Spaces:
Sleeping
Sleeping
File size: 7,361 Bytes
6ad6801 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 |
from utils.transformer_modules import *
from utils.transformer_modules import _gen_timing_signal, _gen_bias_mask
from utils.hparams import HParams
use_cuda = torch.cuda.is_available()
class self_attention_block(nn.Module):
def __init__(self, hidden_size, total_key_depth, total_value_depth, filter_size, num_heads,
bias_mask=None, layer_dropout=0.0, attention_dropout=0.0, relu_dropout=0.0, attention_map=False):
super(self_attention_block, self).__init__()
self.attention_map = attention_map
self.multi_head_attention = MultiHeadAttention(hidden_size, total_key_depth, total_value_depth,hidden_size, num_heads, bias_mask, attention_dropout, attention_map)
self.positionwise_convolution = PositionwiseFeedForward(hidden_size, filter_size, hidden_size, layer_config='cc', padding='both', dropout=relu_dropout)
self.dropout = nn.Dropout(layer_dropout)
self.layer_norm_mha = LayerNorm(hidden_size)
self.layer_norm_ffn = LayerNorm(hidden_size)
def forward(self, inputs):
x = inputs
# Layer Normalization
x_norm = self.layer_norm_mha(x)
# Multi-head attention
if self.attention_map is True:
y, weights = self.multi_head_attention(x_norm, x_norm, x_norm)
else:
y = self.multi_head_attention(x_norm, x_norm, x_norm)
# Dropout and residual
x = self.dropout(x + y)
# Layer Normalization
x_norm = self.layer_norm_ffn(x)
# Positionwise Feedforward
y = self.positionwise_convolution(x_norm)
# Dropout and residual
y = self.dropout(x + y)
if self.attention_map is True:
return y, weights
return y
class bi_directional_self_attention(nn.Module):
def __init__(self, hidden_size, total_key_depth, total_value_depth, filter_size, num_heads, max_length,
layer_dropout=0.0, attention_dropout=0.0, relu_dropout=0.0):
super(bi_directional_self_attention, self).__init__()
self.weights_list = list()
params = (hidden_size,
total_key_depth or hidden_size,
total_value_depth or hidden_size,
filter_size,
num_heads,
_gen_bias_mask(max_length),
layer_dropout,
attention_dropout,
relu_dropout,
True)
self.attn_block = self_attention_block(*params)
params = (hidden_size,
total_key_depth or hidden_size,
total_value_depth or hidden_size,
filter_size,
num_heads,
torch.transpose(_gen_bias_mask(max_length), dim0=2, dim1=3),
layer_dropout,
attention_dropout,
relu_dropout,
True)
self.backward_attn_block = self_attention_block(*params)
self.linear = nn.Linear(hidden_size*2, hidden_size)
def forward(self, inputs):
x, list = inputs
# Forward Self-attention Block
encoder_outputs, weights = self.attn_block(x)
# Backward Self-attention Block
reverse_outputs, reverse_weights = self.backward_attn_block(x)
# Concatenation and Fully-connected Layer
outputs = torch.cat((encoder_outputs, reverse_outputs), dim=2)
y = self.linear(outputs)
# Attention weights for Visualization
self.weights_list = list
self.weights_list.append(weights)
self.weights_list.append(reverse_weights)
return y, self.weights_list
class bi_directional_self_attention_layers(nn.Module):
def __init__(self, embedding_size, hidden_size, num_layers, num_heads, total_key_depth, total_value_depth,
filter_size, max_length=100, input_dropout=0.0, layer_dropout=0.0,
attention_dropout=0.0, relu_dropout=0.0):
super(bi_directional_self_attention_layers, self).__init__()
self.timing_signal = _gen_timing_signal(max_length, hidden_size)
params = (hidden_size,
total_key_depth or hidden_size,
total_value_depth or hidden_size,
filter_size,
num_heads,
max_length,
layer_dropout,
attention_dropout,
relu_dropout)
self.embedding_proj = nn.Linear(embedding_size, hidden_size, bias=False)
self.self_attn_layers = nn.Sequential(*[bi_directional_self_attention(*params) for l in range(num_layers)])
self.layer_norm = LayerNorm(hidden_size)
self.input_dropout = nn.Dropout(input_dropout)
def forward(self, inputs):
# Add input dropout
x = self.input_dropout(inputs)
# Project to hidden size
x = self.embedding_proj(x)
# Add timing signal
x += self.timing_signal[:, :inputs.shape[1], :].type_as(inputs.data)
# A Stack of Bi-directional Self-attention Layers
y, weights_list = self.self_attn_layers((x, []))
# Layer Normalization
y = self.layer_norm(y)
return y, weights_list
class BTC_model(nn.Module):
def __init__(self, config):
super(BTC_model, self).__init__()
self.timestep = config['timestep']
self.probs_out = config['probs_out']
params = (config['feature_size'],
config['hidden_size'],
config['num_layers'],
config['num_heads'],
config['total_key_depth'],
config['total_value_depth'],
config['filter_size'],
config['timestep'],
config['input_dropout'],
config['layer_dropout'],
config['attention_dropout'],
config['relu_dropout'])
self.self_attn_layers = bi_directional_self_attention_layers(*params)
self.output_layer = SoftmaxOutputLayer(hidden_size=config['hidden_size'], output_size=config['num_chords'], probs_out=config['probs_out'])
def forward(self, x, labels):
labels = labels.view(-1, self.timestep)
# Output of Bi-directional Self-attention Layers
self_attn_output, weights_list = self.self_attn_layers(x)
# return logit values for CRF
if self.probs_out is True:
logits = self.output_layer(self_attn_output)
return logits
# Output layer and Soft-max
prediction,second = self.output_layer(self_attn_output)
prediction = prediction.view(-1)
second = second.view(-1)
# Loss Calculation
loss = self.output_layer.loss(self_attn_output, labels)
return prediction, loss, weights_list, second
if __name__ == "__main__":
config = HParams.load("run_config.yaml")
device = torch.device("cuda" if use_cuda else "cpu")
batch_size = 2
timestep = 108
feature_size = 144
num_chords = 25
features = torch.randn(batch_size,timestep,feature_size,requires_grad=True).to(device)
chords = torch.randint(25,(batch_size*timestep,)).to(device)
model = BTC_model(config=config.model).to(device)
prediction, loss, weights_list, second = model(features, chords)
print(prediction.size())
print(loss)
|