399 lines
10 KiB
Python
399 lines
10 KiB
Python
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from argparse import ArgumentParser
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import json
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import pathlib
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import matplotlib.pyplot as plt
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import torch
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import torch.nn as nn
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from torch.utils.data import DataLoader
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from torch.utils.tensorboard import SummaryWriter
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from tqdm import tqdm
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from utils import (
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ParityDataset,
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PetriDishNet,
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ReconstructionLoss,
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RegularizationLoss,
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)
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@torch.no_grad()
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def evaluate(dataloader, module):
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"""Compute relevant metrics.
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Parameters
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----------
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dataloader : DataLoader
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Dataloader that yields batches of `x` and `y`.
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module : PonderNet
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Our pondering network.
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Returns
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-------
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metrics_single : dict
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Scalar metrics. The keys are names and the values are `torch.Tensor`.
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These metrics are computed as mean values over the entire dataset.
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metrics_per_step : dict
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Per step metrics. The keys are names and the values are `torch.Tensor`
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of shape `(max_steps,)`. These metrics are computed as mean values over
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the entire dataset.
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"""
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# Imply device and dtype
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param = next(module.parameters())
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device, dtype = param.device, param.dtype
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metrics_single_ = {
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"accuracy_halted": [],
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"halting_step": [],
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}
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metrics_per_step_ = {
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"accuracy": [],
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"p": [],
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}
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for x_batch, y_true_batch in dataloader:
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x_batch = x_batch.to(device, dtype) # (batch_size, n_elems)
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y_true_batch = y_true_batch.to(device, dtype) # (batch_size,)
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y_pred_batch, p, halting_step = module(x_batch)
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y_halted_batch = y_pred_batch.gather(
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dim=0,
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index=halting_step[None, :] - 1,
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)[
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0
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] # (batch_size,)
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# Computing single metrics (mean over samples in the batch)
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accuracy_halted = (
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((y_halted_batch > 0) == y_true_batch).to(torch.float32).mean()
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)
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metrics_single_["accuracy_halted"].append(accuracy_halted)
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metrics_single_["halting_step"].append(
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halting_step.to(torch.float).mean()
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)
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# Computing per step metrics (mean over samples in the batch)
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accuracy = (
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((y_pred_batch > 0) == y_true_batch[None, :])
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.to(torch.float32)
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.mean(dim=1)
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)
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metrics_per_step_["accuracy"].append(accuracy)
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metrics_per_step_["p"].append(p.mean(dim=1))
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metrics_single = {
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name: torch.stack(values).mean(dim=0).cpu().numpy()
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for name, values in metrics_single_.items()
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}
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metrics_per_step = {
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name: torch.stack(values).mean(dim=0).cpu().numpy()
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for name, values in metrics_per_step_.items()
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}
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return metrics_single, metrics_per_step
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def plot_distributions(target, predicted):
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"""Create a barplot.
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Parameters
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----------
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target, predicted : np.ndarray
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Arrays of shape `(max_steps,)` representing the target and predicted
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probability distributions.
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Returns
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-------
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matplotlib.Figure
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"""
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support = list(range(1, len(target) + 1))
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fig, ax = plt.subplots(dpi=140)
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ax.bar(
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support,
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target,
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color="red",
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label=f"Target - Geometric({target[0].item():.2f})",
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)
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ax.bar(
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support,
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predicted,
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color="green",
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width=0.4,
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label="Predicted",
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)
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ax.set_ylim(0, 0.6)
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ax.set_xticks(support)
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ax.legend()
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ax.grid()
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return fig
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def plot_accuracy(accuracy):
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"""Create a barplot representing accuracy over different halting steps.
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Parameters
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----------
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accuracy : np.array
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1D array representing accuracy if we were to take the output after
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the corresponding step.
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Returns
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-------
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matplotlib.Figure
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"""
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support = list(range(1, len(accuracy) + 1))
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fig, ax = plt.subplots(dpi=140)
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ax.bar(
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support,
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accuracy,
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label="Accuracy over different steps",
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)
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ax.set_ylim(0, 1)
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ax.set_xticks(support)
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ax.legend()
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ax.grid()
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return fig
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def main(argv=None):
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"""CLI for training."""
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parser = ArgumentParser()
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parser.add_argument(
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"log_folder",
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type=str,
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help="Folder where tensorboard logging is saved",
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)
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parser.add_argument(
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"--batch-size",
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type=int,
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default=128,
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help="Batch size",
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)
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parser.add_argument(
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"--beta",
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type=float,
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default=0.01,
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help="Regularization loss coefficient",
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)
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parser.add_argument(
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"-d",
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"--device",
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type=str,
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choices={"cpu", "cuda"},
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default="cpu",
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help="Device to use",
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)
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parser.add_argument(
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"--eval-frequency",
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type=int,
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default=10_000,
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help="Evaluation is run every `eval_frequency` steps",
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)
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parser.add_argument(
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"--lambda-p",
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type=float,
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default=0.4,
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help="True probability of success for a geometric distribution",
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)
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parser.add_argument(
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"--n-iter",
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type=int,
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default=1_000_000,
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help="Number of gradient steps",
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)
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parser.add_argument(
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"--n-elems",
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type=int,
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default=64,
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help="Number of elements",
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)
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parser.add_argument(
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"--n-hidden",
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type=int,
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default=64,
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help="Number of hidden elements in the reccurent cell",
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)
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parser.add_argument(
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"--n-nonzero",
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type=int,
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nargs=2,
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default=(None, None),
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help="Lower and upper bound on nonzero elements in the training set",
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)
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parser.add_argument(
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"--max-steps",
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type=int,
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default=20,
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help="Maximum number of pondering steps",
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)
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# Parameters
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args = parser.parse_args(argv)
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print(args)
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device = torch.device(args.device)
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dtype = torch.float32
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n_eval_samples = 1000
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batch_size_eval = 50
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if args.n_nonzero[0] is None and args.n_nonzero[1] is None:
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threshold = int(0.3 * args.n_elems)
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range_nonzero_easy = (1, threshold)
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range_nonzero_hard = (args.n_elems - threshold, args.n_elems)
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else:
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range_nonzero_easy = (1, args.n_nonzero[1])
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range_nonzero_hard = (args.n_nonzero[1] + 1, args.n_elems)
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# Tensorboard
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log_folder = pathlib.Path(args.log_folder)
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writer = SummaryWriter(log_folder)
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writer.add_text("parameters", json.dumps(vars(args)))
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# Prepare data
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dataloader_train = DataLoader(
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ParityDataset(
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n_samples=args.batch_size * args.n_iter,
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n_elems=args.n_elems,
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n_nonzero_min=args.n_nonzero[0],
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n_nonzero_max=args.n_nonzero[1],
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),
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batch_size=args.batch_size,
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) # consider specifying `num_workers` for speedups
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eval_dataloaders = {
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"test": DataLoader(
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ParityDataset(
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n_samples=n_eval_samples,
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n_elems=args.n_elems,
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n_nonzero_min=args.n_nonzero[0],
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n_nonzero_max=args.n_nonzero[1],
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),
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batch_size=batch_size_eval,
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),
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f"{range_nonzero_easy[0]}_{range_nonzero_easy[1]}": DataLoader(
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ParityDataset(
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n_samples=n_eval_samples,
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n_elems=args.n_elems,
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n_nonzero_min=range_nonzero_easy[0],
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n_nonzero_max=range_nonzero_easy[1],
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),
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batch_size=batch_size_eval,
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),
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f"{range_nonzero_hard[0]}_{range_nonzero_hard[1]}": DataLoader(
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ParityDataset(
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n_samples=n_eval_samples,
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n_elems=args.n_elems,
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n_nonzero_min=range_nonzero_hard[0],
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n_nonzero_max=range_nonzero_hard[1],
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),
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batch_size=batch_size_eval,
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),
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}
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# Model preparation
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module = PetriDashNet(
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n_in=args.n_elems,
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n_neurons=args.n_hidden,
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n_out=1,
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max_steps=args.max_steps,
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)
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module = module.to(device, dtype)
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# Loss preparation
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loss_rec_inst = ReconstructionLoss(
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nn.BCEWithLogitsLoss(reduction="none")
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).to(device, dtype)
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loss_reg_inst = RegularizationLoss(
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lambda_p=args.lambda_p,
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max_steps=args.max_steps,
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).to(device, dtype)
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# Optimizer
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optimizer = torch.optim.Adam(
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module.parameters(),
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lr=0.0003,
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)
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# Training and evaluation loops
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iterator = tqdm(enumerate(dataloader_train), total=args.n_iter)
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for step, (x_batch, y_true_batch) in iterator:
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x_batch = x_batch.to(device, dtype)
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y_true_batch = y_true_batch.to(device, dtype)
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y_pred_batch, p, halting_step = module(x_batch)
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loss_rec = loss_rec_inst(
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p,
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y_pred_batch,
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y_true_batch,
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)
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loss_reg = loss_reg_inst(
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p,
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)
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loss_overall = loss_rec + args.beta * loss_reg
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optimizer.zero_grad()
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loss_overall.backward()
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torch.nn.utils.clip_grad_norm_(module.parameters(), 1)
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optimizer.step()
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# Logging
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writer.add_scalar("loss_rec", loss_rec, step)
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writer.add_scalar("loss_reg", loss_reg, step)
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writer.add_scalar("loss_overall", loss_overall, step)
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# Evaluation
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if step % args.eval_frequency == 0:
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module.eval()
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for dataloader_name, dataloader in eval_dataloaders.items():
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metrics_single, metrics_per_step = evaluate(
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dataloader,
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module,
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)
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fig_dist = plot_distributions(
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loss_reg_inst.p_g.cpu().numpy(),
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metrics_per_step["p"],
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)
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writer.add_figure(
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f"distributions/{dataloader_name}", fig_dist, step
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)
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fig_acc = plot_accuracy(metrics_per_step["accuracy"])
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writer.add_figure(
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f"accuracy_per_step/{dataloader_name}", fig_acc, step
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)
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for metric_name, metric_value in metrics_single.items():
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writer.add_scalar(
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f"{metric_name}/{dataloader_name}",
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metric_value,
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step,
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)
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torch.save(module, log_folder / "checkpoint.pth")
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module.train()
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if __name__ == "__main__":
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main()
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