/
eval.py
2151 lines (1912 loc) · 88.7 KB
/
eval.py
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import numpy as np
import torch
print('-----')
import torch.nn.functional as F
from torch.autograd import Variable
print('-----')
import os
import logging
print('-----')
from sklearn.preprocessing import label_binarize
from sklearn.metrics import f1_score, roc_auc_score, accuracy_score
print('-----')
# from openTSNE import TSNE
import pandas as pd
import uuid
import wandb
import matplotlib.pyplot as plt
# import umap
print('-----')
def feat_get(step, G, Cs, dataset_source, dataset_target, save_path,
ova=True):
G.eval()
for batch_idx, data in enumerate(dataset_source):
if batch_idx == 500:
break
with torch.no_grad():
img_s = data[0]
label_s = data[1]
img_s, label_s = Variable(img_s.cuda()), \
Variable(label_s.cuda())
feat_s = G(img_s)
if batch_idx == 0:
feat_all_s = feat_s.data.cpu().numpy()
label_all_s = label_s.data.cpu().numpy()
else:
feat_s = feat_s.data.cpu().numpy()
label_s = label_s.data.cpu().numpy()
feat_all_s = np.r_[feat_all_s, feat_s]
label_all_s = np.r_[label_all_s, label_s]
for batch_idx, data in enumerate(dataset_target):
if batch_idx == 500:
break
with torch.no_grad():
img_t = data[0]
label_t = data[1]
img_t, label_t = Variable(img_t.cuda()), \
Variable(label_t.cuda())
feat_t = G(img_t)
out_t = Cs[0](feat_t)
pred = out_t.data.max(1)[1]
out_t = F.softmax(out_t)
if ova:
out_open = Cs[1](feat_t)
out_open = F.softmax(out_open.view(out_t.size(0), 2, -1), 1)
tmp_range = torch.arange(out_t.size(0)).long().cuda()
pred_unk = out_open[tmp_range, 0, pred]
weights_open = Cs[1].module.fc.weight.data.cpu().numpy()
else:
pred_unk = -torch.sum(out_t * torch.log(out_t), 1)
if batch_idx == 0:
feat_all = feat_t.data.cpu().numpy()
label_all = label_t.data.cpu().numpy()
unk_all = pred_unk.data.cpu().numpy()
pred_all = pred.data.cpu().numpy()
pred_all_soft = out_t.data.cpu().numpy()
else:
feat_t = feat_t.data.cpu().numpy()
label_t = label_t.data.cpu().numpy()
pred_unk = pred_unk.data.cpu().numpy()
feat_all = np.r_[feat_all, feat_t]
label_all = np.r_[label_all, label_t]
unk_all = np.r_[unk_all, pred_unk]
pred_all = np.r_[pred_all, pred.data.cpu().numpy()]
pred_all_soft = np.r_[pred_all_soft, out_t.data.cpu().numpy()]
if not os.path.exists(save_path):
os.makedirs(save_path, exist_ok=True)
np.save(os.path.join(save_path, "save_%s_ova_%s_target_feat.npy" % (step, ova)), feat_all)
np.save(os.path.join(save_path, "save_%s_ova_%s_target_anom.npy" % (step, ova)), unk_all)
np.save(os.path.join(save_path, "save_%s_ova_%s_target_pred.npy" % (step, ova)), pred_all)
np.save(os.path.join(save_path, "save_%s_ova_%s_target_soft.npy" % (step, ova)), pred_all_soft)
np.save(os.path.join(save_path, "save_%s_ova_%s_source_feat.npy" % (step, ova)), feat_all_s)
np.save(os.path.join(save_path, "save_%s_ova_%s_target_label.npy" % (step, ova)), label_all)
np.save(os.path.join(save_path, "save_%s_ova_%s_source_label.npy" % (step, ova)), label_all_s)
if ova:
np.save(os.path.join(save_path, "save_%s_ova_%s_weight.npy" % (step, ova)), weights_open)
def inference(dataset_test, G, Cs, class_list, entropy, thr, per_class_correct, per_class_num, correct_close, correct, size, thr_open=0.5, sourse_loader_bool=False):
feature_list = []
predict_list = []
path_list = []
pred_unk_list = []
for batch_idx, data in enumerate(dataset_test):
with torch.no_grad():
if sourse_loader_bool == False:
img_t, label_t, path_t = data[0].cuda(), data[1].cuda(), data[2]
else:
img_t, label_t, path_t = data[0][0].cuda(), data[1].cuda(), str(data[2])
path_list += path_t
feat = G(img_t)
# feat = (feat)
out_t = Cs[0](feat)
if batch_idx == 0:
open_class = int(out_t.size(1))
class_list.append(open_class)
pred = out_t.data.max(1)[1]
feature_list.append(feat)
# predict_list.append(pred)
# print('out_t.shape', out_t.shape)
# print('label_t.shape', label_t.shape)
# print('pred.shape', pred.shape)
correct_close += pred.eq(label_t.data).cpu().sum()
out_t = F.softmax(out_t, dim=1)
entr = -torch.sum(out_t * torch.log(out_t), 1).data.cpu().numpy()
if entropy:
pred_unk = -torch.sum(out_t * torch.log(out_t), 1)
ind_unk = np.where(entr > thr)[0]
else:
out_open = Cs[1](feat)
out_open = F.softmax(out_open.view(out_t.size(0), 2, -1),1)
tmp_range = torch.arange(out_t.size(0)).long().cuda()
pred_unk = out_open[tmp_range, 0, pred]
ind_unk = np.where(pred_unk.data.cpu().numpy() > thr_open)[0]
pred_unk_list.append(pred_unk)
pred[ind_unk] = open_class
predict_list.append(pred)
correct += pred.eq(label_t.data).cpu().sum()
pred = pred.cpu().numpy()
k = label_t.data.size()[0]
for i, t in enumerate(class_list):
t_ind = np.where(label_t.data.cpu().numpy() == t)
correct_ind = np.where(pred[t_ind[0]] == t)
per_class_correct[i] += float(len(correct_ind[0]))
per_class_num[i] += float(len(t_ind[0]))
size += k
if open:
label_t = label_t.data.cpu().numpy()
if batch_idx == 0:
label_all = label_t
pred_open = pred_unk.data.cpu().numpy()
pred_all = out_t.data.cpu().numpy()
pred_ent = entr
else:
pred_open = np.r_[pred_open, pred_unk.data.cpu().numpy()]
pred_ent = np.r_[pred_ent, entr]
pred_all = np.r_[pred_all, out_t.data.cpu().numpy()]
label_all = np.r_[label_all, label_t]
return feature_list, predict_list, path_list, pred_unk_list, label_all, pred_open, pred_ent, pred_all, size, correct, correct_close
def inference_amlp(dataset_test, G_mlp, Cs, class_list, entropy, thr, per_class_correct, per_class_num, correct_close, correct, size, thr_open=0.5, sourse_loader_bool=False):
G, mlp = G_mlp
feature_list = []
predict_list = []
path_list = []
pred_unk_list = []
for batch_idx, data in enumerate(dataset_test):
with torch.no_grad():
# if sourse_loader_bool == False:
img_t, label_t, path_t = data[0].cuda(), data[1].cuda(), data[2]
# else:
# img_t, label_t, path_t = data[0][0].cuda(), data[1].cuda(), str(data[2])
path_list += path_t
feat = mlp(G(img_t))
# feat = (feat)
out_t = Cs[0](feat)
if batch_idx == 0:
open_class = int(out_t.size(1))
class_list.append(open_class)
pred = out_t.data.max(1)[1]
feature_list.append(feat)
# predict_list.append(pred)
# print('out_t.shape', out_t.shape)
# print('label_t.shape', label_t.shape)
# print('pred.shape', pred.shape)
correct_close += pred.eq(label_t.data).cpu().sum()
out_t = F.softmax(out_t, dim=1)
entr = -torch.sum(out_t * torch.log(out_t), 1).data.cpu().numpy()
if entropy:
pred_unk = -torch.sum(out_t * torch.log(out_t), 1)
ind_unk = np.where(entr > thr)[0]
else:
out_open = Cs[1](feat)
out_open = F.softmax(out_open.view(out_t.size(0), 2, -1),1)
tmp_range = torch.arange(out_t.size(0)).long().cuda()
pred_unk = out_open[tmp_range, 0, pred]
ind_unk = np.where(pred_unk.data.cpu().numpy() > thr_open)[0]
pred_unk_list.append(pred_unk)
pred[ind_unk] = open_class
predict_list.append(pred)
correct += pred.eq(label_t.data).cpu().sum()
pred = pred.cpu().numpy()
k = label_t.data.size()[0]
for i, t in enumerate(class_list):
t_ind = np.where(label_t.data.cpu().numpy() == t)
correct_ind = np.where(pred[t_ind[0]] == t)
per_class_correct[i] += float(len(correct_ind[0]))
per_class_num[i] += float(len(t_ind[0]))
size += k
if open:
label_t = label_t.data.cpu().numpy()
if batch_idx == 0:
label_all = label_t
pred_open = pred_unk.data.cpu().numpy()
pred_all = out_t.data.cpu().numpy()
pred_ent = entr
else:
pred_open = np.r_[pred_open, pred_unk.data.cpu().numpy()]
pred_ent = np.r_[pred_ent, entr]
pred_all = np.r_[pred_all, out_t.data.cpu().numpy()]
label_all = np.r_[label_all, label_t]
return feature_list, predict_list, path_list, pred_unk_list, label_all, pred_open, pred_ent, pred_all, size, correct, correct_close
def inference_amlp_v2(dataset_test, G_mlp, Cs, class_list, entropy, thr, per_class_correct, per_class_num, correct_close, correct, size, thr_open=0.5, sourse_loader_bool=False):
G, mlp = G_mlp
feature_list = []
predict_list = []
path_list = []
pred_unk_list = []
c1_softmax_all_predict_list = []
c2_softmax_all_predict_list = []
for batch_idx, data in enumerate(dataset_test):
with torch.no_grad():
# if sourse_loader_bool == False:
img_t, label_t, path_t = data[0].cuda(), data[1].cuda(), data[2]
# else:
# img_t, label_t, path_t = data[0][0].cuda(), data[1].cuda(), str(data[2])
path_list += path_t
feat = mlp(G(img_t))
# feat = (feat)
out_t = Cs[0](feat)
c1_softmax_all_predict_list.append(torch.softmax(out_t, dim=1))
# import pdb; pdb.set_trace()
if batch_idx == 0:
open_class = int(out_t.size(1))
class_list.append(open_class)
pred = out_t.data.max(1)[1]
feature_list.append(feat)
# predict_list.append(pred)
# print('out_t.shape', out_t.shape)
# print('label_t.shape', label_t.shape)
# print('pred.shape', pred.shape)
correct_close += pred.eq(label_t.data).cpu().sum()
out_t = F.softmax(out_t, dim=1)
entr = -torch.sum(out_t * torch.log(out_t), 1).data.cpu().numpy()
if entropy:
pred_unk = -torch.sum(out_t * torch.log(out_t), 1)
ind_unk = np.where(entr > thr)[0]
else:
out_open = Cs[1](feat)
out_open = F.softmax(out_open.view(out_t.size(0), 2, -1),1)
c2_softmax_all_predict_list.append(out_open.reshape(out_t.size(0), -1))
tmp_range = torch.arange(out_t.size(0)).long().cuda()
pred_unk = out_open[tmp_range, 0, pred]
ind_unk = np.where(pred_unk.data.cpu().numpy() > thr_open)[0]
pred_unk_list.append(pred_unk)
pred[ind_unk] = open_class
predict_list.append(pred)
correct += pred.eq(label_t.data).cpu().sum()
pred = pred.cpu().numpy()
k = label_t.data.size()[0]
for i, t in enumerate(class_list):
t_ind = np.where(label_t.data.cpu().numpy() == t)
correct_ind = np.where(pred[t_ind[0]] == t)
per_class_correct[i] += float(len(correct_ind[0]))
per_class_num[i] += float(len(t_ind[0]))
size += k
if open:
label_t = label_t.data.cpu().numpy()
if batch_idx == 0:
label_all = label_t
pred_open = pred_unk.data.cpu().numpy()
pred_all = out_t.data.cpu().numpy()
pred_ent = entr
else:
pred_open = np.r_[pred_open, pred_unk.data.cpu().numpy()]
pred_ent = np.r_[pred_ent, entr]
pred_all = np.r_[pred_all, out_t.data.cpu().numpy()]
label_all = np.r_[label_all, label_t]
return c1_softmax_all_predict_list, c2_softmax_all_predict_list, feature_list, predict_list, path_list, pred_unk_list, label_all, pred_open, pred_ent, pred_all, size, correct, correct_close
def inference_amlp_v2_only_c2(dataset_test, G_mlp, Cs, class_list, entropy, thr, per_class_correct, per_class_num, correct_close, correct, size, thr_open=0.5, sourse_loader_bool=False):
G, mlp = G_mlp
feature_list = []
predict_list = []
path_list = []
pred_unk_list = []
c1_softmax_all_predict_list = []
c2_softmax_all_predict_list = []
for batch_idx, data in enumerate(dataset_test):
with torch.no_grad():
# if sourse_loader_bool == False:
img_t, label_t, path_t = data[0].cuda(), data[1].cuda(), data[2]
# else:
# img_t, label_t, path_t = data[0][0].cuda(), data[1].cuda(), str(data[2])
path_list += path_t
feat = mlp(G(img_t))
# feat = (feat)
# out_t = Cs[0](feat)
out_t = Cs[1](feat).view(feat.shape[0], 2, -1)[:,1,:]
c1_softmax_all_predict_list.append(torch.softmax(out_t, dim=1))
# import pdb; pdb.set_trace()
if batch_idx == 0:
open_class = int(out_t.size(1))
class_list.append(open_class)
pred = out_t.data.max(1)[1]
feature_list.append(feat)
# predict_list.append(pred)
# print('out_t.shape', out_t.shape)
# print('label_t.shape', label_t.shape)
# print('pred.shape', pred.shape)
correct_close += pred.eq(label_t.data).cpu().sum()
out_t = F.softmax(out_t, dim=1)
entr = -torch.sum(out_t * torch.log(out_t), 1).data.cpu().numpy()
if entropy:
pred_unk = -torch.sum(out_t * torch.log(out_t), 1)
ind_unk = np.where(entr > thr)[0]
else:
out_open = Cs[1](feat)
out_open = F.softmax(out_open.view(out_t.size(0), 2, -1),1)
c2_softmax_all_predict_list.append(out_open.reshape(out_t.size(0), -1))
tmp_range = torch.arange(out_t.size(0)).long().cuda()
pred_unk = out_open[tmp_range, 0, pred]
ind_unk = np.where(pred_unk.data.cpu().numpy() > thr_open)[0]
pred_unk_list.append(pred_unk)
pred[ind_unk] = open_class
predict_list.append(pred)
correct += pred.eq(label_t.data).cpu().sum()
pred = pred.cpu().numpy()
k = label_t.data.size()[0]
for i, t in enumerate(class_list):
t_ind = np.where(label_t.data.cpu().numpy() == t)
correct_ind = np.where(pred[t_ind[0]] == t)
per_class_correct[i] += float(len(correct_ind[0]))
per_class_num[i] += float(len(t_ind[0]))
size += k
if open:
label_t = label_t.data.cpu().numpy()
if batch_idx == 0:
label_all = label_t
pred_open = pred_unk.data.cpu().numpy()
pred_all = out_t.data.cpu().numpy()
pred_ent = entr
else:
pred_open = np.r_[pred_open, pred_unk.data.cpu().numpy()]
pred_ent = np.r_[pred_ent, entr]
pred_all = np.r_[pred_all, out_t.data.cpu().numpy()]
label_all = np.r_[label_all, label_t]
return c1_softmax_all_predict_list, c2_softmax_all_predict_list, feature_list, predict_list, path_list, pred_unk_list, label_all, pred_open, pred_ent, pred_all, size, correct, correct_close
def inference_amlp_v2_only_c2_maxcompair(dataset_test, G_mlp, Cs, class_list, entropy, thr, per_class_correct, per_class_num, correct_close, correct, size, thr_open=0.5, sourse_loader_bool=False):
G, mlp = G_mlp
feature_list = []
predict_list = []
path_list = []
pred_unk_list = []
c1_softmax_all_predict_list = []
c2_softmax_all_predict_list = []
for batch_idx, data in enumerate(dataset_test):
with torch.no_grad():
# if sourse_loader_bool == False:
img_t, label_t, path_t = data[0].cuda(), data[1].cuda(), data[2]
# else:
# img_t, label_t, path_t = data[0][0].cuda(), data[1].cuda(), str(data[2])
path_list += path_t
feat = mlp(G(img_t))
# feat = (feat)
# out_t = Cs[0](feat)
out_t = Cs[1](feat).view(feat.shape[0], 2, -1)[:,1,:]
c1_softmax_all_predict_list.append(torch.softmax(out_t, dim=1))
# import pdb; pdb.set_trace()
if batch_idx == 0:
open_class = int(out_t.size(1))
class_list.append(open_class)
pred = out_t.data.max(1)[1]
feature_list.append(feat)
# predict_list.append(pred)
# print('out_t.shape', out_t.shape)
# print('label_t.shape', label_t.shape)
# print('pred.shape', pred.shape)
correct_close += pred.eq(label_t.data).cpu().sum()
out_t = F.softmax(out_t, dim=1)
entr = -torch.sum(out_t * torch.log(out_t), 1).data.cpu().numpy()
if entropy:
pred_unk = -torch.sum(out_t * torch.log(out_t), 1)
ind_unk = np.where(entr > thr)[0]
else:
# out_open = Cs[1](feat)
# out_open = F.softmax(out_open.view(out_t.size(0), 2, -1),1)
# c2_softmax_all_predict_list.append(out_open.reshape(out_t.size(0), -1))
# tmp_range = torch.arange(out_t.size(0)).long().cuda()
# pred_unk = out_open[tmp_range, 0, pred]
out_open = Cs[1](feat)
out_open= F.softmax(out_open, 1)
out_open = out_open.view(out_t.size(0), 2, -1)
c2_softmax_all_predict_list.append(out_open.reshape(out_t.size(0), -1))
tmp_range = torch.arange(out_t.size(0)).long().cuda()
pred_unk = out_open[tmp_range, 0, pred]
# ind_unk = np.where(pred_unk.data.cpu().numpy() > thr_open)[0]
ind_unk = torch.tensor(np.where(
np.max(out_open.data.cpu().numpy()[:, 0, :], axis=1) >= \
np.max(out_open.data.cpu().numpy()[:, 1, :], axis=1)
)[0])
# unknown_cont = (out_open.data.cpu().numpy()[:, 0, :] > out_open.data.cpu().numpy()[:, 1, :]).sum(axis=1)
# print(unknown_cont)
pred_unk_list.append(pred_unk)
pred[ind_unk] = open_class
predict_list.append(pred)
correct += pred.eq(label_t.data).cpu().sum()
pred = pred.cpu().numpy()
k = label_t.data.size()[0]
for i, t in enumerate(class_list):
t_ind = np.where(label_t.data.cpu().numpy() == t)
correct_ind = np.where(pred[t_ind[0]] == t)
per_class_correct[i] += float(len(correct_ind[0]))
per_class_num[i] += float(len(t_ind[0]))
size += k
if open:
label_t = label_t.data.cpu().numpy()
if batch_idx == 0:
label_all = label_t
pred_open = pred_unk.data.cpu().numpy()
pred_all = out_t.data.cpu().numpy()
pred_ent = entr
else:
pred_open = np.r_[pred_open, pred_unk.data.cpu().numpy()]
pred_ent = np.r_[pred_ent, entr]
pred_all = np.r_[pred_all, out_t.data.cpu().numpy()]
label_all = np.r_[label_all, label_t]
return c1_softmax_all_predict_list, c2_softmax_all_predict_list, feature_list, predict_list, path_list, pred_unk_list, label_all, pred_open, pred_ent, pred_all, size, correct, correct_close
def inference_amlp_v2_only_c2_maxcompair_eachclass(dataset_test, G_mlp, Cs, class_list, entropy, thr, per_class_correct, per_class_num, correct_close, correct, size, thr_open=0.5, sourse_loader_bool=False):
G, mlp = G_mlp
feature_list = []
predict_list = []
path_list = []
pred_unk_list = []
c1_softmax_all_predict_list = []
c2_softmax_all_predict_list = []
for batch_idx, data in enumerate(dataset_test):
with torch.no_grad():
# if sourse_loader_bool == False:
img_t, label_t, path_t = data[0].cuda(), data[1].cuda(), data[2]
# else:
# img_t, label_t, path_t = data[0][0].cuda(), data[1].cuda(), str(data[2])
path_list += path_t
feat = mlp(G(img_t))
# feat = (feat)
# out_t = Cs[0](feat)
out_t = Cs[1](feat).view(feat.shape[0], 2, -1)[:,1,:]
c1_softmax_all_predict_list.append(torch.softmax(out_t, dim=1))
# import pdb; pdb.set_trace()
if batch_idx == 0:
open_class = int(out_t.size(1))
class_list.append(open_class)
pred = out_t.data.max(1)[1]
feature_list.append(feat)
# predict_list.append(pred)
# print('out_t.shape', out_t.shape)
# print('label_t.shape', label_t.shape)
# print('pred.shape', pred.shape)
correct_close += pred.eq(label_t.data).cpu().sum()
out_t = F.softmax(out_t, dim=1)
entr = -torch.sum(out_t * torch.log(out_t), 1).data.cpu().numpy()
if entropy:
pred_unk = -torch.sum(out_t * torch.log(out_t), 1)
ind_unk = np.where(entr > thr)[0]
else:
# out_open = Cs[1](feat)
# out_open = F.softmax(out_open.view(out_t.size(0), 2, -1),1)
# c2_softmax_all_predict_list.append(out_open.reshape(out_t.size(0), -1))
# tmp_range = torch.arange(out_t.size(0)).long().cuda()
# pred_unk = out_open[tmp_range, 0, pred]
out_open = Cs[1](feat)
out_open= F.softmax(out_open, 1)
out_open = out_open.view(out_t.size(0), 2, -1)
c2_softmax_all_predict_list.append(out_open.reshape(out_t.size(0), -1))
tmp_range = torch.arange(out_t.size(0)).long().cuda()
pred_unk = out_open[tmp_range, 0, pred]
# ind_unk = np.where(pred_unk.data.cpu().numpy() > thr_open)[0]
# ind_unk = torch.tensor(np.where(
# np.max(out_open.data.cpu().numpy()[:, 0, :], axis=1) >= \
# np.max(out_open.data.cpu().numpy()[:, 1, :], axis=1)
# )[0])
unknown_cont = (out_open.data.cpu().numpy()[:, 0, :] > out_open.data.cpu().numpy()[:, 1, :]).sum(axis=1)
ind_unk = (unknown_cont==15)
# print(unknown_cont)
pred_unk_list.append(pred_unk)
pred[ind_unk] = open_class
predict_list.append(pred)
correct += pred.eq(label_t.data).cpu().sum()
pred = pred.cpu().numpy()
k = label_t.data.size()[0]
for i, t in enumerate(class_list):
t_ind = np.where(label_t.data.cpu().numpy() == t)
correct_ind = np.where(pred[t_ind[0]] == t)
per_class_correct[i] += float(len(correct_ind[0]))
per_class_num[i] += float(len(t_ind[0]))
size += k
if open:
label_t = label_t.data.cpu().numpy()
if batch_idx == 0:
label_all = label_t
pred_open = pred_unk.data.cpu().numpy()
pred_all = out_t.data.cpu().numpy()
pred_ent = entr
else:
pred_open = np.r_[pred_open, pred_unk.data.cpu().numpy()]
pred_ent = np.r_[pred_ent, entr]
pred_all = np.r_[pred_all, out_t.data.cpu().numpy()]
label_all = np.r_[label_all, label_t]
return c1_softmax_all_predict_list, c2_softmax_all_predict_list, feature_list, predict_list, path_list, pred_unk_list, label_all, pred_open, pred_ent, pred_all, size, correct, correct_close
def inference_only_c2(dataset_test, G, Cs, class_list, entropy, thr, per_class_correct, per_class_num, correct_close, correct, size, thr_open=0.5, sourse_loader_bool=False):
feature_list = []
predict_list = []
path_list = []
pred_unk_list = []
for batch_idx, data in enumerate(dataset_test):
with torch.no_grad():
if sourse_loader_bool == False:
img_t, label_t, path_t = data[0].cuda(), data[1].cuda(), data[2]
else:
img_t, label_t, path_t = data[0][0].cuda(), data[1].cuda(), str(data[2])
path_list += path_t
feat = G(img_t)
# feat = (feat)
out_t = Cs[1](feat).view(feat.shape[0], 2, -1)[:,1,:]
# print(out_t)
# print('out_t.shape', out_t.shape)
# [:, 1]
if batch_idx == 0:
open_class = int(out_t.size(1))
class_list.append(open_class)
pred = out_t.data.max(1)[1]
feature_list.append(feat)
# predict_list.append(pred)
# print('out_t.shape', out_t.shape)
# print('label_t.shape', label_t.shape)
# print('pred.shape', pred.shape)
correct_close += pred.eq(label_t.data).cpu().sum()
out_t = F.softmax(out_t, dim=1)
entr = -torch.sum(out_t * torch.log(out_t), 1).data.cpu().numpy()
if entropy:
pred_unk = -torch.sum(out_t * torch.log(out_t), 1)
ind_unk = np.where(entr > thr)[0]
else:
out_open = Cs[1](feat)
out_open = F.softmax(out_open.view(out_t.size(0), 2, -1),1)
tmp_range = torch.arange(out_t.size(0)).long().cuda()
pred_unk = out_open[tmp_range, 0, pred]
ind_unk = np.where(pred_unk.data.cpu().numpy() > thr_open)[0]
pred_unk_list.append(pred_unk)
pred[ind_unk] = open_class
predict_list.append(pred)
correct += pred.eq(label_t.data).cpu().sum()
pred = pred.cpu().numpy()
k = label_t.data.size()[0]
for i, t in enumerate(class_list):
t_ind = np.where(label_t.data.cpu().numpy() == t)
correct_ind = np.where(pred[t_ind[0]] == t)
per_class_correct[i] += float(len(correct_ind[0]))
per_class_num[i] += float(len(t_ind[0]))
size += k
if open:
label_t = label_t.data.cpu().numpy()
if batch_idx == 0:
label_all = label_t
pred_open = pred_unk.data.cpu().numpy()
pred_all = out_t.data.cpu().numpy()
pred_ent = entr
else:
pred_open = np.r_[pred_open, pred_unk.data.cpu().numpy()]
pred_ent = np.r_[pred_ent, entr]
pred_all = np.r_[pred_all, out_t.data.cpu().numpy()]
label_all = np.r_[label_all, label_t]
# import pdb; pdb.set_trace()
return feature_list, predict_list, path_list, pred_unk_list, label_all, pred_open, pred_ent, pred_all, size, correct, correct_close
def inference_half(dataset_test, G, Cs, class_list, entropy, thr, per_class_correct, per_class_num, correct_close, correct, size, thr_open=0.5, sourse_loader_bool=False):
feature_list = []
predict_list = []
path_list = []
pred_unk_list = []
for batch_idx, data in enumerate(dataset_test):
with torch.no_grad():
if sourse_loader_bool == False:
img_t, label_t, path_t = data[0].type(torch.HalfTensor).cuda(), data[1].cuda(), data[2]
else:
# import pdb; pdb.set_trace()
img_t, label_t, path_t = data[0][0].type(torch.HalfTensor).cuda(), data[1].cuda(), str(data[2])
# print('img_t.shape', img_t.shape)
# print('label_t.shape', label_t.shape)
path_list += path_t
feat = G(img_t)
# feat = (feat)
out_t = Cs[0](feat)
if batch_idx == 0:
open_class = int(out_t.size(1))
class_list.append(open_class)
pred = out_t.data.max(1)[1]
feature_list.append(feat)
# predict_list.append(pred)
# print('out_t.shape', out_t.shape)
# print('label_t.shape', label_t.shape)
# print('pred.shape', pred.shape)
correct_close += pred.eq(label_t.data).cpu().sum()
out_t = F.softmax(out_t, dim=1)
entr = -torch.sum(out_t * torch.log(out_t), 1).data.cpu().numpy()
if entropy:
pred_unk = -torch.sum(out_t * torch.log(out_t), 1)
ind_unk = np.where(entr > thr)[0]
else:
# print(feat)
# print(Cs[1])
# import pdb; pdb.set_trace()
# print(Cs[1].fc1.weight.data)
out_open =Cs[1](feat)
out_open = F.softmax(out_open.view(out_t.size(0), 2, -1),1)
tmp_range = torch.arange(out_t.size(0)).long().cuda()
pred_unk = out_open[tmp_range, 0, pred]
ind_unk = np.where(pred_unk.data.cpu().numpy() > thr_open)[0]
pred_unk_list.append(pred_unk)
pred[ind_unk] = open_class
predict_list.append(pred)
correct += pred.eq(label_t.data).cpu().sum()
pred = pred.cpu().numpy()
k = label_t.data.size()[0]
for i, t in enumerate(class_list):
t_ind = np.where(label_t.data.cpu().numpy() == t)
correct_ind = np.where(pred[t_ind[0]] == t)
per_class_correct[i] += float(len(correct_ind[0]))
per_class_num[i] += float(len(t_ind[0]))
size += k
if open:
label_t = label_t.data.cpu().numpy()
if batch_idx == 0:
label_all = label_t
pred_open = pred_unk.data.cpu().numpy()
pred_all = out_t.data.cpu().numpy()
pred_ent = entr
else:
pred_open = np.r_[pred_open, pred_unk.data.cpu().numpy()]
pred_ent = np.r_[pred_ent, entr]
pred_all = np.r_[pred_all, out_t.data.cpu().numpy()]
label_all = np.r_[label_all, label_t]
return feature_list, predict_list, path_list, pred_unk_list, label_all, pred_open, pred_ent, pred_all, size, correct, correct_close
def test(step, source_loader, dataset_test, name, n_share, G, Cs, mlp,
open_class = None, open=False, entropy=False, thr=0.5, argsSName=''):
G.eval()
for c in Cs:
c.eval()
## Known Score Calculation.
print('-->')
correct = 0
correct_close = 0
size = 0
per_class_num = np.zeros((n_share + 1))
per_class_correct = np.zeros((n_share + 1)).astype(np.float32)
class_list = [i for i in range(n_share)]
(
feature_list, predict_list, path_list,
pred_unk_list, label_all, pred_open,
pred_ent, pred_all, size,
correct, correct_close
) = inference(
dataset_test, G, Cs, class_list, entropy, thr,
per_class_correct, per_class_num,
correct_close, correct, size, thr_open=0.5,
)
print('-->')
correct_s = 0
correct_close_s = 0
size_s = 0
per_class_num_s = np.zeros((n_share + 1))
per_class_correct_s = np.zeros((n_share + 1)).astype(np.float32)
class_list_s = [i for i in range(n_share)]
(
feature_list_s, predict_list_s, path_list_s,
pred_unk_list_s, label_all_s, pred_open_s,
pred_ent_s, pred_all_s, size_s,
correct_s, correct_close_s
) = inference(
source_loader, G, Cs, class_list_s, entropy, thr,
per_class_correct_s, per_class_num_s,
correct_close_s, correct_s, size_s, sourse_loader_bool=True
)
print('-->')
if open:
Y_test = label_binarize(label_all, classes=[i for i in class_list])
# roc = roc_auc_score(Y_test[:, -1], pred_open)
# roc_ent = roc_auc_score(Y_test[:, -1], pred_ent)
# roc_softmax = roc_auc_score(Y_test[:, -1], -np.max(pred_all, axis=1))
## compute best h-score by grid search. Note that we compupte
## this score just to see the difference between learned threshold
## and best one.
best_th, best_acc, mean_score = select_threshold(
pred_all, pred_open, label_all, class_list)
else:
roc = 0.0
roc_ent = 0.0
best_th = 0.
best_acc = 0.
roc_softmax = 0.0
# import pdb; pdb.set_trace()
print('-->')
logger = logging.getLogger(__name__)
logging.basicConfig(filename=name, format="%(message)s")
logger.setLevel(logging.INFO)
per_class_acc = per_class_correct / per_class_num
acc_all = 100. * float(correct) / float(size)
close_count = float(per_class_num[:len(class_list) - 1].sum())
acc_close_all = 100. *float(correct_close) / close_count
known_acc = per_class_acc[:len(class_list)-1].mean()
unknown = per_class_acc[-1]
h_score = 2 * known_acc * unknown / (known_acc + unknown)
output = ["step %s"%step,
"closed perclass", list(per_class_acc),
"acc per class %s" % (float(per_class_acc.mean())),
"acc %s" % float(acc_all),
"acc close all %s" % float(acc_close_all),
"h score %s" % float(h_score),
# "roc %s"% float(roc),
# "roc ent %s"% float(roc_ent),
# "roc softmax %s"% float(roc_softmax),
"best hscore %s"% float(best_acc),
"best thr %s"% float(best_th)]
logger.info(output)
feature_test = torch.cat(feature_list).detach().cpu().numpy()
pred_unk = torch.cat(pred_unk_list).detach().cpu().numpy()
pred_all = torch.cat(predict_list).detach().cpu().numpy()
# print(feature_test)
feature_test_s = torch.cat(feature_list_s).detach().cpu().numpy()
pred_unk_s = torch.cat(pred_unk_list_s).detach().cpu().numpy()
pred_all_s = torch.cat(predict_list_s).detach().cpu().numpy()
feature_test_t_s = np.concatenate([feature_test, feature_test_s])
num_t_sample = feature_test.shape[0]
print('feature_test_t_s.shape', feature_test_t_s.shape)
if feature_test_t_s.shape[0] < 50000:
feature_test_2d_ts = TSNE().fit(feature_test_t_s)
feature_test_2d = feature_test_2d_ts[:num_t_sample]
plt.scatter(x=feature_test_2d[:,0], y=feature_test_2d[:,1], c=label_all, s=1)
# import pdb; pdb.set_trace()
path_uuidstr_t = str(uuid.uuid4())
plt.colorbar()
plt.savefig('img/t_label{}.png'.format(path_uuidstr_t))
plt.close()
plt.scatter(x=feature_test_2d[:,0], y=feature_test_2d[:,1], c=pred_all, s=1)
plt.colorbar()
plt.savefig('img/t_pred{}.png'.format(path_uuidstr_t))
plt.close()
feature_test_2d_s = feature_test_2d_ts[num_t_sample:]
plt.scatter(x=feature_test_2d_s[:,0], y=feature_test_2d_s[:,1], c=label_all_s, s=1)
# import pdb; pdb.set_trace()
path_uuidstr_s = str(uuid.uuid4())
plt.colorbar()
plt.savefig('img/s_label{}.png'.format(path_uuidstr_s))
plt.close()
plt.scatter(x=feature_test_2d_s[:,0], y=feature_test_2d_s[:,1], c=pred_all_s, s=1)
plt.colorbar()
plt.savefig('img/s_pred{}.png'.format(path_uuidstr_s))
plt.close()
data_path = pd.DataFrame()
data_path['path'] = path_list
data_path['pre'] = pred_all
data_path['lab'] = label_all
data_path['tsne_1'] = feature_test_2d[:,0]
data_path['tsne_2'] = feature_test_2d[:,1]
data_path['pred_unk'] = pred_unk
np.save('file_save/feature_test{}'.format(path_uuidstr_t), feature_test)
np.save('file_save/feature_test_s{}'.format(path_uuidstr_t), feature_test_s)
np.save('file_save/pred_all_s{}'.format(path_uuidstr_t), pred_all_s)
np.save('file_save/pred_all{}'.format(path_uuidstr_t), pred_all)
wandb.log({
'epoch': step,
'entropy'+str(entropy)+'_best best_th':float(best_th),
'entropy'+str(entropy)+'_best hscore':float(best_acc),
'entropy'+str(entropy)+'_t_label':wandb.Image('img/t_label{}.png'.format(path_uuidstr_t)),
'entropy'+str(entropy)+'_t_pred':wandb.Image( 'img/t_pred{}.png'.format(path_uuidstr_t)),
'entropy'+str(entropy)+'_s_label':wandb.Image('img/s_label{}.png'.format(path_uuidstr_s)),
'entropy'+str(entropy)+'_s_pred':wandb.Image( 'img/s_pred{}.png'.format(path_uuidstr_s)),
'feature_test':'file_save/feature_test{}'.format(path_uuidstr_t),
'feature_test_s':'file_save/feature_test_s{}'.format(path_uuidstr_t),
'pred_all_s':'file_save/pred_all_s{}'.format(path_uuidstr_t),
'pred_all':'file_save/pred_all{}'.format(path_uuidstr_t),
'entropy'+str(entropy)+'_acc_all':acc_all,
'entropy'+str(entropy)+'_h_score':h_score,
'entropy'+str(entropy)+'_pre_table': wandb.Table(data=data_path, columns=['path', 'pre', 'label', 'pred_unk'])
})
else:
wandb.log({
'epoch': step,
'entropy'+str(entropy)+'_best best_th':float(best_th),
'entropy'+str(entropy)+'_best hscore':float(best_acc),
'entropy'+str(entropy)+'_acc_all':acc_all,
'entropy'+str(entropy)+'_h_score':h_score,
})
# wandb.save('img/t_label{}.png'.format(path_uuidstr_s))
# wandb.save('img/t_pred{}.png'.format(path_uuidstr_s))
# wandb.save('img/s_label{}.png'.format(path_uuidstr_s))
# wandb.save('img/s_pred{}.png'.format(path_uuidstr_s))
return acc_all, h_score
def test_amlp(step, test_loader_s, dataset_test, name, n_share, G_mlp, Cs, mlp,
open_class = None, open=False, entropy=False, thr=0.5, argsSName='', save_wandb=0):
G, mlp = G_mlp
G.eval()
mlp.eval()
for c in Cs:
c.eval()
## Known Score Calculation.
print('-->')
correct = 0
correct_close = 0
size = 0
per_class_num = np.zeros((n_share + 1))
per_class_correct = np.zeros((n_share + 1)).astype(np.float32)
class_list = [i for i in range(n_share)]
(
feature_list, predict_list, path_list,
pred_unk_list, label_all, pred_open,
pred_ent, pred_all, size,
correct, correct_close
) = inference_amlp(
dataset_test, [G, mlp], Cs, class_list, entropy, thr,
per_class_correct, per_class_num,
correct_close, correct, size, thr_open=0.5,
)
print('-->')
correct_s = 0
correct_close_s = 0
size_s = 0
per_class_num_s = np.zeros((n_share + 1))
per_class_correct_s = np.zeros((n_share + 1)).astype(np.float32)
class_list_s = [i for i in range(n_share)]
(
feature_list_s, predict_list_s, path_list_s,
pred_unk_list_s, label_all_s, pred_open_s,
pred_ent_s, pred_all_s, size_s,
correct_s, correct_close_s
) = inference_amlp(
test_loader_s, [G, mlp], Cs, class_list_s, entropy, thr,
per_class_correct_s, per_class_num_s,
correct_close_s, correct_s, size_s, sourse_loader_bool=True
)
print('-->')
if open:
Y_test = label_binarize(label_all, classes=[i for i in class_list])
# roc = roc_auc_score(Y_test[:, -1], pred_open)
# roc_ent = roc_auc_score(Y_test[:, -1], pred_ent)
# roc_softmax = roc_auc_score(Y_test[:, -1], -np.max(pred_all, axis=1))
## compute best h-score by grid search. Note that we compupte
## this score just to see the difference between learned threshold
## and best one.
best_th, best_acc, mean_score = select_threshold(
pred_all, pred_open, label_all, class_list)
else:
roc = 0.0
roc_ent = 0.0
best_th = 0.
best_acc = 0.
roc_softmax = 0.0
# import pdb; pdb.set_trace()
print('-->')
logger = logging.getLogger(__name__)
logging.basicConfig(filename=name, format="%(message)s")
logger.setLevel(logging.INFO)
per_class_acc = per_class_correct / per_class_num
acc_all = 100. * float(correct) / float(size)
close_count = float(per_class_num[:len(class_list) - 1].sum())
acc_close_all = 100. *float(correct_close) / close_count
known_acc = per_class_acc[:len(class_list)-1].mean()
unknown = per_class_acc[-1]
h_score = 2 * known_acc * unknown / (known_acc + unknown)
output = ["step %s"%step,
"closed perclass", list(per_class_acc),
"acc per class %s" % (float(per_class_acc.mean())),
"acc %s" % float(acc_all),
"acc close all %s" % float(acc_close_all),
"h score %s" % float(h_score),
# "roc %s"% float(roc),
# "roc ent %s"% float(roc_ent),
# "roc softmax %s"% float(roc_softmax),
"best hscore %s"% float(best_acc),
"best thr %s"% float(best_th)]
logger.info(output)
feature_test = torch.cat(feature_list).detach().cpu().numpy()
pred_unk = torch.cat(pred_unk_list).detach().cpu().numpy()
pred_all = torch.cat(predict_list).detach().cpu().numpy()
# print(feature_test)
feature_test_s = torch.cat(feature_list_s).detach().cpu().numpy()
pred_unk_s = torch.cat(pred_unk_list_s).detach().cpu().numpy()
pred_all_s = torch.cat(predict_list_s).detach().cpu().numpy()
feature_test_t_s = np.concatenate([feature_test, feature_test_s])
num_t_sample = feature_test.shape[0]
print('feature_test_t_s.shape', feature_test_t_s.shape)
if feature_test_t_s.shape[0] < 50000:
feature_test_2d_ts = TSNE().fit(feature_test_t_s)
feature_test_2d = feature_test_2d_ts[:num_t_sample]
plt.scatter(x=feature_test_2d[:,0], y=feature_test_2d[:,1], c=label_all, s=1)
# import pdb; pdb.set_trace()
path_uuidstr_t = str(uuid.uuid4())
plt.colorbar()
plt.savefig('img/t_label{}.png'.format(path_uuidstr_t))
plt.close()
plt.scatter(x=feature_test_2d[:,0], y=feature_test_2d[:,1], c=pred_all, s=1)
plt.colorbar()
plt.savefig('img/t_pred{}.png'.format(path_uuidstr_t))