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interpolate.py
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__all__ = ["main"]
import random
import pathlib
import logging
import itertools
from dataclasses import dataclass
from typing import Sequence
import yaap
import torch
import torch.utils.data as td
import torchmodels
import utils
import models
import datasets
from datasets import BatchData
from datasets import Dialog
from datasets import DialogProcessor
@dataclass
class InterpolateInferencer:
model: models.AbstractTDA
processor: DialogProcessor
device: torch.device = torch.device("cpu")
asv_tensor: utils.Stacked1DTensor = None
_num_instances: int = utils.private_field(default=None)
def __post_init__(self):
if self.asv_tensor is None:
self.asv_tensor = self.processor.tensorize_state_vocab("goal_state")
self.asv_tensor = self.asv_tensor.to(self.device)
def prepare_data_batch(self, batch: BatchData) -> dict:
return {
"conv_lens": batch.conv_lens,
"sent": batch.sent.value,
"sent_lens": batch.sent.lens1,
"speaker": batch.speaker.value,
"goal": batch.goal.value,
"goal_lens": batch.goal.lens1,
"state": batch.state.value,
"state_lens": batch.state.lens1,
"asv": self.asv_tensor.value,
"asv_lens": self.asv_tensor.lens
}
def prepare_z_batch(self, batch: torch.Tensor) -> dict:
return {
"zconv": batch,
"asv": self.asv_tensor.value,
"asv_lens": self.asv_tensor.lens
}
def encode(self, dataloader) -> torch.Tensor:
self.model.eval()
self.model.encode()
zconv = []
for batch in dataloader:
batch = batch.to(self.device)
zconv.append(self.model(self.prepare_data_batch(batch)).mu)
return torch.cat(zconv, 0)
def generate(self, dataloader) -> Sequence[Dialog]:
self.model.eval()
self.model.decode_optimal()
dialogs = []
for batch in dataloader:
batch = batch.to(self.device)
pred, _ = self.model(
self.prepare_z_batch(batch),
spkr_scale=0.0,
goal_scale=1.0,
state_scale=0.0,
sent_scale=1.0
)
dialogs.extend(map(self.processor.lexicalize_global, pred))
return dialogs
def create_parser():
parser = yaap.Yaap(
desc="Create z-interpolation between two random data points"
)
# data options
parser.add_pth("data-dir", is_dir=True, must_exist=True,
default=(pathlib.Path(__file__).absolute().parent
.joinpath("tests/data/json")),
help="Path to the data dir. Must contain 'train.json' and "
"'dev.json'.")
parser.add_str("splits", is_list=True, default=("train",),
choices=("train", "dev", "test"),
help="List of splits to evaluate on.")
parser.add_pth("processor-path", required=True, must_exist=True,
help="Path to the processor pickle file.")
parser.add_str("anchor1", regex=r"(train|dev|test)-\d+",
help="Data index of the first anchor. If not provided, "
"a random data point will be chosen.")
parser.add_str("anchor2", regex=r"(train|dev|test)-\d+",
help="Data index of the second anchor. If not provided, "
"a random data point will be chosen.")
# interpolation options
parser.add_int("steps", default=10,
help="Number of intermediate steps between two data points.")
# model options
parser.add_pth("model-path", must_exist=True,
default=(pathlib.Path(__file__).absolute().parent
.joinpath("configs/vhda-mini.yml")),
help="Path to the model configuration file.")
parser.add_pth("ckpt-path", required=True, must_exist=True,
help="Path to the model checkpoint.")
parser.add_int("gpu", min_bound=0,
help="GPU device to use. (e.g. 0, 1, etc.)")
# display options
parser.add_pth("logging-config", must_exist=True,
default=(pathlib.Path(__file__).absolute().parent
.joinpath("configs/logging.yml")),
help="Path to a logging config file (yaml/json).")
parser.add_pth("save-dir", default="out", is_dir=True,
help="Directory to save output files.")
parser.add_bol("overwrite", help="Whether to overwrite save dir.")
return parser
def sample_data(data, idx: str = None):
if idx is None:
return random.choice(list(itertools.chain(*data.values())))
split, idx = idx.split("-")
idx = int(idx)
return data[split][idx]
def main(args=None):
args = utils.parse_args(create_parser(), args)
if args.logging_config is not None:
logging.config.dictConfig(utils.load_yaml(args.logging_config))
save_dir = pathlib.Path(args.save_dir)
if (not args.overwrite and
save_dir.exists() and utils.has_element(save_dir.glob("*.json"))):
raise FileExistsError(f"save directory ({save_dir}) is not empty")
shell = utils.ShellUtils()
shell.mkdir(save_dir, silent=True)
logger = logging.getLogger("interpolate")
data_dir = pathlib.Path(args.data_dir)
data = {
split: list(map(Dialog.from_json,
utils.load_json(data_dir.joinpath(f"{split}.json"))))
for split in set(args.splits)
}
processor: DialogProcessor = utils.load_pickle(args.processor_path)
logger.info("preparing model...")
torchmodels.register_packages(models)
model_cls = torchmodels.create_model_cls(models, args.model_path)
model: models.AbstractTDA = model_cls(processor.vocabs)
model.reset_parameters()
model.load_state_dict(torch.load(args.ckpt_path))
device = torch.device("cpu")
if args.gpu is not None:
device = torch.device(f"cuda:{args.gpu}")
model = model.to(device)
samples = (
sample_data(data, args.anchor1),
sample_data(data, args.anchor2)
)
formatter = utils.DialogTableFormatter()
logger.info(f"first sample: \n{formatter.format(samples[0])}")
logger.info(f"second sample: \n{formatter.format(samples[1])}")
logger.info("preparing environment...")
dataloader = datasets.create_dataloader(
dataset=datasets.DialogDataset(
data=samples,
processor=processor
),
batch_size=1,
shuffle=False,
pin_memory=False
)
inferencer = InterpolateInferencer(
model=model,
processor=processor,
device=device
)
logger.info("interpolating...")
with torch.no_grad():
zconv_a, zconv_b = inferencer.encode(dataloader)
zconv = torch.stack([zconv_a + (zconv_b - zconv_a) / args.steps * i
for i in range(args.steps + 1)])
gen_samples = inferencer.generate(td.DataLoader(zconv, shuffle=False))
# use original data points for two extremes
samples = [samples[0]] + list(gen_samples[1:-1]) + [samples[1]]
logger.info("interpolation results: ")
for i, sample in enumerate(samples):
logger.info(f"interpolation step {i / args.steps:.2%}: \n"
f"{formatter.format(sample)}")
logger.info("saving results...")
json_dir = save_dir.joinpath("json")
json_dir.mkdir(exist_ok=True)
for i, sample in enumerate(samples, 1):
utils.save_json(sample.to_json(), json_dir.joinpath(f"{i:02d}.json"))
tbl_dir = save_dir.joinpath("table")
tbl_dir.mkdir(exist_ok=True)
for i, sample in enumerate(samples, 1):
utils.save_lines([formatter.format(sample)],
tbl_dir.joinpath(f"{i:02d}.txt"))
ltx_dir = save_dir.joinpath("latex")
ltx_dir.mkdir(exist_ok=True)
ltx_formatter = utils.DialogICMLLatexFormatter()
for i, sample in enumerate(samples, 1):
utils.save_lines([ltx_formatter.format(sample)],
ltx_dir.joinpath(f"{i:02d}.tex"))
logger.info("done!")
if __name__ == "__main__":
main()