# Written by Francois Fleuret <francois@fleuret.org>
-import math, sys, argparse, time, tqdm, os, datetime
+import math, sys, argparse, time, tqdm, os, datetime, warnings
import torch, torchvision
from torch import nn
########################################
-parser.add_argument("--nb_epochs", type=int, default=25)
+parser.add_argument("--nb_epochs", type=int, default=50)
parser.add_argument("--batch_size", type=int, default=None)
+parser.add_argument("--physical_batch_size", type=int, default=None)
+
parser.add_argument("--nb_train_samples", type=int, default=None)
parser.add_argument("--nb_test_samples", type=int, default=None)
parser.add_argument("--no_checkpoint", action="store_true", default=False)
-parser.add_argument("--overwrite_results", action="store_true", default=False)
+parser.add_argument("--resume", action="store_true", default=False)
parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
parser.add_argument("--snake_length", type=int, default=200)
+##############################
+# ByHeart options
+
+parser.add_argument("--byheart_separation", type=int, default=1)
+
##############################
# Stack options
parser.add_argument("--stack_nb_digits", type=int, default=3)
-parser.add_argument("--stack_fraction_values_for_train", type=float, default=0.75)
+parser.add_argument("--stack_fraction_values_for_train", type=float, default=None)
##############################
# Expr options
try:
os.mkdir(args.result_dir)
except FileExistsError:
- if not args.overwrite_results:
+ if not args.resume:
print(f"result directory {args.result_dir} already exists")
exit(1)
######################################################################
+if args.physical_batch_size is None:
+ args.physical_batch_size = args.batch_size
+else:
+ assert args.batch_size % args.physical_batch_size == 0
+
+assert args.nb_train_samples % args.batch_size == 0
+assert args.nb_test_samples % args.batch_size == 0
+
if args.task == "file":
assert (
args.filetask_train_file is not None and args.filetask_test_file is not None
args.filetask_test_file,
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
shuffle=True,
device=device,
)
elif args.task == "byheart":
task = tasks.SandBox(
- problem=problems.ProblemByHeart(),
+ problem=problems.ProblemByHeart(separation=args.byheart_separation),
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
logger=log_string,
device=device,
)
problem=problems.ProblemLearnOperator(),
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
logger=log_string,
device=device,
)
problem=problems.ProblemGuessOperator(),
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
logger=log_string,
device=device,
)
problem=problems.ProblemTwoTargets(),
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
logger=log_string,
device=device,
)
problem=problems.ProblemMemory(),
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
logger=log_string,
device=device,
)
),
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
logger=log_string,
device=device,
)
problem=problems.ProblemAddition(),
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
logger=log_string,
device=device,
)
task = tasks.PicoCLVR(
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
height=args.picoclvr_height,
width=args.picoclvr_width,
nb_colors=args.picoclvr_nb_colors,
task = tasks.MNIST(
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
device=device,
)
task = tasks.Maze(
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
height=args.maze_height,
width=args.maze_width,
nb_walls=args.maze_nb_walls,
- device=device,
+ device="cpu",
)
elif args.task == "snake":
task = tasks.Snake(
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
height=args.snake_height,
width=args.snake_width,
nb_colors=args.snake_nb_colors,
task = tasks.Stack(
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
logger=log_string,
nb_steps=args.stack_nb_steps,
nb_stacks=args.stack_nb_stacks,
sequence_length=args.expr_sequence_length,
operand_max=args.expr_operand_max,
result_max=args.expr_result_max,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
device=device,
)
task = tasks.RPL(
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
nb_starting_values=args.rpl_nb_starting_values,
max_input=args.rpl_max_input,
prog_len=args.rpl_prog_len,
task = tasks.Grid(
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
size=args.grid_size,
fraction_play=args.grid_fraction_play,
logger=log_string,
task = tasks.QMLP(
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
result_dir=args.result_dir,
logger=log_string,
device=device,
task = tasks.Greed(
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
+ batch_size=args.physical_batch_size,
height=args.greed_height,
width=args.greed_width,
T=args.greed_T,
##############################
-nb_samples_seen = 0
-
if nb_epochs_finished >= args.nb_epochs:
task.produce_results(
n_epoch=nb_epochs_finished,
model.train()
- nb_train_samples, acc_train_loss = 0, 0.0
+ nb_train_samples, acc_train_loss_ar, acc_train_loss_ae = 0, 0.0, 0.0
for input in task.batches(split="train"):
input = input.to(device)
- output = model(mygpt.BracketedSequence(input)).x
- loss = F.cross_entropy(output.transpose(1, 2), input)
- acc_train_loss += loss.item() * input.size(0)
+
+ if nb_train_samples % args.batch_size == 0:
+ optimizer.zero_grad()
+
+ if args.autoencoder_weight > 0:
+ bs_ar, bs_ae = model(mygpt.BracketedSequence(input), autoencoder=True)
+ output_ar, output_ae = bs_ar.x, bs_ae.x
+ loss_ar = F.cross_entropy(output_ar.transpose(1, 2), input)
+ loss_ae = F.cross_entropy(output_ae[:, 1:].transpose(1, 2), input[:, :-1])
+ else:
+ output = model(mygpt.BracketedSequence(input)).x
+ loss_ar = F.cross_entropy(output.transpose(1, 2), input)
+ loss_ae = loss_ar.new_full((1,), 0.0)
+
+ acc_train_loss_ar += loss_ar.item() * input.size(0)
+ acc_train_loss_ae += loss_ae.item() * input.size(0)
+
nb_train_samples += input.size(0)
- nb_samples_seen += input.size(0)
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
+ (loss_ar + args.autoencoder_weight * loss_ae).backward()
+
+ if nb_train_samples % args.batch_size == 0:
+ optimizer.step()
with torch.autograd.no_grad():
model.eval()
- nb_test_samples, acc_test_loss = 0, 0.0
+ nb_test_samples, acc_test_loss_ar, acc_test_loss_ae = 0, 0.0, 0.0
+ nb_samples_accumulated = 0
for input in task.batches(split="test"):
input = input.to(device)
- output = model(mygpt.BracketedSequence(input)).x
- loss = F.cross_entropy(output.transpose(1, 2), input)
- acc_test_loss += loss.item() * input.size(0)
+ if args.autoencoder_weight > 0:
+ bs_ar, bs_ae = model(mygpt.BracketedSequence(input), autoencoder=True)
+ output_ar, output_ae = bs_ar.x, bs_ae.x
+ loss_ae = F.cross_entropy(
+ output_ae[:, 1:].transpose(1, 2), input[:, :-1]
+ )
+ acc_test_loss_ae += loss_ae.item() * input.size(0)
+ else:
+ bs_ar = model(mygpt.BracketedSequence(input))
+ output_ar = bs_ar.x
+
+ loss_ar = F.cross_entropy(output_ar.transpose(1, 2), input)
+
+ acc_test_loss_ar += loss_ar.item() * input.size(0)
+
nb_test_samples += input.size(0)
- train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
- test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
+ train_ar_perplexity = math.exp(min(100, acc_train_loss_ar / nb_train_samples))
+ test_ar_perplexity = math.exp(min(100, acc_test_loss_ar / nb_test_samples))
log_string(
- f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
+ f"perplexity_ar {n_epoch} train_set {train_set_perplexity} train_prediction {train_ar_perplexity} test_prediction {test_ar_perplexity}"
)
+ if args.autoencoder_weight > 0:
+ train_ae_perplexity = math.exp(
+ min(100, acc_train_loss_ae / nb_train_samples)
+ )
+ test_ae_perplexity = math.exp(min(100, acc_test_loss_ae / nb_test_samples))
+
+ log_string(
+ f"perplexity_ae {n_epoch} train_set {train_set_perplexity} train_prediction {train_ae_perplexity} test_prediction {test_ae_perplexity}"
+ )
+
task.produce_results(
n_epoch=n_epoch,
model=model,
projects / picoclvr.git / blobdiff