mirror of https://github.com/JDAI-CV/DCL.git
82 lines
3.0 KiB
Python
82 lines
3.0 KiB
Python
#coding=utf8
|
|
from __future__ import print_function, division
|
|
import os,time,datetime
|
|
import numpy as np
|
|
import datetime
|
|
from math import ceil
|
|
|
|
import torch
|
|
from torch import nn
|
|
from torch.autograd import Variable
|
|
import torch.nn.functional as F
|
|
|
|
from utils.utils import LossRecord
|
|
|
|
import pdb
|
|
|
|
def dt():
|
|
return datetime.datetime.now().strftime("%Y-%m-%d-%H_%M_%S")
|
|
|
|
def eval_turn(model, data_loader, val_version, epoch_num, log_file):
|
|
|
|
model.train(False)
|
|
|
|
val_corrects1 = 0
|
|
val_corrects2 = 0
|
|
val_corrects3 = 0
|
|
val_size = data_loader.__len__()
|
|
item_count = data_loader.total_item_len
|
|
t0 = time.time()
|
|
get_l1_loss = nn.L1Loss()
|
|
get_ce_loss = nn.CrossEntropyLoss()
|
|
|
|
val_batch_size = data_loader.batch_size
|
|
val_epoch_step = data_loader.__len__()
|
|
num_cls = data_loader.num_cls
|
|
|
|
val_loss_recorder = LossRecord(val_batch_size)
|
|
val_celoss_recorder = LossRecord(val_batch_size)
|
|
print('evaluating %s ...'%val_version, flush=True)
|
|
with torch.no_grad():
|
|
for batch_cnt_val, data_val in enumerate(data_loader):
|
|
inputs = Variable(data_val[0].cuda())
|
|
labels = Variable(torch.from_numpy(np.array(data_val[1])).long().cuda())
|
|
outputs = model(inputs)
|
|
loss = 0
|
|
|
|
ce_loss = get_ce_loss(outputs[0], labels).item()
|
|
loss += ce_loss
|
|
|
|
val_loss_recorder.update(loss)
|
|
val_celoss_recorder.update(ce_loss)
|
|
|
|
if outputs[1].size(1) != 2:
|
|
outputs_pred = outputs[0] + outputs[1][:,0:num_cls] + outputs[1][:,num_cls:2*num_cls]
|
|
else:
|
|
outputs_pred = outputs[0]
|
|
top3_val, top3_pos = torch.topk(outputs_pred, 3)
|
|
|
|
print('{:s} eval_batch: {:-6d} / {:d} loss: {:8.4f}'.format(val_version, batch_cnt_val, val_epoch_step, loss), flush=True)
|
|
|
|
batch_corrects1 = torch.sum((top3_pos[:, 0] == labels)).data.item()
|
|
val_corrects1 += batch_corrects1
|
|
batch_corrects2 = torch.sum((top3_pos[:, 1] == labels)).data.item()
|
|
val_corrects2 += (batch_corrects2 + batch_corrects1)
|
|
batch_corrects3 = torch.sum((top3_pos[:, 2] == labels)).data.item()
|
|
val_corrects3 += (batch_corrects3 + batch_corrects2 + batch_corrects1)
|
|
|
|
val_acc1 = val_corrects1 / item_count
|
|
val_acc2 = val_corrects2 / item_count
|
|
val_acc3 = val_corrects3 / item_count
|
|
|
|
log_file.write(val_version + '\t' +str(val_loss_recorder.get_val())+'\t' + str(val_celoss_recorder.get_val()) + '\t' + str(val_acc1) + '\t' + str(val_acc3) + '\n')
|
|
|
|
t1 = time.time()
|
|
since = t1-t0
|
|
print('--'*30, flush=True)
|
|
print('% 3d %s %s %s-loss: %.4f ||%s-acc@1: %.4f %s-acc@2: %.4f %s-acc@3: %.4f ||time: %d' % (epoch_num, val_version, dt(), val_version, val_loss_recorder.get_val(init=True), val_version, val_acc1,val_version, val_acc2, val_version, val_acc3, since), flush=True)
|
|
print('--' * 30, flush=True)
|
|
|
|
return val_acc1, val_acc2, val_acc3
|
|
|