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