k,v,attn_mask=None,dropout_p=self.dropoutifself.trainingelse0,is_causal=True)else:# manual imp...
运行程序,就可以看到所有的函数、方法 import torch s = dir(torch) for i in s: print(i) 1. 2. 3. 4. 输出有一千多个结果 AVG AggregationType AnyType Argument ArgumentSpec BFloat16Storage BFloat16Tensor BenchmarkConfig BenchmarkExecutionStats Block BoolStorage BoolTensor BoolType BufferDict Byte...
fn() 函数对应的原始的 python ByteCode，和代码对应的关系参见其中的注释：# x = a + b 0 LOAD_FAST 0 (a) 2 LOAD_FAST 1 (b) 4 BINARY_ADD 6 STORE_FAST 2 (x) # x = x / 2.0 8 LOAD_FAST 2 (x) 10 LOAD_CONST 1 (2.0) 12 BINARY_TRUE_DIVIDE 14 STORE_FAST 2 (x) ...
python自带print(isinstance(a, torch.FloatTensor))#True#标量b = torch.tensor(2.)print(b)#tensor(2.)#获取形状print(b.shape)#torch.Size([])print(b.size())#torch.Size([])#向量x = torch.tensor([2.3])
torch.true_divide 是 支持bf16,fp16,fp32,uint8,int8,int16,int32,int64,bool torch.trunc 是 支持fp16,fp32 torch.xlogy 是 支持fp16,fp32,uint8,int8,int16,int32,int64,bool torch.argmax 是 支持bf16,fp16,fp32,fp64,uint8,int8,int16,int32,int64 torch.argmin 是 支持fp...
Add/minus/multiply/divide Matmul(矩阵式相乘) Pow Sqrt/rsqrt Round basic(+ - * / add sub mul div) 建议直接使用运算符 AI检测代码解析 >>> a=torch.rand(3,4) >>> b=torch.rand(4) #broadingcast机制 >>> a+b tensor([[0.2349, 1.7635, 1.4385, 0.5826], ...
0 JUMP_ABSOLUTE 40 2 LOAD_FAST 2 (a) 4 LOAD_GLOBAL 0 (torch) 6 LOAD_ATTR 1 (abs) 8 LOAD_FAST 2 (a) 10 CALL_FUNCTION 1 12 LOAD_CONST 1 (1) 14 BINARY_ADD 16 BINARY_TRUE_DIVIDE 18 STORE_FAST 1 (x) 20 LOAD_FAST 0 (b) 22 LOAD_ATTR 2 (sum) 24 CALL_FUNCTION 0 26 LOA...
rounding_mode(str, optional):“None”-- 不执行舍入,等价于Python中的 / 运算,或者 np.true_divide;“trunc”-- 将除法结果四舍五入到零,等价于C风格的整除运算;“floor”-- 向下舍入除法的结果,等价于Python中的 // 运算,或者 np.floor_divide; 【代码】: ...
torch.Tensor.true_divide_ Supported 337 torch.Tensor.trunc Supported 338 torch.Tensor.trunc_ Supported 339 torch.Tensor.type Supported 340 torch.Tensor.type_as Supported 341 torch.Tensor.unbind Supported 342 torch.Tensor.unfold Supported 343
# 融合 conv+bn 成新的 conv# transpose为True时 为 融合deffuse_conv_bn_eval(conv,bn,transpose=False):assert(not(conv.trainingorbn.training)),"Fusion only for eval!"fused_conv=copy.deepcopy(conv)fused_conv.weight,fused_conv.bias=\fuse_conv_bn_weights(fused_conv.weight,fused_conv.bias,bn...