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JGAN
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Merge branch 'master' of https://github.com/Jittor/gan-jittor

This commit is contained in:
Gword 2020-05-30 00:13:41 +08:00
parent 5a4485f1da 1e91bbb9e1
commit 466c469328
4 changed files with 7 additions and 7 deletions
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4
models/bicyclegan/bicyclegan.py
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@ -48,9 +48,9 @@ optimizer_G = nn.Adam(encoder.parameters() + generator.parameters(), lr=opt.lr,
optimizer_D_VAE = nn.Adam(D_VAE.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D_LR = nn.Adam(D_LR.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
dataloader = ImageDataset("../data/%s" % opt.dataset_name, input_shape).set_attrs(batch_size=opt.batch_size, shuffle=False, num_workers=opt.n_cpu)
dataloader = ImageDataset("../../data/%s" % opt.dataset_name, input_shape).set_attrs(batch_size=opt.batch_size, shuffle=False, num_workers=opt.n_cpu)
valdataloader = ImageDataset("../data/%s" % opt.dataset_name, input_shape, mode="val").set_attrs(batch_size=8, shuffle=False, num_workers=1)
valdataloader = ImageDataset("../../data/%s" % opt.dataset_name, input_shape, mode="val").set_attrs(batch_size=8, shuffle=False, num_workers=1)
def reparameterization(mu, logvar):

4
models/cyclegan/cyclegan.py
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@ -69,9 +69,9 @@ transform_ = [
]
# Training data loader
dataloader = ImageDataset("../data/%s" % opt.dataset_name, transform_=transform_, unaligned=True).set_attrs(batch_size=opt.batch_size, shuffle=True, num_workers=opt.n_cpu)
dataloader = ImageDataset("../../data/%s" % opt.dataset_name, transform_=transform_, unaligned=True).set_attrs(batch_size=opt.batch_size, shuffle=True, num_workers=opt.n_cpu)
val_dataloader = ImageDataset("../data/%s" % opt.dataset_name, transform_=transform_, unaligned=True, mode="test").set_attrs(batch_size=5, shuffle=True, num_workers=1)
val_dataloader = ImageDataset("../../data/%s" % opt.dataset_name, transform_=transform_, unaligned=True, mode="test").set_attrs(batch_size=5, shuffle=True, num_workers=1)
import cv2
def save_image(img, path, nrow=10, padding=5):
N,C,W,H = img.shape

2
models/esrgan/esrgan.py
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@ -91,7 +91,7 @@ criterion_pixel = nn.L1Loss()
# Optimizers
optimizer_G = nn.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D = nn.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
dataloader = ImageDataset("../data/%s" % opt.dataset_name, hr_shape=hr_shape).set_attrs(batch_size=opt.batch_size, shuffle=True, num_workers=opt.n_cpu)
dataloader = ImageDataset("../../data/%s" % opt.dataset_name, hr_shape=hr_shape).set_attrs(batch_size=opt.batch_size, shuffle=True, num_workers=opt.n_cpu)
# ----------
# Training
# ----------

4
models/unit/unit.py
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@ -79,9 +79,9 @@ transform_ = [
transform.ImageNormalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
]
dataloader = ImageDataset("../data/%s" % opt.dataset_name, transforms_=transform_, unaligned=True).set_attrs(batch_size=opt.batch_size, shuffle=True, num_workers=opt.n_cpu)
dataloader = ImageDataset("../../data/%s" % opt.dataset_name, transforms_=transform_, unaligned=True).set_attrs(batch_size=opt.batch_size, shuffle=True, num_workers=opt.n_cpu)
val_dataloader = ImageDataset("../data/%s" % opt.dataset_name, transforms_=transform_, unaligned=True, mode="test").set_attrs(batch_size=5, shuffle=True, num_workers=1)
val_dataloader = ImageDataset("../../data/%s" % opt.dataset_name, transforms_=transform_, unaligned=True, mode="test").set_attrs(batch_size=5, shuffle=True, num_workers=1)
import cv2