/
models.py
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/
models.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
def init_layer(layer):
"""Initialize a Linear or Convolutional layer. """
nn.init.xavier_uniform_(layer.weight)
if hasattr(layer, 'bias'):
if layer.bias is not None:
layer.bias.data.fill_(0.)
def init_bn(bn):
"""Initialize a Batchnorm layer. """
bn.bias.data.fill_(0.)
bn.weight.data.fill_(1.)
class ConvBlock(nn.Module): #for CNN10 & CNN14
def __init__(self, in_channels, out_channels):
super(ConvBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1), bias=False)
self.conv2 = nn.Conv2d(in_channels=out_channels,
out_channels=out_channels,
kernel_size=(3, 3), stride=(1, 1),
padding=(1, 1), bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.bn2 = nn.BatchNorm2d(out_channels)
self.init_weight()
def init_weight(self):
init_layer(self.conv1)
init_layer(self.conv2)
init_bn(self.bn1)
init_bn(self.bn2)
def forward(self, input, pool_size=(2, 2), pool_type='avg'):
x = input
x = F.relu_(self.bn1(self.conv1(x)))
x = F.relu_(self.bn2(self.conv2(x)))
if pool_type == 'max':
x = F.max_pool2d(x, kernel_size=pool_size)
elif pool_type == 'avg':
x = F.avg_pool2d(x, kernel_size=pool_size)
elif pool_type == 'avg+max':
x1 = F.avg_pool2d(x, kernel_size=pool_size)
x2 = F.max_pool2d(x, kernel_size=pool_size)
x = x1 + x2
else:
raise Exception('Incorrect argument!')
return x
class ConvBlock5x5(nn.Module): #for CNN6
def __init__(self, in_channels, out_channels, stride=(1,1)):
super(ConvBlock5x5, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=(5, 5), stride=stride,
padding=(2, 2), bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.init_weight()
def init_weight(self):
init_layer(self.conv1)
init_bn(self.bn1)
def forward(self, input, pool_size=(2, 2), pool_type='avg'):
x = input
x = F.relu_(self.bn1(self.conv1(x)))
if pool_type == 'max':
x = F.max_pool2d(x, kernel_size=pool_size)
elif pool_type == 'avg':
x = F.avg_pool2d(x, kernel_size=pool_size)
elif pool_type == 'avg+max':
x1 = F.avg_pool2d(x, kernel_size=pool_size)
x2 = F.max_pool2d(x, kernel_size=pool_size)
x = x1 + x2
else:
raise Exception('Incorrect argument!')
return x
class _CNN6(nn.Module):
def __init__(self, num_classes=4, do_dropout=False, embed_only=False):
super(_CNN6, self).__init__()
self.embed_only = embed_only
self.do_dropout = do_dropout
self.conv_block1 = ConvBlock5x5(in_channels=1, out_channels=64, stride=(1,1))
self.conv_block2 = ConvBlock5x5(in_channels=64, out_channels=128, stride=(1,1))
self.conv_block3 = ConvBlock5x5(in_channels=128, out_channels=256, stride=(1,1))
self.conv_block4 = ConvBlock5x5(in_channels=256, out_channels=512, stride=(1,1))
self.dropout = nn.Dropout(0.2)
self.linear = nn.Linear(512, num_classes, bias=True)
def forward(self, x):
x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
if self.do_dropout:
x = self.dropout(x)
x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
if self.do_dropout:
x = self.dropout(x)
x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
if self.do_dropout:
x = self.dropout(x)
x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
if self.do_dropout:
x = self.dropout(x)
x = torch.mean(x, dim=3) #mean over time dim
(x1, _) = torch.max(x, dim=2) #max over freq dim
x2 = torch.mean(x, dim=2) #mean over freq dim (after mean over time)
x = x1 + x2
if self.embed_only:
return x
return self.linear(x)
class CNN6(nn.Module):
def __init__(self, num_classes=4, do_dropout=False, embed_only=False, from_scratch=False, path_to_weights="Cnn6_mAP=0.343.pth", device="cuda"):
super(CNN6, self).__init__()
self.cnn6 = _CNN6(num_classes=num_classes, do_dropout=do_dropout, embed_only=embed_only).to(device)
if not from_scratch:
weights = torch.load(path_to_weights, map_location=device)['model']
state_dict = {k: v for k, v in weights.items() if k in self.cnn6.state_dict().keys()}
self.cnn6.load_state_dict(state_dict, strict=False)
def forward(self, x):
return self.cnn6(x)
class _CNN10(nn.Module):
def __init__(self, num_classes=4, do_dropout=False, embed_only=False):
super(_CNN10, self).__init__()
self.embed_only = embed_only
self.do_dropout = do_dropout
self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
self.dropout = nn.Dropout(0.2)
self.linear = nn.Linear(512, num_classes, bias=True)
def forward(self, x):
x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
if self.do_dropout:
x = self.dropout(x)
x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
if self.do_dropout:
x = self.dropout(x)
x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
if self.do_dropout:
x = self.dropout(x)
x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
if self.do_dropout:
x = self.dropout(x)
x = torch.mean(x, dim=3) #mean over time dim
(x1, _) = torch.max(x, dim=2) #max over freq dim
x2 = torch.mean(x, dim=2) #mean over freq dim (after mean over time)
x = x1 + x2
if self.embed_only:
return x
return self.linear(x)
class CNN10(nn.Module):
def __init__(self, num_classes=4, do_dropout=False, embed_only=False, from_scratch=False, path_to_weights="Cnn10_mAP=0.380.pth", device="cuda"):
super(CNN10, self).__init__()
self.cnn10 = _CNN10(num_classes=num_classes, do_dropout=do_dropout, embed_only=embed_only).to(device)
if not from_scratch:
weights = torch.load(path_to_weights, map_location=device)['model']
state_dict = {k: v for k, v in weights.items() if k in self.cnn10.state_dict().keys()}
self.cnn10.load_state_dict(state_dict, strict=False)
def forward(self, x):
return self.cnn10(x)
class _CNN14(nn.Module):
def __init__(self, num_classes=4, do_dropout=False, embed_only=False):
super(_CNN14, self).__init__()
self.embed_only = embed_only
self.do_dropout = do_dropout
self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)
self.dropout = nn.Dropout(0.2)
self.linear = nn.Linear(2048, num_classes, bias=True)
def forward(self, x):
x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
if self.do_dropout:
x = self.dropout(x)
x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
if self.do_dropout:
x = self.dropout(x)
x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
if self.do_dropout:
x = self.dropout(x)
x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
if self.do_dropout:
x = self.dropout(x)
x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg')
if self.do_dropout:
x = self.dropout(x)
x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg')
if self.do_dropout:
x = self.dropout(x)
x = torch.mean(x, dim=3) #mean over time dim
(x1, _) = torch.max(x, dim=2) #max over freq dim
x2 = torch.mean(x, dim=2) #mean over freq dim (after mean over time)
x = x1 + x2
if self.embed_only:
return x
return self.linear(x)
class CNN14(nn.Module):
def __init__(self, num_classes=4, do_dropout=False, embed_only=False, from_scratch=False, path_to_weights="/users/local/Cnn14_mAP=0.431.pth", device="cuda"):
super(CNN14, self).__init__()
self.cnn14 = _CNN14(num_classes=num_classes, do_dropout=do_dropout, embed_only=embed_only).to(device)
if not from_scratch:
weights = torch.load(path_to_weights, map_location=device)['model']
state_dict = {k: v for k, v in weights.items() if k in self.cnn14.state_dict().keys()}
self.cnn14.load_state_dict(state_dict, strict=False)
def forward(self, x):
return self.cnn14(x)
class Projector(nn.Module):
def __init__(self, name='cnn6', out_dim=128, apply_bn=False, device="cpu"):
super(Projector, self).__init__()
_, dim_in = model_dict[name]
self.linear1 = nn.Linear(dim_in, dim_in)
self.linear2 = nn.Linear(dim_in, out_dim)
self.bn = nn.BatchNorm1d(dim_in)
self.relu = nn.ReLU()
if apply_bn:
self.projector = nn.Sequential(self.linear1, self.bn, self.relu, self.linear2)
else:
self.projector = nn.Sequential(self.linear1, self.relu, self.linear2)
self.projector = self.projector.to(device)
def forward(self, x):
return self.projector(x)
class LinearClassifier(nn.Module):
def __init__(self, name='cnn6', num_classes=4, device="cpu"):
super(LinearClassifier, self).__init__()
_, feat_dim = model_dict[name]
self.fc = nn.Linear(feat_dim, num_classes).to(device)
def forward(self, features):
return self.fc(features)
def cnn6(**kwargs):
return CNN6(**kwargs)
def cnn10(**kwargs):
return CNN10(**kwargs)
def cnn14(**kwargs):
return CNN14(**kwargs)
model_dict = {
'cnn6' : [cnn6, 512],
'cnn10' : [cnn10, 512],
'cnn14' : [cnn14, 2048],
}