code stringlengths 3 6.57k |
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UserModelTests(TestCase) |
setUp(self) |
Client() |
self.client.force_login(self.admin_user) |
get_user_model() |
user.set_password(password) |
migrate_speakers(apps, schema_editor) |
apps.get_model("talk", "Talk") |
apps.get_model("talk", "TalkPublishedSpeaker") |
apps.get_model("talk", "TalkDraftSpeaker") |
Talk.objects.using(db_alias) |
all() |
TalkPublishedSpeaker.objects.using(db_alias) |
TalkDraftSpeaker.objects.using(db_alias) |
Migration(migrations.Migration) |
migrations.RunPython(migrate_speakers) |
migrations.RemoveField(model_name="talk", name="draft_speaker") |
migrations.RemoveField(model_name="talk", name="published_speaker") |
Speaker (draft) |
Speaker (public) |
get_no_nncf_trace_context_manager() |
get_no_nncf_trace_context_manager() |
SELayer(nn.Module) |
__init__(self, channel, reduction=4) |
super(SELayer, self) |
__init__() |
nn.AdaptiveAvgPool2d(1) |
nn.Linear(channel, make_divisible(channel // reduction, 8) |
nn.ReLU(inplace=True) |
nn.Linear(make_divisible(channel // reduction, 8) |
HSigmoid() |
forward(self, x) |
no_nncf_se_layer_context() |
x.size() |
self.avg_pool(x) |
view(b, c) |
self.fc(y) |
view(b, c, 1, 1) |
conv_3x3_bn(inp, oup, stride, IN_conv1=False) |
nn.Conv2d(inp, oup, 3, stride, 1, bias=False) |
nn.BatchNorm2d(oup) |
nn.InstanceNorm2d(oup, affine=True) |
HSwish() |
conv_1x1_bn(inp, oup, loss='softmax') |
nn.Conv2d(inp, oup, 1, 1, 0, bias=False) |
nn.BatchNorm2d(oup) |
HSwish() |
nn.PReLU() |
InvertedResidual(nn.Module) |
__init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs) |
super(InvertedResidual, self) |
__init__() |
nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) |
nn.BatchNorm2d(hidden_dim) |
HSwish() |
nn.ReLU(inplace=True) |
SELayer(hidden_dim) |
nn.Identity() |
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False) |
nn.BatchNorm2d(oup) |
nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False) |
nn.BatchNorm2d(hidden_dim) |
HSwish() |
nn.ReLU(inplace=True) |
nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) |
nn.BatchNorm2d(hidden_dim) |
SELayer(hidden_dim) |
nn.Identity() |
HSwish() |
nn.ReLU(inplace=True) |
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False) |
nn.BatchNorm2d(oup) |
forward(self, x) |
self.conv(x) |
self.conv(x) |
MobileNetV3(ModelInterface) |
super() |
__init__(**kwargs) |
nn.InstanceNorm2d(in_channels, affine=True) |
make_divisible(16 * self.width_mult, 8) |
conv_3x3_bn(3, input_channel, stride, IN_conv1) |
if (self.in_size[0] < 100) |
and (s == 2) |
make_divisible(c * self.width_mult, 8) |
make_divisible(input_channel * t, 8) |
layers.append(block(input_channel, exp_size, output_channel, k, s, use_se, use_hs) |
nn.Sequential(*layers) |
conv_1x1_bn(input_channel, exp_size, self.loss) |
make_divisible(output_channel[mode] * self.width_mult, 8) |
nn.Linear(exp_size, output_channel) |
nn.BatchNorm1d(output_channel) |
HSwish() |
Dropout(**self.dropout_cls) |
nn.Linear(output_channel, self.num_classes) |
nn.Linear(exp_size, output_channel) |
nn.BatchNorm1d(output_channel) |
nn.PReLU() |
Dropout(**self.dropout_cls) |
AngleSimpleLinear(output_channel, self.num_classes) |
self._initialize_weights() |
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