpLambda) (1, 256, 152, 32) 0 ['tf.math.add_3[0][0]']
tf.compat.v1.pad_2 (TFOpLa (1, 258, 154, 32) 0 ['tf.nn.relu_3[0][0]']
mbda)
tf.nn.convolution_6 (TFOpL (1, 128, 76, 64) 0 ['tf.compat.v1.pad_2[0][0]']
ambda)
tf.math.add_6 (TFOpLambda) (1, 128, 76, 64) 0 ['tf.nn.convolution_6[0][0]']
tf.nn.relu_6 (TFOpLambda) (1, 128, 76, 64) 0 ['tf.math.add_6[0][0]']
tf.compat.v1.pad_3 (TFOpLa (1, 130, 78, 64) 0 ['tf.nn.relu_6[0][0]']
mbda)
tf.nn.convolution_9 (TFOpL (1, 64, 38, 64) 0 ['tf.compat.v1.pad_3[0][0]']
ambda)
tf.math.add_9 (TFOpLambda) (1, 64, 38, 64) 0 ['tf.nn.convolution_9[0][0]']
tf.nn.relu_9 (TFOpLambda) (1, 64, 38, 64) 0 ['tf.math.add_9[0][0]']
tf.compat.v1.pad_4 (TFOpLa (1, 66, 40, 64) 0 ['tf.nn.relu_9[0][0]']
mbda)
tf.nn.convolution_12 (TFOp (1, 32, 19, 64) 0 ['tf.compat.v1.pad_4[0][0]']
Lambda)
tf.math.add_12 (TFOpLambda (1, 32, 19, 64) 0 ['tf.nn.convolution_12[0][0]']
)
tf.nn.relu_10 (TFOpLambda) (1, 32, 19, 64) 0 ['tf.math.add_12[0][0]']
tf.nn.convolution_13 (TFOp (1, 25, 12, 64) 0 ['tf.nn.relu_10[0][0]']
Lambda)
tf.math.add_13 (TFOpLambda (1, 25, 12, 64) 0 ['tf.nn.convolution_13[0][0]']
)
tf.compat.v1.image.resize_ (1, 15, 8, 64) 0 ['tf.math.add_13[0][0]']
bilinear (TFOpLambda)
tf.compat.v1.pad_5 (TFOpLa (1, 19, 12, 64) 0 ['tf.compat.v1.image.resize_bi
mbda) linear[0][0]']
tf.nn.convolution_14 (TFOp (1, 16, 9, 64) 0 ['tf.compat.v1.pad_5[0][0]']
Lambda)
tf.math.add_14 (TFOpLambda (1, 16, 9, 64) 0 ['tf.nn.convolution_14[0][0]']
)
tf.nn.relu_11 (TFOpLambda) (1, 16, 9, 64) 0 ['tf.math.add_14[0][0]']
tf.compat.v1.image.resize_ (1, 32, 19, 64) 0 ['tf.nn.relu_11[0][0]']
bilinear_1 (TFOpLambda)
tf.compat.v1.pad_7 (TFOpLa (1, 36, 23, 64) 0 ['tf.compat.v1.image.resize_bi
mbda) linear_1[0][0]']
tf.nn.convolution_15 (TFOp (1, 33, 20, 64) 0 ['tf.compat.v1.pad_7[0][0]']
Lambda)
tf.math.add_15 (TFOpLambda (1, 33, 20, 64) 0 ['tf.nn.convolution_15[0][0]']
)
tf.nn.relu_12 (TFOpLambda) (1, 33, 20, 64) 0 ['tf.math.add_15[0][0]']
tf.compat.v1.image.resize_ (1, 63, 37, 64) 0 ['tf.nn.relu_12[0][0]']
bilinear_3 (TFOpLambda)
tf.compat.v1.pad_8 (TFOpLa (1, 67, 41, 64) 0 ['tf.compat.v1.image.resize_bi
mbda) linear_3[0][0]']
tf.nn.convolution_16 (TFOp (1, 64, 38, 64) 0 ['tf.compat.v1.pad_8[0][0]']
Lambda)
tf.math.add_16 (TFOpLambda (1, 64, 38, 64) 0 ['tf.nn.convolution_16[0][0]']
)
tf.nn.relu_13 (TFOpLambda) (1, 64, 38, 64) 0 ['tf.math.add_16[0][0]']
tf.compat.v1.image.resize_ (1, 128, 76, 64) 0 ['tf.nn.relu_13[0][0]']
bilinear_4 (TFOpLambda)
tf.compat.v1.pad_9 (TFOpLa (1, 132, 80, 64) 0 ['tf.compat.v1.image.resize_bi
mbda) linear_4[0][0]']
tf.nn.convolution_17 (TFOp (1, 129, 77, 64) 0 ['tf.compat.v1.pad_9[0][0]']
Lambda)
tf.math.add_17 (TFOpLambda (1, 129, 77, 64) 0 ['tf.nn.convolution_17[0][0]']
)
tf.nn.relu_14 (TFOpLambda) (1, 129, 77, 64) 0 ['tf.math.add_17[0][0]']
tf.compat.v1.image.resize_ (1, 255, 151, 64) 0 ['tf.nn.relu_14[0][0]']
bilinear_5 (TFOpLambda)
tf.nn.convolution (TFOpLam (1, 1021, 605, 128) 0 ['tf.math.divide[0][0]']
bda)
tf.compat.v1.pad_10 (TFOpL (1, 259, 155, 64) 0 ['tf.compat.v1.image.resize_bi
ambda) linear_5[0][0]']
tf.math.add (TFOpLambda) (1, 1021, 605, 128) 0 ['tf.nn.convolution[0][0]']
tf.nn.convolution_1 (TFOpL (1, 1021, 605, 128) 0 ['tf.math.divide[0][0]']
ambda)
tf.nn.convolution_18 (TFOp (1, 256, 152, 64) 0 ['tf.compat.v1.pad_10[0][0]']
Lambda)
tf.nn.relu (TFOpLambda) (1, 1021, 605, 128) 0 ['tf.math.add[0][0]']
tf.math.add_1 (TFOpLambda) (1, 1021, 605, 128) 0 ['tf.nn.convolution_1[0][0]']
tf.math.add_18 (TFOpLambda (1, 256, 152, 64) 0 ['tf.nn.convolution_18[0][0]']
)
tf.compat.v1.nn.avg_pool ( (1, 510, 302, 128) 0 ['tf.nn.relu[0][0]']
TFOpLambda)
tf.nn.relu_1 (TFOpLambda) (1, 1021, 605, 128) 0 ['tf.math.add_1[0][0]']
tf.nn.relu_15 (TFOpLambda) (1, 256, 152, 64) 0 ['tf.math.add_18[0][0]']
tf.nn.convolution_4 (TFOpL (1, 507, 299, 256) 0 ['tf.compat.v1.nn.avg_pool[0][
ambda) 0]']
tf.compat.v1.nn.avg_pool_1 (1, 510, 302, 128) 0 ['tf.nn.relu_1[0][0]']
(TFOpLambda)
tf.compat.v1.image.resize_ (1, 511, 303, 64) 0 ['tf.nn.relu_15[0][0]']
bilinear_6 (TFOpLambda)
tf.math.add_4 (TFOpLambda) (1, 507, 299, 256) 0 ['tf.nn.convolution_4[0][0]']
tf.nn.convolution_5 (TFOpL (1, 507, 299, 256) 0 ['tf.compat.v1.nn.avg_pool_1[0
ambda) ][0]']
tf.compat.v1.pad_11 (TFOpL (1, 515, 307, 64) 0 ['tf.compat.v1.image.resize_bi
ambda) linear_6[0][0]']
tf.nn.relu_4 (TFOpLambda) (1, 507, 299, 256) 0 ['tf.math.add_4[0][0]']
tf.math.add_5 (TFOpLambda) (1, 507, 299, 256) 0 ['tf.nn.convolution_5[0][0]']
tf.nn.convolution_19 (TFOp (1, 512, 304, 64) 0 ['tf.compat.v1.pad_11[0][0]']
Lambda)
tf.compat.v1.nn.avg_pool_2 (1, 253, 149, 256) 0 ['tf.nn.relu_4[0][0]']
(TFOpLambda)
tf.nn.relu_5 (TFOpLambda) (1, 507, 299, 256) 0 ['tf.math.add_5[0][0]']
tf.math.add_19 (TFOpLambda (1, 512, 304, 64) 0 ['tf.nn.convolution_19[0][0]']
)
tf.nn.convolution_7 (TFOpL (1, 251, 147, 256) 0 ['tf.compat.v1.nn.avg_pool_2[0
ambda) ][0]']
tf.compat.v1.nn.avg_pool_3 (1, 253, 149, 256) 0 ['tf.nn.relu_5[0][0]']
(TFOpLambda)
tf.nn.relu_16 (TFOpLambda) (1, 512, 304, 64) 0 ['tf.math.add_19[0][0]']
tf.math.add_7 (TFOpLambda) (1, 251, 147, 256) 0 ['tf.nn.convolution_7[0][0]']
tf.nn.convolution_8 (TFOpL (1, 251, 147, 256) 0 ['tf.compat.v1.nn.avg_pool_3[0
ambda) ][0]']
tf.compat.v1.image.resize_ (1, 248, 144, 64) 0 ['tf.nn.relu_16[0][0]']
bilinear_7 (TFOpLambda)
tf.nn.relu_7 (TFOpLambda) (1, 251, 147, 256) 0 ['tf.math.add_7[0][0]']
tf.math.add_8 (TFOpLambda) (1, 251, 147, 256) 0 ['tf.nn.convolution_8[0][0]']
tf.nn.convolution_20 (TFOp (1, 248, 144, 64) 0 ['tf.compat.v1.image.resize_bi
Lambda) linear_7[0][0]']
tf.nn.convolution_10 (TFOp (1, 248, 144, 384) 0 ['tf.nn.relu_7[0][0]']
Lambda)
tf.nn.relu_8 (TFOpLambda) (1, 251, 147, 256) 0 ['tf.math.add_8[0][0]']
tf.math.add_20 (TFOpLambda (1, 248, 144, 64) 0 ['tf.nn.convolution_20[0][0]']
)
tf.math.add_10 (TFOpLambda (1, 248, 144, 384) 0 ['tf.nn.convolution_10[0][0]']
)
tf.nn.convolution_11 (TFOp (1, 248, 144, 768) 0 ['tf.nn.relu_8[0][0]']
Lambda)
tf.nn.relu_17 (TFOpLambda) (1, 248, 144, 64) 0 ['tf.math.add_20[0][0]']
tf.math.subtract_1 (TFOpLa (1, 248, 144, 384) 0 ['tf.math.add_10[0][0]']
mbda)
tf.math.add_11 (TFOpLambda (1, 248, 144, 768) 0 ['tf.nn.convolution_11[0][0]']
)
tf.nn.convolution_21 (TFOp (1, 248, 144, 384) 0 ['tf.nn.relu_17[0][0]']
Lambda)
tf.math.divide_1 (TFOpLamb (1, 248, 144, 384) 0 ['tf.math.subtract_1[0][0]']
da)
tf.strided_slice (TFOpLamb (1, 248, 144, 384) 0 ['tf.math.add_11[0][0]']
da)
tf.math.add_21 (TFOpLambda (1, 248, 144, 384) 0 ['tf.nn.convolution_21[0][0]']
)
tf.strided_slice_1 (TFOpLa (1, 248, 144, 384) 0 ['tf.math.add_11[0][0]']
mbda)
tf.math.subtract_2 (TFOpLa (1, 248, 144, 384) 0 ['tf.math.divide_1[0][0]',
mbda) 'tf.strided_slice[0][0]']
tf.math.subtract_4 (TFOpLa (1, 248, 144, 384) 0 ['tf.math.add_21[0][0]',
mbda) 'tf.strided_slice_1[0][0]']
tf.math.multiply_6 (TFOpLa (1, 248, 144, 384) 0 ['tf.math.subtract_2[0][0]',
mbda) 'tf.math.subtract_2[0][0]']
tf.math.multiply_11 (TFOpL (1, 248, 144, 384) 0 ['tf.math.subtract_4[0][0]',
ambda) 'tf.math.subtract_4[0][0]']
tf.math.reduce_mean (TFOpL (1, 248, 144, 1) 0 ['tf.math.multiply_6[0][0]']
ambda)
tf.math.reduce_mean_1 (TFO (1, 248, 144, 1) 0 ['tf.math.multiply_11[0][0]']
pLambda)
tf.compat.v1.pad_6 (TFOpLa (1, 256, 152, 1) 0 ['tf.math.reduce_mean[0][0]']
mbda)
tf.compat.v1.pad_12 (TFOpL (1, 256, 152, 1) 0 ['tf.math.reduce_mean_1[0][0]'
ambda) ]
tf.compat.v1.image.resize_ (1, 1024, 608, 1) 0 ['tf.compat.v1.pad_6[0][0]']
bilinear_2 (TFOpLambda)
tf.compat.v1.image.resize_ (1, 1024, 608, 1) 0 ['tf.compat.v1.pad_12[0][0]']
bilinear_8 (TFOpLambda)
tf.math.subtract_3 (TFOpLa (1, 1024, 608, 1) 0 ['tf.compat.v1.image.resize_bi
mbda) linear_2[0][0]']
tf.math.subtract_5 (TFOpLa (1, 1024, 608, 1) 0 ['tf.compat.v1.image.resize_bi
mbda) linear_8[0][0]']
tf.math.multiply_7 (TFOpLa (1, 1024, 608, 1) 0 ['tf.math.subtract_3[0][0]']
mbda)
tf.math.multiply_12 (TFOpL (1, 1024, 608, 1) 0 ['tf.math.subtract_5[0][0]']
ambda)
tf.math.divide_2 (TFOpLamb (1, 1024, 608, 1) 0 ['tf.math.multiply_7[0][0]']
da)
tf.math.divide_3 (TFOpLamb (1, 1024, 608, 1) 0 ['tf.math.multiply_12[0][0]']
da)
tf.math.multiply_8 (TFOpLa (1, 1024, 608, 1) 0 ['tf.math.divide_2[0][0]']
mbda)
tf.math.multiply_13 (TFOpL (1, 1024, 608, 1) 0 ['tf.math.divide_3[0][0]']
ambda)
output (TFOpLambda) (1, 1024, 608, 1) 0 ['tf.math.multiply_8[0][0]',
'tf.math.multiply_13[0][0]']
==================================================================================================
Total params: 0 (0.00 Byte)
Trainable params: 0 (0.00 Byte)
Non-trainable params: 0 (0.00 Byte)
__________________________________________________________________________________________________
Traceback (most recent call last):
File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/home/xxxx/.vscode/extensions/ms-python.debugpy-2024.5.11001012-linux-x64/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py", line 39, in <module>
cli.main()
File "/home/xxxx/.vscode/extensions/ms-python.debugpy-2024.5.11001012-linux-x64/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 430, in main
run()
File "/home/xxxx/.vscode/extensions/ms-python.debugpy-2024.5.11001012-linux-x64/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 284, in run_file
runpy.run_path(target, run_name="__main__")
File "/home/xxxx/.vscode/extensions/ms-python.debugpy-2024.5.11001012-linux-x64/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 321, in run_path
return _run_module_code(code, init_globals, run_name,
File "/home/xxxx/.vscode/extensions/ms-python.debugpy-2024.5.11001012-linux-x64/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 135, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "/home/xxxx/.vscode/extensions/ms-python.debugpy-2024.5.11001012-linux-x64/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 124, in _run_code
exec(code, run_globals)
File "/home/xxxx/work/qat/test.py", line 24, in <module>
quant_aware_model = \
File "/home/xxxx/.local/lib/python3.10/site-packages/tensorflow_model_optimization/python/core/quantization/keras/quantize.py", line 141, in quantize_model
return quantize_apply(
File "/home/xxxx/.local/lib/python3.10/site-packages/tensorflow_model_optimization/python/core/keras/metrics.py", line 74, in inner
raise error
File "/home/xxxx/.local/lib/python3.10/site-packages/tensorflow_model_optimization/python/core/keras/metrics.py", line 69, in inner
results = func(*args, **kwargs)
File "/home/xxxx/.local/lib/python3.10/site-packages/tensorflow_model_optimization/python/core/quantization/keras/quantize.py", line 475, in quantize_apply
_extract_original_model(model_copy)
File "/home/xxxx/.local/lib/python3.10/site-packages/tensorflow_model_optimization/python/core/quantization/keras/quantize.py", line 398, in _extract_original_model
unwrapped_model = keras.models.clone_model(
File "/home/xxxx/.local/lib/python3.10/site-packages/tf_keras/src/models/cloning.py", line 540, in clone_model
return _clone_functional_model(
File "/home/xxxx/.local/lib/python3.10/site-packages/tf_keras/src/models/cloning.py", line 230, in _clone_functional_model
) = functional.reconstruct_from_config(
File "/home/xxxx/.local/lib/python3.10/site-packages/tf_keras/src/engine/functional.py", line 1504, in reconstruct_from_config
if process_node(layer, node_data):
File "/home/xxxx/.local/lib/python3.10/site-packages/tf_keras/src/engine/functional.py", line 1444, in process_node
output_tensors = layer(input_tensors, **kwargs)
File "/home/xxxx/.local/lib/python3.10/site-packages/tf_keras/src/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/xxxx/.local/lib/python3.10/site-packages/tensorflow/python/framework/ops.py", line 1037, in _create_c_op
raise ValueError(e.message)
ValueError: Exception encountered when calling layer "tf.compat.v1.pad" (type TFOpLambda).
Shape must be rank 4 but is rank 7 for '{{node tf.compat.v1.pad/Pad}} = Pad[T=DT_FLOAT, Tpaddings=DT_INT32](tf.compat.v1.pad/Pad/input, tf.compat.v1.pad/Pad/paddings)' with input shapes: [1,1,1,1,1024,608,3], [4,2].
Call arguments received by layer "tf.compat.v1.pad" (type TFOpLambda):
• tensor=['tf.Tensor(shape=(1, 1, 1, 1024, 608, 3), dtype=float32)']
• paddings=[['0', '0'], ['1', '1'], ['1', '1'], ['0', '0']]
• mode='CONSTANT'
• name=None
• constant_values=0
CarlosNacher
github-actions[bot]
Issue Type
Others
OS
Windows
onnx2tf version number
1.19.16
onnx version number
1.15.0
onnxruntime version number
1.17.1
onnxsim (onnx_simplifier) version number
0.4.33
tensorflow version number
2.15.0
Download URL for ONNX
https://we.tl/t-HJjXDINMND
Parameter Replacement JSON
Description
1. Purpose: Create a INT8 model for Edge TPU 2. What: I have succesfully converted my ONNX model to TF ones (float32, float16, integer, fully_integer...), i.e. the code of onnx actually works as expected. The problem I am trying to solve is not the strict scope of onnx2tf, but I think it would be cool it could be perfomed and maybe QAT could be added to onnx2tf supported conversions. Having said that, with the keras model returned by the following code:
I am trying to do QAT (as detailed in https://www.tensorflow.org/model_optimization/guide/quantization/training_comprehensive_guide#create_and_deploy_quantized_model):
but I am getting the following error:
Do you know why it is trying [1, 1, 1024, 608, 3] (rank 5) inside the pad operation instead of rank 4, and/or how can we fix that?
Thank you so much in advance and thank you so much for your valuable work in developing this repo!!!
By the way, I am trying to perform QAT as an alternative of PTQ because with the
model_full_integer_quant.tflitegenerated byonnx2tf.convertmy inferences look all with the same value, as I detailed in https://github.com/PINTO0309/onnx2tf/issues/610