def __init__(self, features, class_num=1000):
super(VGG, self).__init__()
self.features = features
self.flatten = layers.Flatten()
self.classifier = layers.SequentialLayer([
layers.Dense(512 * 7 * 7, 4096),
layers.ReLU(),
layers.Dropout(),
layers.Dense(4096, 4096),
layers.ReLU(),
layers.Dropout(),
layers.Dense(4096, class_num),
])
def __init__(self,
in_channels,
out_channels,
initializer_range=0.02,
dropout_prob=0.1,
compute_type=ts.float32):
super(BertOutput, self).__init__()
self.dense = layers.Dense(in_channels, out_channels,
weight_init=TruncatedNormal(initializer_range)).to_float(compute_type)
self.dropout = layers.Dropout(1 - dropout_prob)
self.dropout_prob = dropout_prob
self.add = P.Add()
self.layernorm = layers.LayerNorm((out_channels,)).to_float(compute_type)
self.cast = P.Cast()
def __init__(self, config, is_training, num_labels=2, dropout_prob=0.0, use_one_hot_embeddings=False,
assessment_method=""):
super(BertCLSModel, self).__init__()
if not is_training:
config.hidden_dropout_prob = 0.0
config.hidden_probs_dropout_prob = 0.0
self.bert = Bert(config, is_training, use_one_hot_embeddings)
self.cast = P.Cast()
self.weight_init = TruncatedNormal(config.initializer_range)
self.log_softmax = P.LogSoftmax(axis=-1)
self.dtype = config.dtype
self.num_labels = num_labels
self.dense_1 = layers.Dense(config.hidden_size, self.num_labels, weight_init=self.weight_init,
has_bias=True).to_float(config.compute_type)
self.dropout = layers.Dropout(1 - dropout_prob)
self.assessment_method = assessment_method
def __init__(self, outer_nc, inner_nc, in_planes=None, dropout=False,
submodule=None, outermost=False, innermost=False, alpha=0.2, norm_mode='batch'):
super(UnetSkipConnectionBlock, self).__init__()
downnorm = layers.BatchNorm2d(inner_nc)
upnorm = layers.BatchNorm2d(outer_nc)
use_bias = False
if norm_mode == 'instance':
downnorm = layers.BatchNorm2d(inner_nc, affine=False)
upnorm = layers.BatchNorm2d(outer_nc, affine=False)
use_bias = True
if in_planes is None:
in_planes = outer_nc
downconv = layers.Conv2d(in_planes, inner_nc, kernel_size=4,
stride=2, padding=1, has_bias=use_bias, pad_mode='pad')
downrelu = layers.LeakyReLU(alpha)
uprelu = layers.ReLU()
if outermost:
upconv = layers.Conv2dTranspose(inner_nc * 2, outer_nc,
kernel_size=4, stride=2,
padding=1, pad_mode='pad')
down = [downconv]
up = [uprelu, upconv, layers.Tanh()]
model = down + [submodule] + up
elif innermost:
upconv = layers.Conv2dTranspose(inner_nc, outer_nc,
kernel_size=4, stride=2,
padding=1, has_bias=use_bias, pad_mode='pad')
down = [downrelu, downconv]
up = [uprelu, upconv, upnorm]
model = down + up
else:
upconv = layers.Conv2dTranspose(inner_nc * 2, outer_nc,
kernel_size=4, stride=2,
padding=1, has_bias=use_bias, pad_mode='pad')
down = [downrelu, downconv, downnorm]
up = [uprelu, upconv, upnorm]
model = down + [submodule] + up
if dropout:
model.append(layers.Dropout(0.5))
self.model = layers.SequentialLayer(model)
self.skip_connections = not outermost
self.concat = Concat(axis=1)
def __init__(self,
dim,
norm_mode='batch',
dropout=True,
pad_mode="CONSTANT"):
super(ResidualBlock, self).__init__()
self.conv1 = ConvNormReLU(dim, dim, 3, 1, 0, norm_mode, pad_mode)
self.conv2 = ConvNormReLU(dim,
dim,
3,
1,
0,
norm_mode,
pad_mode,
use_relu=False)
self.dropout = dropout
if dropout:
self.dropout = layers.Dropout(0.5)
def __init__(self, config, is_training, num_labels=11, use_crf=False, dropout_prob=0.0,
use_one_hot_embeddings=False):
super(BertNERModel, self).__init__()
if not is_training:
config.hidden_dropout_prob = 0.0
config.hidden_probs_dropout_prob = 0.0
self.bert = Bert(config, is_training, use_one_hot_embeddings)
self.cast = P.Cast()
self.weight_init = TruncatedNormal(config.initializer_range)
self.log_softmax = P.LogSoftmax(axis=-1)
self.dtype = config.dtype
self.num_labels = num_labels
self.dense_1 = layers.Dense(config.hidden_size, self.num_labels, weight_init=self.weight_init,
has_bias=True).to_float(config.compute_type)
self.dropout = layers.Dropout(1 - dropout_prob)
self.reshape = P.Reshape()
self.shape = (-1, config.hidden_size)
self.use_crf = use_crf
self.origin_shape = (-1, config.seq_length, self.num_labels)
def __init__(self,
embedding_size,
embedding_shape,
use_relative_positions=False,
use_token_type=False,
token_type_vocab_size=16,
use_one_hot_embeddings=False,
initializer_range=0.02,
max_position_embeddings=512,
dropout_prob=0.1):
super(EmbeddingPostprocessor, self).__init__()
self.use_token_type = use_token_type
self.token_type_vocab_size = token_type_vocab_size
self.use_one_hot_embeddings = use_one_hot_embeddings
self.max_position_embeddings = max_position_embeddings
self.embedding_table = Parameter(initializer
(TruncatedNormal(initializer_range),
[token_type_vocab_size,
embedding_size]),
name='embedding_table')
self.shape_flat = (-1,)
self.one_hot = layers.OneHot()
self.on_value = Tensor(1.0, ts.float32)
self.off_value = Tensor(0.1, ts.float32)
self.array_mul = P.MatMul()
self.reshape = P.Reshape()
self.shape = tuple(embedding_shape)
self.layernorm = layers.LayerNorm((embedding_size,))
self.dropout = layers.Dropout(1 - dropout_prob)
self.gather = P.Gather()
self.use_relative_positions = use_relative_positions
self.slice = P.StridedSlice()
self.full_position_embeddings = Parameter(initializer
(TruncatedNormal(initializer_range),
[max_position_embeddings,
embedding_size]),
name='full_position_embeddings')
def __init__(self,
from_tensor_width,
to_tensor_width,
from_seq_length,
to_seq_length,
num_attention_heads=1,
size_per_head=512,
query_act=None,
key_act=None,
value_act=None,
has_attention_mask=False,
attention_probs_dropout_prob=0.0,
use_one_hot_embeddings=False,
initializer_range=0.02,
do_return_2d_tensor=False,
use_relative_positions=False,
compute_type=ts.float32):
super(BertAttention, self).__init__()
self.from_seq_length = from_seq_length
self.to_seq_length = to_seq_length
self.num_attention_heads = num_attention_heads
self.size_per_head = size_per_head
self.has_attention_mask = has_attention_mask
self.use_relative_positions = use_relative_positions
self.scores_mul = 1.0 / math.sqrt(float(self.size_per_head))
self.reshape = P.Reshape()
self.shape_from_2d = (-1, from_tensor_width)
self.shape_to_2d = (-1, to_tensor_width)
weight = TruncatedNormal(initializer_range)
units = num_attention_heads * size_per_head
self.query_layer = layers.Dense(from_tensor_width,
units,
activation=query_act,
weight_init=weight).to_float(compute_type)
self.key_layer = layers.Dense(to_tensor_width,
units,
activation=key_act,
weight_init=weight).to_float(compute_type)
self.value_layer = layers.Dense(to_tensor_width,
units,
activation=value_act,
weight_init=weight).to_float(compute_type)
self.shape_from = (-1, from_seq_length, num_attention_heads, size_per_head)
self.shape_to = (-1, to_seq_length, num_attention_heads, size_per_head)
self.matmul_trans_b = P.BatchMatMul(transpose_b=True)
self.multiply = P.Mul()
self.transpose = P.Transpose()
self.trans_shape = (0, 2, 1, 3)
self.trans_shape_relative = (2, 0, 1, 3)
self.trans_shape_position = (1, 2, 0, 3)
self.multiply_data = -10000.0
self.matmul = P.BatchMatMul()
self.softmax = layers.Softmax()
self.dropout = layers.Dropout(1 - attention_probs_dropout_prob)
if self.has_attention_mask:
self.expand_dims = P.ExpandDims()
self.sub = P.Sub()
self.add = P.Add()
self.cast = P.Cast()
self.get_dtype = P.DType()
if do_return_2d_tensor:
self.shape_return = (-1, num_attention_heads * size_per_head)
else:
self.shape_return = (-1, from_seq_length, num_attention_heads * size_per_head)
self.cast_compute_type = SaturateCast(dst_type=compute_type)
if self.use_relative_positions:
self._generate_relative_positions_embeddings = \
RelaPosEmbeddingsGenerator(length=to_seq_length,
depth=size_per_head,
max_relative_position=16,
initializer_range=initializer_range,
use_one_hot_embeddings=use_one_hot_embeddings)
