转换和量化 AI 模型

snpe-<framework>-to-dlc --help

required arguments:

-d INPUT_NAME INPUT_DIM, --input_dim INPUT_NAME INPUT_DIM
    The names and dimensions of the network input layers specified in the format
    [input_name comma-separated-dimensions], for example:
    'data' 1,224,224,3
     Note that the quotes should always be included in order to handle special
     characters, spaces, etc.
     For multiple inputs specify multiple --input_dim on the command line like:
     --input_dim 'data1' 1,224,224,3 --input_dim 'data2' 1,50,100,3
--out_node OUT_NAMES, --out_name OUT_NAMES
     Name of the graph's output Tensor Names. Multiple output names should be
     provided separately like:
     --out_name out_1 --out_name out_2
--input_network INPUT_NETWORK, -i INPUT_NETWORK
     Path to the source framework model.

${QAIRT_ROOT}/bin/x86_64-linux-clang/snpe-onnx-to-dlc --input_network ~/models/inception_v3.onnx --output_path ~/models/inception_v3.dlc --input_dim 'x' 1,3,299,299

[ --input_dlc=<val> ]
             Path to the dlc container
             containing the model for which fixed-point encoding metadata should be generated.
             This argument is required.
[ --input_list=<val> ] Path to a file
             specifying the trial inputs. This file should be a plain text file, containing one
             or more absolute file paths per line. These files will be taken to constitute the
             trial set. Each path is expected to point to a binary file containing one trial
             input in the 'raw' format, ready to be consumed by the tool without any further
             modifications. This is similar to how input is provided to snpe-net-run
             application.
[ --output_dlc=<val> ] Path at which the
             metadata-included quantized model container should be written. If this argument is
             omitted, the quantized model will be written at
             <unquantized_model_name>_quantized.dlc.

import os
import numpy as np

input_path_list =[]
BASE_PATH = "/tmp/RandomInputsForInceptionV3"

if not os.path.exists(BASE_PATH):
    os.mkdir(BASE_PATH)

# generate 10 random inputs and save as raw
NUM_IMAGES = 10

#binary files
for img in range(NUM_IMAGES):
    filename = "input_{}.raw".format(img)
    randomTensor = np.random.random((1, 299, 299, 3)).astype(np.float32)
    filename = os.path.join(BASE_PATH, filename)
    randomTensor.tofile(filename)
    input_path_list.append(filename)

#for saving as input_list text
with open("input_list.txt", "w") as f:
    for path in input_path_list:
        f.write(path)
        f.write("\n")

${QAIRT_ROOT}/bin/x86_64-linux-clang/snpe-dlc-quant --input_dlc ~/models/inception_v3.dlc --output_dlc ~/models/inception_v3_quantized.dlc --input_list ~/models/input_list.txt

${QAIRT_ROOT}/bin/x86_64-linux-clang/snpe-dlc-graph-prepare --input_dlc ~/models/inception_v3_quantized.dlc --output_dlc ~/models/inception_v3_quantized_with_htp_cache.dlc --htp_socs qcs6490

${QAIRT_ROOT}/bin/x86_64-linux-clang/snpe-dlc-info -i ~/models/inception_v3_quantized_with_htp_cache.dlc