def try_gpu(i=0):
"""如果存在，则返回gpu(i)，否则返回cpu()。"""
return npx.gpu(i) if npx.num_gpus() >= i + 1 else npx.cpu()
def try_all_gpus():
"""返回所有可用的GPU，如果没有GPU，则返回[cpu()]。"""
devices = [npx.gpu(i) for i in range(npx.num_gpus())]
return devices if devices else [npx.cpu()]
def try_gpu(i=0): #@save
"""Return gpu(i) if exists, otherwise return cpu()."""
return npx.gpu(i) if npx.num_gpus() >= i + 1 else npx.cpu()
def try_all_gpus(): #@save
"""Return all available GPUs, or [cpu()] if no GPU exists."""
devices = [npx.gpu(i) for i in range(npx.num_gpus())]
return devices if devices else [npx.cpu()]
import config
from model import Generator
from model import Discriminator
from mxnet import npx
from mxnet import gluon
from mxnet import init
from tqdm import tqdm
import mxnet as mx
import numpy as np
from mxnet.optimizer import Adam
from mxnet.gluon.data import DataLoader
from mxnet.gluon.loss import SigmoidBCELoss
from engine import train_generator
from engine import train_discriminator
device = npx.gpu() if npx.num_gpus() > 0 else npx.cpu()
gen = Generator()
gen.collect_params().initialize(init=init.Normal(sigma=0.02),
force_reinit=True,
ctx=device)
# noise = random.randn(1, 100, 1, 1)
# output = gen(noise)
# print(output.shape)
dis = Discriminator()
dis.collect_params().initialize(init=init.Normal(sigma=0.02),
force_reinit=True,
ctx=device)
# noise = random.randn(1, 3, 64, 64)
# output = dis(noise)
def try_gpu(i=0):
"""
Return gpu(i) if it exists, else return cpu()
"""
return npx.gpu(i) if npx.num_gpus() >= i + 1 else npx.cpu()
def try_all_gpus():
"""Return all available GPUs, or [cpu(),] if no GPU exists.
Copy from d2l library"""
ctxes = [npx.gpu(i) for i in range(npx.num_gpus())]
return ctxes if ctxes else [npx.cpu()]
