From 7e93579696a31704103fee0f5854cbcc697db81a Mon Sep 17 00:00:00 2001
From: Gonzalo Martin Garcia <gonzalomartingarcia0@gmail.com>
Date: Sat, 22 Jul 2023 15:17:22 +0200
Subject: [PATCH] forgot to update no transform for the celebAHQ dataloader
---
dataloader/load.py | 20 ++++++++------------
1 file changed, 8 insertions(+), 12 deletions(-)
diff --git a/dataloader/load.py b/dataloader/load.py
index 67f0fce..7fba61c 100644
--- a/dataloader/load.py
+++ b/dataloader/load.py
@@ -71,17 +71,17 @@ class UnconditionalDataset_LHQ(Dataset):
# Dataset used when training on CelebAHQ.
class UnconditionalDataset_CelebAHQ(Dataset):
- def __init__(self,fpath,img_size,train,frac =0.8,skip_first_n = 0,ext = ".png",transform=True ):
+ def __init__(self,fpath,img_size,train,frac =0.8,skip_first_n = 0,ext = ".png",transform=True):
"""
Args:
- fpath (string): Path to the folder where images are stored
- img_size (int): size of output image img_size=height=width
- ext (string): type of images used(eg .png)
+ fpath (string): Path to the folder where images are stored
+ img_size (int): Size of output image img_size=height=width
+ ext (string): Type of images used(eg .png)
transform (Bool): Image augmentation for diffusion model
- skip_first_n: skips the first n values. Usefull for datasets that are sorted by increasing Likeliehood
- train (Bool): Choose dataset to be either train set or test set. frac(float) required
- frac (float): value within (0,1] (seeded)random shuffles dataset, then divides into train and test set.
- """
+ skip_first_n: Skips the first n values. Usefull for datasets that are sorted by increasing Likeliehood
+ train (Bool): Choose dataset to be either train set or test set. frac(float) required
+ frac (float): Value within (0,1] (seeded)random shuffles dataset, then divides into train and test set.
+ """
# they provide a fixed train and validation split
if train:
fpath = os.path.join(fpath, 'train')
@@ -96,7 +96,6 @@ class UnconditionalDataset_CelebAHQ(Dataset):
self.df = df[df["Filepath"].str.endswith(ext)]
if transform:
- # for training
intermediate_size = 137
theta = np.pi/4 -np.arccos(intermediate_size/(np.sqrt(2)*img_size)) #Check dataloading.ipynb in analysis-depot for more details
@@ -107,7 +106,6 @@ class UnconditionalDataset_CelebAHQ(Dataset):
transforms.RandomHorizontalFlip(p=0.5),
transforms.Normalize(mean=(0.5,0.5,0.5),std=(0.5,0.5,0.5))])
-
transform_flip = transforms.Compose([transforms.ToTensor(),
transforms.Resize(img_size, antialias=True),
transforms.RandomHorizontalFlip(p=0.5),
@@ -115,9 +113,7 @@ class UnconditionalDataset_CelebAHQ(Dataset):
self.transform = transforms.RandomChoice([transform_rotate_flip,transform_flip])
else :
- # for evaluation
self.transform = transforms.Compose([transforms.ToTensor(),
- transforms.Lambda(lambda x: (x * 255).type(torch.uint8)),
transforms.Resize(img_size)])
def __len__(self):
--
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