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通过Arcgis从逐月平均气温数据中提取并计算年平均气温

扶摇_hyber 2024-07-24 阅读 28

 本文最早发表于电子发烧友论坛:【新提醒】【正点原子i.MX93开发板试用连载体验】基于深度学习的语音本地控制 - 正点原子学习小组 - 电子技术论坛 - 广受欢迎的专业电子论坛! (elecfans.com)

昨天提到要使模型运行的NPU上,必须先将其量化。如果对没有量化的模型使用vela工具进行转换,工具会给出警告,所生成的模型仍然是只能运行在CPU上,而无法运行在NPU上的。

下面就是用vela工具对simple_audio_model_numpy.tflite文件进行转换的结果。

 
root@atk-imx93:~/shell/simple# vela simple_audio_model_numpy.tflite

Warning: Unsupported TensorFlow Lite semantics for RESIZE_BILINEAR 'sequential_2/resizing_2/resize/ResizeBilinear;StatefulPartitionedCall/sequential_2/resizing_2/resize/ResizeBilinear'. Placing on CPU instead

 - Input(s), Output and Weight tensors must have quantization parameters

   Op has tensors with missing quantization parameters: input_3, sequential_2/resizing_2/resize/ResizeBilinear;StatefulPartitionedCall/sequential_2/resizing_2/resize/ResizeBilinear

Warning: Unsupported TensorFlow Lite semantics for CONV_2D 'sequential_2/conv2d_4/Relu;StatefulPartitionedCall/sequential_2/conv2d_4/Relu;sequential_2/conv2d_4/BiasAdd;StatefulPartitionedCall/sequential_2/conv2d_4/BiasAdd;sequential_2/conv2d_4/Conv2D;StatefulPartitionedCall/sequential_2/conv2d_4/Conv2D;StatefulPartitionedCall/sequential_2/conv2d_4/BiasAdd/ReadVariableOp2'. Placing on CPU instead

 - Input(s), Output and Weight tensors must have quantization parameters

   Op has tensors with missing quantization parameters: sequential_2/resizing_2/resize/ResizeBilinear;StatefulPartitionedCall/sequential_2/resizing_2/resize/ResizeBilinear, sequential_2/conv2d_4/Relu;StatefulPartitionedCall/sequential_2/conv2d_4/Relu;sequential_2/conv2d_4/BiasAdd;StatefulPartitionedCall/sequential_2/conv2d_4/BiasAdd;sequential_2/conv2d_4/Conv2D;StatefulPartitionedCall/sequential_2/conv2d_4/Conv2D;StatefulPartitionedCall/sequential_2/conv2d_4/BiasAdd/ReadVariableOp1_reshape, sequential_2/conv2d_4/Relu;StatefulPartitionedCall/sequential_2/conv2d_4/Relu;sequential_2/conv2d_4/BiasAdd;StatefulPartitionedCall/sequential_2/conv2d_4/BiasAdd;sequential_2/conv2d_4/Conv2D;StatefulPartitionedCall/sequential_2/conv2d_4/Conv2D;StatefulPartitionedCall/sequential_2/conv2d_4/BiasAdd/ReadVariableOp2

Warning: Unsupported TensorFlow Lite semantics for CONV_2D 'sequential_2/conv2d_5/Relu;StatefulPartitionedCall/sequential_2/conv2d_5/Relu;sequential_2/conv2d_5/BiasAdd;StatefulPartitionedCall/sequential_2/conv2d_5/BiasAdd;sequential_2/conv2d_5/Conv2D;StatefulPartitionedCall/sequential_2/conv2d_5/Conv2D;StatefulPartitionedCall/sequential_2/conv2d_5/BiasAdd/ReadVariableOp'. Placing on CPU instead

 - Input(s), Output and Weight tensors must have quantization parameters

   Op has tensors with missing quantization parameters: sequential_2/conv2d_4/Relu;StatefulPartitionedCall/sequential_2/conv2d_4/Relu;sequential_2/conv2d_4/BiasAdd;StatefulPartitionedCall/sequential_2/conv2d_4/BiasAdd;sequential_2/conv2d_4/Conv2D;StatefulPartitionedCall/sequential_2/conv2d_4/Conv2D;StatefulPartitionedCall/sequential_2/conv2d_4/BiasAdd/ReadVariableOp2, sequential_2/conv2d_5/Conv2D;StatefulPartitionedCall/sequential_2/conv2d_5/Conv2D_reshape, sequential_2/conv2d_5/Relu;StatefulPartitionedCall/sequential_2/conv2d_5/Relu;sequential_2/conv2d_5/BiasAdd;StatefulPartitionedCall/sequential_2/conv2d_5/BiasAdd;sequential_2/conv2d_5/Conv2D;StatefulPartitionedCall/sequential_2/conv2d_5/Conv2D;StatefulPartitionedCall/sequential_2/conv2d_5/BiasAdd/ReadVariableOp

Warning: Unsupported TensorFlow Lite semantics for MAX_POOL_2D 'sequential_2/max_pooling2d_2/MaxPool;StatefulPartitionedCall/sequential_2/max_pooling2d_2/MaxPool'. Placing on CPU instead

 - Input(s), Output and Weight tensors must have quantization parameters

   Op has tensors with missing quantization parameters: sequential_2/conv2d_5/Relu;StatefulPartitionedCall/sequential_2/conv2d_5/Relu;sequential_2/conv2d_5/BiasAdd;StatefulPartitionedCall/sequential_2/conv2d_5/BiasAdd;sequential_2/conv2d_5/Conv2D;StatefulPartitionedCall/sequential_2/conv2d_5/Conv2D;StatefulPartitionedCall/sequential_2/conv2d_5/BiasAdd/ReadVariableOp, sequential_2/max_pooling2d_2/MaxPool;StatefulPartitionedCall/sequential_2/max_pooling2d_2/MaxPool

Warning: Unsupported TensorFlow Lite semantics for RESHAPE 'sequential_2/flatten_2/Reshape;StatefulPartitionedCall/sequential_2/flatten_2/Reshape'. Placing on CPU instead

 - Input(s), Output and Weight tensors must have quantization parameters

   Op has tensors with missing quantization parameters: sequential_2/max_pooling2d_2/MaxPool;StatefulPartitionedCall/sequential_2/max_pooling2d_2/MaxPool, sequential_2/flatten_2/Reshape;StatefulPartitionedCall/sequential_2/flatten_2/Reshape

Warning: Unsupported TensorFlow Lite semantics for FULLY_CONNECTED 'sequential_2/dense_4/Relu;StatefulPartitionedCall/sequential_2/dense_4/Relu;sequential_2/dense_4/BiasAdd;StatefulPartitionedCall/sequential_2/dense_4/BiasAdd'. Placing on CPU instead

 - Input(s), Output and Weight tensors must have quantization parameters

   Op has tensors with missing quantization parameters: sequential_2/flatten_2/Reshape;StatefulPartitionedCall/sequential_2/flatten_2/Reshape, sequential_2/dense_4/MatMul;StatefulPartitionedCall/sequential_2/dense_4/MatMul_reshape, sequential_2/dense_4/Relu;StatefulPartitionedCall/sequential_2/dense_4/Relu;sequential_2/dense_4/BiasAdd;StatefulPartitionedCall/sequential_2/dense_4/BiasAdd

Warning: Unsupported TensorFlow Lite semantics for FULLY_CONNECTED 'Identity'. Placing on CPU instead

 - Input(s), Output and Weight tensors must have quantization parameters

   Op has tensors with missing quantization parameters: sequential_2/dense_4/Relu;StatefulPartitionedCall/sequential_2/dense_4/Relu;sequential_2/dense_4/BiasAdd;StatefulPartitionedCall/sequential_2/dense_4/BiasAdd, sequential_2/dense_5/MatMul;StatefulPartitionedCall/sequential_2/dense_5/MatMul_reshape, Identity



Network summary for simple_audio_model_numpy

Accelerator configuration               Ethos_U65_256

System configuration                 internal-default

Memory mode                          internal-default

Accelerator clock                                1000 MHz





CPU operators = 7 (100.0%)

NPU operators = 0 (0.0%)



Neural network macs                                 0 MACs/batch

Network Tops/s                                    nan Tops/s



NPU cycles                                          0 cycles/batch

SRAM Access cycles                                  0 cycles/batch

DRAM Access cycles                                  0 cycles/batch

On-chip Flash Access cycles                         0 cycles/batch

Off-chip Flash Access cycles                        0 cycles/batch

Total cycles                                        0 cycles/batch



Batch Inference time                 0.00 ms,     nan inferences/s (batch size 1)



Warning: Could not write the following attributes to RESHAPE 'sequential_2/flatten_2/Reshape;StatefulPartitionedCall/sequential_2/flatten_2/Reshape' ReshapeOptions field: new_shape

这个错误信息明确指出Vela不支持 TensorFlow Lite 对特定操作的支持问题。具体来说,这个警告说明了:量化参数缺失 ,错误信息指出,涉及的输入、输出和权重张量必须具有量化参数,但在这个操作中,某些张量(如 input_3 和 sequential_2/resizing_2/resize/ResizeBilinear)缺失了这些量化参数。由于不支持,相关的操作将被放置在 CPU 上执行,而不是利用可能存在的更高效的硬件加速(NPU)。

我们使用netron.app可以查看一下模型文件。

从中可以看到input_3是float32类型的。

而查看被vela支持的模型,可以看到其输入参数已经被量化,是int8类型的。

如果我们想利用i.MX 93的NPU能力就需要先对模型文件进行量化。当然如果觉得i.MX 93的CPU推理能力已经够用了,此步骤也可以省略。 

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