def train():
paddle.set_device("gpu" if args.n_gpu else "cpu")
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
model = ErnieForGeneration.from_pretrained(args.model_name_or_path)
if "ernie-tiny" in args.model_name_or_path:
tokenizer = ErnieTinyTokenizer.from_pretrained(args.model_name_or_path)
elif "ernie" in args.model_name_or_path:
tokenizer = ErnieTokenizer.from_pretrained(args.model_name_or_path)
elif "roberta" in args.model_name_or_path or "rbt" in args.model_name_or_path:
tokenizer = RobertaTokenizer.from_pretrained(args.model_name_or_path)
elif "electra" in args.model_name_or_path:
tokenizer = ElectraTokenizer.from_pretrained(args.model_name_or_path)
else:
tokenizer = BertTokenizer.from_pretrained(args.model_name_or_path)
if args.init_checkpoint:
model_state = paddle.load(args.init_checkpoint)
model.set_state_dict(model_state)
train_dataset, dev_dataset = Poetry.get_datasets(['train', 'dev'])
attn_id = tokenizer.vocab[
'[ATTN]'] if '[ATTN]' in tokenizer.vocab else tokenizer.vocab['[MASK]']
tgt_type_id = model.sent_emb.weight.shape[0] - 1
trans_func = convert_example(tokenizer=tokenizer,
attn_id=attn_id,
tgt_type_id=tgt_type_id,
max_encode_len=args.max_encode_len,
max_decode_len=args.max_decode_len,
noise_prob=args.noise_prob,
use_random_noice=args.use_random_noice)
train_dataset = train_dataset.apply(trans_func, lazy=True)
train_batch_sampler = paddle.io.DistributedBatchSampler(
train_dataset, batch_size=args.batch_size, shuffle=True)
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id), # src_ids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # src_pids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # src_sids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # tgt_ids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # tgt_pids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # tgt_sids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # attn_ids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # tgt_labels
): after_padding(fn(samples))
train_data_loader = DataLoader(dataset=train_dataset,
batch_sampler=train_batch_sampler,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
dev_dataset = dev_dataset.apply(trans_func, lazy=True)
dev_batch_sampler = paddle.io.BatchSampler(dev_dataset,
batch_size=args.batch_size,
shuffle=False)
dev_data_loader = DataLoader(dataset=dev_dataset,
batch_sampler=dev_batch_sampler,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
label_num = model.word_emb.weight.shape[0]
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
max_steps = (len(train_data_loader) * args.num_epochs)
lr_scheduler = paddle.optimizer.lr.LambdaDecay(
args.learning_rate,
lambda current_step, num_warmup_steps=max_steps * args.
warmup_proportion, num_training_steps=max_steps: float(
current_step) / float(max(1, num_warmup_steps))
if current_step < num_warmup_steps else max(
0.0,
float(num_training_steps - current_step) / float(
max(1, num_training_steps - num_warmup_steps))))
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler,
epsilon=args.adam_epsilon,
parameters=model.parameters(),
weight_decay=args.weight_decay,
grad_clip=nn.ClipGradByGlobalNorm(1.0),
apply_decay_param_fun=lambda x: x in [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
])
rouge1 = Rouge1()
rouge2 = Rouge2()
global_step = 1
tic_train = time.time()
for epoch in range(args.num_epochs):
for step, batch in enumerate(train_data_loader, start=1):
(src_ids, src_sids, src_pids, tgt_ids, tgt_sids, tgt_pids,
attn_ids, mask_src_2_src, mask_tgt_2_srctgt,
mask_attn_2_srctgtattn, tgt_labels, _) = batch
# import pdb; pdb.set_trace()
_, __, info = model(src_ids,
sent_ids=src_sids,
pos_ids=src_pids,
attn_bias=mask_src_2_src,
encode_only=True)
cached_k, cached_v = info['caches']
_, __, info = model(tgt_ids,
sent_ids=tgt_sids,
pos_ids=tgt_pids,
attn_bias=mask_tgt_2_srctgt,
past_cache=(cached_k, cached_v),
encode_only=True)
cached_k2, cached_v2 = info['caches']
past_cache_k = [
paddle.concat([k, k2], 1)
for k, k2 in zip(cached_k, cached_k2)
]
past_cache_v = [
paddle.concat([v, v2], 1)
for v, v2 in zip(cached_v, cached_v2)
]
if args.label_smooth > 0.:
tgt_labels = nn.functional.label_smooth(
nn.functional.one_hot(tgt_labels, label_num),
epsilon=args.label_smooth)
loss, _, __ = model(attn_ids,
sent_ids=tgt_sids,
pos_ids=tgt_pids,
attn_bias=mask_attn_2_srctgtattn,
past_cache=(past_cache_k, past_cache_v),
tgt_labels=tgt_labels,
tgt_pos=paddle.nonzero(attn_ids == attn_id))
if global_step % args.logging_steps == 0:
if (not args.n_gpu > 1) or paddle.distributed.get_rank() == 0:
logger.info(
"global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s, lr: %.3e"
% (global_step, epoch, step, loss, args.logging_steps /
(time.time() - tic_train), lr_scheduler.get_lr()))
tic_train = time.time()
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.clear_gradients()
if global_step % args.save_steps == 0 and (
(not args.n_gpu > 1) or paddle.distributed.get_rank() == 0):
evaluate(model, dev_data_loader, tokenizer, rouge1, rouge2,
attn_id, tgt_type_id, args)
output_dir = os.path.join(args.output_dir,
"model_%d" % global_step)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
global_step += 1
def forward(self,
input_ids=None,
token_type_ids=None,
position_ids=None,
attention_mask=None,
query_input_ids=None,
query_token_type_ids=None,
query_position_ids=None,
query_attention_mask=None,
title_input_ids=None,
title_token_type_ids=None,
title_position_ids=None,
title_attention_mask=None,
seq_lengths=None,
labels=None):
if self.task != 'text-matching':
result = self.model(input_ids, token_type_ids, position_ids,
attention_mask)
else:
query_result = self.model(query_input_ids, query_token_type_ids,
query_position_ids, query_attention_mask)
title_result = self.model(title_input_ids, title_token_type_ids,
title_position_ids, title_attention_mask)
if self.task == 'seq-cls':
logits = result
probs = F.softmax(logits, axis=1)
if labels is not None:
loss = self.criterion(logits, labels)
correct = self.metric.compute(probs, labels)
acc = self.metric.update(correct)
return probs, loss, {'acc': acc}
return probs
elif self.task == 'token-cls':
logits = result
token_level_probs = F.softmax(logits, axis=-1)
preds = token_level_probs.argmax(axis=-1)
if labels is not None:
loss = self.criterion(logits, labels.unsqueeze(-1))
num_infer_chunks, num_label_chunks, num_correct_chunks = \
self.metric.compute(None, seq_lengths, preds, labels)
self.metric.update(num_infer_chunks.numpy(),
num_label_chunks.numpy(),
num_correct_chunks.numpy())
_, _, f1_score = map(float, self.metric.accumulate())
return token_level_probs, loss, {'f1_score': f1_score}
return token_level_probs
elif self.task == 'text-matching':
query_token_embedding = query_result
query_token_embedding = self.dropout(query_token_embedding)
query_attention_mask = paddle.unsqueeze(
(query_input_ids != self.model.pad_token_id).astype(
query_token_embedding.dtype),
axis=2)
query_token_embedding = query_token_embedding * query_attention_mask
query_sum_embedding = paddle.sum(query_token_embedding, axis=1)
query_sum_mask = paddle.sum(query_attention_mask, axis=1)
query_mean = query_sum_embedding / query_sum_mask
title_token_embedding = title_result
title_token_embedding = self.dropout(title_token_embedding)
title_attention_mask = paddle.unsqueeze(
(title_input_ids != self.model.pad_token_id).astype(
title_token_embedding.dtype),
axis=2)
title_token_embedding = title_token_embedding * title_attention_mask
title_sum_embedding = paddle.sum(title_token_embedding, axis=1)
title_sum_mask = paddle.sum(title_attention_mask, axis=1)
title_mean = title_sum_embedding / title_sum_mask
sub = paddle.abs(paddle.subtract(query_mean, title_mean))
projection = paddle.concat([query_mean, title_mean, sub], axis=-1)
logits = self.classifier(projection)
probs = F.softmax(logits)
if labels is not None:
loss = self.criterion(logits, labels)
correct = self.metric.compute(probs, labels)
acc = self.metric.update(correct)
return probs, loss, {'acc': acc}
return probs
else:
sequence_output, pooled_output = result
return sequence_output, pooled_output
def test_quant_concat(self):
out_1 = paddle.concat([self.x, self.y], axis=0)
out_2 = paddle.nn.quant.concat()([self.x, self.y], 0)
self.check(out_1, out_2)
self.assertTrue(out_1.shape == out_2.shape)
def sample(self,
input_ids,
logits_processors,
max_length,
pad_token_id,
eos_token_id,
top_k=None,
top_p=None,
temperature=None,
min_tokens_to_keep=1,
**model_kwargs):
def TopKProcess(probs, top_k, min_tokens_to_keep):
top_k = min(max(top_k, min_tokens_to_keep), probs.shape[-1])
# Remove all tokens with a probability less than the last token of the top-k
topk_probs, _ = paddle.topk(probs, k=top_k)
probs = paddle.where(probs >= topk_probs[:, -1:], probs,
paddle.full_like(probs, 0.0))
return probs
def TopPProcess(probs, top_p, min_tokens_to_keep):
sorted_probs = paddle.sort(probs, descending=True)
sorted_indices = paddle.argsort(probs, descending=True)
cumulative_probs = paddle.cumsum(sorted_probs, axis=-1)
# Remove tokens with cumulative probs above the top_p, But keep at
# least min_tokens_to_keep tokens
sorted_indices_to_remove = cumulative_probs > top_p
if min_tokens_to_keep > 1:
# Set 'min_tokens_to_keep - 1' because the first token is kept
sorted_indices_to_remove[:, :min_tokens_to_keep - 1] = 0
# Keep the first token
sorted_indices_to_remove = paddle.cast(
sorted_indices_to_remove, dtype='int64')
sorted_indices_to_remove[:, 1:] = (
sorted_indices_to_remove[:, :-1].clone())
sorted_indices_to_remove[:, 0] = 0
# Scatter sorted tensors to original indexing
sorted_indices = sorted_indices + paddle.arange(probs.shape[
0]).unsqueeze(-1) * probs.shape[-1]
condition = paddle.scatter(sorted_indices_to_remove.flatten(),
sorted_indices.flatten(),
sorted_indices_to_remove.flatten())
condition = paddle.cast(condition, 'bool').reshape(probs.shape)
probs = paddle.where(condition, paddle.full_like(probs, 0.0), probs)
return probs
batch_size, cur_len = input_ids.shape
origin_len = cur_len
unfinished_flag = paddle.full([batch_size, 1], True, dtype='bool')
scores = paddle.full(
[batch_size, 1], 0.0, dtype=paddle.get_default_dtype())
while cur_len < max_length:
# prepare model inputs & get model output
model_inputs = self.prepare_inputs_for_generation(input_ids,
**model_kwargs)
outputs = self(**model_inputs)
logits = outputs[0] if isinstance(outputs, tuple) else outputs
# [batch_size, vocab_size]
logits = logits[:, -1, :]
# pre-process distribution
logits = self.adjust_logits_during_generation(logits)
logits = logits_processors(input_ids, logits)
# sample
origin_probs = F.softmax(logits)
origin_probs = paddle.log(origin_probs)
if temperature is not None and temperature != 1.0:
logits = logits / temperature
probs = F.softmax(logits)
if top_k is not None and top_k != 0:
probs = TopKProcess(probs, top_k, min_tokens_to_keep)
if top_p is not None and top_p < 1.0:
probs = TopPProcess(probs, top_p, min_tokens_to_keep)
next_tokens = paddle.multinomial(probs)
next_scores = paddle.index_sample(origin_probs, next_tokens)
if eos_token_id is not None:
next_tokens = paddle.where(unfinished_flag, next_tokens,
paddle.full_like(next_tokens,
pad_token_id))
scores = self.update_scores_for_generation(
scores, next_scores, cur_len - origin_len, unfinished_flag)
cur_len += 1
input_ids = paddle.concat([input_ids, next_tokens], axis=1)
if eos_token_id is not None:
unfinished_flag = paddle.logical_and(
unfinished_flag, next_tokens != eos_token_id)
# Stop when there is a </s> in all sentences
if not paddle.any(unfinished_flag):
break
model_kwargs = self.update_model_kwargs_for_generation(outputs,
model_kwargs)
return input_ids[:, origin_len:], scores
def __call__(self,
seg_preds,
seg_masks,
cate_labels,
cate_scores,
sum_masks=None):
# sort and keep top nms_pre
sort_inds = self._sort_score(cate_scores, self.pre_nms_top_n)
seg_masks = paddle.gather(seg_masks, index=sort_inds)
seg_preds = paddle.gather(seg_preds, index=sort_inds)
sum_masks = paddle.gather(sum_masks, index=sort_inds)
cate_scores = paddle.gather(cate_scores, index=sort_inds)
cate_labels = paddle.gather(cate_labels, index=sort_inds)
seg_masks = paddle.flatten(seg_masks, start_axis=1, stop_axis=-1)
# inter.
inter_matrix = paddle.mm(seg_masks,
paddle.transpose(seg_masks, [1, 0]))
n_samples = paddle.shape(cate_labels)
# union.
sum_masks_x = paddle.expand(sum_masks, shape=[n_samples, n_samples])
# iou.
iou_matrix = (inter_matrix /
(sum_masks_x + paddle.transpose(sum_masks_x, [1, 0]) -
inter_matrix))
iou_matrix = paddle.triu(iou_matrix, diagonal=1)
# label_specific matrix.
cate_labels_x = paddle.expand(cate_labels,
shape=[n_samples, n_samples])
label_matrix = paddle.cast(
(cate_labels_x == paddle.transpose(cate_labels_x, [1, 0])),
'float32')
label_matrix = paddle.triu(label_matrix, diagonal=1)
# IoU compensation
compensate_iou = paddle.max((iou_matrix * label_matrix), axis=0)
compensate_iou = paddle.expand(compensate_iou,
shape=[n_samples, n_samples])
compensate_iou = paddle.transpose(compensate_iou, [1, 0])
# IoU decay
decay_iou = iou_matrix * label_matrix
# matrix nms
if self.kernel == 'gaussian':
decay_matrix = paddle.exp(-1 * self.sigma * (decay_iou**2))
compensate_matrix = paddle.exp(-1 * self.sigma *
(compensate_iou**2))
decay_coefficient = paddle.min(decay_matrix / compensate_matrix,
axis=0)
elif self.kernel == 'linear':
decay_matrix = (1 - decay_iou) / (1 - compensate_iou)
decay_coefficient = paddle.min(decay_matrix, axis=0)
else:
raise NotImplementedError
# update the score.
cate_scores = cate_scores * decay_coefficient
y = paddle.zeros(shape=paddle.shape(cate_scores), dtype='float32')
keep = paddle.where(cate_scores >= self.update_threshold, cate_scores,
y)
keep = paddle.nonzero(keep)
keep = paddle.squeeze(keep, axis=[1])
# Prevent empty and increase fake data
keep = paddle.concat(
[keep,
paddle.cast(paddle.shape(cate_scores)[0] - 1, 'int64')])
seg_preds = paddle.gather(seg_preds, index=keep)
cate_scores = paddle.gather(cate_scores, index=keep)
cate_labels = paddle.gather(cate_labels, index=keep)
# sort and keep top_k
sort_inds = self._sort_score(cate_scores, self.post_nms_top_n)
seg_preds = paddle.gather(seg_preds, index=sort_inds)
cate_scores = paddle.gather(cate_scores, index=sort_inds)
cate_labels = paddle.gather(cate_labels, index=sort_inds)
return seg_preds, cate_scores, cate_labels
def forward(self, pixels, regions):
context = self.attention_block(pixels, regions)
feats = paddle.concat([context, pixels], axis=1)
feats = self.conv1x1(feats)
return feats
def concat_unsqueeze2(inputs):
a = (inputs[:, :, 0] + 2) * 7
b = (inputs[:, :, 1] + 3) * 11
c = (inputs[:, :, 2] + 5) * 13
return paddle.concat([paddle.unsqueeze(t, axis=2) for t in [a, b, c]],
axis=2)
def to_tensor(self):
return paddle.concat([self.quaternion, self.translation], axis=-1)
def forward(self, x, fusions):
r_f = paddle.concat([x, fusions], axis=2)
r = F.tanh(self.linear_r(r_f))
g = F.sigmoid(self.linear_g(r_f))
o = g * r + (1-g) * x
return o
def forward(self, indices, segments, positions, input_mask):
r'''
The BertModel forward method, overrides the `__call__()` special method.
Args:
indices (Tensor):
Indices of input sequence tokens in the vocabulary. They are
numerical representations of tokens that build the input sequence.
Its data type should be `int32` and it has a shape of [batch_size * sequence_length].
segments (Tensor):
Segment token indices to indicate different portions of the inputs.
Selected in the range ``[0, type_vocab_size - 1]``.
Its data type should be `int32` and it has a shape of [batch_size * sequence_length].
positions(Tensor):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
max_position_embeddings - 1]``.
Shape as `[batch_size * sequence_length]` and dtype as int32.
input_mask (Tensor, optional):
Mask used in multi-head attention to avoid performing attention on to some unwanted positions,
usually the paddings or the subsequent positions.
If the task is PRETRAINING:
input_mask[0] is the index that masking starts in the mask_tokens
input_mask[1] is the index that masking starts in the rest of the sequence
Otherwise
input_mask is the mask tensor that has -1000 in positions to be masked and 0 otherwise.
Returns:
tuple: Returns tuple (`sequence_output`, `word_embeddings_weights`).
With the fields:
- `sequence_output` (Tensor):
Sequence of hidden-states at the last layer of the model.
It's data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].
'''
with self.config.embeddings_scope:
sequence_output, word_embeddings_weights = self.embedding(
indices, segments, positions)
if self.config.task == "PRETRAINING":
with paddle.static.ipu_shard_guard(index=0, stage=0):
input_mask[0] = self.custom_ops.detach(input_mask[0])
input_mask[1] = self.custom_ops.detach(input_mask[1])
for i in range(self.config.num_hidden_layers):
# Attention
attn_scope = self.config.attn_scopes[i]
with attn_scope:
with paddle.static.name_scope(f"Layer{i}/Attention"):
layer_input = sequence_output
q = self.create_parameter(shape=[
self.config.hidden_size, self.config.hidden_size
],
dtype="float32")
k = self.create_parameter(shape=[
self.config.hidden_size, self.config.hidden_size
],
dtype="float32")
v = self.create_parameter(shape=[
self.config.hidden_size, self.config.hidden_size
],
dtype="float32")
qkv = paddle.concat([q, k, v], axis=1)
qkv = paddle.matmul(sequence_output, qkv)
qkv.block.ops[-1]._set_attr(
'__available_memory',
self.config.available_mem_proportion)
q, k, v = paddle.split(qkv,
num_or_sections=[
self.config.hidden_size,
self.config.hidden_size,
self.config.hidden_size
],
axis=1)
q = paddle.reshape(q, self.qkv_shape)
q = paddle.transpose(q, [0, 2, 1, 3])
k = paddle.reshape(k, self.qkv_shape)
k = paddle.transpose(k, [0, 2, 3, 1])
v = paddle.reshape(v, self.qkv_shape)
v = paddle.transpose(v, [0, 2, 1, 3])
# Attention calculation
with paddle.static.name_scope(f"Z"):
if self.config.task == "PRETRAINING":
if attn_scope.index in self.masks:
final_mask = self.masks[attn_scope.index]
else:
with paddle.static.name_scope("Mask"):
base_value = np.arange(
self.config.seq_len).astype('int32')
base = paddle.fluid.layers.assign(
base_value)
mmask = paddle.less_than(
base, input_mask[0])
mask_value = np.greater_equal(
base_value,
self.config.max_predictions_per_seq)
mask = paddle.fluid.layers.assign(
mask_value)
mmask = paddle.logical_or(mmask, mask)
smask = paddle.less_than(
base, input_mask[1])
final_mask = paddle.logical_and(
mmask, smask)
final_mask = paddle.cast(
final_mask, "float16")
sub_attrs = {
'name': 'constant_sub',
'shape': [1],
'dtype': 'float32',
'value': 1,
}
mul_attrs = {
'name': 'constant_mul',
'shape': [1],
'dtype': 'float32',
'value': 1000,
}
final_mask = paddle.fluid.layers.elementwise_sub(
final_mask,
paddle.fluid.layers.fill_constant(
**sub_attrs))
final_mask = paddle.fluid.layers.elementwise_mul(
final_mask,
paddle.fluid.layers.fill_constant(
**mul_attrs))
final_mask = paddle.reshape(
final_mask,
[-1, 1, 1, self.config.seq_len])
final_mask = self.custom_ops.detach(
final_mask)
self.masks[attn_scope.index] = final_mask
qk = paddle.matmul(q, k)
qk.block.ops[-1]._set_attr(
'__available_memory',
self.config.available_mem_proportion)
qk_scale = paddle.fluid.layers.fill_constant(
**self.qk_scale_attrs)
qk = paddle.fluid.layers.elementwise_mul(qk, qk_scale)
if self.config.task == "PRETRAINING":
qk = paddle.fluid.layers.elementwise_add(
qk, final_mask)
else:
# for SQUAD task, input_mask is calculated in data preprocessing
qk = paddle.fluid.layers.elementwise_add(
qk, input_mask)
qk = paddle.fluid.layers.softmax(qk)
if self.config.task == "SQUAD":
qk = paddle.fluid.layers.dropout(
qk,
self.config.attention_probs_dropout_prob,
dropout_implementation='upscale_in_train')
qkv = paddle.matmul(qk, v)
qkv.block.ops[-1]._set_attr(
'__available_memory',
self.config.available_mem_proportion)
qkv = paddle.transpose(qkv, [0, 2, 1, 3])
qkv = paddle.reshape(qkv,
[-1, self.config.hidden_size])
qkv_linear = nn.Linear(self.config.hidden_size,
self.config.hidden_size,
bias_attr=False)
qkv = qkv_linear(qkv)
qkv.block.ops[-1]._set_attr(
'__available_memory',
self.config.available_mem_proportion)
qkv = paddle.fluid.layers.dropout(
qkv,
self.config.attention_probs_dropout_prob,
dropout_implementation='upscale_in_train')
attention = paddle.add(layer_input, qkv)
layer_norm1 = nn.LayerNorm(self.config.hidden_size,
epsilon=0.001)
attention = layer_norm1(attention)
# FF
with self.config.ff_scopes[i]:
with paddle.static.name_scope(f"Layer{i}/FF"):
ff_linear1 = nn.Linear(self.config.hidden_size,
4 * self.config.hidden_size)
ff_linear2 = nn.Linear(4 * self.config.hidden_size,
self.config.hidden_size)
with paddle.static.name_scope(f"1"):
ff = ff_linear1(attention)
ff.block.ops[-2]._set_attr(
'__available_memory',
self.config.available_mem_proportion)
ff = paddle.fluid.layers.gelu(ff, approximate=True)
with paddle.static.name_scope(f"2"):
ff = ff_linear2(ff)
ff.block.ops[-2]._set_attr(
'__available_memory',
self.config.available_mem_proportion)
ff = paddle.fluid.layers.dropout(
ff,
self.config.attention_probs_dropout_prob,
dropout_implementation='upscale_in_train')
ff = paddle.add(attention, ff)
layer_norm2 = nn.LayerNorm(self.config.hidden_size,
epsilon=0.001)
sequence_output = layer_norm2(ff)
if self.should_checkpoint(i):
with paddle.static.name_scope(f"Layer{i}"):
logging.info(f'add checkpointoutput for ff_{i}')
sequence_output = self.custom_ops.checkpointoutput(
sequence_output)
return sequence_output, word_embeddings_weights
def _encoder_preprocessor(self, position_sequence, n_node, global_context,
particle_types):
# Extract important features from the position_sequence.
most_recent_position = position_sequence[:, -1]
velocity_sequence = time_diff(position_sequence) # Finite-difference.
# Get connectivity of the graph.
(senders, receivers, n_edge
) = connectivity_utils_paddle.compute_connectivity_for_batch_pyfunc(
most_recent_position, n_node, self._connectivity_radius)
# Collect node features.
node_features = []
# node_feat总共包含以下几项：
# 1.flat_velocity_Sequence
# 2.distance_to_lower_boundary
# 3.distance_to_upper_boundary
# 4.particle_type_embeddings
# Normalized velocity sequence, merging spatial an time axis.
velocity_stats = self._normalization_stats["velocity"]
normalized_velocity_sequence = (
velocity_sequence - velocity_stats.mean) / velocity_stats.std
########################################################
flat_velocity_sequence = paddle.reshape(normalized_velocity_sequence,
(1444, 10))
node_features.append(flat_velocity_sequence)
# Normalized clipped distances to lower and upper boundaries.
# boundaries are an array of shape [num_dimensions, 2], where the second
# axis, provides the lower/upper boundaries.
boundaries = np.array(self._boundaries)
# x[:,n]就是取所有集合的第n个数据
distance_to_lower_boundary = most_recent_position - boundaries[:, 0]
distance_to_upper_boundary = boundaries[:, 1] - most_recent_position
distance_to_boundaries = paddle.concat( # 拼接张量操作
[distance_to_lower_boundary, distance_to_upper_boundary],
axis=1)
normalized_clipped_distance_to_boundaries = paddle.clip(
distance_to_boundaries / self._connectivity_radius, min=-1, max=1)
# 将距离控制在-1~1之间
node_features.append(normalized_clipped_distance_to_boundaries)
# Particle type.
# tf.nn.embedding_lookup查找数组中的序号为particle_types的元素
if self._num_particle_types > 1:
particle_type_embeddings = paddle.gather(
self._particle_type_embedding,
particle_types) # particle_types存储着标号
node_features.append(particle_type_embeddings)
# # Collect edge features.
edge_features = []
#1. normalized_realative_displacements = sender - receiver / radius
#2. normalized_relative_distances 向量/矩阵的范数
# Relative displacement and distances normalized to radius
normalized_relative_displacements = (
paddle.gather(most_recent_position, senders) - paddle.gather(
most_recent_position, receivers)) / self._connectivity_radius
# sender - receiver / radius
edge_features.append(normalized_relative_displacements)
normalized_relative_distances = paddle.norm(
normalized_relative_displacements, axis=-1, keepdim=True)
edge_features.append(normalized_relative_distances)
# Normalize the global context.
if global_context is not None:
context_stats = self._normalization_stats["context"]
# Context in some datasets are all zero, so add an epsilon for numerical
# stability.
global_context = (global_context -
context_stats.mean) / paddle.maximum(
context_stats.std, STD_EPSILON)
return pgl.Graph(
edges=n_edge,
num_nodes=n_node,
node_feat=paddle.concat(node_features, axis=-1),
edge_feat=paddle.concat(edge_features, axis=-1),
)
def forward(self,
query_matrix,
key_matrix,
value_matrix,
d_head,
attn_mask=None,
rand_mask_idx=None,
query_mask=None,
key_mask=None,
dropout=None):
'''
query_matrix: [B, H, T, D]
key_matrix: [B, H, T, D]
value_matrix: [B, H, T, D]
query_mask: [B, 1, T, 1] bool mask
key_mask: [B, 1, 1, T] bool mask
rand_mask_idx: [H, T//bs, bs]
Global Attention
Random Attention
Window Attention
'''
B = query_matrix.shape[0] # batch_size
H = self.num_heads
T = query_matrix.shape[2] # sequence_length
D = query_matrix.shape[3] # size per head
G = self.num_global_blocks
GB = self.num_global_blocks_back
GF = self.num_global_blocks_front
R = self.num_rand_blocks
W = self.window_size
bs = self.block_size
L = T // bs # blocked length
blocked_query_matrix = paddle.reshape(query_matrix, [B, H, L, bs, -1])
blocked_key_matrix = paddle.reshape(key_matrix, [B, H, L, bs, -1])
blocked_value_matrix = paddle.reshape(value_matrix, [B, H, L, bs, -1])
blocked_query_mask = paddle.reshape(query_mask, [B, L, bs])
blocked_key_mask = paddle.reshape(key_mask, [B, L, bs])
# 1. global_front_product
global_front_out = self._get_global_out(
query_matrix, key_matrix, value_matrix, key_mask, d_head, dropout)
# 2. global_back_product
global_back_out = self._get_global_out(query_matrix, key_matrix,
value_matrix, key_mask, d_head,
dropout, False)
# 3. second_product
# create second matrix
# [B, 1, L-G, bs, (G+W)*bs]
band_mask = self._get_band_mask(blocked_query_mask, blocked_key_mask, B,
T)
# [B, H, L-G, bs, R*bs]
rand_mask = self._get_rand_mask(blocked_query_mask, blocked_key_mask,
rand_mask_idx, B, T)
# [B, H, L-G, bs, (G+W+R)*bs]
second_mask = paddle.concat([band_mask, rand_mask], axis=4)
# [B, H, L-G, R * bs, -1]
random_keys = self._gather_random_key_value(blocked_key_matrix,
rand_mask_idx, B, T)
random_values = self._gather_random_key_value(blocked_value_matrix,
rand_mask_idx, B, T)
band_keys_matrix = self._get_band_matrix(blocked_key_matrix, B, T)
band_value_matrix = self._get_band_matrix(blocked_value_matrix, B, T)
# [B, H, L - G, bs, -1]
second_query_matrix = blocked_query_matrix[:, :, GF:-GB]
# [B, H, L - G, (G+W+R)*bs, -1]
second_key_matrix = paddle.concat(
[band_keys_matrix, random_keys], axis=3)
# [B, H, L - G, (G+W+R)*bs, -1]
second_value_matrix = paddle.concat(
[band_value_matrix, random_values], axis=3)
second_top_value_matrix, second_middle_value_matrix, second_bottom_value_matrix = \
self._get_splited_matrix(second_value_matrix)
second_product = paddle.matmul(
second_query_matrix, second_key_matrix, transpose_y=True)
second_product = second_product * (d_head**-0.5)
second_product += (1 - second_mask) * -1e6
second_weights = F.softmax(second_product)
second_top_weights, second_middle_weights, second_bottom_weights = \
self._get_splited_matrix(second_weights)
second_top_out = paddle.matmul(second_top_weights,
second_top_value_matrix)
second_middle_out = paddle.matmul(
second_middle_weights[:, :, :, :, GF * bs:-(GB + R) * bs],
second_middle_value_matrix[:, :, :, GF * bs:-(GB + R) * bs])
# add global block attention
second_middle_out += paddle.matmul(
second_middle_weights[:, :, :, :, :GF * bs],
blocked_value_matrix[:, :, 0:GF])
second_middle_out += paddle.matmul(
second_middle_weights[:, :, :, :, -(GB + R) * bs:-R * bs],
blocked_value_matrix[:, :, -GB:])
# add random block attention
second_middle_out += paddle.matmul(
second_middle_weights[:, :, :, :, -R * bs:],
random_values[:, :, GF:-GB])
second_bottom_out = paddle.matmul(second_bottom_weights,
second_bottom_value_matrix)
second_out = paddle.concat(
[second_top_out, second_middle_out, second_bottom_out], axis=2)
second_out = paddle.reshape(second_out, [B, H, (L - G) * bs, -1])
# [B, H, T, D]
out = paddle.concat(
[global_front_out, second_out, global_back_out], axis=2)
out = out * query_mask
return out
def _get_band_matrix(self, blocked_matrix, B, T):
'''
return global and window matrix: [B, H, L-G, (G+W) * bs, -1]
'''
# blocked_matrix: [B, H, L, bs, -1]
GB = self.num_global_blocks_back
GF = self.num_global_blocks_front
G = self.num_global_blocks
R = self.num_rand_blocks
W = self.window_size
bs = self.block_size
L = T // bs # blocked length
H = self.num_heads
# get roll matrix
blocked_list = []
for query_block_id in range(GF, GF + W // 2):
left_block_id = query_block_id - W // 2
right_block_id = query_block_id + W // 2
temp_blocked_matrix_list = [
blocked_matrix[:, :, 0:(right_block_id + 1)],
blocked_matrix[:, :, -(G + W - right_block_id - 1):]
]
temp_blocked_matrix = paddle.concat(
temp_blocked_matrix_list, axis=2)
temp_blocked_matrix = paddle.unsqueeze(temp_blocked_matrix, axis=2)
blocked_list.append(temp_blocked_matrix)
# get window matrix
band_length = L - G - W // 2 * 2
band_matrix_list = []
for query_block_id in range(GF + W // 2, GF + W // 2 + W):
left_block_id = query_block_id - W // 2
right_block_id = query_block_id + W // 2
band_matrix_list.append(
paddle.unsqueeze(
blocked_matrix[:, :, left_block_id:left_block_id +
band_length],
axis=3))
band_matrix = paddle.concat(band_matrix_list, axis=3)
global_blocked_front_matrix = paddle.unsqueeze(
blocked_matrix[:, :, :GF], axis=2)
global_blocked_front_matrix = paddle.expand(
global_blocked_front_matrix, [B, H, band_length, GF, bs, -1])
global_blocked_back_matrix = paddle.unsqueeze(
blocked_matrix[:, :, -GB:], axis=2)
global_blocked_back_matrix = paddle.expand(
global_blocked_back_matrix, [B, H, band_length, GB, bs, -1])
band_matrix = paddle.concat(
[
global_blocked_front_matrix, band_matrix,
global_blocked_back_matrix
],
axis=3)
blocked_list.append(band_matrix)
for query_block_id in range(L - GB - W // 2, L - GB):
left_block_id = query_block_id - W // 2
right_block_id = query_block_id + W // 2
temp_blocked_matrix_list = [
blocked_matrix[:, :, 0:G + W - (L - left_block_id)],
blocked_matrix[:, :, left_block_id:]
]
temp_blocked_matrix = paddle.concat(
temp_blocked_matrix_list, axis=2)
temp_blocked_matrix = paddle.unsqueeze(temp_blocked_matrix, axis=2)
blocked_list.append(temp_blocked_matrix)
band_matrix = paddle.concat(blocked_list, axis=2)
band_matrix = paddle.reshape(band_matrix,
[B, H, L - G, (G + W) * bs, -1])
return band_matrix
def _get_band_mask(self, blocked_query_mask, blocked_key_mask, batch_size,
sequence_length):
'''
Return second mask: [B, 1, L-G, bs, G+W]
'''
GB = self.num_global_blocks_back
GF = self.num_global_blocks_front
G = self.num_global_blocks
R = self.num_rand_blocks
W = self.window_size
bs = self.block_size
T = sequence_length
L = T // bs # blocked length
B = batch_size
H = self.num_heads
# G+W+R
# query_mask: [B, L, bs]
# key_mask: [B, L, bs]
# [B, L-G, bs, 1] * [B, L-G, 1, G*bs] -> [B, L-G, bs, G*bs]
temp_query_mask = paddle.reshape(blocked_query_mask[:, GF:-GB],
[B, L - G, bs, 1])
temp_key_mask_front = paddle.reshape(blocked_key_mask[:, :GF],
[B, 1, 1, GF * bs])
global_block_mask_front = paddle.matmul(temp_query_mask,
temp_key_mask_front)
temp_key_mask_back = paddle.reshape(blocked_key_mask[:, -GB:],
[B, 1, 1, GB * bs])
global_block_mask_back = paddle.matmul(temp_query_mask,
temp_key_mask_back)
# create window block mask
key_mask_list = []
for query_block_id in range(GF, GF + W // 2):
left_block_id = query_block_id - W // 2
right_block_id = query_block_id + W // 2
zero_key_mask = paddle.zeros_like(blocked_key_mask[:, -(W - (
right_block_id + 1 - G)):-GB])
temp_key_mask = paddle.concat(
[blocked_key_mask[:, GF:(right_block_id + 1)], zero_key_mask],
axis=1)
temp_key_mask = paddle.unsqueeze(temp_key_mask, 1)
key_mask_list.append(temp_key_mask)
roll_key_mask1 = paddle.concat(key_mask_list, axis=1)
roll_key_mask1 = paddle.reshape(roll_key_mask1, [0, 0, W * bs])
key_mask_list = []
band_length = L - G - W // 2 * 2
for query_block_id in range(GF + W // 2, GF + W // 2 + W):
left_block_id = query_block_id - W // 2
right_block_id = query_block_id + W // 2
key_mask_list.append(blocked_key_mask[:, left_block_id:left_block_id
+ band_length])
window_key_mask = paddle.concat(key_mask_list, axis=2)
window_key_mask = paddle.reshape(window_key_mask, [0, 0, W * bs])
key_mask_list = []
for query_block_id in range((L - GB) - W // 2, L - GB):
left_block_id = query_block_id - W // 2
right_block_id = query_block_id + W // 2
zero_key_mask = paddle.zeros_like(blocked_key_mask[:, GF:GF + W - (
L - left_block_id - GB)])
temp_key_mask = paddle.concat(
[zero_key_mask, blocked_key_mask[:, left_block_id:-GB]], axis=1)
temp_key_mask = paddle.unsqueeze(temp_key_mask, 1)
key_mask_list.append(temp_key_mask)
roll_key_mask2 = paddle.concat(key_mask_list, axis=1)
roll_key_mask2 = paddle.reshape(roll_key_mask2, [0, 0, W * bs])
window_key_mask = paddle.concat(
[roll_key_mask1, window_key_mask, roll_key_mask2], axis=1)
window_key_mask = paddle.unsqueeze(window_key_mask, axis=2)
# [B, L-G, bs, 1] * [B, L-G, 1, W*bs] -> [B, L-G, bs, W*bs]
window_block_mask = paddle.matmul(temp_query_mask, window_key_mask)
band_mask = paddle.concat(
[
global_block_mask_front, window_block_mask,
global_block_mask_back
],
axis=3)
band_mask = paddle.unsqueeze(band_mask, 1) # for head
band_mask = paddle.expand(band_mask, [B, H, L - G, bs, -1])
return band_mask
def forward(self, inputs, targets=None):
# if and else branch are both needed when you want to assign a variable
# if you modify the var in just one branch, then the modification will not work.
fea = inputs[-1]
if len(fea.shape) == 3:
pass
else:
last_shape = int(np.prod(fea.shape[2:])) # gry added
fea = paddle.reshape(fea, [fea.shape[0], fea.shape[1], last_shape])
fea = fea.transpose([0, 2, 1]) # (NTC)(batch, width, channels)
batch_size = fea.shape[0]
hidden = paddle.zeros((batch_size, self.hidden_size))
output_hiddens = []
if self.training and targets is not None:
structure = targets[0]
for i in range(self.max_elem_length + 1):
elem_onehots = self._char_to_onehot(structure[:, i],
onehot_dim=self.elem_num)
(outputs, hidden), alpha = self.structure_attention_cell(
hidden, fea, elem_onehots)
output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
output = paddle.concat(output_hiddens, axis=1)
structure_probs = self.structure_generator(output)
if self.loc_type == 1:
loc_preds = self.loc_generator(output)
loc_preds = F.sigmoid(loc_preds)
else:
loc_fea = fea.transpose([0, 2, 1])
loc_fea = self.loc_fea_trans(loc_fea)
loc_fea = loc_fea.transpose([0, 2, 1])
loc_concat = paddle.concat([output, loc_fea], axis=2)
loc_preds = self.loc_generator(loc_concat)
loc_preds = F.sigmoid(loc_preds)
else:
temp_elem = paddle.zeros(shape=[batch_size], dtype="int32")
structure_probs = None
loc_preds = None
elem_onehots = None
outputs = None
alpha = None
max_elem_length = paddle.to_tensor(self.max_elem_length)
i = 0
while i < max_elem_length + 1:
elem_onehots = self._char_to_onehot(temp_elem,
onehot_dim=self.elem_num)
(outputs, hidden), alpha = self.structure_attention_cell(
hidden, fea, elem_onehots)
output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
structure_probs_step = self.structure_generator(outputs)
temp_elem = structure_probs_step.argmax(axis=1, dtype="int32")
i += 1
output = paddle.concat(output_hiddens, axis=1)
structure_probs = self.structure_generator(output)
structure_probs = F.softmax(structure_probs)
if self.loc_type == 1:
loc_preds = self.loc_generator(output)
loc_preds = F.sigmoid(loc_preds)
else:
loc_fea = fea.transpose([0, 2, 1])
loc_fea = self.loc_fea_trans(loc_fea)
loc_fea = loc_fea.transpose([0, 2, 1])
loc_concat = paddle.concat([output, loc_fea], axis=2)
loc_preds = self.loc_generator(loc_concat)
loc_preds = F.sigmoid(loc_preds)
return {'structure_probs': structure_probs, 'loc_preds': loc_preds}
def get_loss(self, head_outputs, targets):
"""Here we calculate loss for a batch of images.
We assign anchors to gts in each image and gather all the assigned
postive and negative samples. Then loss is calculated on the gathered
samples.
"""
cls_logits_list, bboxes_reg_list = head_outputs
anchors = self.anchor_generator(cls_logits_list)
anchors = paddle.concat(anchors)
# matches: contain gt_inds
# match_labels: -1(ignore), 0(neg) or 1(pos)
matches_list, match_labels_list = [], []
# assign anchors to gts, no sampling is involved
for gt_bbox in targets['gt_bbox']:
matches, match_labels = self.bbox_assigner(anchors, gt_bbox)
matches_list.append(matches)
match_labels_list.append(match_labels)
# reshape network outputs
cls_logits = [
_.transpose([0, 2, 3, 1]).reshape([0, -1, self.num_classes])
for _ in cls_logits_list
]
bboxes_reg = [
_.transpose([0, 2, 3, 1]).reshape([0, -1, 4])
for _ in bboxes_reg_list
]
cls_logits = paddle.concat(cls_logits, axis=1)
bboxes_reg = paddle.concat(bboxes_reg, axis=1)
cls_pred_list, cls_tar_list = [], []
reg_pred_list, reg_tar_list = [], []
# find and gather preds and targets in each image
for matches, match_labels, cls_logit, bbox_reg, gt_bbox, gt_class in \
zip(matches_list, match_labels_list, cls_logits, bboxes_reg,
targets['gt_bbox'], targets['gt_class']):
pos_mask = (match_labels == 1)
neg_mask = (match_labels == 0)
chosen_mask = paddle.logical_or(pos_mask, neg_mask)
gt_class = gt_class.reshape([-1])
bg_class = paddle.to_tensor([self.num_classes],
dtype=gt_class.dtype)
# a trick to assign num_classes to negative targets
gt_class = paddle.concat([gt_class, bg_class], axis=-1)
matches = paddle.where(
neg_mask, paddle.full_like(matches, gt_class.size - 1),
matches)
cls_pred = cls_logit[chosen_mask]
cls_tar = gt_class[matches[chosen_mask]]
reg_pred = bbox_reg[pos_mask].reshape([-1, 4])
reg_tar = gt_bbox[matches[pos_mask]].reshape([-1, 4])
reg_tar = bbox2delta(anchors[pos_mask], reg_tar, self.weights)
cls_pred_list.append(cls_pred)
cls_tar_list.append(cls_tar)
reg_pred_list.append(reg_pred)
reg_tar_list.append(reg_tar)
cls_pred = paddle.concat(cls_pred_list)
cls_tar = paddle.concat(cls_tar_list)
reg_pred = paddle.concat(reg_pred_list)
reg_tar = paddle.concat(reg_tar_list)
avg_factor = max(1.0, reg_pred.shape[0])
cls_loss = self.loss_class(cls_pred, cls_tar,
reduction='sum') / avg_factor
if reg_pred.shape[0] == 0:
reg_loss = paddle.zeros([1])
reg_loss.stop_gradient = False
else:
reg_loss = self.loss_bbox(reg_pred, reg_tar,
reduction='sum') / avg_factor
loss = cls_loss + reg_loss
out_dict = {
'loss_cls': cls_loss,
'loss_reg': reg_loss,
'loss': loss,
}
return out_dict
def forward(self, inputs):
x = self.primary_conv(inputs)
y = self.cheap_operation(x)
out = paddle.concat([x, y], axis=1)
return out
def forward(self, x: paddle.Tensor) -> paddle.Tensor:
x = self.conv(x)
left = self.left(x)
right = self.right(x)
concat = paddle.concat([left, right], axis=1)
return self.fuse(concat)
def concat_unsqueeze1(inputs):
return paddle.concat(
[inputs[:, 0].unsqueeze(1), inputs[:, 1].unsqueeze(1)], axis=1)
def ops(self, inputs):
"""
operation
"""
concat = paddle.concat(x=inputs, axis=self.axis)
return concat
def get_loss(self, head_outs, gt_meta):
cls_scores, bbox_preds = head_outs
num_level_anchors = [
featmap.shape[-2] * featmap.shape[-1] for featmap in cls_scores
]
num_imgs = gt_meta['im_id'].shape[0]
featmap_sizes = [[featmap.shape[-2], featmap.shape[-1]]
for featmap in cls_scores]
decode_bbox_preds = []
center_and_strides = []
for featmap_size, stride, bbox_pred in zip(featmap_sizes,
self.fpn_stride, bbox_preds):
# center in origin image
yy, xx = self.get_single_level_center_point(featmap_size, stride,
self.cell_offset)
strides = paddle.full((len(xx), ), stride)
center_and_stride = paddle.stack([xx, yy, strides, strides],
-1).tile([num_imgs, 1, 1])
center_and_strides.append(center_and_stride)
center_in_feature = center_and_stride.reshape(
[-1, 4])[:, :-2] / stride
bbox_pred = bbox_pred.transpose([0, 2, 3, 1]).reshape(
[num_imgs, -1, 4 * (self.reg_max + 1)])
pred_distances = self.distribution_project(bbox_pred)
decode_bbox_pred_wo_stride = distance2bbox(
center_in_feature, pred_distances).reshape([num_imgs, -1, 4])
decode_bbox_preds.append(decode_bbox_pred_wo_stride * stride)
flatten_cls_preds = [
cls_pred.transpose([0, 2, 3, 1]).reshape(
[num_imgs, -1, self.cls_out_channels])
for cls_pred in cls_scores
]
flatten_cls_preds = paddle.concat(flatten_cls_preds, axis=1)
flatten_bboxes = paddle.concat(decode_bbox_preds, axis=1)
flatten_center_and_strides = paddle.concat(center_and_strides, axis=1)
gt_boxes, gt_labels = gt_meta['gt_bbox'], gt_meta['gt_class']
pos_num_l, label_l, label_weight_l, bbox_target_l = [], [], [], []
for flatten_cls_pred, flatten_center_and_stride, flatten_bbox,gt_box,gt_label \
in zip(flatten_cls_preds.detach(), flatten_center_and_strides.detach(), \
flatten_bboxes.detach(),gt_boxes,gt_labels):
pos_num, label, label_weight, bbox_target = self._get_target_single(
flatten_cls_pred, flatten_center_and_stride, flatten_bbox,
gt_box, gt_label)
pos_num_l.append(pos_num)
label_l.append(label)
label_weight_l.append(label_weight)
bbox_target_l.append(bbox_target)
labels = paddle.to_tensor(np.stack(label_l, axis=0))
label_weights = paddle.to_tensor(np.stack(label_weight_l, axis=0))
bbox_targets = paddle.to_tensor(np.stack(bbox_target_l, axis=0))
center_and_strides_list = self._images_to_levels(
flatten_center_and_strides, num_level_anchors)
labels_list = self._images_to_levels(labels, num_level_anchors)
label_weights_list = self._images_to_levels(label_weights,
num_level_anchors)
bbox_targets_list = self._images_to_levels(bbox_targets,
num_level_anchors)
num_total_pos = sum(pos_num_l)
try:
num_total_pos = paddle.distributed.all_reduce(num_total_pos.clone(
)) / paddle.distributed.get_world_size()
except:
num_total_pos = max(num_total_pos, 1)
loss_bbox_list, loss_dfl_list, loss_vfl_list, avg_factor = [], [], [], []
for cls_score, bbox_pred, center_and_strides, labels, label_weights, bbox_targets, stride in zip(
cls_scores, bbox_preds, center_and_strides_list, labels_list,
label_weights_list, bbox_targets_list, self.fpn_stride):
center_and_strides = center_and_strides.reshape([-1, 4])
cls_score = cls_score.transpose([0, 2, 3, 1]).reshape(
[-1, self.cls_out_channels])
bbox_pred = bbox_pred.transpose([0, 2, 3, 1]).reshape(
[-1, 4 * (self.reg_max + 1)])
bbox_targets = bbox_targets.reshape([-1, 4])
labels = labels.reshape([-1])
bg_class_ind = self.num_classes
pos_inds = paddle.nonzero(
paddle.logical_and((labels >= 0), (labels < bg_class_ind)),
as_tuple=False).squeeze(1)
# vfl
vfl_score = np.zeros(cls_score.shape)
if len(pos_inds) > 0:
pos_bbox_targets = paddle.gather(bbox_targets, pos_inds, axis=0)
pos_bbox_pred = paddle.gather(bbox_pred, pos_inds, axis=0)
pos_centers = paddle.gather(
center_and_strides[:, :-2], pos_inds, axis=0) / stride
weight_targets = F.sigmoid(cls_score.detach())
weight_targets = paddle.gather(
weight_targets.max(axis=1, keepdim=True), pos_inds, axis=0)
pos_bbox_pred_corners = self.distribution_project(pos_bbox_pred)
pos_decode_bbox_pred = distance2bbox(pos_centers,
pos_bbox_pred_corners)
pos_decode_bbox_targets = pos_bbox_targets / stride
bbox_iou = bbox_overlaps(
pos_decode_bbox_pred.detach().numpy(),
pos_decode_bbox_targets.detach().numpy(),
is_aligned=True)
# vfl
pos_labels = paddle.gather(labels, pos_inds, axis=0)
vfl_score[pos_inds.numpy(), pos_labels] = bbox_iou
pred_corners = pos_bbox_pred.reshape([-1, self.reg_max + 1])
target_corners = bbox2distance(pos_centers,
pos_decode_bbox_targets,
self.reg_max).reshape([-1])
# regression loss
loss_bbox = paddle.sum(
self.loss_bbox(pos_decode_bbox_pred,
pos_decode_bbox_targets) * weight_targets)
# dfl loss
loss_dfl = self.loss_dfl(
pred_corners,
target_corners,
weight=weight_targets.expand([-1, 4]).reshape([-1]),
avg_factor=4.0)
else:
loss_bbox = bbox_pred.sum() * 0
loss_dfl = bbox_pred.sum() * 0
weight_targets = paddle.to_tensor([0], dtype='float32')
# vfl loss
num_pos_avg_per_gpu = num_total_pos
vfl_score = paddle.to_tensor(vfl_score)
loss_vfl = self.loss_vfl(
cls_score, vfl_score, avg_factor=num_pos_avg_per_gpu)
loss_bbox_list.append(loss_bbox)
loss_dfl_list.append(loss_dfl)
loss_vfl_list.append(loss_vfl)
avg_factor.append(weight_targets.sum())
avg_factor = sum(avg_factor)
try:
avg_factor = paddle.distributed.all_reduce(avg_factor.clone())
avg_factor = paddle.clip(
avg_factor / paddle.distributed.get_world_size(), min=1)
except:
avg_factor = max(avg_factor.item(), 1)
if avg_factor <= 0:
loss_vfl = paddle.to_tensor(0, dtype='float32', stop_gradient=False)
loss_bbox = paddle.to_tensor(
0, dtype='float32', stop_gradient=False)
loss_dfl = paddle.to_tensor(0, dtype='float32', stop_gradient=False)
else:
losses_bbox = list(map(lambda x: x / avg_factor, loss_bbox_list))
losses_dfl = list(map(lambda x: x / avg_factor, loss_dfl_list))
loss_vfl = sum(loss_vfl_list)
loss_bbox = sum(losses_bbox)
loss_dfl = sum(losses_dfl)
loss_states = dict(
loss_vfl=loss_vfl, loss_bbox=loss_bbox, loss_dfl=loss_dfl)
return loss_states
def _concat(x, out):
# concatenate along channel axis
return paddle.concat((x, out), 1)
def beam_search(self, input_ids, beam_scorer, logits_processors, max_length,
pad_token_id, eos_token_id, **model_kwargs):
batch_size = len(beam_scorer._beam_hyps)
num_beams = beam_scorer.num_beams
batch_beam_size, cur_len = input_ids.shape
origin_len = cur_len
assert (
num_beams * batch_size == batch_beam_size
), "Batch dimension of `input_ids` should be {}, but received {}.".format(
num_beams * batch_size, batch_beam_size)
beam_scores = paddle.zeros(
(batch_size, num_beams), dtype=paddle.get_default_dtype())
beam_scores[:, 1:] = -1e9
beam_scores = paddle.reshape(beam_scores, [-1])
while cur_len < max_length:
# prepare model inputs & get model output
model_inputs = self.prepare_inputs_for_generation(input_ids,
**model_kwargs)
outputs = self(**model_inputs)
logits = outputs[0] if isinstance(outputs, tuple) else outputs
# [batch_size, vocab_size]
logits = logits[:, -1, :]
# pre-process distribution
logits = self.adjust_logits_during_generation(logits)
logits = logits_processors(input_ids, logits)
# beam search
# [batch_size * num_beams, vocab_size]
next_scores = F.softmax(logits)
next_scores = paddle.log(next_scores)
next_scores = next_scores + beam_scores.unsqueeze(-1)
# reshape for beam search
vocab_size = next_scores.shape[-1]
next_scores = next_scores.reshape(
[batch_size, num_beams * vocab_size])
next_scores, next_tokens = paddle.topk(
next_scores, 2 * num_beams, axis=1)
next_indices = next_tokens // vocab_size
next_tokens = next_tokens % vocab_size
# stateless
beam_outputs = beam_scorer.process(
input_ids,
next_scores,
next_tokens,
next_indices,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id, )
beam_scores = beam_outputs["next_beam_scores"]
beam_next_tokens = beam_outputs["next_beam_tokens"]
beam_idx = beam_outputs["next_beam_indices"]
cur_len += 1
input_ids = paddle.concat(
[
paddle.index_select(input_ids, beam_idx),
beam_next_tokens.unsqueeze(-1)
],
axis=-1)
if beam_scorer.is_done:
break
model_kwargs = self.update_model_kwargs_for_generation(outputs,
model_kwargs)
if model_kwargs["cache"] is not None:
# reorder the cache
model_kwargs["cache"] = map_structure(
lambda x: paddle.index_select(x, beam_idx),
model_kwargs["cache"])
pred_ids, scores = beam_scorer.finalize(
input_ids,
beam_scores,
next_tokens,
next_indices,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id)
return pred_ids[:, origin_len:], scores
def get_loss(self, scores, deltas, targets, rois, bbox_weight):
"""
scores (Tensor): scores from bbox head outputs
deltas (Tensor): deltas from bbox head outputs
targets (list[List[Tensor]]): bbox targets containing tgt_labels, tgt_bboxes and tgt_gt_inds
rois (List[Tensor]): RoIs generated in each batch
"""
cls_name = 'loss_bbox_cls'
reg_name = 'loss_bbox_reg'
loss_bbox = {}
# TODO: better pass args
tgt_labels, tgt_bboxes, tgt_gt_inds = targets
# bbox cls
tgt_labels = paddle.concat(
tgt_labels) if len(tgt_labels) > 1 else tgt_labels[0]
valid_inds = paddle.nonzero(tgt_labels >= 0).flatten()
if valid_inds.shape[0] == 0:
loss_bbox[cls_name] = paddle.zeros([1], dtype='float32')
else:
tgt_labels = tgt_labels.cast('int64')
tgt_labels.stop_gradient = True
loss_bbox_cls = F.cross_entropy(input=scores,
label=tgt_labels,
reduction='mean')
loss_bbox[cls_name] = loss_bbox_cls
# bbox reg
cls_agnostic_bbox_reg = deltas.shape[1] == 4
fg_inds = paddle.nonzero(
paddle.logical_and(tgt_labels >= 0,
tgt_labels < self.num_classes)).flatten()
if fg_inds.numel() == 0:
loss_bbox[reg_name] = paddle.zeros([1], dtype='float32')
return loss_bbox
if cls_agnostic_bbox_reg:
reg_delta = paddle.gather(deltas, fg_inds)
else:
fg_gt_classes = paddle.gather(tgt_labels, fg_inds)
reg_row_inds = paddle.arange(fg_gt_classes.shape[0]).unsqueeze(1)
reg_row_inds = paddle.tile(reg_row_inds, [1, 4]).reshape([-1, 1])
reg_col_inds = 4 * fg_gt_classes.unsqueeze(1) + paddle.arange(4)
reg_col_inds = reg_col_inds.reshape([-1, 1])
reg_inds = paddle.concat([reg_row_inds, reg_col_inds], axis=1)
reg_delta = paddle.gather(deltas, fg_inds)
reg_delta = paddle.gather_nd(reg_delta, reg_inds).reshape([-1, 4])
rois = paddle.concat(rois) if len(rois) > 1 else rois[0]
tgt_bboxes = paddle.concat(
tgt_bboxes) if len(tgt_bboxes) > 1 else tgt_bboxes[0]
reg_target = bbox2delta(rois, tgt_bboxes, bbox_weight)
reg_target = paddle.gather(reg_target, fg_inds)
reg_target.stop_gradient = True
if self.bbox_loss is not None:
reg_delta = self.bbox_transform(reg_delta)
reg_target = self.bbox_transform(reg_target)
loss_bbox_reg = self.bbox_loss(
reg_delta, reg_target).sum() / tgt_labels.shape[0]
loss_bbox_reg *= self.num_classes
else:
loss_bbox_reg = paddle.abs(reg_delta -
reg_target).sum() / tgt_labels.shape[0]
loss_bbox[reg_name] = loss_bbox_reg
return loss_bbox
def forward(self, inputs):
"""modeling forward stage of encoder
"""
seq_hidden, cls_hidden = self.base_encoder(inputs['src_ids'],
inputs['sent_ids'])
if self.pretrain_model_type != 'ERNIE' and self.pretrain_model_type != 'BERT':
cls_hidden, seq_hidden = seq_hidden, cls_hidden
question_tokens_index = inputs["question_tokens_index"]
table_indexes = inputs["table_indexes"]
column_indexes = inputs["column_indexes"]
value_indexes = inputs["value_indexes"]
question_encs = nn_utils.batch_gather_2d(seq_hidden,
question_tokens_index)
table_encs = nn_utils.batch_gather_2d(seq_hidden, table_indexes)
column_encs = nn_utils.batch_gather_2d(seq_hidden, column_indexes)
value_encs = nn_utils.batch_gather_2d(seq_hidden, value_indexes)
if self.enc_value_with_col:
value_num = value_encs.shape[1] // 2
value_encs = value_encs.reshape(
[value_encs.shape[0], value_num, 2, -1]).sum(axis=2)
orig_inputs = inputs['orig_inputs']
column_pointer_maps = [{
i: [i]
for i in range(len(orig_input.columns))
} for orig_input in orig_inputs]
table_pointer_maps = [{i: [i]
for i in range(len(orig_input.tables))}
for orig_input in orig_inputs]
value_pointer_maps = [{i: [i]
for i in range(len(orig_input.values))}
for orig_input in orig_inputs]
enc_results = []
# calculate relation encoding one-by-one
for batch_idx, orig_input in enumerate(orig_inputs):
q_len = orig_input.column_indexes[0] - 2
col_size = len(orig_input.columns)
tab_size = len(orig_input.tables)
val_size = len(orig_input.values)
q_enc = question_encs[batch_idx][:q_len]
tab_enc = table_encs[batch_idx][:tab_size]
col_enc = column_encs[batch_idx][:col_size]
val_enc = value_encs[batch_idx][:val_size]
c_boundary = list(range(col_size + 1))
t_boundary = list(range(tab_size + 1))
v_e_input = val_enc.unsqueeze(0) if self.rel_has_value else None
(q_enc_new, c_enc_new, t_enc_new,
v_enc_new), align_mat = self.encs_update.forward_unbatched(
q_enc.unsqueeze(0), col_enc.unsqueeze(0),
tab_enc.unsqueeze(0), c_boundary, t_boundary,
orig_input.relations, v_e_input)
memory = []
if 'question' in self.include_in_memory:
memory.append(q_enc_new)
if 'table' in self.include_in_memory:
memory.append(t_enc_new)
if 'column' in self.include_in_memory:
memory.append(c_enc_new)
if 'value' in self.include_in_memory and self.rel_has_value:
memory.append(v_enc_new)
memory = paddle.concat(memory, axis=1)
if not self.rel_has_value:
v_enc_new = val_enc.unsqueeze(0)
m2v_align_mat = self.value_align(memory,
v_enc_new,
relations=None)
align_mat[2] = m2v_align_mat
schema_memory = (c_enc_new, t_enc_new)
if self.rel_has_value:
schema_memory += (v_enc_new, )
enc_results.append(
EncoderState(
state=None,
cls_hidden=cls_hidden[batch_idx],
memory=memory,
question_memory=q_enc_new,
schema_memory=paddle.concat(schema_memory, axis=1),
words=orig_input.question_tokens,
pointer_memories={
'table': t_enc_new,
'column': c_enc_new,
'value': v_enc_new,
},
pointer_maps={
'column': column_pointer_maps[batch_idx],
'table': table_pointer_maps[batch_idx],
'value': value_pointer_maps[batch_idx],
},
m2c_align_mat=align_mat[0],
m2t_align_mat=align_mat[1],
m2v_align_mat=align_mat[2],
))
return enc_results
def forward(self, x):
bn_x = self.bnorm(x[:, :self.bnorm_channels, :, :])
in_x = self.inorm(x[:, self.bnorm_channels:, :, :])
return paddle.concat((bn_x, in_x), 1)
def predictEnsembleThree(model,
model_1,
model_crop,
model_path,
model_path_1,
model_path_crop,
transforms,
transforms_crop,
image_list,
image_dir=None,
save_dir='output',
aug_pred=False,
scales=1.0,
flip_horizontal=True,
flip_vertical=False,
is_slide=False,
stride=None,
crop_size=None):
"""
predict and visualize the image_list.
Args:
model (nn.Layer): Used to predict for input image.
model_path (str): The path of pretrained model.
transforms (transform.Compose): Preprocess for input image.
image_list (list): A list of image path to be predicted.
image_dir (str, optional): The root directory of the images predicted. Default: None.
save_dir (str, optional): The directory to save the visualized results. Default: 'output'.
aug_pred (bool, optional): Whether to use mulit-scales and flip augment for predition. Default: False.
scales (list|float, optional): Scales for augment. It is valid when `aug_pred` is True. Default: 1.0.
flip_horizontal (bool, optional): Whether to use flip horizontally augment. It is valid when `aug_pred` is True. Default: True.
flip_vertical (bool, optional): Whether to use flip vertically augment. It is valid when `aug_pred` is True. Default: False.
is_slide (bool, optional): Whether to predict by sliding window. Default: False.
stride (tuple|list, optional): The stride of sliding window, the first is width and the second is height.
It should be provided when `is_slide` is True.
crop_size (tuple|list, optional): The crop size of sliding window, the first is width and the second is height.
It should be provided when `is_slide` is True.
"""
utils.utils.load_entire_model(model, model_path)
model.eval()
utils.utils.load_entire_model(model_1, model_path_1)
model_1.eval()
utils.utils.load_entire_model(model_crop, model_path_crop)
model_crop.eval()
nranks = paddle.distributed.get_world_size()
local_rank = paddle.distributed.get_rank()
if nranks > 1:
img_lists = partition_list(image_list, nranks)
else:
img_lists = [image_list]
added_saved_dir = os.path.join(save_dir, 'added_prediction')
pred_saved_dir = os.path.join(save_dir, 'pseudo_color_prediction')
logger.info("Start to predict...")
progbar_pred = progbar.Progbar(target=len(img_lists[0]), verbose=1)
with paddle.no_grad():
for i, im_path in enumerate(img_lists[local_rank]):
im_origin = cv2.imread(im_path)
ori_shape = im_origin.shape[:2]
im, _ = transforms(im_origin)
im = im[np.newaxis, ...]
im = paddle.to_tensor(im)
ims, _ = transforms_crop(im_origin)
im1 = ims[:, 540:540 + 720, 320:320 + 1280]
im2 = ims[:, 540:540 + 720, 960:960 + 1280]
im3 = ims[:, 540:540 + 720, 1600:1600 + 1280]
im1 = im1[np.newaxis, ...]
im1 = paddle.to_tensor(im1)
im2 = im2[np.newaxis, ...]
im2 = paddle.to_tensor(im2)
im3 = im3[np.newaxis, ...]
im3 = paddle.to_tensor(im3)
ims_ = [im1, im2, im3]
if aug_pred:
pred = infer_ensemble.aug_inference(
model,
model_1,
im,
ori_shape=ori_shape,
transforms=transforms.transforms,
scales=scales,
flip_horizontal=flip_horizontal,
flip_vertical=flip_vertical,
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
else:
pred = infer_ensemble.inference(
model,
model_1,
im,
ori_shape=ori_shape,
transforms=transforms.transforms,
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
preds = []
for ii in range(3):
im_ = ims_[ii]
if aug_pred:
pred_crop = infer_crop.aug_inference(
model,
im_,
ori_shape=ori_shape,
transforms=transforms.transforms,
scales=scales,
flip_horizontal=flip_horizontal,
flip_vertical=flip_vertical,
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
else:
pred_crop = infer_crop.inference(
model,
im_,
ori_shape=ori_shape,
transforms=transforms.transforms,
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
preds.append(pred_crop)
left_ensem = (preds[0][:, :, :, 640:1280] +
preds[1][:, :, :, 0:640]) / 2
right_ensem = (preds[1][:, :, :, 640:1280] +
preds[2][:, :, :, 0:640]) / 2
pred_ensem = paddle.concat([
preds[0][:, :, :, 0:640], left_ensem, right_ensem,
preds[2][:, :, :, 640:1280]
],
axis=3)
logit = F.interpolate(pred_ensem, (432, 768), mode='bilinear')
pred_logit = pred.clone()
pred_logit[:, :, 324:756, 576:1344] = logit
pred = pred + pred_logit
pred = F.interpolate(pred, ori_shape, mode='bilinear')
pred = paddle.argmax(pred, axis=1, keepdim=True, dtype='int32')
pred = paddle.squeeze(pred)
pred = pred.numpy().astype('uint8')
# get the saved name
if image_dir is not None:
im_file = im_path.replace(image_dir, '')
else:
im_file = os.path.basename(im_path)
if im_file[0] == '/':
im_file = im_file[1:]
# save added image
added_image = utils.visualize.visualize(im_path, pred, weight=0.6)
added_image_path = os.path.join(added_saved_dir, im_file)
mkdir(added_image_path)
cv2.imwrite(added_image_path, added_image)
# save pseudo color prediction
pred_mask = utils.visualize.get_pseudo_color_map(pred)
pred_saved_path = os.path.join(pred_saved_dir,
im_file.rsplit(".")[0] + ".png")
mkdir(pred_saved_path)
pred_mask.save(pred_saved_path)
# pred_im = utils.visualize(im_path, pred, weight=0.0)
# pred_saved_path = os.path.join(pred_saved_dir, im_file)
# mkdir(pred_saved_path)
# cv2.imwrite(pred_saved_path, pred_im)
progbar_pred.update(i + 1)
def main(args):
paddle.seed(12345)
config = load_yaml(args.config_yaml)
use_gpu = config.get("dygraph.use_gpu", True)
train_data_dir = config.get("dygraph.train_data_dir", None)
epochs = config.get("dygraph.epochs", None)
print_interval = config.get("dygraph.print_interval", None)
model_save_path = config.get("dygraph.model_save_path", "model_output")
dense_input_dim = config.get('hyper_parameters.dense_input_dim', None)
print("***********************************")
logger.info(
"use_gpu: {}, train_data_dir: {}, epochs: {}, print_interval: {}, model_save_path: {}"
.format(use_gpu, train_data_dir, epochs, print_interval,
model_save_path))
print("***********************************")
place = paddle.set_device('gpu' if use_gpu else 'cpu')
fm_model = create_model(config)
model_init_path = config.get("dygraph.model_init_path", None)
if model_init_path is not None:
load_model(model_init_path, fm_model)
# to do : add optimizer function
optimizer = paddle.optimizer.Adam(parameters=fm_model.parameters())
file_list = [
os.path.join(train_data_dir, x) for x in os.listdir(train_data_dir)
]
print("read data")
dataset = CriteoDataset(file_list)
train_dataloader = create_data_loader(dataset, place=place, config=config)
last_epoch_id = config.get("last_epoch", -1)
for epoch_id in range(last_epoch_id + 1, epochs):
# set train mode
fm_model.train()
auc_metric = paddle.metric.Auc("ROC")
epoch_begin = time.time()
interval_begin = time.time()
train_reader_cost = 0.0
train_run_cost = 0.0
total_samples = 0
reader_start = time.time()
for batch_id, batch in enumerate(train_dataloader()):
train_reader_cost += time.time() - reader_start
optimizer.clear_grad()
train_start = time.time()
batch_size = len(batch[0])
label, sparse_tensor, dense_tensor = create_feeds(
batch, dense_input_dim)
pred = fm_model(sparse_tensor, dense_tensor)
loss = create_loss(pred, label)
loss.backward()
optimizer.step()
train_run_cost += time.time() - train_start
total_samples += batch_size
label_int = paddle.cast(label, 'int64')
# for auc
predict_2d = paddle.concat(x=[1 - pred, pred], axis=1)
auc_metric.update(preds=predict_2d.numpy(),
labels=label_int.numpy())
if batch_id % print_interval == 0:
logger.info(
"epoch: {}, batch_id: {}, auc: {:.6f}, avg_reader_cost: {:.5f} sec, avg_batch_cost: {:.5f} sec, avg_samples: {:.5f}, ips: {:.5f} images/sec"
.format(
epoch_id, batch_id, auc_metric.accumulate(),
train_reader_cost / print_interval,
(train_reader_cost + train_run_cost) / print_interval,
total_samples / print_interval,
total_samples / (train_reader_cost + train_run_cost)))
train_reader_cost = 0.0
train_run_cost = 0.0
total_samples = 0
reader_start = time.time()
logger.info("epoch: {} done, auc: {:.6f}, epoch time:{:.2f} s".format(
epoch_id, auc_metric.accumulate(),
time.time() - epoch_begin))
save_model(fm_model,
optimizer,
model_save_path,
epoch_id,
prefix='rec')
def get_loss(self, cate_preds, kernel_preds, ins_pred, ins_labels,
cate_labels, grid_order_list, fg_num):
"""
Get loss of network of SOLOv2.
Args:
cate_preds (list): Tensor list of categroy branch output.
kernel_preds (list): Tensor list of kernel branch output.
ins_pred (list): Tensor list of instance branch output.
ins_labels (list): List of instance labels pre batch.
cate_labels (list): List of categroy labels pre batch.
grid_order_list (list): List of index in pre grid.
fg_num (int): Number of positive samples in a mini-batch.
Returns:
loss_ins (Tensor): The instance loss Tensor of SOLOv2 network.
loss_cate (Tensor): The category loss Tensor of SOLOv2 network.
"""
batch_size = paddle.shape(grid_order_list[0])[0]
ins_pred_list = []
for kernel_preds_level, grid_orders_level in zip(kernel_preds,
grid_order_list):
if grid_orders_level.shape[1] == 0:
ins_pred_list.append(None)
continue
grid_orders_level = paddle.reshape(grid_orders_level, [-1])
reshape_pred = paddle.reshape(
kernel_preds_level,
shape=(paddle.shape(kernel_preds_level)[0],
paddle.shape(kernel_preds_level)[1], -1))
reshape_pred = paddle.transpose(reshape_pred, [0, 2, 1])
reshape_pred = paddle.reshape(
reshape_pred, shape=(-1, paddle.shape(reshape_pred)[2]))
gathered_pred = paddle.gather(reshape_pred, index=grid_orders_level)
gathered_pred = paddle.reshape(
gathered_pred,
shape=[batch_size, -1, paddle.shape(gathered_pred)[1]])
cur_ins_pred = ins_pred
cur_ins_pred = paddle.reshape(
cur_ins_pred,
shape=(paddle.shape(cur_ins_pred)[0],
paddle.shape(cur_ins_pred)[1], -1))
ins_pred_conv = paddle.matmul(gathered_pred, cur_ins_pred)
cur_ins_pred = paddle.reshape(
ins_pred_conv,
shape=(-1, paddle.shape(ins_pred)[-2],
paddle.shape(ins_pred)[-1]))
ins_pred_list.append(cur_ins_pred)
num_ins = paddle.sum(fg_num)
cate_preds = [
paddle.reshape(
paddle.transpose(cate_pred, [0, 2, 3, 1]),
shape=(-1, self.cate_out_channels)) for cate_pred in cate_preds
]
flatten_cate_preds = paddle.concat(cate_preds)
new_cate_labels = []
for cate_label in cate_labels:
new_cate_labels.append(paddle.reshape(cate_label, shape=[-1]))
cate_labels = paddle.concat(new_cate_labels)
loss_ins, loss_cate = self.solov2_loss(
ins_pred_list, ins_labels, flatten_cate_preds, cate_labels, num_ins)
return {'loss_ins': loss_ins, 'loss_cate': loss_cate}
def forward(self, inputs_tensor, is_infer=0):
# input
inputs = inputs_tensor[0] # sparse_tensor
dense_tensor = inputs_tensor[1]
self.btag_his = inputs[:, 0:50]
self.cate_his = inputs[:, 50:100]
self.brand_his = inputs[:, 100:150]
self.mask = inputs[:, 150:200]
self.match_mask = inputs[:, 200:250]
self.uid = inputs[:, 250]
self.cms_segid = inputs[:, 251]
self.cms_group_id = inputs[:, 252]
self.final_gender_code = inputs[:, 253]
self.age_level = inputs[:, 254]
self.pvalue_level = inputs[:, 255]
self.shopping_level = inputs[:, 256]
self.occupation = inputs[:, 257]
self.new_user_class_level = inputs[:, 258]
self.mid = inputs[:, 259]
self.cate_id = inputs[:, 260]
self.campaign_id = inputs[:, 261]
self.customer = inputs[:, 262]
self.brand = inputs[:, 263]
self.price = dense_tensor.astype('float32')
self.pid = inputs[:, 265]
if is_infer == 0:
self.labels = inputs[:, 266]
# embedding layer
self.uid_batch_embedded = self.uid_embeddings_var(self.uid)
self.mid_batch_embedded = self.mid_embeddings_var(self.mid)
self.cat_batch_embedded = self.cat_embeddings_var(self.cate_id)
self.cat_his_batch_embedded = self.cat_embeddings_var(self.cate_his)
self.brand_batch_embedded = self.brand_embeddings_var(self.brand)
self.brand_his_batch_embedded = self.brand_embeddings_var(
self.brand_his)
self.btag_his_batch_embedded = self.btag_embeddings_var(self.btag_his)
self.dm_btag_his_batch_embedded = self.dm_btag_embeddings_var(
self.btag_his)
self.campaign_id_batch_embedded = self.campaign_id_embeddings_var(
self.campaign_id)
self.customer_batch_embedded = self.customer_embeddings_var(
self.customer)
self.cms_segid_batch_embedded = self.cms_segid_embeddings_var(
self.cms_segid)
self.cms_group_id_batch_embedded = self.cms_group_id_embeddings_var(
self.cms_group_id)
self.final_gender_code_batch_embedded = self.final_gender_code_embeddings_var(
self.final_gender_code)
self.age_level_batch_embedded = self.age_level_embeddings_var(
self.age_level)
self.pvalue_level_batch_embedded = self.pvalue_level_embeddings_var(
self.pvalue_level)
self.shopping_level_batch_embedded = self.shopping_level_embeddings_var(
self.shopping_level)
self.occupation_batch_embedded = self.occupation_embeddings_var(
self.occupation)
self.new_user_class_level_batch_embedded = self.new_user_class_level_embeddings_var(
self.new_user_class_level)
self.pid_batch_embedded = self.pid_embeddings_var(self.pid)
self.user_feat = paddle.concat([
self.uid_batch_embedded, self.cms_segid_batch_embedded,
self.cms_group_id_batch_embedded,
self.final_gender_code_batch_embedded,
self.age_level_batch_embedded, self.pvalue_level_batch_embedded,
self.shopping_level_batch_embedded, self.occupation_batch_embedded,
self.new_user_class_level_batch_embedded
], -1)
self.item_his_eb = paddle.concat(
[self.cat_his_batch_embedded, self.brand_his_batch_embedded], -1)
self.item_his_eb_sum = paddle.sum(self.item_his_eb, 1)
self.item_feat = paddle.concat([
self.mid_batch_embedded, self.cat_batch_embedded,
self.brand_batch_embedded, self.campaign_id_batch_embedded,
self.customer_batch_embedded, self.price
], -1)
self.item_eb = paddle.concat(
[self.cat_batch_embedded, self.brand_batch_embedded], -1)
self.context_feat = self.pid_batch_embedded
self.position_his_eb = self.position_embeddings_var(
self.position_his) # T, E
self.position_his_eb = paddle.tile(
self.position_his_eb, [paddle.shape(self.mid)[0], 1]) # B*T, E
self.position_his_eb = paddle.reshape(self.position_his_eb, [
paddle.shape(self.mid)[0], -1,
paddle.shape(self.position_his_eb)[1]
]) # B, T, E
self.dm_position_his_eb = self.dm_position_embeddings_var(
self.dm_position_his) # T, E
self.dm_position_his_eb = paddle.tile(
self.dm_position_his_eb, [paddle.shape(self.mid)[0], 1]) # B*T, E
self.dm_position_his_eb = paddle.reshape(self.dm_position_his_eb, [
paddle.shape(self.mid)[0], -1,
paddle.shape(self.dm_position_his_eb)[1]
]) # B, T, E
self.position_his_eb = paddle.concat(
[self.position_his_eb, self.btag_his_batch_embedded], -1)
self.dm_position_his_eb = paddle.concat(
[self.dm_position_his_eb, self.dm_btag_his_batch_embedded], -1)
# User-to-Item Network
# Auxiliary Match Network
self.match_mask = paddle.cast(self.match_mask, 'float32')
self.aux_loss, self.dm_user_vector, scores = self._deep_match(
self.item_his_eb, self.dm_position_his_eb, self.mask,
self.match_mask, self.cate_his, self.dm_item_vectors_var.weight,
self.dm_item_biases, self.cate_size)
self.aux_loss *= 0.1
self.dm_item_vec = self.dm_item_vectors_var(self.cate_id)
rel_u2i = paddle.sum(self.dm_user_vector * self.dm_item_vec,
-1,
keepdim=True) # B,1
self.rel_u2i = rel_u2i
# Item-to-Item Network
att_outputs, alphas, scores_unnorm = self._dmr_fcn_attention(
self.item_eb, self.item_his_eb, self.position_his_eb, self.mask)
rel_i2i = paddle.unsqueeze(paddle.sum(scores_unnorm, [1, 2]), -1)
self.rel_i2i = rel_i2i
self.scores = paddle.sum(alphas, 1)
inp = paddle.concat([
self.user_feat, self.item_feat, self.context_feat,
self.item_his_eb_sum, self.item_eb * self.item_his_eb_sum, rel_u2i,
rel_i2i, att_outputs
], -1)
# build fcn net
inp = self.inp_layer(inp)
dnn0 = self.dnn0_layer(inp)
dnn0 = self.dnn0_prelu(dnn0)
dnn1 = self.dnn1_layer(dnn0)
dnn1 = self.dnn1_prelu(dnn1)
dnn2 = self.dnn2_layer(dnn1)
dnn2 = self.dnn2_prelu(dnn2)
dnn3 = self.dnn3_layer(dnn2)
dnn3 = self.dnn3_prelu(dnn3)
# prediction
self.y_hat = F.sigmoid(dnn3)
if is_infer == False:
# Cross-entropy loss and optimizer initialization
x = paddle.sum(dnn3, 1)
BCE = paddle.nn.BCEWithLogitsLoss()
ctr_loss = paddle.mean(BCE(x, label=self.labels.astype('float32')))
self.ctr_loss = ctr_loss
self.loss = self.ctr_loss + self.aux_loss
return self.y_hat, self.loss
else:
return self.y_hat, paddle.ones(shape=[1])
