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Support regnet_x_400mf and regnet_y_400mf (pytorch#4925)
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# Copyright (c) Qualcomm Innovation Center, Inc. | ||
# All rights reserved | ||
# | ||
# This source code is licensed under the BSD-style license found in the | ||
# LICENSE file in the root directory of this source tree. | ||
|
||
import json | ||
import os | ||
import sys | ||
from multiprocessing.connection import Client | ||
|
||
import numpy as np | ||
import torch | ||
from executorch.backends.qualcomm.quantizer.quantizer import QuantDtype | ||
from executorch.examples.qualcomm.utils import ( | ||
build_executorch_binary, | ||
make_output_dir, | ||
parse_skip_delegation_node, | ||
setup_common_args_and_variables, | ||
SimpleADB, | ||
topk_accuracy, | ||
) | ||
|
||
from torchvision.models import ( | ||
regnet_x_400mf, | ||
RegNet_X_400MF_Weights, | ||
regnet_y_400mf, | ||
RegNet_Y_400MF_Weights, | ||
) | ||
|
||
|
||
def get_dataset(dataset_path, data_size): | ||
from torchvision import datasets, transforms | ||
|
||
def get_data_loader(): | ||
preprocess = transforms.Compose( | ||
[ | ||
transforms.Resize(256), | ||
transforms.CenterCrop(224), | ||
transforms.ToTensor(), | ||
transforms.Normalize( | ||
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] | ||
), | ||
] | ||
) | ||
imagenet_data = datasets.ImageFolder(dataset_path, transform=preprocess) | ||
return torch.utils.data.DataLoader( | ||
imagenet_data, | ||
shuffle=True, | ||
) | ||
|
||
# prepare input data | ||
inputs, targets, input_list = [], [], "" | ||
data_loader = get_data_loader() | ||
for index, data in enumerate(data_loader): | ||
if index >= data_size: | ||
break | ||
feature, target = data | ||
inputs.append((feature,)) | ||
for element in target: | ||
targets.append(element) | ||
input_list += f"input_{index}_0.raw\n" | ||
|
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return inputs, targets, input_list | ||
|
||
|
||
def main(args): | ||
skip_node_id_set, skip_node_op_set = parse_skip_delegation_node(args) | ||
|
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# ensure the working directory exist. | ||
os.makedirs(args.artifact, exist_ok=True) | ||
|
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if not args.compile_only and args.device is None: | ||
raise RuntimeError( | ||
"device serial is required if not compile only. " | ||
"Please specify a device serial by -s/--device argument." | ||
) | ||
|
||
data_num = 100 | ||
inputs, targets, input_list = get_dataset( | ||
dataset_path=f"{args.dataset}", | ||
data_size=data_num, | ||
) | ||
|
||
if args.weights == "regnet_y_400mf": | ||
weights = RegNet_Y_400MF_Weights.DEFAULT | ||
model = regnet_y_400mf(weights=weights).eval() | ||
pte_filename = "regnet_y_400mf" | ||
else: | ||
weights = RegNet_X_400MF_Weights.DEFAULT | ||
model = regnet_x_400mf(weights=weights).eval() | ||
pte_filename = "regnet_x_400mf" | ||
|
||
build_executorch_binary( | ||
model, | ||
inputs[0], | ||
args.model, | ||
f"{args.artifact}/{pte_filename}", | ||
inputs, | ||
quant_dtype=QuantDtype.use_8a8w, | ||
) | ||
|
||
if args.compile_only: | ||
sys.exit(0) | ||
|
||
adb = SimpleADB( | ||
qnn_sdk=os.getenv("QNN_SDK_ROOT"), | ||
build_path=f"{args.build_folder}", | ||
pte_path=f"{args.artifact}/{pte_filename}.pte", | ||
workspace=f"/data/local/tmp/executorch/{pte_filename}", | ||
device_id=args.device, | ||
host_id=args.host, | ||
soc_model=args.model, | ||
) | ||
adb.push(inputs=inputs, input_list=input_list) | ||
adb.execute() | ||
|
||
# collect output data | ||
output_data_folder = f"{args.artifact}/outputs" | ||
make_output_dir(output_data_folder) | ||
|
||
adb.pull(output_path=args.artifact) | ||
|
||
# top-k analysis | ||
predictions = [] | ||
for i in range(data_num): | ||
predictions.append( | ||
np.fromfile( | ||
os.path.join(output_data_folder, f"output_{i}_0.raw"), dtype=np.float32 | ||
) | ||
) | ||
|
||
k_val = [1, 5] | ||
topk = [topk_accuracy(predictions, targets, k).item() for k in k_val] | ||
if args.ip and args.port != -1: | ||
with Client((args.ip, args.port)) as conn: | ||
conn.send(json.dumps({f"top_{k}": topk[i] for i, k in enumerate(k_val)})) | ||
else: | ||
for i, k in enumerate(k_val): | ||
print(f"top_{k}->{topk[i]}%") | ||
|
||
|
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if __name__ == "__main__": | ||
parser = setup_common_args_and_variables() | ||
parser.add_argument( | ||
"-a", | ||
"--artifact", | ||
help="path for storing generated artifacts by this example. Default ./regnet", | ||
default="./regnet", | ||
type=str, | ||
) | ||
|
||
parser.add_argument( | ||
"-d", | ||
"--dataset", | ||
help=( | ||
"path to the validation folder of ImageNet dataset. " | ||
"e.g. --dataset imagenet-mini/val " | ||
"for https://www.kaggle.com/datasets/ifigotin/imagenetmini-1000)" | ||
), | ||
type=str, | ||
required=True, | ||
) | ||
|
||
parser.add_argument( | ||
"--weights", | ||
type=str, | ||
choices=["regnet_y_400mf", "regnet_x_400mf"], | ||
help="Specify which regent weights/model to execute", | ||
required=True, | ||
) | ||
|
||
args = parser.parse_args() | ||
try: | ||
main(args) | ||
except Exception as e: | ||
if args.ip and args.port != -1: | ||
with Client((args.ip, args.port)) as conn: | ||
conn.send(json.dumps({"Error": str(e)})) | ||
else: | ||
raise Exception(e) |
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