Ross Wightman
5 years ago
committed by
GitHub
commit
ebf82b84ac
@ -0,0 +1,61 @@
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import torch
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import torch.nn.parallel
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import torch.nn as nn
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import torch.nn.functional as F
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class AntiAliasDownsampleLayer(nn.Module):
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def __init__(self, remove_aa_jit: bool = False, filt_size: int = 3, stride: int = 2,
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channels: int = 0):
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super(AntiAliasDownsampleLayer, self).__init__()
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if not remove_aa_jit:
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self.op = DownsampleJIT(filt_size, stride, channels)
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else:
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self.op = Downsample(filt_size, stride, channels)
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def forward(self, x):
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return self.op(x)
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@torch.jit.script
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class DownsampleJIT(object):
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def __init__(self, filt_size: int = 3, stride: int = 2, channels: int = 0):
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self.stride = stride
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self.filt_size = filt_size
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self.channels = channels
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assert self.filt_size == 3
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assert stride == 2
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a = torch.tensor([1., 2., 1.])
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filt = (a[:, None] * a[None, :]).clone().detach()
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filt = filt / torch.sum(filt)
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self.filt = filt[None, None, :, :].repeat((self.channels, 1, 1, 1)).cuda().half()
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def __call__(self, input: torch.Tensor):
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if input.dtype != self.filt.dtype:
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self.filt = self.filt.float()
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input_pad = F.pad(input, (1, 1, 1, 1), 'reflect')
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return F.conv2d(input_pad, self.filt, stride=2, padding=0, groups=input.shape[1])
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class Downsample(nn.Module):
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def __init__(self, filt_size=3, stride=2, channels=None):
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super(Downsample, self).__init__()
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self.filt_size = filt_size
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self.stride = stride
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self.channels = channels
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assert self.filt_size == 3
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a = torch.tensor([1., 2., 1.])
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filt = (a[:, None] * a[None, :])
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filt = filt / torch.sum(filt)
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# self.filt = filt[None, None, :, :].repeat((self.channels, 1, 1, 1))
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self.register_buffer('filt', filt[None, None, :, :].repeat((self.channels, 1, 1, 1)))
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def forward(self, input):
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input_pad = F.pad(input, (1, 1, 1, 1), 'reflect')
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return F.conv2d(input_pad, self.filt, stride=self.stride, padding=0, groups=input.shape[1])
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@ -0,0 +1,53 @@
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import torch
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import torch.nn as nn
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class SpaceToDepth(nn.Module):
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def __init__(self, block_size=4):
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super().__init__()
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assert block_size == 4
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self.bs = block_size
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def forward(self, x):
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N, C, H, W = x.size()
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x = x.view(N, C, H // self.bs, self.bs, W // self.bs, self.bs) # (N, C, H//bs, bs, W//bs, bs)
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x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # (N, bs, bs, C, H//bs, W//bs)
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x = x.view(N, C * (self.bs ** 2), H // self.bs, W // self.bs) # (N, C*bs^2, H//bs, W//bs)
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return x
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@torch.jit.script
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class SpaceToDepthJit(object):
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def __call__(self, x: torch.Tensor):
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# assuming hard-coded that block_size==4 for acceleration
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N, C, H, W = x.size()
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x = x.view(N, C, H // 4, 4, W // 4, 4) # (N, C, H//bs, bs, W//bs, bs)
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x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # (N, bs, bs, C, H//bs, W//bs)
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x = x.view(N, C * 16, H // 4, W // 4) # (N, C*bs^2, H//bs, W//bs)
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return x
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class SpaceToDepthModule(nn.Module):
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def __init__(self, remove_model_jit=False):
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super().__init__()
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if not remove_model_jit:
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self.op = SpaceToDepthJit()
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else:
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self.op = SpaceToDepth()
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def forward(self, x):
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return self.op(x)
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class DepthToSpace(nn.Module):
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def __init__(self, block_size):
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super().__init__()
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self.bs = block_size
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def forward(self, x):
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N, C, H, W = x.size()
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x = x.view(N, self.bs, self.bs, C // (self.bs ** 2), H, W) # (N, bs, bs, C//bs^2, H, W)
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x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # (N, C//bs^2, H, bs, W, bs)
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x = x.view(N, C // (self.bs ** 2), H * self.bs, W * self.bs) # (N, C//bs^2, H * bs, W * bs)
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return x
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@ -0,0 +1,292 @@
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"""
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TResNet: High Performance GPU-Dedicated Architecture
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https://arxiv.org/pdf/2003.13630.pdf
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Original model: https://github.com/mrT23/TResNet
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"""
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from functools import partial
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import torch
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import torch.nn as nn
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from collections import OrderedDict
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from .layers import SpaceToDepthModule, AntiAliasDownsampleLayer
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from .registry import register_model
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from .helpers import load_pretrained
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try:
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from inplace_abn import InPlaceABN
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has_iabn = True
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except ImportError:
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has_iabn = False
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__all__ = ['tresnet_m', 'tresnet_l', 'tresnet_xl']
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def _cfg(url='', **kwargs):
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return {
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'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
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'crop_pct': 0.875, 'interpolation': 'bilinear',
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'mean': (0, 0, 0), 'std': (1, 1, 1),
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'first_conv': 'layer0.conv1', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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'tresnet_m':
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_cfg(url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/tresnet/tresnet_m_80_8.pth'),
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'tresnet_l':
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_cfg(url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/tresnet/tresnet_l_81_5.pth'),
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'tresnet_xl':
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_cfg(url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/tresnet/tresnet_xl_82_0.pth')
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}
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class FastGlobalAvgPool2d(nn.Module):
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def __init__(self, flatten=False):
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super(FastGlobalAvgPool2d, self).__init__()
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self.flatten = flatten
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def forward(self, x):
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if self.flatten:
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in_size = x.size()
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return x.view((in_size[0], in_size[1], -1)).mean(dim=2)
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else:
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return x.view(x.size(0), x.size(1), -1).mean(-1).view(x.size(0), x.size(1), 1, 1)
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class FastSEModule(nn.Module):
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def __init__(self, channels, reduction_channels, inplace=True):
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super(FastSEModule, self).__init__()
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self.avg_pool = FastGlobalAvgPool2d()
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self.fc1 = nn.Conv2d(channels, reduction_channels, kernel_size=1, padding=0, bias=True)
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self.relu = nn.ReLU(inplace=inplace)
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self.fc2 = nn.Conv2d(reduction_channels, channels, kernel_size=1, padding=0, bias=True)
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self.activation = nn.Sigmoid()
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def forward(self, x):
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x_se = self.avg_pool(x)
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x_se2 = self.fc1(x_se)
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x_se2 = self.relu(x_se2)
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x_se = self.fc2(x_se2)
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x_se = self.activation(x_se)
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return x * x_se
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def IABN2Float(module: nn.Module) -> nn.Module:
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"If `module` is IABN don't use half precision."
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if isinstance(module, InPlaceABN):
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module.float()
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for child in module.children(): IABN2Float(child)
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return module
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def conv2d_ABN(ni, nf, stride, activation="leaky_relu", kernel_size=3, activation_param=1e-2, groups=1):
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return nn.Sequential(
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nn.Conv2d(ni, nf, kernel_size=kernel_size, stride=stride, padding=kernel_size // 2, groups=groups,
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bias=False),
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InPlaceABN(num_features=nf, activation=activation, activation_param=activation_param)
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)
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True, anti_alias_layer=None):
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super(BasicBlock, self).__init__()
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if stride == 1:
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self.conv1 = conv2d_ABN(inplanes, planes, stride=1, activation_param=1e-3)
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else:
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if anti_alias_layer is None:
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self.conv1 = conv2d_ABN(inplanes, planes, stride=2, activation_param=1e-3)
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else:
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self.conv1 = nn.Sequential(conv2d_ABN(inplanes, planes, stride=1, activation_param=1e-3),
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anti_alias_layer(channels=planes, filt_size=3, stride=2))
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self.conv2 = conv2d_ABN(planes, planes, stride=1, activation="identity")
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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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reduce_layer_planes = max(planes * self.expansion // 4, 64)
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self.se = FastSEModule(planes * self.expansion, reduce_layer_planes) if use_se else None
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def forward(self, x):
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if self.downsample is not None:
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residual = self.downsample(x)
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else:
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residual = x
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out = self.conv1(x)
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out = self.conv2(out)
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if self.se is not None: out = self.se(out)
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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 Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True, anti_alias_layer=None):
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super(Bottleneck, self).__init__()
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self.conv1 = conv2d_ABN(inplanes, planes, kernel_size=1, stride=1, activation="leaky_relu",
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activation_param=1e-3)
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if stride == 1:
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self.conv2 = conv2d_ABN(planes, planes, kernel_size=3, stride=1, activation="leaky_relu",
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activation_param=1e-3)
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else:
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if anti_alias_layer is None:
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self.conv2 = conv2d_ABN(planes, planes, kernel_size=3, stride=2, activation="leaky_relu",
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activation_param=1e-3)
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else:
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self.conv2 = nn.Sequential(conv2d_ABN(planes, planes, kernel_size=3, stride=1,
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activation="leaky_relu", activation_param=1e-3),
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anti_alias_layer(channels=planes, filt_size=3, stride=2))
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self.conv3 = conv2d_ABN(planes, planes * self.expansion, kernel_size=1, stride=1,
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activation="identity")
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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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reduce_layer_planes = max(planes * self.expansion // 8, 64)
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self.se = FastSEModule(planes, reduce_layer_planes) if use_se else None
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def forward(self, x):
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if self.downsample is not None:
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residual = self.downsample(x)
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else:
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residual = x
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out = self.conv1(x)
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out = self.conv2(out)
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if self.se is not None: out = self.se(out)
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out = self.conv3(out)
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out = out + residual # no inplace
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out = self.relu(out)
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return out
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class TResNet(nn.Module):
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def __init__(self, layers, in_chans=3, num_classes=1000, width_factor=1.0, remove_aa_jit=False):
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if not has_iabn:
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raise " For TResNet models, please install InplaceABN: 'pip install git+https://github.com/mapillary/inplace_abn.git@v1.0.11' "
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super(TResNet, self).__init__()
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# JIT layers
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space_to_depth = SpaceToDepthModule()
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anti_alias_layer = partial(AntiAliasDownsampleLayer, remove_aa_jit=remove_aa_jit)
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global_pool_layer = FastGlobalAvgPool2d(flatten=True)
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# TResnet stages
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self.inplanes = int(64 * width_factor)
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self.planes = int(64 * width_factor)
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conv1 = conv2d_ABN(in_chans * 16, self.planes, stride=1, kernel_size=3)
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layer1 = self._make_layer(BasicBlock, self.planes, layers[0], stride=1, use_se=True,
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anti_alias_layer=anti_alias_layer) # 56x56
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layer2 = self._make_layer(BasicBlock, self.planes * 2, layers[1], stride=2, use_se=True,
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anti_alias_layer=anti_alias_layer) # 28x28
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layer3 = self._make_layer(Bottleneck, self.planes * 4, layers[2], stride=2, use_se=True,
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anti_alias_layer=anti_alias_layer) # 14x14
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layer4 = self._make_layer(Bottleneck, self.planes * 8, layers[3], stride=2, use_se=False,
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anti_alias_layer=anti_alias_layer) # 7x7
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# body
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self.body = nn.Sequential(OrderedDict([
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('SpaceToDepth', space_to_depth),
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('conv1', conv1),
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('layer1', layer1),
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('layer2', layer2),
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('layer3', layer3),
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('layer4', layer4)]))
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# head
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self.embeddings = []
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self.global_pool = nn.Sequential(OrderedDict([('global_pool_layer', global_pool_layer)]))
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self.num_features = (self.planes * 8) * Bottleneck.expansion
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fc = nn.Linear(self.num_features, num_classes)
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self.head = nn.Sequential(OrderedDict([('fc', fc)]))
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# model initilization
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='leaky_relu')
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elif isinstance(m, nn.BatchNorm2d) or isinstance(m, InPlaceABN):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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# residual connections special initialization
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for m in self.modules():
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if isinstance(m, BasicBlock):
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m.conv2[1].weight = nn.Parameter(torch.zeros_like(m.conv2[1].weight)) # BN to zero
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if isinstance(m, Bottleneck):
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m.conv3[1].weight = nn.Parameter(torch.zeros_like(m.conv3[1].weight)) # BN to zero
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if isinstance(m, nn.Linear): m.weight.data.normal_(0, 0.01)
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def _make_layer(self, block, planes, blocks, stride=1, use_se=True, anti_alias_layer=None):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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layers = []
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if stride == 2:
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# avg pooling before 1x1 conv
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layers.append(nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=True, count_include_pad=False))
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layers += [conv2d_ABN(self.inplanes, planes * block.expansion, kernel_size=1, stride=1,
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activation="identity")]
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downsample = nn.Sequential(*layers)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample, use_se=use_se,
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anti_alias_layer=anti_alias_layer))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks): layers.append(
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block(self.inplanes, planes, use_se=use_se, anti_alias_layer=anti_alias_layer))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.body(x)
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self.embeddings = self.global_pool(x)
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logits = self.head(self.embeddings)
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return logits
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def filter_fn(input):
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return input['model']
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@register_model
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def tresnet_m(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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default_cfg = default_cfgs['tresnet_m']
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model = TResNet(layers=[3, 4, 11, 3], num_classes=num_classes, in_chans=in_chans)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans, filter_fn=filter_fn)
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return model
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@register_model
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def tresnet_l(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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default_cfg = default_cfgs['tresnet_l']
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model = TResNet(layers=[4, 5, 18, 3], num_classes=num_classes, in_chans=in_chans, width_factor=1.2)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans, filter_fn=filter_fn)
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return model
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@register_model
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def tresnet_xl(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
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default_cfg = default_cfgs['tresnet_xl']
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model = TResNet(layers=[4, 5, 24, 3], num_classes=num_classes, in_chans=in_chans, width_factor=1.3)
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model.default_cfg = default_cfg
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if pretrained:
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load_pretrained(model, default_cfg, num_classes, in_chans, filter_fn=filter_fn)
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return model
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Reference in new issue