mirror of
https://github.com/wassname/attentive-neural-processes.git
synced 2026-08-03 12:40:14 +08:00
194 lines
7.2 KiB
Python
194 lines
7.2 KiB
Python
import os
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import numpy as np
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import pandas as pd
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import torch
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from tqdm.auto import tqdm
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from torch import nn
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from torch.nn import functional as F
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from torch.utils.data import DataLoader
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from torchvision.datasets import MNIST
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from test_tube import Experiment, HyperOptArgumentParser
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from neural_processes.data.smart_meter import (
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collate_fns,
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SmartMeterDataSet,
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get_smartmeter_df,
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)
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import torchvision.transforms as transforms
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from neural_processes.plot import plot_from_loader_to_tensor, plot_from_loader
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from argparse import ArgumentParser
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import json
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import pytorch_lightning as pl
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import math
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from matplotlib import pyplot as plt
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import torch
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import io
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import PIL
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import optuna
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from torchvision.transforms import ToTensor
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from neural_processes.data.smart_meter import get_smartmeter_df
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from neural_processes.modules import BatchNormSequence
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from neural_processes.utils import ObjectDict
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from neural_processes.lightning import PL_Seq2Seq
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from ..logger import logger
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from ..utils import hparams_power
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class TransformerSeq2SeqNet(nn.Module):
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def __init__(self, hparams):
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super().__init__()
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hparams = hparams_power(hparams)
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self.hparams = hparams
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self._min_std = hparams.min_std
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hidden_out_size = self.hparams.hidden_out_size
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self.enc_norm = BatchNormSequence(self.hparams.input_size)
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self.enc_emb = nn.Linear(self.hparams.input_size, hidden_out_size)
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encoder_norm = nn.LayerNorm(hidden_out_size)
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layer_enc = nn.TransformerEncoderLayer(
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d_model=hidden_out_size,
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dim_feedforward=self.hparams.hidden_size,
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dropout=self.hparams.attention_dropout,
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nhead=self.hparams.nhead,
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# activation
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)
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self.encoder = nn.TransformerEncoder(
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layer_enc, num_layers=self.hparams.nlayers, norm=encoder_norm
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)
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self.dec_norm = BatchNormSequence(self.hparams.input_size_decoder)
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self.dec_emb = nn.Linear(self.hparams.input_size_decoder, hidden_out_size)
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layer_dec = nn.TransformerDecoderLayer(
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d_model=hidden_out_size,
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dim_feedforward=self.hparams.hidden_size,
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dropout=self.hparams.attention_dropout,
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nhead=self.hparams.nhead,
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)
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decoder_norm = nn.LayerNorm(hidden_out_size)
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self.decoder = nn.TransformerDecoder(
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layer_dec, num_layers=self.hparams.nlayers, norm=decoder_norm
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)
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self.mean = nn.Linear(hidden_out_size, self.hparams.output_size)
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self.std = nn.Linear(hidden_out_size, self.hparams.output_size)
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self._use_lvar = False
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# self._reset_parameters()
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def _reset_parameters(self):
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r"""Initiate parameters in the transformer model."""
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for p in self.parameters():
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if p.dim() > 1:
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torch.nn.init.xavier_uniform_(p)
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def forward(self, context_x, context_y, target_x, target_y=None):
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device = next(self.parameters()).device
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x = torch.cat([context_x, context_y], -1)
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# Size([B, C, input_dim])
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x = self.enc_emb(self.enc_norm(x)).permute(1, 0, 2)
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# Size([C, B, emb_dim])
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memory = self.encoder(x)
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# Size([C, B, emb_dim])
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target_x = self.dec_emb(self.dec_norm(target_x)).permute(1, 0, 2)
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# Size([T, B, input_target_dim]) -> Size([B, T, emb_dim])
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# In transformers the memory and target_x need to be the same length. Lets use a permutation invariant agg on the context
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# Then expand it, so it's available as we decode, conditional on target_x
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memory = memory.max(dim=0, keepdim=True)[0].expand_as(target_x)
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outputs = self.decoder(target_x, memory).permute(1, 0, 2).contiguous()
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# Size([B, T, emb_dim])
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mean = self.mean(outputs)
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log_sigma = self.std(outputs)
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if self._use_lvar:
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log_sigma = torch.clamp(
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log_sigma, math.log(self._min_std), -math.log(self._min_std)
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)
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sigma = torch.exp(log_sigma)
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else:
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sigma = self._min_std + (1 - self._min_std) * F.softplus(log_sigma)
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y_dist = torch.distributions.Normal(mean, sigma)
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# Loss
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loss_mse = loss_p = loss_p_weighted = None
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if target_y is not None:
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loss_mse = F.mse_loss(mean, target_y, reduction="none")
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if self._use_lvar:
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loss_p = -log_prob_sigma(target_y, mean, log_sigma)
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else:
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loss_p = -y_dist.log_prob(target_y).mean(-1)
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if self.hparams["context_in_target"]:
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loss_p[: context_x.size(1)] /= 100
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loss_mse[: context_x.size(1)] /= 100
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# Weight loss nearer to prediction time?
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weight = (torch.arange(loss_p.shape[1]) + 1).float().to(device)[None, :]
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loss_p_weighted = loss_p / torch.sqrt(weight) # We want to weight nearer stuff more
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y_pred = y_dist.rsample if self.training else y_dist.loc
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return (
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y_pred,
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dict(loss=loss_p.mean(), loss_p=loss_p.mean(), loss_mse=loss_mse.mean(), loss_p_weighted=loss_p_weighted.mean()),
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dict(log_sigma=log_sigma, y_dist=y_dist),
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)
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class TransformerSeq2Seq_PL(PL_Seq2Seq):
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def __init__(self, hparams, MODEL_CLS=TransformerSeq2SeqNet, **kwargs):
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super().__init__(hparams, MODEL_CLS=MODEL_CLS, **kwargs)
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DEFAULT_ARGS = {
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"agg": "max",
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"attention_dropout": 0.2,
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"hidden_out_size_power": 4,
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"hidden_size_power": 5,
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"learning_rate": 0.006,
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"nhead_power": 3,
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"nlayers": 2,
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}
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@staticmethod
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def add_suggest(trial: optuna.Trial, user_attrs={}):
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"""
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Add hyperparam ranges to an optuna trial and typical user attrs.
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Usage:
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trial = optuna.trial.FixedTrial(
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params={
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'hidden_size': 128,
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}
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)
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trial = add_suggest(trial)
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trainer = pl.Trainer()
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model = LSTM_PL(dict(**trial.params, **trial.user_attrs), dataset_train,
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dataset_test, cache_base_path, norm)
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trainer.fit(model)
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"""
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trial.suggest_loguniform("learning_rate", 1e-6, 1e-2)
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trial.suggest_uniform("attention_dropout", 0, 0.75)
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# we must have nhead<==hidden_size
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# so nhead_power.max()<==hidden_size_power.min()
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trial.suggest_discrete_uniform("hidden_size_power", 4, 10, 1)
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trial.suggest_discrete_uniform("hidden_out_size_power", 4, 9, 1)
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trial.suggest_discrete_uniform("nhead_power", 1, 4, 1)
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trial.suggest_int("nlayers", 1, 12)
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user_attrs_default = {
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"batch_size": 16,
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"grad_clip": 40,
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"max_nb_epochs": 200,
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"num_workers": 4,
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"num_extra_target": 24 * 4,
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"vis_i": "670",
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"num_context": 24 * 4,
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"input_size": 18,
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"input_size_decoder": 17,
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"context_in_target": False,
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"output_size": 1,
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"patience": 3,
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'min_std': 0.005,
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}
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[trial.set_user_attr(k, v) for k, v in user_attrs_default.items()]
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[trial.set_user_attr(k, v) for k, v in user_attrs.items()]
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return trial
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