def __init__(self,
d_model,
nhead,
dim_feedforward=2048,
attention_dropout_rate=0.0,
residual_dropout_rate=0.1):
super(TransformerDecoderLayer, self).__init__()
self.self_attn = MultiheadAttention(d_model,
nhead,
dropout=attention_dropout_rate)
self.multihead_attn = MultiheadAttention(
d_model, nhead, dropout=attention_dropout_rate)
self.conv1 = Conv2D(in_channels=d_model,
out_channels=dim_feedforward,
kernel_size=(1, 1))
self.conv2 = Conv2D(in_channels=dim_feedforward,
out_channels=d_model,
kernel_size=(1, 1))
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
self.norm3 = LayerNorm(d_model)
self.dropout1 = Dropout(residual_dropout_rate)
self.dropout2 = Dropout(residual_dropout_rate)
self.dropout3 = Dropout(residual_dropout_rate)
def __init__(self,
d_model,
nhead,
dim_feedforward,
dropout=0.1,
activation="relu",
attn_dropout=None,
act_dropout=None,
normalize_before=False,
weight_attr=None,
bias_attr=None,
attention_type="bigbird",
block_size=1,
window_size=3,
num_global_blocks=1,
num_rand_blocks=1,
seed=None):
self._config = locals()
self._config.pop("self")
self._config.pop("__class__", None) # py3
super(TransformerEncoderLayer, self).__init__()
attn_dropout = dropout if attn_dropout is None else attn_dropout
act_dropout = dropout if act_dropout is None else act_dropout
self.normalize_before = normalize_before
weight_attrs = _convert_param_attr_to_list(weight_attr, 2)
bias_attrs = _convert_param_attr_to_list(bias_attr, 2)
self.self_attn = MultiHeadAttention(
d_model,
nhead,
dropout=attn_dropout,
weight_attr=weight_attrs[0],
bias_attr=bias_attrs[0],
attention_type=attention_type,
block_size=block_size,
window_size=window_size,
num_global_blocks=num_global_blocks,
num_rand_blocks=num_rand_blocks,
seed=seed)
self.linear1 = Linear(d_model,
dim_feedforward,
weight_attrs[1],
bias_attr=bias_attrs[1])
self.dropout = Dropout(act_dropout, mode="upscale_in_train")
self.linear2 = Linear(dim_feedforward,
d_model,
weight_attrs[1],
bias_attr=bias_attrs[1])
self.norm1 = LayerNorm(d_model, epsilon=1e-12)
self.norm2 = LayerNorm(d_model, epsilon=1e-12)
self.dropout1 = Dropout(dropout, mode="upscale_in_train")
self.dropout2 = Dropout(dropout, mode="upscale_in_train")
self.activation = getattr(F, activation)
self.d_model = d_model
def __init__(self, encoder_layer, num_layers):
super(TransformerEncoder, self).__init__()
self.layers = LayerList([(encoder_layer if i == 0 else
type(encoder_layer)(**encoder_layer._config))
for i in range(num_layers)])
self.num_layers = num_layers
self.norm = LayerNorm(self.layers[0].d_model, epsilon=1e-12)
self.normalize_before = self.layers[0].normalize_before
def __init__(self):
super(ModelCase4, self).__init__()
self.bn1 = BatchNorm2D(3)
self.ln1 = LayerNorm([3 * 16 * 16])
self.relu1 = ReLU()
self.fc1 = paddle.nn.Linear(3 * 16 * 16, 3 * 16 * 16)
