mirror of
https://github.com/huggingface/pytorch-image-models.git
synced 2025-06-03 15:01:08 +08:00
Some TResNet cleanup.
* allow use of global pool arg, test-time-pooling * clean checkpoints to just contain state dict, add 448 res checkpoints * support DataParallel via lazy filter creation for JIT Downsample * some minor formatting (mostly alignment) preferences
This commit is contained in:
Ross Wightman
parent
64fe37d008
commit
0004f37d25
@ -70,10 +70,11 @@ class AdaptiveCatAvgMaxPool2d(nn.Module):
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class SelectAdaptivePool2d(nn.Module):
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"""Selectable global pooling layer with dynamic input kernel size
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"""
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def __init__(self, output_size=1, pool_type='avg'):
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def __init__(self, output_size=1, pool_type='avg', flatten=False):
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super(SelectAdaptivePool2d, self).__init__()
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self.output_size = output_size
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self.pool_type = pool_type
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self.flatten = flatten
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if pool_type == 'avgmax':
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self.pool = AdaptiveAvgMaxPool2d(output_size)
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elif pool_type == 'catavgmax':
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@ -86,7 +87,10 @@ class SelectAdaptivePool2d(nn.Module):
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self.pool = nn.AdaptiveAvgPool2d(output_size)
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def forward(self, x):
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return self.pool(x)
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x = self.pool(x)
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if self.flatten:
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x = x.flatten(1)
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return x
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def feat_mult(self):
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return adaptive_pool_feat_mult(self.pool_type)
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@ -5,13 +5,14 @@ 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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def __init__(self, no_jit: bool = False, filt_size: int = 3, stride: int = 2, 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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if no_jit:
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self.op = Downsample(filt_size, stride, channels)
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else:
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self.op = DownsampleJIT(filt_size, stride, channels)
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# FIXME I should probably override _apply and clear DownsampleJIT filter cache for .cuda(), .half(), etc calls
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def forward(self, x):
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return self.op(x)
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@ -23,20 +24,21 @@ class DownsampleJIT(object):
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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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self.filt = {} # lazy init by device for DataParallel compat
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filt = (a[:, None] * a[None, :]).clone().detach()
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def _create_filter(self, like: torch.Tensor):
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filt = torch.tensor([1., 2., 1.], dtype=like.dtype, device=like.device)
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filt = filt[:, None] * filt[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)).cuda().half()
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filt = filt[None, None, :, :].repeat((self.channels, 1, 1, 1))
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return filt
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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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filt = self.filt.get(str(input.device), self._create_filter(input))
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return F.conv2d(input_pad, filt, stride=2, padding=0, groups=input.shape[1])
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class Downsample(nn.Module):
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@ -46,11 +48,9 @@ class Downsample(nn.Module):
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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 = torch.tensor([1., 2., 1.])
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filt = filt[:, None] * filt[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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@ -58,4 +58,4 @@ class Downsample(nn.Module):
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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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return F.conv2d(input_pad, self.filt, stride=self.stride, padding=0, groups=input.shape[1])
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@ -28,9 +28,9 @@ class SpaceToDepthJit(object):
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class SpaceToDepthModule(nn.Module):
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def __init__(self, remove_model_jit=False):
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def __init__(self, no_jit=False):
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super().__init__()
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if not remove_model_jit:
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if not no_jit:
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self.op = SpaceToDepthJit()
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else:
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self.op = SpaceToDepth()
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@ -8,8 +8,9 @@ Original model: https://github.com/mrT23/TResNet
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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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import torch.nn.functional as F
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from collections import OrderedDict
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from .layers import SpaceToDepthModule, AntiAliasDownsampleLayer
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from .layers import SpaceToDepthModule, AntiAliasDownsampleLayer, SelectAdaptivePool2d
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from .registry import register_model
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from .helpers import load_pretrained
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@ -27,18 +28,27 @@ def _cfg(url='', **kwargs):
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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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'first_conv': 'layer0.conv1', 'classifier': 'head.fc',
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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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'tresnet_m': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_m_80_8-dbc13962.pth'),
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'tresnet_l': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_81_5-235b486c.pth'),
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'tresnet_xl': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_xl_82_0-a2d51b00.pth'),
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'tresnet_m_448': _cfg(
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input_size=(3, 448, 448),
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_m_448-bc359d10.pth'),
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'tresnet_l_448': _cfg(
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input_size=(3, 448, 448),
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_448-940d0cd1.pth'),
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'tresnet_xl_448': _cfg(
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input_size=(3, 448, 448),
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_xl_448-8c1815de.pth')
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}
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@ -54,6 +64,9 @@ class FastGlobalAvgPool2d(nn.Module):
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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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def feat_mult(self):
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return 1
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class FastSEModule(nn.Module):
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@ -78,14 +91,15 @@ 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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for child in module.children():
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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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nn.Conv2d(
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ni, nf, kernel_size=kernel_size, stride=stride, padding=kernel_size // 2, groups=groups, bias=False),
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InPlaceABN(num_features=nf, activation=activation, activation_param=activation_param)
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)
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@ -101,8 +115,9 @@ class BasicBlock(nn.Module):
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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.conv1 = nn.Sequential(
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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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@ -120,12 +135,11 @@ class BasicBlock(nn.Module):
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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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if self.se is not None:
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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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@ -134,22 +148,22 @@ class Bottleneck(nn.Module):
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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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self.conv1 = conv2d_ABN(
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inplanes, planes, kernel_size=1, stride=1, activation="leaky_relu", 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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self.conv2 = conv2d_ABN(
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planes, planes, kernel_size=3, stride=1, activation="leaky_relu", 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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self.conv2 = conv2d_ABN(
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planes, planes, kernel_size=3, stride=2, activation="leaky_relu", 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.conv2 = nn.Sequential(
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conv2d_ABN(planes, planes, kernel_size=3, stride=1, 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.conv3 = conv2d_ABN(
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planes, planes * self.expansion, kernel_size=1, 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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@ -166,7 +180,8 @@ class Bottleneck(nn.Module):
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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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if self.se is not None:
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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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@ -176,29 +191,32 @@ class Bottleneck(nn.Module):
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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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def __init__(self, layers, in_chans=3, num_classes=1000, width_factor=1.0, no_aa_jit=False,
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global_pool='avg', drop_rate=0.):
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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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raise ImportError(
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"For TResNet models, please install InplaceABN: "
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"'pip install git+https://github.com/mapillary/inplace_abn.git@v1.0.11'")
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self.num_classes = num_classes
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self.drop_rate = drop_rate
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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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anti_alias_layer = partial(AntiAliasDownsampleLayer, no_jit=no_aa_jit)
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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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layer1 = self._make_layer(
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BasicBlock, self.planes, layers[0], stride=1, use_se=True, anti_alias_layer=anti_alias_layer) # 56x56
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layer2 = self._make_layer(
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BasicBlock, self.planes * 2, layers[1], stride=2, use_se=True, anti_alias_layer=anti_alias_layer) # 28x28
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layer3 = self._make_layer(
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Bottleneck, self.planes * 4, layers[2], stride=2, use_se=True, anti_alias_layer=anti_alias_layer) # 14x14
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layer4 = self._make_layer(
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Bottleneck, self.planes * 8, layers[3], stride=2, use_se=False, 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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@ -210,11 +228,10 @@ class TResNet(nn.Module):
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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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self.global_pool = None
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self.head = None
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self.reset_classifier(num_classes, global_pool)
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# model initilization
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for m in self.modules():
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@ -239,54 +256,104 @@ class TResNet(nn.Module):
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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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layers += [conv2d_ABN(
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self.inplanes, planes * block.expansion, kernel_size=1, stride=1, 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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layers.append(block(
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self.inplanes, planes, stride, downsample, use_se=use_se, 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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for i in range(1, blocks):
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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 get_classifier(self):
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return self.head.fc
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def reset_classifier(self, num_classes, global_pool='avg'):
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self.num_classes = num_classes
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if global_pool == 'avg':
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self.global_pool = FastGlobalAvgPool2d(flatten=True)
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else:
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self.global_pool = SelectAdaptivePool2d(pool_type=global_pool, flatten=True)
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self.head = None
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if num_classes:
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self.head = nn.Sequential(OrderedDict([
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('fc', nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes))]))
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def forward_features(self, x):
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return self.body(x)
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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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x = self.forward_features(x)
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x = self.global_pool(x)
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if self.drop_rate:
|
||||
x = F.dropout(x, p=float(self.drop_rate), training=self.training)
|
||||
x = self.head(x)
|
||||
return x
|
||||
|
||||
|
||||
@register_model
|
||||
def tresnet_m(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||
default_cfg = default_cfgs['tresnet_m']
|
||||
model = TResNet(layers=[3, 4, 11, 3], num_classes=num_classes, in_chans=in_chans)
|
||||
model = TResNet(layers=[3, 4, 11, 3], num_classes=num_classes, in_chans=in_chans, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans, filter_fn=filter_fn)
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def tresnet_l(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||
default_cfg = default_cfgs['tresnet_l']
|
||||
model = TResNet(layers=[4, 5, 18, 3], num_classes=num_classes, in_chans=in_chans, width_factor=1.2)
|
||||
model = TResNet(
|
||||
layers=[4, 5, 18, 3], num_classes=num_classes, in_chans=in_chans, width_factor=1.2, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans, filter_fn=filter_fn)
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def tresnet_xl(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||
default_cfg = default_cfgs['tresnet_xl']
|
||||
model = TResNet(layers=[4, 5, 24, 3], num_classes=num_classes, in_chans=in_chans, width_factor=1.3)
|
||||
model = TResNet(
|
||||
layers=[4, 5, 24, 3], num_classes=num_classes, in_chans=in_chans, width_factor=1.3, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans, filter_fn=filter_fn)
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def tresnet_m_448(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||
default_cfg = default_cfgs['tresnet_m_448']
|
||||
model = TResNet(layers=[3, 4, 11, 3], num_classes=num_classes, in_chans=in_chans, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def tresnet_l_448(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||
default_cfg = default_cfgs['tresnet_l_448']
|
||||
model = TResNet(
|
||||
layers=[4, 5, 18, 3], num_classes=num_classes, in_chans=in_chans, width_factor=1.2, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def tresnet_xl_448(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||
default_cfg = default_cfgs['tresnet_xl_448']
|
||||
model = TResNet(
|
||||
layers=[4, 5, 24, 3], num_classes=num_classes, in_chans=in_chans, width_factor=1.3, **kwargs)
|
||||
model.default_cfg = default_cfg
|
||||
if pretrained:
|
||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||
return model
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user