def vis(self):
self.model.eval()
gallery_path = data.testset.imgs
query_path = data.queryset.imgs
# Extract feature
print('extract features, this may take a few minutes')
#query_feature = extract_feature(model, tqdm([(torch.unsqueeze(data.query_image, 0), 1)]))
query_feature = extract_feature(model, tqdm(data.query_loader))
gallery_feature = extract_feature(model, tqdm(data.test_loader))
print(query_feature.size())
for query_Index in range(query_feature.size()[0]):
# sort images
query_img_path = query_path[query_Index]
query_feature_now = query_feature[query_Index].unsqueeze(1)
print(query_feature_now.size())
#query_feature = query_feature.view(-1, 1)
#print(query_feature.size())
score = torch.mm(gallery_feature, query_feature_now)
score = score.squeeze(1).cpu()
score = score.numpy()
index = np.argsort(score) # from small to large
index = index[::-1] # from large to small
# # Remove junk images
# junk_index = np.argwhere(gallery_label == -1)
# mask = np.in1d(index, junk_index, invert=True)
# index = index[mask]
# Visualize the rank result
fig = plt.figure(figsize=(16, 4))
ax = plt.subplot(1, 11, 1)
ax.axis('off')
plt.imshow(plt.imread(query_img_path))
ax.set_title('query')
#print('Top 10 images are as follow:')
for i in range(10):
img_path = gallery_path[index[i]]
#print(img_path)
ax = plt.subplot(1, 11, i + 2)
ax.axis('off')
plt.imshow(plt.imread(img_path))
ax.set_title(img_path.split('/')[-1][:9])
fig.savefig(
"/home/wangminjie/Desktop/wmj/projects/Part-reID_2/result/detect/{}.png"
.format(query_img_path.split('/')[-1][:9]))
print('result saved to show{}.png'.format(
query_img_path.split('/')[-1][:9]))
print("task end")
def run_HetGNN(model, hg, het_graph, config):
# het_graph is used to sample neighbour
hg = hg.to('cpu')
category = config.category
train_mask = hg.nodes[category].data.pop('train_mask')
test_mask = hg.nodes[category].data.pop('test_mask')
train_idx = th.nonzero(train_mask, as_tuple=False).squeeze()
test_idx = th.nonzero(test_mask, as_tuple=False).squeeze()
labels = hg.nodes[category].data.pop('label')
emd = hg.nodes[category].data['dw_embedding']
train_batch = load_link_pred('./a_a_list_train.txt')
test_batch = load_link_pred('./a_a_list_test.txt')
# HetGNN Sampler
batch_sampler = SkipGramBatchSampler(hg, config.batch_size,
config.window_size)
neighbor_sampler = NeighborSampler(het_graph, hg.ntypes,
batch_sampler.num_nodes, config.device)
collator = HetGNNCollator(neighbor_sampler, hg)
dataloader = DataLoader(batch_sampler,
collate_fn=collator.collate_train,
num_workers=config.num_workers)
opt = th.optim.Adam(model.parameters())
pred = ScorePredictor()
dataloader_it = iter(dataloader)
for i in range(config.max_epoch):
model.train()
for batch_id in tqdm.trange(config.batches_per_epoch):
positive_graph, negative_graph, blocks = next(dataloader_it)
blocks = [b.to(config.device) for b in blocks]
positive_graph = positive_graph.to(config.device)
negative_graph = negative_graph.to(config.device)
# we need extract multi-feature
input_features = extract_feature(blocks[0], hg.ntypes)
x = model(blocks[0], input_features)
loss = compute_loss(pred(positive_graph, x),
pred(negative_graph, x))
opt.zero_grad()
loss.backward()
opt.step()
print('Epoch {:05d} |Train - Loss: {:.4f}'.format(i, loss.item()))
input_features = extract_feature(het_graph, hg.ntypes)
x = model(het_graph, input_features)
author_link_prediction(x['author'].to('cpu').detach(), train_batch,
test_batch)
micro_f1, macro_f1 = Hetgnn_evaluate(
x[config.category].to('cpu').detach(), labels, train_idx, test_idx)
print('<Classification> Micro-F1 = %.4f, Macro-F1 = %.4f' %
(micro_f1, macro_f1))
pass
def test_feature_extraction():
print("Testing Feature Extraction with test file..")
result = extract_feature(list_of_files[0])
if len(result) > 1:
print("FEATURE EXTRACTION PASSED")
else:
print("FEATURE EXTRACTION FAILED")
print("Testing Feature Extraction with empty input..")
result = extract_feature("")
if result == "File Doesn't Exist":
print("FEATURE EXTRACTION PASSED")
else:
print("FEATURE EXTRACTION FAILED")
def evaluate(model):
model.eval()
with open(test_file, 'r') as file:
lines = file.readlines()
angles = []
start = time.time()
with torch.no_grad():
for line in tqdm(lines):
tokens = line.split()
file0 = tokens[0]
mel0 = extract_feature(input_file=file0,
feature='fbank',
dim=hp.n_mels,
cmvn=True)
mel0 = build_LFR_features(mel0, m=hp.LFR_m, n=hp.LFR_n)
mel0 = torch.unsqueeze(torch.from_numpy(mel0), dim=0)
mel0 = mel0.to(hp.device)
with torch.no_grad():
output = model(mel0)[0]
feature0 = output.cpu().numpy()
file1 = tokens[1]
mel1 = extract_feature(input_file=file1,
feature='fbank',
dim=hp.n_mels,
cmvn=True)
mel1 = build_LFR_features(mel1, m=hp.LFR_m, n=hp.LFR_n)
mel1 = torch.unsqueeze(torch.from_numpy(mel1), dim=0)
with torch.no_grad():
mel1 = mel1.to(hp.device)
output = model(mel1)[0]
feature1 = output.cpu().numpy()
x0 = feature0 / np.linalg.norm(feature0)
x1 = feature1 / np.linalg.norm(feature1)
cosine = np.dot(x0, x1)
theta = math.acos(cosine)
theta = theta * 180 / math.pi
is_same = tokens[2]
angles.append('{} {}\n'.format(theta, is_same))
elapsed_time = time.time() - start
print('elapsed time(sec) per audio: {}'.format(elapsed_time / (6000 * 2)))
with open(angles_file, 'w') as file:
file.writelines(angles)
def vis(self):
self.model.eval()
gallery_path = data.testset.imgs
gallery_label = data.testset.ids
# Extract feature
print('extract features, this may take a few minutes')
query_feature = extract_feature(
model, tqdm([(torch.unsqueeze(data.query_image, 0), 1)]))
gallery_feature = extract_feature(model, tqdm(data.test_loader))
# sort images
query_feature = query_feature.view(-1, 1)
score = torch.mm(gallery_feature, query_feature)
score = score.squeeze(1).cpu()
score = score.numpy()
index = np.argsort(score) # from small to large
index = index[::-1] # from large to small
# # Remove junk images
# junk_index = np.argwhere(gallery_label == -1)
# mask = np.in1d(index, junk_index, invert=True)
# index = index[mask]
# Visualize the rank result
fig = plt.figure(figsize=(16, 4))
ax = plt.subplot(1, 11, 1)
ax.axis('off')
plt.imshow(plt.imread(opt.query_image))
ax.set_title('query')
print('Top 10 images are as follow:')
for i in range(10):
img_path = gallery_path[index[i]]
print(img_path)
ax = plt.subplot(1, 11, i + 2)
ax.axis('off')
plt.imshow(plt.imread(img_path))
ax.set_title(img_path.split('/')[-1][:9])
fig.savefig("show.png")
print('result saved to show.png')
def choose_best_vec():
train_sets_names = ["A", "B", "C", "D"]
print("Choosing best vectorization method:")
clf = LinearDiscriminantAnalysis()
best_score = 0
best_vec_method = ""
for vec_method in train_sets_names:
X_train, y_train, X_test, _ = training_data()
X_train, X_test = extract_feature(X_train, X_test, vec_method)
# Split Training set to predefined train and cross validation
X_t, X_cv, y_t, y_cv, _ = split_data(X_train, y_train)
model = clf.fit(X_t, y_t)
y_pred = model.predict(X_cv)
score = accuracy_score(y_cv, y_pred)
print("Method:", vec_method, "cv accuracy:", score)
if score > best_score:
best_score = score
best_vec_method = vec_method
print("Best vectorization method:", best_vec_method, "with score of:",
best_score)
return best_vec_method
def get_feature(img):
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# 反向二值化，将黑色的数字变为白色，白色背景变成黑色
retval, binary = cv.threshold(gray, 0, 255,
cv.THRESH_BINARY_INV | cv.THRESH_OTSU)
# 定位到各个数字的RoI区域
contours, hierarchy = cv.findContours(binary, cv.RETR_EXTERNAL,
cv.CHAIN_APPROX_SIMPLE)
rects = []
for c in contours:
rect = cv.boundingRect(c)
# print(rect)
rects.append(rect)
cv.rectangle(img, rect, (0, 0, 255), 1, 8)
# 按左上角x坐标从小到大排列，如图像td1.png，从左到右排列，分别为1-9、0
rects = sorted(rects, key=lambda x: x[0], reverse=False)
feature_list = []
for i, r in enumerate(rects):
x, y, w, h = r
roi = binary[y:y + h, x:x + w]
feature = extract_feature(roi)
feature_list.append(feature)
# print(f"number {(i + 1) % 10} 's features: ", feature)
return np.array(feature_list), np.array(rects) # (n, 40), (n, 4)
def predict(self, audio_path):
feature = extract_feature(audio_path, **self.audio_config).reshape(
(1, 1, self.input_length))
if self.classification:
return self.int2emotions[self.model.predict_classes(feature)[0][0]]
else:
return self.model.predict(feature)[0][0][0]
def pre(self):
json_file = 'results/result_' + time.strftime("%Y%m%d%H%M%S") + '.json'
json_file_rk = 'results/result_' + time.strftime(
"%Y%m%d%H%M%S") + '_rk.json'
self.model.eval()
t1 = time.time()
print('extract features, this may take a few minutes')
feats = extract_feature(self.model, tqdm(self.test_loader))
print('feats:', feats.shape)
qf = feats[:self.num_query]
gf = feats[self.num_query:]
print('qf,gf:', qf.shape, gf.shape)
m, n = qf.shape[0], gf.shape[0]
distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
distmat.addmm_(1, -2, qf, gf.t())
distmat = distmat.cpu().numpy()
self.writeResult(distmat, json_file)
print('re_ranking ...')
distmat_rk = reRanking(qf, gf, 7, 3, 0.85)
self.writeResult(distmat_rk, json_file_rk)
print('Time cost is: {:.2f} s'.format(time.time() - t1))
print('over!')
def gen_feat(opt, model_, data_loader, feat_path):
opt.model_name = model_['model_name']
opt.model_path = os.path.expanduser(model_['model_path'])
opt.weight = os.path.expanduser(model_['weight'])
logger.debug(opt)
model = build_model(opt, 2432)
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
model = model.to("cuda")
model.eval()
with torch.no_grad():
feat = extract_feature(model, tqdm(data_loader))
print('feat:', feat.shape)
# Save to Matlab for check
feature = feat.numpy()
result = {'test_f': feature}
feat_dir, feat_file = os.path.split(feat_path)
if not os.path.exists(feat_dir):
os.makedirs(feat_dir, exist_ok=True)
scipy.io.savemat(feat_path, result)
return feature
def __getitem__(self, i):
sample = self.samples[i]
wave = sample['wave']
trn = sample['trn']
feature = extract_feature(wave)
return feature, trn
def gen_feat(opt, model_, data):
opt.model_name = model_['model_name']
opt.model_path = os.path.expanduser(model_['model_path'])
opt.weight = model_['weight']
print(opt,'\n')
if opt.model_name == 'se_resnext50':
model = build_model(opt, 2432)
# model.load_state_dict(torch.load(opt.weight))
model.load_param(opt.weight)
else:
# model = build_model(opt, 4950)
model = build_model(opt, data.num_classes)
model.load_state_dict(torch.load(opt.weight)['state_dict'])
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
model = model.to("cuda")
model.eval()
with torch.no_grad():
feat = extract_feature(model, tqdm(data.test_loader))
logger.debug('feat: {}'.format(feat.shape))
# Save to Matlab for check
feature = feat.numpy()
result = {'test_f': feature}
mat_dir, mat_file = os.path.split(model_['mat_path'])
if not os.path.exists(mat_dir):
os.makedirs(mat_dir, exist_ok=True)
scipy.io.savemat(model_['mat_path'], result)
return feature
def results():
"""
This route is used to save the file, convert the audio to 16000hz monochannel,
and predict the emotion using the saved binary model
"""
if not os.path.isdir("./audio"):
os.mkdir("audio")
if request.method == 'POST':
try:
f = request.files['file']
filename = secure_filename(f.filename)
f.save(os.path.join(app.config["UPLOAD_FOLDER"], filename))
except:
return render_template('main.html', value="")
wav_file_pre = os.listdir("./audio")[0]
wav_file_pre = f"{os.getcwd()}/audio/{wav_file_pre}"
wav_file = convert(wav_file_pre)
os.remove(wav_file_pre)
model = pickle.load(open(f"{os.getcwd()}/model.model", "rb"))
x_test =extract_feature(wav_file)
y_pred=model.predict(np.array([x_test]))
os.remove(wav_file)
print(y_pred)
return render_template('main.html', value=y_pred[0])
def test(self, use_cuda, use_fed=False):
print("="*10)
print("Start Testing！")
print("="*10)
print('We use the scale: %s' % self.multiple_scale)
for dataset in self.data.datasets:
# if self.use_clustering:
if use_fed and not self.use_clustering:
print("Using federated model")
client_model = self.federated_model.eval()
if use_cuda:
client_model = self.federated_model.cuda()
else:
print("Using local model")
client_model = self.clients[dataset].get_model().eval() # self.federated_model.eval()
if use_cuda:
client_model = client_model.cuda() # self.federated_model.cuda()
# else:
# self.federated_model = self.federated_model.eval()
# if use_cuda:
# self.federated_model = self.federated_model.cuda()
with torch.no_grad():
gallery_feature = extract_feature(client_model, self.data.test_loaders[dataset]['gallery'], self.multiple_scale)
query_feature = extract_feature(client_model, self.data.test_loaders[dataset]['query'], self.multiple_scale)
result = {
'gallery_f': gallery_feature.numpy(),
'gallery_label': self.data.gallery_meta[dataset]['labels'],
'gallery_cam': self.data.gallery_meta[dataset]['cameras'],
'query_f': query_feature.numpy(),
'query_label': self.data.query_meta[dataset]['labels'],
'query_cam': self.data.query_meta[dataset]['cameras']
}
print("====== before loading =======")
# for i in result:
# print(i, np.array(result[i]).shape, result[i][:3])
# file_path = os.path.join(self.project_dir,
# 'model',
# self.model_name,
# 'pytorch_result_{}_{}.mat'.format(dataset, random.randint(0, 100000000)))
# scipy.io.savemat(file_path, result)
print(self.model_name)
print(dataset)
testing_model(result, dataset)
def predict(self, audio_path):
"""
given an `audio_path`, this method extracts the features
and predicts the emotion
"""
feature = extract_feature(audio_path,
**self.audio_config).reshape(1, -1)
return self.model.predict(feature)[0]
def __getitem__(self, i):
sample = self.samples[i]
wave = sample['wave']
trn = sample['trn']
feature = extract_feature(input_file=wave, feature='fbank', dim=80, cmvn=True, delta=True, delta_delta=True)
return feature, trn
def predict_proba(self, audio_path):
""""""
feature = extract_feature(audio_path,
**self.audio_config).reshape(1, -1)
proba = self.model.predict_proba(feature)[0]
result = {}
for emotion, prob in zip(self.emotions, proba):
result[emotion] = prob
return result
def predict_proba(self, audio_path):
if self.classification:
feature = extract_feature(audio_path, **self.audio_config).reshape((1, 1, self.input_length))
proba = self.model.predict(feature)[0][0]
result = {}
for prob, emotion in zip(proba, self.emotions):
result[emotion] = prob
return result
else:
raise NotImplementedError("Probability prediction doesn't make sense for regression")
def evaluate(self):
self.model.eval()
print('extract features, this may take a few minutes')
qf = extract_feature(self.model, tqdm(self.query_loader)).numpy()
gf = extract_feature(self.model, tqdm(self.test_loader)).numpy()
# mqf = extract_feature(self.model, tqdm(self.query_loader)).numpy()
def rank(dist):
r = cmc(dist, self.queryset.ids, self.testset.ids, self.queryset.cameras, self.testset.cameras,
separate_camera_set=False,
single_gallery_shot=False,
first_match_break=True)
m_ap = mean_ap(dist, self.queryset.ids, self.testset.ids, self.queryset.cameras, self.testset.cameras)
return r, m_ap
#
# ######################## re rank##########################
# q_g_dist = np.dot(qf, np.transpose(gf))
# q_q_dist = np.dot(qf, np.transpose(qf))
# g_g_dist = np.dot(gf, np.transpose(gf))
# dist = re_ranking(q_g_dist, q_q_dist, g_g_dist)
#
# r, m_ap = rank(dist)
#
# print('[With Re-Ranking] mAP: {:.4f} rank1: {:.4f} rank3: {:.4f} rank5: {:.4f} rank10: {:.4f}'
# .format(m_ap, r[0], r[2], r[4], r[9]))
#
#
# #########################no re rank##########################
dist = cdist(qf, gf)
r, m_ap = rank(dist)
index = np.argsort(dist, axis=1) # from small to large
index = index[:, :100] + 1
file = opt.weight
file = file.split('/model')[0]
np.savetxt(os.path.join('weights',file, 'rank.txt'), index, delimiter=' ', fmt='%d')
print('[Without Re-Ranking] mAP: {:.4f} rank1: {:.4f} rank3: {:.4f} rank5: {:.4f} rank10: {:.4f}'
.format(m_ap, r[0], r[2], r[4], r[9]))
def evaluate(self):
self.model.eval()
print('extract features, this may take a few minutes')
qf = extract_feature(self.model, tqdm(self.query_loader)).numpy()
gf = extract_feature(self.model, tqdm(self.test_loader)).numpy()
def rank(dist):
r = cmc(dist,
self.queryset.ids,
self.testset.ids,
self.queryset.cameras,
self.testset.cameras,
separate_camera_set=False,
single_gallery_shot=False,
first_match_break=True)
m_ap = mean_ap(dist, self.queryset.ids, self.testset.ids,
self.queryset.cameras, self.testset.cameras)
return r, m_ap
######################### re rank##########################
q_g_dist = np.dot(qf, np.transpose(gf))
q_q_dist = np.dot(qf, np.transpose(qf))
g_g_dist = np.dot(gf, np.transpose(gf))
dist = re_ranking(q_g_dist, q_q_dist, g_g_dist)
r, m_ap = rank(dist)
print(
'[With Re-Ranking] mAP: {:.4f} rank1: {:.4f} rank3: {:.4f} rank5: {:.4f} rank10: {:.4f}'
.format(m_ap, r[0], r[2], r[4], r[9]))
#########################no re rank##########################
dist = cdist(qf, gf)
r, m_ap = rank(dist)
print(
'[Without Re-Ranking] mAP: {:.4f} rank1: {:.4f} rank3: {:.4f} rank5: {:.4f} rank10: {:.4f}'
.format(m_ap, r[0], r[2], r[4], r[9]))
def __getitem__(self, i):
sample = self.samples[i]
wave = sample['audiopath']
label = sample['label']
feature = extract_feature(input_file=wave,
feature='fbank',
dim=hp.n_mels,
cmvn=True)
feature = build_LFR_features(feature, m=hp.LFR_m, n=hp.LFR_n)
return feature, label
def test(self, use_cuda):
print("=" * 10)
print("Start Tesing!")
print("=" * 10)
print('We use the scale: %s' % self.multiple_scale)
for dataset in self.data.datasets:
self.federated_model = self.federated_model.eval()
if use_cuda:
self.federated_model = self.federated_model.cuda()
with torch.no_grad():
gallery_feature = extract_feature(
self.federated_model,
self.data.test_loaders[dataset]['gallery'],
self.multiple_scale)
query_feature = extract_feature(
self.federated_model,
self.data.test_loaders[dataset]['query'],
self.multiple_scale)
result = {
'gallery_f': gallery_feature.numpy(),
'gallery_label': self.data.gallery_meta[dataset]['labels'],
'gallery_cam': self.data.gallery_meta[dataset]['cameras'],
'query_f': query_feature.numpy(),
'query_label': self.data.query_meta[dataset]['labels'],
'query_cam': self.data.query_meta[dataset]['cameras']
}
scipy.io.savemat(
os.path.join(self.project_dir, 'model', self.model_name,
'pytorch_result.mat'), result)
print(self.model_name)
print(dataset)
os.system('python evaluate.py --result_dir {} --dataset {}'.format(
os.path.join(self.project_dir, 'model', self.model_name),
dataset))
def pre(self):
json_file = 'results/result_' + time.strftime("%Y%m%d%H%M%S") + '.json'
json_file_rk = 'results/result_' + time.strftime(
"%Y%m%d%H%M%S") + '_rk.json'
self.model.eval()
t1 = time.time()
print('extract features, this may take a few minutes')
feats = extract_feature(self.model, tqdm(self.test_loader))
print('feats:', feats.shape)
qf = feats[:self.num_query]
gf = feats[self.num_query:]
print('qf,gf:', qf.shape, gf.shape)
m, n = qf.shape[0], gf.shape[0]
distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
distmat.addmm_(1, -2, qf, gf.t())
distmat = distmat.cpu().numpy()
self.writeResult(distmat, json_file)
print('re_ranking ...')
distmat_rk = reRanking(qf, gf, 7, 3, 0.85)
self.writeResult(distmat_rk, json_file_rk)
# print('distmat:', distmat.shape)
# index = np.argsort(distmat) # from small to large
# max_index = index[:, :200]
# print(max_index.shape)
#
# # Visualize the rank result
# results = {}
# json_file = 'results/result_' + time.strftime("%Y%m%d%H%M%S") + '.json'
# json_file_rk = 'results/result_' + time.strftime("%Y%m%d%H%M%S") + '_rk.json'
# for i in range(len(self.query_paths)):
# query_name = self.query_paths[i].split('/')[-1]
# index_mask = max_index[i]
# gallery_name = [self.gallery_paths[k].split('/')[-1] for k in index_mask]
# #print(index_mask)
#
# results[query_name] = gallery_name
# #print(res)
#
# with open(json_file, 'w', encoding='utf-8') as fp:
# json.dump(results, fp)
print('Time cost is: {:.2f} s'.format(time.time() - t1))
print('over!')
def __getitem__(self, i):
sample = self.samples[i]
wave = sample['wave']
trn = sample['trn']
feature = extract_feature(input_file=wave,
feature='fbank',
dim=self.args.d_input,
cmvn=True)
feature = build_LFR_features(feature,
m=self.args.LFR_m,
n=self.args.LFR_n)
return feature, trn
def sub_extract(args):
iscount = args.count
k, gap, lam, n_jobs = args.kmer, args.gap, args.lam, args.process
if args.directory:
out = Path(args.output[0])
out.mkdir(exist_ok=True)
ul.batch_extract(args.file,
out,
k,
gap,
lam,
n_jobs=n_jobs,
count=iscount)
else:
if args.raa:
raa = list(args.raa)
else:
raa = list(Path(args.file[0]).stem.split('-')[-1])
xy_ls = []
aa_ls = [''.join(aa) for aa in product(raa, repeat=k)]
for idx, file in enumerate(args.file):
feature_file = Path(file)
xy = ul.extract_feature(feature_file,
raa,
k,
gap,
lam,
count=iscount)
new_aa_ls = aa_ls
if args.index:
_, (fea_idx, _) = ul.load_data(args.index,
normal=False,
label_exist=False)
fea_idx = fea_idx.astype(int).flatten()
xy = xy[:, fea_idx]
new_aa_ls = [aa_ls[i] for i in fea_idx]
if args.label_f:
y = np.array([[idx]] * xy.shape[0])
xy = np.hstack([y, xy])
xy_ls.append(xy)
new_aa_ls.insert(0, 'label') if args.label_f else 0
header = ','.join(new_aa_ls)
if args.merge:
out = args.output[0]
seq_mtx = np.vstack(xy_ls)
ul.write_array(Path(out), seq_mtx, header=header)
exit()
for idx, o in enumerate(args.output):
ul.write_array(Path(o), xy_ls[idx], header=header)
def predictSpeech():
print("Please talk")
filename = "test.wav"
# record the file (start talking)
record_to_file(filename)
# load the saved model (after training)
model = pickle.load(open("result/mlp_classifier.model", "rb"))
# extract features and reshape it
features = extract_feature(file_name="test.wav", mfcc=True, chroma=True, mel=True).reshape(1, -1)
# predict
result = model.predict(features)[0]
# show the result !
print("result:", result)
return result
def predict_proba(self, audio_path):
"""
Predicts the probability of each emotion.
"""
if self.classification:
feature = extract_feature(audio_path,
**self.audio_config).reshape(1, -1)
proba = self.model.predict_proba(feature)[0]
result = {}
for emotion, prob in zip(self.model.classes_, proba):
result[emotion] = prob
return result
else:
raise NotImplementedError(
"Probability prediction doesn't make sense for regression")
def __getitem__(self, i):
sample = self.samples[i]
wave = sample['wave']
trn = sample['trn']
feature = extract_feature(input_file=wave,
feature='fbank',
dim=self.args.d_input,
cmvn=True)
# zero mean and unit variance
feature = (feature - feature.mean()) / feature.std()
feature = spec_augment(feature)
feature = build_LFR_features(feature,
m=self.args.LFR_m,
n=self.args.LFR_n)
return feature, trn
return sample_width, r
def record_to_file(path):
"Records from the microphone and outputs the resulting data to 'path'"
sample_width, data = record()
data = pack('<' + ('h' * len(data)), *data)
wf = wave.open(path, 'wb')
wf.setnchannels(1)
wf.setsampwidth(sample_width)
wf.setframerate(RATE)
wf.writeframes(data)
wf.close()
if __name__ == "__main__":
# load the saved model (after training)
model = pickle.load(open("result/mlp_classifier.model", "rb"))
print("Please talk")
filename = "test.wav"
# record the file (start talking)
record_to_file(filename)
# extract features and reshape it
features = extract_feature(filename, mfcc=True, chroma=True,
mel=True).reshape(1, -1)
# predict
result = model.predict(features)[0]
# show the result !
print("result:", result)
def compute_true_false_miss(self, conf, log_dir, feat_path, tta):
def gen_distmat(qf, q_pids, gf, g_pids):
m, n = qf.shape[0], gf.shape[0]
logger.debug('query shape {}, gallery shape {}'.format(qf.shape, gf.shape))
# logger.debug('q_pids {}, g_pids {}'.format(q_pids, g_pids))
distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
distmat.addmm_(1, -2, qf, gf.t())
distmat = distmat.cpu().numpy()
return distmat# embeddings[idx:idx + conf.batch_size] = l2_norm(emb_batch).cpu()
def distance(emb1, emb2):
diff = np.subtract(emb1, emb2)
dist = np.sum(np.square(diff), 1)
return dist
self.model.eval()
if conf.gen_feature:
with torch.no_grad():
query_feature, query_label = extract_feature(conf, self.model, self.loader['query']['dl'], tta)
gallery_feature, gallery_label = extract_feature(conf, self.model, self.loader['gallery']['dl'], tta)
result = {'query_feature': query_feature.numpy(), 'query_label': query_label.numpy(),
'gallery_feature': gallery_feature.numpy(), 'gallery_label': gallery_label.numpy()}
scipy.io.savemat(feat_path, result)
else:
result = scipy.io.loadmat(feat_path)
query_feature = torch.from_numpy(result['query_feature'])
query_label = torch.from_numpy(result['query_label'])[0]
gallery_feature = torch.from_numpy(result['gallery_feature'])
gallery_label = torch.from_numpy(result['gallery_label'])[0]
distmat = gen_distmat(query_feature, query_label, gallery_feature, gallery_label)
# record txt
with open(os.path.join(log_dir, 'result.txt'),'at') as f:
f.write('%s\t%s\t%s\t%s\n' % ('threshold', 'acc', 'err', 'miss'))
# record excel
xls_file = xlwt.Workbook()
sheet_1 = xls_file.add_sheet('sheet_1', cell_overwrite_ok=True)
row = 0
path_excel = os.path.join(log_dir, 'result.xls')
sheet_title = ['threshold', 'acc', 'err', 'miss']
for i_sheet in range(len(sheet_title)):
sheet_1.write(row, i_sheet, sheet_title[i_sheet])
xls_file.save(path_excel)
row += 1
index = np.argsort(distmat) # from small to large
max_index = index[:, 0]
query_list_file = 'data/probe.txt'
gallery_list_file = 'data/gallery.txt'
err_rank1 = os.path.join(log_dir, 'err_rank1.txt')
data_path = DataPath(query_list_file, gallery_list_file)
with open(err_rank1,'at') as f:
f.write('%s\t\t\t%s\n' % ('query', 'gallery'))
thresholds = np.arange(0.4, 2, 0.01)
for threshold in thresholds:
acc, err, miss = compute_rank1(distmat, max_index, query_label, gallery_label, threshold, data_path, err_rank1)
# record txt
with open(os.path.join(log_dir, 'result.txt'),'at') as f:
f.write('%.6f\t%.6f\t%.6f\t%.6f\n' % (threshold, acc, err, miss))
# record excel
list_data = [threshold, acc, err, miss]
for i_1 in range(len(list_data)):
sheet_1.write(row, i_1, list_data[i_1])
xls_file.save(path_excel)
row += 1
