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model: | ||
base_learning_rate: 4.5e-6 | ||
target: taming.models.vqgan.VQModel | ||
params: | ||
embed_dim: 256 | ||
n_embed: 1024 | ||
ddconfig: | ||
double_z: False | ||
z_channels: 256 | ||
resolution: 256 | ||
in_channels: 3 | ||
out_ch: 3 | ||
ch: 128 | ||
ch_mult: [ 1,1,2,2,4] # num_down = len(ch_mult)-1 | ||
num_res_blocks: 2 | ||
attn_resolutions: [16] | ||
dropout: 0.0 | ||
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lossconfig: | ||
target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator | ||
params: | ||
disc_conditional: False | ||
disc_in_channels: 3 | ||
disc_start: 10000 | ||
disc_weight: 0.8 | ||
codebook_weight: 1.0 | ||
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data: | ||
target: main.DataModuleFromConfig | ||
params: | ||
batch_size: 5 | ||
num_workers: 8 | ||
train: | ||
target: taming.data.custom.CustomTrain | ||
params: | ||
training_images_list_file: some/training.txt | ||
size: 256 | ||
validation: | ||
target: taming.data.custom.CustomTest | ||
params: | ||
test_images_list_file: some/test.txt | ||
size: 256 | ||
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import os | ||
import numpy as np | ||
import albumentations | ||
from torch.utils.data import Dataset | ||
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from taming.data.base import ImagePaths, NumpyPaths, ConcatDatasetWithIndex | ||
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class CustomBase(Dataset): | ||
def __init__(self, *args, **kwargs): | ||
super().__init__() | ||
self.data = None | ||
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def __len__(self): | ||
return len(self.data) | ||
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def __getitem__(self, i): | ||
example = self.data[i] | ||
return example | ||
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class CustomTrain(CustomBase): | ||
def __init__(self, size, training_images_list_file): | ||
super().__init__() | ||
with open(training_images_list_file, "r") as f: | ||
paths = f.read().splitlines() | ||
self.data = ImagePaths(paths=paths, size=size, random_crop=False) | ||
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class CustomTest(CustomBase): | ||
def __init__(self, size, test_images_list_file): | ||
super().__init__() | ||
with open(test_images_list_file, "r") as f: | ||
paths = f.read().splitlines() | ||
self.data = ImagePaths(paths=paths, size=size, random_crop=False) | ||
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