def test_dcgan(self):
# dcgan is flaky on some seeds, see:
# https://github.com/ProjectToffee/onnx/pull/70
torch.manual_seed(1)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(1)
netD = dcgan._netD(1)
netD.apply(dcgan.weights_init)
input = Variable(torch.randn(BATCH_SIZE, 3, dcgan.imgsz, dcgan.imgsz))
self.run_model_test(netD,
train=False,
batch_size=BATCH_SIZE,
input=input)
netG = dcgan._netG(1)
netG.apply(dcgan.weights_init)
state_dict = model_zoo.load_url(model_urls['dcgan_b'], progress=False)
# state_dict = model_zoo.load_url(model_urls['dcgan_f'], progress=False)
noise = Variable(torch.randn(BATCH_SIZE, dcgan.nz, 1, 1).normal_(0, 1))
self.run_model_test(netG,
train=False,
batch_size=BATCH_SIZE,
input=noise,
state_dict=state_dict,
rtol=1e-2,
atol=1e-6)
def test_dcgan(self):
# note, could have more than 1 gpu
netG = _netG(1)
netG.apply(weights_init)
netD = _netD(1)
netD.apply(weights_init)
input = torch.Tensor(bsz, 3, imgsz, imgsz)
noise = torch.Tensor(bsz, nz, 1, 1)
fixed_noise = torch.Tensor(bsz, nz, 1, 1).normal_(0, 1)
fixed_noise = Variable(fixed_noise)
netD.zero_grad()
inputv = Variable(input)
self.exportTest(toC(netD), toC(inputv), "dcgan-netD")
noise.resize_(bsz, nz, 1, 1).normal_(0, 1)
noisev = Variable(noise)
self.exportTest(toC(netG), toC(noisev), "dcgan-netG")
def test_dcgan(self):
# dcgan is flaky on some seeds, see:
# https://github.com/ProjectToffee/onnx/pull/70
torch.manual_seed(1)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(1)
netD = dcgan._netD(1)
netD.apply(dcgan.weights_init)
input = Variable(torch.randn(BATCH_SIZE, 3, dcgan.imgsz, dcgan.imgsz))
self.run_model_test(netD, train=False, batch_size=BATCH_SIZE,
input=input)
netG = dcgan._netG(1)
netG.apply(dcgan.weights_init)
state_dict = model_zoo.load_url(model_urls['dcgan_b'], progress=False)
# state_dict = model_zoo.load_url(model_urls['dcgan_f'], progress=False)
noise = Variable(
torch.randn(BATCH_SIZE, dcgan.nz, 1, 1).normal_(0, 1))
self.run_model_test(netG, train=False, batch_size=BATCH_SIZE,
input=noise, state_dict=state_dict, rtol=1e-2, atol=1e-6)
def test_dcgan_netG(self):
netG = _netG(1)
netG.apply(weights_init)
input = Variable(torch.Tensor(bsz, nz, 1, 1).normal_(0, 1))
self.exportTest(toC(netG), toC(input))
def test_dcgan_netG(self):
netG = _netG(1)
netG.apply(weights_init)
input = Variable(torch.Tensor(bsz, nz, 1, 1).normal_(0, 1))
self.exportTest(toC(netG), toC(input))