MLECO-2493 Add python OD example with TFLite delegate
Signed-off-by: Raviv Shalev <raviv.shalev@arm.com>
Change-Id: I25fcccbf912be0c5bd4fbfd2e97552341958af35
diff --git a/python/pyarmnn/examples/common/utils.py b/python/pyarmnn/examples/common/utils.py
index d4dadf8..beca0d3 100644
--- a/python/pyarmnn/examples/common/utils.py
+++ b/python/pyarmnn/examples/common/utils.py
@@ -8,7 +8,7 @@
from pathlib import Path
import numpy as np
-import pyarmnn as ann
+import datetime
def dict_labels(labels_file_path: str, include_rgb=False) -> dict:
@@ -42,67 +42,76 @@
return labels
-def prepare_input_tensors(audio_data, input_binding_info, mfcc_preprocessor):
+def prepare_input_data(audio_data, input_data_type, input_quant_scale, input_quant_offset, mfcc_preprocessor):
"""
Takes a block of audio data, extracts the MFCC features, quantizes the array, and uses ArmNN to create the
input tensors.
Args:
audio_data: The audio data to process
- mfcc_instance: the mfcc class instance
- input_binding_info: the model input binding info
- mfcc_preprocessor: the mfcc preprocessor instance
+ mfcc_instance: The mfcc class instance
+ input_data_type: The model's input data type
+ input_quant_scale: The model's quantization scale
+ input_quant_offset: The model's quantization offset
+ mfcc_preprocessor: The mfcc preprocessor instance
Returns:
- input_tensors: the prepared input tensors, ready to be consumed by the ArmNN NetworkExecutor
+ input_data: The prepared input data
"""
- data_type = input_binding_info[1].GetDataType()
- input_tensor = mfcc_preprocessor.extract_features(audio_data)
- if data_type != ann.DataType_Float32:
- input_tensor = quantize_input(input_tensor, input_binding_info)
- input_tensors = ann.make_input_tensors([input_binding_info], [input_tensor])
- return input_tensors
+ input_data = mfcc_preprocessor.extract_features(audio_data)
+ if input_data_type != np.float32:
+ input_data = quantize_input(input_data, input_data_type, input_quant_scale, input_quant_offset)
+ return input_data
-def quantize_input(data, input_binding_info):
+def quantize_input(data, input_data_type, input_quant_scale, input_quant_offset):
"""Quantize the float input to (u)int8 ready for inputting to model."""
if data.ndim != 2:
raise RuntimeError("Audio data must have 2 dimensions for quantization")
- quant_scale = input_binding_info[1].GetQuantizationScale()
- quant_offset = input_binding_info[1].GetQuantizationOffset()
- data_type = input_binding_info[1].GetDataType()
-
- if data_type == ann.DataType_QAsymmS8:
- data_type = np.int8
- elif data_type == ann.DataType_QAsymmU8:
- data_type = np.uint8
- else:
+ if (input_data_type != np.int8) and (input_data_type != np.uint8):
raise ValueError("Could not quantize data to required data type")
- d_min = np.iinfo(data_type).min
- d_max = np.iinfo(data_type).max
+ d_min = np.iinfo(input_data_type).min
+ d_max = np.iinfo(input_data_type).max
for row in range(data.shape[0]):
for col in range(data.shape[1]):
- data[row, col] = (data[row, col] / quant_scale) + quant_offset
+ data[row, col] = (data[row, col] / input_quant_scale) + input_quant_offset
data[row, col] = np.clip(data[row, col], d_min, d_max)
- data = data.astype(data_type)
+ data = data.astype(input_data_type)
return data
-def dequantize_output(data, output_binding_info):
+def dequantize_output(data, is_output_quantized, output_quant_scale, output_quant_offset):
"""Dequantize the (u)int8 output to float"""
- if output_binding_info[1].IsQuantized():
+ if is_output_quantized:
if data.ndim != 2:
raise RuntimeError("Data must have 2 dimensions for quantization")
- quant_scale = output_binding_info[1].GetQuantizationScale()
- quant_offset = output_binding_info[1].GetQuantizationOffset()
-
data = data.astype(float)
for row in range(data.shape[0]):
for col in range(data.shape[1]):
- data[row, col] = (data[row, col] - quant_offset)*quant_scale
+ data[row, col] = (data[row, col] - output_quant_offset)*output_quant_scale
return data
+
+
+class Profiling:
+ def __init__(self, enabled: bool):
+ self.m_start = 0
+ self.m_end = 0
+ self.m_enabled = enabled
+
+ def profiling_start(self):
+ if self.m_enabled:
+ self.m_start = datetime.datetime.now()
+
+ def profiling_stop_and_print_us(self, msg):
+ if self.m_enabled:
+ self.m_end = datetime.datetime.now()
+ period = self.m_end - self.m_start
+ period_us = period.seconds * 1_000_000 + period.microseconds
+ print(f'Profiling: {msg} : {period_us:,} microSeconds')
+ return period_us
+ return 0