Comments (3)
The file in bin/ is a Python file. At the bottom, it looks like it is using the fire package to call the train_from_folder function in that same file. I'm guessing that using "--help" with the command will give you a list of the options, but you could also look at the arguments to the train_from_folder function as well. Let me know if that helps.
from stylegan2-pytorch.
The file in bin/ is a Python file.
Thank you! This makes things clearer!
I did not know that the file in bin/
was a text file: I assumed it might be some pre-compiled binary file.
from stylegan2-pytorch.
So, Python Fire is a library to generate command-line interfaces.
The list of command-line arguments, and their default values, are given by the train_from_folder()
function, as you mentioned:
def train_from_folder(
data = './data',
results_dir = './results',
models_dir = './models',
name = 'default',
new = False,
load_from = -1,
image_size = 128,
network_capacity = 16,
batch_size = 3,
gradient_accumulate_every = 5,
num_train_steps = 100000,
learning_rate = 2e-4,
num_workers = None,
generate = False,
num_image_tiles = 8
):
# [...]
from stylegan2-pytorch.
Related Issues (20)
- Parameters for generating faces HOT 1
- Performance on MNIST
- Out of memory after exactly 5024 iterations? HOT 1
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- Uneven GPU utilization.
- Trainning on images with one (single) channel HOT 1
- Bug: random_hflip function HOT 1
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- Bug: gradient_accumulate_contexts function HOT 1
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- /torch_utils/custom_ops.py - _find_compiler_bindir: incorrect Visual Studio Path
- Inconsistent evaluation of self.av HOT 2
- How to Train on a Single Image
- Examples on save_every and evaluate_every in README section needed.
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