Python torch.nn.ReLU() Examples
The following are 30
code examples of torch.nn.ReLU().
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Example #1
| Source File: encoder.py From hgraph2graph with MIT License | 9 votes |
|
def __init__(self, rnn_type, input_size, node_fdim, hidden_size, depth, dropout):
super(MPNEncoder, self).__init__()
self.hidden_size = hidden_size
self.input_size = input_size
self.depth = depth
self.W_o = nn.Sequential(
nn.Linear(node_fdim + hidden_size, hidden_size),
nn.ReLU(),
nn.Dropout(dropout)
)
if rnn_type == 'GRU':
self.rnn = GRU(input_size, hidden_size, depth)
elif rnn_type == 'LSTM':
self.rnn = LSTM(input_size, hidden_size, depth)
else:
raise ValueError('unsupported rnn cell type ' + rnn_type)
Example #2
| Source File: model.py From cat-bbs with MIT License | 8 votes |
|
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(MyResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
# note the increasing dilation
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilation=1)
self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4)
# these layers will not be used
self.avgpool = nn.AvgPool2d(7)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
Example #3
| Source File: resnet_v1.py From Collaborative-Learning-for-Weakly-Supervised-Object-Detection with MIT License | 7 votes |
|
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
# maxpool different from pytorch-resnet, to match tf-faster-rcnn
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
# use stride 1 for the last conv4 layer (same as tf-faster-rcnn)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
Example #4
| Source File: decoder.py From DDPAE-video-prediction with MIT License | 7 votes |
|
def __init__(self, input_size, n_channels, ngf, n_layers, activation='tanh'):
super(ImageDecoder, self).__init__()
ngf = ngf * (2 ** (n_layers - 2))
layers = [nn.ConvTranspose2d(input_size, ngf, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True)]
for i in range(1, n_layers - 1):
layers += [nn.ConvTranspose2d(ngf, ngf // 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf // 2),
nn.ReLU(True)]
ngf = ngf // 2
layers += [nn.ConvTranspose2d(ngf, n_channels, 4, 2, 1, bias=False)]
if activation == 'tanh':
layers += [nn.Tanh()]
elif activation == 'sigmoid':
layers += [nn.Sigmoid()]
else:
raise NotImplementedError
self.main = nn.Sequential(*layers)
Example #5
| Source File: 22_vgg.py From deep-learning-note with MIT License | 7 votes |
|
def vgg(conv_arch, fc_features, fc_hidden_units=4096):
net = nn.Sequential()
# 卷积层部分
for i, (num_convs, in_channels, out_channels) in enumerate(conv_arch):
# 每经过一个 vgg_block 宽高减半
net.add_module('vgg_block_' + str(i+1), vgg_block(num_convs, in_channels, out_channels))
# 全连接部分
net.add_module('fc', nn.Sequential(
utils.FlattenLayer(),
nn.Linear(fc_features, fc_hidden_units),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(fc_hidden_units, fc_hidden_units),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(fc_hidden_units, 10)
))
return net
Example #6
| Source File: albunet.py From neural-pipeline with MIT License | 6 votes |
|
def __init__(self, block, layers, in_channels=3):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
Example #7
| Source File: model.py From models with MIT License | 6 votes |
|
def __init__( self ):
super(CNN, self).__init__()
self.elmo_feature_extractor = nn.Sequential(
nn.Conv2d( 1024, 32, kernel_size=(7,1), padding=(3,0) ),
nn.ReLU(),
nn.Dropout( 0.25 ),
)
n_final_in = 32
self.dssp3_classifier = nn.Sequential(
nn.Conv2d( n_final_in, 3, kernel_size=(7,1), padding=(3,0))
)
self.dssp8_classifier = nn.Sequential(
nn.Conv2d( n_final_in, 8, kernel_size=(7,1), padding=(3,0))
)
self.diso_classifier = nn.Sequential(
nn.Conv2d( n_final_in, 2, kernel_size=(7,1), padding=(3,0))
)
Example #8
| Source File: BEV_Unet.py From PolarSeg with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def __init__(self, in_ch, out_ch,group_conv,dilation=1):
super(double_conv_circular, self).__init__()
if group_conv:
self.conv1 = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, padding=(1,0),groups = min(out_ch,in_ch)),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True)
)
self.conv2 = nn.Sequential(
nn.Conv2d(out_ch, out_ch, 3, padding=(1,0),groups = out_ch),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True)
)
else:
self.conv1 = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, padding=(1,0)),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True)
)
self.conv2 = nn.Sequential(
nn.Conv2d(out_ch, out_ch, 3, padding=(1,0)),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True)
)
Example #9
| Source File: BEV_Unet.py From PolarSeg with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def __init__(self, in_ch, out_ch,group_conv,dilation=1):
super(double_conv, self).__init__()
if group_conv:
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, padding=1,groups = min(out_ch,in_ch)),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
nn.Conv2d(out_ch, out_ch, 3, padding=1,groups = out_ch),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True)
)
else:
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
nn.Conv2d(out_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True)
)
Example #10
| Source File: googlenet.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def __init__(self):
super(GoogLeNet, self).__init__()
self.pre_layers = nn.Sequential(
nn.Conv2d(3, 192, kernel_size=3, padding=1),
nn.BatchNorm2d(192),
nn.ReLU(True),
)
self.a3 = Inception(192, 64, 96, 128, 16, 32, 32)
self.b3 = Inception(256, 128, 128, 192, 32, 96, 64)
self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
self.a4 = Inception(480, 192, 96, 208, 16, 48, 64)
self.b4 = Inception(512, 160, 112, 224, 24, 64, 64)
self.c4 = Inception(512, 128, 128, 256, 24, 64, 64)
self.d4 = Inception(512, 112, 144, 288, 32, 64, 64)
self.e4 = Inception(528, 256, 160, 320, 32, 128, 128)
self.a5 = Inception(832, 256, 160, 320, 32, 128, 128)
self.b5 = Inception(832, 384, 192, 384, 48, 128, 128)
self.avgpool = nn.AvgPool2d(8, stride=1)
self.linear = nn.Linear(1024, 10)
Example #11
| Source File: vgg.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def _make_layers(self, cfg):
layers = []
in_channels = 3
for x in cfg:
if x == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
nn.BatchNorm2d(x),
nn.ReLU(inplace=True)]
in_channels = x
layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
return nn.Sequential(*layers)
# net = VGG('VGG11')
# x = torch.randn(2,3,32,32)
# print(net(Variable(x)).size())
Example #12
| Source File: model.py From VSE-C with MIT License | 6 votes |
|
def __init__(self, vocab_size, word_dim, embed_size, use_abs=False,
glove_path='data/glove.pkl'):
super(EncoderTextDeepCNN, self).__init__()
self.use_abs = use_abs
self.embed_size = embed_size
# word embedding
self.embed = nn.Embedding(vocab_size, word_dim-300, padding_idx=0)
_, embed_weight = pickle.load(open(glove_path, 'rb'))
self.glove = Variable(torch.cuda.FloatTensor(embed_weight), requires_grad=False)
channel_num = embed_size
self.conv1 = nn.Conv1d(word_dim, embed_size, 2, stride=2) # [batch_size, dim, 30]
self.conv2 = nn.Conv1d(embed_size, embed_size, 4, stride=2) # [batch_size, dim, 14]
self.conv3 = nn.Conv1d(embed_size, embed_size, 5, stride=2) # [batch_size, dim, 5]
self.conv4 = nn.Conv1d(embed_size, channel_num, 5)
self.drop = nn.Dropout(p=0.5)
self.relu = nn.ReLU()
# self.mlp = nn.Linear(embed_size, embed_size)
self.init_weights()
Example #13
| Source File: encoder.py From hgraph2graph with MIT License | 6 votes |
|
def __init__(self, rnn_type, input_size, node_fdim, hidden_size, depth, dropout):
super(MPNEncoder, self).__init__()
self.hidden_size = hidden_size
self.input_size = input_size
self.depth = depth
self.W_o = nn.Sequential(
nn.Linear(node_fdim + hidden_size, hidden_size),
nn.ReLU(),
nn.Dropout(dropout)
)
if rnn_type == 'GRU':
self.rnn = GRU(input_size, hidden_size, depth)
elif rnn_type == 'LSTM':
self.rnn = LSTM(input_size, hidden_size, depth)
else:
raise ValueError('unsupported rnn cell type ' + rnn_type)
Example #14
| Source File: vgg.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def _make_layers(self, cfg):
layers = []
in_channels = 3
for x in cfg:
if x == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
nn.BatchNorm2d(x),
nn.ReLU(inplace=True)]
in_channels = x
layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
return nn.Sequential(*layers)
# net = VGG('VGG11')
# x = torch.randn(2,3,32,32)
# print(net(Variable(x)).size())
Example #15
| Source File: fovea_head.py From mmdetection with Apache License 2.0 | 6 votes |
|
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
deformable_groups=4):
super(FeatureAlign, self).__init__()
offset_channels = kernel_size * kernel_size * 2
self.conv_offset = nn.Conv2d(
4, deformable_groups * offset_channels, 1, bias=False)
self.conv_adaption = DeformConv(
in_channels,
out_channels,
kernel_size=kernel_size,
padding=(kernel_size - 1) // 2,
deformable_groups=deformable_groups)
self.relu = nn.ReLU(inplace=True)
Example #16
| Source File: googlenet.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def __init__(self):
super(GoogLeNet, self).__init__()
self.pre_layers = nn.Sequential(
nn.Conv2d(3, 192, kernel_size=3, padding=1),
nn.BatchNorm2d(192),
nn.ReLU(True),
)
self.a3 = Inception(192, 64, 96, 128, 16, 32, 32)
self.b3 = Inception(256, 128, 128, 192, 32, 96, 64)
self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
self.a4 = Inception(480, 192, 96, 208, 16, 48, 64)
self.b4 = Inception(512, 160, 112, 224, 24, 64, 64)
self.c4 = Inception(512, 128, 128, 256, 24, 64, 64)
self.d4 = Inception(512, 112, 144, 288, 32, 64, 64)
self.e4 = Inception(528, 256, 160, 320, 32, 128, 128)
self.a5 = Inception(832, 256, 160, 320, 32, 128, 128)
self.b5 = Inception(832, 384, 192, 384, 48, 128, 128)
self.avgpool = nn.AvgPool2d(8, stride=1)
self.linear = nn.Linear(1024, 10)
Example #17
| Source File: guided_anchor_head.py From mmdetection with Apache License 2.0 | 6 votes |
|
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
deformable_groups=4):
super(FeatureAdaption, self).__init__()
offset_channels = kernel_size * kernel_size * 2
self.conv_offset = nn.Conv2d(
2, deformable_groups * offset_channels, 1, bias=False)
self.conv_adaption = DeformConv(
in_channels,
out_channels,
kernel_size=kernel_size,
padding=(kernel_size - 1) // 2,
deformable_groups=deformable_groups)
self.relu = nn.ReLU(inplace=True)
Example #18
| Source File: vgg.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def _make_layers(self, cfg):
layers = []
in_channels = 3
for x in cfg:
if x == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
nn.BatchNorm2d(x),
nn.ReLU(inplace=True)]
in_channels = x
layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
return nn.Sequential(*layers)
# net = VGG('VGG11')
# x = torch.randn(2,3,32,32)
# print(net(Variable(x)).size())
Example #19
| Source File: model.py From VSE-C with MIT License | 6 votes |
|
def __init__(self, vocab_size, word_dim, embed_size, use_abs=False, glove_path='data/glove.pkl'):
super(EncoderTextCNN, self).__init__()
self.use_abs = use_abs
self.embed_size = embed_size
# word embedding
self.embed = nn.Embedding(vocab_size, word_dim-300, padding_idx=0) # 0 for <pad>
_, embed_weight = pickle.load(open(glove_path, 'rb'))
self.glove = Variable(torch.cuda.FloatTensor(embed_weight), requires_grad=False)
channel_num = embed_size // 4
self.conv2 = nn.Conv1d(word_dim, channel_num, 2)
self.conv3 = nn.Conv1d(word_dim, channel_num, 3)
self.conv4 = nn.Conv1d(word_dim, channel_num, 4)
self.conv5 = nn.Conv1d(word_dim, channel_num, 5)
self.drop = nn.Dropout(p=0.5)
self.relu = nn.ReLU()
# self.mlp = nn.Linear(embed_size, embed_size)
self.init_weights()
Example #20
| Source File: hrnet.py From mmdetection with Apache License 2.0 | 6 votes |
|
def __init__(self,
num_branches,
blocks,
num_blocks,
in_channels,
num_channels,
multiscale_output=True,
with_cp=False,
conv_cfg=None,
norm_cfg=dict(type='BN')):
super(HRModule, self).__init__()
self._check_branches(num_branches, num_blocks, in_channels,
num_channels)
self.in_channels = in_channels
self.num_branches = num_branches
self.multiscale_output = multiscale_output
self.norm_cfg = norm_cfg
self.conv_cfg = conv_cfg
self.with_cp = with_cp
self.branches = self._make_branches(num_branches, blocks, num_blocks,
num_channels)
self.fuse_layers = self._make_fuse_layers()
self.relu = nn.ReLU(inplace=False)
Example #21
| Source File: wrn.py From Random-Erasing with Apache License 2.0 | 5 votes |
|
def __init__(self, depth, num_classes, widen_factor=1, dropRate=0.0):
super(WideResNet, self).__init__()
nChannels = [16, 16*widen_factor, 32*widen_factor, 64*widen_factor]
assert (depth - 4) % 6 == 0, 'depth should be 6n+4'
n = (depth - 4) // 6
block = BasicBlock
# 1st conv before any network block
self.conv1 = nn.Conv2d(1, nChannels[0], kernel_size=3, stride=1,
padding=1, bias=False)
# 1st block
self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, dropRate)
# 2nd block
self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, dropRate)
# 3rd block
self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate)
# global average pooling and classifier
self.bn1 = nn.BatchNorm2d(nChannels[3])
self.relu = nn.ReLU(inplace=True)
self.fc = nn.Linear(nChannels[3], num_classes)
self.nChannels = nChannels[3]
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
m.bias.data.zero_()
Example #22
| Source File: resnet.py From Random-Erasing with Apache License 2.0 | 5 votes |
|
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
Example #23
| Source File: resnet.py From Random-Erasing with Apache License 2.0 | 5 votes |
|
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
Example #24
| Source File: wrn.py From Random-Erasing with Apache License 2.0 | 5 votes |
|
def __init__(self, depth, num_classes, widen_factor=1, dropRate=0.0):
super(WideResNet, self).__init__()
nChannels = [16, 16*widen_factor, 32*widen_factor, 64*widen_factor]
assert (depth - 4) % 6 == 0, 'depth should be 6n+4'
n = (depth - 4) / 6
block = BasicBlock
# 1st conv before any network block
self.conv1 = nn.Conv2d(3, nChannels[0], kernel_size=3, stride=1,
padding=1, bias=False)
# 1st block
self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, dropRate)
# 2nd block
self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, dropRate)
# 3rd block
self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate)
# global average pooling and classifier
self.bn1 = nn.BatchNorm2d(nChannels[3])
self.relu = nn.ReLU(inplace=True)
self.fc = nn.Linear(nChannels[3], num_classes)
self.nChannels = nChannels[3]
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
m.bias.data.zero_()
Example #25
| Source File: resnet.py From Random-Erasing with Apache License 2.0 | 5 votes |
|
def __init__(self, depth, num_classes=1000):
super(ResNet, self).__init__()
# Model type specifies number of layers for CIFAR-10 model
assert (depth - 2) % 6 == 0, 'depth should be 6n+2'
n = (depth - 2) // 6
block = Bottleneck if depth >=44 else BasicBlock
self.inplanes = 16
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1,
bias=False)
self.bn1 = nn.BatchNorm2d(16)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(block, 16, n)
self.layer2 = self._make_layer(block, 32, n, stride=2)
self.layer3 = self._make_layer(block, 64, n, stride=2)
self.avgpool = nn.AvgPool2d(8)
self.fc = nn.Linear(64 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
Example #26
| Source File: resnet.py From Random-Erasing with Apache License 2.0 | 5 votes |
|
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
Example #27
| Source File: resnet.py From Random-Erasing with Apache License 2.0 | 5 votes |
|
def __init__(self, depth, num_classes=1000):
super(ResNet, self).__init__()
# Model type specifies number of layers for CIFAR-10 model
assert (depth - 2) % 6 == 0, 'depth should be 6n+2'
n = (depth - 2) / 6
block = Bottleneck if depth >=44 else BasicBlock
self.inplanes = 16
self.conv1 = nn.Conv2d(1, 16, kernel_size=3, padding=1,
bias=False)
self.bn1 = nn.BatchNorm2d(16)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(block, 16, n)
self.layer2 = self._make_layer(block, 32, n, stride=2)
self.layer3 = self._make_layer(block, 64, n, stride=2)
self.avgpool = nn.AvgPool2d(7)
self.fc = nn.Linear(64 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
Example #28
| Source File: cnns.py From cvpr2018-hnd with MIT License | 5 votes |
|
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
Example #29
| Source File: resnet.py From Random-Erasing with Apache License 2.0 | 5 votes |
|
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
Example #30
| Source File: wrn.py From Random-Erasing with Apache License 2.0 | 5 votes |
|
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu1 = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_planes)
self.relu2 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1,
padding=1, bias=False)
self.droprate = dropRate
self.equalInOut = (in_planes == out_planes)
self.convShortcut = (not self.equalInOut) and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
padding=0, bias=False) or None
