Margrate

I use different architectures to train classify model. sometime it shows "out of memory" .

> @JaminFong I've looked at your implementation and only found instructions for running the 2nd order version of the algorithm. Could you specify the instructions on running the algorithm on...

Can train.py run on muti-gpus? drop_path is not supported?

> > Did the author use cutout while training? I didn't find cutout except parser. > > just here: parser.add_argument('cutout', action='store_true', default='False').But it didn't appear in the following main code....

It takes a long time to search architecture and train from start. If I use other code with Resnet to train from start. It only takes 1/5 time of DARTS...

Does DARTS_V2 in genotypes.py mean the architecture of DARTS(second order) ?

but the function didn't use the line: self.mask[i * _out_channels: (i + 1) * _out_channels, i * _in_channels: (i + 1) * _in_channels, : , :] = def forward(self, x):...

You add the two lines： dist_ap *= (1.0 + self.hard_factor) dist_an *= (1.0 - self.hard_factor) then, it becomes harder triplet loss?

I got acc95.95% using PDARTS in genotypes.py without change anything. (GPU:Tesla_V100-SXM2-32G)

> @Margrate Maybe you missed option terms `--cutout` and/or `--auxiliary` according to the result. I run it again by adding option term --cutout and --auxiliary. Just got acc 97.01%