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Fix torchscript compat and features_only behaviour in GhostNet PR. A few minor formatting changes. Reuse existing layers.

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pull/571/head
Ross Wightman 4 years ago
parent d793deb51a
commit 2df77ee5cb
1 changed files with 44 additions and 91 deletions
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timm/models/ghostnet.py
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@ -4,13 +4,17 @@ GhostNet: More Features from Cheap Operations. https://arxiv.org/abs/1911.11907
The train script of the model is similar to that of MobileNetV3
Original model: https://github.com/huawei-noah/CV-backbones/tree/master/ghostnet_pytorch
"""
import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .layers import SelectAdaptivePool2d
from .layers import SelectAdaptivePool2d, Linear, hard_sigmoid
from .efficientnet_blocks import SqueezeExcite, ConvBnAct, make_divisible
from .helpers import build_model_with_cfg
from .registry import register_model
@ -36,62 +40,7 @@ default_cfgs = {
}
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
"""
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v
def hard_sigmoid(x, inplace: bool = False):
if inplace:
return x.add_(3.).clamp_(0., 6.).div_(6.)
else:
return F.relu6(x + 3.) / 6.
class SqueezeExcite(nn.Module):
def __init__(self, in_chs, se_ratio=0.25, reduced_base_chs=None,
act_layer=nn.ReLU, gate_fn=hard_sigmoid, divisor=4, **_):
super(SqueezeExcite, self).__init__()
self.gate_fn = gate_fn
reduced_chs = _make_divisible((reduced_base_chs or in_chs) * se_ratio, divisor)
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True)
self.act1 = act_layer(inplace=True)
self.conv_expand = nn.Conv2d(reduced_chs, in_chs, 1, bias=True)
def forward(self, x):
x_se = self.avg_pool(x)
x_se = self.conv_reduce(x_se)
x_se = self.act1(x_se)
x_se = self.conv_expand(x_se)
x = x * self.gate_fn(x_se)
return x
class ConvBnAct(nn.Module):
def __init__(self, in_chs, out_chs, kernel_size,
stride=1, act_layer=nn.ReLU):
super(ConvBnAct, self).__init__()
self.conv = nn.Conv2d(in_chs, out_chs, kernel_size, stride, kernel_size//2, bias=False)
self.bn1 = nn.BatchNorm2d(out_chs)
self.act1 = act_layer(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.bn1(x)
x = self.act1(x)
return x
_SE_LAYER = partial(SqueezeExcite, gate_fn=hard_sigmoid, divisor=4)
class GhostModule(nn.Module):
@ -134,26 +83,27 @@ class GhostBottleneck(nn.Module):
# Depth-wise convolution
if self.stride > 1:
self.conv_dw = nn.Conv2d(mid_chs, mid_chs, dw_kernel_size, stride=stride,
padding=(dw_kernel_size-1)//2,
groups=mid_chs, bias=False)
self.conv_dw = nn.Conv2d(
mid_chs, mid_chs, dw_kernel_size, stride=stride,
padding=(dw_kernel_size-1)//2, groups=mid_chs, bias=False)
self.bn_dw = nn.BatchNorm2d(mid_chs)
else:
self.conv_dw = None
self.bn_dw = None
# Squeeze-and-excitation
if has_se:
self.se = SqueezeExcite(mid_chs, se_ratio=se_ratio)
else:
self.se = None
self.se = _SE_LAYER(mid_chs, se_ratio=se_ratio) if has_se else None
# Point-wise linear projection
self.ghost2 = GhostModule(mid_chs, out_chs, relu=False)
# shortcut
if (in_chs == out_chs and self.stride == 1):
if in_chs == out_chs and self.stride == 1:
self.shortcut = nn.Sequential()
else:
self.shortcut = nn.Sequential(
nn.Conv2d(in_chs, in_chs, dw_kernel_size, stride=stride,
nn.Conv2d(
in_chs, in_chs, dw_kernel_size, stride=stride,
padding=(dw_kernel_size-1)//2, groups=in_chs, bias=False),
nn.BatchNorm2d(in_chs),
nn.Conv2d(in_chs, out_chs, 1, stride=1, padding=0, bias=False),
@ -168,7 +118,7 @@ class GhostBottleneck(nn.Module):
x = self.ghost1(x)
# Depth-wise convolution
if self.stride > 1:
if self.conv_dw is not None:
x = self.conv_dw(x)
x = self.bn_dw(x)
@ -184,52 +134,55 @@ class GhostBottleneck(nn.Module):
class GhostNet(nn.Module):
def __init__(self, cfgs, num_classes=1000, width=1.0, dropout=0.2, in_chans=3):
def __init__(self, cfgs, num_classes=1000, width=1.0, dropout=0.2, in_chans=3, output_stride=32):
super(GhostNet, self).__init__()
# setting of inverted residual blocks
assert output_stride == 32, 'only output_stride==32 is valid, dilation not supported'
self.cfgs = cfgs
self.num_classes = num_classes
self.dropout = dropout
self.feature_info = []
# building first layer
output_channel = _make_divisible(16 * width, 4)
self.conv_stem = nn.Conv2d(in_chans, output_channel, 3, 2, 1, bias=False)
self.feature_info.append(dict(num_chs=output_channel, reduction=2, module=f'conv_stem'))
self.bn1 = nn.BatchNorm2d(output_channel)
stem_chs = make_divisible(16 * width, 4)
self.conv_stem = nn.Conv2d(in_chans, stem_chs, 3, 2, 1, bias=False)
self.feature_info.append(dict(num_chs=stem_chs, reduction=2, module=f'conv_stem'))
self.bn1 = nn.BatchNorm2d(stem_chs)
self.act1 = nn.ReLU(inplace=True)
input_channel = output_channel
prev_chs = stem_chs
# building inverted residual blocks
stages = nn.ModuleList([])
block = GhostBottleneck
stage_idx = 0
net_stride = 2
for cfg in self.cfgs:
layers = []
s = 1
for k, exp_size, c, se_ratio, s in cfg:
output_channel = _make_divisible(c * width, 4)
hidden_channel = _make_divisible(exp_size * width, 4)
layers.append(block(input_channel, hidden_channel, output_channel, k, s,
se_ratio=se_ratio))
input_channel = output_channel
out_chs = make_divisible(c * width, 4)
mid_chs = make_divisible(exp_size * width, 4)
layers.append(block(prev_chs, mid_chs, out_chs, k, s, se_ratio=se_ratio))
prev_chs = out_chs
if s > 1:
self.feature_info.append(dict(num_chs=output_channel, reduction=2**(stage_idx+2),
module=f'blocks.{stage_idx}'))
net_stride *= 2
self.feature_info.append(dict(
num_chs=prev_chs, reduction=net_stride, module=f'blocks.{stage_idx}'))
stages.append(nn.Sequential(*layers))
stage_idx += 1
output_channel = _make_divisible(exp_size * width, 4)
stages.append(nn.Sequential(ConvBnAct(input_channel, output_channel, 1)))
self.pool_dim = input_channel = output_channel
out_chs = make_divisible(exp_size * width, 4)
stages.append(nn.Sequential(ConvBnAct(prev_chs, out_chs, 1)))
self.pool_dim = prev_chs = out_chs
self.blocks = nn.Sequential(*stages)
# building last several layers
self.num_features = output_channel = 1280
self.num_features = out_chs = 1280
self.global_pool = SelectAdaptivePool2d(pool_type='avg')
self.conv_head = nn.Conv2d(input_channel, output_channel, 1, 1, 0, bias=True)
self.conv_head = nn.Conv2d(prev_chs, out_chs, 1, 1, 0, bias=True)
self.act2 = nn.ReLU(inplace=True)
self.classifier = nn.Linear(output_channel, num_classes)
self.classifier = Linear(out_chs, num_classes)
def get_classifier(self):
return self.classifier

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