| /third_party/mindspore/tests/ |
| D | train_step_wrap.py | 28 def __init__(self, network): argument
30 self.network = network
31 self.network.set_train()
32 self.weights = ParameterTuple(network.trainable_params())
39 grads = self.grad(self.network, weights)(x, label)
48 def __init__(self, network): argument
51 self.network = network
54 predict = self.network(x)
58 def train_step_with_loss_warp(network): argument
59 return TrainStepWrap(NetWithLossClass(network))
[all …]
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| /third_party/mindspore/mindspore/train/ |
| D | amp.py | 41 def _do_keep_batchnorm_fp32(network): argument
43 cells = network.name_cells()
47 if subcell == network:
50 network._cells[name] = _OutputTo16(subcell.to_float(mstype.float32))
54 if isinstance(network, nn.SequentialCell) and change:
55 network.cell_list = list(network.cells())
90 def _add_loss_network(network, loss_fn, cast_model_type): argument
108 network = WithLossCell(network, loss_fn)
110 network = nn.WithLossCell(network, loss_fn)
111 return network
[all …]
|
| D | dataset_helper.py | 63 …def __init__(self, network, dataset_types, dataset_shapes, queue_name, min_shapes=None, max_shapes… argument
64 super(_DataWrapper, self).__init__(auto_prefix=False, flags=network.get_flags())
66 flags = getattr(network.__class__.construct, "_mindspore_flags", {})
75 self.network = network
79 return self.network(*outputs)
82 def _generate_dataset_sink_mode_net(network, dataset_shapes, dataset_types, queue_name, argument
84 if not isinstance(network, _DataWrapper):
85 … network = _DataWrapper(network, dataset_types, dataset_shapes, queue_name, min_shapes, max_shapes)
86 return network
96 def _generate_network_with_dataset(network, dataset_helper, queue_name): argument
[all …]
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| /third_party/wpa_supplicant/wpa_supplicant-2.9_standard/wpa_supplicant/ |
| D | wpa_supplicant.conf | 1679 network={
1687 network={
1695 network={
1706 network={
1718 network={
1735 network={
1749 network={
1762 network={
1775 network={
1793 network={
[all …]
|
| /third_party/wpa_supplicant/wpa_supplicant-2.9/wpa_supplicant/ |
| D | wpa_supplicant.conf | 1528 network={
1536 network={
1544 network={
1555 network={
1567 network={
1584 network={
1598 network={
1611 network={
1624 network={
1642 network={
[all …]
|
| /third_party/cef/libcef/browser/net_service/ |
| D | proxy_url_loader_factory.h | 54 network::ResourceRequest* request,
65 network::ResourceRequest* request,
76 const network::ResourceRequest& request, in ProcessRequestHeaders()
87 const network::ResourceRequest& request, in ProcessResponseHeaders()
109 network::ResourceRequest* request,
117 const network::ResourceRequest& request,
123 const network::ResourceRequest& request, in OnRequestComplete()
124 const network::URLLoaderCompletionStatus& status) {} in OnRequestComplete()
128 const network::ResourceRequest& request, in OnRequestError()
138 : public network::mojom::URLLoaderFactory,
[all …]
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| D | proxy_url_loader_factory.cc | 53 mojo::PendingReceiver<network::mojom::URLLoaderFactory> loader_receiver, in CreateProxyHelper()
150 class CorsPreflightRequest : public network::mojom::TrustedHeaderClient {
153 mojo::PendingReceiver<network::mojom::TrustedHeaderClient> receiver) in CorsPreflightRequest()
180 mojo::Receiver<network::mojom::TrustedHeaderClient> header_client_receiver_{
192 class InterceptedRequest : public network::mojom::URLLoader,
193 public network::mojom::URLLoaderClient,
194 public network::mojom::TrustedHeaderClient {
200 const network::ResourceRequest& request,
202 mojo::PendingReceiver<network::mojom::URLLoader> loader_receiver,
203 mojo::PendingRemote<network::mojom::URLLoaderClient> client,
[all …]
|
| D | url_loader_factory_getter.h | 19 namespace network {
45 scoped_refptr<network::SharedURLLoaderFactory> GetURLLoaderFactory();
54 std::unique_ptr<network::PendingSharedURLLoaderFactory>
56 network::mojom::URLLoaderFactoryPtrInfo proxy_factory_ptr_info,
57 network::mojom::URLLoaderFactoryRequest proxy_factory_request);
62 std::unique_ptr<network::PendingSharedURLLoaderFactory> loader_factory_info_;
63 scoped_refptr<network::SharedURLLoaderFactory> lazy_factory_;
64 network::mojom::URLLoaderFactoryPtrInfo proxy_factory_ptr_info_;
65 network::mojom::URLLoaderFactoryRequest proxy_factory_request_;
|
| /third_party/mindspore/tests/ut/python/communication/ |
| D | test_comm.py | 150 network = AllReduceNet(2, 1, op)
152 optimizer = Momentum(filter(lambda x: x.requires_grad, network.get_parameters()),
155 network = WithLossCell(network, loss_fn)
156 network = TrainOneStepCell(network, optimizer)
157 _cell_graph_executor.compile(network, input_tensor, label_tensor)
174 network = AllGatherNet(2, 1)
176 optimizer = Momentum(filter(lambda x: x.requires_grad, network.get_parameters()),
179 network = WithLossCell(network, loss_fn)
180 network = TrainOneStepCell(network, optimizer)
181 _cell_graph_executor.compile(network, input_tensor, label_tensor)
[all …]
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| /third_party/mindspore/mindspore/compression/export/ |
| D | quant_export.py | 202 def __init__(self, network, mean, std_dev, *inputs, is_mindir=False): argument
203 network = Validator.check_isinstance('network', network, (nn.Cell,))
205 self.network = copy.deepcopy(network)
206 self.network_bk = copy.deepcopy(network)
216 graph_id, _ = _executor.compile(self.network, *inputs, phase=phase_name, do_convert=False)
221 self.network.update_cell_prefix()
222 network = self.network
223 if isinstance(network, _AddFakeQuantInput):
224 network = network.network
225 network = self._convert_quant2deploy(network)
[all …]
|
| /third_party/mindspore/tests/st/ops/cpu/ |
| D | test_dot_op.py | 41 network = NetDot()
42 ms_result_np = network(x1_tensor, x2_tensor)
55 network = NetDot()
56 ms_result_np = network(x1_tensor, x2_tensor)
69 network = NetDot()
70 ms_result_np = network(x1_tensor, x2_tensor)
112 network = NetDot()
113 ms_result_np = network(x1_tensor, x2_tensor)
128 network = NetDot()
129 ms_result_np = network(x1_tensor, x2_tensor)
[all …]
|
| /third_party/mindspore/tests/st/control/ |
| D | test_if_mindir.py | 75 def __init__(self, network): argument
78 self.network = network
81 predict = self.network(x)
86 def __init__(self, network): argument
88 self.network = network
89 self.network.set_train()
90 self.weights = ParameterTuple(network.trainable_params())
97 grads = self.grad(self.network, weights)(x, label)
118 network = LeNet5()
119 network.set_train()
[all …]
|
| /third_party/mindspore/tests/st/fl/albert/src/ |
| D | utils.py | 32 def save_params(network, param_dict=None): argument
34 return {param.name: copy.deepcopy(param) for param in network.trainable_params()
36 for param in network.trainable_params():
42 def restore_params(network, param_dict, init_adam=True): argument
43 for param in network.trainable_params():
82 def upload_to_server(network, worker_upload_list): argument
83 for param in network.trainable_params():
109 def download_from_server(network, worker_download_list): argument
110 for param in network.trainable_params():
123 def freeze(network, freeze_list): argument
[all …]
|
| /third_party/mindspore/tests/st/export_and_load/ |
| D | test_train_mindir.py | 75 def __init__(self, network): argument
78 self.network = network
81 predict = self.network(x)
86 def __init__(self, network): argument
88 self.network = network
89 self.network.set_train()
90 self.weights = ParameterTuple(network.trainable_params())
97 grads = self.grad(self.network, weights)(x, label)
107 network = LeNet5()
108 network.set_train()
[all …]
|
| /third_party/mindspore/mindspore/nn/wrap/ |
| D | cell_wrapper.py | 166 def __init__(self, network, loss_fn=None, sens=None): argument
168 self.network = network
170 self.weights = ParameterTuple(network.trainable_params())
174 self.network_with_loss = network
176 self.network_with_loss = WithLossCell(self.network, self.loss_fn)
250 def __init__(self, network, weights=None, get_all=False, get_by_list=False, sens_param=False): argument
252 if not isinstance(network, (Cell, FunctionType, MethodType)):
262 self.network = network
263 if isinstance(network, Cell):
264 self.network.set_grad()
[all …]
|
| /third_party/mindspore/tests/mindspore_test_framework/utils/ |
| D | block_util.py | 67 def __init__(self, network, output_index): argument
68 if isinstance(network, nn.Cell):
72 self.network = network
76 predict = self.network(*inputs)[self.output_index]
80 def get_output_cell(network, num_input, output_index, training=True): argument
82 net = IthOutputCell(network, output_index)
88 def __init__(self, network, output_num): argument
91 self.network = network
96 return self.reduce_sum(self.network(*inputs), None)
99 predict = self.network(*inputs)[index]
[all …]
|
| /third_party/mindspore/tests/st/networks/ |
| D | test_gradient_accumulation.py | 75 def __init__(self, network, optimizer, grad_sum, sens=1.0): argument
77 self.network = network
78 self.network.set_grad()
79 self.network.add_flags(defer_inline=True)
80 self.weights = ParameterTuple(network.trainable_params())
89 loss = self.network(*inputs)
91 grads = self.grad(self.network, weights)(*inputs, sens)
118 def __init__(self, network, loss_fn, optimizer): argument
119 self._network = network
132 network = self._network
[all …]
|
| /third_party/mindspore/tests/st/ops/gpu/ |
| D | test_tensordot_op.py | 36 def __init__(self, network): argument
39 self.network = network
42 gout = self.grad(self.network)(input_data_a, input_data_b, sens)
60 network = NetTensorDot(axes)
61 ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
74 network = NetTensorDot(axes)
75 ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
88 network = NetTensorDot(axes)
89 ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
102 network = NetTensorDot(axes)
[all …]
|
| /third_party/mindspore/mindspore/boost/ |
| D | boost.py | 70 def network_auto_process_train(self, network, optimizer): argument
73 network = LessBN(network, fn_flag=self._fn_flag)
75 group_params = self._param_processer.assign_parameter_group(network.trainable_params(),
80 optimizer_process.add_grad_centralization(network)
86 network, optimizer = freeze_processer.freeze_generate(network, optimizer)
90 return network, optimizer
92 def network_auto_process_eval(self, network): argument
95 network = LessBN(network)
97 return network
|
| /third_party/mindspore/tests/ut/python/parallel/ |
| D | test_strategy_checkpoint.py | 34 def __init__(self, network): argument
37 self.network = network
40 predict = self.network(x1, x6)
44 def __init__(self, network): argument
46 self.network = network
49 return grad_all(self.network)(x1, x6)
98 def __init__(self, network): argument
101 self.network = network
104 predict = self.network(x1, x6, x7)
108 def __init__(self, network): argument
[all …]
|
| D | test_semi_auto_two_subgraphs.py | 43 def __init__(self, network): argument
47 self.net = network
57 def __init__(self, network, output_index): argument
59 self.network = network
63 predict = self.network(x1)[self.output_index]
68 def __init__(self, network, sens=1000.0): argument
70 self.network = network
71 self.network.set_train()
72 self.trainable_params = network.trainable_params()
91 self.loss_net_w = IthOutputCell(network, output_index=0)
[all …]
|
| /third_party/mindspore/tests/ut/cpp/python_input/gtest_input/pipeline/parse/ |
| D | parse_class.py | 46 def __init__(self, network, tensor, use_net=False): argument
51 self.network = network
53 self.network = None
58 z = self.network(z, x)
76 network = SimpleNet(ResNet(X), Y)
77 return network
83 network = SimpleNet(ResNet(X), Y, True)
84 print(network.parameters_dict())
85 return _cell_graph_executor.compile(network, X, Y)
|
| /third_party/mindspore/tests/st/quantization/lenet_quant/ |
| D | test_lenet_quant.py | 47 network = LeNet5Fusion(cfg.num_classes)
51 load_nonquant_param_into_quant_net(network, param_dict)
69 network = quantizer.quantize(network)
74 net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
82 model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
95 network = LeNet5Fusion(cfg.num_classes)
113 network = quantizer.quantize(network)
118 net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
121 model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
125 not_load_param = load_param_into_net(network, param_dict)
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|
| /third_party/glib/gio/ |
| D | gwin32networkmonitor.c | 110 GInetAddressMask *network; in get_network_mask() local
118 network = g_inet_address_mask_new (dest_addr, len, NULL); in get_network_mask()
121 return network; in get_network_mask()
146 GInetAddressMask *network; in win_network_monitor_process_table() local
156 network = get_network_mask (family, dest, len); in win_network_monitor_process_table()
157 if (network == NULL) in win_network_monitor_process_table()
160 g_ptr_array_add (networks, network); in win_network_monitor_process_table()
176 GInetAddressMask *network; in add_network() local
178 network = get_network_mask (family, dest, dest_len); in add_network()
179 if (network != NULL) in add_network()
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| /third_party/mindspore/tests/ut/python/utils/ |
| D | test_export.py | 61 def __init__(self, network): argument
64 self.network = network
67 predict = self.network(x)
72 def __init__(self, network): argument
74 self.network = network
75 self.network.set_train()
76 self.weights = ParameterTuple(network.trainable_params())
83 grads = self.grad(self.network, weights)(x, label)
89 network = LeNet5()
90 network.set_train()
[all …]
|