How to derive the final architecture? (question for PyTorch version) about pcdarts-tf2 HOT 4 CLOSED

Did you use ptflops.get_model_complexity_info to calculate the Flops? I saw even the oldest version, the requirements of it is Pytorch 0.4.1 or 1.0, torchvision 0.2.1 but the official PC-DARTS was implemented on pytorch(0.3)...

Is that a problem?

As I know, the official PC-DARTS will be OOM in the newer PyTorch version in here. Maybe
that's the same reason. The operators are not equal mechanism in different versions.

Thanks for your suggestion.

I just checked that it is not caused by the PyTorch version. For the OOM, it is mainly because the newer PyTorch deprecated the volatile and volatile=True does not work anymore. So once you want to assign a parameter without gradient you can use with torch.no_grad() instead. At the very beginning, I have corrected it.

After I get the searched genotype, I want to stack some cells or layers to make a new model (like residual block and resnet50). Usually, in neural architecture search, our aim is to get a simple and small model while maintaining the same level of accuracy, which is suitable for deployment. But the latency (10 times as resnet50) and model complexity (12000M FLOPs) seems a little strange under the ImageNet setting (8 cells). Since the reported FLOPs under ImageNet setting is about 597M FLOPs.

As you can see in the following test codes, I set the output classes = 1000, init_channels=36, cells=14 with the searched genotype, which is the same as the original paper.

from model import NetworkCIFAR as Network
import genotypes

genotype = genotype = eval("genotypes.%s" % "PCDARTS")

with torch.cuda.device(0):
    model = Network(36, 1000, 14, True, genotype)
    model.drop_path_prob = 0.3
    model.eval()
    flops, params =  get_model_complexity_info(model, (3, 224, 224), as_strings=True, print_per_layer_stat=True)
    print("{:<30}  {:<8}".format("Computational complexity: ", flops))
    print("{:<30}  {:<8}".format("Number of parameters: ", params))

I will continue to figure it out.

from pcdarts-tf2.

from model import NetworkImageNet as Network

Is the model imported from different way?
You can check it from here.
The feature maps resolution in layers are totally different in each model. HAHAHA...

from pcdarts-tf2.

Yes, you are right! I got it!!!

Thanks for your gentle help. Have a nice day. Hahahaha

from pcdarts-tf2.

Did you use ptflops.get_model_complexity_info to calculate the Flops? I saw even the oldest version, the requirements of it is Pytorch 0.4.1 or 1.0, torchvision 0.2.1 but the official PC-DARTS was implemented on pytorch(0.3)...

Is that a problem?

As I know, the official PC-DARTS will be OOM in the newer PyTorch version in here. Maybe
that's the same reason. The operators are not equal mechanism in different versions.

from pcdarts-tf2.