From 1e91bbb9e19ef26f70343a40acd4c074448c2b65 Mon Sep 17 00:00:00 2001
From: zwy <576825820@qq.com>
Date: Sat, 30 May 2020 00:02:11 +0800
Subject: [PATCH] update

---
 models/bicyclegan/bicyclegan.py | 4 ++--
 models/cyclegan/cyclegan.py     | 4 ++--
 models/esrgan/esrgan.py         | 2 +-
 models/unit/unit.py             | 4 ++--
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/models/bicyclegan/bicyclegan.py b/models/bicyclegan/bicyclegan.py
index 28ce787..4b15254 100644
--- a/models/bicyclegan/bicyclegan.py
+++ b/models/bicyclegan/bicyclegan.py
@@ -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):
diff --git a/models/cyclegan/cyclegan.py b/models/cyclegan/cyclegan.py
index cc6e907..56bc4e3 100644
--- a/models/cyclegan/cyclegan.py
+++ b/models/cyclegan/cyclegan.py
@@ -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
diff --git a/models/esrgan/esrgan.py b/models/esrgan/esrgan.py
index 0298059..46dd871 100644
--- a/models/esrgan/esrgan.py
+++ b/models/esrgan/esrgan.py
@@ -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
 # ----------
diff --git a/models/unit/unit.py b/models/unit/unit.py
index cd4f200..fb3ee47 100644
--- a/models/unit/unit.py
+++ b/models/unit/unit.py
@@ -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