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**Full name of submitter**: Brian Bi
**Reference (section label)**: [expr.reinterpret.cast]
**Issue description**: In [expr.reinterpret.cast]/11, we have a similar issue to the one described in …
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The EfficientDet paper uses depth-wise convolution of BiFPN, however, in this implementation, clearly depth-wise convolution is not used (in module.py).
`conv_cfg = {
'Conv': nn.Conv2d,
'…
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在网络搜索完后,得到的最高精度网络结构为genotype = Genotype(normal=[('sep_conv_5x5', 1), ('dil_conv_3x3', 0), ('dil_conv_5x5', 2), ('sep_conv_3x3', 1), ('avg_pool_3x3', 3), ('avg_pool_3x3', 1), ('sep_conv_5x5', 1), ('dil…
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I have managed to get a model converted using the conversion script that I modified:
```py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_fun…
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```python
class IBEConvModule(BaseModule):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, dilation=1, groups=1, bias=False, act_cfg=None, **k…
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Thanks for your code. But I think your code is very different from the method which the paper mentioned.
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I tried to create a new header file for the DS-CNN by running:
```
python train.py --model_architecture ds_cnn ...
python fold_batchnorm.py --model_architecture ds_cnn ...
python quant_test.…
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I am seeing the following compile error from both OPAM and HEAD when the --enable-conv flag is set (which happens automatically in OPAM when you have meta_conv installed):
```
[garth@xxx]$ make build…
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```rust
#![feature(f128, f16)]
use core::hint::black_box;
extern "C" {
fn __truncdfhf2(a: f64) -> f16;
}
fn main() {
let a = -0_f64;
let res_sys: f16 = unsafe { __truncdfhf2(…
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```bash
RuntimeError: /io/build/temp.linux-x86_64-cpython-37/spconv/build/core_cc/src/cumm/conv/main/ConvMainUnitTest/ConvMainUnitTest_matmul_split_Simt_f32f32f32_0.cu(222)
int64_t(N) * int64_t(C) *…