padded_mask = np.zeros( (mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8) padded_mask[1:-1, 1:-1] = mask contours = find_contours(padded_mask, 0.5) for verts in contours: # Subtract the padding and flip (y, x) to (x, y) verts = np.fliplr(verts) - 1 p = ...
_buffers.items(), prefix=prefix, recurse=recurse) for elem in gen: yield elem def children(self) -> Iterator['Module']: for name, module in self.named_children(): yield module def named_children(self) -> Iterator[Tuple[str, 'Module']]: memo = set() for name, module in self._...
通过函数去剔除不满足这些类别的COCO数据: def _gen_seg_mask(self, target, h, w): mask = np.zeros((h, w), dtype=np.uint8) coco_mask = self.coco_mask for instance in target: rle = coco_mask.frPyObjects(instance['segmentation'], h, w) m = coco_mask.decode(rle) cat = instance[...
padding_mask=None, Tensor? atten_mask=None, float scale=1., float keep_prob=1., int pre_tockens=2147483647, int next_tockens=2147483647, int inner_precise=0, int[]? prefix=None, int[]? actual_seq_qlen=None, int[]? actual_seq_kvlen=None, int sparse_mode=0, bool gen_mask_paralle...
🐛 Describe the bug torch.compile raises attn_mask dtype RuntimeError import math import torch torch.manual_seed(420) class Model(torch.nn.Module): def __init__(self): super(Model, self).__init__() def forward(self, query: torch.Tensor, k...
n_seq = len(val_tokens) mask_dims = (n_seq, n_seq) target_mask = torch.from_numpy( (val_tokens != TOKENS['<pad>'])[..., np.newaxis] \ & (np.triu(np.ones(mask_dims), k=1).astype('uint8') == 0))PolyGen还广泛使用了无效的预测遮罩，以确保其生成的顶点和面序列编码有效...
mask_all=np.bitwise_or(mask_all.astype(int),mask.astype(int)) returnmask_all,mask_miss defdwaw_gen_masks(self,img,mask,color=(0,0,1)): bimsk=np.repeat(mask[:,:,np.newaxis],3,axis=2) mskd=img*bimsk.astype(np.int32)
atten_mask=None, scale=1.0, keep_prob=1.0, pre_tockens=2147483647, next_tockens=2147483647, inner_precise=0, prefix=None, sparse_mode=0, gen_mask_parallel=True, sync=False): return torch.empty_like(query) @impl(m, "npu_fusion_attention_grad") def npu_fusion_attention_backwar...
importtorch.nn.functionalasFdata=dataset[0]optimizer=torch.optim.Adam(model.parameters(),lr=0.01)forepochinrange(200):pred=model(data.x,data.edge_index)loss=F.cross_entropy(pred[data.train_mask],data.y[data.train_mask])# Backpropagationoptimizer.zero_grad()loss.backward()optimizer.step() ...
PolyGen的总体目标是双重的:首先为3D模型生成一组可能的顶点(可能由图像,体素或类标签来限制),然后生成一系列的面,一个接一个地连接 顶点在一起,为该模型提供了一个合理的表面。 组合模型将网格p(M)上的分布表示为两个模型之间的联合分布:代表顶点的顶点模型p(V)和代表以顶点为条件的面的模型p(F | V)。