import copy
import math
from pyexpat import model
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import argparse
import os
import random
import shutil
import time
import torch.utils.data as data
import sys
import pickle
import logging
from tqdm import tqdm
sys.path.append("..")
from helpers.utils import *
from helpers.metrics import *
from .basemethod import BaseMethod
eps_cst = 1e-8
# This really doesn't work well on no bencmark,
[docs]class MixtureOfExperts(BaseMethod):
"""Implementation of Madras et al., 2018"""
def __init__(self, model, device, plotting_interval=100):
self.plotting_interval = plotting_interval
self.model = model
self.device = device
[docs] def mixtures_of_experts_loss(self, outputs, human_is_correct, labels):
"""
Implmentation of Mixtures of Experts loss from Madras et al., 2018
"""
batch_size = outputs.size()[0] # batch_size
human_loss = torch.cuda.FloatTensor(1 - human_is_correct * 1.0)
rejector_probability = torch.sigmoid(
outputs[:, -1] + eps_cst
) # probability of rejection
outputs_class = F.softmax(outputs[:, :-1], dim=1)
classifier_loss = -torch.log2(
outputs_class[range(batch_size), labels] + eps_cst
)
loss = (
classifier_loss * (1 - rejector_probability)
+ human_loss * rejector_probability
)
return torch.sum(loss) / batch_size
[docs] def fit_epoch(self, dataloader, optimizer, verbose=True, epoch=1):
"""
Fit the model for one epoch
"""
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
end = time.time()
self.model.train()
for batch, (data_x, data_y, hum_preds) in enumerate(dataloader):
data_x = data_x.to(self.device)
data_y = data_y.to(self.device)
hum_preds = hum_preds.to(self.device)
m = (hum_preds == data_y) * 1
m = torch.tensor(m).to(self.device)
outputs = self.model(data_x)
# apply softmax to outputs
loss = self.mixtures_of_experts_loss(outputs, m, data_y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
prec1 = accuracy(outputs.data, data_y, topk=(1,))[0]
losses.update(loss.data.item(), data_x.size(0))
top1.update(prec1.item(), data_x.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if torch.isnan(loss):
print("Nan loss")
logging.warning(f"NAN LOSS")
break
if verbose and batch % self.plotting_interval == 0:
logging.info(
"Epoch: [{0}][{1}/{2}]\t"
"Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t"
"Loss {loss.val:.4f} ({loss.avg:.4f})\t"
"Prec@1 {top1.val:.3f} ({top1.avg:.3f})".format(
epoch,
batch,
len(dataloader),
batch_time=batch_time,
loss=losses,
top1=top1,
)
)
[docs] def fit(
self,
dataloader_train,
dataloader_val,
dataloader_test,
epochs,
optimizer,
lr,
verbose=True,
test_interval=5,
scheduler=None,
):
optimizer = optimizer(self.model.parameters(), lr=lr)
if scheduler is not None:
scheduler = scheduler(optimizer, len(dataloader_train)*epochs)
for epoch in tqdm(range(epochs)):
self.fit_epoch(dataloader_train, optimizer, verbose, epoch)
if verbose and epoch % test_interval == 0:
data_test = self.test(dataloader_val)
logging.info(compute_deferral_metrics(data_test))
if scheduler is not None:
scheduler.step()
final_test = self.test(dataloader_test)
return compute_deferral_metrics(final_test)
[docs] def test(self, dataloader):
defers_all = []
truths_all = []
hum_preds_all = []
rej_score = []
predictions_all = [] # classifier only
self.model.eval()
with torch.no_grad():
for batch, (data_x, data_y, hum_preds) in enumerate(dataloader):
data_x = data_x.to(self.device)
data_y = data_y.to(self.device)
hum_preds = hum_preds.to(self.device)
outputs = self.model(data_x)
outputs_soft = F.softmax(outputs[:, :-1], dim=1)
_, predicted_class = torch.max(outputs_soft.data, 1)
predictions_all.extend(predicted_class.cpu().numpy())
rejector_outputs = torch.sigmoid(outputs[:, -1])
defers_all.extend((rejector_outputs.cpu().numpy() >= 0.5).astype(int))
truths_all.extend(data_y.cpu().numpy())
hum_preds_all.extend(hum_preds.cpu().numpy())
rej_score.extend(rejector_outputs.cpu().numpy())
# convert to numpy
defers_all = np.array(defers_all)
truths_all = np.array(truths_all)
hum_preds_all = np.array(hum_preds_all)
predictions_all = np.array(predictions_all)
data = {
"defers": defers_all,
"labels": truths_all,
"hum_preds": hum_preds_all,
"preds": predictions_all,
"rej_score": rej_score,
}
return data