/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/tutorials/frontend/ |
H A D | using_external_lib.py | 57 simple_net = relay.nn.conv2d( variable 60 simple_net = relay.nn.batch_norm(simple_net, bn_gamma, bn_beta, bn_mmean, bn_mvar)[0] variable 61 simple_net = relay.nn.relu(simple_net) variable 62 simple_net = relay.Function(relay.analysis.free_vars(simple_net), simple_net) variable 65 net, params = testing.create_workload(simple_net) 496 net, params = testing.create_workload(simple_net)
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/dports/misc/tvm/incubator-tvm-0.6.1/tutorials/frontend/ |
H A D | using_external_lib.py | 56 simple_net = relay.nn.conv2d(data=data, weight=weight, kernel_size=(3,3), channels=out_channels, pa… variable 57 simple_net = relay.nn.batch_norm(simple_net, bn_gamma, bn_beta, bn_mmean, bn_mvar)[0] variable 58 simple_net = relay.nn.relu(simple_net) variable 59 simple_net = relay.Function(relay.analysis.free_vars(simple_net), simple_net) variable 62 net, params = testing.create_workload(simple_net) 494 net, params = testing.create_workload(simple_net)
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/dports/misc/py-tvm/incubator-tvm-0.6.1/tutorials/frontend/ |
H A D | using_external_lib.py | 56 simple_net = relay.nn.conv2d(data=data, weight=weight, kernel_size=(3,3), channels=out_channels, pa… variable 57 simple_net = relay.nn.batch_norm(simple_net, bn_gamma, bn_beta, bn_mmean, bn_mvar)[0] variable 58 simple_net = relay.nn.relu(simple_net) variable 59 simple_net = relay.Function(relay.analysis.free_vars(simple_net), simple_net) variable 62 net, params = testing.create_workload(simple_net) 494 net, params = testing.create_workload(simple_net)
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/dports/misc/tvm/incubator-tvm-0.6.1/nnvm/tutorials/ |
H A D | using_external_lib.py | 49 simple_net = sym.conv2d(data=data, kernel_size=(3,3), channels=out_channels, padding = (1, 1), use_… variable 50 simple_net = sym.batch_norm(data=simple_net) variable 51 simple_net = sym.relu(data=simple_net) variable 55 net, params = utils.create_workload(simple_net, batch_size, data_shape[1:]) 173 net, params = utils.create_workload(simple_net, batch_size, data_shape[1:])
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/dports/misc/py-tvm/incubator-tvm-0.6.1/nnvm/tutorials/ |
H A D | using_external_lib.py | 49 simple_net = sym.conv2d(data=data, kernel_size=(3,3), channels=out_channels, padding = (1, 1), use_… variable 50 simple_net = sym.batch_norm(data=simple_net) variable 51 simple_net = sym.relu(data=simple_net) variable 55 net, params = utils.create_workload(simple_net, batch_size, data_shape[1:]) 173 net, params = utils.create_workload(simple_net, batch_size, data_shape[1:])
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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/mkldnn/doc/build/ |
H A D | link.md | 49 g++ -std=c++11 -I${DNNLROOT}/include -L${DNNLROOT}/lib simple_net.cpp -ldnnl 50 clang++ -std=c++11 -I${DNNLROOT}/include -L${DNNLROOT}/lib simple_net.cpp -ldnnl 51 icpc -std=c++11 -I${DNNLROOT}/include -L${DNNLROOT}/lib simple_net.cpp -ldnnl 92 icl /I%DNNLROOT%\include /Qstd=c++11 /qopenmp simple_net.cpp %DNNLROOT%\lib\dnnl.lib 93 cl /I%DNNLROOT%\include simple_net.cpp %DNNLROOT%\lib\dnnl.lib
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/dports/math/onednn/oneDNN-2.5.1/doc/build/ |
H A D | link.md | 49 g++ -std=c++11 -I${DNNLROOT}/include -L${DNNLROOT}/lib simple_net.cpp -ldnnl 50 clang++ -std=c++11 -I${DNNLROOT}/include -L${DNNLROOT}/lib simple_net.cpp -ldnnl 51 icpc -std=c++11 -I${DNNLROOT}/include -L${DNNLROOT}/lib simple_net.cpp -ldnnl 92 icl /I%DNNLROOT%\include /Qstd=c++11 /qopenmp simple_net.cpp %DNNLROOT%\lib\dnnl.lib 93 cl /I%DNNLROOT%\include simple_net.cpp %DNNLROOT%\lib\dnnl.lib
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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/mkldnn/tests/mkldnn_compat/examples/ |
H A D | cnn_training_f32.cpp | 40 void simple_net(engine::kind engine_kind) { in simple_net() function 431 simple_net(parse_engine_kind(argc, argv)); in main()
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H A D | cpu_cnn_training_bf16.cpp | 43 void simple_net() { in simple_net() function 435 simple_net(); in main()
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H A D | cnn_inference_f32.c | 148 mkldnn_status_t simple_net(mkldnn_engine_kind_t engine_kind) { in simple_net() function 457 mkldnn_status_t result = simple_net(parse_engine_kind(argc, argv)); in main()
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H A D | rnn_training_f32.cpp | 67 void simple_net(engine::kind engine_kind) { in simple_net() function 711 simple_net(parse_engine_kind(argc, argv)); in main()
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H A D | cpu_rnn_inference_f32.cpp | 163 void simple_net() { in simple_net() function 800 simple_net(); in main()
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H A D | cpu_cnn_training_f32.c | 166 mkldnn_status_t simple_net() { in simple_net() function 813 mkldnn_status_t result = simple_net(); in main()
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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/mkldnn/examples/ |
H A D | cnn_training_bf16.cpp | 39 void simple_net(engine::kind engine_kind) { in simple_net() function 476 return handle_example_errors(simple_net, parse_engine_kind(argc, argv)); in main()
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H A D | cnn_training_f32.cpp | 36 void simple_net(engine::kind engine_kind) { in simple_net() function 446 return handle_example_errors(simple_net, parse_engine_kind(argc, argv)); in main()
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H A D | cnn_inference_f32.c | 164 void simple_net(dnnl_engine_kind_t engine_kind) { in simple_net() function 478 simple_net(engine_kind); in main()
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H A D | rnn_training_f32.cpp | 69 void simple_net(engine::kind engine_kind) { in simple_net() function 708 return handle_example_errors(simple_net, parse_engine_kind(argc, argv)); in main()
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H A D | cpu_cnn_training_f32.c | 149 void simple_net() { in simple_net() function 785 simple_net(); in main()
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H A D | cpu_rnn_inference_f32.cpp | 167 void simple_net() { in simple_net() function 802 return handle_example_errors({engine::kind::cpu}, simple_net); in main()
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/dports/math/onednn/oneDNN-2.5.1/examples/ |
H A D | cnn_inference_f32.c | 164 void simple_net(dnnl_engine_kind_t engine_kind) { in simple_net() function 485 simple_net(engine_kind); in main()
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H A D | cnn_training_bf16.cpp | 39 void simple_net(engine::kind engine_kind) { in simple_net() function 476 return handle_example_errors(simple_net, parse_engine_kind(argc, argv)); in main()
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H A D | cnn_training_f32.cpp | 36 void simple_net(engine::kind engine_kind) { in simple_net() function 446 return handle_example_errors(simple_net, parse_engine_kind(argc, argv)); in main()
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H A D | rnn_training_f32.cpp | 69 void simple_net(engine::kind engine_kind) { in simple_net() function 708 return handle_example_errors(simple_net, parse_engine_kind(argc, argv)); in main()
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H A D | cpu_rnn_inference_f32.cpp | 167 void simple_net() { in simple_net() function 802 return handle_example_errors({engine::kind::cpu}, simple_net); in main()
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H A D | cpu_cnn_training_f32.c | 149 void simple_net() { in simple_net() function 792 simple_net(); in main()
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