edgetpu.basic.basic_engine

class
edgetpu.basic.basic_engine.
BasicEngine
(model_path, device_path=None)¶ Base inference engine to execute a TensorFlow Lite model on the Edge TPU.
Parameters:  model_path (str) – Path to a TensorFlow Lite (
.tflite
) file. This model must be compiled for the Edge TPU; otherwise, it simply executes on the host CPU.  device_path (str) – The device path for the Edge TPU this engine should use. This argument is needed only when you have multiple Edge TPUs and more inference engines than available Edge TPUs. For details, read how to use multiple Edge TPUs.

RunInference
(input)¶ Performs inference with a raw input tensor.
Parameters: input – ( numpy.ndarray
): A 1D array as the input tensor. You can query the required size for this array withrequired_input_array_size()
.Returns: A 2tuple with the inference latency in milliseconds (float) and a 1D array ( numpy.ndarray
) representing the output tensor. If there are multiple output tensors, they are compressed into a single 1D array. For example, if the model outputs 2 tensors with values [1, 2, 3] and [0.1, 0.4, 0.9], the returned 1D array is [1, 2, 3, 0.1, 0.4, 0.9]. You can calculate the size and offset for each tensor usingget_all_output_tensors_sizes()
,get_num_of_output_tensors()
, andget_output_tensor_size()
.

device_path
()¶ Gets the path for the Edge TPU that’s associated with this inference engine.
See how to run multiple models with multiple Edge TPUs.
Returns: A string representing this engine’s Edge TPU device path.

get_all_output_tensors_sizes
()¶ Gets the size of each output tensor.
A model may output several tensors, but the return from
RunInference()
andget_raw_output()
concatenates them together into a 1D array. So this function provides the size for each original output tensor, allowing you to calculate the offset for each tensor within the concatenated array.Returns: An array ( numpy.ndarray
) with the length of each output tensor (this assumes that all output tensors are 1D).

get_inference_time
()¶ Gets the latency of the most recent inference.
This can be used by higher level engines for debugging.
Returns: A float representing the inference latency (in milliseconds).

get_input_tensor_shape
()¶ Gets the shape required for the input tensor.
For models trained for image classification / detection, the shape is always [1, height, width, channels]. To be used as input for
RunInference()
, this tensor shape must be flattened into a 1D array with sizeheight * width * channels
. To instead get that 1D array size, userequired_input_array_size()
.Returns: A 1D array ( numpy.ndarray
) representing the required input tensor shape.

get_num_of_output_tensors
()¶ Gets the number of output tensors.
Returns: An integer representing number of output tensors.

get_output_tensor_size
(index)¶ Gets the size of a specific output tensor.
Parameters: index (int) – The index position of the output tensor. Returns: An integer representing the size of the output tensor.

get_raw_output
()¶ Gets the output of the most recent inference.
This can be used by higher level engines for debugging.
Returns: A 1D array ( numpy.ndarray
) representing the output tensor. If there are multiple output tensors, they are compressed into a single 1D array. (Same as what’s returned byRunInference()
.)

model_path
()¶ Gets the file path for model loaded by this inference engine.
Returns: A string representing the model file’s path.

required_input_array_size
()¶ Gets the required size for the
input_tensor
given toRunInference()
.This is the total size of the 1D array, once the tensor shape is flattened.
Returns: An integer representing the required input tensor size.

total_output_array_size
()¶ Gets the expected size of the 1D output array returned by
RunInference()
andget_raw_output()
.Returns: An integer representing the output tensor size.
 model_path (str) – Path to a TensorFlow Lite (
Is this content helpful?