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train_fast.py
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train_fast.py
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from pathlib import Path
from itertools import count
import pickle
import sys
import jax
import jax.numpy as np
from jax import jit, value_and_grad
from jax_utils import key, vsplit
import wandb
import optax
import models
run_name = "wrn34g-v9-r300"
use_wandb = True
save_checkpoints = 10
label_pair = (9, 4)
batch_size = 16
from neural_tangents import stax
init_fn, apply_fn, kernel_fn = models.WideResnet(block_size=4, k=5, num_classes=1)
root = Path(".")
checkpoint_dir = root / "output" / "checkpoints" / run_name
if not checkpoint_dir.exists():
checkpoint_dir.mkdir()
def loss(params, batch):
inputs, targets = batch
outputs = apply_fn(params, inputs).ravel()
return ((outputs - targets) ** 2).sum(), outputs
def update(i, opt_state, params, batch):
inputs, targets = batch
(l, outputs), g = value_and_grad(loss, has_aux=True)(params, batch)
updates, opt_state = tx.update(g, opt_state, params)
correct = np.sum(np.sign(outputs) == targets)
return l, correct, opt_state, optax.apply_updates(params, updates)
def accuracy(opt_state, params, batch):
inputs, targets = batch
l, outputs = loss(params, batch)
correct = np.sum(np.sign(outputs) == targets)
return l, correct
lr = 1e-4 / batch_size
_, params = init_fn(key, input_shape=(1, 32, 32, 3))
tx = optax.sgd(lr, momentum=0.9)
opt_state = tx.init(params)
data_file = Path("output/X-94-1xs-perm.npy")
n, m = 5000, 1000
X = np.load(data_file)
x_train_c, x_train_p, x_train_pp, x_test_c, x_test_p, x_test_pp = vsplit(
X, n, n, n, m, m, m
)
Xd = np.vstack([x_train_c, x_train_pp])
Yd = np.hstack([np.full(len(x_train_c), 1), np.full(len(x_train_pp), -1)])
# Xp = x_train_p
# Yp = np.full(len(x_train_p), 1)
Xt = np.vstack([x_test_c, x_test_pp])
Yt = np.hstack([np.full(len(x_test_c), 1), np.full(len(x_test_pp), -1)])
Xa = x_test_p
Ya = np.full(len(x_test_p), 1)
s, t = 8000, 0
D = np.s_[(2 * n - s) // 2 : -(2 * n - s) // 2 or None]
Xd, Yd = Xd[D], Yd[D]
# poison_file = "output/wrn34g-1xs/ebd-v9-10-tr2.2.npy"
# Xp = np.load(poison_file)
poison_file = None
# Xp = np.array([]).reshape(0, 32, 32, 3)
Xp = x_train_p[:300]
# mix = 32
# Xp = 1/mix * Xp + (1 - 1/mix) * Xd[s//2:][:10]
Yp = np.ones(len(Xp))
eps = len(Xp)
Xdp = np.vstack([Xd, Xp])
Ydp = np.hstack([Yd, Yp])
ttx = Xt.reshape(-1, 100, *Xt.shape[1:])
tty = Yt.reshape(-1, 100)
tax = Xa.reshape(-1, 100, *Xa.shape[1:])
tay = Ya.reshape(-1, 100)
@jit
def iter(epoch, params, opt_state, key):
key, subkey = jax.random.split(key)
P = jax.random.permutation(subkey, np.arange(len(Xdp)))
tdx = Xdp[P][: len(Xdp) - len(Xdp) % batch_size].reshape(-1, batch_size, *Xdp.shape[1:])
tdy = Ydp[P][: len(Xdp) - len(Xdp) % batch_size].reshape(-1, batch_size)
def train_scanner(carry, xy):
opt_state, params = carry
l, correct, opt_state, params = update(epoch, opt_state, params, xy)
return (opt_state, params), (l, correct)
(opt_state, params), (l, correct) = jax.lax.scan(
train_scanner, (opt_state, params), (tdx, tdy)
)
avg_loss, avg_acc = l.sum(), correct.sum()
leftovers = (
Xdp[P][-(len(Xdp) % batch_size) or len(Xdp):],
Ydp[P][-(len(Xdp) % batch_size) or len(Xdp):]
)
l, correct, opt_state, params = update(epoch, opt_state, params, leftovers)
avg_loss += l
avg_acc += correct
avg_loss /= len(Xdp)
avg_acc /= len(Xdp)
def acc_mapper(xy):
return accuracy(opt_state, params, xy)
l, correct = jax.lax.map(acc_mapper, (ttx, tty))
avg_tloss = l.sum() / len(Xt)
avg_tacc = correct.sum() / len(Xt)
l, correct = jax.lax.map(acc_mapper, (tax, tay))
avg_ploss = l.sum() / len(Xa)
avg_pacc = correct.sum() / len(Xa)
if eps > 0:
p_pred = apply_fn(params, Xp)
else:
p_pred = np.zeros(1)
return (params, opt_state, key), (
avg_loss,
avg_acc,
avg_tloss,
avg_tacc,
avg_ploss,
avg_pacc,
p_pred,
)
# if __name__ == "__main__":
# if use_wandb:
# wandb.init(
# project="ntk-backdoor",
# entity="jhayase",
# settings=wandb.Settings(start_method="fork"),
# )
# wandb.run.name = run_name
# print(f"Running {wandb.run.name}")
# wandb.config.update(
# {
# "learning_rate": lr,
# "batch_size": batch_size,
# "eps": eps,
# "dataset": "cifar10",
# "source_label": label_pair[0],
# "target_label": label_pair[1],
# "optimizer": "sgd",
# "poison_type": "grad_opt",
# "poison_trigger": "periodic",
# "poison_source": poison_file,
# "model": "wrn34g-5x",
# "float_bits": 64 if jax.config.FLAGS.jax_enable_x64 else 32,
# "save_checkpoints": save_checkpoints,
# }
# )
# key = jax.random.PRNGKey(0)
# for epoch in count(0):
# (params, opt_state, key), (
# avg_loss,
# avg_acc,
# avg_tloss,
# avg_tacc,
# avg_ploss,
# avg_pacc,
# p_pred,
# ) = iter(epoch, params, opt_state, key)
# print(
# f"{run_name}: {epoch:04d}, "
# f"tr: ({avg_loss:.07f}, {avg_acc:.04f}), "
# f"te: ({avg_tloss:.05f}, {avg_tacc:.04f}), "
# f"p: ({avg_ploss:.05f}, {avg_pacc:.03f}), "
# f"{p_pred.mean():+.06f}"
# )
# if use_wandb:
# wandb.log(
# {
# "train_loss": avg_loss,
# "train_acc": avg_acc,
# "test_loss": avg_tloss,
# "test_acc": avg_tacc,
# "poison_loss": avg_ploss,
# "poison_acc": avg_pacc,
# "poison_pred": float(p_pred.mean()),
# }
# )
# if save_checkpoints and epoch % save_checkpoints == 0:
# pickle.dump(params, open(checkpoint_dir / f"params_{epoch:06d}.pkl", "wb"))
# pickle.dump(
# {"epoch": epoch, "opt_state": opt_state},
# open(checkpoint_dir / "state.pkl", "wb"),
# )
# sys.stdout.flush()
# if avg_loss < 1e-6:
# break