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Copy pathevaluator.py
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155 lines (137 loc) · 6.76 KB
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import time
import models
import torch
import torch.optim as optim
import util
from torch.autograd import Variable
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
class Evaluator():
def __init__(self, data_loader, logger, config, data_type):
self.loss_meters = util.AverageMeter()
self.acc_meters = util.AverageMeter()
self.acc5_meters = util.AverageMeter()
self.criterion = torch.nn.CrossEntropyLoss()
self.data_loader = data_loader
self.logger = logger
self.log_frequency = config.log_frequency if config.log_frequency is not None else 100
self.config = config
self.current_acc = 0
self.current_acc_top5 = 0
self.confusion_matrix = torch.zeros(config.num_classes, config.num_classes)
self.data_type = data_type
self.recover_loss_meters = util.AverageMeter()
self.recover_acc_meters = util.AverageMeter()
self.recover_acc5_meters = util.AverageMeter()
self.recover_confusion_matrix = torch.zeros(self.config.num_classes, self.config.num_classes)
return
def _reset_stats(self):
self.loss_meters = util.AverageMeter()
self.acc_meters = util.AverageMeter()
self.acc5_meters = util.AverageMeter()
self.confusion_matrix = torch.zeros(self.config.num_classes, self.config.num_classes)
self.recover_loss_meters = util.AverageMeter()
self.recover_acc_meters = util.AverageMeter()
self.recover_acc5_meters = util.AverageMeter()
self.recover_confusion_matrix = torch.zeros(self.config.num_classes, self.config.num_classes)
return
def eval(self, epoch, model):
model.eval()
for i, (images, labels) in enumerate(self.data_loader["test_dataset"]):
if self.data_type == 'PathMNIST' or self.data_type == 'DermaMNIST' or self.data_type == 'OCTMNIST' or\
self.data_type == 'PneumoniaMNIST' or self.data_type == 'RetinaMNIST' or self.data_type == 'BreastMNIST' or \
self.data_type == 'BloodMNIST' or self.data_type == 'TissueMNIST' or self.data_type == 'OrganAMNIST' or \
self.data_type == 'OrganCMNIST' or self.data_type == 'OrganSMNIST' or self.data_type == 'OrganMNIST3D'or \
self.data_type == 'NoduleMNIST3D' or self.data_type =='FractureMNIST3D' or self.data_type == 'AdrenalMNIST3D'or \
self.data_type =='VesselMNIST3D' or self.data_type =='SynapseMNIST3D':
labels = torch.squeeze(labels, 1).long()
start = time.time()
log_payload = self.eval_batch(images=images, labels=labels, model=model)
end = time.time()
time_used = end - start
display = util.log_display(epoch=epoch,
global_step=i,
time_elapse=time_used,
**log_payload)
if self.logger is not None:
self.logger.info(display)
model.eval()
for i, (images, labels, _) in enumerate(self.data_loader["recover_test_dataset"]):
start = time.time()
recover_log_payload = self.eval_recover_batch(images=images, labels=labels, model=model)
end = time.time()
time_used = end - start
display = util.log_display(epoch=epoch,
global_step=i,
time_elapse=time_used,
**recover_log_payload)
if self.logger is not None:
self.logger.info(display)
return
def eval_model(self, epoch, model):
model.eval()
for i, (images, labels) in enumerate(self.data_loader["test_dataset"]):
labels = torch.squeeze(labels, 1).long()
start = time.time()
log_payload = self.eval_batch(images=images, labels=labels, model=model)
end = time.time()
time_used = end - start
display = util.log_display(epoch=epoch,
global_step=i,
time_elapse=time_used,
**log_payload)
if self.logger is not None:
self.logger.info(display)
return
def eval_batch(self, images, labels, model):
images, labels = images.to(device, non_blocking=True), labels.to(device, non_blocking=True)
with torch.no_grad():
pred = model(images)
loss = self.criterion(pred, labels)
if pred.shape[1] >= 5:
acc, acc5 = util.accuracy(pred, labels, topk=(1, 5))
else:
acc, = util.accuracy(pred, labels, topk=(1,))
acc5 = 1
# acc, acc5 = util.accuracy(pred, labels, topk=(1, 5))
_, preds = torch.max(pred, 1)
for t, p in zip(labels.view(-1), preds.view(-1)):
self.confusion_matrix[t.long(), p.long()] += 1
self.loss_meters.update(loss.item(), n=images.size(0))
self.acc_meters.update(acc.item(), n=images.size(0))
# self.acc5_meters.update(acc5.item(), n=images.size(0))
self.acc5_meters.update(acc5, n=images.size(0))
payload = {"acc": acc.item(),
"acc_avg": self.acc_meters.avg,
"acc5": acc5,
"acc5_avg": self.acc5_meters.avg,
"loss": loss.item(),
"loss_avg": self.loss_meters.avg}
return payload
def eval_recover_batch(self, images, labels, model):
images, labels = images.to(device, non_blocking=True), labels.to(device, non_blocking=True)
with torch.no_grad():
pred = model(images)
loss = self.criterion(pred, labels)
if pred.shape[1] >= 5:
acc, acc5 = util.accuracy(pred, labels, topk=(1, 5))
else:
acc, = util.accuracy(pred, labels, topk=(1,))
acc5 = 1
# acc, acc5 = util.accuracy(pred, labels, topk=(1, 5))
_, preds = torch.max(pred, 1)
for t, p in zip(labels.view(-1), preds.view(-1)):
self.recover_confusion_matrix[t.long(), p.long()] += 1
self.recover_loss_meters.update(loss.item(), n=images.size(0))
self.recover_acc_meters.update(acc.item(), n=images.size(0))
# self.recover_acc5_meters.update(acc5.item(), n=images.size(0))
self.acc5_meters.update(acc5, n=images.size(0))
recover_payload = {"acc": acc.item(),
"acc_avg": self.recover_acc_meters.avg,
"acc5": acc5,
"acc5_avg": self.recover_acc5_meters.avg,
"loss": loss.item(),
"loss_avg": self.recover_loss_meters.avg}
return recover_payload