DeepLab 257x257 is great but 2049x2049 is even better #8
Description
Do you know how to produce a TFLite file of any arbitary dimension from the deeplab models here:
https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
I got pretty close.
I have some test code
import numpy as np
import tensorflow as tf
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path="deeplabv3_257_mv_gpu.tflite")
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Test model on random input data.
input_shape = input_details[0]['shape']
input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data)
Which executes flawlessly
but for my own model that I have converted
(tensorflow-v1.13.1) [samh@apollo-centos6 tmp]$ more speedy.py
import numpy as np
import tensorflow as tf
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path="speedy.tflite")
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Test model on random input data.
input_shape = input_details[0]['shape']
input_data = np.array(np.random.random_sample(input_shape), dtype=np.uint8)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data)
(tensorflow-v1.13.1) [samh@apollo-centos6 tmp]$ python speedy.py
Traceback (most recent call last):
File "speedy.py", line 17, in <module>
interpreter.invoke()
File "/home/samh/anaconda3/envs/tensorflow-v1.13.1/lib/python3.6/site-packages/tensorflow/lite/python/interpreter.py", line 277, in invoke
self._interpreter.Invoke()
File "/home/samh/anaconda3/envs/tensorflow-v1.13.1/lib/python3.6/site-packages/tensorflow/lite/python/interpreter_wrapper/tensorflow_wrap_interpreter_wrapper.py", line 109, in Invoke
return _tensorflow_wrap_interpreter_wrapper.InterpreterWrapper_Invoke(self)
RuntimeError: tensorflow/lite/kernels/depthwise_conv.cc:99 params->depth_multiplier * SizeOfDimension(input, 3) != SizeOfDimension(filter, 3) (0 != 64)Node number 33 (DEPTHWISE_CONV_2D) failed to prepare.
It is erroring on the line .invoke()
the .pb file was created using the export_model.py script here:
https://github.com/tensorflow/models/blob/master/research/deeplab/export_model.py
Using the docs here
https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/export_model.md
It is an xception_65 model
I quantised as follows
tflite_convert --output_file=speedy.tflite --graph_def_file=frozen_graph.pb --inference_type=FLOAT --inference_input_type=QUANTIZED_UINT8 --input_arrays=ImageTensor --input_shapes=1,2049,2049,3 --output_arrays='SemanticPredictions' --std_dev_values=128 --mean_values=127
Which ends cleanly.
Now I know that this will take a while to run on a mobile phone but the end game is to run it on a GPU in OpenGL ES on Linux and Metal on Apple desktop.
Do you have any hints
To repeat here is the error message
File "/home/samh/anaconda3/envs/tensorflow-v1.13.1/lib/python3.6/site-packages/tensorflow/lite/python/interpreter_wrapper/tensorflow_wrap_interpreter_wrapper.py", line 109, in Invoke
return _tensorflow_wrap_interpreter_wrapper.InterpreterWrapper_Invoke(self)
RuntimeError: tensorflow/lite/kernels/depthwise_conv.cc:99 params->depth_multiplier * SizeOfDimension(input, 3) != SizeOfDimension(filter, 3) (0 != 64)Node number 33 (DEPTHWISE_CONV_2D) failed to prepare.