Home
last modified time | relevance | path

Searched refs:has_bias (Results 1 – 25 of 165) sorted by relevance

1234567

/third_party/mindspore/tests/ut/python/parallel/
Dtest_allreduce_fusion.py53 def __init__(self, has_bias=True, activation='relu'): argument
55 self.fc1 = nn.Dense(128, 128, has_bias=has_bias, activation=activation)
56 self.fc2 = nn.Dense(128, 128, has_bias=has_bias, activation=activation)
57 self.fc3 = nn.Dense(128, 128, has_bias=has_bias, activation=activation)
58 self.fc4 = nn.Dense(128, 128, has_bias=has_bias, activation=activation)
69 def __init__(self, has_bias=True, activation='relu'): argument
71 self.fc1 = nn.Dense(128, 128, has_bias=has_bias, activation=activation)
72 self.fc2 = nn.Dense(128, 128, has_bias=has_bias, activation=activation)
73 self.fc3 = nn.Dense(128, 128, has_bias=has_bias, activation=activation)
74 self.fc4 = nn.Dense(128, 128, has_bias=has_bias, activation=activation)
[all …]
/third_party/mindspore/tests/ut/python/nn/
Dtest_lstm.py27 def __init__(self, input_size, hidden_size, num_layers, has_bias, batch_first, bidirectional): argument
32 has_bias=has_bias,
43 'cell': LstmTestNet(10, 12, 2, has_bias=True, batch_first=False, bidirectional=False),
47 'cell': LstmTestNet(10, 12, 2, has_bias=False, batch_first=False, bidirectional=False),
51 'cell': LstmTestNet(10, 12, 2, has_bias=True, batch_first=False, bidirectional=True),
55 'cell': LstmTestNet(10, 12, 2, has_bias=False, batch_first=False, bidirectional=True),
59 'cell': LstmTestNet(10, 12, 2, has_bias=True, batch_first=True, bidirectional=False),
63 'cell': LstmTestNet(10, 12, 2, has_bias=False, batch_first=True, bidirectional=False),
67 'cell': LstmTestNet(10, 12, 2, has_bias=True, batch_first=True, bidirectional=True),
71 'cell': LstmTestNet(10, 12, 2, has_bias=False, batch_first=True, bidirectional=True),
Dtest_conv.py40 has_bias=True, argument
52 has_bias,
68 net = Net(3, 64, 4, has_bias=False, weight_init='normal')
74 net = Net(3, 64, (3, 5), has_bias=False, weight_init='normal')
80 net = Net(3, 64, (3, 5), pad_mode="same", padding=0, has_bias=False, weight_init='normal')
86 net = Net(3, 64, (3, 5), pad_mode="valid", padding=0, has_bias=False, weight_init='normal')
92 net = Net(3, 64, (3, 5), pad_mode="pad", padding=1, has_bias=False, weight_init='normal')
138 has_bias=False, argument
150 has_bias,
165 net = NetConv2dTranspose(3, 64, 4, has_bias=True, weight_init='normal')
[all …]
Dtest_dense.py97 has_bias=True, argument
104 has_bias,
130 net = Net(64, 8, weight=weight, has_bias=False)
135 net_train = Net(64, 8, weight=weight, has_bias=False)
160 net = Net(128, 10, has_bias=False)
165 net_train = Net(128, 10, has_bias=False)
/third_party/mindspore/tests/st/ops/ascend/
Dtest_lstm_op.py41 … def __init__(self, input_s, hidden_s, num_layers, has_bias, batch_first, bidirectional, dropout): argument
43 ….lstm = nn.LSTM(input_size=input_s, hidden_size=hidden_s, num_layers=num_layers, has_bias=has_bias,
51 def __init__(self, num_layers, has_bias, input_s, num_directions, hidden_s, bidirectional): argument
53 self.has_bias = has_bias
72 if self.has_bias:
83 np.float16) if self.has_bias else b0
97 has_bias = True
102 fact = LSTMWeightBias(num_layers, has_bias, input_s, num_directions, hidden_s, bidirectional)
111 …net = LSTM(input_s=input_s, hidden_s=16, num_layers=num_layers, has_bias=has_bias, batch_first=Fal…
119 …net_pynative = LSTM(input_s=input_s, hidden_s=16, num_layers=num_layers, has_bias=has_bias, batch_…
[all …]
Dtest_gru_op.py40 …def __init__(self, input_size, hidden_size, num_layers, has_bias, batch_first, bidirectional, drop… argument
42 … = nn.GRU(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, has_bias=has_bias,
50 … def __init__(self, num_layers, has_bias, input_size, num_directions, hidden_size, bidirectional): argument
52 self.has_bias = has_bias
77 if self.has_bias:
98 has_bias = True
103 … fact = GRUWeightBias(num_layers, has_bias, input_size, num_directions, hidden_size, bidirectional)
111 …net = GRU(input_size=input_size, hidden_size=16, num_layers=num_layers, has_bias=has_bias, batch_f…
121 …net_pynative = GRU(input_size=input_size, hidden_size=16, num_layers=num_layers, has_bias=has_bias,
139 has_bias = True
[all …]
Dtest_rnn_op.py40 …def __init__(self, input_size, hidden_size, num_layers, has_bias, batch_first, bidirectional, drop… argument
42 … = nn.RNN(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, has_bias=has_bias,
50 … def __init__(self, num_layers, has_bias, input_size, num_directions, hidden_size, bidirectional): argument
52 self.has_bias = has_bias
77 if self.has_bias:
98 has_bias = True
103 … fact = RNNWeightBias(num_layers, has_bias, input_size, num_directions, hidden_size, bidirectional)
111 …net = RNN(input_size=input_size, hidden_size=16, num_layers=num_layers, has_bias=has_bias, batch_f…
121 …net_pynative = RNN(input_size=input_size, hidden_size=16, num_layers=num_layers, has_bias=has_bias,
139 has_bias = True
[all …]
/third_party/mindspore/mindspore/nn/probability/bnn_layers/
Ddense_variational.py37 has_bias=True, argument
45 self.has_bias = Validator.check_bool(has_bias)
51 if self.has_bias:
74 if self.has_bias:
84 self.weight_posterior.untransformed_std, self.has_bias)
85 if self.has_bias:
104 if self.has_bias:
184 has_bias=True, argument
193 has_bias=has_bias,
278 has_bias=True, argument
[all …]
Dconv_variational.py39 has_bias=False, argument
56 has_bias,
73 self.has_bias = has_bias
82 if self.has_bias:
104 if self.has_bias:
114 self.has_bias)
115 if self.has_bias:
127 if self.has_bias:
242 has_bias=False, argument
256 has_bias=has_bias,
/third_party/mindspore/mindspore/lite/examples/export_models/models/
DNetworkInNetwork.py31 … nn.Conv2d(in_channels=num_channel, out_channels=192, kernel_size=5, stride=1, has_bias=False),
33 nn.Conv2d(in_channels=192, out_channels=160, kernel_size=1, stride=1, has_bias=True),
35 nn.Conv2d(in_channels=160, out_channels=96, kernel_size=1, stride=1, has_bias=True),
42 nn.Conv2d(in_channels=96, out_channels=192, kernel_size=5, stride=1, has_bias=False),
44 nn.Conv2d(in_channels=192, out_channels=192, kernel_size=1, stride=1, has_bias=True),
46 nn.Conv2d(in_channels=192, out_channels=192, kernel_size=1, stride=1, has_bias=True),
53 nn.Conv2d(in_channels=192, out_channels=192, kernel_size=3, stride=1, has_bias=False),
55 nn.Conv2d(in_channels=192, out_channels=192, kernel_size=1, stride=1, has_bias=True),
57 … nn.Conv2d(in_channels=192, out_channels=num_classes, kernel_size=1, stride=1, has_bias=True),
Dmini_alexnet.py21 def conv(in_channels, out_channels, kernel_size, stride=1, padding=0, pad_mode="valid", has_bias=Tr… argument
23 has_bias=has_bias, pad_mode=pad_mode)
26 def fc_with_initialize(input_channels, out_channels, has_bias=True): argument
27 return nn.Dense(input_channels, out_channels, has_bias=has_bias)
36 self.conv1 = conv(channel, 12, 11, stride=2, pad_mode="same", has_bias=True)
37 self.conv2 = conv(12, 20, 3, pad_mode="same", has_bias=True)
Deffnet.py73 …onv_reduce = nn.Conv2d(in_channels=channel, out_channels=reduced_chs, kernel_size=1, has_bias=True,
76 …onv_expand = nn.Conv2d(in_channels=reduced_chs, out_channels=channel, kernel_size=1, has_bias=True)
99 pad_mode="pad", padding=1, has_bias=False, group=in_chs)
109 …= nn.Conv2d(in_channels=in_chs, out_channels=out_chs, kernel_size=1, stride=stride, has_bias=False)
134 has_bias=False, pad_mode='pad'),
143 has_bias=False),
160 …v_pw = nn.Conv2d(in_channels=in_chs, out_channels=mid_chs, kernel_size=1, stride=1, has_bias=False)
167 … padding=padding, has_bias=False, group=mid_chs, pad_mode='same')
170 padding=padding, has_bias=False, group=mid_chs, pad_mode='pad')
181 …pwl = nn.Conv2d(in_channels=mid_chs, out_channels=out_chs, kernel_size=1, stride=1, has_bias=False)
[all …]
Demoji_model.py48 has_bias=True)
51 has_bias=True)
54 has_bias=True)
57 has_bias=True)
61 has_bias=True)
/third_party/mindspore/tests/st/gnn/
Daggregator.py74 has_bias=True): argument
78 self.has_bias = Validator.check_bool(has_bias)
87 if self.has_bias:
101 if self.has_bias:
108 if self.has_bias:
109 s += ', has_bias={}'.format(self.has_bias)
145 has_bias=True, argument
155 self.has_bias = has_bias
160 has_bias=self.has_bias)
201 has_bias=True, argument
[all …]
/third_party/mindspore/tests/st/ops/cpu/
Dtest_lstm_op.py40 has_bias=True, argument
62 has_bias=has_bias,
72 if has_bias:
102 …def __init__(self, batch_size, input_size, hidden_size, num_layers, has_bias, bidirectional, dropo… argument
109 self.lstm = StackLSTM(input_size, hidden_size, num_layers, has_bias, bidirectional, dropout)
160 has_bias = True
166 net = LstmNet(batch_size, input_size, hidden_size, num_layers, has_bias, bidirectional, dropout)
205 …def __init__(self, batch_size, input_size, hidden_size, num_layers, has_bias, bidirectional, dropo… argument
212 …StackLSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, has_bias=has_bias,
242 bias_size = 0 if not has_bias else num_directions * hidden_size * 4
[all …]
/third_party/mindspore/mindspore/nn/layer/
Dconv.py45 has_bias, argument
81 self.has_bias = has_bias
101 if Validator.check_bool(has_bias, "has_bias", self.cls_name):
232 has_bias=False, argument
250 has_bias,
267 if self.has_bias:
284 self.has_bias,
398 has_bias=False, argument
432 has_bias,
455 if self.has_bias:
[all …]
Dcombined.py107 has_bias=False, argument
127 has_bias=has_bias,
206 has_bias=True, argument
220 has_bias)
/third_party/mindspore/mindspore/train/train_thor/
Dconvert_utils.py50 has_bias=subcell.has_bias,
60 has_bias=subcell.has_bias,
64 if subcell.has_bias:
105 has_bias = subcell.has_bias
110 has_bias=has_bias, weight_init=weight)
/third_party/mindspore/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/
Dscale_int8.cc33 const std::vector<int> &out_shape, int8_t *output_data, int axis, bool has_bias);
67 … const std::vector<int> &out_shape, int8_t *output_data, int axis, bool has_bias) { in Prepare() argument
80 if (has_bias) { in Prepare()
109 bool has_bias = true; in TEST_F() local
121 has_bias); in TEST_F()
135 bool has_bias = true; in TEST_F() local
147 has_bias); in TEST_F()
161 bool has_bias = false; in TEST_F() local
173 has_bias); in TEST_F()
/third_party/mindspore/mindspore/core/ops/fusion/
Dfull_connection.cc23 void FullConnection::set_has_bias(const bool has_bias) { (void)this->AddAttr(kHasBias, MakeValue(ha… in set_has_bias() argument
54 void FullConnection::Init(const bool has_bias, const int64_t axis, const bool use_axis, in Init() argument
56 this->set_has_bias(has_bias); in Init()
72 auto has_bias = GetValue<bool>(primitive->GetAttr(kHasBias)); in FullConnectionInfer() local
75 if (has_bias) { in FullConnectionInfer()
97 if (has_bias) { in FullConnectionInfer()
/third_party/mindspore/tests/ut/python/pynative_mode/ge/ops/
Dtest_conv.py29 stride=1, padding=0, has_bias=False, bias=None): argument
36 stride=1, padding=0, has_bias=False, bias=None): argument
43 has_bias=has_bias,
50 net = Net(weight, in_channel, out_channel, kernel_size, stride, padding, has_bias, bias)
/third_party/mindspore/tests/st/ops/gpu/
Dtest_lstm_op.py32 …def __init__(self, seq_len, batch_size, input_size, hidden_size, num_layers, has_bias, bidirection… argument
39 self.lstm = P.LSTM(input_size, hidden_size, num_layers, has_bias, bidirectional, dropout)
118 has_bias = True
126 …net = LstmNet(seq_len, batch_size, input_size, hidden_size, num_layers, has_bias, bidirectional, d…
164 …def __init__(self, seq_len, batch_size, input_size, hidden_size, num_layers, has_bias, bidirection… argument
171 self.lstm = P.LSTM(input_size, hidden_size, num_layers, has_bias, bidirectional, dropout)
268 has_bias = True
276 …net = BiLstmNet(seq_len, batch_size, input_size, hidden_size, num_layers, has_bias, bidirectional,…
321 …def __init__(self, seq_len, batch_size, input_size, hidden_size, num_layers, has_bias, bidirection… argument
328 self.lstm = P.LSTM(input_size, hidden_size, num_layers, has_bias, bidirectional, dropout)
[all …]
/third_party/mindspore/tests/ut/cpp/ops/
Dtest_ops_full_connection.cc36 bool has_bias = false; in TEST_F() local
39 op->Init(has_bias, axis, use_axis, NO_ACTIVATION); in TEST_F()
66 bool has_bias = true; in TEST_F() local
69 op->Init(has_bias, axis, use_axis, NO_ACTIVATION); in TEST_F()
97 bool has_bias = false; in TEST_F() local
100 op->Init(has_bias, axis, use_axis, NO_ACTIVATION); in TEST_F()
/third_party/mindspore/mindspore/lite/examples/transfer_learning/model/
Deffnet.py68 … in_channels=channel, out_channels=reduced_chs, kernel_size=1, has_bias=True, weight_init=weight)
71 in_channels=reduced_chs, out_channels=channel, kernel_size=1, has_bias=True)
97 … stride=stride, pad_mode="pad", padding=1, has_bias=False, group=in_chs)
107 in_channels=in_chs, out_channels=out_chs, kernel_size=1, stride=stride, has_bias=False)
130 padding=1, weight_init=weight, has_bias=False, pad_mode='pad'),
139 stride=1, padding=0, weight_init=weight, has_bias=False),
157 in_channels=in_chs, out_channels=mid_chs, kernel_size=1, stride=1, has_bias=False)
162 … stride=stride, padding=padding, has_bias=False, group=mid_chs, pad_mode='same')
165 … stride=stride, padding=padding, has_bias=False, group=mid_chs, pad_mode='pad')
177 in_channels=mid_chs, out_channels=out_chs, kernel_size=1, stride=1, has_bias=False)
[all …]
/third_party/mindspore/mindspore/boost/
Dless_batch_normalization.py61 has_bias=True): argument
69 self.has_bias = has_bias
70 if self.has_bias:
79 if self.has_bias:

1234567