def __init__(
self,
vocab_size,
embedding_size,
input_dropout,
fc_hidden_size,
num_classes,
rnn_units,
rnn_layers,
rnn_dropout,
):
super().__init__()
self.embedding = nn.Embedding(
vocab_size + 1,
embedding_size,
padding_idx=0,
)
self.dropout = nn.Dropout(input_dropout)
self.rnn = nn.GRU(
input_size=embedding_size,
hidden_size=rnn_units,
num_layers=rnn_layers,
batch_first=True,
dropout=rnn_dropout,
bidirectional=True,
)
self.global_pooling_max = nn.AdaptiveMaxPool1d(1)
self.global_pooling_avg = nn.AdaptiveAvgPool1d(1)
self.fc = nn.Sequential(
nn.Linear(2 * 2 * rnn_units, fc_hidden_size),
nn.ReLU(),
nn.Linear(fc_hidden_size, num_classes),
)
def __init__(self, n_classes: int, embedding_size: int, kernel_sizes_cnn: List[int],
filters_cnn: Union[int, List[int]], dense_size: int, dropout_rate: float = 0.0,
embedded_tokens: bool = True, vocab_size: Optional[int] = None, **kwargs):
super().__init__()
self.embedded_tokens = embedded_tokens
self.kernel_sizes_cnn = kernel_sizes_cnn
if not embedded_tokens and vocab_size:
self.embedding = nn.Embedding(vocab_size, embedding_size)
if isinstance(filters_cnn, int):
filters_cnn = len(kernel_sizes_cnn) * [filters_cnn]
for i in range(len(kernel_sizes_cnn)):
setattr(self, "conv_" + str(i), nn.Conv1d(embedding_size, filters_cnn[i], kernel_sizes_cnn[i],
padding=kernel_sizes_cnn[i]))
setattr(self, "bn_" + str(i), nn.BatchNorm1d(filters_cnn[i]))
setattr(self, "relu_" + str(i), nn.ReLU())
setattr(self, "pool_" + str(i), nn.AdaptiveMaxPool1d(1))
self.dropout = nn.Dropout(dropout_rate)
self.dense = nn.Linear(sum(filters_cnn), dense_size)
self.relu_dense = nn.ReLU()
self.final_dense = nn.Linear(dense_size, n_classes)
def __init__(self, input_channel, output_class):
super(SignalRecognitionNet_v4, self).__init__()
self.conv1 = nn.Conv1d(input_channel, 4, 3, 1, 1)
self.bn1 = nn.BatchNorm1d(4)
self.conv2 = nn.Conv1d(4, 4, 3, stride=1, padding=1) # P0
self.bn2 = nn.BatchNorm1d(4)
self.conv3 = nn.Conv1d(4, 8, 3, 2, 1) # P1 /2
self.bn3 = nn.BatchNorm1d(8)
self.conv4 = nn.Conv1d(8, 16, 3, 1, 1) # P2 /4
self.bn4 = nn.BatchNorm1d(16)
self.conv5 = nn.Conv1d(16, 32, 3, 2, 1) # P3 /8
self.bn5 = nn.BatchNorm1d(32)
self.conv6 = nn.Conv1d(32, 64, 3, 1, 1) # P4 /16
self.bn6 = nn.BatchNorm1d(64)
self.conv7 = nn.Conv1d(64, 32, 3, 2, 1) # P5 /32
self.bn7 = nn.BatchNorm1d(32)
self.roi_pooling = nn.AdaptiveMaxPool1d(32)
self.liner1 = nn.Linear(32 * 32, 32 * 16)
self.liner2 = nn.Linear(32 * 16, 16 * 16)
self.liner3 = nn.Linear(16 * 16, 16 * 8)
self.liner4 = nn.Linear(128, output_class)
def __init__(self,
block=ResidualBlock,
layers=[2, 2, 2, 2, 2, 2],
num_classes=28):
super(KKstreamModelRNNCNN3, self).__init__()
self.in_channels = 16
self.bi_gru = nn.GRU(23,
128,
num_layers=3,
dropout=0.375,
bidirectional=True)
self.conv1 = conv1x7(256, 16, padding=3)
self.bn1 = nn.BatchNorm1d(16)
self.elu = nn.ELU(inplace=True)
self.layer1 = self.make_layer(block, 16, layers[0])
self.layer2 = self.make_layer(block, 32, layers[1], 2)
self.layer3 = self.make_layer(block, 64, layers[2], 2) #small
self.layer4 = self.make_layer(block, 128, layers[3], 2) #big
self.layer5 = self.make_layer(block, 256, layers[4], 2) #large
self.layer6 = self.make_layer(block, 512, layers[5], 2) #large
self.gap = nn.AdaptiveMaxPool1d(1)
self.fc = nn.Linear(512, num_classes)
self.ativ = nn.Sigmoid()
def __init__(self,
input_channel_count,
output_channel_count,
kernel_size=5):
"""
Creates a model for combining character embeddings through a 1D
convolution.
Args:
- input_channel_count: The number of channels in the input
(input depth).
- output_channel_count (int): The number of filters used in the 1D
convolution. This gives the number of output
channels (output depth). In our case filter
will be embed_word_size (e_word)
- kernel_size (int): The size of the window used to compute features.
Default: 5
"""
super(CNN, self).__init__()
self.conv = nn.Conv1d(in_channels=input_channel_count,
out_channels=output_channel_count,
kernel_size=kernel_size)
self.maxpool = nn.AdaptiveMaxPool1d(output_size=1)
def __init__(self, bert_vocab_num, emb_dim, hidden_dim, output_dim):
super(Baseline, self).__init__()
self.bert_vocab_num = bert_vocab_num
self.emb_dim = emb_dim
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.embedding_layer = nn.Embedding(
num_embeddings=bert_vocab_num,
embedding_dim=emb_dim,
padding_idx=0,
)
self.gru_layer = PackingRNN(
nn.GRU(input_size=emb_dim,
hidden_size=hidden_dim,
num_layers=1,
batch_first=True,
bidirectional=True))
self.conv_layer = SamePaddingConv1d(emb_dim, hidden_dim, kernel_size=3)
self.max_pooler = nn.AdaptiveMaxPool1d(1)
self.fc = nn.Sequential(nn.Linear(hidden_dim * 3, hidden_dim),
nn.BatchNorm1d(hidden_dim), nn.ReLU(),
nn.Linear(hidden_dim, output_dim))
def __init__(self, ntokens: int, char_embedding_dim: int,
char_padding_idx: int, dropout: float, kernel_size: int,
out_channels: int, target_emb: int, use_highway: bool):
super(WordCharCNNEmbedding, self).__init__()
self._use_highway = use_highway
self._char_padding_idx = char_padding_idx
self.embedding_size = out_channels
if self._use_highway and out_channels != target_emb:
raise ValueError("out_channels and target_emb must be "
"equal in highway setting")
print("asdfsadf", ntokens)
self.char_embedding = nn.Embedding(ntokens, char_embedding_dim,
char_padding_idx)
self.conv_embedding = nn.Sequential(
nn.Dropout(p=dropout),
nn.Conv1d(in_channels=char_embedding_dim,
out_channels=out_channels,
kernel_size=kernel_size,
padding=kernel_size - 1), nn.AdaptiveMaxPool1d(1))
self.proj_layer = TimeDistributed(nn.Linear(out_channels, target_emb))
self.out_dropout = nn.Dropout(p=dropout)
def __init__(self, in_channel=1, out_channel=10):
super(Mexhat_LeNet, self).__init__()
self.conv1 = nn.Sequential(
Mexh_fast(64, 16),
nn.BatchNorm1d(64),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2, stride=2),
)
self.conv2 = nn.Sequential(
nn.Conv1d(64, 16, 5),
nn.BatchNorm1d(16),
nn.ReLU(),
nn.AdaptiveMaxPool1d(25) # adaptive change the outputsize to (16,5)
)
self.fc1 = nn.Sequential(
nn.Linear(16 * 25, 120),
nn.ReLU()
)
self.fc2 = nn.Sequential(
nn.Linear(120, 84),
nn.ReLU()
)
self.fc3 = nn.Linear(84, out_channel)
def __init__(self, encoder, w_init=xavier_init):
super().__init__()
self.encoder = encoder
self.total_labels = 264
self.layers = nn.ModuleList([
nn.Conv1d(in_channels=64, out_channels=32,
kernel_size=3, stride=1, padding=1),
nn.BatchNorm1d(32),
nn.ReLU(inplace=True),
nn.Conv1d(in_channels=32, out_channels=16,
kernel_size=3, stride=1, padding=1),
nn.BatchNorm1d(16),
nn.ReLU(inplace=True),
nn.Conv1d(in_channels=16, out_channels=1,
kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.AdaptiveMaxPool1d(1000),
nn.ReLU(inplace=True),
nn.Linear(1000, 264),
nn.Softmax(dim=0),
])
for layer in self.layers:
w_init(layer)
def __init__(self, num_classes: int, embedding_dim: int, k_max: int,
vocab: Vocab) -> None:
"""
:param num_classes: the number of classes
:param embedding_dim: Dimension of embedding vector for token
:param k_max: k_max pooling
:param vocab: gluonnlp
"""
super(VDCNN, self).__init__()
self._structure = nn.Sequential(
nn.Embedding(16, embedding_dim, 0), Permute(),
nn.Conv1d(embedding_dim, 64, kernel_size=(3, 1, 1)),
ConvBlock(64, 64), ConvBlock(64, 64), nn.MaxPool1d(2, 2),
ConvBlock(64, 128), ConvBlock(128, 128), nn.MaxPool1d(2, 2),
ConvBlock(128, 256), ConvBlock(256, 256), nn.MaxPool1d(2, 2),
ConvBlock(256, 512), ConvBlock(512, 512),
nn.AdaptiveMaxPool1d(k_max), FullyConnected())
self._classifier = nn.Sequential(nn.Linear(512 * k_max,
2048), nn.ReLU(),
nn.Linear(2048, 2048), nn.ReLU(),
nn.Linear(2048, num_classes))
def __init__(self):
super(NetworkCnn, self).__init__()
"""
TODO:
Create and initialise weights and biases for the layers.
"""
self.conv1 = tnn.Sequential(
tnn.Conv1d(in_channels=50,
out_channels=50,
kernel_size=8,
padding=5), tnn.ReLU(), tnn.MaxPool1d(kernel_size=4))
self.conv2 = tnn.Sequential(
tnn.Conv1d(in_channels=50,
out_channels=50,
kernel_size=8,
padding=5), tnn.ReLU(), tnn.MaxPool1d(kernel_size=4))
self.conv3 = tnn.Sequential(
tnn.Conv1d(in_channels=50,
out_channels=50,
kernel_size=8,
padding=5), tnn.ReLU(),
tnn.AdaptiveMaxPool1d(output_size=1))
self.fc1 = tnn.Linear(in_features=50, out_features=1)
def __init__(self, chunk_size=65536, overlap=512, min_chunk_size=1024):
"""
chunk_size: how many bytes at a time to process. Increasing may improve compute efficent, but use more memory. Total memory use will be a function of chunk_size, and not of the length of the input sequence L
overlap: how many bytes of overlap to use between chunks
"""
super(LowMemConvBase, self).__init__()
self.chunk_size = chunk_size
self.overlap = overlap
self.min_chunk_size = min_chunk_size
# Used for pooling over time in a meory efficent way
self.pooling = nn.AdaptiveMaxPool1d(1)
# self.pooling.register_backward_hook(drop_zeros_hook)
self.cat = CatMod()
self.cat.register_backward_hook(drop_zeros_hook)
self.receptive_field = None
# Used to force checkpoint code to behave correctly due to poor design https://discuss.pytorch.org/t/checkpoint-with-no-grad-requiring-inputs-problem/19117/11
self.dummy_tensor = torch.ones(1,
dtype=torch.float32,
requires_grad=True)
def __init__(self, one_action_actor_init):
network_init = one_action_actor_init['network']
super(NActionActor, self).__init__()
torch.manual_seed(one_action_actor_init['seed'])
self.action_dim = network_init["o_size"] // 3
self.entropy_learning_rate = one_action_actor_init[
'entropy_learning_rate']
self.optimizer = None
self.loss_history = list()
self.state_representation = one_action_actor_init[
"state_representation"]
self.relu = nn.ReLU()
self.conv1 = nn.Sequential(nn.ConstantPad1d(15 // 2, 0.25),
nn.Conv1d(4, 400, 15), nn.LeakyReLU(0.1),
nn.AdaptiveMaxPool1d(1))
self.l1 = nn.Linear(network_init["i_size"], network_init["l1_size"])
self.l2 = nn.Linear(network_init["l1_size"], network_init["l2_size"])
self.l3 = nn.Linear(network_init["l2_size"], network_init["o_size"])
self.softmax = nn.Softmax(dim=1)
self.softmax_dim_0 = nn.Softmax(dim=0)
def __init__(self, backbone, num_classes, feature_dim, criterion=None):
super(MultiTaskWithLoss, self).__init__()
self.basemodel = backbones.__dict__[backbone]()
self.criterion = criterion
self.num_tasks = len(num_classes)
#普通
self.fcs = nn.ModuleList([
nn.Linear(feature_dim, num_classes[k])
for k in range(self.num_tasks)
])
# 第一任务加空间注意力 -- 要注意backbone里要去掉avg_pooling及之后的层，都搬到外面来
# self.global_avg_pooling = nn.AdaptiveAvgPool2d(1)
# self.ca = ChannelAttention(feature_dim)
# self.sa = SpatialAttention()
# self.fcs = nn.ModuleList()
# for k in range(self.num_tasks):
# # if k == 1:
# # self.fcs.append(nn.Sequential(self.sa,nn.Linear(feature_dim, num_classes[k]))) #sa
# self.fcs.append(nn.Sequential(self.ca,self.sa,nn.Linear(feature_dim, num_classes[k])))
# else:
# self.fcs.append(nn.Linear(feature_dim, num_classes[k]))
self.layers = nn.AdaptiveMaxPool1d(10)
def __init__(self):
super(CharEncoder, self).__init__()
self.char_embedding = nn.Embedding(num_embeddings=2000, # 字符表的大小
embedding_dim=100, # 字符向量的维度
padding_idx=0) # 0索引的向量置0
nn.init.xavier_uniform_(self.char_embedding.weight, gain=nn.init.calculate_gain('relu'))
# nn.init.uniform_(self.char_embedding.weight.data, -0.32, 0.32)
self._win_sizes = [3, 4, 5]
self._cnn_encoder = nn.ModuleList([
nn.Sequential(
nn.Conv1d(in_channels=100, # 字符向量维度
out_channels=50, # 卷积输出的特征维度
kernel_size=w),
nn.ReLU(),
nn.AdaptiveMaxPool1d(1)
) for w in self._win_sizes
])
# 使卷积提取的特征更加稠密
self.cnn_dense = nn.Linear(in_features=50 * len(self._win_sizes),
out_features=150)
self.bilstm = RNNNet(input_size=150,
hidden_size=200,
nb_layer=1,
dropout=0.0,
batch_first=True,
bidirectional=True)
# 分类
self.dense = nn.Sequential(
nn.Linear(200, 100),
nn.ReLU(),
nn.Linear(100, 3)
)
def __init__(self, pretrained=False, in_channel=1, out_channel=10):
super(CNN, self).__init__()
if pretrained == True:
warnings.warn("Pretrained model is not available")
self.layer1 = nn.Sequential(
nn.Conv1d(in_channel, 16, kernel_size=15), # 16, 26 ,26
nn.BatchNorm1d(16),
nn.ReLU(inplace=True))
self.layer2 = nn.Sequential(
nn.Conv1d(16, 32, kernel_size=3), # 32, 24, 24
nn.BatchNorm1d(32),
nn.ReLU(inplace=True),
nn.MaxPool1d(kernel_size=2, stride=2),
) # 32, 12,12 (24-2) /2 +1
self.layer3 = nn.Sequential(
nn.Conv1d(32, 64, kernel_size=3), # 64,10,10
nn.BatchNorm1d(64),
nn.ReLU(inplace=True))
self.layer4 = nn.Sequential(
nn.Conv1d(64, 128, kernel_size=3), # 128,8,8
nn.BatchNorm1d(128),
nn.ReLU(inplace=True),
nn.AdaptiveMaxPool1d(4)) # 128, 4,4
self.layer5 = nn.Sequential(
nn.Linear(128 * 4, 256),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(256, 256),
nn.ReLU(inplace=True),
nn.Dropout(),
)
self.fc = nn.Linear(256, out_channel)
def __init__(self, config):
super().__init__()
self.output_channel = config.output_channel
self.mode = config.mode
self.batchnorm = config.batchnorm
self.beta_ema = config.beta_ema
self.dynamic_pool = config.dynamic_pool
self.dynamic_pool_length = config.dynamic_pool_length
self.has_bottleneck = config.bottleneck_layer
self.bottleneck_units = config.bottleneck_units
self.filter_widths = 3
config.vocab = config.dataset.CODE_FIELD.vocab.vectors
self.collection_encoder = CollectionEncoder(config)
self.dropout = nn.Dropout(config.dropout)
if self.dynamic_pool:
self.dynamic_pool = nn.AdaptiveMaxPool1d(self.dynamic_pool_length) # Dynamic pooling
if self.has_bottleneck:
self.fc1 = nn.Linear(self.filter_widths * self.output_channel * self.dynamic_pool_length, self.bottleneck_units)
self.fc2 = nn.Linear(self.bottleneck_units, config.target_class)
else:
self.fc1 = nn.Linear(self.filter_widths * self.output_channel * self.dynamic_pool_length, config.target_class)
else:
if self.has_bottleneck:
self.fc1 = nn.Linear(self.filter_widths * self.output_channel, self.bottleneck_units)
self.fc2 = nn.Linear(self.bottleneck_units, config.target_class)
else:
self.fc1 = nn.Linear(self.filter_widths * self.output_channel, config.target_class)
if self.beta_ema > 0:
self.avg_param = deepcopy([p.data for p in self.parameters()])
if torch.cuda.is_available():
self.avg_param = [a.cuda() for a in self.avg_param]
self.steps_ema = 0.0
def __init__(self, max_features, token_len, embedding_size, weights):
super(AvitorWord, self).__init__()
self.max_features = max_features
self.embedding_size = embedding_size
self.token_len = token_len
self.n_word_layer = len(token_len)
self.word_layers = []
for i, tkl in enumerate(token_len):
embedding = nn.Embedding(self.max_features, self.embedding_size)
embedding.weight = nn.Parameter(
torch.from_numpy(np.array(weights)).double())
embedding.weight.requires_grad = False
word_layer = nn.Sequential(
embedding,
# FloatTensor(),
# nn.Dropout(0.5),
TensorRotate(),
# BiRNN(self.embedding_size, 32, 2, 16),
nn.BatchNorm1d(self.embedding_size),
nn.Conv1d(in_channels=self.embedding_size,
out_channels=50,
kernel_size=3),
nn.ReLU(),
nn.AdaptiveMaxPool1d(1),
Flatten(),
nn.Dropout(0.5))
self.add_module(f"word_layer_{i}", word_layer)
self.word_layers.append(word_layer)
self.out_features = 50
for m in self.modules():
if isinstance(m, nn.Conv1d):
nn.init.xavier_normal_(m.weight)
def __init__(self, vocab, device, num_classes=3, hidden_size=512, attention_dim=256, embeddings_dim=300, linear_pre_1dim=32, linear_pre_2dim=64, pad_token='<pad>', use_pretrained_embeddings=True, dropout_rate=0.1):
super(RNN_Combined_Model, self).__init__()
self.embedding_size = (embeddings_dim if not use_pretrained_embeddings else vocab.vectors.shape[1])
self.vocabulary_size = len(vocab)
self.hidden_size=hidden_size
self.device=device
self.sentence1_embedding = nn.Embedding(self.vocabulary_size, self.embedding_size, padding_idx=vocab.stoi[pad_token])
self.sentence2_embedding = nn.Embedding(self.vocabulary_size, self.embedding_size, padding_idx=vocab.stoi[pad_token])
if use_pretrained_embeddings:
self.sentence1_embedding.from_pretrained(vocab.vectors, freeze=False, padding_idx=vocab.stoi[pad_token])
self.sentence2_embedding.from_pretrained(vocab.vectors, freeze=False, padding_idx=vocab.stoi[pad_token])
self.sentence1_dropout = nn.Dropout(dropout_rate)
self.sentence2_dropout = nn.Dropout(dropout_rate)
self.sentence1_lstm=nn.LSTM(input_size=self.embedding_size, hidden_size=hidden_size, bidirectional=True, batch_first=True)
self.sentence2_lstm = nn.LSTM(input_size=self.embedding_size, hidden_size=hidden_size, bidirectional=True, batch_first=True)
self.linear_sentence1=nn.Linear(self.hidden_size * 2, attention_dim)
self.linear_sentence2 = nn.Linear(self.hidden_size * 2, attention_dim)
self.linear_combined = nn.Linear(3*attention_dim, attention_dim)
self.pool = nn.AdaptiveAvgPool1d(1)
self.pool1 = nn.AdaptiveMaxPool1d(1)
self.linear_pre2_classifier=nn.Linear(attention_dim,linear_pre_2dim)
self.bn_pre2_classifier=nn.BatchNorm1d(linear_pre_2dim)
self.relu2 = nn.ReLU()
self.linear_pre1_classifier=nn.Linear(linear_pre_2dim, linear_pre_1dim)
self.bn_pre1_classifier = nn.BatchNorm1d(linear_pre_1dim)
self.relu1 = nn.ReLU()
self.classifier=nn.Linear(linear_pre_1dim, num_classes)
def forward(self, x):
batch_size = x.size(0)
x = self.conv1(
x
) # (batch_size, 3*2, num_points, k) -> (batch_size, 64, num_points, k)
x = self.conv1_2(x)
x = self.conv2(
x
) # (batch_size, 64, num_points, k) -> (batch_size, 128, num_points, k)
m = nn.AdaptiveMaxPool2d((x.size(2), 1)) #.view(batch_size, -1)
x = m(x).squeeze(-1)
#x = x.max(dim=-1, keepdim=False)[0] # (batch_size, 128, num_points, k) -> (batch_size, 128, num_points)
x = self.conv3(
x
) # (batch_size, 128, num_points) -> (batch_size, 1024, num_points)
m = nn.AdaptiveMaxPool1d(1) #.view(batch_size, -1)
x = m(x).squeeze(-1)
#x = x.max(dim=-1, keepdim=False)[0] # (batch_size, 1024, num_points) -> (batch_size, 1024)
x = F.leaky_relu(
self.bn4(self.linear1(x)),
negative_slope=0.2) # (batch_size, 1024) -> (batch_size, 512)
x = F.leaky_relu(
self.bn5(self.linear2(x)),
negative_slope=0.2) # (batch_size, 512) -> (batch_size, 256)
#initialize as identity
init = torch.eye(self.k, requires_grad=True).repeat(batch_size, 1, 1)
if x.is_cuda:
init = init.cuda()
x = self.transform(x) # (batch_size, 256) -> (batch_size, 3*3)
x = x.view(batch_size, self.k,
self.k) + init # (batch_size, 3*3) -> (batch_size, 3, 3)
return x
def __init__(self, n_classes):
super(Net2, self).__init__()
self.n_classes = n_classes
kernel_size = 5
padding = (kernel_size - 1) / 2
stride = 1
width = 32
self.dropout = nn.Dropout(0.2)
self.conv1 = nn.Conv1d(13,
width,
kernel_size,
stride,
padding,
1,
bias=False)
self.bn1 = nn.BatchNorm1d(width)
self.conv2 = nn.Conv1d(width,
2 * width,
kernel_size,
stride,
padding,
2,
bias=False)
self.bn2 = nn.BatchNorm1d(2 * width)
self.conv3 = nn.Conv1d(2 * width,
4 * width,
kernel_size,
stride,
padding,
4,
bias=False)
self.bn3 = nn.BatchNorm1d(4 * width)
self.pool = nn.MaxPool1d(2)
self.globalpool = nn.AdaptiveMaxPool1d(1)
self.fc = nn.Linear(4 * width, n_classes)
def __init__(self, num_classes: int, embedding_dim: int, k_max: int,
embedding_size: int) -> None:
"""Instantiating VDCNN class
Args:
num_classes (int): the number of classes
embedding_dim (int): embedding dimension of token
k_max (int): parameter of k-max pooling following last convolution block
embedding_size (dict): token2idx
"""
super(VDCNN, self).__init__()
self._extractor = nn.Sequential(
nn.Embedding(embedding_size, embedding_dim, 1), Permute(),
nn.Conv1d(embedding_dim, 64, 3, 1, 1), ConvBlock(64, 64),
ConvBlock(64, 64), nn.MaxPool1d(2, 2), ConvBlock(64, 128),
ConvBlock(128, 128), nn.MaxPool1d(2, 2), ConvBlock(128, 256),
ConvBlock(256, 256), nn.MaxPool1d(2, 2), ConvBlock(256, 512),
ConvBlock(512, 512), nn.AdaptiveMaxPool1d(k_max), Flatten())
self._classifier = nn.Sequential(nn.Linear(512 * k_max,
2048), nn.ReLU(),
nn.Linear(2048, 2048), nn.ReLU(),
nn.Linear(2048, num_classes))
def __init__(self, num_classes=40, use_normals=True, points=512,
blocks=[1, 2, 1, 1], embed_channel=32, k_neighbors=[16, 16, 16, 16],
heads=8, dim_head=16, expansion=2, reducer=4, pool="avg", **kwargs):
super(Develop1, self).__init__()
self.stages = len(blocks)
self.num_classes = num_classes
channel = 6 if use_normals else 3
self.linear = nn.Linear(channel, embed_channel)
self.transformer_stages = nn.ModuleList()
self.transformer_downs = nn.ModuleList()
self.groupers = nn.ModuleList()
for stage, block_num in enumerate(blocks):
# for appending transformer blocks
factor = expansion ** stage
factor_d = int(math.sqrt(factor))
factor_h = factor // factor_d
transformer_blocks = []
for _ in range(block_num):
transformer_blocks.append(
TransformerBlock(dim=embed_channel * factor, heads=heads * factor_h, dim_head=dim_head * factor_d)
)
transformer_blocks = nn.Sequential(*transformer_blocks)
self.transformer_stages.append(transformer_blocks)
# for appending transformer groups
knn = k_neighbors[stage]
self.groupers.append(FPSKNNGrouper(points=points // (reducer ** stage), knn=knn))
# for appending transformer downs
self.transformer_downs.append(
TransformerDown(in_dim=embed_channel * factor, out_dim=embed_channel * factor * expansion,
hid_dim=embed_channel)
)
self.pool = nn.AdaptiveAvgPool1d(1) if pool=="avg" else nn.AdaptiveMaxPool1d(1)
self.classify = nn.Linear(embed_channel * factor * expansion, num_classes)
def __init__(self):
super(LSTMEncoder, self).__init__()
self.input_dim = 1
self.hidden_dim = 16
self.nl = 1
self.bidir = True
self.direction = 1
if self.bidir:
self.direction = 2
# Layers
# self.pool0 = nn.MaxPool1d(10, 1)
self.pool0 = nn.AdaptiveMaxPool1d(400)
self.rnn = nn.LSTM(input_size=self.input_dim,
hidden_size=self.hidden_dim,
num_layers=self.nl,
bidirectional=self.bidir,
dropout=0.1,
batch_first=True)
self.fc1 = Dense(32, 32)
self.fc2 = Dense(32, 16)
self.fc3 = Dense(16, 8)
self.act = nn.ELU()
self.fc_out = nn.Linear(16, 1, bias=True)
def __init__(self, word_dim, num_channels:list, kernel_sizes:list, is_batch_normal:bool):
super(baseline_TextCNN_encoder, self).__init__()
self.pool = nn.AdaptiveMaxPool1d(output_size=1)
self.convs = nn.ModuleList()
if is_batch_normal:
for c, k in zip(num_channels, kernel_sizes):
self.convs.append(
nn.Sequential(
nn.Conv1d(in_channels=word_dim,
out_channels=c,
kernel_size=k),
nn.BatchNorm1d(c)
)
)
else:
for c, k in zip(num_channels, kernel_sizes):
self.convs.append(
nn.Conv1d(in_channels=word_dim,
out_channels=c,
kernel_size=k)
)
def __init__(self,
input_size,
filter_sizes,
num_filter_per_size,
activation,
dropout=0,
num_pool=0,
channel_last=False):
super(CNNEncoder, self).__init__()
if not filter_sizes:
raise ValueError(f'CNNEncoder expect non-empty filter_sizes. '
f'Got: {filter_sizes}')
self.channel_last = channel_last
self.convs = nn.ModuleList()
for filter_size in filter_sizes:
conv = nn.Conv1d(in_channels=input_size,
out_channels=num_filter_per_size,
kernel_size=filter_size)
self.convs.append(conv)
self.num_pool = num_pool
if num_pool > 1:
self.pool = nn.AdaptiveMaxPool1d(num_pool)
self.activation = getattr(torch, activation, getattr(F, activation))
self.dropout = nn.Dropout(dropout)
def __init__(self):
super(NetworkCnn, self).__init__()
"""
Create and initialise weights and biases for the layers.
"""
self.conv1 = tnn.Conv1d(in_channels=50,
kernel_size=8,
padding=5,
out_channels=50)
self.ReLU1 = tnn.ReLU()
self.maxpool1 = tnn.MaxPool1d(4)
self.conv2 = tnn.Conv1d(in_channels=50,
kernel_size=8,
padding=5,
out_channels=50)
self.ReLU2 = tnn.ReLU()
self.maxpool2 = tnn.MaxPool1d(4)
self.conv3 = tnn.Conv1d(in_channels=50,
kernel_size=8,
padding=5,
out_channels=50)
self.ReLU3 = tnn.ReLU()
self.maxpoolglobal = tnn.AdaptiveMaxPool1d(50)
self.fc1 = tnn.Linear(50 * 50, 1)
def __init__(self, word_vecs, model_type, h, feature, k, p, max_l):
super(CNN, self).__init__()
v = word_vecs.size()[0]
# Embedding Layer
self.ch = 1
self.emb = nn.Embedding(v, k, padding_idx=0)
if model_type != "rand":
self.emb.weight.data.copy_(word_vecs)
if model_type == "static":
self.emb.weight.requires_grad = False
elif model_type == "multichannel":
self.emb_multi = nn.Embedding(v, k, padding_idx=0)
self.emb_multi.weight.data.copy_(word_vecs)
self.emb_multi.weight.requires_grad = False
self.ch = 2
# Convolutional Layer
for w in h:
conv = nn.Conv1d(self.ch, feature, w * k, stride=k)
setattr(self, 'conv%d' % w, conv)
# Pooling Layer
self.pool = nn.AdaptiveMaxPool1d(1)
# FC Layer
self.fc = nn.Linear(len(h) * feature, 2)
# Other Layers
self.dropout = nn.Dropout(p)
self.relu = nn.ReLU()
self.h = h
self.feature = feature
self.k = k
self.max_l = max_l
def __init__(self):
super(NetworkCnn, self).__init__()
"""
TODO:
Create and initialise weights and biases for the layers.
"""
self.conv1 = tnn.Conv1d(in_channels=50,
out_channels=50,
kernel_size=8,
padding=5)
self.conv2 = tnn.Conv1d(in_channels=50,
out_channels=50,
kernel_size=8,
padding=5)
self.conv3 = tnn.Conv1d(in_channels=50,
out_channels=50,
kernel_size=8,
padding=5)
self.dropout = tnn.Dropout(p=0.3)
self.maxpool = tnn.MaxPool1d(kernel_size=4)
self.relu = tnn.ReLU()
self.globalmaxpool = tnn.AdaptiveMaxPool1d(1)
self.fc1 = tnn.Linear(50, 1)
def __init__(self, input_size,
output_size,
kernel_sizes=(1, 3, 5),
filter_nums=(50, 100, 150),
dropout=0.0):
super(MWCNN, self).__init__()
self.dropout = dropout
for k in kernel_sizes:
assert k % 2 == 1
self.convs = nn.ModuleList([
nn.Sequential(
nn.Conv1d(in_channels=input_size,
out_channels=filter_nums[i],
kernel_size=k,
padding=k // 2),
nn.ReLU(),
nn.AdaptiveMaxPool1d(1)
) for i, k in enumerate(kernel_sizes)
])
self.fc = nn.Linear(sum(filter_nums), output_size)