1# Copyright (c) Qualcomm Innovation Center, Inc. 2# All rights reserved 3# 4# This source code is licensed under the BSD-style license found in the 5# LICENSE file in the root directory of this source tree. 6 7import json 8import os 9from multiprocessing.connection import Client 10 11import numpy as np 12 13import torch 14from executorch.backends.qualcomm.quantizer.quantizer import QuantDtype 15from executorch.examples.models.inception_v3.model import InceptionV3Model 16from executorch.examples.qualcomm.utils import ( 17 build_executorch_binary, 18 get_imagenet_dataset, 19 make_output_dir, 20 parse_skip_delegation_node, 21 setup_common_args_and_variables, 22 SimpleADB, 23 topk_accuracy, 24) 25 26 27def main(args): 28 skip_node_id_set, skip_node_op_set = parse_skip_delegation_node(args) 29 30 # ensure the working directory exist. 31 os.makedirs(args.artifact, exist_ok=True) 32 33 if not args.compile_only and args.device is None: 34 raise RuntimeError( 35 "device serial is required if not compile only. " 36 "Please specify a device serial by -s/--device argument." 37 ) 38 39 data_num = 100 40 if args.compile_only: 41 inputs = [(torch.rand(1, 3, 224, 224),)] 42 else: 43 inputs, targets, input_list = get_imagenet_dataset( 44 dataset_path=f"{args.dataset}", 45 data_size=data_num, 46 image_shape=(256, 256), 47 crop_size=224, 48 ) 49 pte_filename = "ic3_qnn_q8" 50 instance = InceptionV3Model() 51 build_executorch_binary( 52 instance.get_eager_model().eval(), 53 instance.get_example_inputs(), 54 args.model, 55 f"{args.artifact}/{pte_filename}", 56 inputs, 57 skip_node_id_set=skip_node_id_set, 58 skip_node_op_set=skip_node_op_set, 59 quant_dtype=QuantDtype.use_8a8w, 60 shared_buffer=args.shared_buffer, 61 ) 62 63 if args.compile_only: 64 return 65 66 adb = SimpleADB( 67 qnn_sdk=os.getenv("QNN_SDK_ROOT"), 68 build_path=f"{args.build_folder}", 69 pte_path=f"{args.artifact}/{pte_filename}.pte", 70 workspace=f"/data/local/tmp/executorch/{pte_filename}", 71 device_id=args.device, 72 host_id=args.host, 73 soc_model=args.model, 74 shared_buffer=args.shared_buffer, 75 ) 76 adb.push(inputs=inputs, input_list=input_list) 77 adb.execute() 78 79 # collect output data 80 output_data_folder = f"{args.artifact}/outputs" 81 make_output_dir(output_data_folder) 82 83 adb.pull(output_path=args.artifact) 84 85 # top-k analysis 86 predictions = [] 87 for i in range(data_num): 88 predictions.append( 89 np.fromfile( 90 os.path.join(output_data_folder, f"output_{i}_0.raw"), dtype=np.float32 91 ) 92 ) 93 94 k_val = [1, 5] 95 topk = [topk_accuracy(predictions, targets, k).item() for k in k_val] 96 if args.ip and args.port != -1: 97 with Client((args.ip, args.port)) as conn: 98 conn.send(json.dumps({f"top_{k}": topk[i] for i, k in enumerate(k_val)})) 99 else: 100 for i, k in enumerate(k_val): 101 print(f"top_{k}->{topk[i]}%") 102 103 104if __name__ == "__main__": 105 parser = setup_common_args_and_variables() 106 107 parser.add_argument( 108 "-d", 109 "--dataset", 110 help=( 111 "path to the validation folder of ImageNet dataset. " 112 "e.g. --dataset imagenet-mini/val " 113 "for https://www.kaggle.com/datasets/ifigotin/imagenetmini-1000)" 114 ), 115 type=str, 116 required=False, 117 ) 118 119 parser.add_argument( 120 "-a", 121 "--artifact", 122 help="path for storing generated artifacts by this example. " 123 "Default ./inception_v3", 124 default="./inception_v3", 125 type=str, 126 ) 127 128 args = parser.parse_args() 129 try: 130 main(args) 131 except Exception as e: 132 if args.ip and args.port != -1: 133 with Client((args.ip, args.port)) as conn: 134 conn.send(json.dumps({"Error": str(e)})) 135 else: 136 raise Exception(e) 137