1 for mixed,2 for background patch_vectors : A tensor of shape:[B,num_class,patch_dct_vector_dim,scale,scale] or a dict of tensors(for multi-stage PatchDCT) DCT vector for each patch (only calculate loss for mixed patch) """ for layer in self.conv_norm_relus: #四层输入维度为256...
PatchDCT输入和输出的特征尺寸完全一致,因此可以使用多个PatchDCT进行多阶段精炼。如下图所示: 图中P表示PatchDCT模块,M表示mask,F表示FPN-P2输出的特征。 损失函数如下: \mathcal{L}_{\text {mask }}=\lambda_0 \mathcal{L}_{\text {dct }_N}+\sum_{s>0} \lambda_s\left(\mathcal{L}_{\text {...
[PatchDCT: Patch Refinement for High Quality Instance Segmentation] Qinrou Wen, Jirui Yang, Xue Yang, Kewei Liang arXiv preprint(arXiv:2302.02693) In this repository, we release code for PatchDCT in Detectron2. Contributions PatchDCT is the fist compressed vector based multi-stage refinement fram...
几篇论文实现代码:《PatchDCT: Patch Refinement for High Quality Instance Segmentation》(ICLR 2023) GitHub: github.com/olivia-w12/PatchDCT 《EDA: Explicit Text-Decoupling and Dense Alignment for 3D...
Sarawut Tae-o-sot, Akinori Nishihara, "DCT Inpainting with Patch Shifting Scheme", The Center for Research and Development of Educational Technology, Tokyo Institute of Technology, 2011.DCT inpainting with patch shifting scheme. NISHIHARA A. Asia-Pacific Signal and Information Processing Association ...
- if n in dct.get('_unsynchronized_methods', []): + if n in blacklist: pass elif isinstance(obj, FunctionType): obj = exclusive(obj) diff --git a/blivet/udev.py b/blivet/udev.py index a8297f3f..e1b67845 100644 --- a/blivet/udev.py +++ b/blivet/udev.py @@ -...
def __new__(cls, name, bases, dct): class MyClass(object): __metaclass__ = MyMetaClass pass 这样创建的MyClass就是由MyMetaClass来创建的,在MyMetaClass里就可以修改你想创建的类了,这样理解起来是不是不那么抽象了,里面的参数查查API就明白了,用时在查。
dctx->mgf1md = sctx->mgf1md; dctx->saltlen = sctx->saltlen; + dctx->implicit_rejection = sctx->implicit_rejection; if (sctx->oaep_label) { OPENSSL_free(dctx->oaep_label); dctx->oaep_label = OPENSSL_memdup(sctx->oaep_label, sctx->oaep_labellen); ...
patches = dct.dct_2d(x[:, indices]) # normlize patches on h and w patches = time_norm(patches, dim=1)x[:, indices] = patches return xdef to_patch(x, replace=False): # b 512 32 32 b, t, h, w = x.shape x = time_norm(x, dim=1)...
...if'__complex__' in dct:...returncomplex(dct['real'], dct['imag'])...returndct ... >>> json.loads('{"__complex__": true, "real": 1, "imag": 2}',... object_hook=as_complex) (1+2j) >>>fromdecimalimportDecimal>>> json.loads('1.1', parse_float=Decimal) == Decim...