grant-arm commented on a change in pull request #8922: URL: https://github.com/apache/tvm/pull/8922#discussion_r723417616
########## File path: apps/microtvm/ethosu/convert_image.py ########## @@ -0,0 +1,88 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +import os +import pathlib +import re +import sys +from PIL import Image +import numpy as np + + +def create_header_file(name, section, npy_data, output_path): + """ + This function generates a header file containing the data from the numpy array provided. + """ + file_path = pathlib.Path(f"{output_path}/" + name).resolve() + + # Create header file with npy_data as a C array + raw_path = file_path.with_suffix(".h").resolve() + with open(raw_path, "w") as header_file: + header_file.write("#include <tvmgen_default.h>\n") + for tensor_name in npy_data.keys(): + sanitized_tensor_name = re.sub(r"\W+", "_", tensor_name) + header_file.write( + f"const size_t {sanitized_tensor_name}_len = {npy_data[tensor_name].size};\n" + ) + + # Convert numpy data type to C data type + if npy_data[tensor_name].dtype == np.uint8: + c_type = "uint8_t" + elif npy_data[tensor_name].dtype == np.int8: + c_type = "int8_t" + else: + raise RuntimeError(f"Data type {str(npy_data[tensor_name].dtype)} not supported") + + header_file.write( + f'{c_type} {sanitized_tensor_name}[] __attribute__((section("{section}"), aligned(16))) = "' + ) + + data_hexstr = npy_data[tensor_name].tobytes().hex() + for i in range(0, len(data_hexstr), 2): + header_file.write(f"\\x{data_hexstr[i:i+2]}") + header_file.write('";\n\n') Review comment: @Mousius I made the changes you suggested. ########## File path: apps/microtvm/ethosu/convert_image.py ########## @@ -0,0 +1,88 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +import os +import pathlib +import re +import sys +from PIL import Image +import numpy as np + + +def create_header_file(name, section, npy_data, output_path): + """ + This function generates a header file containing the data from the numpy array provided. + """ + file_path = pathlib.Path(f"{output_path}/" + name).resolve() + + # Create header file with npy_data as a C array + raw_path = file_path.with_suffix(".h").resolve() + with open(raw_path, "w") as header_file: + header_file.write("#include <tvmgen_default.h>\n") + for tensor_name in npy_data.keys(): + sanitized_tensor_name = re.sub(r"\W+", "_", tensor_name) + header_file.write( + f"const size_t {sanitized_tensor_name}_len = {npy_data[tensor_name].size};\n" + ) + + # Convert numpy data type to C data type + if npy_data[tensor_name].dtype == np.uint8: + c_type = "uint8_t" + elif npy_data[tensor_name].dtype == np.int8: + c_type = "int8_t" + else: + raise RuntimeError(f"Data type {str(npy_data[tensor_name].dtype)} not supported") + + header_file.write( + f'{c_type} {sanitized_tensor_name}[] __attribute__((section("{section}"), aligned(16))) = "' + ) + + data_hexstr = npy_data[tensor_name].tobytes().hex() + for i in range(0, len(data_hexstr), 2): + header_file.write(f"\\x{data_hexstr[i:i+2]}") + header_file.write('";\n\n') + + +def create_headers(image_name): + """ + This function generates C header files for the input and output arrays required to run inferences + """ + img_path = os.path.join("./", f"{image_name}") + + # Resize image to 224x224 + resized_image = Image.open(img_path).resize((224, 224)) + img_data = np.asarray(resized_image).astype("float32") + + # Convert input to NCHW + img_data = np.transpose(img_data, (2, 0, 1)) + + # Create input header file + input_data = {"input": img_data.astype(np.uint8)} + create_header_file("inputs", "ethosu_scratch", input_data, "./include") + + # Create output header file + output_data = {"output": np.zeros([1001], np.uint8)} + create_header_file( + "outputs", + "output_data_sec", + output_data, + "./include", + ) Review comment: @Mousius I made the changes you suggested. -- This is an automated message from the Apache Git Service. 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