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def ShuffleNetG2():
cfg = {'out_planes': [200, 400, 800], 'num_blocks': [4, 8, 4], 'groups': 2}
return ShuffleNet(cfg)
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def ShuffleNetG3():
cfg = {'out_planes': [240, 480, 960], 'num_blocks': [4, 8, 4], 'groups': 3}
return ShuffleNet(cfg)
|
def test():
net = ShuffleNetG2()
x = torch.randn(1, 3, 32, 32)
y = net(x)
print(y)
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class VGG(nn.Module):
def __init__(self, vgg_name):
super(VGG, self).__init__()
self.features = self._make_layers(cfg[vgg_name])
self.classifier = nn.Linear(512, 10)
def forward(self, x):
out = self.features(x)
out = out.view(out.size(0), (- 1))
out = self.cla... |
def test():
net = VGG('VGG11')
x = torch.randn(2, 3, 32, 32)
y = net(x)
print(y.size())
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def narcissus_gen(dataset_path=dataset_path, lab=lab):
noise_size = 32
l_inf_r = (16 / 255)
surrogate_model = ResNet18_201().cuda()
generating_model = ResNet18_201().cuda()
surrogate_epochs = 200
generating_lr_warmup = 0.1
warmup_round = 5
generating_lr_tri = 0.01
gen_round = 1000
... |
class DemandDataset(torch.utils.data.Dataset):
def __init__(self, data_dir, cut_len=(16000 * 2)):
self.cut_len = cut_len
self.clean_dir = os.path.join(data_dir, 'clean')
self.noisy_dir = os.path.join(data_dir, 'noisy')
self.clean_wav_name = os.listdir(self.clean_dir)
self.... |
def load_data(ds_dir, batch_size, n_cpu, cut_len):
torchaudio.set_audio_backend('sox_io')
train_dir = os.path.join(ds_dir, 'train')
test_dir = os.path.join(ds_dir, 'test')
train_ds = DemandDataset(train_dir, cut_len)
test_ds = DemandDataset(test_dir, cut_len)
train_dataset = torch.utils.data.D... |
def exists(val):
return (val is not None)
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def default(val, d):
return (val if exists(val) else d)
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def calc_same_padding(kernel_size):
pad = (kernel_size // 2)
return (pad, (pad - ((kernel_size + 1) % 2)))
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class Swish(nn.Module):
def forward(self, x):
return (x * x.sigmoid())
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class GLU(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
(out, gate) = x.chunk(2, dim=self.dim)
return (out * gate.sigmoid())
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class DepthWiseConv1d(nn.Module):
def __init__(self, chan_in, chan_out, kernel_size, padding):
super().__init__()
self.padding = padding
self.conv = nn.Conv1d(chan_in, chan_out, kernel_size, groups=chan_in)
def forward(self, x):
x = F.pad(x, self.padding)
return self.... |
class Scale(nn.Module):
def __init__(self, scale, fn):
super().__init__()
self.fn = fn
self.scale = scale
def forward(self, x, **kwargs):
return (self.fn(x, **kwargs) * self.scale)
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class PreNorm(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.fn = fn
self.norm = nn.LayerNorm(dim)
def forward(self, x, **kwargs):
x = self.norm(x)
return self.fn(x, **kwargs)
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class Attention(nn.Module):
def __init__(self, dim, heads=8, dim_head=64, dropout=0.0, max_pos_emb=512):
super().__init__()
inner_dim = (dim_head * heads)
self.heads = heads
self.scale = (dim_head ** (- 0.5))
self.to_q = nn.Linear(dim, inner_dim, bias=False)
self.t... |
class FeedForward(nn.Module):
def __init__(self, dim, mult=4, dropout=0.0):
super().__init__()
self.net = nn.Sequential(nn.Linear(dim, (dim * mult)), Swish(), nn.Dropout(dropout), nn.Linear((dim * mult), dim), nn.Dropout(dropout))
def forward(self, x):
return self.net(x)
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class ConformerConvModule(nn.Module):
def __init__(self, dim, causal=False, expansion_factor=2, kernel_size=31, dropout=0.0):
super().__init__()
inner_dim = (dim * expansion_factor)
padding = (calc_same_padding(kernel_size) if (not causal) else ((kernel_size - 1), 0))
self.net = n... |
class ConformerBlock(nn.Module):
def __init__(self, *, dim, dim_head=64, heads=8, ff_mult=4, conv_expansion_factor=2, conv_kernel_size=31, attn_dropout=0.0, ff_dropout=0.0, conv_dropout=0.0):
super().__init__()
self.ff1 = FeedForward(dim=dim, mult=ff_mult, dropout=ff_dropout)
self.attn = ... |
def pesq_loss(clean, noisy, sr=16000):
try:
pesq_score = pesq(sr, clean, noisy, 'wb')
except:
pesq_score = (- 1)
return pesq_score
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def batch_pesq(clean, noisy):
pesq_score = Parallel(n_jobs=(- 1))((delayed(pesq_loss)(c, n) for (c, n) in zip(clean, noisy)))
pesq_score = np.array(pesq_score)
if ((- 1) in pesq_score):
return None
pesq_score = ((pesq_score - 1) / 3.5)
return torch.FloatTensor(pesq_score).to('cuda')
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class Discriminator(nn.Module):
def __init__(self, ndf, in_channel=2):
super().__init__()
self.layers = nn.Sequential(nn.utils.spectral_norm(nn.Conv2d(in_channel, ndf, (4, 4), (2, 2), (1, 1), bias=False)), nn.InstanceNorm2d(ndf, affine=True), nn.PReLU(ndf), nn.utils.spectral_norm(nn.Conv2d(ndf, (... |
def kaiming_init(m):
if isinstance(m, nn.Linear):
torch.nn.init.kaiming_normal_(m.weight)
if (m.bias is not None):
m.bias.data.fill_(0.01)
if isinstance(m, nn.Conv2d):
torch.nn.init.kaiming_normal_(m.weight)
if (m.bias is not None):
m.bias.data.fill_(0.0... |
def power_compress(x):
real = x[(..., 0)]
imag = x[(..., 1)]
spec = torch.complex(real, imag)
mag = torch.abs(spec)
phase = torch.angle(spec)
mag = (mag ** 0.3)
real_compress = (mag * torch.cos(phase))
imag_compress = (mag * torch.sin(phase))
return torch.stack([real_compress, imag... |
def power_uncompress(real, imag):
spec = torch.complex(real, imag)
mag = torch.abs(spec)
phase = torch.angle(spec)
mag = (mag ** (1.0 / 0.3))
real_compress = (mag * torch.cos(phase))
imag_compress = (mag * torch.sin(phase))
return torch.stack([real_compress, imag_compress], (- 1))
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class LearnableSigmoid(nn.Module):
def __init__(self, in_features, beta=1):
super().__init__()
self.beta = beta
self.slope = nn.Parameter(torch.ones(in_features))
self.slope.requiresGrad = True
def forward(self, x):
return (self.beta * torch.sigmoid((self.slope * x)))... |
class Bottleneck(nn.Module):
def __init__(self, in_planes, growth_rate):
super(Bottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, (4 * growth_rate), kernel_size=1, bias=False)
self.bn2 = nn.BatchNorm2d((4 * growth_rate))
sel... |
class Transition(nn.Module):
def __init__(self, in_planes, out_planes):
super(Transition, self).__init__()
self.bn = nn.BatchNorm2d(in_planes)
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False)
def forward(self, x):
out = self.conv(F.relu(self.bn(x)))
... |
class DenseNet(nn.Module):
def __init__(self, block, nblocks, growth_rate=12, reduction=0.5, num_classes=10):
super(DenseNet, self).__init__()
self.growth_rate = growth_rate
num_planes = (2 * growth_rate)
self.conv1 = nn.Conv2d(3, num_planes, kernel_size=3, padding=1, bias=False)
... |
def DenseNet121():
return DenseNet(Bottleneck, [6, 12, 24, 16], growth_rate=32)
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def DenseNet169():
return DenseNet(Bottleneck, [6, 12, 32, 32], growth_rate=32)
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def DenseNet201():
return DenseNet(Bottleneck, [6, 12, 48, 32], growth_rate=32)
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def DenseNet161():
return DenseNet(Bottleneck, [6, 12, 36, 24], growth_rate=48)
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def densenet_cifar():
return DenseNet(Bottleneck, [6, 12, 24, 16], growth_rate=12)
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def test():
net = densenet_cifar()
x = torch.randn(1, 3, 32, 32)
y = net(x)
print(y)
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class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2... |
class Root(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1):
super(Root, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=((kernel_size - 1) // 2), bias=False)
self.bn = nn.BatchNorm2d(out_channels)
def forward(s... |
class Tree(nn.Module):
def __init__(self, block, in_channels, out_channels, level=1, stride=1):
super(Tree, self).__init__()
self.level = level
if (level == 1):
self.root = Root((2 * out_channels), out_channels)
self.left_node = block(in_channels, out_channels, str... |
class DLA(nn.Module):
def __init__(self, block=BasicBlock, num_classes=10):
super(DLA, self).__init__()
self.base = nn.Sequential(nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False), nn.BatchNorm2d(16), nn.ReLU(True))
self.layer1 = nn.Sequential(nn.Conv2d(16, 16, kernel_size=... |
def test():
net = DLA()
print(net)
x = torch.randn(1, 3, 32, 32)
y = net(x)
print(y.size())
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class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2... |
class Root(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1):
super(Root, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=((kernel_size - 1) // 2), bias=False)
self.bn = nn.BatchNorm2d(out_channels)
def forward(s... |
class Tree(nn.Module):
def __init__(self, block, in_channels, out_channels, level=1, stride=1):
super(Tree, self).__init__()
self.root = Root((2 * out_channels), out_channels)
if (level == 1):
self.left_tree = block(in_channels, out_channels, stride=stride)
self.ri... |
class SimpleDLA(nn.Module):
def __init__(self, block=BasicBlock, num_classes=10):
super(SimpleDLA, self).__init__()
self.base = nn.Sequential(nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False), nn.BatchNorm2d(16), nn.ReLU(True))
self.layer1 = nn.Sequential(nn.Conv2d(16, 16, ... |
def test():
net = SimpleDLA()
print(net)
x = torch.randn(1, 3, 32, 32)
y = net(x)
print(y.size())
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def swish(x):
return (x * x.sigmoid())
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def drop_connect(x, drop_ratio):
keep_ratio = (1.0 - drop_ratio)
mask = torch.empty([x.shape[0], 1, 1, 1], dtype=x.dtype, device=x.device)
mask.bernoulli_(keep_ratio)
x.div_(keep_ratio)
x.mul_(mask)
return x
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class SE(nn.Module):
'Squeeze-and-Excitation block with Swish.'
def __init__(self, in_channels, se_channels):
super(SE, self).__init__()
self.se1 = nn.Conv2d(in_channels, se_channels, kernel_size=1, bias=True)
self.se2 = nn.Conv2d(se_channels, in_channels, kernel_size=1, bias=True)
... |
class Block(nn.Module):
'expansion + depthwise + pointwise + squeeze-excitation'
def __init__(self, in_channels, out_channels, kernel_size, stride, expand_ratio=1, se_ratio=0.0, drop_rate=0.0):
super(Block, self).__init__()
self.stride = stride
self.drop_rate = drop_rate
self.... |
class EfficientNet(nn.Module):
def __init__(self, cfg, num_classes=1000):
super(EfficientNet, self).__init__()
self.cfg = cfg
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(32)
self.layers = self._make_layers(in_chan... |
def EfficientNetB0():
cfg = {'num_blocks': [1, 2, 2, 3, 3, 4, 1], 'expansion': [1, 6, 6, 6, 6, 6, 6], 'out_channels': [16, 24, 40, 80, 112, 192, 320], 'kernel_size': [3, 3, 5, 3, 5, 5, 3], 'stride': [1, 2, 2, 2, 1, 2, 1], 'dropout_rate': 0.2, 'drop_connect_rate': 0.2}
return EfficientNet(cfg)
|
def test():
net = EfficientNetB0()
x = torch.randn(2, 3, 32, 32)
y = net(x)
print(y.shape)
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class Inception(nn.Module):
def __init__(self, in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):
super(Inception, self).__init__()
self.b1 = nn.Sequential(nn.Conv2d(in_planes, n1x1, kernel_size=1), nn.BatchNorm2d(n1x1), nn.ReLU(True))
self.b2 = nn.Sequential(nn.Conv2d(in_planes... |
class GoogLeNet(nn.Module):
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, 1... |
def test():
net = GoogLeNet()
x = torch.randn(1, 3, 32, 32)
y = net(x)
print(y.size())
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class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(((16 * 5) * 5), 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x)... |
class Block(nn.Module):
'Depthwise conv + Pointwise conv'
def __init__(self, in_planes, out_planes, stride=1):
super(Block, self).__init__()
self.conv1 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=in_planes, bias=False)
self.bn1 = nn.BatchNorm2d(in... |
class MobileNet(nn.Module):
cfg = [64, (128, 2), 128, (256, 2), 256, (512, 2), 512, 512, 512, 512, 512, (1024, 2), 1024]
def __init__(self, num_classes=10):
super(MobileNet, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.Ba... |
def test():
net = MobileNet()
x = torch.randn(1, 3, 32, 32)
y = net(x)
print(y.size())
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class Block(nn.Module):
'expand + depthwise + pointwise'
def __init__(self, in_planes, out_planes, expansion, stride):
super(Block, self).__init__()
self.stride = stride
planes = (expansion * in_planes)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, stride=1, padding... |
class MobileNetV2(nn.Module):
cfg = [(1, 16, 1, 1), (6, 24, 2, 1), (6, 32, 3, 2), (6, 64, 4, 2), (6, 96, 3, 1), (6, 160, 3, 2), (6, 320, 1, 1)]
def __init__(self, num_classes=10):
super(MobileNetV2, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False)... |
def test():
net = MobileNetV2()
x = torch.randn(2, 3, 32, 32)
y = net(x)
print(y.size())
|
class SepConv(nn.Module):
'Separable Convolution.'
def __init__(self, in_planes, out_planes, kernel_size, stride):
super(SepConv, self).__init__()
self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding=((kernel_size - 1) // 2), bias=False, groups=in_planes)
self.bn... |
class CellA(nn.Module):
def __init__(self, in_planes, out_planes, stride=1):
super(CellA, self).__init__()
self.stride = stride
self.sep_conv1 = SepConv(in_planes, out_planes, kernel_size=7, stride=stride)
if (stride == 2):
self.conv1 = nn.Conv2d(in_planes, out_planes,... |
class CellB(nn.Module):
def __init__(self, in_planes, out_planes, stride=1):
super(CellB, self).__init__()
self.stride = stride
self.sep_conv1 = SepConv(in_planes, out_planes, kernel_size=7, stride=stride)
self.sep_conv2 = SepConv(in_planes, out_planes, kernel_size=3, stride=strid... |
class PNASNet(nn.Module):
def __init__(self, cell_type, num_cells, num_planes):
super(PNASNet, self).__init__()
self.in_planes = num_planes
self.cell_type = cell_type
self.conv1 = nn.Conv2d(3, num_planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchN... |
def PNASNetA():
return PNASNet(CellA, num_cells=6, num_planes=44)
|
def PNASNetB():
return PNASNet(CellB, num_cells=6, num_planes=32)
|
def test():
net = PNASNetB()
x = torch.randn(1, 3, 32, 32)
y = net(x)
print(y)
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class PreActBlock(nn.Module):
'Pre-activation version of the BasicBlock.'
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(PreActBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride,... |
class PreActBottleneck(nn.Module):
'Pre-activation version of the original Bottleneck module.'
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(PreActBottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, planes, ker... |
class PreActResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10):
super(PreActResNet, self).__init__()
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.layer1 = self._make_layer(block, 64, num_blocks[0], str... |
def PreActResNet18():
return PreActResNet(PreActBlock, [2, 2, 2, 2])
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def PreActResNet34():
return PreActResNet(PreActBlock, [3, 4, 6, 3])
|
def PreActResNet50():
return PreActResNet(PreActBottleneck, [3, 4, 6, 3])
|
def PreActResNet101():
return PreActResNet(PreActBottleneck, [3, 4, 23, 3])
|
def PreActResNet152():
return PreActResNet(PreActBottleneck, [3, 8, 36, 3])
|
def test():
net = PreActResNet18()
y = net(torch.randn(1, 3, 32, 32))
print(y.size())
|
class SE(nn.Module):
'Squeeze-and-Excitation block.'
def __init__(self, in_planes, se_planes):
super(SE, self).__init__()
self.se1 = nn.Conv2d(in_planes, se_planes, kernel_size=1, bias=True)
self.se2 = nn.Conv2d(se_planes, in_planes, kernel_size=1, bias=True)
def forward(self, x)... |
class Block(nn.Module):
def __init__(self, w_in, w_out, stride, group_width, bottleneck_ratio, se_ratio):
super(Block, self).__init__()
w_b = int(round((w_out * bottleneck_ratio)))
self.conv1 = nn.Conv2d(w_in, w_b, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(w_b)
... |
class RegNet(nn.Module):
def __init__(self, cfg, num_classes=10):
super(RegNet, self).__init__()
self.cfg = cfg
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_... |
def RegNetX_200MF():
cfg = {'depths': [1, 1, 4, 7], 'widths': [24, 56, 152, 368], 'strides': [1, 1, 2, 2], 'group_width': 8, 'bottleneck_ratio': 1, 'se_ratio': 0}
return RegNet(cfg)
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def RegNetX_400MF():
cfg = {'depths': [1, 2, 7, 12], 'widths': [32, 64, 160, 384], 'strides': [1, 1, 2, 2], 'group_width': 16, 'bottleneck_ratio': 1, 'se_ratio': 0}
return RegNet(cfg)
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def RegNetY_400MF():
cfg = {'depths': [1, 2, 7, 12], 'widths': [32, 64, 160, 384], 'strides': [1, 1, 2, 2], 'group_width': 16, 'bottleneck_ratio': 1, 'se_ratio': 0.25}
return RegNet(cfg)
|
def test():
net = RegNetX_200MF()
print(net)
x = torch.randn(2, 3, 32, 32)
y = net(x)
print(y.shape)
|
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2... |
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_s... |
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10):
super(ResNet, self).__init__()
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(blo... |
def ResNet18():
return ResNet(BasicBlock, [2, 2, 2, 2])
|
def ResNet18_11():
return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=11)
|
def ResNet18_201():
return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=201)
|
def ResNet34():
return ResNet(BasicBlock, [3, 4, 6, 3])
|
def ResNet50():
return ResNet(Bottleneck, [3, 4, 6, 3])
|
def ResNet101():
return ResNet(Bottleneck, [3, 4, 23, 3])
|
def ResNet152():
return ResNet(Bottleneck, [3, 8, 36, 3])
|
def test():
net = ResNet18()
y = net(torch.randn(1, 3, 32, 32))
print(y.size())
|
class Block(nn.Module):
'Grouped convolution block.'
expansion = 2
def __init__(self, in_planes, cardinality=32, bottleneck_width=4, stride=1):
super(Block, self).__init__()
group_width = (cardinality * bottleneck_width)
self.conv1 = nn.Conv2d(in_planes, group_width, kernel_size=1... |
class ResNeXt(nn.Module):
def __init__(self, num_blocks, cardinality, bottleneck_width, num_classes=10):
super(ResNeXt, self).__init__()
self.cardinality = cardinality
self.bottleneck_width = bottleneck_width
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=1,... |
def ResNeXt29_2x64d():
return ResNeXt(num_blocks=[3, 3, 3], cardinality=2, bottleneck_width=64)
|
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