code stringlengths 3 6.57k |
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x2.size() |
F.pad(x1[:, :, :, :], (0, 0, 0, 0, ch_diff//2, ch_diff//2) |
F.pad(x1[:, :, :, :], (0, 0, 0, 0, ch_diff//2, (ch_diff//2) |
F.pad(x2[:, :, :, :], (0, 0, 0, 0, ch_diff//2, ch_diff//2) |
F.pad(x2[:, :, :, :], (0, 0, 0, 0, ch_diff//2, (ch_diff//2) |
conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1) |
conv1x1(in_planes, out_planes, stride=1) |
nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) |
BasicBlock(nn.Module) |
super(BasicBlock, self) |
__init__() |
ValueError("BasicBlock only supports groups=1 and base_width=64") |
NotImplementedError("Dilation > 1 not supported in BasicBlock") |
conv3x3(n_in_channels, n_channels1, stride) |
norm_layer(n_channels1) |
nn.ReLU(inplace=True) |
conv3x3(n_channels1, n_channels2) |
norm_layer(n_channels2) |
downsample(n_in_channels, n_channels3) |
forward(self, x) |
self.conv1(x) |
self.bn1(out) |
self.relu(out) |
self.conv2(out) |
self.bn2(out) |
self.downsample(x) |
zero_padding(out, identity) |
self.relu(out) |
ResNet34(nn.Module) |
super(ResNet34, self) |
__init__() |
len(replace_stride_with_dilation) |
format(replace_stride_with_dilation) |
nn.Conv2d(3, ch_conv1, kernel_size=7, stride=2, padding=3, bias=False) |
norm_layer(ch_conv1) |
nn.ReLU(inplace=True) |
nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
max(ch_conv1, ch_l10_2) |
max(in_ch_l11, ch_l11_2) |
max(in_ch_l12, ch_l12_2) |
max(ch_l20_ds, ch_l20_2) |
max(in_ch_l21, ch_l21_2) |
max(in_ch_l22, ch_l22_2) |
max(in_ch_l23, ch_l23_2) |
max(ch_l30_ds, ch_l30_2) |
max(in_ch_l31, ch_l31_2) |
max(in_ch_l32, ch_l32_2) |
max(in_ch_l33, ch_l33_2) |
max(in_ch_l34, ch_l34_2) |
max(in_ch_l35, ch_l35_2) |
max(ch_l40_ds, ch_l40_2) |
max(in_ch_l41, ch_l41_2) |
max(in_ch_l42, ch_l42_2) |
nn.AdaptiveAvgPool2d((1, 1) |
nn.Linear(in_ch_fc, num_classes) |
self.modules() |
isinstance(m, nn.Conv2d) |
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") |
isinstance(m, (nn.BatchNorm2d, nn.GroupNorm) |
nn.init.constant_(m.weight, 1) |
nn.init.constant_(m.bias, 0) |
self.modules() |
isinstance(m, Bottleneck) |
nn.init.constant_(m.bn3.weight, 0) |
isinstance(m, BasicBlock) |
nn.init.constant_(m.bn2.weight, 0) |
nn.Sequential( conv1x1(n_in_channels0, n_channels_ds, stride) |
norm_layer(n_channels_ds) |
nn.Sequential(*layers) |
nn.Sequential( conv1x1(n_in_channels0, n_channels_ds, stride) |
norm_layer(n_channels_ds) |
nn.Sequential(*layers) |
nn.Sequential( conv1x1(n_in_channels0, n_channels_ds, stride) |
norm_layer(n_channels_ds) |
nn.Sequential(*layers) |
forward(self, x) |
self.conv1(x) |
self.bn1(x) |
self.relu(x) |
self.maxpool(x) |
self.layer1(x) |
self.layer2(x) |
self.layer3(x) |
self.layer4(x) |
self.avgpool(x) |
x.reshape(x.size(0) |
self.fc(x) |
AllegationsDownloadTestCase(SimpleTestCase) |
patch('allegation.services.download_allegations.xlsxwriter.Workbook') |
test_write_disclaimer(self, mock_workbook) |
Setting.objects.first() |
SettingFactory() |
DownloadFactory() |
format(line_1=line_1, line_2=line_2) |
setting.save() |
MagicMock() |
mock_workbook() |
patch('allegation.services.download_allegations.os') |
AllegationsDownload(download.id) |
allegation_download.init_workbook() |
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