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KOLMO_LIN.py
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KOLMO_LIN.py
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# borrowed @ francois-rozet
import h5py
import json
import torch
import math
import matplotlib.pyplot as plt
import numpy as np
import os
import pickle
import random
import seaborn as sns
import time
import wandb
from dawgz import job, schedule
from pathlib import Path
from tqdm import trange
from typing import *
from zuko.distributions import DiagNormal
from zuko.flows import Unconditional
from dasbi.inference.models import VPScoreLinear as NSE
from dasbi.networks.embedding import EmbedObs
from dasbi.simulators.sim_2D import LZ2D as sim
SCRATCH = os.environ.get("HOME", ".")
DATA = os.environ.get("SCRATCH", ".")
PATH = Path(SCRATCH) / "nse_2D/linear"
PATH.mkdir(parents=True, exist_ok=True)
fact = 5
N_grid = [2**i for i in range(5,6)]
Y_grid = [int(np.ceil(x/6)) for x in N_grid]
lN = len(N_grid)
window = 1
max_epochs = 2048
dp = {
8 : 2,
16 : 2,
32 : 2,
64 : 3,
128 : 3,
256 : 4
}
CONFIG = {
# Architecture
"embedding": [3]*lN,
"depth": [3]*lN,
"input_h": [64]*lN,
"N_ms": ["score2D_lin"]*lN,
# Training
# "epochs": [512]*lN,
"batch_size": [512]*lN,
"step_per_batch": [32]*lN,
"optimizer": ["AdamW"]*lN,
"learning_rate": [1e-4]*lN, # np.geomspace(1e-3, 1e-4).tolist(),
"weight_decay": [1e-4]*lN, # np.geomspace(1e-2, 1e-4).tolist(),
"scheduler": ["linear"]*lN, # , 'cosine', 'exponential'],
# Data
"points": N_grid,
"noise": [0.5]*lN,
"train_sim": [819]*lN,
"val_sim": [102]*lN,
"device": ['cuda']*lN,
# Test with assimilation window
"x_dim": [(1, 2, sp, sp) for sp in N_grid],
"y_dim": [(1, 2*window, spy, spy) for spy in Y_grid],
"y_dim_emb": [(1, 20, sp, sp) for sp in N_grid],
'obs_mask': [True]*lN, #+1 in y_dim
"observer_fp": [f"experiments/observer2D.pickle" for _ in N_grid],
}
def build(**config):
mod_args = {
"input_c": 2*config["y_dim_emb"][1], #try with better state !
"output_c": config["x_dim"][1],
"depth": config["depth"],
"input_hidden": config["input_h"],
"type": "2D",
'n_c': 4,
# "in_d":torch.tensor(config["x_dim"]).prod() + torch.tensor(config["y_dim_emb"]).prod(),
# 'out_d': torch.tensor(config["x_dim"]).prod()
}
observer = None
if config['obs_mask']:
with open(config["observer_fp"], "rb") as handle:
observer = pickle.load(handle)
return NSE(state_dim=config["x_dim"], targ_c=2*config["y_dim_emb"][1] - 5 - 2,
observer=observer, noise = config['noise'], **mod_args)
def process_sim(simulator):
MUX = simulator.data.mean(dim=(0, 1))
SIGMAX = simulator.data.std(dim=(0, 1))
MUY = simulator.obs.mean(dim=(0, 1))
SIGMAY = simulator.obs.std(dim=(0, 1))
MUT = simulator.time.mean(dim=(0, 1))
SIGMAT = simulator.time.std(dim=(0, 1))
simulator.data = (simulator.data - MUX) / SIGMAX
simulator.obs = (simulator.obs - MUY) / SIGMAY
simulator.time = (simulator.time - MUT) / SIGMAT
ret_ls = [MUX, SIGMAX, MUY, SIGMAY, MUT, SIGMAT]
ret_ls = [x.to(CONFIG['device'][0]) for x in ret_ls]
return ret_ls
def vorticity(x):
*batch, _, h, w = x.shape
y = x.reshape(-1, 2, h, w)
y = torch.nn.functional.pad(y, (1, 1, 1, 1), mode='circular')
du, = torch.gradient(y[:, 0], dim=-1)
dv, = torch.gradient(y[:, 1], dim=-2)
y = du - dv
y = y[:, 1:-1, 1:-1]
y = y.reshape(*batch, h, w)
return y
def coarsen(x, r=2):
*batch, h, w = x.shape
x = x.reshape(*batch, h // r, r, w // r, r)
x = x.mean(axis=(-3, -1))
return x
def load_data(file):
filep = Path(DATA) / file
with h5py.File(filep, mode='r') as f:
data = f['x'][:]
data = torch.from_numpy(data)
# data = coarsen(data)
return data
@job(array=fact*lN, cpus=3, gpus=1, ram="32GB", time="2-12:00:00")
def Score_train(i: int):
# config = {key: random.choice(values) for key, values in CONFIG.items()}
config = {key : values[i%lN] for key,values in CONFIG.items()}
with open(config["observer_fp"], "rb") as handle:
observer = pickle.load(handle)
gr = 'step' if window == 1 else 'assim'
run = wandb.init(project="dasbi", config=config, group=f"LZ2D_{gr}")
runpath = PATH / f"runs/{run.name}_{run.id}"
runpath.mkdir(parents=True, exist_ok=True)
with open(runpath / "config.json", "w") as f:
json.dump(config, f)
# Data
# tmax = 50
traj_len = 64
times = torch.arange(traj_len).float()
simt = sim(N=config["points"], M=config["points"], noise=config["noise"])
simt.init_observer(observer)
simt.data = load_data('train.h5')
simt.obs = simt.observe()
simt.time = times[None,...].repeat(config["train_sim"],1)
mx, sx, _, _, mt, st = process_sim(simt)
mx = mx.cpu()
sx = sx.cpu()
mt = mt.cpu()
st = st.cpu()
simv = sim(N=config["points"], M=config["points"], noise=config["noise"])
simv.init_observer(observer)
simv.data = load_data('valid.h5')
simv.obs = simv.observe()
simv.time = times[None,...].repeat(config["val_sim"],1)
mvx, svx, mvy, svy, _, _ = process_sim(simv)
col = sns.color_palette("icefire", as_cmap=True)
gt, obs = simv.data[0,traj_len//2].cuda(),\
simv.obs[0,traj_len//2].cuda()
gt = vorticity(gt[None,...]).squeeze()
plt.imshow(gt.cpu(), cmap=col)
plt.title('GT')
run.log({"GT state" : wandb.Image(plt)})
plt.close()
obs = vorticity(obs[None,...]).squeeze()
plt.imshow(obs.cpu(), cmap=col)
plt.title('GT')
run.log({"GT obs" : wandb.Image(plt)})
plt.close()
# Network
conv_nse = build(**config).cuda()
# wandb.watch(conv_npe, log = 'all', log_freq = 128)
size = sum(param.numel() for param in conv_nse.parameters())
run.config.num_param = size
# Training
# epochs = config["epochs"]
batch_size = config["batch_size"]
step_per_batch = config["step_per_batch"]
best = 1000
prev_loss = best
time_buff = 1024
count = 0
## Optimizer
if config["optimizer"] == "AdamW":
optimizer = torch.optim.AdamW(
conv_nse.parameters(),
lr=config["learning_rate"],
weight_decay=config["weight_decay"],
)
else:
raise ValueError()
if config["scheduler"] == "linear":
lr = lambda t: 1 - (t / max_epochs)
# elif config["scheduler"] == "cosine":
# lr = lambda t: (1 + math.cos(math.pi * t / epochs)) / 2
# elif config["scheduler"] == "exponential":
# lr = lambda t: math.exp(-7 * (t / epochs) ** 2)
else:
raise ValueError()
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lr)
## Loop
# for epoch in trange(epochs, ncols=88):
epoch = 0
while True:
losses_train = []
losses_val = []
### Train
i = np.random.choice(
len(simt.data),
size=batch_size,
replace=False
)
start = time.time()
simt.data = simt.data*sx + mx
simt.obs = simt.observe()
simt.time = simt.time*st + mt
process_sim(simt)
for xb, yb, tb in zip(
simt.data[i].cuda(), simt.obs[i].cuda(), simt.time[i].cuda()
):
subset_data = np.random.choice(
np.arange(window - 1, traj_len),#because window of 10
size=step_per_batch,
replace=False,
)
sh_y = yb.shape
x, y, t = (
xb[subset_data],
torch.cat([(yb[i - window + 1 : i + 1].reshape(window*2, sh_y[-2], sh_y[-1])).unsqueeze(0) for i in subset_data], dim=0),
tb[subset_data],
)
# x = x[:, None, ...]
optimizer.zero_grad()
l = conv_nse.loss(x, t)
l.backward()
norm = torch.nn.utils.clip_grad_norm_(conv_nse.parameters(), 1)
if torch.isfinite(norm):
optimizer.step()
losses_train.append(l.detach())
end = time.time()
### Valid
i = np.random.choice(
len(simv.data),
size=batch_size//8,
replace=False,
)
with torch.no_grad():
for xb, yb, tb in zip(
simv.data[i].cuda(), simv.obs[i].cuda(), simv.time[i].cuda()
):
subset_data = np.random.choice(
np.arange(window - 1, traj_len),
size=step_per_batch,
replace=False,
)
sh_y = yb.shape
x, y, t = (
xb[subset_data],
torch.cat([(yb[i - window + 1 : i + 1].reshape(window*2, sh_y[-2], sh_y[-1])).unsqueeze(0) for i in subset_data], dim=0),
tb[subset_data],
)
# x = x[:, None, ...]
losses_val.append(conv_nse.loss(x, t))
gt, obs, tm = simv.data[0,traj_len//2].cuda(),\
(simv.obs[0,traj_len//2-window+1:traj_len//2 + 1].reshape(2*window, sh_y[-2], sh_y[-1])).cuda(),\
simv.time[0,traj_len//2].cuda()
# gt = vorticity(gt[None,...]).squeeze()
# plt.imshow(gt.cpu(), cmap=col)
# plt.title('GT')
# run.log({"GT state" : wandb.Image(plt)})
# plt.close()
# plt.imshow(obs[-1], cmap=col)
# plt.title('GT obs')
# run.log({"GT observation" : wandb.Image(plt)})
# plt.close()
if epoch %10 == 0:
samp = conv_nse.sample(obs[None,...], tm[None,...], 1, [mvx,svx,mvy,svy]).squeeze(0)
obs_samp = simv.observe(samp.cpu())
samp = vorticity(samp).squeeze()
plt.imshow(samp.cpu(), cmap=col)
plt.title('SAMPLE')
run.log({"Sampled state" : wandb.Image(plt)})
plt.close()
obs_samp = vorticity(obs_samp).squeeze()
plt.imshow(obs_samp.cpu(), cmap=col)
plt.title('SAMPLE')
run.log({"Sampled obs" : wandb.Image(plt)})
plt.close()
# plt.imshow(gt, cmap=col)
# plt.title('GT')
# run.log({"GT state" : wandb.Image(plt)})
# plt.close()
### Logs
loss_train = torch.stack(losses_train).mean().item()
loss_val = torch.stack(losses_val).mean().item()
run.log(
{
"loss": loss_train,
"loss_val": loss_val,
"time_epoch": (end - start),
"lr": optimizer.param_groups[0]["lr"],
"plateau_buffer": count,
"epoch": epoch
}
)
### Checkpoint
if (prev_loss - loss_val) > 1e-5:
prev_loss = loss_val
torch.save(
conv_nse.state_dict(),
runpath / f"checkpoint.pth",
)
count = 0
else:
count += 1
epoch += 1
if count == time_buff or epoch == max_epochs:
break
scheduler.step()
run.finish()
if __name__ == "__main__":
schedule(
Score_train,
name="LZ2D_train",
backend="slurm",
settings={"export": "ALL"},
env=[
"conda activate DASBI",
"export WANDB_SILENT=true",
],
)