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PaddleOCR
提交 91f6f243 编写于 作者: T tink2123
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ppocr/modeling/heads/self_attention/model.py
浏览文件 @ 91f6f243
......@@ -7,6 +7,11 @@ import paddle.fluid.layers as layers
# Set seed for CE
dropout_seed = None
encoder_data_input_fields = (
"src_word",
"src_pos",
"src_slf_attn_bias", )
def wrap_layer_with_block(layer, block_idx):
"""
......@@ -45,25 +50,6 @@ def wrap_layer_with_block(layer, block_idx):
return layer_wrapper
def position_encoding_init(n_position, d_pos_vec):
"""
Generate the initial values for the sinusoid position encoding table.
"""
channels = d_pos_vec
position = np.arange(n_position)
num_timescales = channels // 2
log_timescale_increment = (np.log(float(1e4) / float(1)) /
(num_timescales - 1))
inv_timescales = np.exp(np.arange(
num_timescales)) * -log_timescale_increment
scaled_time = np.expand_dims(position, 1) * np.expand_dims(inv_timescales,
0)
signal = np.concatenate([np.sin(scaled_time), np.cos(scaled_time)], axis=1)
signal = np.pad(signal, [[0, 0], [0, np.mod(channels, 2)]], 'constant')
position_enc = signal
return position_enc.astype("float32")
def multi_head_attention(queries,
keys,
values,
......@@ -271,7 +257,7 @@ post_process_layer = pre_post_process_layer
def prepare_encoder(
src_word, #[b,t,c]
src_word, # [b,t,c]
src_pos,
src_vocab_size,
src_emb_dim,
......@@ -286,7 +272,7 @@ def prepare_encoder(
This module is used at the bottom of the encoder stacks.
"""
src_word_emb = src_word #layers.concat(res,axis=1)
src_word_emb = src_word # layers.concat(res,axis=1)
src_word_emb = layers.cast(src_word_emb, 'float32')
# print("src_word_emb",src_word_emb)
......@@ -338,12 +324,6 @@ def prepare_decoder(src_word,
is_test=False) if dropout_rate else enc_input
# prepare_encoder = partial(
# prepare_encoder_decoder, pos_enc_param_name=pos_enc_param_names[0])
# prepare_decoder = partial(
# prepare_encoder_decoder, pos_enc_param_name=pos_enc_param_names[1])
def encoder_layer(enc_input,
attn_bias,
n_head,
......@@ -412,234 +392,6 @@ def encoder(enc_input,
return enc_output
def decoder_layer(dec_input,
enc_output,
slf_attn_bias,
dec_enc_attn_bias,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
cache=None,
gather_idx=None):
""" The layer to be stacked in decoder part.
The structure of this module is similar to that in the encoder part except
a multi-head attention is added to implement encoder-decoder attention.
"""
slf_attn_output = multi_head_attention(
pre_process_layer(dec_input, preprocess_cmd, prepostprocess_dropout),
None,
None,
slf_attn_bias,
d_key,
d_value,
d_model,
n_head,
attention_dropout,
cache=cache,
gather_idx=gather_idx)
slf_attn_output = post_process_layer(
dec_input,
slf_attn_output,
postprocess_cmd,
prepostprocess_dropout, )
enc_attn_output = multi_head_attention(
pre_process_layer(slf_attn_output, preprocess_cmd,
prepostprocess_dropout),
enc_output,
enc_output,
dec_enc_attn_bias,
d_key,
d_value,
d_model,
n_head,
attention_dropout,
cache=cache,
gather_idx=gather_idx,
static_kv=True)
enc_attn_output = post_process_layer(
slf_attn_output,
enc_attn_output,
postprocess_cmd,
prepostprocess_dropout, )
ffd_output = positionwise_feed_forward(
pre_process_layer(enc_attn_output, preprocess_cmd,
prepostprocess_dropout),
d_inner_hid,
d_model,
relu_dropout, )
dec_output = post_process_layer(
enc_attn_output,
ffd_output,
postprocess_cmd,
prepostprocess_dropout, )
return dec_output
def decoder(dec_input,
enc_output,
dec_slf_attn_bias,
dec_enc_attn_bias,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
caches=None,
gather_idx=None):
"""
The decoder is composed of a stack of identical decoder_layer layers.
"""
for i in range(n_layer):
dec_output = decoder_layer(
dec_input,
enc_output,
dec_slf_attn_bias,
dec_enc_attn_bias,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
cache=None if caches is None else caches[i],
gather_idx=gather_idx)
dec_input = dec_output
dec_output = pre_process_layer(dec_output, preprocess_cmd,
prepostprocess_dropout)
return dec_output
def make_all_inputs(input_fields):
"""
Define the input data layers for the transformer model.
"""
inputs = []
for input_field in input_fields:
input_var = layers.data(
name=input_field,
shape=input_descs[input_field][0],
dtype=input_descs[input_field][1],
lod_level=input_descs[input_field][2]
if len(input_descs[input_field]) == 3 else 0,
append_batch_size=False)
inputs.append(input_var)
return inputs
def make_all_py_reader_inputs(input_fields, is_test=False):
reader = layers.py_reader(
capacity=20,
name="test_reader" if is_test else "train_reader",
shapes=[input_descs[input_field][0] for input_field in input_fields],
dtypes=[input_descs[input_field][1] for input_field in input_fields],
lod_levels=[
input_descs[input_field][2]
if len(input_descs[input_field]) == 3 else 0
for input_field in input_fields
])
return layers.read_file(reader), reader
def transformer(src_vocab_size,
trg_vocab_size,
max_length,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
weight_sharing,
label_smooth_eps,
bos_idx=0,
use_py_reader=False,
is_test=False):
if weight_sharing:
assert src_vocab_size == trg_vocab_size, (
"Vocabularies in source and target should be same for weight sharing."
)
data_input_names = encoder_data_input_fields + \
decoder_data_input_fields[:-1] + label_data_input_fields
if use_py_reader:
all_inputs, reader = make_all_py_reader_inputs(data_input_names,
is_test)
else:
all_inputs = make_all_inputs(data_input_names)
# print("all inputs",all_inputs)
enc_inputs_len = len(encoder_data_input_fields)
dec_inputs_len = len(decoder_data_input_fields[:-1])
enc_inputs = all_inputs[0:enc_inputs_len]
dec_inputs = all_inputs[enc_inputs_len:enc_inputs_len + dec_inputs_len]
label = all_inputs[-2]
weights = all_inputs[-1]
enc_output = wrap_encoder(
src_vocab_size, 64, n_layer, n_head, d_key, d_value, d_model,
d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout,
preprocess_cmd, postprocess_cmd, weight_sharing, enc_inputs)
predict = wrap_decoder(
trg_vocab_size,
max_length,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
weight_sharing,
dec_inputs,
enc_output, )
# Padding index do not contribute to the total loss. The weights is used to
# cancel padding index in calculating the loss.
if label_smooth_eps:
label = layers.label_smooth(
label=layers.one_hot(
input=label, depth=trg_vocab_size),
epsilon=label_smooth_eps)
cost = layers.softmax_with_cross_entropy(
logits=predict,
label=label,
soft_label=True if label_smooth_eps else False)
weighted_cost = cost * weights
sum_cost = layers.reduce_sum(weighted_cost)
token_num = layers.reduce_sum(weights)
token_num.stop_gradient = True
avg_cost = sum_cost / token_num
return sum_cost, avg_cost, predict, token_num, reader if use_py_reader else None
def wrap_encoder_forFeature(src_vocab_size,
max_length,
n_layer,
......@@ -662,44 +414,8 @@ def wrap_encoder_forFeature(src_vocab_size,
img
"""
if enc_inputs is None:
# This is used to implement independent encoder program in inference.
conv_features, src_pos, src_slf_attn_bias = make_all_inputs(
encoder_data_input_fields)
else:
conv_features, src_pos, src_slf_attn_bias = enc_inputs #
b, t, c = conv_features.shape
#"""
# insert cnn
#"""
#import basemodel
# feat = basemodel.resnet_50(img)
# mycrnn = basemodel.CRNN()
# feat = mycrnn.ocr_convs(img,use_cudnn=TrainTaskConfig.use_gpu)
# b, c, w, h = feat.shape
# src_word = layers.reshape(feat, shape=[-1, c, w * h])
#myconv8 = basemodel.conv8()
#feat = myconv8.net(img )
#b , c, h, w = feat.shape#h=6
#print(feat)
#layers.Print(feat,message="conv_feat",summarize=10)
#feat =layers.conv2d(feat,c,filter_size =[4 , 1],act="relu")
#feat = layers.pool2d(feat,pool_stride=(3,1),pool_size=(3,1))
#src_word = layers.squeeze(feat,axes=[2]) #src_word [-1,c,ww]
#feat = layers.transpose(feat, [0,3,1,2])
#src_word = layers.reshape(feat,[-1,w, c*h])
#src_word = layers.im2sequence(
# input=feat,
# stride=[1, 1],
# filter_size=[feat.shape[2], 1])
#layers.Print(src_word,message="src_word",summarize=10)
# print('feat',feat)
#print("src_word",src_word)
conv_features, src_pos, src_slf_attn_bias = enc_inputs #
b, t, c = conv_features.shape
enc_input = prepare_encoder(
conv_features,
......@@ -755,37 +471,7 @@ def wrap_encoder(src_vocab_size,
encoder_data_input_fields)
else:
src_word, src_pos, src_slf_attn_bias = enc_inputs #
#"""
# insert cnn
#"""
#import basemodel
# feat = basemodel.resnet_50(img)
# mycrnn = basemodel.CRNN()
# feat = mycrnn.ocr_convs(img,use_cudnn=TrainTaskConfig.use_gpu)
# b, c, w, h = feat.shape
# src_word = layers.reshape(feat, shape=[-1, c, w * h])
#myconv8 = basemodel.conv8()
#feat = myconv8.net(img )
#b , c, h, w = feat.shape#h=6
#print(feat)
#layers.Print(feat,message="conv_feat",summarize=10)
#feat =layers.conv2d(feat,c,filter_size =[4 , 1],act="relu")
#feat = layers.pool2d(feat,pool_stride=(3,1),pool_size=(3,1))
#src_word = layers.squeeze(feat,axes=[2]) #src_word [-1,c,ww]
#feat = layers.transpose(feat, [0,3,1,2])
#src_word = layers.reshape(feat,[-1,w, c*h])
#src_word = layers.im2sequence(
# input=feat,
# stride=[1, 1],
# filter_size=[feat.shape[2], 1])
#layers.Print(src_word,message="src_word",summarize=10)
# print('feat',feat)
#print("src_word",src_word)
enc_input = prepare_decoder(
src_word,
src_pos,
......@@ -811,248 +497,3 @@ def wrap_encoder(src_vocab_size,
preprocess_cmd,
postprocess_cmd, )
return enc_output
def wrap_decoder(trg_vocab_size,
max_length,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
weight_sharing,
dec_inputs=None,
enc_output=None,
caches=None,
gather_idx=None,
bos_idx=0):
"""
The wrapper assembles together all needed layers for the decoder.
"""
if dec_inputs is None:
# This is used to implement independent decoder program in inference.
trg_word, trg_pos, trg_slf_attn_bias, trg_src_attn_bias, enc_output = \
make_all_inputs(decoder_data_input_fields)
else:
trg_word, trg_pos, trg_slf_attn_bias, trg_src_attn_bias = dec_inputs
dec_input = prepare_decoder(
trg_word,
trg_pos,
trg_vocab_size,
d_model,
max_length,
prepostprocess_dropout,
bos_idx=bos_idx,
word_emb_param_name="src_word_emb_table"
if weight_sharing else "trg_word_emb_table")
dec_output = decoder(
dec_input,
enc_output,
trg_slf_attn_bias,
trg_src_attn_bias,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
caches=caches,
gather_idx=gather_idx)
return dec_output
# Reshape to 2D tensor to use GEMM instead of BatchedGEMM
dec_output = layers.reshape(
dec_output, shape=[-1, dec_output.shape[-1]], inplace=True)
if weight_sharing:
predict = layers.matmul(
x=dec_output,
y=fluid.default_main_program().global_block().var(
"trg_word_emb_table"),
transpose_y=True)
else:
predict = layers.fc(input=dec_output,
size=trg_vocab_size,
bias_attr=False)
if dec_inputs is None:
# Return probs for independent decoder program.
predict = layers.softmax(predict)
return predict
def fast_decode(src_vocab_size,
trg_vocab_size,
max_in_len,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
weight_sharing,
beam_size,
max_out_len,
bos_idx,
eos_idx,
use_py_reader=False):
"""
Use beam search to decode. Caches will be used to store states of history
steps which can make the decoding faster.
"""
data_input_names = encoder_data_input_fields + fast_decoder_data_input_fields
if use_py_reader:
all_inputs, reader = make_all_py_reader_inputs(data_input_names)
else:
all_inputs = make_all_inputs(data_input_names)
enc_inputs_len = len(encoder_data_input_fields)
dec_inputs_len = len(fast_decoder_data_input_fields)
enc_inputs = all_inputs[0:enc_inputs_len] #enc_inputs tensor
dec_inputs = all_inputs[enc_inputs_len:enc_inputs_len +
dec_inputs_len] #dec_inputs tensor
enc_output = wrap_encoder(
src_vocab_size,
64, ##to do !!!!!????
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
weight_sharing,
enc_inputs,
bos_idx=bos_idx)
start_tokens, init_scores, parent_idx, trg_src_attn_bias = dec_inputs
def beam_search():
max_len = layers.fill_constant(
shape=[1],
dtype=start_tokens.dtype,
value=max_out_len,
force_cpu=True)
step_idx = layers.fill_constant(
shape=[1], dtype=start_tokens.dtype, value=0, force_cpu=True)
cond = layers.less_than(x=step_idx, y=max_len) # default force_cpu=True
while_op = layers.While(cond)
# array states will be stored for each step.
ids = layers.array_write(
layers.reshape(start_tokens, (-1, 1)), step_idx)
scores = layers.array_write(init_scores, step_idx)
# cell states will be overwrited at each step.
# caches contains states of history steps in decoder self-attention
# and static encoder output projections in encoder-decoder attention
# to reduce redundant computation.
caches = [
{
"k": # for self attention
layers.fill_constant_batch_size_like(
input=start_tokens,
shape=[-1, n_head, 0, d_key],
dtype=enc_output.dtype,
value=0),
"v": # for self attention
layers.fill_constant_batch_size_like(
input=start_tokens,
shape=[-1, n_head, 0, d_value],
dtype=enc_output.dtype,
value=0),
"static_k": # for encoder-decoder attention
layers.create_tensor(dtype=enc_output.dtype),
"static_v": # for encoder-decoder attention
layers.create_tensor(dtype=enc_output.dtype)
} for i in range(n_layer)
]
with while_op.block():
pre_ids = layers.array_read(array=ids, i=step_idx)
# Since beam_search_op dosen't enforce pre_ids' shape, we can do
# inplace reshape here which actually change the shape of pre_ids.
pre_ids = layers.reshape(pre_ids, (-1, 1, 1), inplace=True)
pre_scores = layers.array_read(array=scores, i=step_idx)
# gather cell states corresponding to selected parent
pre_src_attn_bias = layers.gather(
trg_src_attn_bias, index=parent_idx)
pre_pos = layers.elementwise_mul(
x=layers.fill_constant_batch_size_like(
input=pre_src_attn_bias, # cann't use lod tensor here
value=1,
shape=[-1, 1, 1],
dtype=pre_ids.dtype),
y=step_idx,
axis=0)
logits = wrap_decoder(
trg_vocab_size,
max_in_len,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
weight_sharing,
dec_inputs=(pre_ids, pre_pos, None, pre_src_attn_bias),
enc_output=enc_output,
caches=caches,
gather_idx=parent_idx,
bos_idx=bos_idx)
# intra-beam topK
topk_scores, topk_indices = layers.topk(
input=layers.softmax(logits), k=beam_size)
accu_scores = layers.elementwise_add(
x=layers.log(topk_scores), y=pre_scores, axis=0)
# beam_search op uses lod to differentiate branches.
accu_scores = layers.lod_reset(accu_scores, pre_ids)
# topK reduction across beams, also contain special handle of
# end beams and end sentences(batch reduction)
selected_ids, selected_scores, gather_idx = layers.beam_search(
pre_ids=pre_ids,
pre_scores=pre_scores,
ids=topk_indices,
scores=accu_scores,
beam_size=beam_size,
end_id=eos_idx,
return_parent_idx=True)
layers.increment(x=step_idx, value=1.0, in_place=True)
# cell states(caches) have been updated in wrap_decoder,
# only need to update beam search states here.
layers.array_write(selected_ids, i=step_idx, array=ids)
layers.array_write(selected_scores, i=step_idx, array=scores)
layers.assign(gather_idx, parent_idx)
layers.assign(pre_src_attn_bias, trg_src_attn_bias)
length_cond = layers.less_than(x=step_idx, y=max_len)
finish_cond = layers.logical_not(layers.is_empty(x=selected_ids))
layers.logical_and(x=length_cond, y=finish_cond, out=cond)
finished_ids, finished_scores = layers.beam_search_decode(
ids, scores, beam_size=beam_size, end_id=eos_idx)
return finished_ids, finished_scores
finished_ids, finished_scores = beam_search()
return finished_ids, finished_scores, reader if use_py_reader else None
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