print(mask.size()) b = a.masked_fill(mask==0, value=torch.tensor(-1e9)) print(b) print(b.size()) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 输出: tensor([[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7]], [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, ...
mask = torch.tensor([[1, 0, 0], [0, 1, 0], [0, 0, 1]]).bool() # 1为感兴趣区域位置,0为非感兴趣,可将其值填为0 b=a.masked_fill_(~mask, 0) # 注意按照上述定义,需取反~ print(b) # tensor([[[ 77, 0, 0], # [ 0, 187, 0], # [ 0, 0, 138]], # [[223, 0...
本文简要介绍 python 语言中 numpy.ma.MaskedArray.filled 的用法。 用法: ma.MaskedArray.filled(fill_value=None)返回self 的副本,其中掩码值填充给定值。然而,如果没有要填充的掩码值,则 self 将作为 ndarray 返回。参数: fill_value: 数组,可选 用于无效条目的值。可以是标量或非标量。如果非标量,则生成的...
def attention(q, k, v, d_k, mask=None, dropout=None):scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)if mask is not None: mask = mask.unsqueeze(1) scores = scores.masked_fill(mask == 0, -1e9)scores = F.softmax(scores, dim=-1) if dropout is not None...
(src, self.src_mask) output = self.decoder(output) return output def _generate_square_subsequent_mask(self, sz): mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1) mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0)) ...
# Blend the image and the mask masked = (mask_stack * frame) + ((1-mask_stack) * mask_color) masked = (masked * 255).astype('uint8') cv2.imshow("Foreground", masked) 最后,蒙版和框架混合在一起,这样背景就被涂黑了。最后一行显示结果。 # Use the q button to quit the operation if...
[# "BertForMaskedLM"# ],# "attention_probs_dropout_prob": 0.1,# "classifier_dropout": null,# "gradient_checkpointing": false,# "hidden_act": "gelu",# "hidden_dropout_prob": 0.1,# "hidden_size": 768,# "initializer_range": 0.02,# "intermediate_size": 3072,# "layer_norm_eps":...
在这个函数中,query、key和value是多头注意力的输入,mask是掩码矩阵。函数首先计算注意力分数,然后使用masked_fill函数将掩码矩阵中为0的位置对应的注意力分数设置为一个非常大的负数,然后计算注意力权重,并根据权重和value计算输出。 最后总结NLP模型特点:
att=(q @ k.transpose(-2,-1))*(1.0/math.sqrt(k.size(-1)))att=att.masked_fill(self.bias[:,:,:T,:T]==0,float('-inf'))att=F.softmax(att,dim=-1)y=att @ v #(B,nh,T,T)x(B,nh,T,hs)->(B,nh,T,hs)y=y.transpose(1,2).contiguous().view(B,T,C)# re-assemble al...
# Next we'llcreate a masked edges imageusingcv2.fillPoly()mask = np.zeros_like(edges)ignore_mask_color =255 # This time we are defining a four sided polygon to maskimshape = image.shapevertices = np.array([[(0,imshape[0]),(0,0), (imshape[1...