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validation.py
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validation.py
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from torch.autograd import Variable
import warnings
warnings.filterwarnings("ignore")
import torch
import torch.optim as optim
from datetime import datetime, timedelta
from torch.utils.data import DataLoader
import torch.nn as nn
from data.dataset import NCPDataset, NCP2DDataset, NCPJPGDataset, NCPJPGDataset_new, NCPJPGtestDataset_new
import os, cv2
import numpy as np
def _validate(modelOutput, length, labels, total=None, wrong=None):
averageEnergies = torch.mean(modelOutput.data, 1)
for i in range(modelOutput.size(0)):
# print(modelOutput[i,:length[i]].sum(0).shape)
averageEnergies[i] = modelOutput[i, :length[i]].mean(0)
maxvalues, maxindices = torch.max(averageEnergies, 1)
# print(maxindices.cpu().numpy())
# print(labels.cpu().numpy())
count = 0
for i in range(0, labels.squeeze(1).size(0)):
l = int(labels.squeeze(1)[i].cpu())
if total is not None:
if l not in total:
total[l] = 1
else:
total[l] += 1
if maxindices[i] == labels.squeeze(1)[i]:
count += 1
else:
if wrong is not None:
if l not in wrong:
wrong[l] = 1
else:
wrong[l] += 1
return (averageEnergies, count)
class Validator():
def __init__(self, options, mode, model, savenpy):
self.R = 'R' in options['general'].keys()
self.model = model
self.cls_num = options['general']['class_num']
self.use_plus = options['general']['use_plus']
self.use_3d = options['general']['use_3d']
self.usecudnn = options["general"]["usecudnn"]
self.use_lstm = options["general"]["use_lstm"]
self.batchsize = options["input"]["batchsize"]
self.use_slice = options['general']['use_slice']
self.asinput = options['general']['plus_as_input']
self.USE_25D = options['general']['use25d']
if options['general']['use_slice']:
if self.USE_25D:
f = 'data/3cls_test.list'
self.validationdataset = NCPJPGtestDataset_new(options[mode]["data_root"],
options["training"]["padding"],
f, cls_num=self.cls_num, mod=options['general']['mod'],
options=options)
else:
self.validationdataset = NCPJPGDataset_new(options[mode]["data_root"],
options[mode]["index_root"],
options[mode]["padding"],
False, cls_num=self.cls_num,
mod=options['general']['mod'],
options=options)
else:
if options['general']['use_3d']:
self.validationdataset = NCPDataset(options[mode]["index_root"],
options[mode]["padding"],
False,
z_length=options["model"]["z_length"])
else:
self.validationdataset = NCP2DDataset(options[mode]["data_root"],
options[mode]["index_root"],
options[mode]["padding"],
False)
self.savingnpy = savenpy
self.tot_data = len(self.validationdataset)
self.validationdataloader = DataLoader(
self.validationdataset,
batch_size=options["input"]["batchsize"],
shuffle=True,
num_workers=options["input"]["numworkers"],
drop_last=False
)
self.mode = mode
self.epoch = 0
def __call__(self):
self.epoch += 1
with torch.no_grad():
print("Starting {}...".format(self.mode))
count = np.zeros((self.cls_num + self.use_plus * 2 * (1 - self.asinput)))
Matrix = np.zeros((self.cls_num, self.cls_num))
if self.use_3d:
validator_function = self.model.validator_function()
if self.use_lstm:
validator_function = _validate
self.model.eval()
LL = []
GG = []
AA = []
if (self.usecudnn):
net = nn.DataParallel(self.model).cuda()
error_dir = 'error/'
os.makedirs(error_dir, exist_ok=True)
cnt = 0
num_samples = np.zeros((self.cls_num + self.use_plus * 2 * (1 - self.asinput)))
for i_batch, sample_batched in enumerate(self.validationdataloader):
input = Variable(sample_batched['temporalvolume']).cuda()
labels = Variable(sample_batched['label']).cuda()
# length = len(sample_batched['length'][1])
names = sample_batched['name']
outputs, f = net(input)
output_numpy = np.exp(outputs.cpu().numpy())
output_numpy = output_numpy[:, [0, -1]]
# output_numpy_ab=np.sum(output_numpy[:,1:],1)
# output_numpy=np.stack([output_numpy[:,0],output_numpy_ab],-1)
output_numpy = output_numpy / output_numpy.sum(1, keepdims=True)
pre = np.array(output_numpy[:, -1] > 0.5, np.int)
isacc = labels.cpu().numpy().reshape(labels.size(0)) == pre
label_numpy = labels.cpu().numpy()[:, 0]
# argmax = (-vector.cpu().numpy()).argsort()
for i in range(labels.size(0)):
LL.append([names[i], output_numpy[i, -1], label_numpy[i]])
Matrix[label_numpy[i], pre[i]] += 1
if isacc[i] == 1:
count[labels[i]] += 1
num_samples[labels[i]] += 1
if self.mode == 'validation':
if labels[i] == 1 and output_numpy[i, -1] > 0.99:
I = np.array(input[i, :, :, :].cpu().numpy() * 255, np.uint8).transpose(1, 2, 0)[:, :,
[2, 1, 0]]
cv2.imwrite('/mnt/data9/covid_detector_jpgs/selected_train1/abnor/abnor_' +
names[i].split('/')[-1].split('.')[0] + '.jpg', I)
if labels[i] == 0 and output_numpy[i, 0] > 0.99:
I = np.array(input[i, :, :, :].cpu().numpy() * 255, np.uint8).transpose(1, 2, 0)[:, :,
[2, 1, 0]]
cv2.imwrite('/mnt/data9/covid_detector_jpgs/selected_train1/nor/nor_' +
names[i].split('/')[-1].split('.')[0] + '.jpg', I)
print(count[:self.cls_num].sum() / num_samples[:self.cls_num].sum(), np.mean(AA))
LL = np.array(LL)
np.save(self.savingnpy, LL)
print(Matrix)
return count / num_samples, count[:self.cls_num].sum() / num_samples[:self.cls_num].sum()