def forward(net, input_data, net_config, phase='train', deploy=False):
"""Defines and creates the ReInspect network given the net, input data
and configurations."""
net.clear_forward()
batch_ws_i = input_data["ws_i"]
batch_stop_i = [net_config['max_len']] * net_config['batch_size']
wordvec_layer = input_data["wordvec_layer"] # 128*38*100*1
net.f(NumpyData("target_words",
data=np.array(input_data["target_words"]))) # 128*100*1*1
tops = []
slice_point = []
for i in range(net_config['max_len']):
tops.append('label%d' % i)
if i != 0:
slice_point.append(i)
net.f(
Slice("label_slice_layer",
slice_dim=1,
bottoms=["target_words"],
tops=tops,
slice_point=slice_point))
net.f(NumpyData("target_wordvec%d" % 0,
data=wordvec_layer[:, :, 0, 0])) # start symbol, 128*38
filler = Filler("uniform", net_config["init_range"])
for i in range(net_config['max_len']):
if i == 0:
net.f(
NumpyData(
"dummy_layer",
np.zeros((net_config["batch_size"],
net_config["lstm_num_cells"]))))
net.f(
NumpyData(
"dummy_mem_cell",
np.zeros((net_config["batch_size"],
net_config["lstm_num_cells"]))))
for j in range(net_config['lstm_num_stacks']):
bottoms = []
if j == 0:
bottoms.append('target_wordvec%d' % i)
if j >= 1:
bottoms.append('dropout%d_%d' % (j - 1, i))
if i == 0:
bottoms.append("dummy_layer")
else:
bottoms.append('lstm%d_hidden%d' % (j, i - 1))
net.f(Concat('concat%d_layer%d' % (j, i), bottoms=bottoms))
param_names = []
for k in range(4):
param_names.append('lstm%d_param_%d' % (j, k))
bottoms = ['concat%d_layer%d' % (j, i)]
if i == 0:
bottoms.append('dummy_mem_cell')
else:
bottoms.append('lstm%d_mem_cell%d' % (j, i - 1))
net.f(
LstmUnit('lstm%d_layer%d' % (j, i),
net_config["lstm_num_cells"],
weight_filler=filler,
param_names=param_names,
bottoms=bottoms,
tops=[
'lstm%d_hidden%d' % (j, i),
'lstm%d_mem_cell%d' % (j, i)
]))
net.f(
Dropout('dropout%d_%d' % (j, i),
net_config["dropout_ratio"],
bottoms=['lstm%d_hidden%d' % (j, i)]))
net.f(
InnerProduct("ip%d" % i,
net_config['vocab_size'],
bottoms=[
'dropout%d_%d' %
(net_config['lstm_num_stacks'] - 1, i)
],
weight_filler=filler))
if i < net_config['max_len'] - 1:
tar_wordvec = np.array(wordvec_layer[:, :, i + 1, 0]) # 128*38
if phase == 'test':
net.f(Softmax("word_probs%d" % i, bottoms=["ip%d" % i]))
probs = net.blobs["word_probs%d" % i].data
for bi in range(net_config['batch_size']):
if i >= batch_ws_i[bi] and i < batch_stop_i[bi]:
vec = [0] * net_config["vocab_size"]
peakIndex = np.argmax(probs[bi, :])
if peakIndex == net_config['whitespace_symbol']:
batch_stop_i[bi] = i + 1
vec[peakIndex] = 1
tar_wordvec[bi, :] = vec
net.f(NumpyData("target_wordvec%d" % (i + 1), data=tar_wordvec))
bottoms = []
for i in range(net_config['max_len']):
bottoms.append("ip%d" % i)
net.f(Concat('ip_concat', bottoms=bottoms, concat_dim=0))
bottoms = []
for i in range(net_config['max_len']):
bottoms.append('label%d' % i)
net.f(Concat('label_concat', bottoms=bottoms, concat_dim=0))
if deploy:
net.f(Softmax("word_probs", bottoms=["ip_concat"]))
net.f(
SoftmaxWithLoss("word_loss",
bottoms=["ip_concat", "label_concat"],
ignore_label=net_config['zero_symbol']))