code stringlengths 3 6.57k |
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squeeze(-1) |
squeeze(-1) |
Yolact(nn.Module) |
Parameters (in cfg.backbone) |
len(selected_layers) |
scales (see PredictionModule) |
len(selected_layers) |
__init__(self, only_last_layer=False) |
super() |
__init__() |
construct_backbone(cfg.backbone) |
self.freeze_bn() |
torch.Tensor(np.load(cfg.mask_proto_grid_file) |
self.grid.size(0) |
make_net(in_channels, cfg.mask_proto_net, include_last_relu=False) |
FastMaskIoUNet() |
FPN([src_channels[i] for i in self.selected_layers]) |
list(range(len(self.selected_layers) |
len(self.selected_layers) |
nn.ModuleList() |
len(self.selected_layers) |
enumerate(self.selected_layers) |
self.prediction_layers.append(pred) |
nn.Linear(src_channels[-1], cfg.num_classes - 1) |
nn.Conv2d(src_channels[0], cfg.num_classes-1, kernel_size=1) |
save_weights(self, path) |
torch.save(self.state_dict() |
load_weights(self, path) |
torch.load(path) |
these (the new variable is called layers) |
list(state_dict.keys() |
key.startswith('backbone.layer') |
key.startswith('backbone.layers') |
key.startswith('fpn.downsample_layers.') |
int(key.split('.') |
self.load_state_dict(state_dict) |
self.load_state_dict(state_dict) |
print('Ignoring "' + str(e) |
init_weights(self, backbone_path) |
self.backbone.init_backbone(backbone_path) |
getattr(nn.Conv2d(1, 1, 1) |
all_in(x, y) |
self.named_modules() |
type(module) |
hasattr(module, 'original_name') |
all_in(module.__dict__['_constants_set'], conv_constants) |
all_in(conv_constants, module.__dict__['_constants_set']) |
isinstance(module, nn.Conv2d) |
nn.init.xavier_uniform_(module.weight.data) |
sum(exp(x) |
exp(x_0) |
log(z * (1 - pi) |
log(z / c) |
simplicity (and because we have a degree of freedom here) |
log((1 - pi) |
log(c) |
np.log((1 - cfg.focal_loss_init_pi) |
np.log(module.bias.size(0) |
np.log(cfg.focal_loss_init_pi / (1 - cfg.focal_loss_init_pi) |
np.log((1 - cfg.focal_loss_init_pi) |
module.bias.data.zero_() |
train(self, mode=True) |
super() |
train(mode) |
self.freeze_bn() |
freeze_bn(self, enable=False) |
self.modules() |
isinstance(module, nn.BatchNorm2d) |
module.train() |
module.eval() |
forward(self, x) |
x.size() |
timer.env('backbone') |
self.backbone(x) |
timer.env('fpn') |
self.fpn(outs) |
timer.env('proto') |
self.grid.repeat(proto_x.size(0) |
torch.cat([proto_x, grids], dim=1) |
self.proto_net(proto_x) |
cfg.mask_proto_prototype_activation(proto_out) |
proto_out.clone() |
proto_out.detach() |
proto_out.permute(0, 2, 3, 1) |
contiguous() |
proto_out.size() |
torch.cat([proto_out, torch.ones(*bias_shape) |
timer.env('pred_heads') |
zip(self.selected_layers, self.prediction_layers) |
F.interpolate(proto_downsampled, size=outs[idx].size() |
torch.cat([pred_x, proto_downsampled], dim=1) |
pred_layer(pred_x.detach() |
pred_layer(pred_x) |
p.items() |
append(v) |
pred_outs.items() |
torch.cat(v, -2) |
self.class_existence_fc(outs[-1].mean(dim=(2, 3) |
self.semantic_seg_conv(outs[0]) |
torch.sigmoid(pred_outs['score']) |
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