def test_avg(iterations, test_file, beam_size):
data = prepare_data.read_file(test_file)
feature = Feature()
decoder = Decoder(beam_size, feature.get_score)
count = 0
data_size = len(data)
model_file = open(
'/home/xzt/CWS/model_result/avg-model_beam-size-' + str(beam_size) +
'.pkl', 'rb')
feature.load_model(model_file)
model_file.close()
for line in data:
z = decoder.beamSearch(line)
seg_data = ' '.join(z)
seg_data_file = '/home/xzt/CWS/test_seg_data/avg-test-seg-data' + '_beam-size-' + str(
beam_size) + '.txt'
with open(seg_data_file, 'a') as f:
f.write(seg_data + '\n')
count += 1
if count % 1000 == 0:
print("segment with avg-model, finish %.2f%%" %
((count / data_size) * 100))
f.close()
print("segment with avg model finish")
def generate_data(filename, version, block_size=16):
input_path = os.path.join(PGM_ORIGINAL_PATH, filename + PGM_SUFFIX)
if not os.path.exists(BITSTREAM_PATH):
os.mkdir(BITSTREAM_PATH)
bitstream_path = os.path.join(BITSTREAM_PATH, filename + BITSTREAM_SUFFIX)
if not os.path.exists(PGM_RECONSTRUCTION_PATH):
os.mkdir(PGM_RECONSTRUCTION_PATH)
output_path = os.path.join(PGM_RECONSTRUCTION_PATH, filename + PGM_SUFFIX)
df = pd.DataFrame(columns=['bpp', 'db'])
for index, quality in enumerate([8, 12, 16, 20, 24]):
enc = Encoder(input_path, bitstream_path, block_size, quality, False)
enc.encode_image()
dec = Decoder(bitstream_path, output_path, pgm=True)
dec.decode_all_frames()
process = subprocess.run(
[PSNR_TOOL_PATH, input_path, output_path, bitstream_path],
stdout=subprocess.PIPE,
)
stdout = process.stdout.decode("utf-8")
bpp, db = stdout.split(' bpp ')
bpp, db = float(bpp), float(db.replace(' dB', ''))
db = 0.0 if math.isinf(db) else db
df.loc[index] = [bpp, db]
version_path = os.path.join(DATA_ROOT_PATH, version)
print(version)
if not os.path.exists(version_path):
os.mkdir(version_path)
df.to_pickle(os.path.join(version_path, filename + DATA_SUFFIX))
def train(iterations, train_file, beam_size):
data = prepare_data.read_file(train_file)
feature = Feature()
decoder = Decoder(beam_size, feature.get_score)
for t in range(iterations):
count = 0
data_size = len(data)
for line in data:
y = line.split()
z = decoder.beamSearch(line)
if z != y:
feature.update_weight(y, z)
train_seg = ' '.join(z)
seg_data_file = '/home/xzt/CWS/train_seg_data/train-seg-data_ model-' + str(
t) + '.txt'
with open(seg_data_file, 'a') as f:
f.write(train_seg + '\n')
count += 1
if count % 1000 == 0:
print("iter %d , finish %.2f%%" % (t,
(count / data_size) * 100))
model_file = open(
"/home/xzt/CWS/model_result/model-" + str(t) + "_beam-size-" +
str(beam_size) + '.pkl', 'wb')
feature.save_model(model_file)
model_file.close()
f.close()
print("segment with model-%d finish" % t)
print("iteration %d finish" % t)
def predict(self, y, phi_gmm, encoder_layers, decoder_layers, seed=0):
"""
Args:
y: data to cluster and reconstruct
phi_gmm: latent phi param
encoder_layers: encoder NN architecture
decoder_layers: encoder NN architecture
seed: random seed
Returns:
reconstructed y and most probable cluster allocation
"""
nb_samples = 1
phi_enc_model = Encoder(layerspecs=encoder_layers)
phi_enc = phi_enc_model.forward(y)
x_k_samples, log_r_nk, _, _ = e_step(phi_enc,
phi_gmm,
nb_samples,
seed=0)
x_samples = subsample_x(x_k_samples, log_r_nk, seed)[:, 0, :]
y_recon_model = Decoder(layerspecs=decoder_layers)
y_mean, _ = y_recon_model.forward(x_samples)
return (y_mean, torch.argmax(log_r_nk, dim=1))
def call_Decoder(self,nlayer,Elt_state,\
Rlt_top,Rlt_state,epoch):
# h = hidden layers
R_lt_next = None
Elt_state_in = None
Decode_lt = None
# return hidden layers size:
h_l_down_in, h_l_top_out,\
h_l_down_out, h_Elt = self.hidden_layers_selctor(nlayer)
if Elt_state is None:
Elt_state_in = self.get_init_Elt_tensor(self.error_states[nlayer +
1])
else:
Elt_state_in = Elt_state
if Rlt_top is None:
Decode_lt = Decoder(h_Elt, 0, h_l_down_out,\
self.kernel_size).cuda()
Elt_state = self.get_init_Elt_tensor(self.error_states[nlayer + 1])
R_lt_next = Decode_lt(Elt_state_in, None, None)
else:
Decode_lt = Decoder(h_Elt, Rlt_top.data.size()[1],\
h_l_top_out,self.kernel_size).cuda()
R_lt_next = Decode_lt(Elt_state_in,\
Rlt_top,Rlt_state)
if self.saveModel == True:
if epoch % self.numSaveIter == 0:
self.save_models(Decode_lt, epoch, "Decoder")
return R_lt_next, Decode_lt.parameters()
def encoder_decoder(self, inputs):
encoded = self.encoder.encoder(inputs, self.target_layer)
model = Decoder()
decoded, _ = model.decoder(encoded, self.target_layer)
decoded_encoded = self.encoder.encoder(decoded, self.target_layer)
return encoded, decoded, decoded_encoded
def __init__(self, w2i, i2w):
"""
Args:
args: parameters of the model
textData: the dataset object
"""
super(Model, self).__init__()
print("Model creation...")
self.word2index = w2i
self.index2word = i2w
self.max_length = args['maxLengthDeco']
self.dtype = 'float32'
self.NLLloss = torch.nn.NLLLoss(reduction='none')
self.CEloss = torch.nn.CrossEntropyLoss(reduction='none')
self.embedding = nn.Embedding(args['vocabularySize'],
args['embeddingSize'])
self.emo_embedding = nn.Embedding(args['emo_labelSize'],
args['embeddingSize'])
self.encoder = Encoder(w2i, i2w, self.embedding)
self.decoder = Decoder(w2i, i2w, self.embedding)
self.tanh = nn.Tanh()
self.softmax = nn.Softmax(dim=-1)
def __init__(self, args):
super(Mem2SeqRunner, self).__init__(args)
# Model parameters
self.gru_size = 128
self.emb_size = 128
#TODO: Try hops 4 with task 3
self.hops = 3
self.dropout = 0.2
self.encoder = Encoder(self.hops, self.nwords, self.gru_size)
self.decoder = Decoder(self.emb_size, self.hops, self.gru_size,
self.nwords)
self.optim_enc = torch.optim.Adam(self.encoder.parameters(), lr=0.001)
self.optim_dec = torch.optim.Adam(self.decoder.parameters(), lr=0.001)
if self.loss_weighting:
self.optim_loss_weights = torch.optim.Adam([self.loss_weights],
lr=0.0001)
self.scheduler = lr_scheduler.ReduceLROnPlateau(self.optim_dec,
mode='max',
factor=0.5,
patience=1,
min_lr=0.0001,
verbose=True)
if self.use_cuda:
self.cross_entropy = self.cross_entropy.cuda()
self.encoder = self.encoder.cuda()
self.decoder = self.decoder.cuda()
if self.loss_weighting:
self.loss_weights = self.loss_weights.cuda()
def __init__(self, mem_size: int = 10000):
"""
The following is an abstraction for a bank of values
such as valA, which will be used during each cycle.
It's set up as an object to avoid circular import.
"""
self.ValBank = ValBank()
"""
The following are functional units like memory,
registers, or flags
"""
self.Memory = Memory(mem_size)
self.RegisterBank = RegisterBank()
self.ZF = CCFlag("ZF") # zero flag
self.OF = CCFlag("OF") # overflow flag
self.SF = CCFlag("SF") # sign flag
self.ErrorFlag = StateFlag("Error Flag", error_lib)
self.StateFlag = StateFlag("State Flag", state_lib)
self.ALU = ALU(self.ValBank, self.StateFlag, self.ErrorFlag, self.SF, self.OF, self.ZF)
"""
The following are functional abstractions of operations
that the processor performs
"""
self.Fetcher = Fetcher(self.ValBank, self.RegisterBank, self.Memory, self.StateFlag, self.ErrorFlag)
self.Decoder = Decoder(self.ValBank, self.RegisterBank, self.Memory)
self.Executor = Executor(self.ValBank, self.ALU, self.OF, self.ZF, self.SF)
self.Memorizer = Memorizer(self.ValBank, self.Memory)
self.RegWriter = RegWriter(self.RegisterBank, self.ValBank)
self.PCUpdater = PCUpdater(self.RegisterBank, self.ValBank)
def test_folder(folder):
""" Test all images inside a folder
Use Zbar library to test the image.
Args:
folder: The path of your target folder
Returns:
(succ, fail, rate): The number of success, failure,
and the "success rate" = (succ) / (succ + fail)
"""
def is_img(path):
# Add more extensions if you need
img_ext = ['jpg', 'png', 'bmp']
return path.split('.')[-1] in img_ext
dc = Decoder()
for root, folders, files in os.walk(folder):
img_list = [os.path.join(root, file) for file in files if is_img(file)]
succ = fail = 0
for img in img_list:
pil = Image.open(img).convert('L')
code = dc.decode(pil)
if len(code) > 0:
succ += 1
else:
fail += 1
rate = float(succ) / (succ + fail)
return (succ, fail, rate)
def __init__(self, input_size, hidden_size, batch_size, learning_rate,
num_epoch, method):
dataset = Seq2SeqDataset()
self.vocab = sorted(set(dataset.full_text))
self.vocab_size = len(self.vocab)
self.char2ind, self.ind2char = self.get_vocab()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = self.vocab_size
self.method = method
self.learning_rate = learning_rate
self.batch_size = batch_size
self.num_epoch = num_epoch
self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
self.dataloader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True)
self.encoder = Encoder(input_size, hidden_size, self.vocab_size)
self.decoder = Decoder(hidden_size, self.output_size, method)
self.encoder = self.encoder.to(self.device)
self.decoder = self.decoder.to(self.device)
self.loss_function = NLLLoss()
self.encoder_optim = optim.Adam(self.encoder.parameters(),
lr=self.learning_rate)
self.decoder_optim = optim.Adam(self.decoder.parameters(),
lr=self.learning_rate)
def __init__(self, config):
super(Model, self).__init__()
self.config = config
# 定义嵌入层
self.embedding = Embedding(
config.num_vocab, # 词汇表大小
config.embedding_size, # 嵌入层维度
config.pad_id, # pad_id
config.dropout)
# post编码器
self.post_encoder = Encoder(
config.post_encoder_cell_type, # rnn类型
config.embedding_size, # 输入维度
config.post_encoder_output_size, # 输出维度
config.post_encoder_num_layers, # rnn层数
config.post_encoder_bidirectional, # 是否双向
config.dropout) # dropout概率
# response编码器
self.response_encoder = Encoder(
config.response_encoder_cell_type,
config.embedding_size, # 输入维度
config.response_encoder_output_size, # 输出维度
config.response_encoder_num_layers, # rnn层数
config.response_encoder_bidirectional, # 是否双向
config.dropout) # dropout概率
# 先验网络
self.prior_net = PriorNet(
config.post_encoder_output_size, # post输入维度
config.latent_size, # 潜变量维度
config.dims_prior) # 隐藏层维度
# 识别网络
self.recognize_net = RecognizeNet(
config.post_encoder_output_size, # post输入维度
config.response_encoder_output_size, # response输入维度
config.latent_size, # 潜变量维度
config.dims_recognize) # 隐藏层维度
# 初始化解码器状态
self.prepare_state = PrepareState(
config.post_encoder_output_size + config.latent_size,
config.decoder_cell_type, config.decoder_output_size,
config.decoder_num_layers)
# 解码器
self.decoder = Decoder(
config.decoder_cell_type, # rnn类型
config.embedding_size, # 输入维度
config.decoder_output_size, # 输出维度
config.decoder_num_layers, # rnn层数
config.dropout) # dropout概率
# 输出层
self.projector = nn.Sequential(
nn.Linear(config.decoder_output_size, config.num_vocab),
nn.Softmax(-1))
def aptidao(self, cromossomo):
decodificacao = Decoder(self.custos, sqrt(self.TAM_CROM))
'''
Transforma as chaves aleatorias em binarios, verifica as restrições e
retorna o custo a ser minimizado
'''
Z = decodificacao.decode(cromossomo)
return Z
def encoder_decoder(self, inputs):
encoded = self.encoder.encoder(inputs, self.target_layer) #特征提取
model = Decoder() #生成图像model
decoded, _ = model.decoder(encoded, self.target_layer) #生成图像
# 再对生成图像，进行特征提取
decoded_encoded = self.encoder.encoder(decoded, self.target_layer)
# 返回值为 a.提取的特征 b.由特征生成的图片 c.由生成图片提取的特征
return encoded, decoded, decoded_encoded
def __init__(self,target_layer,content_path,style_path,alpha,pretrained_vgg,output_path,decoder_weights) :
self.target_layer = target_layer
self.content_path = content_path
self.style_path = style_path
self.output_path = output_path
self.alpha = alpha
self.encoder = Vgg19(pretrained_vgg)
self.decoder = Decoder()
self.decoder_weights = decoder_weights
def Decode(input, msg, coder, transSymbols=None):
print("----------")
print("Wejscie: " + input)
decoder = Decoder(None, None, coder)
if transSymbols != None:
print("Zdekodowano: " +
decoder.DecodeAllLevelsString(msg, transSymbols))
else:
print("Zdekodowano: " + decoder.DecodeString(msg))
print("----------")
def __init__(self,content_path,style_path,alpha,pretrained_vgg,output_path) :
self.content_path = content_path
self.style_path = style_path
self.output_path = output_path
self.alpha = alpha
self.encoder = Vgg19(pretrained_vgg)
self.decoder = Decoder()
#添加第五个权重
#add ——>models/decoder_5.ckpt
self.decoder_weights = ['models/decoder_1.ckpt','models/decoder_2.ckpt','models/decoder_3.ckpt','models/decoder_4.ckpt','models/model.ckpt-15004']
def network():
# define encoder input data
enc_token_ids = fluid.layers.data(name="enc_token_ids", shape=[None, SEQ_MAX_LEN], dtype='int64')
enc_segment_ids = fluid.layers.data(name="enc_segment_ids", shape=[None, SEQ_MAX_LEN], dtype='int64')
enc_pos_ids = fluid.layers.data(name="enc_pos_ids", shape=[None, SEQ_MAX_LEN], dtype='int64')
enc_input_length = fluid.layers.data(name='enc_input_length', shape=[None, SEQ_MAX_LEN, 1], dtype='int64')
# define decoder input data
dec_token_ids = fluid.layers.data(name="token_ids", shape=[None, SEQ_MAX_LEN], dtype='int64')
dec_segment_ids = fluid.layers.data(name="segment_ids", shape=[None, SEQ_MAX_LEN], dtype='int64')
dec_pos_ids = fluid.layers.data(name="pos_ids", shape=[None, SEQ_MAX_LEN], dtype='int64')
dec_enc_slf_attn = fluid.layers.data(name='enc_slf_attn', shape=[None, SEQ_MAX_LEN, SEQ_MAX_LEN], dtype='int64')
# task label
dec_lm_label_mat = fluid.layers.data(name='lm_label_mat', shape=[None, SEQ_MAX_LEN], dtype='int64')
dec_lm_pos_mask = fluid.layers.data(name='lm_pos_mask', shape=[None, SEQ_MAX_LEN, SEQ_MAX_LEN], dtype='int64')
dec_lm_pos_len = fluid.layers.data(name='lm_pos_len', shape=[None, 1], dtype='int64')
goal_type_pos = fluid.layers.data(name="goal_type_pos", shape=[None, 2], dtype='int64')
goal_type_label = fluid.layers.data(name="goal_type_label", shape=[None], dtype='int64')
# enc_dec_mask
enc_dec_mask = fluid.layers.data(name='enc_dec_mask', shape=[None, SEQ_MAX_LEN, SEQ_MAX_LEN], dtype='int64')
# network
encode = Encoder(enc_token_ids, enc_pos_ids, enc_segment_ids,
enc_input_length, config)
enc_output = encode.get_sequence_output()
decode = Decoder(dec_token_ids, dec_pos_ids, dec_segment_ids,
dec_enc_slf_attn, config=config, enc_input=enc_output, enc_input_mask=enc_dec_mask)
loss, goal_type_acc = decode.pretrain(goal_type_pos, goal_type_label,
dec_lm_label_mat, dec_lm_pos_mask, dec_lm_pos_len)
input_name_list = [
enc_token_ids.name,
enc_segment_ids.name,
enc_pos_ids.name,
enc_input_length.name,
dec_token_ids.name,
dec_segment_ids.name,
dec_pos_ids.name,
dec_enc_slf_attn.name,
enc_dec_mask.name,
dec_lm_label_mat.name,
dec_lm_pos_mask.name,
dec_lm_pos_len.name,
goal_type_pos.name,
goal_type_label.name
]
return loss.name, goal_type_acc.name, input_name_list
class Processor:
"""
This represents the processor itself. Most work and operations
will be outsourced to other objects referenced by the processor.
The processor holds the values of valP, valE, valA, valB, valC,
and rA, rB.
"""
def __init__(self, mem_size: int = 10000):
"""
The following is an abstraction for a bank of values
such as valA, which will be used during each cycle.
It's set up as an object to avoid circular import.
"""
self.ValBank = ValBank()
"""
The following are functional units like memory,
registers, or flags
"""
self.Memory = Memory(mem_size)
self.RegisterBank = RegisterBank()
self.ZF = CCFlag("ZF") # zero flag
self.OF = CCFlag("OF") # overflow flag
self.SF = CCFlag("SF") # sign flag
self.ErrorFlag = StateFlag("Error Flag", error_lib)
self.StateFlag = StateFlag("State Flag", state_lib)
self.ALU = ALU(self.ValBank, self.StateFlag, self.ErrorFlag, self.SF, self.OF, self.ZF)
"""
The following are functional abstractions of operations
that the processor performs
"""
self.Fetcher = Fetcher(self.ValBank, self.RegisterBank, self.Memory, self.StateFlag, self.ErrorFlag)
self.Decoder = Decoder(self.ValBank, self.RegisterBank, self.Memory)
self.Executor = Executor(self.ValBank, self.ALU, self.OF, self.ZF, self.SF)
self.Memorizer = Memorizer(self.ValBank, self.Memory)
self.RegWriter = RegWriter(self.RegisterBank, self.ValBank)
self.PCUpdater = PCUpdater(self.RegisterBank, self.ValBank)
def run(self):
"""
This is the only necessary public method for the processor.
Currently the way to operate this is by manually loading values
into the memory using the place_instruction method, then calling
the 'run' method for the processor. Afterwards calling print
on the memory object will reveal the finish state of the processor.
"""
while self.StateFlag.State == 0:
self.Fetcher.fetch()
self.Decoder.decode()
self.Executor.execute()
self.Memorizer.memory_write()
self.RegWriter.write_back()
self.PCUpdater.update_pc()
def __init__(self): threading.Thread.__init__(self) self.shutdown_flag = threading.Event() self.motor = Motorcontroller() self.buzzer = Buzzer() self.xbee = Xbee() self.decoder = Decoder() self.servo1 = 96 self.servo2 = 75 self.joycalc = Joystick() self.motor.setServo1(self.servo1) self.motor.setServo2(self.servo2) self.lastSavedTime = 0
def __init__(self, w2i, i2w, embs=None, title_emb = None, info=None):
"""
Args:
args: parameters of the model
textData: the dataset object
"""
super(LSTM_CTE_Model_with_action, self).__init__()
print("Model creation...")
self.word2index = w2i
self.index2word = i2w
self.max_length = args['maxLengthDeco']
self.info = info
self.NLLloss = torch.nn.NLLLoss(ignore_index=0)
self.CEloss = torch.nn.CrossEntropyLoss(ignore_index=0)
if embs is not None:
self.embedding = nn.Embedding.from_pretrained(embs)
else:
self.embedding = nn.Embedding(args['vocabularySize'], args['embeddingSize'])
if title_emb is not None:
self.field_embedding = nn.Embedding.from_pretrained(title_emb)
else:
self.field_embedding = nn.Embedding(args['TitleNum'], args['embeddingSize'])
self.encoder = Encoder(w2i, i2w, bidirectional=True)
# self.encoder_answer_only = Encoder(w2i, i2w)
self.encoder_no_answer = Encoder(w2i, i2w)
self.encoder_pure_answer = Encoder(w2i, i2w)
self.decoder_answer = Decoder(w2i, i2w, self.embedding, copy='pure', max_dec_len=10)
self.decoder_no_answer = Decoder(w2i, i2w, self.embedding,
input_dim = args['embeddingSize']*2,# + args['hiddenSize'] ,
copy='semi')
self.ansmax2state_h = nn.Linear(args['embeddingSize'], args['hiddenSize']*2, bias=False)
self.ansmax2state_c = nn.Linear(args['embeddingSize'], args['hiddenSize']*2, bias=False)
self.tanh = nn.Tanh()
self.relu = nn.ReLU()
self.softmax = nn.Softmax(dim=-1)
self.sigmoid = nn.Sigmoid()
self.att_size_r = 60
# self.grm = GaussianOrthogonalRandomMatrix()
# self.att_projection_matrix = Parameter(self.grm.get_2d_array(args['embeddingSize'], self.att_size_r))
self.M = Parameter(torch.randn([args['embeddingSize'], args['hiddenSize']*2,2]))
self.shrink_copy_input= nn.Linear(args['hiddenSize']*2, args['hiddenSize'], bias=False)
self.emb2hid = nn.Linear(args['embeddingSize'], args['hiddenSize'], bias=False)
def __init__(self, config):
super(Model, self).__init__()
self.config = config
# 情感嵌入层
self.affect_embedding = AffectEmbedding(config.num_vocab,
config.affect_embedding_size,
config.pad_id)
# 定义嵌入层
self.embedding = WordEmbedding(config.num_vocab, # 词汇表大小
config.embedding_size, # 嵌入层维度
config.pad_id) # pad_id
# 情感编码器
self.affect_encoder = Encoder(config.encoder_decoder_cell_type, # rnn类型
config.affect_embedding_size, # 输入维度
config.affect_encoder_output_size, # 输出维度
config.encoder_decoder_num_layers, # 层数
config.encoder_bidirectional, # 是否双向
config.dropout)
# 编码器
self.encoder = Encoder(config.encoder_decoder_cell_type, # rnn类型
config.embedding_size, # 输入维度
config.encoder_decoder_output_size, # 输出维度
config.encoder_decoder_num_layers, # rnn层数
config.encoder_bidirectional, # 是否双向
config.dropout) # dropout概率
self.prepare_state = PrepareState(config.encoder_decoder_cell_type,
config.encoder_decoder_output_size + config.affect_encoder_output_size,
config.encoder_decoder_output_size)
self.linear_prepare_input = nn.Linear(config.embedding_size + config.affect_encoder_output_size,
config.decoder_input_size)
# 解码器
self.decoder = Decoder(config.encoder_decoder_cell_type, # rnn类型
config.decoder_input_size, # 输入维度
config.encoder_decoder_output_size, # 输出维度
config.encoder_decoder_num_layers, # rnn层数
config.dropout) # dropout概率
# 输出层
self.projector = nn.Sequential(
nn.Linear(config.encoder_decoder_output_size, config.num_vocab),
nn.Softmax(-1)
)
def __init__(self, config):
super(Model, self).__init__()
self.config = config
self.embedding = Embedding(config.num_vocab,
config.embedding_size,
config.pad_id,
config.dropout)
self.affect_embedding = Embedding(config.num_vocab,
config.affect_embedding_size,
config.pad_id,
config.dropout)
self.affect_embedding.embedding.weight.requires_grad = False
self.post_encoder = Encoder(config.encoder_cell_type,
config.embedding_size + config.affect_embedding_size,
config.encoder_output_size,
config.encoder_num_layers,
config.encoder_bidirectional,
config.dropout)
self.response_encoder = Encoder(config.encoder_cell_type,
config.embedding_size + config.affect_embedding_size,
config.encoder_output_size,
config.encoder_num_layers,
config.encoder_bidirectional,
config.dropout)
self.prior_net = PriorNet(config.encoder_output_size,
config.latent_size,
config.dims_prior)
self.recognize_net = RecognizeNet(config.encoder_output_size,
config.encoder_output_size,
config.latent_size,
config.dims_recognize)
self.prepare_state = PrepareState(config.encoder_output_size + config.latent_size,
config.decoder_cell_type,
config.decoder_output_size,
config.decoder_num_layers)
self.decoder = Decoder(config.decoder_cell_type,
config.embedding_size + config.affect_embedding_size + config.encoder_output_size,
config.decoder_output_size,
config.decoder_num_layers,
config.dropout)
self.projector = nn.Sequential(nn.Linear(config.decoder_output_size, config.num_vocab), nn.Softmax(-1))
def __init__(self, content_path, style_path, alpha_wct, alpha_swap,
pretrained_vgg, output_path):
self.content_path = content_path # 内容图片路径
self.style_path = style_path # 风格图片路径
self.output_path = output_path # 融合后图片输出路径
self.alpha_wct = alpha_wct # wct方法内容与风格图片比重
self.alpha_swap = alpha_swap #wct_swap方法内容与风格图片比重
self.encoder = Vgg19(pretrained_vgg) # 导入VGG19 模型
self.decoder = Decoder() # 导入Decoder 反卷积
# 导入Decoder模型参数
self.decoder_weights = [
'models/decoder_1.ckpt', 'models/decoder_2.ckpt',
'models/decoder_3.ckpt', 'models/decoder_4.ckpt'
]
def encoder_decoder(self, inputs):
"""
encoder_decoder function:
Outputs:
encoded: feature map obtained by processing the original image with the encoder
decoded: reconstructed image obtained by processing the original image with both the encoder and decoder
decoded_encoded: feature map obtained by processing the reconstructed image with the encoder
"""
encoded = self.encoder.encoder(inputs, self.target_layer)
model = Decoder()
decoded, _ = model.decoder(encoded, self.target_layer)
decoded_encoded = self.encoder.encoder(decoded, self.target_layer)
return encoded, decoded, decoded_encoded
def __init__(self,
in_channels,
hidden_channels,
num_resblocks,
res_channels,
D,
K,
beta=0.25,
gamma=0.99):
"""
in_channels: number of channels of the input image
hidden_channels: the number of channels that are used by the hidden conv layers
num_resblocks: the number of residual blocks used in both the encoder and the decoder
res_channels: the number of channels that are used by the residual blocks
D: dimensionality of each embedding vector, or embedding_dim in the sonnet's implementation
K: the size of the discrete space (the number of embedding vectors), or num_embeddings in the sonnet's implementation
beta: the hyperparameter that acts as a weighting to the lost term, or commitment_cost in the sonnet's implementation
recommendation from the paper, beta=0.25
gamma: controls the speed of the EMA, or decay in the sonnet's implementation
recommendation from the paper, gamma=0.99
"""
super(VQVAE, self).__init__()
self.encoder = Encoder(in_channels, hidden_channels, num_resblocks,
res_channels)
# the following is the additional layer added in the author's original implementation
# to make sure that the number of channels equals to D (embedding dimension)
self.pre_vq = nn.Conv2d(in_channels=hidden_channels,
out_channels=D,
kernel_size=1,
stride=1)
self.vectorquantizer = VectorQuantizerEMA(D, K, beta, gamma)
self.decoder = Decoder(D, hidden_channels, num_resblocks, res_channels,
in_channels)
def __build(self):
self.encoder = Encoder(self.src_vocab_size, self.src_emb_dim,
self.enc_units)
self.decoder = Decoder(self.tgt_vocab_size, self.tgt_emb_dim,
self.enc_units, self.dec_units, self.att_units,
self.score_type)
self.sess = tf.Session()
def __init__(self, vocab_size, embed_size, hidden_size, choose_model,
use_gpu, gpu_id):
# gpu Setting
model = Alex_RNNLM(hidden_size)
if choose_model == "Alex_RNNLM":
model = Alex_RNNLM(hidden_size)
if choose_model == "AlexBn_RNNLM":
model = AlexBn_RNNLM(hidden_size)
if use_gpu:
cuda.get_device(gpu_id).use()
model.to_gpu()
# Setting Model
super(EncoderDecoder, self).__init__(
enc=model,
dec=Decoder(vocab_size, embed_size, hidden_size),
)
self.vocab_size = vocab_size
self.embed_size = embed_size
self.hidden_size = hidden_size
self.common_function = CommonFunction()
self.use_gpu = use_gpu
self.gpu_id = gpu_id
self.choose_model = choose_model
self.__set_gpu()
def __init__(self,
vocab_size,
max_len,
device,
num_layers=6,
stack_layers=6,
d_model=512,
num_heads=8,
ffn_dim=2048,
dropout=0.2):
super(Transformer, self).__init__()
self.device = device
self.encoder = Encoder(vocab_size, max_len, num_layers, d_model,
num_heads, ffn_dim, dropout)
self.decoder = Decoder(vocab_size, max_len, device, num_layers,
d_model, num_heads, ffn_dim, dropout)
self.generator = Generator(vocab_size, max_len, device, stack_layers,
d_model, num_heads, ffn_dim, dropout)
self.embedding = nn.Embedding(vocab_size, d_model)
self.linear = nn.Linear(d_model, vocab_size, bias=False)
self.softmax = nn.Softmax(dim=2)
def make_model(src_vocab,
tgt_vocab,
N=6,
d_model=512,
d_ff=2048,
h=8,
dropout=0.1):
"Helper: Construct a model from hyperparameters."
c = copy.deepcopy
attn = MultiHeadedAttention(h, d_model)
ff = PositionwiseFeedForward(d_model, d_ff, dropout)
position = PositionalEncoding(d_model, dropout)
model = EncoderDecoder(
Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N),
Decoder(DecoderLayer(d_model, c(attn), c(attn), c(ff), dropout), N),
nn.Sequential(Embeddings(d_model, src_vocab), c(position)),
nn.Sequential(Embeddings(d_model, tgt_vocab), c(position)),
Generator(d_model, tgt_vocab))
# This was important from their code.
# Initialize parameters with Glorot / fan_avg.
for p in model.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
"""https://zhuanlan.zhihu.com/p/74274453
#權值初始化 Xavier均勻分佈"""
return model
def decode(lyricsWithModels, observationFeatures):
'''
same as decoder.decodeAudio() without the parts with WITH_Duration flag.
'''
alpha = 0.97
deviationInSec = 0.1
ONLY_MIDDLE_STATE=False
params = Parameters(alpha, ONLY_MIDDLE_STATE, deviationInSec)
decoder = Decoder(lyricsWithModels, params.ALPHA, params.deviationInSec)
# decodes
decoder.hmmNetwork.initDecodingParameters(observationFeatures)
chiBackPointer, psiBackPointer = decoder.hmmNetwork._viterbiForcedDur(observationFeatures)
# backtrack
path = Path(chiBackPointer, psiBackPointer)
detectedWordList = decoder.path2ResultWordList(path)
# DEBUG
decoder.lyricsWithModels.printWordsAndStatesAndDurations(decoder.path)
path.printDurations()
# tu jest moj main
import sys
import matplotlib
from Decoder import Decoder
matplotlib.use("Tkagg")
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
from matplotlib.figure import Figure
import tkinter as tk
from tkinter import ttk
LARGE_FONT = ("Verdana", 12)
data = Decoder()
allData = data.getData()
class SeaofBTCapp(tk.Tk):
def __init__(self, *args, **kwargs):
tk.Tk.__init__(self, *args, **kwargs)
tk.Tk.wm_title(self, "BlackBox")
container = tk.Frame(self)
container.pack(side="top", fill="both", expand=True)
container.grid_rowconfigure(0, weight=1)
container.grid_columnconfigure(0, weight=1)
self.frames = {}
for F in (StartPage, PageOne, PageTwo, PageThree):
# get average signal power
signal_power = mapper.get_signal_average_power()
# what is the noise power and standard deviation when SNR is 10dB?
noise_power = signal_power / 10.0
noise_std_dev = math.sqrt(noise_power)
# initialize random number generator. this seed is fixed constant in order
# to get deterministic results in this example.
random.seed(314159265359)
# add white gaussian noise at 10dB to signal
print "Adding white gaussian noise at 10dB."
noisy_symbols = [sym + random.gauss(0, noise_std_dev) for sym in symbols]
# round to closest integer
noisy_symbols = [int(x + 0.5) for x in noisy_symbols]
print "noisy symbols:", noisy_symbols
# instantiate decoder
decoder = Decoder(k, B, d, map_func)
# update decoder with gathered points
for i in xrange(spine_length):
decoder.advance([noisy_symbols[i],
noisy_symbols[i+spine_length],
noisy_symbols[i+2*spine_length]])
print "decoded hex:", decoder.get_most_likely().encode("hex")
# make sure we got the message we started with
assert(decoder.get_most_likely() == message)
socket = ServerUDP.initServerSocket()
print "Running and waiting..."
while True:
try:
encoded_text = ServerUDP.readSocket(socket)
print "Data received!"
file = open(Util.ENCODED_FILE, "w")
file.write(encoded_text)
file.close()
print "Decoding data..."
Decoder.decode()
print "File successfully decoded!"
print "\nRunning and waiting..."
except Exception as e:
print e
print("Error!")
# finally:
# socket.close()
# os._exit(0)
def decodeRaw(inputStream, outputStream, intervalLength, options):
Coder.checkInterval(intervalLength)
decoder = Decoder(inputStream, outputStream, options)
amountProcessed = 0
while not decoder.decode(intervalLength):
amountProcessed += intervalLength