Using xtal2png with denoising_diffusion_pytorch
[1]:
%pip install xtal2png mp-time-split denoising_diffusion_pytorch
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
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Installing collected packages: six, ruamel.yaml.clib, future, uncertainties, sympy, spglib, scipy, ruamel.yaml, requests, monty, pymatgen, pint, latexcodec, pybtex, matminer, kaleido, ema-pytorch, einops, accelerate, xtal2png, mp-time-split, denoising-diffusion-pytorch
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.28.1 which is incompatible.
google-colab 1.0.0 requires six~=1.15.0, but you have six 1.16.0 which is incompatible.
datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.
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Data type cannot be displayed: application/vnd.colab-display-data+json
[2]:
from os import path
from pathlib import Path
from uuid import uuid4
from denoising_diffusion_pytorch import GaussianDiffusion, Trainer, Unet
from mp_time_split.core import MPTimeSplit
from xtal2png.core import XtalConverter
MPTimeSplit
Get the first fold of the Materials Project time split benchmark.
[3]:
dummy = True #@param {type:"boolean"}
mpt = MPTimeSplit()
mpt.load(dummy=dummy)
fold = 4 # last fold
train_inputs, val_inputs, train_outputs, val_outputs = mpt.get_train_and_val_data(fold)
Reading file /usr/local/lib/python3.7/dist-packages/mp_time_split/utils/mp_dummy_time_summary.json.gz: 0it [00:00, ?it/s]
Decoding objects from /usr/local/lib/python3.7/dist-packages/mp_time_split/utils/mp_dummy_time_summary.json.gz: 100%|##########| 11/11 [00:00<00:00, 830.17it/s]
Convert via xtal2png
[36]:
channels = 3#@param ["1", "3"] {type:"raw", allow-input: true}
[37]:
data_path = path.join("data", "preprocessed", "mp-time-split", f"fold={fold}")
xc = XtalConverter(
save_dir=data_path,
encode_cell_type="primitive_standard",
decode_cell_type="primitive_standard",
channels=channels,
)
xc.xtal2png(train_inputs.tolist())
100%|██████████| 7/7 [00:00<00:00, 16.84it/s]
[37]:
[<PIL.Image.Image image mode=RGB size=64x64 at 0x7F8F514D3C10>,
<PIL.Image.Image image mode=RGB size=64x64 at 0x7F8F514D3A50>,
<PIL.Image.Image image mode=RGB size=64x64 at 0x7F8F514D3CD0>,
<PIL.Image.Image image mode=RGB size=64x64 at 0x7F8F514D3C50>,
<PIL.Image.Image image mode=RGB size=64x64 at 0x7F8EB437C710>,
<PIL.Image.Image image mode=RGB size=64x64 at 0x7F8F514CB5D0>,
<PIL.Image.Image image mode=RGB size=64x64 at 0x7F8F514CBE90>]
Denoising Diffusion Probabilistic Model Training
[38]:
if dummy:
train_num_steps = 100
timesteps = 10
train_batch_size = 2
else:
train_num_steps = 700000
timesteps = 1000
train_batch_size = 32
model = Unet(dim=64, dim_mults=(1, 2, 4, 8), channels=channels).cuda()
diffusion = GaussianDiffusion(
model, channels=channels, image_size=64, timesteps=timesteps, loss_type="l1"
).cuda()
print("train_batch_size: ", train_batch_size)
results_folder = path.join(
"data", "interim", "denoising_diffusion_pytorch", f"fold={fold}", str(uuid4())[0:4]
)
Path(results_folder).mkdir(exist_ok=True, parents=True)
trainer = Trainer(
diffusion,
data_path,
train_batch_size=train_batch_size,
train_lr=2e-5,
train_num_steps=train_num_steps, # total training steps
gradient_accumulate_every=2, # gradient accumulation steps
ema_decay=0.995, # exponential moving average decay
amp=True, # turn on mixed precision
augment_horizontal_flip=False,
results_folder=results_folder,
)
trainer.train()
train_batch_size: 2
training complete
Unconditionally Sample New Images
Note that the results will be non-converged/without much meaning if using the dummy dataset and training parameters.
[40]:
from PIL import Image
sampled_images = diffusion.sample(batch_size=100)
arr = sampled_images[0].cpu().numpy()
mode = "RGB" if channels == 3 else "L"
if mode == "RGB":
arr = arr.transpose(1, 2, 0)
Image.fromarray(arr, mode=mode)
[40]:
