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JittorMirror
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Merge branch 'master' of https://github.com/Jittor/jittor

This commit is contained in:
Dun Liang 2020-03-22 14:11:08 +08:00
parent dcb530119d 1260e19235
commit 0a85b202c0
4 changed files with 18 additions and 18 deletions
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10
README.cn.md
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@ -298,11 +298,11 @@ help(jt.ops)
[1]: notebooks/example.md "示例"
[2]: notebooks/basics.md "基本概念"
[3]: notebooks/meta_op.md "元算子"
[4]: notebooks/custom_op.md "自定义算子"
[5]: notebooks/profiler.md "性能分析器"
[1]: notebook/example.src.md "示例"
[2]: notebook/basics.src.md "基本概念"
[3]: notebook/meta_op.src.md "元算子"
[4]: notebook/custom_op.src.md "自定义算子"
[5]: notebook/profiler.src.md "性能分析器"
这些notebooks可以通过python3.7 -m jittor.notebook在您自己的计算机中运行。

2
README.md
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@ -235,7 +235,7 @@ print(c.name())
# Output: c
```
### Operations
###Operations
Jittor'op is simular with numpy. Let's try some operations. We create Var `a` and `b` via operation `jt.float32`, and add them. Printing those variables shows they have the same shape and dtype.

20
README.src.md
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@ -364,16 +364,16 @@ If you want to know more about Jittor, please check out the notebooks below:
[1]: notebooks/example.md "example"
[2]: notebooks/basics.md "basics"
[3]: notebooks/meta_op.md "meta_op"
[4]: notebooks/custom_op.md "custom_op"
[5]: notebooks/profiler.md "profiler"
[1]: notebooks/example.md "示例"
[2]: notebooks/basics.md "基本概念"
[3]: notebooks/meta_op.md "元算子"
[4]: notebooks/custom_op.md "自定义算子"
[5]: notebooks/profiler.md "性能分析器"
[1]: notebook/example.src.md "example"
[2]: notebook/basics.src.md "basics"
[3]: notebook/meta_op.src.md "meta_op"
[4]: notebook/custom_op.src.md "custom_op"
[5]: notebook/profiler.src.md "profiler"
[1]: notebook/example.src.md "示例"
[2]: notebook/basics.src.md "基本概念"
[3]: notebook/meta_op.src.md "元算子"
[4]: notebook/custom_op.src.md "自定义算子"
[5]: notebook/profiler.src.md "性能分析器"
Those notebooks can be started in your own computer by `python3.7 -m jittor.notebook`

4
notebook/meta_op.src.md
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@ -170,9 +170,9 @@ for i0 in range(shape[0]):
for i5 in range(shape[5]):
for i6 in range(shape[6]):
if is_overflow(i0,i1,i2,i3,i4,i5,i6):
y[i0,i1,...,in] = 0
xx[i0,i1,...,in] = 0
else:
y[i0,i1,i2,i3,i4,i5,i6] = x[i0,i1+i3,i2+i4,i5]
xx[i0,i1,i2,i3,i4,i5,i6] = x[i0,i1+i3,i2+i4,i5]
# expansion of w.broadcast_var(xx)
ww = np.zeros(shape, x.dtype)