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274
base_models.py
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274
base_models.py
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import numpy as np, pdb
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import functools
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.utils.data import Dataset
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class SSLDataSet(Dataset):
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def __init__(self, data, clicks):
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super(SSLDataSet, self).__init__()
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self.data = data
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self.clicks = clicks
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def __getitem__(self, idx):
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return torch.tensor(self.data[idx], dtype=torch.float), torch.tensor(self.clicks[idx], dtype=torch.float)
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def __len__(self):
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return self.data.shape[0]
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class FTDataSet(Dataset):
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def __init__(self, data, label, clicks, actions=None, multi_label=False):
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super(FTDataSet, self).__init__()
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self.data = data
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self.label = label
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self.clicks = clicks
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self.multi_label = multi_label
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self.actions = actions
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def __getitem__(self, index):
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if self.multi_label:
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if self.actions is None:
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return (torch.tensor(self.data[index], dtype=torch.float), torch.tensor(self.label[index], dtype=torch.float), torch.tensor(self.clicks[index], dtype=torch.float))
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else:
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return (torch.tensor(self.data[index], dtype=torch.float), torch.tensor(self.label[index], dtype=torch.float), torch.tensor(self.clicks[index], dtype=torch.float), torch.tensor(self.actions[index], dtype=torch.float))
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else:
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return (torch.tensor(self.data[index], dtype=torch.float), torch.tensor(self.label[index], dtype=torch.long), torch.tensor(self.clicks[index], dtype=torch.float))
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def __len__(self):
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return self.data.shape[0]
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# Resnet 1d
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def conv(in_planes, out_planes, stride=1, kernel_size=3):
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"convolution with padding 自动使用zeros进行padding"
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return nn.Conv1d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
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padding=(kernel_size - 1) // 2, bias=False)
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class ZeroPad1d(nn.Module):
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def __init__(self, pad_left, pad_right):
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super().__init__()
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self.pad_left = pad_left
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self.pad_right = pad_right
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def forward(self, x):
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return F.pad(x, (self.pad_left, self.pad_right))
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class BasicBlock1d(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super().__init__()
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self.conv1 = conv(inplanes, planes, stride=stride, kernel_size=3)
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self.bn1 = nn.BatchNorm1d(planes)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = conv(planes, planes, kernel_size=3)
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self.bn2 = nn.BatchNorm1d(planes)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class Bottleneck1d(nn.Module):
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"""Bottleneck for ResNet52 ..."""
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super().__init__()
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kernel_size = 3
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self.conv1 = nn.Conv1d(inplanes, planes, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm1d(planes)
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self.conv2 = nn.Conv1d(planes, planes, kernel_size=kernel_size, stride=stride,
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padding=(kernel_size - 1) // 2, bias=False)
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self.bn2 = nn.BatchNorm1d(planes)
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self.conv3 = nn.Conv1d(planes, planes * 4, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm1d(planes * 4)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class ResNet1d(nn.Module):
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'''1d adaptation of the torchvision resnet'''
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def __init__(self, block, layers, kernel_size=3, input_channels=12, inplanes=64,
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fix_feature_dim=False, kernel_size_stem=None, stride_stem=2, pooling_stem=True,
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stride=2):
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super(ResNet1d, self).__init__()
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self.inplanes = inplanes
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layers_tmp = []
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if kernel_size_stem is None:
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kernel_size_stem = kernel_size[0] if isinstance(kernel_size, list) else kernel_size
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# conv-bn-relu (basic feature extraction)
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layers_tmp.append(nn.Conv1d(input_channels, inplanes,
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kernel_size=kernel_size_stem,
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stride=stride_stem,
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padding=(kernel_size_stem - 1) // 2, bias=False))
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layers_tmp.append(nn.BatchNorm1d(inplanes))
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layers_tmp.append(nn.ReLU(inplace=True))
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if pooling_stem is True:
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layers_tmp.append(nn.MaxPool1d(kernel_size=3, stride=2, padding=1))
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for i, l in enumerate(layers):
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if i == 0:
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layers_tmp.append(self._make_block(block, inplanes, layers[0]))
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else:
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layers_tmp.append(
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self._make_block(block, inplanes if fix_feature_dim else (2 ** i) * inplanes, layers[i],
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stride=stride))
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self.feature_extractor = nn.Sequential(*layers_tmp)
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def _make_block(self, block, planes, blocks, stride=1, kernel_size=3):
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down_sample = None
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# 注定会进行下采样
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if stride != 1 or self.inplanes != planes * block.expansion:
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down_sample = nn.Sequential(
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nn.Conv1d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm1d(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, down_sample))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes))
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return nn.Sequential(*layers)
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def forward(self, x):
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return self.feature_extractor(x)
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def resnet1d14(inplanes, input_channels):
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return ResNet1d(BasicBlock1d, [2,2,2], inplanes=inplanes, input_channels=input_channels)
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def resnet1d18(**kwargs):
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return ResNet1d(BasicBlock1d, [2, 2, 2, 2], **kwargs)
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# MLP
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class MLP(nn.Module):
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def __init__(self, in_channels, hidden_channels, n_classes, bn = True):
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super(MLP, self).__init__()
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self.in_channels = in_channels
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self.n_classes = n_classes
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self.hidden_channels = hidden_channels
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self.fc1 = nn.Linear(self.in_channels, self.hidden_channels)
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self.fc2 = nn.Linear(self.hidden_channels, self.n_classes)
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self.ac = nn.ReLU()
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self.bn = nn.BatchNorm1d(hidden_channels)
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self.ln = nn.LayerNorm(hidden_channels)
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self.fc3 = nn.Linear(self.hidden_channels, self.hidden_channels)
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def forward(self, x):
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hidden = self.fc1(x)
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hidden = self.bn(hidden)
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hidden = self.ac(hidden)
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'''hidden = self.fc3(hidden)
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hidden = self.ln2(hidden)
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hidden = self.ac(hidden)'''
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out = self.fc2(hidden)
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return out
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# Time-steps features -> aggregated features
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class Flatten(nn.Module):
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def __init__(self):
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super(Flatten, self).__init__()
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def forward(self, tensor):
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b = tensor.size(0)
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return tensor.reshape(b, -1)
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class AdaptiveConcatPool1d(nn.Module):
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"Layer that concats `AdaptiveAvgPool1d` and `AdaptiveMaxPool1d`."
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def __init__(self, sz=None):
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"Output will be 2*sz or 2 if sz is None"
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super().__init__()
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sz = sz or 1
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self.ap, self.mp = nn.AdaptiveAvgPool1d(sz), nn.AdaptiveMaxPool1d(sz)
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def forward(self, x):
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"""x is shaped of B, C, T"""
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return torch.cat([self.mp(x), self.ap(x), x[..., -1:]], 1)
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def bn_drop_lin(n_in, n_out, bn, p, actn):
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"`n_in`->bn->dropout->linear(`n_in`,`n_out`)->`actn`"
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layers = list()
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if bn:
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layers.append(nn.BatchNorm1d(n_in))
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if p > 0.:
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layers.append(nn.Dropout(p=p))
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layers.append(nn.Linear(n_in, n_out))
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if actn is not None:
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layers.append(actn)
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return layers
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def create_head1d(nf: int, nc: int, lin_ftrs=[512, ], dropout=0.5, bn: bool = True, act="relu"):
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lin_ftrs = [3 * nf] + lin_ftrs + [nc]
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activations = [nn.ReLU(inplace=True) if act == "relu" else nn.ELU(inplace=True)] * (len(lin_ftrs) - 2) + [None]
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layers = [AdaptiveConcatPool1d(), Flatten()]
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for ni, no, actn in zip(lin_ftrs[:-1], lin_ftrs[1:], activations):
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layers += bn_drop_lin(ni, no, bn, dropout, actn)
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layers += [nn.Sigmoid()]
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return nn.Sequential(*layers)
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