torch.masked_select(input, mask, out) output = input.masked_select(mask) selected_ele = torch.masked_select(input=imgs, mask=mask)#true表示selected,false则未选中,所以这里没有取反 #tensor([182., 92., 86., 157., 148., 56.]) 3)torch.masked_scatter(input, mask, source) 说明:将从inp...
# 索引和切片 mask=x>1 selected=torch.masked_select(x,mask) print(selected) # 设备管理 iftorch.cuda.is_available(): device=torch.device('cuda') x=x.to(device) print(x.device) 如果需要更详细的信息,可以参考PyTorch 官方文档。 点我分享笔记...
maskSelected可以用来做dropout这种功能,通过制定一个mask (0,1)矩阵来决定哪个要哪个不要。
mask (ByteTensor)– the tensor containing the binary mask to index with out (Tensor, optional)– the output tensor. Example: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 复制 >>> x = torch.randn(3, 4) >>> x tensor([[ 0.3552, -2.3825, -0.8297, 0.3477], [-1.2035, 1.2252,...
import torch # 创建一个示例张量 tensor = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # 指定要删除的列索引 col_index = 1 # 创建布尔索引 mask = torch.ones(tensor.size(1), dtype=torch.bool) mask[col_index] = False # 删除指定列 new_tensor = tensor[:, mask] print...
mask (BoolTensor)– the boolean mask source (Tensor)– the tensor to copy from Note The mask operates on the self tensor, not on the given source tensor. masked_scatter(mask, tensor)→ Tensor Out-of-place version of torch.Tensor.masked_scatter_() masked_fill_(mask, value) Fills elem...
checkpoint函数接收一个函数和若干个输入,并返回该函数的输出。这里将自定义的前向传递函数、输入的隐藏状态hidden_states、注意力掩码attention_mask和因果注意力掩码causal_attention_mask作为参数传递给了checkpoint函数,返回的是encoder_layer的输出。 如果模型没有开启梯度检查点,则直接调用encoder_layer的正常前向传递...
masked_fill_(triu_mask, 0) C = torch.cat((Cread, Cwrite), dim=-1) E = utils.fill_with_neg_inf(emissions[0].new(tgt_length, src_length, N)) for t, (subC, subE) in enumerate(zip(controls, emissions)): select = [indices[t].to(C.device)] C[t].index_put_(select, subC....
If the following conditions are satisfied: 1) cudnn is enabled, 2) input data is on the GPU 3) input data has dtypetorch.float164) V100 GPU is used, 5) input data is not inPackedSequenceformat persistent algorithm can be selected to improve performance. ...
valid_true_boxes = tf.boolean_mask(y_true[..., 0:4], tf.cast(object_mask[..., 0], 'bool')) # shape: [V, 2] # 将gt目标框的中心和高度宽度分离成两个矩阵,每个矩阵的shape都是[V, 2] valid_true_box_xy = valid_true_boxes[:, 0:2] valid_true_box_wh = valid_true_boxes[:...