Comments (5)
TimYao18
commented on June 7, 2024
1
hi @TimYao18 ,I also encountered this problem on mobile device, hope you can share how to solve it, thanks~
I use this tool onnx2tf to convert and it default convert into the right format.
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TimYao18
commented on June 7, 2024
The outputs of the step 3 during conversion to TFLite as below:
2024-01-10 10:59:33.953038: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:378] Ignored output_format.
2024-01-10 10:59:33.953063: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:381] Ignored drop_control_dependency.
Summary on the non-converted ops:
---------------------------------
* Accepted dialects: tfl, builtin, func
* Non-Converted Ops: 17122, Total Ops 34728, % non-converted = 49.30 %
* 17122 ARITH ops
- arith.constant: 17122 occurrences (f32: 17109, i32: 13)
(f32: 99)
(f32: 24)
(f32: 17001)
(f32: 73)
(f32: 45)
(uq_8: 70)
(f32: 7)
(f32: 1)
(f32: 5)
(f32: 97)
(f32: 1)
(f32: 180)
2024-01-10 10:59:39.877035: I tensorflow/compiler/mlir/lite/flatbuffer_export.cc:2989] Estimated count of arithmetic ops: 9.246 G ops, equivalently 4.623 G MACsfrom midas.
TimYao18
commented on June 7, 2024
Now I can convert the model-small.onnx to tflite, but the result images are blurry. Where was I doing wrong? Please help me~
- I fix the onnx model's key problem ( the model input tensor name = '0' ==> KeyError: '0')
import onnx
from onnx import helper
onnx_model_path = "model-small.onnx"
onnx_model = onnx.load(onnx_model_path)
# Define a mapping from old names to new names
name_map = {"0": "arg_0"}
# Initialize a list to hold the new inputs
new_inputs = []
# Iterate over the inputs and change their names if needed
for inp in onnx_model.graph.input:
if inp.name in name_map:
# Create a new ValueInfoProto with the new name
new_inp = helper.make_tensor_value_info(name_map[inp.name],
inp.type.tensor_type.elem_type,
[dim.dim_value for dim in inp.type.tensor_type.shape.dim])
new_inputs.append(new_inp)
else:
new_inputs.append(inp)
# Clear the old inputs and add the new ones
onnx_model.graph.ClearField("input")
onnx_model.graph.input.extend(new_inputs)
# Go through all nodes in the model and replace the old input name with the new one
for node in onnx_model.graph.node:
for i, input_name in enumerate(node.input):
if input_name in name_map:
node.input[i] = name_map[input_name]
# Save the renamed ONNX model
onnx_model_path = "model-small-fix.onnx"
onnx.save(onnx_model, onnx_model_path)- Convert it into TensorFlow saved model format (the result of the TF model is confirmed OK):
import onnx
from onnx_tf.backend import prepare
model_path = "model-small-fix.onnx"
output_path = "modified_model_2"
onnx_model = onnx.load(model_path) # load onnx model
tf_rep = prepare(onnx_model) # prepare tf representation
tf_rep.export_graph(output_path) # export the model- Convert the TensorFlow saved model into TFLite:
import tensorflow as tf
import io
path = 'modified_model_2'
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir=path)
tf_lite_model = converter.convert()
open('model_1.tflite', 'wb').write(tf_lite_model)- When last python running, the log out as below:
python convert_tf2tflite_savedmodel.py
2024-01-10 14:26:37.071501: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:378] Ignored output_format.
2024-01-10 14:26:37.071524: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:381] Ignored drop_control_dependency.
2024-01-10 14:26:37.072913: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: /modified_model_2
2024-01-10 14:26:37.089615: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }
2024-01-10 14:26:37.089642: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: /modified_model_2
2024-01-10 14:26:37.110969: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled
2024-01-10 14:26:37.118290: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle.
2024-01-10 14:26:37.263435: I tensorflow/cc/saved_model/loader.cc:217] Running initialization op on SavedModel bundle at path: /modified_model_2
2024-01-10 14:26:37.389643: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 316731 microseconds.
2024-01-10 14:26:37.545668: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
Summary on the non-converted ops:
---------------------------------
* Accepted dialects: tfl, builtin, func
* Non-Converted Ops: 293, Total Ops 804, % non-converted = 36.44 %
* 293 ARITH ops
- arith.constant: 293 occurrences (f32: 281, i32: 12)
(f32: 99)
(f32: 73)
(f32: 24)
(f32: 73)
(f32: 45)
(f32: 7)
(f32: 1)
(f32: 5)
(f32: 1)
(f32: 180)
2024-01-10 14:26:38.047603: I tensorflow/compiler/mlir/lite/flatbuffer_export.cc:2989] Estimated count of arithmetic ops: 9.246 G ops, equivalently 4.623 G MACsfrom midas.
TimYao18
commented on June 7, 2024
I found that the input shape (1, 3,256,256) in TensorFlow Lite is different from the MiDaS provided model (1,256,256,3)
I use the 'onnx2tf' that it automatically transposes (N,C,H,W) to (N,H,W,C) So I will close this issue.
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visonpon
commented on June 7, 2024
hi @TimYao18 ,I follw your steps and the test results are normal, but also encountered this problem on mobile device, hope you can share how to solve it, thanks~
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