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from pycoral.utils import edgetpu
import time
from abc import ABC, abstractmethod
from pathlib import Path
import numpy as np
DEFAULT_MODEL_PATH = str(Path(__file__).parent / "models/portiloop_model_quant.tflite")
print(DEFAULT_MODEL_PATH)
class AbstractQuantizedModelForInference(ABC):
@abstractmethod
def add_datapoints(self, input_float):
return NotImplemented
class QuantizedModelForInference(AbstractQuantizedModelForInference):
def __init__(self, num_models_parallel=8, window_size=54, seq_stride=42, model_path=None, verbose=False, channel=2):
model_path = DEFAULT_MODEL_PATH if model_path is None else model_path
self.verbose = verbose
self.channel = channel
self.num_models_parallel = num_models_parallel
self.interpreters = []
for i in range(self.num_models_parallel):
self.interpreters.append(edgetpu.make_interpreter(model_path))
self.interpreters[i].allocate_tensors()
self.interpreter_counter = 0
self.input_details = self.interpreters[0].get_input_details()
self.output_details = self.interpreters[0].get_output_details()
self.buffer = []
self.seq_stride = seq_stride
self.window_size = window_size
self.stride_counters = [np.floor((self.seq_stride / self.num_models_parallel) * i) for i in range(self.num_models_parallel)]
for idx, i in enumerate(self.stride_counters[1:]):
self.stride_counters[idx+1] = i - self.stride_counters[idx]
self.current_stride_counter = self.stride_counters[0] - 1
def add_datapoints(self, inputs_float):
res = []
for inp in inputs_float:
result = self.add_datapoint(inp)
if result is not None:
res.append(result)
return res
def add_datapoint(self, input_float):
input_float = input_float[self.channel-1]
result = None
self.buffer.append(input_float)
if len(self.buffer) > self.window_size:
self.buffer = self.buffer[1:]
self.current_stride_counter += 1
if self.current_stride_counter == self.stride_counter[self.interpreter_counter]:
result = self.call_model(self.interpreter_counter, self.buffer)
self.interpreter_counter += 1
self.interpreter_counter %= self.num_model_parallel
self.current_stride_counter = 0
return result
def call_model(self, idx, input_float=None):
if input_float is None:
# For debuggin purposes
input_shape = input_details[0]['shape']
input = np.array(np.random.random_sample(input_shape), dtype=np.int8)
else:
# Convert float input to Int
input_scale, input_zero_point = input_details[0]["quantization"]
input = np.asarray(input_float) / input_scale + input_zero_point
input = input.astype(input_details[0]["dtype"])
interpreter.set_tensor(input_details[0]['index'], input)
if self.verbose:
start_time = time.time()
interpreter.invoke()
if self.verbose:
end_time = time.time()
output = interpreter.get_tensor(output_details[0]['index'])
output_scale, output_zero_point = input_details[0]["quantization"]
output = float(output - output_zero_point) * output_scale
if self.verbose:
print(f"Computed output {output} in {end_time - start_time} seconds")
return output
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