def load(self, fdata, use_char=False, n_context=1, max_len=10):
sentences = self.preprocess(fdata)
x, y, char_x, lens = [], [], [], []
for wordseq, tagseq in sentences:
wiseq = [self.wdict.get(w, self.unk_wi) for w in wordseq]
tiseq = [self.tdict[t] for t in tagseq]
# 获取每个词汇的上下文
if n_context > 1:
x.append(self.get_context(wiseq, n_context))
else:
x.append(torch.tensor(wiseq, dtype=torch.long))
y.append(torch.tensor(tiseq, dtype=torch.long))
# 不足最大长度的部分用0填充
char_x.append(torch.tensor([
[self.cdict.get(c, self.unk_ci)
for c in w[:max_len]] + [0] * (max_len - len(w))
for w in wordseq
]))
lens.append(len(tiseq))
x = pad_sequence(x, True)
y = pad_sequence(y, True)
char_x = pad_sequence(char_x, True)
lens = torch.tensor(lens)
if use_char:
dataset = TensorDataset(x, y, char_x, lens)
else:
dataset = TensorDataset(x, y, lens)
return dataset
def postprocess_sequence(self, X):
"""Embed (variable-length) sequences
Parameters
----------
X : list
List of input sequences
Returns
-------
fX : numpy array
Batch of sequence embeddings.
"""
lengths = torch.tensor([len(x) for x in X])
sorted_lengths, sort = torch.sort(lengths, descending=True)
_, unsort = torch.sort(sort)
sequences = [torch.tensor(X[i],
dtype=torch.float32,
device=self.device) for i in sort]
padded = pad_sequence(sequences, batch_first=True, padding_value=0)
packed = pack_padded_sequence(padded, sorted_lengths,
batch_first=True)
cpu = torch.device('cpu')
fX = self.model(packed).detach().to(cpu).numpy()
return fX[unsort]
def _process(self, index):
if type(index) is list:
dict_list = sorted(
[self._transform(s, t) for s, t in self.dataset[index]],
key=lambda x: x["num_frames"],
reverse=True)
spectrogram = pack_sequence([d["spectrogram"] for d in dict_list])
target_attr = pad_sequence(
[d["target_attr"] for d in dict_list], batch_first=True)
silent_mask = pad_sequence(
[d["silent_mask"] for d in dict_list], batch_first=True)
return spectrogram, target_attr, silent_mask
elif type(index) is int:
s, t = self.dataset[index]
data_dict = self._transform(s, t)
return data_dict["spectrogram"], \
data_dict["target_attr"], \
data_dict["silent_mask"]
else:
raise ValueError("Unsupported index type({})".format(type(index)))
def test_rnn_init_predict_split(self):
model = nn.LSTM(RNN_INPUT_SIZE, RNN_HIDDEN_SIZE, 3, bidirectional=True)
seq_lengths = np.random.randint(1, RNN_SEQUENCE_LENGTH + 1, size=7)
seq_lengths = list(reversed(sorted(map(int, seq_lengths))))
input = [Variable(torch.randn(l, RNN_INPUT_SIZE)) for l in seq_lengths]
input = rnn_utils.pad_sequence(input)
# Test that we are correctly splitting between init and
# predict net. When we embed parameters, there should be more
# ops in the init net.
mp = onnx.ModelProto.FromString(do_export(model, input, export_params=self.embed_params)[0])
prepared = c2.prepare(mp, device='CPU')
if self.embed_params:
assert len(prepared.init_net.op) == 1038
assert len(prepared.predict_net.op) == 101
else:
assert len(prepared.init_net.op) == 27
assert len(prepared.predict_net.op) == 1112
def make_input(batch_size):
seq_lengths = np.random.randint(1, RNN_SEQUENCE_LENGTH + 1, size=batch_size)
seq_lengths = list(reversed(sorted(map(int, seq_lengths))))
inputs = [Variable(torch.randn(l, RNN_INPUT_SIZE)) for l in seq_lengths]
inputs = rnn_utils.pad_sequence(inputs)
if packed_sequence == 2:
inputs = inputs.transpose(0, 1)
inputs = [inputs]
directions = 2 if bidirectional else 1
if initial_state:
h0 = Variable(torch.randn(directions * layers, batch_size, RNN_HIDDEN_SIZE))
inputs.append(h0)
if packed_sequence != 0:
inputs.append(Variable(torch.IntTensor(seq_lengths)))
if len(inputs) == 1:
input = inputs[0]
else:
input = tuple(inputs)
return input
def forward(self, x, char_x, lens):
B, T = x.shape
# 获取掩码
mask = x.gt(0)
# 获取词嵌入向量
x = self.embed(x)
# 获取字嵌入向量
char_x = self.char_lstm(char_x[mask])
char_x = pad_sequence(torch.split(char_x, lens.tolist()), True)
# 获取词表示与字表示的拼接
x = torch.cat((x, char_x), dim=-1)
x = self.drop(x)
x = pack_padded_sequence(x, lens, True)
x, _ = self.word_lstm(x)
x, _ = pad_packed_sequence(x, True)
x = self.drop(x)
return self.out(x)
policy.parameters()),
lr=0.01)
for i in range(num_batch):
policy_optimizer.zero_grad()
actor_batch = []
critic_batch = []
reward_batch = []
regex = []
for j in range(batch_size):
actions, reg = policy.sample_regex(max_len_regex)
reward_batch.append(evaluator.evaluate(reg))
actor_outs, critic_outs, _, _ = policy.evaluate_solution(actions)
actor_batch.append(actor_outs)
critic_batch.append(critic_outs)
regex.append(reg)
actor_batch = pad_sequence(actor_batch, True)
critic_batch = pad_sequence(critic_batch, True)
reward_batch = (torch.FloatTensor(reward_batch)
if not torch.cuda.is_available() else
torch.FloatTensor(reward_batch).cuda())
reward_batch = reward_batch.unsqueeze(1)
print("max_reward :", reward_batch.max())
print("regex max :", regex[int(reward_batch.argmax())])
loss = -1.0 * (actor_batch * reward_batch).sum()
print("loss :", loss)
loss.backward()
policy_optimizer.step()
def batch_loss(self, batch, model, device, writer=None, **kwargs):
lengths = torch.tensor([len(x) for x in batch['X']])
variable_lengths = len(set(lengths)) > 1
if variable_lengths:
sorted_lengths, sort = torch.sort(lengths, descending=True)
_, unsort = torch.sort(sort)
sequences = [torch.tensor(batch['X'][i],
dtype=torch.float32,
device=device) for i in sort]
padded = pad_sequence(sequences, batch_first=True, padding_value=0)
packed = pack_padded_sequence(padded, sorted_lengths,
batch_first=True)
batch['X'] = packed
else:
batch['X'] = torch.tensor(np.stack(batch['X']),
dtype=torch.float32,
device=device)
# forward pass
fX = model(batch['X'])
if variable_lengths:
fX = fX[unsort]
# log embedding norms
if writer is not None:
norm_npy = np.linalg.norm(self.to_numpy(fX), axis=1)
self.log_norm_.append(norm_npy)
batch['fX'] = fX
batch = self.aggregate(batch)
fX = batch['fX']
y = batch['y']
# pre-compute pairwise distances
distances = self.pdist(fX)
# sample triplets
triplets = getattr(self, 'batch_{0}'.format(self.sampling))
anchors, positives, negatives = triplets(y, distances)
# compute loss for each triplet
losses, deltas, _, _ = self.triplet_loss(
distances, anchors, positives, negatives,
return_delta=True)
if writer is not None:
pdist_npy = self.to_numpy(distances)
delta_npy = self.to_numpy(deltas)
same_speaker = pdist(y.reshape((-1, 1)), metric='equal')
self.log_positive_.append(pdist_npy[np.where(same_speaker)])
self.log_negative_.append(pdist_npy[np.where(~same_speaker)])
self.log_delta_.append(delta_npy)
# average over all triplets
return torch.mean(losses)
def pad(batch, device=torch.device('cpu')):
batch_lengths = torch.tensor(_.map_(batch, len),
dtype=torch.long,
device=device)
return (pad_sequence(batch, batch_first=True,
padding_value=1).to(device), batch_lengths)
print("weights: ", weights)
print("===================")
test_acc=[]
train_acc=[]
weighted_acc = []
test_loss=[]
train_loss=[]
for epoch in range(100): # again, normally you would NOT do 300 epochs, it is toy data
print("===================================" + str(epoch+1) + "==============================================")
losses = 0
correct=0
model.train()
for j, (input_lstm,input, target,seq_length) in enumerate(train_loader):
if (j+1)%20==0: print("=================================Train Batch"+ str(j+1)+ str(weight)+"===================================================")
model.zero_grad()
input_lstm = pad_sequence(sequences=input_lstm,batch_first=True)
losses_batch,correct_batch= model(input_lstm,input, target,seq_length)
loss = torch.mean(losses_batch,dim=0)
correct_batch=torch.sum(correct_batch,dim=0)
losses += loss.item() * batch_size
loss.backward()
weight=model.module.state_dict()["weight"]
weight=torch.exp(10*weight)/(1+torch.exp(10*weight)).item()
optimizer.step()
correct += correct_batch.item()
accuracy=correct*1.0/((j+1)*batch_size)
losses=losses / ((j+1)*batch_size)
losses_test = 0
correct_test = 0
losses_test_ce=0
def stroke_embed(batch,
initials,
embedder,
bezier_degree,
bezier_degree_low,
variational=False,
inf_loss=False):
h_initial, c_initial = initials
# Redundant, but thats fine
device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
# accumulate all info into these empty lists
sketches_ctrlpt, sketches_ratw, sketches_st_starts, sketches_stopbits = [], [], [], []
deg_losses = []
n_strokes = []
for sk, _ in batch:
# for each sketch in the batch
st_starts = torch.tensor([st[0, :2] for st in sk], device=device)
sk = [
torch.tensor(st[:, :-1], device=device) - st_start
for st, st_start in zip(sk, st_starts)
]
ls = [st.shape[0] for st in sk]
sk = pad_sequence(sk, batch_first=True)
sk = pack_padded_sequence(sk,
ls,
batch_first=True,
enforce_sorted=False)
if embedder.rational:
emb_ctrlpt, emb_ratw = embedder(sk, h_initial, c_initial)
else:
if not inf_loss:
emb_ctrlpt = embedder(sk, h_initial, c_initial, inf_loss=False)
else:
emb_ctrlpt, deg_loss = embedder(sk,
h_initial,
c_initial,
inf_loss=True)
# breakpoint()
if not inf_loss:
emb_ctrlpt = emb_ctrlpt[bezier_degree - bezier_degree_low]
sketches_ctrlpt.append(emb_ctrlpt.view(len(ls), -1))
else:
sketches_ctrlpt.append(emb_ctrlpt)
deg_losses.append(deg_loss)
# breakpoint()
if embedder.rational:
sketches_ratw.append(emb_ratw)
sketches_st_starts.append(st_starts)
# create stopbits
stopbit = torch.zeros(len(ls), 1, device=device)
stopbit[-1, 0] = 1.
sketches_stopbits.append(stopbit)
n_strokes.append(len(ls))
n_strokes = torch.tensor(n_strokes, device=device)
if not inf_loss:
sketches_ctrlpt = pad_sequence(sketches_ctrlpt, batch_first=True)
if embedder.rational:
sketches_ratw = pad_sequence(sketches_ratw, batch_first=True)
sketches_st_starts = pad_sequence(sketches_st_starts, batch_first=True)
sketches_stopbits = pad_sequence(sketches_stopbits,
batch_first=True,
padding_value=1.0)
# For every sketch in a batch:
# For every stroke in the sketch:
# 1. (Control Point, Rational Weights) pair
# 2. Start location of the stroke with respect to a global reference (of the sketch)
if embedder.rational:
return sketches_ctrlpt, sketches_ratw, sketches_st_starts, sketches_stopbits, n_strokes
else:
if not inf_loss:
return sketches_ctrlpt, sketches_st_starts, sketches_stopbits, n_strokes
else:
return (
sketches_ctrlpt,
deg_losses), sketches_st_starts, sketches_stopbits, n_strokes
def fit(self, X, y=None, y_for_verification=None, plot=False):
# assert not self.semisupervised, "semisupervised not supported yet"
self.best_delta_mi = -1
self.best_full_net = None
self.best_embedding_net = None
# self.final_model = None
# self.final_model_trained = False
self.best_n_clusters = 1
self.zero_cutoff = self.initial_zero_cutoff
self.exp_dist = 0
if self.random_seed is not None:
np.random.seed(self.random_seed)
use_y_to_verify_performance = y_for_verification is not None
self.semisupervised = self.semisupervised and y is not None
if self.semisupervised and self.semisupervised_weight is None:
self.semisupervised_weight = np.sum(y != -1) / y.shape[0]
if self.semisupervised:
n_classes = np.unique(y[y != -1]).shape[0] # because of the -1
if use_y_to_verify_performance:
verify_n_classes = np.unique(y_for_verification).shape[0]
self._print_with_verbosity(
f"number of classes in verification set: {verify_n_classes}",
3)
if self.model == "auto":
self.model = self._select_model(X)
if self.is_tokens:
X = pad_sequence(X, padding_value=0, batch_first=True)
if type(X) is not torch.Tensor:
X = torch.Tensor(X)
self.device = torch.device("cuda") if (
torch.cuda.is_available()
and self.use_gpu) else torch.device("cpu")
if self.device.type == "cpu":
self._print_with_verbosity("WARNING: using CPU, may be very slow",
0,
strict=True)
self._print_with_verbosity(f"using torch device {self.device}", 1)
self._print_with_verbosity("building dataset", 1)
dataset = self._build_dataset(
X,
y=y if self.semisupervised else None,
)
data_loader = DataLoader(dataset,
shuffle=True,
batch_size=self.batch_size)
self.model = self.model.to(self.device)
if self.optimizer_override is None:
self.optimizer = optim.Adam(self.model.parameters(),
lr=self.learning_rate)
else:
self.optimizer = self.optimizer_override(self.model.parameters(),
lr=self.learning_rate)
if self.semisupervised:
label_subnet = ClusterNet(self.n_components,
n_classes).to(self.device)
self.semisupervised_model = FullNet(self.model,
label_subnet).to(self.device)
self.optimizer = optim.Adam(self.semisupervised_model.parameters(),
lr=self.learning_rate)
self._print_with_verbosity("training", 1)
for i in self._progressbar_with_verbosity(range(self.epochs),
0,
strict=True):
self.model.train()
self._print_with_verbosity(f"this is epoch {i}", 1)
self._train_siamese_one_epoch(data_loader)
self.model.eval()
transformed = self.transform(X, model=self.model)
self._get_exp_dist(data_loader)
self._print_with_verbosity(
f"found expected distance between related points as {self.exp_dist}",
3)
cluster_assignments = self._cluster(transformed)
self._print_with_verbosity(f"found {self.n_clusters} clusters", 1)
preds = self._build_cluster_subnet(X, transformed,
cluster_assignments)
if use_y_to_verify_performance:
nmi_score = normalized_mutual_info_score(
cluster_assignments, y_for_verification, 'geometric')
self._print_with_verbosity(
f"NMI of cluster labels with y: {nmi_score}", 2)
nmi_score = normalized_mutual_info_score(
preds, y_for_verification, 'geometric')
self._print_with_verbosity(
f"NMI of network predictions with y: {nmi_score}", 1)
if self.n_clusters == verify_n_classes:
acc_score = get_accuracy(cluster_assignments,
y_for_verification)
self._print_with_verbosity(
f"accuracy of cluster labels: {acc_score}", 2)
if np.unique(preds).shape[0] == verify_n_classes:
acc_score = get_accuracy(preds, y_for_verification)
self._print_with_verbosity(
f"accuracy of network predictions: {acc_score}", 1)
else:
self._print_with_verbosity(
f"number of predicted classes did not match number of clusters so not computing accuracy, correct {verify_n_classes} vs {self.n_clusters}",
2)
if plot:
if self.n_components == 2:
plot_2d(transformed,
cluster_assignments,
show=False,
no_legend=True)
if use_y_to_verify_performance:
plot_2d(transformed,
y_for_verification,
show=False,
no_legend=True)
plt.show()
elif self.n_components == 3:
plot_3d(transformed, cluster_assignments, show=False)
if use_y_to_verify_performance:
plot_3d(transformed, y_for_verification, show=False)
plt.show()
sequences_1 = [sequence[0] for sequence in input_variables]
sequences_2 = [sequence[1] for sequence in input_variables]
batch_size = len(sequences_1)
# Make a tensor for the similarity scores
sim_scores_2d = torch.zeros([batch_size, 2])
for j in range(batch_size):
if similarity_scores[j] == 0:
sim_scores_2d[j] = fake_label
else:
sim_scores_2d[j] = real_label
sim_scores_2d = sim_scores_2d.cuda()
temp = rnn.pad_sequence(sequences_1 + sequences_2)
sequences_1 = temp[:, :batch_size]
sequences_2 = temp[:, batch_size:]
model_optimizer.zero_grad()
loss_s = 0.0
optimizerG.zero_grad()
loss_g = 0.0
optimizerD.zero_grad()
loss_d = 0.0
loss_f = 0.0
# Initialise the hidden state and pass through the maLSTM
def to_torch(batch, **kwargs):
return pad_sequence(
[torch.tensor(b, dtype=torch.long) for b in batch], batch_first=False)
def collate_batch(batch):
"""Collate a whole batch of utterances."""
flatten = [u for s in batch for u in s]
return pad_sequence(flatten, batch_first=True, padding_value=0)
stocks = stocks.set_index('symbol', drop=True)
train_df = stocks.drop(test_symbols, axis=0)
test_df = stocks.drop(stocks.index.difference(test_symbols), axis=0)
train_symbols = train_df.index.unique().tolist()
train_tensors = []
train_seq_lens = []
for sym in train_symbols:
stock_data, stock_data_len = prepare_stock_data(sym)
stock_data = normalize_stock_data(stock_data)
stock_tensor = torch.Tensor(stock_data)
train_seq_lens.append(stock_data_len)
train_tensors.append(stock_tensor)
X = pad_sequence(train_tensors).T.unsqueeze(-1)
y = torch.Tensor(train_seq_lens)
train_dataset = TensorDataset(X, y)
test_seq_lens = []
test_tensors = []
for sym in test_symbols:
stock_data, stock_data_len = prepare_stock_data(sym)
stock_data = normalize_stock_data(stock_data)
test_seq_lens.append(stock_data_len)
stock_tensor = torch.Tensor(stock_data)
test_tensors.append(stock_tensor)
X = pad_sequence(test_tensors).T.unsqueeze(-1)
y = torch.Tensor(test_seq_lens)
test_dataset = TensorDataset(X, y)
hidden_state = torch.cat([ht_final[i] for i in range(ht_final.size(0))], dim=1)
# apply attention
hidden_state = hidden_state.unsqueeze(2) # (B, hidden * 2, 1)
attention_scores = torch.bmm(out, hidden_state).squeeze(2)
soft_attention_weights = F.softmax(attention_scores, 1).unsqueeze(2) # (B, L, 1)
attention_out = torch.bmm(out.permute(0, 2, 1), soft_attention_weights).squeeze(2)
features = torch.cat([hidden_state.squeeze(2), attention_out], dim=1)
features = self.dropout_1(features)
features = self.fc1(features)
features = self.bn1(features)
features = F.relu(features)
features = self.dropout_2(features)
logits = self.fc2(features)
return logits
if __name__ == '__main__':
import torch
from torch.nn.utils.rnn import pad_sequence
sentences = [torch.LongTensor([2, 3, 4, 5]), torch.LongTensor([6, 7, 8]), torch.LongTensor([9, 10])]
x = pad_sequence(sentences, batch_first=True,
padding_value=0)
masks = (x != 0).type(torch.FloatTensor)
len_x = [4, 3, 2]
model = TopicClassLSTM(vocab_size=11, emb_size=10, embedding_tensor=None, freeze=False,
dropout=0.2, lstm_hidden=200, num_classes=16)
out = model(x, len_x)
def train_model(cls, online_net, target_net, optimizer, batch, batch_size,
sequence_length, gamma, use_deeper_net):
# def slice_burn_in(item):
# return item[:, burn_in_length:, :]
# batch.state is a list of tensors of shape (seq_length, input_dim)
# so seq.size()[0] = the length of the sequence
lengths = np.array([seq.size()[0] for seq in batch.state])
max_length = int(np.max(lengths))
# ===== compute loss mask =====
# for example, if sequence_length == 3, then lower_triangular_matrix =
# 1 0 0
# 1 1 0
# 1 1 1
# suppose lengths == np.array([2, 3, 1]), then lengths - 1 == np.array([1, 2, 0]) and
# the loss_mask computed from lower_triangular_matrix[lengths-1] is
# 1 1 0
# 1 1 1
# 1 0 0
# which corresponds to lengths correctly
lower_triangular_matrix = np.tril(
np.ones((sequence_length, sequence_length)))
loss_mask = lower_triangular_matrix[
lengths - 1] # first convert from 1-based to 0-based indexing
loss_mask = torch.tensor(loss_mask) # has shape (bs, seq_len)
if use_deeper_net:
states = pad_sequence(batch.state, batch_first=True)
next_states = pad_sequence(batch.next_state, batch_first=True)
else:
states = pack_padded_sequence(pad_sequence(batch.state,
batch_first=True),
lengths=lengths,
batch_first=True,
enforce_sorted=False)
next_states = pack_padded_sequence(pad_sequence(batch.next_state,
batch_first=True),
lengths=lengths,
batch_first=True,
enforce_sorted=False)
# max_length == sequence_length most of the times, but not always
actions = pad_sequence(batch.action, batch_first=True).view(
batch_size, max_length, -1).long() # has shape (bs, seq_len, 1)
rewards = pad_sequence(batch.reward, batch_first=True).view(
batch_size, max_length, -1) # has shape (bs, seq_len, 1)
masks = pad_sequence(batch.mask, batch_first=True).view(
batch_size, max_length, -1) # has shape (bs, seq_len, 1)
h0 = torch.stack([
rnn_state[0, 0, :] for rnn_state in batch.rnn_state
]).unsqueeze(0).detach() # has shape (1, bs, hidden_size)
c0 = torch.stack([
rnn_state[0, 1, :] for rnn_state in batch.rnn_state
]).unsqueeze(0).detach() # has shape (1, bs, hidden_size)
h1 = torch.stack([
rnn_state[1, 0, :] for rnn_state in batch.rnn_state
]).unsqueeze(0).detach() # has shape (1, bs, hidden_size)
c1 = torch.stack([
rnn_state[1, 1, :] for rnn_state in batch.rnn_state
]).unsqueeze(0).detach() # has shape (1, bs, hidden_size)
# states = torch.stack(batch.state).view(batch_size, sequence_length, online_net.num_inputs)
# next_states = torch.stack(batch.next_state).view(batch_size, sequence_length, online_net.num_inputs)
# actions = torch.stack(batch.action).view(batch_size, sequence_length, -1).long()
# rewards = torch.stack(batch.reward).view(batch_size, sequence_length, -1)
# masks = torch.stack(batch.mask).view(batch_size, sequence_length, -1)
# rnn_state = torch.stack(batch.rnn_state).view(batch_size, sequence_length, 2, -1)
# [h0, c0] = rnn_state[:, 0, :, :].transpose(0, 1)
# h0 = h0.unsqueeze(0).detach()
# c0 = c0.unsqueeze(0).detach()
# [h1, c1] = rnn_state[:, 1, :, :].transpose(0, 1)
# h1 = h1.unsqueeze(0).detach()
# c1 = c1.unsqueeze(0).detach()
pred, _ = online_net(states, (h0, c0),
inference=False,
max_length=max_length,
lengths=lengths)
next_pred, _ = target_net(next_states, (h1, c1),
inference=False,
max_length=max_length,
lengths=lengths)
# if burn_in_length > 0:
# pred = slice_burn_in(pred)
# next_pred = slice_burn_in(next_pred)
# actions = slice_burn_in(actions)
# rewards = slice_burn_in(rewards)
# masks = slice_burn_in(masks)
pred = pred.gather(2, actions).squeeze() # has shape (bs, seq_len)
target = rewards + masks * gamma * next_pred.max(2, keepdim=True)[0]
target = target.squeeze() # has shape (bs, seq_len)
loss = torch.mean(((pred - target.detach())**2) * loss_mask.float())
# loss = F.mse_loss(pred, target.detach())
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(online_net.parameters(), 1.0)
optimizer.step()
return loss
def padded_collate(batch):
# Sort batch by the longest sequence desc
batch.sort(key=lambda sequence: len(sequence[3]), reverse=True)
graph_ids, targetss, char_indices, *index_groups = zip(*batch)
#print("sens",[(i.shape, j.shape) for i,j in zip(index_groups[0], index_groups[-1])])
# The number of words in each sequence
seq_lengths = torch.LongTensor(
[len(indices) for indices in index_groups[0]])
max_word_count = seq_lengths[0]
#print(max_word_count, targets[0].shape, len(heads[0]), len(index_groups[0][0]))
padded_targetss = [] # when only having a primary (no other) loss
unpadding_mask = None
targetss = tuple(zip(*targetss))
#print(seq_lengths)
#print(len(targetss[0]))
#print([x.shape for x in targetss[0]])
for targets in targetss:
if not targets[0] is None:
padded_targets = torch.zeros(
len(seq_lengths), max_word_count, max_word_count, dtype=torch.long)
if unpadding_mask is None:
#unpadding_mask = torch.zeros_like(padded_targets, dtype=torch.uint8)
unpadding_mask = torch.zeros_like(padded_targets, dtype=torch.bool)
for i, target in enumerate(targets):
padded_targets[i, :seq_lengths[i], :seq_lengths[i]] = target
unpadding_mask[i, :seq_lengths[i], :seq_lengths[i]] = 1
else:
padded_targets = None
unpadding_mask = None
padded_targetss.append(padded_targets)
# Batch specific word vocabulary where each word
# is expressed by its character indices
batch_voc = list({word for sentence in char_indices for word in sentence})
batch_voc.append(PAD_WORD)
batch_voc.sort(key=lambda word: len(word), reverse=True)
voc_lengths = torch.LongTensor([len(word) for word in batch_voc])
voc_lookup = {word: i for i, word in enumerate(batch_voc)}
batch_voc = pad_sequence([torch.LongTensor(tup) for tup in batch_voc],
batch_first=True)
index_mapping = torch.full(
size=(len(batch), max_word_count),
fill_value=voc_lookup[PAD_WORD],
dtype=torch.long)
# Map each word in the batch to an index in the char word vocabulary
for i, sentence in enumerate(char_indices):
for j, word in enumerate(sentence):
index_mapping[i, j] = voc_lookup[word]
padded = PaddedBatch(
graph_ids, padded_targetss,
unpadding_mask,
CharContainer(index_mapping, batch_voc, voc_lengths), seq_lengths,
[pad_sequence(indices, batch_first=True) for indices in index_groups])
return padded
def test_case4(self):
device = torch.device('cuda', device_id)
# combine case1 to case3 to a minibatch
# the first example (a): input_length: 1, label_length: 1
# the second example (c, c): input_length: 3, label_length: 2
# the third example (b, c): input_length: 3, label_length: 2
label_lengths_tensor = torch.tensor([1, 2, 2], dtype=torch.int32)
input_lengths_tensor = torch.tensor([1, 3, 3], dtype=torch.int32)
alphabet_size = 5
minibatch = 3
info = ctc.CtcOptions()
info.loc = ctc.CtcComputeLocation.CTC_GPU
info.blank_label = 0
label_lengths = kaldi.IntSubVectorFromDLPack(
to_dlpack(label_lengths_tensor))
input_lengths = kaldi.IntSubVectorFromDLPack(
to_dlpack(input_lengths_tensor))
status, size_in_bytes = ctc.GetWorkspaceSize(
label_lengths=label_lengths,
input_lengths=input_lengths,
alphabet_size=alphabet_size,
minibatch=minibatch,
info=info)
self.assertEqual(status, ctc.CtcStatus.CTC_STATUS_SUCCESS)
num_floats = size_in_bytes // 4 + 1
workspace_tensor = torch.empty(
num_floats, dtype=torch.float32).contiguous().to(device)
ex1 = torch.tensor([[0.2, 0.2, 0.2, 0.2, 0.2]], dtype=torch.float32)
ex2 = torch.tensor(
[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]],
dtype=torch.float32)
ex3 = torch.tensor([[-5, -4, -3, -2, -1], [-10, -9, -8, -7, -6],
[-15, -14, -13, -12, -11]],
dtype=torch.float32)
activations_tensor = pad_sequence([ex1, ex2, ex3], batch_first=False)
activations_tensor = activations_tensor.contiguous().view(-1).to(device)
gradients_tensor = torch.empty_like(activations_tensor)
# labels are: (a), (c, c) (b, c)
# which are: (1), (3, 3), (2, 3)
flat_labels_tensor = torch.tensor([1, 3, 3, 2, 3], dtype=torch.int32)
costs_tensor = torch.empty(minibatch, dtype=torch.float32)
activations = kaldi.CuSubVectorFromDLPack(to_dlpack(activations_tensor))
gradients = kaldi.CuSubVectorFromDLPack(to_dlpack(gradients_tensor))
flat_labels = kaldi.IntSubVectorFromDLPack(
to_dlpack(flat_labels_tensor))
costs = kaldi.FloatSubVectorFromDLPack(to_dlpack(costs_tensor))
workspace = kaldi.CuSubVectorFromDLPack(to_dlpack(workspace_tensor))
status = ctc.ComputeCtcLossGpu(activations=activations,
gradients=gradients,
flat_labels=flat_labels,
label_lengths=label_lengths,
input_lengths=input_lengths,
alphabet_size=alphabet_size,
minibatch=minibatch,
costs=costs,
workspace=workspace,
options=info)
self.assertAlmostEqual(costs[0], 1.6094379425049)
self.assertAlmostEqual(costs[1], 7.355742931366)
self.assertAlmostEqual(costs[2], 4.938850402832, places=6)
def collate(batch):
return [
pad_sequence(x, batch_first=True).to(dtype=torch.long)
for x in zip(*batch)
]
def pad_collate(batch):
sent, label = zip(*batch)
sent_pad = pad_sequence(sent, batch_first=True, padding_value=0)
return sent_pad, torch.LongTensor(label)
def collate(batch):
q_tokens, qids, qrel_sets = zip(*batch)
padded = pad_sequence(q_tokens, batch_first=True).to(dtype=torch.long)
return padded, qids, qrel_sets
def _process_data_batch(self, data_batch):
# pad the sequences, each seq must be (L, *)
seq_lens = [len(x) for x in data_batch]
seq_batch = pad_sequence(data_batch, batch_first=True)
return seq_batch.unsqueeze(1).cuda(), seq_lens
def collate_fn(samples):
# samples: [(seq_len, channel), ...]
samples = pad_sequence(samples, batch_first=True)
# samples: (batch_size, max_len, channel)
return samples.transpose(-1, -2).contiguous()
def forward(self, features, labels,
records, logger, prefix, global_step, **kwargs):
"""
This function will be used in both train/dev/test, you can use
self.training (bool) to control the different behavior for
training or evaluation (dev/test)
Args:
features:
list of unpadded features [feat1, feat2, ...]
each feat is in torch.FloatTensor and already
put in the device assigned by command-line args
your_other_contents1, ... :
in the order defined by your dataloader (dataset + collate_fn)
these are all in cpu, and you can move them to the same device
as features
records:
defaultdict(list), by dumping contents into records,
these contents can be averaged and logged on Tensorboard
later by self.log_records
Note1. downstream/runner.py will call self.log_records
1. every log_step during training
2. once after evalute the whole dev/test dataloader
Note2. log_step is defined in your downstream config
logger:
Tensorboard SummaryWriter, given here for logging/debugging convenience
please use f'{prefix}your_content_name' as key name
to log your customized contents
prefix:
used to indicate downstream and train/test on Tensorboard
eg. 'phone/train-'
global_step:
global_step in runner, which is helpful for Tensorboard logging
Return:
loss:
the loss to be optimized, should not be detached
a single scalar in torch.FloatTensor
"""
features_pad = pad_sequence(features, batch_first=True)
attention_mask = [torch.ones((feature.shape[0])) for feature in features]
attention_mask_pad = pad_sequence(attention_mask,batch_first=True)
attention_mask_pad = (1.0 - attention_mask_pad) * -100000.0
features_pad = self.connector(features_pad)
predicted = self.model(features_pad, attention_mask_pad.cuda())
labels = torch.LongTensor(labels).to(features_pad.device)
loss = self.objective(predicted, labels)
predicted_classid = predicted.max(dim=-1).indices
records['acc'] += (predicted_classid == labels).view(-1).cpu().float().tolist()
if not self.training:
# some evaluation-only processing, eg. decoding
pass
return loss
def collect(sequences):
sequences = sorted(sequences, key = lambda x: x.size(), reverse = True)
mols = [seq for seq in sequences]
lengths = [seq.size(0) for seq in mols]
return pad_sequence(mols,batch_first=True), lengths
def collate_fn(batch):
token, label = zip(*batch)
label = torch.tensor(label)
token = pad_sequence(token, batch_first=True)
return token, label
def forward(self, mode, features, utter_idx, labels, records, **kwargs):
"""
Args:
features:
the features extracted by upstream
put in the device assigned by command-line args
labels:
the speaker labels
records:
defaultdict(list), by appending scalars into records,
these scalars will be averaged and logged on Tensorboard
logger:
Tensorboard SummaryWriter, given here for logging/debugging
convenience, please use "self.downstream/your_content_name" as key
name to log your customized contents
global_step:
global_step in runner, which is helpful for Tensorboard logging
Return:
loss:
the loss to be optimized, should not be detached
"""
features_pad = pad_sequence(features, batch_first=True)
if self.modelrc['module'] == "XVector":
# TDNN layers in XVector will decrease the total sequence length by fixed 14
attention_mask = [
torch.ones((feature.shape[0] - 14)) for feature in features
]
else:
attention_mask = [
torch.ones((feature.shape[0])) for feature in features
]
attention_mask_pad = pad_sequence(attention_mask, batch_first=True)
attention_mask_pad = (1.0 - attention_mask_pad) * -100000.0
features_pad = self.connector(features_pad)
if mode == 'train':
agg_vec = self.model(features_pad, attention_mask_pad.cuda())
labels = torch.LongTensor(labels).to(features_pad.device)
loss = self.objective(agg_vec, labels)
records['loss'].append(loss.item())
return loss
elif mode in ['dev', 'test']:
agg_vec = self.model.inference(features_pad,
attention_mask_pad.cuda())
agg_vec = torch.nn.functional.normalize(agg_vec, dim=-1)
# separate batched data to pair data.
vec1, vec2 = self.separate_data(agg_vec)
names1, names2 = self.separate_data(utter_idx)
scores = self.score_fn(vec1, vec2).cpu().detach().tolist()
records['scores'].extend(scores)
records['labels'].extend(labels)
records['pair_names'].extend(
[f"{name1}_{name2}" for name1, name2 in zip(names1, names2)])
return torch.tensor(0)
def collect_fn_quadkey(batch,
data_source,
sampler,
region_processer,
loc2quadkey=None,
k=5,
with_trg_quadkey=True):
src, trg = zip(*batch)
user, loc, time, region = [], [], [], []
data_size = []
trg_ = []
trg_probs_ = []
for e in src:
u_, l_, t_, r_, b_ = zip(*e)
data_size.append(len(u_))
user.append(torch.tensor(u_))
loc.append(torch.tensor(l_))
time.append(torch.tensor(t_))
r_ = region_processer.numericalize(list(r_)) # (L, LEN_QUADKEY)
region.append(r_)
user_ = pad_sequence(user,
batch_first=True) # (N,T) 下同，返回时通过.t()变为(T,N)
loc_ = pad_sequence(loc, batch_first=True)
time_ = pad_sequence(time, batch_first=True)
# (T, N, LEN_QUADKEY)
region_ = pad_sequence(region, batch_first=False)
if with_trg_quadkey:
batch_trg_regs = []
for i, seq in enumerate(trg):
pos = torch.tensor([[e[1]] for e in seq])
neg, probs = sampler(seq, k, user=seq[0][0])
# (L, k+1), k即为负采样的k
trg_seq = torch.cat([pos, neg], dim=-1)
trg_.append(trg_seq)
trg_regs = []
for trg_seq_idx in range(trg_seq.size(0)):
regs = []
for loc in trg_seq[trg_seq_idx]:
regs.append(loc2quadkey[loc])
trg_regs.append(region_processer.numericalize(regs))
batch_trg_regs.append(torch.stack(trg_regs))
trg_probs_.append(probs)
# (N, T, k+1, LEN_QUADKEY)
batch_trg_regs = pad_sequence(batch_trg_regs, batch_first=True)
# [(1+k) * T, N, LEN_QUADKEY)
batch_trg_regs = batch_trg_regs.permute(2, 1, 0,
3).contiguous().view(
-1,
batch_trg_regs.size(0),
batch_trg_regs.size(3))
trg_ = pad_sequence(trg_, batch_first=True)
trg_probs_ = pad_sequence(trg_probs_,
batch_first=True,
padding_value=1.0)
trg_ = trg_.permute(2, 1, 0).contiguous().view(-1, trg_.size(0))
trg_nov_ = [[not e[-1] for e in seq] for seq in trg]
return user_.t(), loc_.t(), time_.t(
), region_, trg_, batch_trg_regs, trg_nov_, trg_probs_, data_size
else:
for i, seq in enumerate(trg):
pos = torch.tensor([[e[1]] for e in seq])
neg, probs = sampler(seq, k, user=seq[0][0])
trg_.append(torch.cat([pos, neg], dim=-1))
trg_probs_.append(probs)
trg_ = pad_sequence(trg_, batch_first=True)
trg_probs_ = pad_sequence(trg_probs_,
batch_first=True,
padding_value=1.0)
trg_ = trg_.permute(2, 1, 0).contiguous().view(-1, trg_.size(0))
trg_nov_ = [[not e[-1] for e in seq] for seq in trg]
return user_.t(), loc_.t(), time_.t(
), region_, trg_, trg_nov_, trg_probs_, data_size
def forward(self, sentences, doc_lens=[], batch_sent_lens=[], log=False):
word_embeddings = self.embedding(sentences)
batch_len, word_len, embedding_dim = word_embeddings.shape
packed_word_embeddings = pack_padded_sequence(
word_embeddings,
torch.LongTensor(batch_sent_lens),
batch_first=True,
enforce_sorted=False,
)
packed_word_encoder_hidden_states = self.word_encoder(packed_word_embeddings)
word_encoder_hidden_states, _ = pad_packed_sequence(
packed_word_encoder_hidden_states, batch_first=True
)
word_attention_weights = self.word_attention(word_encoder_hidden_states)
sentence_vectors = torch.squeeze(
torch.matmul(
torch.unsqueeze(word_attention_weights, 1), word_encoder_hidden_states
)
)
n = 0
batch_sentence_vectors = []
for doc_len in doc_lens:
batch_sentence_vectors.append(sentence_vectors[n:n+doc_len])
n += doc_len
padded_sentence_vectors = pad_sequence(batch_sentence_vectors, batch_first=True)
packed_sentence_vectors = pack_padded_sequence(
padded_sentence_vectors,
torch.LongTensor(doc_lens),
batch_first=True,
enforce_sorted=False,
)
packed_sentence_encoder_hidden_states = self.sentence_encoder(packed_sentence_vectors)
sentence_encoder_hidden_states, _ = pad_packed_sequence(
packed_sentence_encoder_hidden_states, batch_first=True
)
sentence_attention_weights = self.sentence_attention(
sentence_encoder_hidden_states
)
document_vectors = torch.squeeze(
torch.matmul(
torch.unsqueeze(sentence_attention_weights, 1),
sentence_encoder_hidden_states,
)
)
batch_probs = []
Cs = []
Ms = []
Ns = []
Ps = []
Pros = []
for doc_index, doc_len in enumerate(doc_lens):
o = torch.zeros(
2 * self.hparams.lstm_hidden_size,
device=self.device,
)
document_vector = document_vectors[doc_index]
probs = []
for pos in range(doc_len):
sentence_vector = padded_sentence_vectors[doc_index, pos, :]
C = self.content(sentence_vector)
M = self.salience(sentence_vector, document_vector)
N = self.novelty(sentence_vector, torch.tanh(o))
pos_forward = self.pos_forward_embed(
torch.tensor([pos], dtype=torch.long, device=self.device)
).view(-1)
pos_backward = self.pos_backward_embed(
torch.tensor(
[doc_len - pos - 1],
dtype=torch.long,
device=self.device,
)
).view(-1)
positional_embedding = torch.cat((pos_forward, pos_backward))
P = self.position(positional_embedding)
prob = torch.sigmoid(C + M - N + P + self.bias)
if doc_index == 0:
Cs.append(C.item())
Ms.append(M.item())
Ns.append(N.item())
Ps.append(P.item())
Pros.append(prob.item())
# print(C, M, N, P, prob)
o = o + (prob * sentence_vector)
if log:
print(
f"doc {doc_index+1}, sentence {pos+1}, C: {C.item():10.4f}, M: {M.item():10.4f}, N: {N.item():10.4f}, bias: {self.bias.item():10.4f}, prob: {prob.item():10.4f}, o: {o}"
)
probs.append(prob)
batch_probs.append(torch.cat(probs))
return pad_sequence(batch_probs, batch_first=True)
def collate(examples: List[torch.Tensor]):
if tokenizer._pad_token is None:
return pad_sequence(examples, batch_first=True)
return pad_sequence(examples,
batch_first=True,
padding_value=tokenizer.pad_token_id)
def _collate_fn(self, data):
word = pad_sequence([x[0] for x in data], batch_first=True,
padding_value=0)
label = pad_sequence([x[-1] for x in data], batch_first=True,
padding_value=0)
return word, label
stats_loss = []
t0 = time.time()
for i in range(max_iter):
encoder_optimizer.zero_grad()
decoder_optimizer.zero_grad()
# data batch : TODO - Loader needed
rnd_idx = np.random.randint(len(libri), size=batch_size)
batches = [libri[r] for r in rnd_idx]
xbs = [torch.tensor(b[0]) for b in batches] # t,d
xlen = torch.tensor([get_padded_len(_x, n_conv_layers)
for _x in xbs]).cuda()
#xlen = torch.tensor([len(_x) for _x in xbs]).cuda()
x_batches = pad_sequence(xbs).cuda()
ybs = [b[1][:-1] for b in batches]
y_inputs = pad_sequence(ybs, padding_value=EOS_TOKEN).long().cuda()
ybs = [b[1][1:] for b in batches]
y_outputs = pad_sequence(ybs, padding_value=PAD_TOKEN).long().cuda()
out_enc = encoder(x_batches)
h_enc = torch.zeros([batch_size, h_dim]).cuda()
# model forward path
# out_enc, h_enc = encoder(x_batches, xlen)
prediction, ce_loss = decoder(y_inputs, y_outputs, out_enc, h_enc, i)
ce_loss.backward()
_ = nn.utils.clip_grad_norm_(encoder.parameters(), grad_clip)
def test_step(self, batch, batch_idx):
sent_order = []
sum_ids = []
sum_sent_lens = []
sum_sent_toks = []
sum_len = 0
source_ids_flat_pad, sum_ids_flat_pad, target_dist, counts, masks, metadata = batch
gold_sent_order = list(np.argsort(tens_to_np(-target_dist.squeeze())))
num_sents = len(metadata['source_sents'][0])
mrn = metadata['mrn'][0]
rel_ranks = []
account = metadata['account'][0]
for _ in range(min(num_sents, MAX_GEN_SUM_SENTS)):
i0 = source_ids_flat_pad.to(self.device_name)
i1 = sum_ids_flat_pad.to(self.device_name)
i2 = {}
i3 = {}
for k, v in counts.items():
i2[k] = v.to(self.device_name)
for k, v in masks.items():
i3[k] = v.to(self.device_name)
y_hat_scores = self(i0, i1, i2, i3)
y_hat_scores = tens_to_np(y_hat_scores.squeeze(0))
if len(sent_order) > 0:
y_hat_scores[sent_order] = float('-inf')
max_idx = np.argmax(y_hat_scores)
rel_ranks.append(gold_sent_order.index(max_idx))
sent_sum_len = counts['source_sent_lens_flat'][max_idx]
sum_len += sent_sum_len
if sum_len > MAX_GEN_SUM_TOK_CT:
break
sent_order.append(max_idx)
chosen_sent_toks = metadata['source_sents'][0][max_idx]
sum_sent_toks.append(chosen_sent_toks)
num_sum_sents = len(sent_order)
chosen_sent_ids = list(
tens_to_np(source_ids_flat_pad[max_idx][:sent_sum_len]))
sum_ids.append(chosen_sent_ids)
sum_sent_lens.append(sent_sum_len)
sum_ids_flat = list(map(torch.LongTensor, sum_ids))
sum_ids_flat_pad = pad_sequence(sum_ids_flat,
batch_first=True,
padding_value=0)
sum_att_mask = mask_2D([num_sum_sents])
counts['sum_sent_lens_flat'] = torch.LongTensor(sum_sent_lens)
counts['sum_lens'] = torch.LongTensor([len(sent_order)])
masks['sum_att_mask'] = sum_att_mask
result = pl.EvalResult()
result.mrn = mrn
result.account = account
result.sent_order = ','.join([str(s) for s in sent_order])
result.sum_sent_toks = ' <s> '.join(sum_sent_toks)
result.reference = metadata['reference'][0]
result.rel_r1 = rel_ranks[0]
result.rel_r2 = rel_ranks[1]
result.rel_r3 = rel_ranks[2]
result.rel_r4 = rel_ranks[3]
result.rel_r5 = rel_ranks[4]
result.rel_r5plus = sum(rel_ranks[5:]) / float(len(rel_ranks[5:]))
return result
def __call__(self, batch):
dat = pd.DataFrame(batch)
return [self.pad_collate(dat[i]) if i==0 else \
pad_sequence(dat[i], True) if i < 7 else \
dat[i].tolist() for i in dat]
embeddings.weight.data[idx] = (torch.FloatTensor(values))
model = BiLSTM_CRF(len(word_to_ix), tag_to_ix, EMBEDDING_DIM, HIDDEN_DIM,
BS).to(device)
load_fastext_embeeding(model.word_embeds, word_to_ix, "wiki-news-300d-1M.vec")
model.word_embeds.requires_grad = False
optimizer = optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-4)
best_f1 = -1
for epoch in range(epochs):
for i, batch in enumerate(train_dataloader):
model.zero_grad()
sents, labs, lens = batch
sents = pad_sequence(sents, batch_first=True).to(device)
labs = pad_sequence(labs, batch_first=True).to(device)
lens = torch.tensor(lens).to(device)
lens, idx = torch.sort(lens, descending=True)
sents = sents[idx]
labs = labs[idx]
loss = model.neg_log_likelihood(sents, labs, lens)
loss.backward()
optimizer.step()
score, preds = model(sents, lens)
true_labs = [id2lab(labs[i, :l]) for i, l in enumerate(lens)]
pred_labs = [id2lab(preds[i, :l]) for i, l in enumerate(lens)]
acc = accuracy_score(true_labs, pred_labs)
f1 = f1_score(true_labs, pred_labs)
print(
"Epoch {}, batch {}, train loss {:.4f}, train acc {:.4f}, train f1 {:.4f} "
