def synthesis(text, args):
m = Model()
m_post = ModelPostNet()
m.load_state_dict(load_checkpoint(args.restore_step1, "transformer"))
m_post.load_state_dict(load_checkpoint(args.restore_step2, "postnet"))
text = np.asarray(text_to_sequence(text, [hp.cleaners]))
text = t.LongTensor(text).unsqueeze(0)
text = text.cuda()
mel_input = t.zeros([1,1, 80]).cuda()
pos_text = t.arange(1, text.size(1)+1).unsqueeze(0)
pos_text = pos_text.cuda()
m=m.cuda()
m_post = m_post.cuda()
m.train(False)
m_post.train(False)
pbar = tqdm(range(args.max_len))
with t.no_grad():
for i in pbar:
pos_mel = t.arange(1,mel_input.size(1)+1).unsqueeze(0).cuda()
mel_pred, postnet_pred, attn, stop_token, _, attn_dec = m.forward(text, mel_input, pos_text, pos_mel)
mel_input = t.cat([t.zeros([1,1, 80]).cuda(),postnet_pred], dim=1)
mag_pred = m_post.forward(postnet_pred)
wav = spectrogram2wav(mag_pred.squeeze(0).cpu().numpy())
write(hp.sample_path + "/test.wav", hp.sr, wav)
def synthesis(text, args):
m = Model()
m.load_state_dict(load_checkpoint(args.restore_path))
print("[%s][%s] Synthesizing:" % (args.lang, args.spk), text)
text = np.asarray([1] + list(text.encode('utf-8')) + [2])
text = t.LongTensor(text).unsqueeze(0)
text = text
mel_input = t.zeros([1, 1, 80])
pos_text = t.arange(1, text.size(1) + 1).unsqueeze(0)
pos_text = pos_text
lang_to_id = json.load(open(os.path.join(args.data_path, 'lang_id.json')))
spk_to_id = json.load(open(os.path.join(args.data_path, 'spk_id.json')))
lang_id = lang_to_id[args.lang]
spk_id = spk_to_id[args.spk]
lang_id = t.LongTensor([lang_id])
spk_id = t.LongTensor([spk_id])
m.train(False)
pbar = tqdm(range(args.max_len))
with t.no_grad():
for i in pbar:
pos_mel = t.arange(1, mel_input.size(1) + 1).unsqueeze(0)
mel_pred, postnet_pred, attn, stop_token, _, attn_dec = \
m.forward(text, mel_input, pos_text, pos_mel, lang_id, spk_id)
mel_input = t.cat([mel_input, mel_pred[:, -1:, :]], dim=1)
if stop_token[:, -1].item() > 0:
break
mel = postnet_pred.squeeze(0).cpu().numpy()
wav = mel2wav(mel)
np.save(args.out_path + "_mel.npy", mel)
write(args.out_path + ".wav", hp.sr, wav)
plot_mel(args.out_path + "_mel.png", mel)
plot_attn(attn, args.out_path + '_align.png')
def synthesis(text, num):
m = Model()
# m_post = ModelPostNet()
m.load_state_dict(load_checkpoint(num, "transformer"))
# m_post.load_state_dict(load_checkpoint(args.restore_step2, "postnet"))
text = np.asarray(text_to_sequence(text, [hp.cleaners]))
text = t.LongTensor(text).unsqueeze(0)
text = text.cuda()
mel_input = t.zeros([1, 1, 80]).cuda()
pos_text = t.arange(1, text.size(1) + 1).unsqueeze(0)
pos_text = pos_text.cuda()
m = m.cuda()
# m_post = m_post.cuda()
m.train(False)
# m_post.train(False)
# pbar = tqdm(range(args.max_len))
with t.no_grad():
for _ in range(1000):
pos_mel = t.arange(1, mel_input.size(1) + 1).unsqueeze(0).cuda()
mel_pred, postnet_pred, attn, stop_token, _, attn_dec = m.forward(
text, mel_input, pos_text, pos_mel)
mel_input = t.cat([mel_input, postnet_pred[:, -1:, :]], dim=1)
# mag_pred = m_post.forward(postnet_pred)
# wav = spectrogram2wav(mag_pred.squeeze(0).cpu().numpy())
mel_postnet = postnet_pred[0].cpu().numpy().T
plot_data([mel_postnet for _ in range(2)])
wav = audio.inv_mel_spectrogram(mel_postnet)
wav = wav[0:audio.find_endpoint(wav)]
audio.save_wav(wav, "result.wav")
def test(path):
model = Model()
model.to("cuda:0")
model.eval()
checkpoint = torch.load("./model.pth")
model.load_state_dict(checkpoint["model"])
img = np.array(Image.open(path).resize([448, 448]))[np.newaxis]
img = np.transpose(img, axes=[0, 3, 1, 2]) / 255
img = torch.tensor(img, dtype=torch.float32).to("cuda:0")
preds = model(img).cpu().detach().numpy()
cell_h, cell_w = IMG_H / S, IMG_W / S
x, y = np.meshgrid(range(S), range(S))
preds_xywhs = []
for i in range(B):
preds_x = (preds[0, :, :, i * 4] + x) * cell_w
preds_y = (preds[0, :, :, i * 4 + 1] + y) * cell_h
preds_w = preds[0, :, :, i * 4 + 2] * IMG_W
preds_h = preds[0, :, :, i * 4 + 3] * IMG_H
preds_xywh = np.dstack((preds_x, preds_y, preds_w, preds_h))
preds_xywhs.append(preds_xywh)
preds_xywhs = np.dstack(preds_xywhs)
preds_xywhs = np.reshape(preds_xywhs, [-1, 4])
preds_class = preds[0, :, :, 10:]
preds_class = np.reshape(preds_class, [-1, 20])
preds_c = preds[0, :, :, 8:10]
preds_c = np.reshape(preds_c, [-1, 1])
max_arg = np.argmax(preds_c, axis=0)
print("max confidence: %f" % (preds_c[max_arg]))
max_arg_ = np.argmax(preds_class[int(max_arg // 2)])
print("class confidence: %f" % (preds_class[max_arg // 2, max_arg_]))
print("class category: %s" % (CLASSES[int(max_arg_)]))
Image.fromarray(
np.uint8(
draw_bboxes(np.array(Image.open(path).resize([448, 448])),
preds_xywhs[max_arg[0]:max_arg[0] + 1]))).show()
def synthesis(text, args):
m = Model()
m_post = ModelPostNet()
m.load_state_dict(load_checkpoint(args.step1, "transformer"))
m_post.load_state_dict(load_checkpoint(args.step2, "postnet"))
text = np.asarray(text_to_sequence(text, [hp.cleaners]))
text = torch.LongTensor(text).unsqueeze(0)
text = text.cuda()
mel_input = np.load('3_0.pt.npy')
pos_text = torch.arange(1, text.size(1) + 1).unsqueeze(0)
pos_text = pos_text.cuda()
m = m.cuda()
m_post = m_post.cuda()
m.train(False)
m_post.train(False)
with torch.no_grad():
mag_pred = m_post.forward(
torch.from_numpy(mel_input).unsqueeze(0).cuda())
wav = spectrogram2wav(mag_pred.squeeze(0).cpu().numpy())
write(hp.sample_path + "/test.wav", hp.sr, wav)
def synthesis(text, args, num):
m = Model()
m_post = ModelPostNet()
m.load_state_dict(load_checkpoint(args.restore_step1, "transformer"))
m_post.load_state_dict(load_checkpoint(args.restore_step2, "postnet"))
text = np.asarray(text_to_sequence(text, [hp.cleaners]))
text = t.LongTensor(text).unsqueeze(0)
text = text.cuda()
mel_input = t.zeros([1, 1, 80]).cuda()
pos_text = t.arange(1, text.size(1) + 1).unsqueeze(0)
pos_text = pos_text.cuda()
m = m.cuda()
m_post = m_post.cuda()
m.train(False)
m_post.train(False)
pbar = tqdm(range(args.max_len))
with t.no_grad():
for i in pbar:
pos_mel = t.arange(1, mel_input.size(1) + 1).unsqueeze(0).cuda()
mel_pred, postnet_pred, attn, stop_token, _, attn_dec = m.forward(
text, mel_input, pos_text, pos_mel)
# print('mel_pred==================',mel_pred.shape)
# print('postnet_pred==================', postnet_pred.shape)
mel_input = t.cat([mel_input, postnet_pred[:, -1:, :]], dim=1)
#print(postnet_pred[:, -1:, :])
#print(t.argmax(attn[1][1][i]).item())
#print('mel_input==================', mel_input.shape)
# #直接用真实mel测试postnet效果
#aa = t.from_numpy(np.load('D:\SSHdownload\\000101.pt.npy')).cuda().unsqueeze(0)
# # print(aa.shape)
mag_pred = m_post.forward(postnet_pred)
#real_mag = t.from_numpy((np.load('D:\SSHdownload\\003009.mag.npy'))).cuda().unsqueeze(dim=0)
#wav = spectrogram2wav(postnet_pred)
#print('shappe============',attn[2][0].shape)
# count = 0
# for j in range(4):
# count += 1
# attn1 = attn[0][j].cpu()
# plot_alignment(attn1, path='./training_loss/'+ str(args.restore_step1)+'_'+str(count)+'_'+'S'+str(num)+'.png', title='sentence'+str(num))
attn1 = attn[0][1].cpu()
plot_alignment(attn1,
path='./training_loss/' + str(args.restore_step1) + '_' +
'S' + str(num) + '.png',
title='sentence' + str(num))
wav = spectrogram2wav(mag_pred.squeeze(0).cpu().detach().numpy())
write(
hp.sample_path + '/' + str(args.restore_step1) + '-' + "test" +
str(num) + ".wav", hp.sr, wav)
def ensemble(state, X_test, y_test, g):
mod1 = Model().to(state['device'])
mod2 = Model().to(state['device'])
mod3 = Model().to(state['device'])
mod4 = Model().to(state['device'])
mod = Model().to(state['device'])
mod1.load_state_dict(torch.load(state['path1'])['model_state_dict'])
mod2.load_state_dict(torch.load(state['path2'])['model_state_dict'])
mod3.load_state_dict(torch.load(state['path3'])['model_state_dict'])
mod4.load_state_dict(torch.load(state['path4'])['model_state_dict'])
for p, p1, p2, p3, p4 in zip(mod.parameters(), mod1.parameters(),
mod2.parameters(), mod3.parameters(),
mod4.parameters()):
p.data.copy_(
p1.data.mul(0.25).add(p2.data.mul(0.25)).add(
p3.data.mul(0.25)).add(p4.data.mul(0.25)))
mod.state_dict()
acc = test_with_dropout(X_test, y_test, mod, state['device'],
state['cuda'])
path = g + str(state['itr']) + 'epoch.' + str(state['acq']) + 'acq.pth.tar'
state['rep'] = path
torch.save({'model_state_dict': mod.state_dict()}, state['rep'])
return mod, acc
def model(dataset, model_name=None, device=None, train=True):
"""加载模型"""
device = device or torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
net = Model(vocab_size=dataset.vocab_size,
embedding_dim=config.embedding_dim,
output_size=dataset.target_vocab_size,
encoder_hidden_size=config.encoder_hidden_size,
decoder_hidden_size=config.decoder_hidden_size,
encoder_layers=config.encoder_layers,
decoder_layers=config.decoder_layers,
dropout=config.dropout,
embedding_weights=dataset.vector_weights,
device=device)
if model_name: # 如果指定了模型名称, 就加载对应的模型
pre_trained_state_dict = torch.load(FILE_PATH + config.model_path +
model_name,
map_location=device)
state_dict = net.state_dict()
state_dict.update(pre_trained_state_dict)
net.load_state_dict(state_dict)
net.train() if train else net.eval()
return net
np.random.seed(random_seed)
np.random.shuffle(indices)
val_indices = indices[:split]
# check if dataset load order is correct
# for ind in val_indices:
# print(ind)
# data = my_dataset[ind]
# img = data['image']
# plt.figure()
# plt.imshow(img.permute(1,2,0))
# plt.show()
# load model
model = Model().to(device=device)
model.load_state_dict(torch.load('model_saved.pth'))
model = model.float()
model.eval()
for ind in val_indices:
data = my_dataset[ind]
img = data['image']
img = img.to(device=device)
img = img.unsqueeze(dim=0)
position_map, feature_maps = model(img)
position_map = position_map.squeeze() # must be (128,128)
feature_maps = feature_maps.squeeze() # should be (16,128,128)
feature_maps = feature_maps.permute(1, 2, 0) # should be (128,128,16)
position_map = position_map.detach().cpu().numpy()
def synthesis(args):
m = Model()
m_post = ModelPostNet()
m_stop = ModelStopToken()
m.load_state_dict(load_checkpoint(args.restore_step1, "transformer"))
m_stop.load_state_dict(load_checkpoint(args.restore_step3, "stop_token"))
m_post.load_state_dict(load_checkpoint(args.restore_step2, "postnet"))
m=m.cuda()
m_post = m_post.cuda()
m_stop = m_stop.cuda()
m.train(False)
m_post.train(False)
m_stop.train(False)
test_dataset = get_dataset(hp.test_data_csv)
test_dataloader = DataLoader(test_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn_transformer, drop_last=True, num_workers=1)
ref_dataset = get_dataset(hp.test_data_csv)
ref_dataloader = DataLoader(ref_dataset, batch_size=1, shuffle=True, collate_fn=collate_fn_transformer, drop_last=True, num_workers=1)
writer = get_writer(hp.checkpoint_path, hp.log_directory)
ref_dataloader_iter = iter(ref_dataloader)
for i, data in enumerate(test_dataloader):
character, mel, mel_input, pos_text, pos_mel, text_length, mel_length, fname = data
ref_character, ref_mel, ref_mel_input, ref_pos_text, ref_pos_mel, ref_text_length, ref_mel_length, ref_fname = next(ref_dataloader_iter)
stop_tokens = t.abs(pos_mel.ne(0).type(t.float) - 1)
mel_input = t.zeros([1,1,80]).cuda()
stop=[]
character = character.cuda()
mel = mel.cuda()
mel_input = mel_input.cuda()
pos_text = pos_text.cuda()
pos_mel = pos_mel.cuda()
ref_character = ref_character.cuda()
ref_mel = ref_mel.cuda()
ref_mel_input = ref_mel_input.cuda()
ref_pos_text = ref_pos_text.cuda()
ref_pos_mel = ref_pos_mel.cuda()
with t.no_grad():
start=time.time()
for i in range(args.max_len):
pos_mel = t.arange(1,mel_input.size(1)+1).unsqueeze(0).cuda()
mel_pred, postnet_pred, attn_probs, decoder_output, attns_enc, attns_dec, attns_style = m.forward(character, mel_input, pos_text, pos_mel, ref_mel, ref_pos_mel)
stop_token = m_stop.forward(decoder_output)
mel_input = t.cat([mel_input, postnet_pred[:,-1:,:]], dim=1)
stop.append(t.sigmoid(stop_token).squeeze(-1)[0,-1])
if stop[-1] > 0.5:
print("stop token at " + str(i) + " is :", stop[-1])
print("model inference time: ", time.time() - start)
break
if stop[-1] == 0:
continue
mag_pred = m_post.forward(postnet_pred)
inf_time = time.time() - start
print("inference time: ", inf_time)
wav = spectrogram2wav(mag_pred.squeeze(0).cpu().numpy())
print("rtx : ", (len(wav)/hp.sr) / inf_time)
wav_path = os.path.join(hp.sample_path, 'wav')
if not os.path.exists(wav_path):
os.makedirs(wav_path)
write(os.path.join(wav_path, "text_{}_ref_{}_synth.wav".format(fname, ref_fname)), hp.sr, wav)
print("written as text{}_ref_{}_synth.wav".format(fname, ref_fname))
attns_enc_new=[]
attns_dec_new=[]
attn_probs_new=[]
attns_style_new=[]
for i in range(len(attns_enc)):
attns_enc_new.append(attns_enc[i].unsqueeze(0))
attns_dec_new.append(attns_dec[i].unsqueeze(0))
attn_probs_new.append(attn_probs[i].unsqueeze(0))
attns_style_new.append(attns_style[i].unsqueeze(0))
attns_enc = t.cat(attns_enc_new, 0)
attns_dec = t.cat(attns_dec_new, 0)
attn_probs = t.cat(attn_probs_new, 0)
attns_style = t.cat(attns_style_new, 0)
attns_enc = attns_enc.contiguous().view(attns_enc.size(0), 1, hp.n_heads, attns_enc.size(2), attns_enc.size(3))
attns_enc = attns_enc.permute(1,0,2,3,4)
attns_dec = attns_dec.contiguous().view(attns_dec.size(0), 1, hp.n_heads, attns_dec.size(2), attns_dec.size(3))
attns_dec = attns_dec.permute(1,0,2,3,4)
attn_probs = attn_probs.contiguous().view(attn_probs.size(0), 1, hp.n_heads, attn_probs.size(2), attn_probs.size(3))
attn_probs = attn_probs.permute(1,0,2,3,4)
attns_style = attns_style.contiguous().view(attns_style.size(0), 1, hp.n_heads, attns_style.size(2), attns_style.size(3))
attns_style = attns_style.permute(1,0,2,3,4)
save_dir = os.path.join(hp.sample_path, 'figure', "text_{}_ref_{}_synth.wav".format(fname, ref_fname))
if not os.path.exists(save_dir):
os.makedirs(save_dir)
writer.add_alignments(attns_enc.detach().cpu(), attns_dec.detach().cpu(), attn_probs.detach().cpu(), attns_style.detach().cpu(), mel_length, text_length, args.restore_step1, 'Validation', save_dir)
def synthesis(args):
m = Model()
m.load_state_dict(load_checkpoint(args.restore_step1, "transformer"))
m = m.cuda()
m.train(False)
vocoder = SmartVocoder(Hyperparameters(parse_args()))
vocoder.load_state_dict(
t.load('./mel2audio/merged_STFT_checkpoint.pth')["state_dict"])
vocoder = vocoder.cuda()
vocoder.eval()
with open('./hifi_gan/config.json') as f:
data = f.read()
json_config = json.loads(data)
h = AttrDict(json_config)
hifi_gan = Generator(h).cuda()
state_dict_g = t.load('./hifi_gan/g_00334000', map_location='cuda')
hifi_gan.load_state_dict(state_dict_g['generator'])
hifi_gan.eval()
hifi_gan.remove_weight_norm()
test_dataset = get_dataset(hp.test_data_csv)
test_dataloader = DataLoader(test_dataset,
batch_size=1,
shuffle=False,
collate_fn=collate_fn_transformer,
drop_last=True,
num_workers=1)
ref_dataset = get_dataset(hp.test_data_csv_shuf)
ref_dataloader = DataLoader(ref_dataset,
batch_size=1,
shuffle=False,
collate_fn=collate_fn_transformer,
drop_last=True,
num_workers=1)
writer = get_writer(hp.checkpoint_path, hp.log_directory)
mel_basis = t.from_numpy(
librosa.filters.mel(hp.sr, hp.n_fft, hp.n_mels, 50,
11000)).unsqueeze(0) # (n_mels, 1+n_fft//2)
ref_dataloader_iter = iter(ref_dataloader)
_, ref_mel, _, _, _, ref_pos_mel, _, _, ref_fname = next(
ref_dataloader_iter)
for i, data in enumerate(test_dataloader):
character, _, _, _, pos_text, _, text_length, _, fname = data
mel_input = t.zeros([1, 1, 80]).cuda()
character = character.cuda()
ref_mel = ref_mel.cuda()
mel_input = mel_input.cuda()
pos_text = pos_text.cuda()
with t.no_grad():
start = time.time()
memory, c_mask, attns_enc, duration_mask = m.encoder(character,
pos=pos_text)
style, coarse_emb = m.ref_encoder(ref_mel)
memory = t.cat((memory, coarse_emb.expand(-1, memory.size(1), -1)),
-1)
memory = m.memory_coarse_layer(memory)
duration_predictor_output = m.duration_predictor(
memory, duration_mask)
duration = t.ceil(duration_predictor_output)
duration = duration * duration_mask
# max_length = t.sum(duration).type(t.LongTensor)
# print("length : ", max_length)
monotonic_interpolation, pos_mel_, weights = m.length_regulator(
memory, duration, duration_mask)
kv_mask = t.zeros([1, mel_input.size(1),
character.size(1)]).cuda() # B, t', N
kv_mask[:, :, :3] = 1
kv_mask = kv_mask.eq(0)
stop_flag = False
ctr = 0
for j in range(1200):
pos_mel = t.arange(1,
mel_input.size(1) + 1).unsqueeze(0).cuda()
mel_pred, postnet_pred, attn_probs, decoder_output, attns_dec, attns_style = m.decoder(
memory,
style,
mel_input,
c_mask,
pos=pos_mel,
ref_pos=ref_pos_mel,
mono_inter=monotonic_interpolation[:, :mel_input.shape[1]],
kv_mask=kv_mask)
mel_input = t.cat([mel_input, postnet_pred[:, -1:, :]], dim=1)
# print("j", j, "mel_input", mel_input.shape)
if stop_flag and ctr == 10:
break
elif stop_flag:
ctr += 1
kv_mask, stop_flag = update_kv_mask(
kv_mask, attn_probs) # B, t', N --> B, t'+1, N
postnet_pred = t.cat((postnet_pred,
t.zeros(postnet_pred.size(0), 5,
postnet_pred.size(-1)).cuda()), 1)
gen_length = mel_input.size(1)
# print("gen_length", gen_length)
post_linear = m.postnet(postnet_pred)
post_linear = resample(post_linear,
seq_len=mel_input.size(1),
scale=args.rhythm_scale)
postnet_pred = resample(mel_input,
seq_len=mel_input.size(1),
scale=args.rhythm_scale)
inf_time = time.time() - start
print("inference time: ", inf_time)
# print("speech_rate: ", len(postnet_pred[0])/len(character[0]))
postnet_pred_v = postnet_pred.transpose(2, 1)
postnet_pred_v = (postnet_pred_v * 100 + 20 - 100) / 20
B, C, T = postnet_pred_v.shape
z = t.randn(1, 1, T * hp.hop_length).cuda()
z = z * 0.6 # Temp
t.cuda.synchronize()
timestemp = time.time()
with t.no_grad():
y_gen = vocoder.reverse(z, postnet_pred_v).squeeze()
t.cuda.synchronize()
print('{} seconds'.format(time.time() - timestemp))
wav = y_gen.to(t.device("cpu")).data.numpy()
wav = np.pad(
wav, [0, 4800], mode='constant',
constant_values=0) #pad 0 for 0.21 sec silence at the end
post_linear_v = post_linear.transpose(1, 2)
post_linear_v = 10**((post_linear_v * 100 + 20 - 100) / 20)
mel_basis = mel_basis.repeat(post_linear_v.shape[0], 1, 1)
post_linear_mel_v = t.log10(t.bmm(mel_basis.cuda(), post_linear_v))
B, C, T = post_linear_mel_v.shape
z = t.randn(1, 1, T * hp.hop_length).cuda()
z = z * 0.6 # Temp
t.cuda.synchronize()
timestemp = time.time()
with t.no_grad():
y_gen_linear = vocoder.reverse(z, post_linear_mel_v).squeeze()
t.cuda.synchronize()
wav_linear = y_gen_linear.to(t.device("cpu")).data.numpy()
wav_linear = np.pad(
wav_linear, [0, 4800], mode='constant',
constant_values=0) #pad 0 for 0.21 sec silence at the end
wav_hifi = hifi_gan(post_linear_mel_v).squeeze().clamp(
-1, 1).detach().cpu().numpy()
wav_hifi = np.pad(
wav_hifi, [0, 4800], mode='constant',
constant_values=0) #pad 0 for 0.21 sec silence at the end
mel_path = os.path.join(hp.sample_path + '_' + str(args.rhythm_scale),
'mel')
if not os.path.exists(mel_path):
os.makedirs(mel_path)
np.save(
os.path.join(
mel_path,
'text_{}_ref_{}_synth_{}.mel'.format(i, ref_fname,
str(args.rhythm_scale))),
postnet_pred.cpu())
linear_path = os.path.join(
hp.sample_path + '_' + str(args.rhythm_scale), 'linear')
if not os.path.exists(linear_path):
os.makedirs(linear_path)
np.save(
os.path.join(
linear_path, 'text_{}_ref_{}_synth_{}.linear'.format(
i, ref_fname, str(args.rhythm_scale))), post_linear.cpu())
wav_path = os.path.join(hp.sample_path + '_' + str(args.rhythm_scale),
'wav')
if not os.path.exists(wav_path):
os.makedirs(wav_path)
write(
os.path.join(
wav_path,
"text_{}_ref_{}_synth_{}.wav".format(i, ref_fname,
str(args.rhythm_scale))),
hp.sr, wav)
print("rtx : ", (len(wav) / hp.sr) / inf_time)
wav_linear_path = os.path.join(
hp.sample_path + '_' + str(args.rhythm_scale), 'wav_linear')
if not os.path.exists(wav_linear_path):
os.makedirs(wav_linear_path)
write(
os.path.join(
wav_linear_path,
"text_{}_ref_{}_synth_{}.wav".format(i, ref_fname,
str(args.rhythm_scale))),
hp.sr, wav_linear)
wav_hifi_path = os.path.join(
hp.sample_path + '_' + str(args.rhythm_scale), 'wav_hifi')
if not os.path.exists(wav_hifi_path):
os.makedirs(wav_hifi_path)
write(
os.path.join(
wav_hifi_path,
"text_{}_ref_{}_synth_{}.wav".format(i, ref_fname,
str(args.rhythm_scale))),
hp.sr, wav_hifi)
show_weights = weights.contiguous().view(weights.size(0), 1, 1,
weights.size(1),
weights.size(2))
attns_enc_new = []
attns_dec_new = []
attn_probs_new = []
attns_style_new = []
for i in range(len(attns_enc)):
attns_enc_new.append(attns_enc[i].unsqueeze(0))
attns_dec_new.append(attns_dec[i].unsqueeze(0))
attn_probs_new.append(attn_probs[i].unsqueeze(0))
attns_style_new.append(attns_style[i].unsqueeze(0))
attns_enc = t.cat(attns_enc_new, 0)
attns_dec = t.cat(attns_dec_new, 0)
attn_probs = t.cat(attn_probs_new, 0)
attns_style = t.cat(attns_style_new, 0)
attns_enc = attns_enc.contiguous().view(attns_enc.size(0), 1,
hp.n_heads, attns_enc.size(2),
attns_enc.size(3))
attns_enc = attns_enc.permute(1, 0, 2, 3, 4)
attns_dec = attns_dec.contiguous().view(attns_dec.size(0), 1,
hp.n_heads, attns_dec.size(2),
attns_dec.size(3))
attns_dec = attns_dec.permute(1, 0, 2, 3, 4)
attn_probs = attn_probs.contiguous().view(attn_probs.size(0),
1, hp.n_heads,
attn_probs.size(2),
attn_probs.size(3))
attn_probs = attn_probs.permute(1, 0, 2, 3, 4)
attns_style = attns_style.contiguous().view(attns_style.size(0), 1,
hp.n_heads,
attns_style.size(2),
attns_style.size(3))
attns_style = attns_style.permute(1, 0, 2, 3, 4)
save_dir = os.path.join(
hp.sample_path + '_' + str(args.rhythm_scale), 'figure',
"text_{}_ref_{}_synth_{}.wav".format(fname, ref_fname,
str(args.rhythm_scale)))
if not os.path.exists(save_dir):
os.makedirs(save_dir)
writer.add_alignments(attns_enc.detach().cpu(),
attns_dec.detach().cpu(),
attn_probs.detach().cpu(),
attns_style.detach().cpu(),
show_weights.detach().cpu(),
[t.tensor(gen_length).type(t.LongTensor)],
text_length, args.restore_step1, 'Inference',
save_dir)
def random_run(acquisition_iterations, X_Pool, y_Pool, pool_subset,
dropout_iterations, nb_classes, Queries, X_test, y_test, rep,
X_old, y_old, device, itr, cuda, g):
mod = Model().to(device)
if cuda:
cp = torch.load(rep)
print("\n ********load gpu version******* \n")
else:
cp = torch.load(rep, map_location='cpu')
mod.load_state_dict(cp['model_state_dict'])
optimizer = optim.Adam(mod.parameters(), lr=0.001,
weight_decay=0.5) #,weight_decay=0.5
#optimizer = optim.SGD(mod.parameters(), lr=0.001,weight_decay=0.5)
optimizer.load_state_dict(cp['optimizer_state_dict'])
criterion = nn.CrossEntropyLoss()
X_train = np.empty([0, 1, 28, 28])
y_train = np.empty([
0,
])
AA = []
losses_train = []
#acc = test(test_loader,mod,device,cuda)
acc = test(X_test, y_test, mod, device, cuda)
AA.append(acc)
print('initial test accuracy: ', acc)
for i in range(acquisition_iterations):
pool_subset_dropout = np.asarray(
random.sample(range(0, X_Pool.shape[0]), pool_subset))
X_Pool_Dropout = X_Pool[pool_subset_dropout, :, :, :]
y_Pool_Dropout = y_Pool[pool_subset_dropout]
x_pool_index = np.random.choice(X_Pool_Dropout.shape[0],
Queries,
replace=False)
Pooled_X = X_Pool_Dropout[x_pool_index, :, :, :]
Pooled_Y = y_Pool_Dropout[x_pool_index]
delete_Pool_X = np.delete(X_Pool, (pool_subset_dropout), axis=0)
delete_Pool_Y = np.delete(y_Pool, (pool_subset_dropout), axis=0)
delete_Pool_X_Dropout = np.delete(X_Pool_Dropout, (x_pool_index),
axis=0)
delete_Pool_Y_Dropout = np.delete(y_Pool_Dropout, (x_pool_index),
axis=0)
X_Pool = np.concatenate((delete_Pool_X, delete_Pool_X_Dropout), axis=0)
y_Pool = np.concatenate((delete_Pool_Y, delete_Pool_Y_Dropout), axis=0)
print('updated pool size is ', X_Pool.shape[0])
X_train = np.concatenate((X_train, Pooled_X), axis=0)
y_train = np.concatenate((y_train, Pooled_Y), axis=0)
print('number of data points from pool', X_train.shape[0])
batch_size = 100
X = np.vstack((X_old, Pooled_X))
y = np.hstack((y_old, Pooled_Y))
X, y = shuffle(X, y)
num_batch = X.shape[0] // batch_size
print("number of batch: ", num_batch)
mod.train()
for h in range(itr):
losses = 0
for j in range(num_batch):
slce = get_slice(j, batch_size)
X_fog_ = torch.from_numpy(X[slce]).float().to(device)
y_fog_ = torch.from_numpy(y[slce]).long().to(device)
optimizer.zero_grad()
out = mod(X_fog_)
train_loss = criterion(out, y_fog_)
losses += train_loss
train_loss.backward()
optimizer.step()
losses_train.append(losses.item() / num_batch)
acc = test(X_test, y_test, mod, device, cuda)
print('test accuracy: ', acc)
AA.append(acc)
torch.save(
{
'model_state_dict': mod.state_dict(),
'optimizer_state_dict': optimizer.state_dict()
}, g)
return AA, mod, X_train, y_train, losses_train, optimizer
'GPUs': 0
}
if isinstance(parameters['GPUs'], int):
parameters['GPUs'] = (parameters['GPUs'], )
testset = MyDataset(
filelist='../dataset/wp1_real.txt',
input_transform=transforms.Compose([
Resize((300, 300)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
)
model = Model(parameters['n_classes'])
model.load_state_dict(torch.load('wp1-cold.pth'))
if parameters['GPUs']:
model = model.cuda(parameters['GPUs'][0])
if len(parameters['GPUs']) > 1:
model = nn.DataParallel(model, device_ids=parameters['GPUs'])
model.eval()
all_features, all_outputs, all_preds, all_labels = predict(model, testset, **parameters)
recall = np.sum(all_preds == all_labels) / float(len(testset))
ap = AP(all_outputs, all_labels)
mean_ap = meanAP(all_outputs, all_labels)
print('Mean Recall: ', recall)
import torch import torchvision from network import Model from PIL import Image PATH = './state_dict.pth' state_dict = torch.load(PATH) model = Model() model = torch.nn.DataParallel(model) model.load_state_dict(state_dict) sample = torch.randn(64, 10) data = model.module.fc4(model.module.fc3(sample)).view(64, 10, 7, 7) data = model.module.decoder(data) torchvision.utils.save_image(data.view(64, 1, 28, 28), 'result2.png') # sample = model.module.decoder(model.module.fc2(sample).view(64, 128, 7, 7)).cpu() # torchvision.utils.save_image(sample.data.view(64, 1, 28, 28), 'result/sample_' + str(1) + '.png')
validation_split = .1
shuffle_dataset = True
random_seed = 42
# Creating data indices for training and validation splits:
dataset_size = len(my_dataset)
indices = list(range(dataset_size))
split = int(np.floor(validation_split * dataset_size))
if shuffle_dataset:
np.random.seed(random_seed)
np.random.shuffle(indices)
train_indices = indices[split:]
# load model
model = Model().to(device=device)
model.load_state_dict(
torch.load('stats/model_saved.pth', map_location=torch.device(device)))
model = model.float()
for ind in train_indices:
data = my_dataset[ind]
img = data['image']
position_target = data['point_map']
img = img.to(device=device)
position_target = position_target.to(device=device)
img = img.unsqueeze(dim=0)
position_target = position_target.unsqueeze(dim=0)
pred = model(img)
logits = pred['logits']
