def forward(self, incoming): inp = Storage() inp.resp_length = incoming.data.resp_length inp.embedding = incoming.resp.embedding inp.init_h = incoming.conn.init_h incoming.gen = gen = Storage() self.teacherForcing(inp, gen) w_o_f = flattenSequence(gen.w, incoming.data.resp_length-1) data_f = flattenSequence(incoming.data.resp[1:], incoming.data.resp_length-1) incoming.result.word_loss = self.lossCE(w_o_f, data_f) incoming.result.perplexity = torch.exp(incoming.result.word_loss)
def forward(self, incoming):
inp = Storage()
inp.length = incoming.data.sent_length
inp.embedding = incoming.sent.embedding
incoming.gen = gen = Storage()
self.teacherForcing(inp, gen)
w_o_f = flattenSequence(gen.w, incoming.data.sent_length - 1)
data_f = flattenSequence(
incoming.data.sent.transpose(0, 1)[1:],
incoming.data.sent_length - 1)
incoming.result.word_loss = self.lossCE(w_o_f, data_f)
incoming.result.perplexity = torch.exp(incoming.result.word_loss)
def teacherForcingForward(incoming): #Copied from GenNetwork.forward inp = Storage() inp.resp_length = incoming.data.resp_length inp.embedding = incoming.resp.embedding inp.post = incoming.hidden.h inp.post_length = incoming.data.post_length inp.init_h = incoming.conn.init_h incoming.gen = gen = Storage() self.teacherForcing(inp, gen) w_o_f = flattenSequence(gen.w, incoming.data.resp_length-1) data_f = flattenSequence(incoming.data.resp[1:], incoming.data.resp_length-1) incoming.result.word_loss = self.lossCE(w_o_f, data_f) incoming.result.perplexity = torch.exp(incoming.result.word_loss)
def forward(self, incoming):
inp = Storage()
inp.resp_length = incoming.data.resp_length
inp.embedding = incoming.resp.embedding
inp.post = incoming.hidden.h
inp.post_length = incoming.data.post_length
inp.init_h = incoming.conn.init_h
incoming.gen = gen = Storage()
self.teacherForcing(inp, gen)
if self.training:
incoming.result.word_loss = raml_loss(
gen.w, incoming.data.resp[1:], incoming.data.resp_length - 1,
incoming.data.rewards_ts, self.lossCE)
else:
w_o_f = flattenSequence(gen.w, incoming.data.resp_length - 1)
data_f = flattenSequence(incoming.data.resp[1:],
incoming.data.resp_length - 1)
incoming.result.word_loss = self.lossCE(w_o_f, data_f)
incoming.result.perplexity = torch.exp(incoming.result.word_loss)
def forward(self, incoming):
inp = Storage()
inp.embedding = incoming.resp.embedding
inp.post = incoming.hidden.h
inp.post_length = incoming.data.post_length
inp.resp_length = incoming.data.resp_length
incoming.gen = gen = Storage()
inp.init_h = incoming.conn.init_h
inp.embLayer = incoming.resp.embLayer
inp.max_sent_length = self.args.max_sent_length
inp.sampling_proba = incoming.args.sampling_proba
inp.dm = self.param.volatile.dm
inp.batch_size = incoming.data.batch_size
self.scheduledTeacherForcing(inp, gen)
w_o_f = flattenSequence(gen.w_pro, incoming.data.resp_length - 1)
data_f = flattenSequence(incoming.data.resp[1:],
incoming.data.resp_length - 1)
incoming.result.word_loss = self.lossCE(w_o_f, data_f)
incoming.result.perplexity = torch.exp(incoming.result.word_loss)
def forward(self, incoming):
# incoming.data.wiki_sen: batch * wiki_len * wiki_sen_len
# incoming.wiki_hidden.h1: wiki_sen_len * (batch *wiki_len) * (eh_size * 2)
# incoming.wiki_hidden.h_n1: wiki_len * batch * (eh_size * 2)
# incoming.wiki_hidden.h2: wiki_len * batch * (eh_size * 2)
# incoming.wiki_hidden.h_n2: batch * (eh_size * 2)
i = incoming.state.num
valid_sen = incoming.state.valid_sen
reverse_valid_sen = incoming.state.reverse_valid_sen
inp = Storage()
inp.wiki_sen = incoming.conn.selected_wiki_sen
inp.wiki_hidden = incoming.conn.selected_wiki_h
raw_resp_length = torch.tensor(incoming.data.resp_length[:, i],
dtype=torch.long)
raw_embedding = incoming.resp.embedding
resp_length = inp.resp_length = torch.index_select(
raw_resp_length, 0, valid_sen.cpu()).numpy()
inp.embedding = torch.index_select(
raw_embedding, 0,
valid_sen).transpose(0, 1) # length * valid_num * embedding_dim
resp = torch.index_select(incoming.data.resp[:, i], 0,
valid_sen).transpose(0, 1)[1:]
inp.init_h = incoming.conn.init_h
inp.wiki_cv = incoming.conn.wiki_cv
incoming.gen = gen = Storage()
self.teacherForcing(inp, gen)
# gen.h_n: valid_num * dh_dim
w_slice = torch.index_select(gen.w, 1, reverse_valid_sen)
if w_slice.shape[0] < self.args.max_sent_length:
w_slice = torch.cat([
w_slice,
zeros(self.args.max_sent_length - w_slice.shape[0],
w_slice.shape[1], w_slice.shape[2])
], 0)
if i == 0:
incoming.state.w_all = w_slice.unsqueeze(0)
else:
incoming.state.w_all = torch.cat([
incoming.state.w_all,
w_slice.unsqueeze(0)
], 0) #state.w_all: sen_num * sen_length * batch_size * vocab_size
w_o_f = flattenSequence(torch.log(gen.p), resp_length - 1)
data_f = flattenSequence(resp, resp_length - 1)
incoming.statistic.sen_num += incoming.state.valid_num
now = 0
for l in resp_length:
loss = self.lossCE(w_o_f[now:now + l - 1, :],
data_f[now:now + l - 1])
if incoming.result.word_loss is None:
incoming.result.word_loss = loss.clone()
else:
incoming.result.word_loss += loss.clone()
incoming.statistic.sen_loss.append(loss.item())
now += l - 1
if i == incoming.state.last - 1:
incoming.statistic.sen_loss = torch.tensor(
incoming.statistic.sen_loss)
incoming.result.perplexity = torch.mean(
torch.exp(incoming.statistic.sen_loss))
