train.py

#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.

# Copyright 2023-present Edoardo Mello Rella, Ajad Chhatkuli, Ender Konukoglu & Luc Van Gool

import torch
import torch.nn.functional as F
import torch.utils.data as data_utils
import signal
import sys
import os
import logging
import math
import json
import time

import implicit_vf
import implicit_vf.workspace as ws
from implicit_vf.decoder import Decoder


class LearningRateSchedule:
    def get_learning_rate(self, epoch):
        pass


class ConstantLearningRateSchedule(LearningRateSchedule):
    def __init__(self, value):
        self.value = value

    def get_learning_rate(self, epoch):
        return self.value


class StepLearningRateSchedule(LearningRateSchedule):
    def __init__(self, initial, interval, factor):
        self.initial = initial
        self.interval = interval
        self.factor = factor

    def get_learning_rate(self, epoch):
        return self.initial * (self.factor ** (epoch // self.interval))


class WarmupLearningRateSchedule(LearningRateSchedule):
    def __init__(self, initial, warmed_up, length):
        self.initial = initial
        self.warmed_up = warmed_up
        self.length = length

    def get_learning_rate(self, epoch):
        if epoch > self.length:
            return self.warmed_up
        return self.initial + (self.warmed_up - self.initial) * epoch / self.length


def get_learning_rate_schedules(specs):
    schedule_specs = specs["LearningRateSchedule"]

    schedules = []

    for schedule_specs in schedule_specs:
        if schedule_specs["Type"] == "Step":
            schedules.append(
                StepLearningRateSchedule(
                    schedule_specs["Initial"],
                    schedule_specs["Interval"],
                    schedule_specs["Factor"],
                )
            )
        elif schedule_specs["Type"] == "Warmup":
            schedules.append(
                WarmupLearningRateSchedule(
                    schedule_specs["Initial"],
                    schedule_specs["Final"],
                    schedule_specs["Length"],
                )
            )
        elif schedule_specs["Type"] == "Constant":
            schedules.append(ConstantLearningRateSchedule(schedule_specs["Value"]))

        else:
            raise Exception(
                'no known learning rate schedule of type "{}"'.format(
                    schedule_specs["Type"]
                )
            )

    return schedules


def save_model(experiment_directory, filename, decoder, epoch):
    model_params_dir = ws.get_model_params_dir(experiment_directory, True)

    torch.save(
        {"epoch": epoch, "model_state_dict": decoder.state_dict()},
        os.path.join(model_params_dir, filename),
    )


def save_optimizer(experiment_directory, filename, optimizer, epoch):
    optimizer_params_dir = ws.get_optimizer_params_dir(experiment_directory, True)

    torch.save(
        {"epoch": epoch, "optimizer_state_dict": optimizer.state_dict()},
        os.path.join(optimizer_params_dir, filename),
    )


def load_optimizer(experiment_directory, filename, optimizer):
    full_filename = os.path.join(
        ws.get_optimizer_params_dir(experiment_directory), filename
    )

    if not os.path.isfile(full_filename):
        raise Exception(
            'optimizer state dict "{}" does not exist'.format(full_filename)
        )

    data = torch.load(full_filename)

    optimizer.load_state_dict(data["optimizer_state_dict"])

    return data["epoch"]


def save_latent_vectors(experiment_directory, filename, latent_vec, epoch):
    latent_codes_dir = ws.get_latent_codes_dir(experiment_directory, True)

    all_latents = latent_vec.state_dict()

    torch.save(
        {"epoch": epoch, "latent_codes": all_latents},
        os.path.join(latent_codes_dir, filename),
    )


def load_latent_vectors(experiment_directory, filename, lat_vecs):
    full_filename = os.path.join(
        ws.get_latent_codes_dir(experiment_directory), filename
    )

    if not os.path.isfile(full_filename):
        raise Exception('latent state file "{}" does not exist'.format(full_filename))

    data = torch.load(full_filename)

    if isinstance(data["latent_codes"], torch.Tensor):
        # for backwards compatibility
        if not lat_vecs.num_embeddings == data["latent_codes"].size()[0]:
            raise Exception(
                "num latent codes mismatched: {} vs {}".format(
                    lat_vecs.num_embeddings, data["latent_codes"].size()[0]
                )
            )

        if not lat_vecs.embedding_dim == data["latent_codes"].size()[2]:
            raise Exception("latent code dimensionality mismatch")

        for i, lat_vec in enumerate(data["latent_codes"]):
            lat_vecs.weight.data[i, :] = lat_vec

    else:
        lat_vecs.load_state_dict(data["latent_codes"])

    return data["epoch"]


def save_logs(
    experiment_directory,
    loss_log,
    lr_log,
    timing_log,
    lat_mag_log,
    param_mag_log,
    epoch,
):
    torch.save(
        {
            "epoch": epoch,
            "loss": loss_log,
            "learning_rate": lr_log,
            "timing": timing_log,
            "latent_magnitude": lat_mag_log,
            "param_magnitude": param_mag_log,
        },
        os.path.join(experiment_directory, ws.logs_filename),
    )


def load_logs(experiment_directory):
    full_filename = os.path.join(experiment_directory, ws.logs_filename)

    if not os.path.isfile(full_filename):
        raise Exception('log file "{}" does not exist'.format(full_filename))

    data = torch.load(full_filename)

    return (
        data["loss"],
        data["learning_rate"],
        data["timing"],
        data["latent_magnitude"],
        data["param_magnitude"],
        data["epoch"],
    )


def clip_logs(loss_log, lr_log, timing_log, lat_mag_log, param_mag_log, epoch):
    iters_per_epoch = len(loss_log) // len(lr_log)

    loss_log = loss_log[: (iters_per_epoch * epoch)]
    lr_log = lr_log[:epoch]
    timing_log = timing_log[:epoch]
    lat_mag_log = lat_mag_log[:epoch]
    for n in param_mag_log:
        param_mag_log[n] = param_mag_log[n][:epoch]

    return (loss_log, lr_log, timing_log, lat_mag_log, param_mag_log)


def get_spec_with_default(specs, key, default):
    try:
        return specs[key]
    except KeyError:
        return default


def get_mean_latent_vector_magnitude(latent_vectors):
    return torch.mean(torch.norm(latent_vectors.weight.data.detach(), dim=1))


def append_parameter_magnitudes(param_mag_log, model):
    for name, param in model.named_parameters():
        if len(name) > 7 and name[:7] == "module.":
            name = name[7:]
        if name not in param_mag_log.keys():
            param_mag_log[name] = []
        param_mag_log[name].append(param.data.norm().item())


def main_function(
    experiment_directory,
):
    logging.debug("running " + experiment_directory)

    specs = ws.load_experiment_specifications(experiment_directory)

    logging.info("Experiment description: \n" + specs["Description"][0])

    data_source = specs["DataSource"]
    train_split_file = specs["TrainSplit"]

    logging.debug(specs["NetworkSpecs"])

    latent_size = specs["CodeLength"]

    checkpoints = list(
        range(
            specs["SnapshotFrequency"],
            specs["NumEpochs"] + 1,
            specs["SnapshotFrequency"],
        )
    )

    for checkpoint in specs["AdditionalSnapshots"]:
        checkpoints.append(checkpoint)
    checkpoints.sort()

    lr_schedules = get_learning_rate_schedules(specs)

    grad_clip = get_spec_with_default(specs, "GradientClipNorm", None)
    if grad_clip is not None:
        logging.debug("clipping gradients to max norm {}".format(grad_clip))

    def save_latest(epoch):
        save_model(experiment_directory, "latest.pth", decoder, epoch)
        save_optimizer(experiment_directory, "latest.pth", optimizer_all, epoch)
        save_latent_vectors(experiment_directory, "latest.pth", lat_vecs, epoch)

    def save_checkpoints(epoch):
        save_model(experiment_directory, str(epoch) + ".pth", decoder, epoch)
        save_optimizer(experiment_directory, str(epoch) + ".pth", optimizer_all, epoch)
        save_latent_vectors(experiment_directory, str(epoch) + ".pth", lat_vecs, epoch)

    def signal_handler(sig, frame):
        logging.info("Stopping early...")
        sys.exit(0)

    def adjust_learning_rate(lr_schedules, optimizer, epoch):
        for i, param_group in enumerate(optimizer.param_groups):
            param_group["lr"] = lr_schedules[i].get_learning_rate(epoch)

    signal.signal(signal.SIGINT, signal_handler)

    num_samp_per_scene = specs["SamplesPerScene"]
    scene_per_batch = specs["ScenesPerBatch"]

    do_code_regularization = get_spec_with_default(specs, "CodeRegularization", True)
    code_reg_lambda = get_spec_with_default(specs, "CodeRegularizationLambda", 1e-4)

    code_bound = get_spec_with_default(specs, "CodeBound", None)

    decoder = Decoder(
        latent_size,
        **specs["NetworkSpecs"],
    ).cuda()
    logging.info("training with {} GPU(s)".format(torch.cuda.device_count()))

    decoder = torch.nn.DataParallel(decoder)

    num_epochs = specs["NumEpochs"]
    log_frequency = get_spec_with_default(specs, "LogFrequency", 10)

    with open(train_split_file, "r") as f:
        train_split = json.load(f)

    vf_dataset = implicit_vf.data.OpenVFSamples(
        data_source, train_split, num_samp_per_scene
    )

    num_data_loader_threads = get_spec_with_default(specs, "DataLoaderThreads", 1)
    logging.debug("loading data with {} threads".format(num_data_loader_threads))

    vf_loader = data_utils.DataLoader(
        vf_dataset,
        batch_size=scene_per_batch,
        shuffle=True,
        num_workers=num_data_loader_threads,
        drop_last=True,
    )

    logging.debug("torch num_threads: {}".format(torch.get_num_threads()))

    num_scenes = len(vf_dataset)

    logging.info("There are {} scenes".format(num_scenes))

    logging.debug(decoder)

    lat_vecs = torch.nn.Embedding(num_scenes, latent_size, max_norm=code_bound)
    torch.nn.init.normal_(
        lat_vecs.weight.data,
        0.0,
        get_spec_with_default(specs, "CodeInitStdDev", 1.0) / math.sqrt(latent_size),
    )

    logging.debug(
        "initialized with mean magnitude {}".format(
            get_mean_latent_vector_magnitude(lat_vecs)
        )
    )
    loss_l2 = torch.nn.MSELoss(reduction="sum")

    optimizer_all = torch.optim.Adam(
        [
            {
                "params": decoder.parameters(),
                "lr": lr_schedules[0].get_learning_rate(0),
            },
            {
                "params": lat_vecs.parameters(),
                "lr": lr_schedules[1].get_learning_rate(0),
            },
        ]
    )

    loss_log = []
    lr_log = []
    lat_mag_log = []
    timing_log = []
    param_mag_log = {}

    start_epoch = 1

    logging.info("starting from epoch {}".format(start_epoch))

    logging.info(
        "Number of decoder parameters: {}".format(
            sum(p.data.nelement() for p in decoder.parameters())
        )
    )
    logging.info(
        "Number of shape code parameters: {} (# codes {}, code dim {})".format(
            lat_vecs.num_embeddings * lat_vecs.embedding_dim,
            lat_vecs.num_embeddings,
            lat_vecs.embedding_dim,
        )
    )
    batch_loss = 0.0

    for epoch in range(start_epoch, num_epochs + 1):
        start = time.time()

        logging.info("epoch {}... loss {}".format(epoch, batch_loss))

        decoder.train()

        adjust_learning_rate(lr_schedules, optimizer_all, epoch)

        for vf_data, indices in vf_loader:
            # Process the input data
            vf_data = vf_data.reshape(-1, 6)
            num_vf_samples = vf_data.shape[0]

            vf_data.requires_grad = False

            xyz = vf_data[:, :3]
            vf_gt = vf_data[:, 3:]
            xyz.requires_grad = True

            indices = indices.unsqueeze(-1).repeat(1, num_samp_per_scene).view(-1)

            optimizer_all.zero_grad()
            batch_vecs = lat_vecs(indices)
            input = torch.cat([batch_vecs, xyz], dim=1)

            # NN optimization
            pred = decoder(input)

            vf_loss = loss_l2(pred, vf_gt) / num_vf_samples

            if do_code_regularization:
                l2_size_loss = torch.sum(torch.norm(batch_vecs, dim=1))
                reg_loss = (
                    code_reg_lambda * min(1, epoch / 100) * l2_size_loss
                ) / num_vf_samples

                vf_loss = vf_loss + reg_loss.cuda()

            vf_loss.backward()

            batch_loss = (batch_loss + vf_loss.item()) / 2
            logging.debug("loss = {}".format(batch_loss))
            loss_log.append(batch_loss)

            if grad_clip is not None:
                torch.nn.utils.clip_grad_norm_(decoder.parameters(), grad_clip)

            optimizer_all.step()

        end = time.time()
        seconds_elapsed = end - start
        timing_log.append(seconds_elapsed)
        lr_log.append([schedule.get_learning_rate(epoch) for schedule in lr_schedules])
        lat_mag_log.append(get_mean_latent_vector_magnitude(lat_vecs))
        append_parameter_magnitudes(param_mag_log, decoder)

        if epoch in checkpoints:
            save_checkpoints(epoch)

        if epoch % log_frequency == 0:
            save_latest(epoch)
            save_logs(
                experiment_directory,
                loss_log,
                lr_log,
                timing_log,
                lat_mag_log,
                param_mag_log,
                epoch,
            )


if __name__ == "__main__":
    import argparse

    arg_parser = argparse.ArgumentParser(description="Train a ImplicitVF autodecoder")
    arg_parser.add_argument(
        "--experiment",
        "-e",
        dest="experiment_directory",
        required=True,
        help="The experiment directory. This directory should include "
        + "experiment specifications in 'specs.json', and logging will be "
        + "done in this directory as well.",
    )

    implicit_vf.add_common_args(arg_parser)
    args = arg_parser.parse_args()
    implicit_vf.configure_logging(args)

    main_function(args.experiment_directory)