def elmo_encoder(word_ids, elmo_l2_coef):
"""
param:word_ids
param:elmo_l2_coef
"""
x_emb = layers.embedding(input=word_ids,
size=[vocab_size, emb_size],
dtype='float32',
is_sparse=False,
param_attr=fluid.ParamAttr(name='embedding_para'))
x_emb_r = fluid.layers.sequence_reverse(x_emb, name=None)
fw_hiddens, fw_hiddens_ori = encoder_wrapper(x_emb,
vocab_size,
emb_size,
para_name='fw_',
args=None)
bw_hiddens, bw_hiddens_ori = encoder_wrapper(x_emb_r,
vocab_size,
emb_size,
para_name='bw_',
args=None)
num_layers = len(fw_hiddens_ori)
token_embeddings = layers.concat(input=[x_emb, x_emb], axis=1)
token_embeddings.stop_gradient = True
concate_embeddings = [token_embeddings]
for index in range(num_layers):
embedding = layers.concat(
input=[fw_hiddens_ori[index], bw_hiddens_ori[index]], axis=1)
embedding = dropout(embedding)
embedding.stop_gradient = True
concate_embeddings.append(embedding)
weighted_emb = weight_layers(concate_embeddings, l2_coef=elmo_l2_coef)
return weighted_emb
def __split_heads_qkv(queries, keys, values, n_head, d_key, d_value):
"""
Reshape input tensors at the last dimension to split multi-heads
and then transpose. Specifically, transform the input tensor with shape
[bs, max_sequence_length, n_head * hidden_dim] to the output tensor
with shape [bs, n_head, max_sequence_length, hidden_dim].
"""
# The value 0 in shape attr means copying the corresponding dimension
# size of the input as the output dimension size.
reshaped_q = layers.reshape(x=queries,
shape=[0, 0, n_head, d_key],
inplace=True)
# permuate the dimensions into:
# [batch_size, n_head, max_sequence_len, hidden_size_per_head]
q = layers.transpose(x=reshaped_q, perm=[0, 2, 1, 3])
# For encoder-decoder attention in inference, insert the ops and vars
# into global block to use as cache among beam search.
reshape_layer = _wrap_layer_with_block(
layers.reshape,
fluid.default_main_program().current_block().parent_idx
) if cache is not None and static_kv else layers.reshape
transpose_layer = _wrap_layer_with_block(
layers.transpose,
fluid.default_main_program().current_block().parent_idx
) if cache is not None and static_kv else layers.transpose
reshaped_k = reshape_layer(x=keys,
shape=[0, 0, n_head, d_key],
inplace=True)
k = transpose_layer(x=reshaped_k, perm=[0, 2, 1, 3])
reshaped_v = reshape_layer(x=values,
shape=[0, 0, n_head, d_value],
inplace=True)
v = transpose_layer(x=reshaped_v, perm=[0, 2, 1, 3])
if cache is not None: # only for faster inference
if static_kv: # For encoder-decoder attention in inference
cache_k, cache_v = cache["static_k"], cache["static_v"]
# To init the static_k and static_v in cache.
# Maybe we can use condition_op(if_else) to do these at the first
# step in while loop to replace these, however it might be less
# efficient.
static_cache_init = _wrap_layer_with_block(
layers.assign,
fluid.default_main_program().current_block().parent_idx)
static_cache_init(k, cache_k)
static_cache_init(v, cache_v)
else: # For decoder self-attention in inference
cache_k, cache_v = cache["k"], cache["v"]
# gather cell states corresponding to selected parent
select_k = layers.gather(cache_k, index=gather_idx)
select_v = layers.gather(cache_v, index=gather_idx)
if not static_kv:
# For self attention in inference, use cache and concat time steps.
select_k = layers.concat([select_k, k], axis=2)
select_v = layers.concat([select_v, v], axis=2)
# update cell states(caches) cached in global block
layers.assign(select_k, cache_k)
layers.assign(select_v, cache_v)
return q, select_k, select_v
return q, k, v
def greedy_search_infilling(model,
q_ids,
q_sids,
sos_id,
eos_id,
attn_id,
max_encode_len=640,
max_decode_len=100,
tgt_type_id=3):
model.eval()
_, logits, info = model(q_ids, q_sids)
gen_ids = L.argmax(logits, -1)
d_batch, d_seqlen = q_ids.shape
seqlen = L.reduce_sum(L.cast(q_ids != 0, 'int64'), 1, keep_dim=True)
has_stopped = np.zeros([d_batch], dtype=np.bool)
gen_seq_len = np.zeros([d_batch], dtype=np.int64)
output_ids = []
past_cache = info['caches']
cls_ids = L.ones([d_batch], dtype='int64') * sos_id
attn_ids = L.ones([d_batch], dtype='int64') * attn_id
ids = L.stack([cls_ids, attn_ids], -1)
for step in range(max_decode_len):
bias = gen_bias(q_ids, ids, step)
pos_ids = D.to_variable(
np.tile(np.array([[step, step + 1]], dtype=np.int64),
[d_batch, 1]))
pos_ids += seqlen
_, logits, info = model(ids,
L.ones_like(ids) * tgt_type_id,
pos_ids=pos_ids,
attn_bias=bias,
past_cache=past_cache)
gen_ids = L.argmax(logits, -1)
past_cached_k, past_cached_v = past_cache
cached_k, cached_v = info['caches']
cached_k = [
L.concat([pk, k[:, :1, :]], 1)
for pk, k in zip(past_cached_k, cached_k)
] # concat cached
cached_v = [
L.concat([pv, v[:, :1, :]], 1)
for pv, v in zip(past_cached_v, cached_v)
]
past_cache = (cached_k, cached_v)
gen_ids = gen_ids[:, 1]
ids = L.stack([gen_ids, attn_ids], 1)
gen_ids = gen_ids.numpy()
has_stopped |= (gen_ids == eos_id).astype(np.bool)
gen_seq_len += (1 - has_stopped.astype(np.int64))
output_ids.append(gen_ids.tolist())
if has_stopped.all():
break
output_ids = np.array(output_ids).transpose([1, 0])
return output_ids
def bbox_ciou(self, boxes1_x0y0x1y1, boxes2_x0y0x1y1):
'''
计算ciou = iou - p2/c2 - av
:param boxes1: (batch_size, num_priors, 4) pred_x0y0x1y1
:param boxes2: (batch_size, num_priors, 4) label_x0y0x1y1
:return:
'''
# 得到中心点坐标、宽高
boxes1 = P.concat(
[(boxes1_x0y0x1y1[:, :, :2] + boxes1_x0y0x1y1[:, :, 2:]) * 0.5,
boxes1_x0y0x1y1[:, :, 2:] - boxes1_x0y0x1y1[:, :, :2]],
axis=-1)
boxes2 = P.concat(
[(boxes2_x0y0x1y1[:, :, :2] + boxes2_x0y0x1y1[:, :, 2:]) * 0.5,
boxes2_x0y0x1y1[:, :, 2:] - boxes2_x0y0x1y1[:, :, :2]],
axis=-1)
# 两个矩形的面积
boxes1_area = (boxes1_x0y0x1y1[:, :, 2] - boxes1_x0y0x1y1[:, :, 0]) * (
boxes1_x0y0x1y1[:, :, 3] - boxes1_x0y0x1y1[:, :, 1])
boxes2_area = (boxes2_x0y0x1y1[:, :, 2] - boxes2_x0y0x1y1[:, :, 0]) * (
boxes2_x0y0x1y1[:, :, 3] - boxes2_x0y0x1y1[:, :, 1])
# 相交矩形的左上角坐标、右下角坐标
left_up = P.elementwise_max(boxes1_x0y0x1y1[:, :, :2],
boxes2_x0y0x1y1[:, :, :2])
right_down = P.elementwise_min(boxes1_x0y0x1y1[:, :, 2:],
boxes2_x0y0x1y1[:, :, 2:])
# 相交矩形的面积inter_area。iou
inter_section = P.relu(right_down - left_up)
inter_area = inter_section[:, :, 0] * inter_section[:, :, 1]
union_area = boxes1_area + boxes2_area - inter_area
iou = inter_area / union_area
# 包围矩形的左上角坐标、右下角坐标
enclose_left_up = P.elementwise_min(boxes1_x0y0x1y1[:, :, :2],
boxes2_x0y0x1y1[:, :, :2])
enclose_right_down = P.elementwise_max(boxes1_x0y0x1y1[:, :, 2:],
boxes2_x0y0x1y1[:, :, 2:])
# 包围矩形的对角线的平方
enclose_wh = enclose_right_down - enclose_left_up
enclose_c2 = P.pow(enclose_wh[:, :, 0], 2) + P.pow(
enclose_wh[:, :, 1], 2)
# 两矩形中心点距离的平方
p2 = P.pow(boxes1[:, :, 0] - boxes2[:, :, 0], 2) + P.pow(
boxes1[:, :, 1] - boxes2[:, :, 1], 2)
# 增加av。分母boxes2[:, :, 3]可能为0，所以加了极小的常数防止nan
atan1 = P.atan(boxes1[:, :, 2] / (boxes1[:, :, 3] + 1e-9))
atan2 = P.atan(boxes2[:, :, 2] / (boxes2[:, :, 3] + 1e-9))
v = 4.0 * P.pow(atan1 - atan2, 2) / (math.pi**2)
a = v / (1 - iou + v)
ciou = iou - 1.0 * p2 / enclose_c2 - 1.0 * a * v
return ciou
def std_gen_interpolate(batch_size=8, seed=None, out_path='data/out',
levels=None, interpolate_mode=0):
default_levels = ("y;z0;z11;z12;z21;z22;z31;z32;z41;z42;z51;z52;z61;z62")
if levels is None:
levels = default_levels
default_levels = default_levels.split(';')
img_save_dir = os.path.join('/tmp', out_path+'.dir')
os.system(f'rm -rf {img_save_dir}')
os.system(f'mkdir {img_save_dir} -p')
with dg.no_grad():
model_cache.train_mode = False
model_cache.initialized = False
if seed is not None:
rds.rng = np.random.RandomState(seed)
elif rds.rng is None:
rds.rng = np.random
G = model_cache.G
x_np = rds.rng.randn(batch_size,140).astype('float32')
y_np = rds.rng.randint(0,1000,size=[batch_size]).astype('int64')
x = dg.to_variable(x_np)
y_cls = dg.to_variable(y_np)
y_hot = layers.one_hot(layers.unsqueeze(y_cls,[1]), depth=1000)
y_embed = G.embed_y(y_hot)
x = layers.concat([x, x[:1]], 0)
y_embed = layers.concat([y_embed, y_embed[:1]], 0)
levels = levels.split(';')
for level in default_levels:
if len(level) == 1:
locals()[level] = y_embed
locals()['_'+level] = y_embed[:1]
if len(level) >= 2:
idx = int(level[1])*20
locals()[level] = x[:,idx:idx+20]
locals()['_'+level] = x[:1,idx:idx+20]
imgs = []
for i in range(batch_size):
for j in range(40):
alpha = j / 40
if interpolate_mode == 1:
alpha = alpha**2 * (3 - 2 * alpha)
for level in levels:
locals()['_'+level] = (1 - alpha) * locals()[level][i:i+1] + alpha * locals()[level][i+1:i+2]
inputs = []
for level in default_levels[1:]:
inputs.append(locals()['_'+level])
img_pd = G(inputs, locals()['_'+default_levels[0]], True)
img = np.uint8(img_pd.numpy().clip(0,1)*255)[0].transpose([1,2,0])
imgs.append(Image.fromarray(img))
stdout.write(f'{i*40+j+1}/{40*batch_size}\r')
stdout.flush()
print('')
for i, img in enumerate(imgs):
img.save(os.path.join(img_save_dir, str(i).zfill(5)+'.png'))
imgs[0].save(out_path+'.gif', save_all=True, append_images=imgs[1:], duration=40, loop=0)
out_path = out_path + '.mp4'
os.system(f'ffmpeg -r 40 -i {img_save_dir}/%05d.png -hide_banner -loglevel warning -nostats -c:v libx264 -crf 23 -y {out_path}')
os.system(f'rm -rf {img_save_dir}')
def __split_heads_qkv(queries, keys, values, n_head, d_key, d_value):
"""
Reshape input tensors at the last dimension to split multi-heads
and then transpose. Specifically, transform the input tensor with shape
[bs, max_sequence_length, n_head * hidden_dim] to the output tensor
with shape [bs, n_head, max_sequence_length, hidden_dim].
"""
# The value 0 in shape attr means copying the corresponding dimension
# size of the input as the output dimension size.
reshaped_q = layers.reshape(x=queries,
shape=[0, 0, n_head, d_key],
inplace=True)
# permuate the dimensions into:
# [batch_size, n_head, max_sequence_len, hidden_size_per_head]
q = layers.transpose(x=reshaped_q, perm=[0, 2, 1, 3])
# For encoder-decoder attention in inference, insert the ops and vars
# into global block to use as cache among beam search.
reshape_layer = wrap_layer_with_block(
layers.reshape,
fluid.default_main_program().current_block().parent_idx
) if cache is not None and static_kv else layers.reshape
transpose_layer = wrap_layer_with_block(
layers.transpose,
fluid.default_main_program().current_block().parent_idx
) if cache is not None and static_kv else layers.transpose
reshaped_k = reshape_layer(x=keys,
shape=[0, 0, n_head, d_key],
inplace=True)
k = transpose_layer(x=reshaped_k, perm=[0, 2, 1, 3])
reshaped_v = reshape_layer(x=values,
shape=[0, 0, n_head, d_value],
inplace=True)
v = transpose_layer(x=reshaped_v, perm=[0, 2, 1, 3])
if cache is not None: # only for faster inference
cache_, i = cache
if static_kv: # For encoder-decoder attention in inference
cache_k, cache_v = cache_["static_k"], cache_["static_v"]
# To init the static_k and static_v in global block.
static_cache_init = wrap_layer_with_block(
layers.assign,
fluid.default_main_program().current_block().parent_idx)
static_cache_init(
k,
fluid.default_main_program().global_block().var(
"static_k_%d" % i))
static_cache_init(
v,
fluid.default_main_program().global_block().var(
"static_v_%d" % i))
k, v = cache_k, cache_v
else: # For decoder self-attention in inference
# use cache and concat time steps.
cache_k, cache_v = cache_["k"], cache_["v"]
k = layers.concat([cache_k, k], axis=2)
v = layers.concat([cache_v, v], axis=2)
cache_["k"], cache_["v"] = (k, v)
return q, k, v
def bbox_iou(boxes1, boxes2):
'''
预测框 boxes1 (?, grid_h, grid_w, 3, 1, 4)，神经网络的输出(tx, ty, tw, th)经过了后处理求得的(bx, by, bw, bh)
图片中所有的gt boxes2 (?, 1, 1, 1, 150, 4)
paddle里不支持省略号，boxes1_area = boxes1[..., 2] * boxes1[..., 3]
冒号要写完
'''
boxes1_area = boxes1[:, :, :, :, :, 2] * boxes1[:, :, :, :, :,
3] # 所有格子的3个预测框的面积
boxes2_area = boxes2[:, :, :, :, :, 2] * boxes2[:, :, :, :, :,
3] # 所有ground truth的面积
# (x, y, w, h)变成(x0, y0, x1, y1)
boxes1 = P.concat([
boxes1[:, :, :, :, :, :2] - boxes1[:, :, :, :, :, 2:] * 0.5,
boxes1[:, :, :, :, :, :2] + boxes1[:, :, :, :, :, 2:] * 0.5
],
axis=-1)
boxes2 = P.concat([
boxes2[:, :, :, :, :, :2] - boxes2[:, :, :, :, :, 2:] * 0.5,
boxes2[:, :, :, :, :, :2] + boxes2[:, :, :, :, :, 2:] * 0.5
],
axis=-1)
# 所有格子的3个预测框 分别 和 150个ground truth 计算iou。 所以left_up和right_down的shape = (?, grid_h, grid_w, 3, 150, 2)
expand_boxes1 = P.expand(boxes1,
[1, 1, 1, 1, P.shape(boxes2)[4], 1
]) # 不同于pytorch和tf，boxes1和boxes2都要扩展为相同shape
expand_boxes2 = P.expand(
boxes2,
[1,
P.shape(boxes1)[1],
P.shape(boxes1)[2],
P.shape(boxes1)[3], 1, 1]) # 不同于pytorch和tf，boxes1和boxes2都要扩展为相同shape
left_up = P.elementwise_max(expand_boxes1[:, :, :, :, :, :2],
expand_boxes2[:, :, :, :, :, :2]) # 相交矩形的左上角坐标
right_down = P.elementwise_min(expand_boxes1[:, :, :, :, :, 2:],
expand_boxes2[:, :, :, :, :,
2:]) # 相交矩形的右下角坐标
inter_section = P.relu(
right_down -
left_up) # 相交矩形的w和h，是负数时取0 (?, grid_h, grid_w, 3, 150, 2)
inter_area = inter_section[:, :, :, :, :,
0] * inter_section[:, :, :, :, :,
1] # 相交矩形的面积 (?, grid_h, grid_w, 3, 150)
expand_boxes1_area = P.expand(boxes1_area,
[1, 1, 1, 1, P.shape(boxes2)[4]])
expand_boxes2_area = P.expand(boxes2_area, [
1,
P.shape(expand_boxes1_area)[1],
P.shape(expand_boxes1_area)[2],
P.shape(expand_boxes1_area)[3], 1
])
union_area = expand_boxes1_area + expand_boxes2_area - inter_area # union_area (?, grid_h, grid_w, 3, 150)
iou = 1.0 * inter_area / union_area # iou (?, grid_h, grid_w, 3, 150)
return iou
def forward(self, input_tensor, cur_state):
h_cur = cur_state
x_in = concat([input_tensor, h_cur], axis=1)
update = sigmoid(self.update_gate(x_in))
reset = sigmoid(self.reset_gate(x_in))
x_out = tanh(
self.out_gate(concat([input_tensor, h_cur * reset], axis=1)))
h_new = h_cur * (1 - update) + x_out * update
return h_new
def beam_search_step(state, logits, eos_id, beam_width, is_first_step,
length_penalty):
"""logits.shape == [B*W, V]"""
_, vocab_size = logits.shape
bsz, beam_width = state.log_probs.shape
onehot_eos = L.cast(F.one_hot(L.ones([1], 'int64') * eos_id, vocab_size),
'int64') #[1, V]
probs = L.log(L.softmax(logits)) #[B*W, V]
probs = mask_prob(probs, onehot_eos, state.finished) #[B*W, V]
allprobs = L.reshape(state.log_probs, [-1, 1]) + probs #[B*W, V]
not_finished = 1 - L.reshape(state.finished, [-1, 1]) #[B*W,1]
not_eos = 1 - onehot_eos
length_to_add = not_finished * not_eos #[B*W,V]
alllen = L.reshape(state.lengths, [-1, 1]) + length_to_add
allprobs = L.reshape(allprobs, [-1, beam_width * vocab_size])
alllen = L.reshape(alllen, [-1, beam_width * vocab_size])
allscore = hyp_score(allprobs, alllen, length_penalty)
if is_first_step:
allscore = L.reshape(
allscore,
[bsz, beam_width, -1])[:, 0, :] # first step only consiter beam 0
scores, idx = L.topk(allscore, k=beam_width) #[B, W]
next_beam_id = idx // vocab_size #[B, W]
next_word_id = idx % vocab_size
gather_idx = L.concat([L.where(idx != -1)[:, :1],
L.reshape(idx, [-1, 1])], 1)
next_probs = L.reshape(L.gather_nd(allprobs, gather_idx), idx.shape)
next_len = L.reshape(L.gather_nd(alllen, gather_idx), idx.shape)
gather_idx = L.concat(
[L.where(next_beam_id != -1)[:, :1],
L.reshape(next_beam_id, [-1, 1])], 1)
next_finished = L.reshape(
L.gather_nd(state.finished, gather_idx), state.finished.shape
) #[gather new beam state according to new beam id]
#log.debug(gather_idx.numpy())
#log.debug(state.finished.numpy())
#log.debug(next_finished.numpy())
next_finished += L.cast(next_word_id == eos_id, 'int64')
next_finished = L.cast(next_finished > 0, 'int64')
#log.debug(next_word_id.numpy())
#log.debug(next_beam_id.numpy())
next_state = BeamSearchState(log_probs=next_probs,
lengths=next_len,
finished=next_finished)
output = BeamSearchOutput(scores=scores,
predicted_ids=next_word_id,
beam_parent_ids=next_beam_id)
return output, next_state
def beam_search_step(state, logits, eos_id, beam_width, is_first_step,
length_penalty):
"""logits.shape == [B*W, V]"""
beam_size, vocab_size = logits.shape # as batch size=1 in this hub module. the first dim means bsz * beam_size equals beam_size
logits_np = logits.numpy()
for i in range(beam_size):
logits_np[i][17963] = 0 # make [UNK] prob = 0
logits = D.to_variable(logits_np)
bsz, beam_width = state.log_probs.shape
onehot_eos = L.cast(F.one_hot(L.ones([1], 'int64') * eos_id, vocab_size),
'int64') #[1, V]
probs = L.log(L.softmax(logits)) #[B*W, V]
probs = mask_prob(probs, onehot_eos, state.finished) #[B*W, V]
allprobs = L.reshape(state.log_probs, [-1, 1]) + probs #[B*W, V]
not_finished = 1 - L.reshape(state.finished, [-1, 1]) #[B*W,1]
not_eos = 1 - onehot_eos
length_to_add = not_finished * not_eos #[B*W,V]
alllen = L.reshape(state.lengths, [-1, 1]) + length_to_add
allprobs = L.reshape(allprobs, [-1, beam_width * vocab_size])
alllen = L.reshape(alllen, [-1, beam_width * vocab_size])
allscore = hyp_score(allprobs, alllen, length_penalty)
if is_first_step:
allscore = L.reshape(
allscore,
[bsz, beam_width, -1])[:, 0, :] # first step only consiter beam 0
scores, idx = L.topk(allscore, k=beam_width) #[B, W]
next_beam_id = idx // vocab_size #[B, W]
next_word_id = idx % vocab_size
gather_idx = L.concat([L.where(idx != -1)[:, :1],
L.reshape(idx, [-1, 1])], 1)
next_probs = L.reshape(L.gather_nd(allprobs, gather_idx), idx.shape)
next_len = L.reshape(L.gather_nd(alllen, gather_idx), idx.shape)
gather_idx = L.concat(
[L.where(next_beam_id != -1)[:, :1],
L.reshape(next_beam_id, [-1, 1])], 1)
next_finished = L.reshape(
L.gather_nd(state.finished, gather_idx), state.finished.shape
) #[gather new beam state according to new beam id]
next_finished += L.cast(next_word_id == eos_id, 'int64')
next_finished = L.cast(next_finished > 0, 'int64')
next_state = BeamSearchState(log_probs=next_probs,
lengths=next_len,
finished=next_finished)
output = BeamSearchOutput(scores=scores,
predicted_ids=next_word_id,
beam_parent_ids=next_beam_id)
return output, next_state
def get_single_direction_output(rnn_input,
encode_hidden,
unit_list,
mask=None,
direc_index=0):
rnn = StaticRNN()
#print(rnn_input.shape)
with rnn.step():
step_input = rnn.step_input(rnn_input)
if mask:
step_mask = rnn.step_input(mask)
for i in range(num_layers):
if init_hidden:
pre_hidden = rnn.memory(init=init_hidden[i, direc_index])
else:
pre_hidden = rnn.memory(batch_ref=rnn_input,
shape=[-1, hidden_size],
ref_batch_dim_idx=1)
encode_h = encode_hidden[i]
pre_encode_hidden = layers.concat([pre_hidden, encode_h], axis=1)
new_hidden = unit_list[i](step_input, pre_encode_hidden)
if mask:
new_hidden = layers.elementwise_mul(
new_hidden, step_mask, axis=0) - layers.elementwise_mul(
pre_hidden, (step_mask - 1), axis=0)
rnn.update_memory(pre_hidden, new_hidden)
rnn.step_output(new_hidden)
step_input = new_hidden
if dropout_prob is not None and dropout_prob > 0.0:
step_input = layers.dropout(step_input, dropout_prob=dropout_prob, )
rnn.step_output(step_input)
rnn_out = rnn()
last_hidden_array = []
all_hidden_array = [] # 增加这个来得到所有隐含状态
rnn_output = rnn_out[-1]
for i in range(num_layers):
last_hidden = rnn_out[i]
all_hidden_array.append(last_hidden)
last_hidden = last_hidden[-1]
last_hidden_array.append(last_hidden)
all_hidden_array = layers.concat(all_hidden_array, axis=0)
all_hidden_array = layers.reshape(all_hidden_array, shape=[num_layers, input.shape[0], -1, hidden_size])
last_hidden_output = layers.concat(last_hidden_array, axis=0)
last_hidden_output = layers.reshape(last_hidden_output, shape=[num_layers, -1, hidden_size])
return rnn_output, last_hidden_output, all_hidden_array
def flow_generation(self, label, ref_labels, ref_images, prev_labels,
prev_images, ref_idx):
"""
Generates flows and masks for warping reference / previous images.
Args:
label (NxCxHxW): Target label map.
ref_labels (NxKxCxHxW): Reference label maps.
ref_images (NxKx3xHxW): Reference images.
prev_labels (NxTxCxHxW): Previous label maps.
prev_images (NxTx3xHxW): Previous images.
ref_idx (Nx1): index for which image to use from the reference images.
Returns:
- flow (list of Nx2xHxW): Optical flows.
- occ_mask (list of Nx1xHxW): Occlusion masks.
- img_warp (list of Nx3xHxW): Warped reference /previous images.
- cond_inputs (list of Nx4xHxW): conditional inputs for SPADE combination
"""
# Pick an image in the reference imagegs using ref_idx.
ref_label, ref_image = pick_image([ref_labels, ref_images], ref_idx)
# Only start using prev frames when enough prev frames are generated.
has_prev = prev_labels is not None and prev_labels.shape[
1] == self.num_frames_G - 1
flow, occ_mask, img_warp, cond_inputs = [None] * 2, [None] * 2, [
None
] * 2, [None] * 2
if self.warp_ref:
# Generate flows / masks for warping the reference image.
flow_ref, occ_mask_ref = self.flow_network_ref(
label, ref_label, ref_image)
ref_image_warp = resample(ref_image, flow_ref)
flow[0], occ_mask[0], img_warp[
0] = flow_ref, occ_mask_ref, ref_image_warp[:, :3]
# Concat warped image and occlusion mask to form the conditional input.
cond_inputs[0] = L.concat([img_warp[0], occ_mask[0]], axis=1)
if self.temporal_initialized and has_prev:
# Generate flows / masks for warping the previous image.
b, t, c, h, w = prev_labels.shape
prev_labels_concat = L.reshape(prev_labels, (b, -1, h, w))
prev_images_concat = L.reshape(prev_images, (b, -1, h, w))
flow_prev, occ_mask_prev = self.flow_network_temp(
label, prev_labels_concat, prev_images_concat)
img_prev_warp = resample(prev_images[:, -1], flow_prev)
flow[1], occ_mask[1], img_warp[
1] = flow_prev, occ_mask_prev, img_prev_warp
cond_inputs[1] = L.concat([img_warp[1], occ_mask[1]], axis=1)
return flow, occ_mask, img_warp, cond_inputs
def __call__(self, x):
x_1 = x
x_2 = self.max_pool1(x)
x_3 = self.max_pool2(x)
x_4 = self.max_pool3(x)
if self.seq == 'desc':
out = L.concat([x_4, x_3, x_2, x_1], axis=1)
else:
out = L.concat([x_1, x_2, x_3, x_4], axis=1)
return out
def _build_distribution(self, enc_final_state=None):
enc_hidden = [
layers.concat(state, axis=-1) for state in enc_final_state
]
enc_hidden = layers.concat(enc_hidden, axis=-1)
z_mean_log_var = layers.fc(input=enc_hidden,
size=self.latent_size * 2,
name='fc_dist')
z_mean, z_log_var = layers.split(z_mean_log_var, 2, -1)
return z_mean, z_log_var
def build_model(self):
node_features = self.graph_wrapper.node_feat["feat"]
output = self.gcn(gw=self.graph_wrapper,
feature=node_features,
hidden_size=self.hidden_size,
activation="relu",
norm=self.graph_wrapper.node_feat["norm"],
name="gcn_layer_1")
output1 = output
output = self.gcn(gw=self.graph_wrapper,
feature=output,
hidden_size=self.hidden_size,
activation="relu",
norm=self.graph_wrapper.node_feat["norm"],
name="gcn_layer_2")
output2 = output
output = self.gcn(gw=self.graph_wrapper,
feature=output,
hidden_size=self.hidden_size,
activation="relu",
norm=self.graph_wrapper.node_feat["norm"],
name="gcn_layer_3")
output = L.concat(input=[output1, output2, output], axis=-1)
output, ratio_length = sag_pool(gw=self.graph_wrapper,
feature=output,
ratio=self.pooling_ratio,
graph_id=self.graph_id,
dataset=self.args.dataset_name,
name="sag_pool_1")
output = L.lod_reset(output, self.graph_wrapper.graph_lod)
cat1 = L.sequence_pool(output, "sum")
ratio_length = L.cast(ratio_length, dtype="float32")
cat1 = L.elementwise_div(cat1, ratio_length, axis=-1)
cat2 = L.sequence_pool(output, "max")
output = L.concat(input=[cat2, cat1], axis=-1)
output = L.fc(output, size=self.hidden_size, act="relu")
output = L.dropout(output, dropout_prob=self.dropout_ratio)
output = L.fc(output, size=self.hidden_size // 2, act="relu")
output = L.fc(output,
size=self.num_classes,
act=None,
param_attr=fluid.ParamAttr(name="final_fc"))
self.labels = L.cast(self.labels, dtype="float32")
loss = L.sigmoid_cross_entropy_with_logits(x=output, label=self.labels)
self.loss = L.mean(loss)
pred = L.sigmoid(output)
self.pred = L.argmax(x=pred, axis=-1)
correct = L.equal(self.pred, self.labels_1dim)
correct = L.cast(correct, dtype="int32")
self.correct = L.reduce_sum(correct)
def forward(self):
""" forward
"""
src, dst = L.read_file(self.pyreader)
if self.is_sparse:
# sparse mode use 2 dims input.
src = L.reshape(src, [-1, 1])
dst = L.reshape(dst, [-1, 1])
src_embed = split_embedding(src, self.num_nodes, self.hidden_size,
self.embed_init, "weight", self.num_part,
self.is_sparse)
dst_embed = split_embedding(dst, self.num_nodes, self.hidden_size,
self.embed_init, "weight", self.num_part,
self.is_sparse)
if self.is_sparse:
src_embed = L.reshape(src_embed,
[-1, 1, self.num_featuers, self.hidden_size])
dst_embed = L.reshape(
dst_embed,
[-1, self.neg_num + 1, self.num_featuers, self.hidden_size])
src_embed = L.reduce_mean(src_embed, 2)
dst_embed = L.reduce_mean(dst_embed, 2)
logits = L.matmul(src_embed, dst_embed,
transpose_y=True) # [batch_size, 1, neg_num+1]
pos_label = L.fill_constant_batch_size_like(logits, [-1, 1, 1],
"float32", 1)
neg_label = L.fill_constant_batch_size_like(logits,
[-1, 1, self.neg_num],
"float32", 0)
label = L.concat([pos_label, neg_label], -1)
pos_weight = L.fill_constant_batch_size_like(logits, [-1, 1, 1],
"float32", self.neg_num)
neg_weight = L.fill_constant_batch_size_like(logits,
[-1, 1, self.neg_num],
"float32", 1)
weight = L.concat([pos_weight, neg_weight], -1)
weight.stop_gradient = True
label.stop_gradient = True
loss = L.sigmoid_cross_entropy_with_logits(logits, label)
loss = loss * weight
loss = L.reduce_mean(loss)
loss = loss * ((self.neg_num + 1) / 2 / self.neg_num)
loss.persistable = True
self.loss = loss
return loss
def forward(self, input):
x = self.DownBlock(input)
gap = adaptive_pool2d(x, pool_size=[1, 1], pool_type='avg')
gap_ = reshape(x=gap, shape=(x.shape[0], -1))
gap_logit = self.gap_fc(gap_)
gap_weight = self.gap_fc.parameters()[0]
gap_weight = transpose(gap_weight, perm=[1, 0])
gap_weight = unsqueeze(gap_weight, axes=2)
gap_weight = unsqueeze(gap_weight, axes=3)
gap = x * gap_weight
gmp = adaptive_pool2d(x, pool_size=[1, 1], pool_type='max')
gmp_ = reshape(x=gmp, shape=(x.shape[0], -1))
gmp_logit = self.gmp_fc(gmp_)
gmp_weight = self.gmp_fc.parameters()[0]
gmp_weight = transpose(gmp_weight, perm=[1, 0])
gmp_weight = unsqueeze(gmp_weight, axes=2)
gmp_weight = unsqueeze(gmp_weight, axes=3)
gmp = x * gmp_weight
cam_logit = concat(input=[gap_logit, gmp_logit], axis=1)
x = concat(input=[gap, gmp], axis=1)
x = self.relu(self.conv1x1(x))
heatmap = reduce_sum(x, dim=1, keep_dim=True)
if self.light:
x_ = adaptive_pool2d(x, pool_size=[1, 1], pool_type='avg')
x_ = reshape(x=x_, shape=(x_.shape[0], -1))
x_ = self.FC(x_)
else:
x_ = reshape(x, shape=(x.shape[0], -1))
x_ = self.FC(x_)
gamma, beta = self.gamma(x_), self.beta(x_)
for i in range(self.n_blocks):
x = getattr(self, 'UpBlock1_' + str(i + 1))(x, gamma, beta)
out = self.UpBlock2(x)
return out, cam_logit, heatmap
def gen_bias(encoder_inputs, decoder_inputs, step):
decoder_bsz, decoder_seqlen = decoder_inputs.shape[:2]
attn_bias = L.reshape(L.range(0, decoder_seqlen, 1, dtype='float32') + 1, [1, -1, 1])
decoder_bias = L.cast((L.matmul(attn_bias, 1. / attn_bias, transpose_y=True) >= 1.),
'float32') #[1, 1, decoderlen, decoderlen]
encoder_bias = L.unsqueeze(L.cast(L.ones_like(encoder_inputs), 'float32'), [1]) #[bsz, 1, encoderlen]
encoder_bias = L.expand(encoder_bias, [1, decoder_seqlen, 1]) #[bsz,decoderlen, encoderlen]
decoder_bias = L.expand(decoder_bias, [decoder_bsz, 1, 1]) #[bsz, decoderlen, decoderlen]
if step > 0:
bias = L.concat([encoder_bias, L.ones([decoder_bsz, decoder_seqlen, step], 'float32'), decoder_bias], -1)
else:
bias = L.concat([encoder_bias, decoder_bias], -1)
return bias
def rotation_z(theta):
"""
:param theta: must be a scale, shape [1], 'float32'
:return:
"""
cos_value = cos(theta/2)
sin_value = sin(theta/2)
zero_pd = pp_zeros([1], "float32")
rz_re = concat([cos_value, zero_pd, zero_pd, cos_value], axis=0)
rz_im = concat([-sin_value, zero_pd, zero_pd, sin_value], axis=0)
return ComplexVariable(reshape(rz_re, [2, 2]), reshape(rz_im, [2, 2]))
def _ranking(self, inputs, predictions):
""" Reranking generated responses. """
src_token = inputs["src_token"]
src_mask = inputs["src_mask"]
src_pos = inputs["src_pos"]
src_type = inputs["src_type"]
src_turn = inputs["src_turn"]
src_embed = self.embedder(src_token, src_pos, src_type, src_turn)
batch_size, num_latent, tgt_seq_len = predictions.shape
# shape: [batch_size, num_latent, seq_len, 1]
preds_token = F.unsqueeze(predictions, [3])
preds_mask = F.not_equal(preds_token, self.padding_idx, "int64")
preds_pos = layers.range(0, tgt_seq_len, 1, dtype="float32")
preds_pos = F.unsqueeze(preds_pos, [0, 0, 1])
preds_pos = layers.expand(preds_pos, [batch_size, num_latent, 1, 1])
preds_pos = layers.cast(preds_pos, "int64")
preds_type = layers.zeros_like(preds_token)
preds_turn = layers.zeros_like(preds_token)
scores = []
for i in range(num_latent):
pred_token = preds_token[:, i]
pred_mask = preds_mask[:, i]
pred_pos = preds_pos[:, i]
pred_type = preds_type[:, i]
pred_turn = preds_turn[:, i]
input_mask = layers.concat([src_mask, pred_mask], axis=1)
input_mask.stop_gradient = True
pred_embed = self.embedder(pred_token, pred_pos, pred_type,
pred_turn)
embed = layers.concat([src_embed, pred_embed], axis=1)
embed = self.embed_layer_norm(embed)
mask_embed = self.mask_embed
mask_embed = layers.expand(mask_embed, [batch_size, 1, 1])
mask_embed = self.embed_layer_norm(mask_embed)
out = layers.concat([mask_embed, embed], axis=1)
mask = self._create_mask(input_mask, append_head=True)
for layer in self.layers:
out = layer(out, mask, None)
mask_embed = out[:, 0]
score = self.discriminator(mask_embed)
scores.append(score[:, 0])
scores = layers.stack(scores, axis=1)
return scores
def _attn_forward(self,
queries,
keys,
values,
attn_bias,
past_cache,
head_mask=None):
assert len(queries.shape) == len(keys.shape) == len(values.shape) == 3
q = self.q(queries)
k = self.k(keys)
v = self.v(values)
cache = (k, v)
if past_cache is not None:
cached_k, cached_v = past_cache
k = L.concat([cached_k, k], 1)
v = L.concat([cached_v, v], 1)
if hasattr(self.q, 'fn') and self.q.fn.cur_config['expand_ratio'] != None:
n_head = int(self.n_head * self.q.fn.cur_config['expand_ratio'])
else:
n_head = self.n_head
q = L.transpose(
L.reshape(q, [0, 0, n_head, q.shape[-1] // n_head]),
[0, 2, 1, 3]) #[batch, head, seq, dim]
k = L.transpose(
L.reshape(k, [0, 0, n_head, k.shape[-1] // n_head]),
[0, 2, 1, 3]) #[batch, head, seq, dim]
v = L.transpose(
L.reshape(v, [0, 0, n_head, v.shape[-1] // n_head]),
[0, 2, 1, 3]) #[batch, head, seq, dim]
q = L.scale(q, scale=self.d_key**-0.5)
score = L.matmul(q, k, transpose_y=True)
if attn_bias is not None:
score += attn_bias
score = L.softmax(score, use_cudnn=True)
score = self.dropout(score)
if head_mask is not None:
score = score * head_mask
out = L.matmul(score, v)
out = L.transpose(out, [0, 2, 1, 3])
out = L.reshape(out, [0, 0, out.shape[2] * out.shape[3]])
out = self.o(out)
return out, cache
def get_l2_norm_pow(params_grads, sum_dtype=None):
sum_square_list = []
sum_square_list_fp16 = []
sum_square_list_fp32 = []
for p, g in params_grads:
if g is None:
continue
if getattr(p, 'need_clip', True) is False:
continue
merge_grad = g
if g.type == core.VarDesc.VarType.SELECTED_ROWS:
merge_grad = layers.merge_selected_rows(g)
merge_grad = layers.get_tensor_from_selected_rows(merge_grad)
sum_square = _squared_l2_norm(merge_grad)
if sum_square.dtype == core.VarDesc.VarType.FP16:
sum_square_list_fp16.append(sum_square)
elif sum_square.dtype == core.VarDesc.VarType.FP32:
sum_square_list_fp32.append(sum_square)
else:
sum_square_list.append(sum_square)
# all parameters have been filterd out
if len(sum_square_list) + len(sum_square_list_fp16) + len(
sum_square_list_fp32) == 0:
return None, None
assert sum_dtype in ["float64", "float32", None], \
"sum's type must be float64/ float32 / None"
if sum_dtype != "float64":
sum_dtype = 'float64' if len(sum_square_list) > 0 else "float32"
global_norm_var = []
if len(sum_square_list_fp16) > 0:
global_norm_var_fp16 = layers.concat(sum_square_list_fp16)
global_norm_var_fp16 = layers.reduce_sum(global_norm_var_fp16)
global_norm_var.append(global_norm_var_fp16.astype(sum_dtype))
if len(sum_square_list_fp32) > 0:
global_norm_var_fp32 = layers.concat(sum_square_list_fp32)
global_norm_var_fp32 = layers.reduce_sum(global_norm_var_fp32)
if sum_dtype == 'float32':
global_norm_var.append(global_norm_var_fp32)
else:
global_norm_var.append(global_norm_var_fp32.astype(sum_dtype))
if len(sum_square_list) > 0:
global_norm_var_fp64 = layers.concat(sum_square_list)
global_norm_var_fp64 = layers.reduce_sum(global_norm_var_fp64)
global_norm_var.append(global_norm_var_fp64)
global_norm_var = layers.concat(global_norm_var)
global_norm_var = layers.reduce_sum(global_norm_var)
return global_norm_var, sum_dtype
def forward(self, x):
features = self.feature(x)
x1 = self.stage1(features)
x2 = L.concat([x1, features], 1)
x2 = self.stage2(x2)
x3 = L.concat([x2, features], 1)
x3 = self.stage3(x3)
x4 = L.concat([x3, features], 1)
x4 = self.stage4(x4)
x5 = L.concat([x4, features], 1)
x5 = self.stage5(x5)
x6 = L.concat([x5, features], 1)
x6 = self.stage6(x6)
return [x1, x2, x3, x4, x5, x6]
def forward(self, gw):
x = self._atom_encoder(gw)
patch_repr = []
for i in range(self.num_layers):
e = self._bond_encoder(gw, name='l%d'%i)
x = gin_layer(gw, x, e, 'gin_%s' % i)
x = L.batch_norm(
x, param_attr=F.ParamAttr(name='batchnorm_%s' % i))
patch_repr.append(x) # $h_i^{(k)}$
patch_summary = L.concat(patch_repr, axis=1) # $h_{\phi}^i$
patch_pool = [pgl.layers.graph_pooling(gw, x, 'sum')
for x in patch_repr]
global_repr = L.concat(patch_pool, axis=1)
return global_repr, patch_summary
def _decode(self,
x,
y,
w,
h,
anchors,
stride,
scale_x_y,
eps,
is_gt=False):
conv_shape = x.shape # (8, 13, 13, 3)
batch_size = conv_shape[0]
n_grid = conv_shape[1]
anchor_per_scale = conv_shape[3]
_x = L.unsqueeze(x, 4)
_y = L.unsqueeze(y, 4)
conv_raw_dxdy = L.concat([_x, _y], -1) # (8, 13, 13, 3, 2)
_w = L.unsqueeze(w, 4)
_h = L.unsqueeze(h, 4)
conv_raw_dwdh = L.concat([_w, _h], -1) # (8, 13, 13, 3, 2)
rows = L.range(0, n_grid, 1, 'float32')
cols = L.range(0, n_grid, 1, 'float32')
rows = L.expand(L.reshape(rows, (1, -1, 1)), [n_grid, 1, 1])
cols = L.expand(L.reshape(cols, (-1, 1, 1)), [1, n_grid, 1])
offset = L.concat([rows, cols], axis=-1)
offset = L.reshape(offset, (1, n_grid, n_grid, 1, 2))
offset = L.expand(offset, [batch_size, 1, 1, anchor_per_scale, 1])
if is_gt:
decode_xy = (conv_raw_dxdy + offset) / n_grid
else:
if (abs(scale_x_y - 1.0) < eps):
decode_xy = L.sigmoid(conv_raw_dxdy)
decode_xy = (decode_xy + offset) / n_grid
else:
# Grid Sensitive
decode_xy = scale_x_y * L.sigmoid(conv_raw_dxdy) - 0.5 * (
scale_x_y - 1.0)
decode_xy = (decode_xy + offset) / n_grid
anchor_t = fluid.layers.assign(np.copy(anchors).astype(np.float32))
decode_wh = (L.exp(conv_raw_dwdh) * anchor_t) / (n_grid * stride)
decode_xywh = L.concat([decode_xy, decode_wh], axis=-1)
if is_gt:
decode_xywh.stop_gradient = True
return decode_xywh # (8, 13, 13, 3, 4)
def get_prediction(self, body_feats, im_size):
"""
Get prediction result of YOLOv3 network
Args:
input (list): List of Variables, output of backbone stages
im_size (Variable): Variable of size([h, w]) of each image
Returns:
pred (Variable): shape = [bs, keep_top_k, 6]
"""
# outputs里为大中小感受野的输出
outputs = self._get_outputs(body_feats)
boxes = []
scores = []
for i, output in enumerate(outputs):
if self.iou_aware:
output = get_iou_aware_score(output,
len(self.anchor_masks[i]),
self.num_classes,
self.iou_aware_factor)
box, score = fluid.layers.yolo_box(
x=output,
img_size=im_size,
anchors=self.mask_anchors[i],
class_num=self.num_classes,
conf_thresh=self.nms_cfg['score_threshold'],
downsample_ratio=self.downsample[i],
name="yolo_box" + str(i),
clip_bbox=self.clip_bbox,
scale_x_y=self.scale_x_y)
boxes.append(box)
scores.append(fluid.layers.transpose(score, perm=[0, 2, 1]))
yolo_boxes = L.concat(boxes, axis=1)
yolo_scores = L.concat(scores, axis=2)
# nms
nms_cfg = copy.deepcopy(self.nms_cfg)
nms_type = nms_cfg.pop('nms_type')
batch_size = 1
if nms_type == 'matrix_nms':
pred = fluid.layers.matrix_nms(yolo_boxes, yolo_scores, background_label=-1, **nms_cfg)
elif nms_type == 'multiclass_nms':
pred = fluid.layers.multiclass_nms(yolo_boxes, yolo_scores, background_label=-1, **nms_cfg)
return pred
def test_nce(self):
window_size = 5
words = []
for i in range(window_size):
words.append(
layers.data(name='word_{0}'.format(i),
shape=[1],
dtype='int64'))
dict_size = 10000
label_word = int(window_size // 2) + 1
embs = []
for i in range(window_size):
if i == label_word:
continue
emb = layers.embedding(input=words[i],
size=[dict_size, 32],
param_attr='emb.w',
is_sparse=True)
embs.append(emb)
embs = layers.concat(input=embs, axis=1)
loss = layers.nce(input=embs,
label=words[label_word],
num_total_classes=dict_size,
param_attr='nce.w',
bias_attr='nce.b')
avg_loss = layers.mean(loss)
self.assertIsNotNone(avg_loss)
print(str(default_main_program()))
def loss_boxes(self, outputs, targets, indices, num_boxes):
"""
Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss
targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]
The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size.
"""
assert "pred_boxes" in outputs
idx = self._get_src_permutation_idx(indices)
src_boxes = outputs["pred_boxes"].numpy()[
idx[0].numpy(), idx[1].numpy(), :] # [num_objects, 4]
src_boxes = dg.to_variable(src_boxes)
target_boxes = [
t["boxes"].numpy()[i.numpy()]
for t, (_, i) in zip(targets, indices)
]
target_boxes = [dg.to_variable(t) for t in target_boxes]
target_boxes = L.concat(target_boxes,
0).astype("float32") # [num_objects, 4]
loss_bbox = F.loss.l1_loss(src_boxes, target_boxes, reduction="sum")
losses = {}
losses["loss_bbox"] = loss_bbox / num_boxes
num_boxes = src_boxes.shape[0]
mask = T.creation.diag(dg.to_variable(
np.ones(num_boxes))) # mask out non-diag element
loss_giou = (1 - box_ops.generalied_box_iou(
box_ops.box_cxcywh_to_xyxy(src_boxes),
box_ops.box_cxcywh_to_xyxy(target_boxes))) * mask
losses["loss_giou"] = L.reduce_sum(loss_giou) / num_boxes
return losses
def distributed_embedding(input,
dict_size,
hidden_size,
initializer,
name,
num_part=16,
is_sparse=False,
learning_rate=1.0):
_part_size = hidden_size // num_part
if hidden_size % num_part != 0:
_part_size += 1
output_embedding = []
p_num = 0
while hidden_size > 0:
_part_size = min(_part_size, hidden_size)
hidden_size -= _part_size
print("part", p_num, "size=", (dict_size, _part_size))
part_embedding = L.embedding(input=input,
size=(dict_size, int(_part_size)),
is_sparse=is_sparse,
is_distributed=False,
param_attr=F.ParamAttr(
name=name + '_part%s' % p_num,
initializer=initializer,
learning_rate=learning_rate))
p_num += 1
output_embedding.append(part_embedding)
return L.concat(output_embedding, -1)
def test_nce(self):
window_size = 5
words = []
for i in xrange(window_size):
words.append(
layers.data(
name='word_{0}'.format(i), shape=[1], dtype='int64'))
dict_size = 10000
label_word = int(window_size / 2) + 1
embs = []
for i in xrange(window_size):
if i == label_word:
continue
emb = layers.embedding(
input=words[i],
size=[dict_size, 32],
param_attr='emb.w',
is_sparse=True)
embs.append(emb)
embs = layers.concat(input=embs, axis=1)
loss = layers.nce(input=embs,
label=words[label_word],
num_total_classes=dict_size,
param_attr='nce.w',
bias_attr='nce.b')
avg_loss = layers.mean(loss)
self.assertIsNotNone(avg_loss)
print(str(default_main_program()))
def get_activations(data_loader,
key_real,
key_fake,
generator=None,
sample_size=None,
preprocess=None):
inception = build_inception()
inception.eval()
batch_y = []
for it, data in enumerate(data_loader.batch_reader(2)()):
if preprocess is not None:
data = preprocess(data)
if generator is None:
images = data[key_real]
else:
net_G_output = generator(data)
images = net_G_output[key_fake]
# Clamp the image for models that do not set the output to between
# -1, 1. For models that employ tanh, this has not effect.
# images = L.clip(images, -1, 1)
images = apply_image_net_normalization(images)
images = nn.functional.interpolate(images,
size=(299, 299),
mode='bilinear',
align_corners=True)
y = inception(images)
batch_y += [y]
batch_y = L.concat(batch_y).numpy()
if sample_size is not None:
batch_y = batch_y[:sample_size]
# print(batch_y.shape)
return batch_y
def get_usr_combined_features():
# FIXME(dzh) : old API integer_value(10) may have range check.
# currently we don't have user configurated check.
USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1
uid = layers.data(name='user_id', shape=[1], dtype='int64')
usr_emb = layers.embedding(
input=uid,
dtype='float32',
size=[USR_DICT_SIZE, 32],
param_attr='user_table',
is_sparse=IS_SPARSE)
usr_fc = layers.fc(input=usr_emb, size=32)
USR_GENDER_DICT_SIZE = 2
usr_gender_id = layers.data(name='gender_id', shape=[1], dtype='int64')
usr_gender_emb = layers.embedding(
input=usr_gender_id,
size=[USR_GENDER_DICT_SIZE, 16],
param_attr='gender_table',
is_sparse=IS_SPARSE)
usr_gender_fc = layers.fc(input=usr_gender_emb, size=16)
USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table)
usr_age_id = layers.data(name='age_id', shape=[1], dtype="int64")
usr_age_emb = layers.embedding(
input=usr_age_id,
size=[USR_AGE_DICT_SIZE, 16],
is_sparse=IS_SPARSE,
param_attr='age_table')
usr_age_fc = layers.fc(input=usr_age_emb, size=16)
USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1
usr_job_id = layers.data(name='job_id', shape=[1], dtype="int64")
usr_job_emb = layers.embedding(
input=usr_job_id,
size=[USR_JOB_DICT_SIZE, 16],
param_attr='job_table',
is_sparse=IS_SPARSE)
usr_job_fc = layers.fc(input=usr_job_emb, size=16)
concat_embed = layers.concat(
input=[usr_fc, usr_gender_fc, usr_age_fc, usr_job_fc], axis=1)
usr_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
return usr_combined_features
def get_mov_combined_features():
MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1
mov_id = layers.data(name='movie_id', shape=[1], dtype='int64')
mov_emb = layers.embedding(
input=mov_id,
dtype='float32',
size=[MOV_DICT_SIZE, 32],
param_attr='movie_table',
is_sparse=IS_SPARSE)
mov_fc = layers.fc(input=mov_emb, size=32)
CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories())
category_id = layers.data(
name='category_id', shape=[1], dtype='int64', lod_level=1)
mov_categories_emb = layers.embedding(
input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_categories_hidden = layers.sequence_pool(
input=mov_categories_emb, pool_type="sum")
MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict())
mov_title_id = layers.data(
name='movie_title', shape=[1], dtype='int64', lod_level=1)
mov_title_emb = layers.embedding(
input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_title_conv = nets.sequence_conv_pool(
input=mov_title_emb,
num_filters=32,
filter_size=3,
act="tanh",
pool_type="sum")
concat_embed = layers.concat(
input=[mov_fc, mov_categories_hidden, mov_title_conv], axis=1)
# FIXME(dzh) : need tanh operator
mov_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
return mov_combined_features
def test_word_embedding(self):
program = Program()
with program_guard(program, startup_program=Program()):
dict_size = 10000
embed_size = 32
first_word = layers.data(name='firstw', shape=[1], dtype='int64')
second_word = layers.data(name='secondw', shape=[1], dtype='int64')
third_word = layers.data(name='thirdw', shape=[1], dtype='int64')
forth_word = layers.data(name='forthw', shape=[1], dtype='int64')
next_word = layers.data(name='nextw', shape=[1], dtype='int64')
embed_first = layers.embedding(
input=first_word,
size=[dict_size, embed_size],
dtype='float32',
param_attr='shared_w')
embed_second = layers.embedding(
input=second_word,
size=[dict_size, embed_size],
dtype='float32',
param_attr='shared_w')
embed_third = layers.embedding(
input=third_word,
size=[dict_size, embed_size],
dtype='float32',
param_attr='shared_w')
embed_forth = layers.embedding(
input=forth_word,
size=[dict_size, embed_size],
dtype='float32',
param_attr='shared_w')
concat_embed = layers.concat(
input=[embed_first, embed_second, embed_third, embed_forth],
axis=1)
hidden1 = layers.fc(input=concat_embed, size=256, act='sigmoid')
predict_word = layers.fc(input=hidden1,
size=dict_size,
act='softmax')
cost = layers.cross_entropy(input=predict_word, label=next_word)
avg_cost = layers.mean(cost)
self.assertIsNotNone(avg_cost)
print(str(program))
