FM / ideep

/usr/local/lib64/python3.6/site-packages/caffe2/python/ideep/

/usr/local/lib64/python3.6/site-packages/caffe2/python/ideep

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
adam_op_test.py	3212	0644	edit dl rm
blobs_queue_db_test.py	4045	0644	edit dl rm
channel_shuffle_op_test.py	1286	0644	edit dl rm
concat_split_op_test.py	5532	0644	edit dl rm
convfusion_op_test.py	31919	0644	edit dl rm
conv_op_test.py	5666	0644	edit dl rm
conv_transpose_test.py	2595	0644	edit dl rm
copy_op_test.py	3074	0644	edit dl rm
dropout_op_test.py	2075	0644	edit dl rm
elementwise_sum_op_test.py	6317	0644	edit dl rm
expanddims_squeeze_op_test.py	4285	0644	edit dl rm
fc_op_test.py	11745	0644	edit dl rm
leaky_relu_op_test.py	2854	0644	edit dl rm
LRN_op_test.py	1195	0644	edit dl rm
moment_sgd_op_test.py	1777	0644	edit dl rm
operator_fallback_op_test.py	3450	0644	edit dl rm
order_switch_op_test.py	2299	0644	edit dl rm
pool_op_test.py	4296	0644	edit dl rm
pre_convert_test.py	4115	0644	edit dl rm
relu_op_test.py	3705	0644	edit dl rm
reshape_op_test.py	5919	0644	edit dl rm
shape_op_test.py	2631	0644	edit dl rm
sigmoid_op_test.py	775	0644	edit dl rm
softmax_op_test.py	932	0644	edit dl rm
spatial_bn_op_test.py	5237	0644	edit dl rm
test_ideep_net.py	4094	0644	edit dl rm
transform_ideep_net.py	11683	0644	edit dl rm
transpose_op_test.py	1284	0644	edit dl rm
weightedsum_op_test.py	1560	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/ideep/test_ideep_net.py (4094B)

from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
from caffe2.python.models.download import ModelDownloader
import numpy as np
import argparse
import time

def GetArgumentParser():
    parser = argparse.ArgumentParser(description="Caffe2 benchmark.")
    parser.add_argument(
        "--batch_size",
        type=int,
        default=128,
        help="The batch size."
    )
    parser.add_argument("--model", type=str, help="The model to benchmark.")
    parser.add_argument(
        "--order",
        type=str,
        default="NCHW",
        help="The order to evaluate."
    )
    parser.add_argument(
        "--device",
        type=str,
        default="CPU",
        help="device to evaluate on."
    )
    parser.add_argument(
        "--cudnn_ws",
        type=int,
        help="The cudnn workspace size."
    )
    parser.add_argument(
        "--iterations",
        type=int,
        default=10,
        help="Number of iterations to run the network."
    )
    parser.add_argument(
        "--warmup_iterations",
        type=int,
        default=10,
        help="Number of warm-up iterations before benchmarking."
    )
    parser.add_argument(
        "--forward_only",
        action='store_true',
        help="If set, only run the forward pass."
    )
    parser.add_argument(
        "--layer_wise_benchmark",
        action='store_true',
        help="If True, run the layer-wise benchmark as well."
    )
    parser.add_argument(
        "--engine",
        type=str,
        default="",
        help="If set, blindly prefer the given engine(s) for every op.")
    parser.add_argument(
        "--dump_model",
        action='store_true',
        help="If True, dump the model prototxts to disk."
    )
    parser.add_argument("--net_type", type=str, default="simple")
    parser.add_argument("--num_workers", type=int, default=2)
    parser.add_argument("--use-nvtx", default=False, action='store_true')
    parser.add_argument("--htrace_span_log_path", type=str)
    return parser

def benchmark(args):
    print('Batch size: {}'.format(args.batch_size))
    mf = ModelDownloader()
    init_net, pred_net, value_info = mf.get_c2_model(args.model)
    input_shapes = {k : [args.batch_size] + v[-1][1:] for (k, v) in value_info.items()}
    print("input info: {}".format(input_shapes))
    external_inputs = {}
    for k, v in input_shapes.items():
        external_inputs[k] = np.random.randn(*v).astype(np.float32)

if args.device == 'CPU':
        device_option = core.DeviceOption(caffe2_pb2.CPU)
    elif args.device == 'MKL':
        device_option = core.DeviceOption(caffe2_pb2.MKLDNN)
    elif args.device == 'IDEEP':
        device_option = core.DeviceOption(caffe2_pb2.IDEEP)
    else:
        raise Exception("Unknown device: {}".format(args.device))
    print("Device option: {}, {}".format(args.device, device_option))
    pred_net.device_option.CopyFrom(device_option)
    for op in pred_net.op:
        op.device_option.CopyFrom(device_option)

# Hack to initialized weights into MKL/IDEEP context
    workspace.RunNetOnce(init_net)
    bb = workspace.Blobs()
    weights = {}
    for b in bb:
        weights[b] = workspace.FetchBlob(b)
    for k, v in external_inputs.items():
        weights[k] = v
    workspace.ResetWorkspace()

with core.DeviceScope(device_option):
        for name, blob in weights.items():
            #print("{}".format(name))
            workspace.FeedBlob(name, blob, device_option)
        workspace.CreateNet(pred_net)
        start = time.time()
        res = workspace.BenchmarkNet(pred_net.name,
                                     args.warmup_iterations,
                                     args.iterations,
                                     args.layer_wise_benchmark)
        print("FPS: {:.2f}".format(1/res[0]*1000*args.batch_size))

if __name__ == '__main__':
    args, extra_args = GetArgumentParser().parse_known_args()
    if (
        not args.batch_size or not args.model or not args.order
    ):
        GetArgumentParser().print_help()
    benchmark(args)

from caffe2.proto import caffe2_pb2 from caffe2.python import core, workspace from caffe2.python.models.download import ModelDownloader import numpy as np import argparse import time def GetArgumentParser(): parser = argparse.ArgumentParser(description="Caffe2 benchmark.") parser.add_argument( "--batch_size", type=int, default=128, help="The batch size." ) parser.add_argument("--model", type=str, help="The model to benchmark.") parser.add_argument( "--order", type=str, default="NCHW", help="The order to evaluate." ) parser.add_argument( "--device", type=str, default="CPU", help="device to evaluate on." ) parser.add_argument( "--cudnn_ws", type=int, help="The cudnn workspace size." ) parser.add_argument( "--iterations", type=int, default=10, help="Number of iterations to run the network." ) parser.add_argument( "--warmup_iterations", type=int, default=10, help="Number of warm-up iterations before benchmarking." ) parser.add_argument( "--forward_only", action='store_true', help="If set, only run the forward pass." ) parser.add_argument( "--layer_wise_benchmark", action='store_true', help="If True, run the layer-wise benchmark as well." ) parser.add_argument( "--engine", type=str, default="", help="If set, blindly prefer the given engine(s) for every op.") parser.add_argument( "--dump_model", action='store_true', help="If True, dump the model prototxts to disk." ) parser.add_argument("--net_type", type=str, default="simple") parser.add_argument("--num_workers", type=int, default=2) parser.add_argument("--use-nvtx", default=False, action='store_true') parser.add_argument("--htrace_span_log_path", type=str) return parser def benchmark(args): print('Batch size: {}'.format(args.batch_size)) mf = ModelDownloader() init_net, pred_net, value_info = mf.get_c2_model(args.model) input_shapes = {k : [args.batch_size] + v[-1][1:] for (k, v) in value_info.items()} print("input info: {}".format(input_shapes)) external_inputs = {} for k, v in input_shapes.items(): external_inputs[k] = np.random.randn(*v).astype(np.float32) if args.device == 'CPU': device_option = core.DeviceOption(caffe2_pb2.CPU) elif args.device == 'MKL': device_option = core.DeviceOption(caffe2_pb2.MKLDNN) elif args.device == 'IDEEP': device_option = core.DeviceOption(caffe2_pb2.IDEEP) else: raise Exception("Unknown device: {}".format(args.device)) print("Device option: {}, {}".format(args.device, device_option)) pred_net.device_option.CopyFrom(device_option) for op in pred_net.op: op.device_option.CopyFrom(device_option) # Hack to initialized weights into MKL/IDEEP context workspace.RunNetOnce(init_net) bb = workspace.Blobs() weights = {} for b in bb: weights[b] = workspace.FetchBlob(b) for k, v in external_inputs.items(): weights[k] = v workspace.ResetWorkspace() with core.DeviceScope(device_option): for name, blob in weights.items(): #print("{}".format(name)) workspace.FeedBlob(name, blob, device_option) workspace.CreateNet(pred_net) start = time.time() res = workspace.BenchmarkNet(pred_net.name, args.warmup_iterations, args.iterations, args.layer_wise_benchmark) print("FPS: {:.2f}".format(1/res[0]*1000*args.batch_size)) if __name__ == '__main__': args, extra_args = GetArgumentParser().parse_known_args() if ( not args.batch_size or not args.model or not args.order ): GetArgumentParser().print_help() benchmark(args)