File size: 8,359 Bytes
6b25f66
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
import math
import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
from typing import Any, Dict, List, Optional, Tuple, Type, Union


class Chomp1d(nn.Module):
    def __init__(self, chomp_size):
        super(Chomp1d, self).__init__()
        self.chomp_size = chomp_size

    def forward(self, x):
        return x[:, :, : -self.chomp_size].contiguous()


class TemporalBlock(nn.Module):
    def __init__(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding, dropout=0.2):
        super(TemporalBlock, self).__init__()
        self.conv1 = weight_norm(nn.Conv1d(n_inputs, n_outputs, kernel_size, stride=stride, padding=padding, dilation=dilation))
        self.chomp1 = Chomp1d(padding)
        self.relu1 = nn.ReLU()
        self.dropout1 = nn.Dropout(dropout)

        self.conv2 = weight_norm(nn.Conv1d(n_outputs, n_outputs, kernel_size, stride=stride, padding=padding, dilation=dilation))
        self.chomp2 = Chomp1d(padding)
        self.relu2 = nn.ReLU()
        self.dropout2 = nn.Dropout(dropout)

        self.net = nn.Sequential(self.conv1, self.chomp1, self.relu1, self.dropout1, self.conv2, self.chomp2, self.relu2, self.dropout2)
        self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None
        self.relu = nn.ReLU()
        self.init_weights()

    def init_weights(self):
        self.conv1.weight.data.normal_(0, 0.01)
        self.conv2.weight.data.normal_(0, 0.01)
        if self.downsample is not None:
            self.downsample.weight.data.normal_(0, 0.01)

    def forward(self, x):
        out = self.net(x)
        res = x if self.downsample is None else self.downsample(x)
        return self.relu(out + res)


class TemporalConvNet(nn.Module):
    def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2):
        super(TemporalConvNet, self).__init__()
        layers = []
        num_levels = len(num_channels)
        for i in range(num_levels):
            dilation_size = 2**i
            in_channels = num_inputs if i == 0 else num_channels[i - 1]
            out_channels = num_channels[i]
            layers += [
                TemporalBlock(
                    in_channels,
                    out_channels,
                    kernel_size,
                    stride=1,
                    dilation=dilation_size,
                    padding=(kernel_size - 1) * dilation_size,
                    dropout=dropout,
                )
            ]

        self.network = nn.Sequential(*layers)

    def forward(self, x):
        return self.network(x)


class TextEncoderTCN(nn.Module):
    """
    based on https://github.com/locuslab/TCN/blob/master/TCN/word_cnn/model.py
    Licensed under: https://github.com/locuslab/TCN/blob/master/LICENSE
    """

    def __init__(
        self, args, n_words=11195, embed_size=300, pre_trained_embedding=None, kernel_size=2, dropout=0.3, emb_dropout=0.1, word_cache=False
    ):
        super(TextEncoderTCN, self).__init__()
        num_channels = [args.hidden_size]  # * args.n_layer
        self.tcn = TemporalConvNet(embed_size, num_channels, kernel_size, dropout=dropout)
        self.decoder = nn.Linear(num_channels[-1], args.word_f)
        self.drop = nn.Dropout(emb_dropout)
        self.init_weights()

    def init_weights(self):
        self.decoder.bias.data.fill_(0)
        self.decoder.weight.data.normal_(0, 0.01)

    def forward(self, input):
        y = self.tcn(input.transpose(1, 2)).transpose(1, 2)
        y = self.decoder(y)
        return y, torch.max(y, dim=1)[0]


def reparameterize(mu, logvar):
    std = torch.exp(0.5 * logvar)
    eps = torch.randn_like(std)
    return mu + eps * std


def ConvNormRelu(in_channels, out_channels, downsample=False, padding=0, batchnorm=True):
    if not downsample:
        k = 3
        s = 1
    else:
        k = 4
        s = 2
    conv_block = nn.Conv1d(in_channels, out_channels, kernel_size=k, stride=s, padding=padding)
    norm_block = nn.BatchNorm1d(out_channels)
    if batchnorm:
        net = nn.Sequential(conv_block, norm_block, nn.LeakyReLU(0.2, True))
    else:
        net = nn.Sequential(conv_block, nn.LeakyReLU(0.2, True))
    return net


class BasicBlock(nn.Module):
    """
    based on timm: https://github.com/huggingface/pytorch-image-models/blob/f689c850b90b16a45cc119a7bc3b24375636fc63/timm/models/resnet.py#L34
    Licensed under: https://github.com/huggingface/pytorch-image-models/blob/main/LICENSE
    """

    def __init__(
        self,
        inplanes: int,
        planes: int,
        ker_size: int,  # add kernel size
        stride: int = 1,
        downsample: Optional[nn.Module] = None,
        cardinality: int = 1,
        base_width: int = 64,
        reduce_first: int = 1,
        dilation: int = 1,
        first_dilation: Optional[int] = None,
        act_layer: Type[nn.Module] = nn.LeakyReLU,  # change activation function from ReLU to LeakyReLU
        norm_layer: Type[nn.Module] = nn.BatchNorm1d,  # change norm layer from BatchNorm2d to BatchNorm1d
        attn_layer: Optional[Type[nn.Module]] = None,
        aa_layer: Optional[Type[nn.Module]] = None,
        drop_block: Optional[Type[nn.Module]] = None,
        drop_path: Optional[nn.Module] = None,
    ):
        super(BasicBlock, self).__init__()

        """
        Original Layer definition:
        https://github.com/huggingface/pytorch-image-models/blob/f689c850b90b16a45cc119a7bc3b24375636fc63/timm/models/resnet.py#L75C1-L100C35
        """
        # Revised from here
        self.conv1 = nn.Conv1d(inplanes, planes, kernel_size=ker_size, stride=stride, padding=first_dilation, dilation=dilation, bias=True)
        self.bn1 = norm_layer(planes)
        self.act1 = act_layer(inplace=True)
        self.conv2 = nn.Conv1d(planes, planes, kernel_size=ker_size, padding=ker_size // 2, dilation=dilation, bias=True)
        self.bn2 = norm_layer(planes)
        self.act2 = act_layer(inplace=True)
        if downsample is not None:
            self.downsample = nn.Sequential(
                nn.Conv1d(inplanes, planes, stride=stride, kernel_size=ker_size, padding=first_dilation, dilation=dilation, bias=True),
                norm_layer(planes),
            )
        else:
            self.downsample = None
        self.stride = stride
        self.dilation = dilation
        self.drop_block = drop_block
        self.drop_path = drop_path
        # Until here

    def zero_init_last(self):
        if getattr(self.bn2, "weight", None) is not None:
            nn.init.zeros_(self.bn2.weight)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        shortcut = x
        """
        Original Layer Sequence:
        https://github.com/huggingface/pytorch-image-models/blob/f689c850b90b16a45cc119a7bc3b24375636fc63/timm/models/resnet.py#L209C1-L226C34
        """
        # Revised from here
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.act1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        # Until here

        if self.downsample is not None:
            shortcut = self.downsample(shortcut)
        x += shortcut
        x = self.act2(x)
        return x


def init_weight(m):
    if isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear) or isinstance(m, nn.ConvTranspose1d):
        nn.init.xavier_normal_(m.weight)
        # m.bias.data.fill_(0.01)
        if m.bias is not None:
            nn.init.constant_(m.bias, 0)


def init_weight_skcnn(m):
    if isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear) or isinstance(m, nn.ConvTranspose1d):
        nn.init.kaiming_uniform_(m.weight, a=math.sqrt(5))
        # m.bias.data.fill_(0.01)
        if m.bias is not None:
            # nn.init.constant_(m.bias, 0)
            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(m.weight)
            bound = 1 / math.sqrt(fan_in)
            nn.init.uniform_(m.bias, -bound, bound)


class ResBlock(nn.Module):
    def __init__(self, channel):
        super(ResBlock, self).__init__()
        self.model = nn.Sequential(
            nn.Conv1d(channel, channel, kernel_size=3, stride=1, padding=1),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv1d(channel, channel, kernel_size=3, stride=1, padding=1),
        )

    def forward(self, x):
        residual = x
        out = self.model(x)
        out += residual
        return out