def get_backend(backend, dataset, max_ind_range, data_sub_sample_rate, use_gpu,
use_ipex):
if backend == "pytorch-native":
from backend_pytorch_native import BackendPytorchNative
# NOTE: pass model parameters here, the following options are available
if dataset == "kaggle":
# 1. Criteo Kaggle Display Advertisement Challenge Dataset (see ./bench/dlrm_s_criteo_kaggle.sh)
backend = BackendPytorchNative(
m_spa=16,
ln_emb=np.array([
1460, 583, 10131227, 2202608, 305, 24, 12517, 633, 3,
93145, 5683, 8351593, 3194, 27, 14992, 5461306, 10, 5652,
2173, 4, 7046547, 18, 15, 286181, 105, 142572
]),
ln_bot=np.array([13, 512, 256, 64, 16]),
ln_top=np.array([367, 512, 256, 1]),
use_gpu=use_gpu)
elif dataset == "terabyte":
if max_ind_range == 10000000:
# 2. Criteo Terabyte (see ./bench/dlrm_s_criteo_terabyte.sh [--sub-sample=0.875] --max-in-range=10000000)
backend = BackendPytorchNative(
m_spa=64,
ln_emb=np.array([
9980333, 36084, 17217, 7378, 20134, 3, 7112, 1442, 61,
9758201, 1333352, 313829, 10, 2208, 11156, 122, 4, 970,
14, 9994222, 7267859, 9946608, 415421, 12420, 101, 36
]),
ln_bot=np.array([13, 512, 256, 64]),
ln_top=np.array([415, 512, 512, 256, 1]),
use_gpu=use_gpu)
elif max_ind_range == 40000000:
# 3. Criteo Terabyte MLPerf training (see ./bench/run_and_time.sh --max-in-range=40000000)
backend = BackendPytorchNative(
m_spa=128,
ln_emb=np.array([
39884406, 39043, 17289, 7420, 20263, 3, 7120, 1543, 63,
38532951, 2953546, 403346, 10, 2208, 11938, 155, 4,
976, 14, 39979771, 25641295, 39664984, 585935, 12972,
108, 36
]),
ln_bot=np.array([13, 512, 256, 128]),
ln_top=np.array([479, 1024, 1024, 512, 256, 1]),
use_gpu=use_gpu,
use_ipex=use_ipex,
server=True)
else:
raise ValueError("only --max-in-range 10M or 40M is supported")
else:
raise ValueError(
"only kaggle|terabyte dataset options are supported")
else:
raise ValueError("unknown backend: " + backend)
return backend
def get_backend(backend):
if backend == "tensorflow":
from backend_tf import BackendTensorflow
backend = BackendTensorflow()
elif backend == "onnxruntime":
from backend_onnxruntime import BackendOnnxruntime
backend = BackendOnnxruntime()
elif backend == "null":
from backend_null import BackendNull
backend = BackendNull()
elif backend == "pytorch":
from backend_pytorch import BackendPytorch
backend = BackendPytorch()
elif backend == "pytorch-native":
from backend_pytorch_native import BackendPytorchNative
backend = BackendPytorchNative()
elif backend == "tflite":
from backend_tflite import BackendTflite
backend = BackendTflite()
elif backend == "tflite-calibrate":
from backend_tflite_calibrate import BackendTflite
backend = BackendTflite()
elif backend == "tflite-ncore":
from backend_tflite_ncore import BackendTfliteNcore
backend = BackendTfliteNcore()
elif backend == "tflite-ncore-offline-imagenet":
from backend_tflite_ncore_offline_imagenet import BackendTfliteNcoreOfflineImagenet
backend = BackendTfliteNcoreOfflineImagenet()
elif backend == "tflite-ncore-offline-ssd":
from backend_tflite_ncore_offline_ssd import BackendTfliteNcoreOfflineSSD
backend = BackendTfliteNcoreOfflineSSD()
else:
raise ValueError("unknown backend: " + backend)
return backend
def get_backend(backend):
if backend == "tensorflow":
from backend_tf import BackendTensorflow
backend = BackendTensorflow()
elif backend == "onnxruntime":
from backend_onnxruntime import BackendOnnxruntime
backend = BackendOnnxruntime()
elif backend == "null":
from backend_null import BackendNull
backend = BackendNull()
elif backend == "pytorch":
from backend_pytorch import BackendPytorch
backend = BackendPytorch()
elif backend == "pytorch-native":
from backend_pytorch_native import BackendPytorchNative
backend = BackendPytorchNative()
elif backend == "tflite":
from backend_tflite import BackendTflite
backend = BackendTflite()
elif backend == "tvm":
from backend_tvm import BackendTvm
backend = BackendTvm()
else:
raise ValueError("unknown backend: " + backend)
return backend
def get_backend(backend):
if backend == "tensorflow":
from backend_tf import BackendTensorflow
backend = BackendTensorflow()
elif backend == "onnxruntime":
from backend_onnxruntime import BackendOnnxruntime
backend = BackendOnnxruntime()
elif backend == "null":
from backend_null import BackendNull
backend = BackendNull()
elif backend == "pytorch":
from backend_pytorch import BackendPytorch
backend = BackendPytorch()
elif backend == "pytorch-native":
from backend_pytorch_native import BackendPytorchNative
backend = BackendPytorchNative()
elif backend == "pytorch-centaur":
from backend_pytorch_centaur import BackendPytorchCentaur
backend = BackendPytorchCentaur()
elif backend == "pytorch-native-calibrate":
from backend_pytorch_native_calibrate import BackendPytorchNativeCalibrate
backend = BackendPytorchNativeCalibrate()
elif backend == "tflite":
from backend_tflite import BackendTflite
backend = BackendTflite()
elif backend == "tflite-calibrate":
from backend_tflite_calibrate import BackendTflite
backend = BackendTflite()
elif backend == "tflite-ncore":
from backend_tflite_ncore import BackendTfliteNcore
backend = BackendTfliteNcore()
elif backend == "tflite-ncore-mobilenet":
from backend_libncoretflite import BackendTfliteNcoreMobileNetV1
backend = BackendTfliteNcoreMobileNetV1()
backend.inputs = ["image_tensor:0"]
elif backend == "tflite-ncore-resnet":
from backend_libncoretflite import BackendTfliteNcoreResnet
backend = BackendTfliteNcoreResnet()
backend.inputs = ["image_tensor:0"]
elif backend == "tflite-ncore-ssd":
from backend_libncoretflite import BackendTfliteNcoreSSD
backend = BackendTfliteNcoreSSD()
backend.inputs = ["image_tensor:0"]
elif backend == "tflite-ncore-mobilenet-offline":
from backend_libncoretflite import BackendTfliteNcoreMobileNetV1Offline
backend = BackendTfliteNcoreMobileNetV1Offline()
backend.inputs = ["image_tensor:0"]
elif backend == "tflite-ncore-resnet-offline":
from backend_libncoretflite import BackendTfliteNcoreResnetOffline
backend = BackendTfliteNcoreResnetOffline()
backend.inputs = ["image_tensor:0"]
elif backend == "tflite-ncore-ssd-offline":
from backend_libncoretflite import BackendTfliteNcoreSSDOffline
backend = BackendTfliteNcoreSSDOffline()
backend.inputs = ["image_tensor:0"]
else:
raise ValueError("unknown backend: " + backend)
return backend
def get_backend(backend, dataset_path, dataset_calibration_list):
if backend == "tensorflow":
from backend_tf import BackendTensorflow
backend = BackendTensorflow()
elif backend == "onnxruntime":
from backend_onnxruntime import BackendOnnxruntime
backend = BackendOnnxruntime()
elif backend == "null":
from backend_null import BackendNull
backend = BackendNull()
elif backend == "pytorch":
from backend_pytorch import BackendPytorch
backend = BackendPytorch()
elif backend == "pytorch-native":
from backend_pytorch_native import BackendPytorchNative
backend = BackendPytorchNative()
elif backend == "pytorch-jit-traced":
from backend_pytorch_jit_traced import BackendPytorchJITTraced
backend = BackendPytorchJITTraced()
elif backend == "pytorch-fp32":
from backend_pytorch_fp32 import BackendPytorchFP32
backend = BackendPytorchFP32()
elif backend == "pytorch-ssd-jit-traced":
from backend_pytorch_ssd_jit_traced import BackendPytorchSSDJITTraced
backend = BackendPytorchSSDJITTraced()
elif backend == "pytorch-yolov3-jit-traced":
from backend_pytorch_yolov3_jit_traced import BackendPytorchYOLOv3JITTraced
backend = BackendPytorchYOLOv3JITTraced()
elif backend == "pytorch-yolov3-fp32":
from backend_pytorch_yolov3_fp32 import BackendPytorchYOLOv3FP32
backend = BackendPytorchYOLOv3FP32()
elif backend == "tflite":
from backend_tflite import BackendTflite
backend = BackendTflite()
elif backend == "edgecortix":
from backend_edgecortix import BackendEdgecortix
backend = BackendEdgecortix(dataset_path, dataset_calibration_list)
else:
raise ValueError("unknown backend: " + backend)
return backend