"""
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_classifier
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs
__all__ = ['PNASNet5Large']
class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding=''):
super(SeparableConv2d, self).__init__()
self.depthwise_conv2d = create_conv2d(
in_channels, in_channels, kernel_size=kernel_size,
stride=stride, padding=padding, groups=in_channels)
self.pointwise_conv2d = create_conv2d(
in_channels, out_channels, kernel_size=1, padding=padding)
def forward(self, x):
x = self.depthwise_conv2d(x)
x = self.pointwise_conv2d(x)
return x
class BranchSeparables(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, stem_cell=False, padding=''):
super(BranchSeparables, self).__init__()
middle_channels = out_channels if stem_cell else in_channels
self.act_1 = nn.ReLU()
self.separable_1 = SeparableConv2d(
in_channels, middle_channels, kernel_size, stride=stride, padding=padding)
self.bn_sep_1 = nn.BatchNorm2d(middle_channels, eps=0.001)
self.act_2 = nn.ReLU()
self.separable_2 = SeparableConv2d(
middle_channels, out_channels, kernel_size, stride=1, padding=padding)
self.bn_sep_2 = nn.BatchNorm2d(out_channels, eps=0.001)
def forward(self, x):
x = self.act_1(x)
x = self.separable_1(x)
x = self.bn_sep_1(x)
x = self.act_2(x)
x = self.separable_2(x)
x = self.bn_sep_2(x)
return x
class ActConvBn(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=''):
super(ActConvBn, self).__init__()
self.act = nn.ReLU()
self.conv = create_conv2d(
in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
def forward(self, x):
x = self.act(x)
x = self.conv(x)
x = self.bn(x)
return x
class FactorizedReduction(nn.Module):
def __init__(self, in_channels, out_channels, padding=''):
super(FactorizedReduction, self).__init__()
self.act = nn.ReLU()
self.path_1 = nn.Sequential(OrderedDict([
('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)),
('conv', create_conv2d(in_channels, out_channels // 2, kernel_size=1, padding=padding)),
]))
self.path_2 = nn.Sequential(OrderedDict([
('pad', nn.ZeroPad2d((-1, 1, -1, 1))), # shift
('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)),
('conv', create_conv2d(in_channels, out_channels // 2, kernel_size=1, padding=padding)),
]))
self.final_path_bn = nn.BatchNorm2d(out_channels, eps=0.001)
def forward(self, x):
x = self.act(x)
x_path1 = self.path_1(x)
x_path2 = self.path_2(x)
out = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
return out
class CellBase(nn.Module):
def cell_forward(self, x_left, x_right):
x_comb_iter_0_left = self.comb_iter_0_left(x_left)
x_comb_iter_0_right = self.comb_iter_0_right(x_left)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_right)
x_comb_iter_1_right = self.comb_iter_1_right(x_right)
x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
x_comb_iter_2_left = self.comb_iter_2_left(x_right)
x_comb_iter_2_right = self.comb_iter_2_right(x_right)
x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
x_comb_iter_3_left = self.comb_iter_3_left(x_comb_iter_2)
x_comb_iter_3_right = self.comb_iter_3_right(x_right)
x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right
x_comb_iter_4_left = self.comb_iter_4_left(x_left)
if self.comb_iter_4_right is not None:
x_comb_iter_4_right = self.comb_iter_4_right(x_right)
else:
x_comb_iter_4_right = x_right
x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
x_out = torch.cat([x_comb_iter_0, x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
return x_out
class CellStem0(CellBase):
def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
super(CellStem0, self).__init__()
self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, kernel_size=1, padding=pad_type)
self.comb_iter_0_left = BranchSeparables(
in_chs_left, out_chs_left, kernel_size=5, stride=2, stem_cell=True, padding=pad_type)
self.comb_iter_0_right = nn.Sequential(OrderedDict([
('max_pool', create_pool2d('max', 3, stride=2, padding=pad_type)),
('conv', create_conv2d(in_chs_left, out_chs_left, kernel_size=1, padding=pad_type)),
('bn', nn.BatchNorm2d(out_chs_left, eps=0.001)),
]))
self.comb_iter_1_left = BranchSeparables(
out_chs_right, out_chs_right, kernel_size=7, stride=2, padding=pad_type)
self.comb_iter_1_right = create_pool2d('max', 3, stride=2, padding=pad_type)
self.comb_iter_2_left = BranchSeparables(
out_chs_right, out_chs_right, kernel_size=5, stride=2, padding=pad_type)
self.comb_iter_2_right = BranchSeparables(
out_chs_right, out_chs_right, kernel_size=3, stride=2, padding=pad_type)
self.comb_iter_3_left = BranchSeparables(
out_chs_right, out_chs_right, kernel_size=3, padding=pad_type)
self.comb_iter_3_right = create_pool2d('max', 3, stride=2, padding=pad_type)
self.comb_iter_4_left = BranchSeparables(
in_chs_right, out_chs_right, kernel_size=3, stride=2, stem_cell=True, padding=pad_type)
self.comb_iter_4_right = ActConvBn(
out_chs_right, out_chs_right, kernel_size=1, stride=2, padding=pad_type)
def forward(self, x_left):
x_right = self.conv_1x1(x_left)
x_out = self.cell_forward(x_left, x_right)
return x_out
class Cell(CellBase):
def __init__(
self,
in_chs_left,
out_chs_left,
in_chs_right,
out_chs_right,
pad_type='',
is_reduction=False,
match_prev_layer_dims=False,
):
super(Cell, self).__init__()
# If `is_reduction` is set to `True` stride 2 is used for
# convolution and pooling layers to reduce the spatial size of
# the output of a cell approximately by a factor of 2.
stride = 2 if is_reduction else 1
# If `match_prev_layer_dimensions` is set to `True`
# `FactorizedReduction` is used to reduce the spatial size
# of the left input of a cell approximately by a factor of 2.
self.match_prev_layer_dimensions = match_prev_layer_dims
if match_prev_layer_dims:
self.conv_prev_1x1 = FactorizedReduction(in_chs_left, out_chs_left, padding=pad_type)
else:
self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, kernel_size=1, padding=pad_type)
self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, kernel_size=1, padding=pad_type)
self.comb_iter_0_left = BranchSeparables(
out_chs_left, out_chs_left, kernel_size=5, stride=stride, padding=pad_type)
self.comb_iter_0_right = create_pool2d('max', 3, stride=stride, padding=pad_type)
self.comb_iter_1_left = BranchSeparables(
out_chs_right, out_chs_right, kernel_size=7, stride=stride, padding=pad_type)
self.comb_iter_1_right = create_pool2d('max', 3, stride=stride, padding=pad_type)
self.comb_iter_2_left = BranchSeparables(
out_chs_right, out_chs_right, kernel_size=5, stride=stride, padding=pad_type)
self.comb_iter_2_right = BranchSeparables(
out_chs_right, out_chs_right, kernel_size=3, stride=stride, padding=pad_type)
self.comb_iter_3_left = BranchSeparables(out_chs_right, out_chs_right, kernel_size=3)
self.comb_iter_3_right = create_pool2d('max', 3, stride=stride, padding=pad_type)
self.comb_iter_4_left = BranchSeparables(
out_chs_left, out_chs_left, kernel_size=3, stride=stride, padding=pad_type)
if is_reduction:
self.comb_iter_4_right = ActConvBn(
out_chs_right, out_chs_right, kernel_size=1, stride=stride, padding=pad_type)
else:
self.comb_iter_4_right = None
def forward(self, x_left, x_right):
x_left = self.conv_prev_1x1(x_left)
x_right = self.conv_1x1(x_right)
x_out = self.cell_forward(x_left, x_right)
return x_out
class PNASNet5Large(nn.Module):
def __init__(
self,
num_classes=1000,
in_chans=3,
output_stride=32,
drop_rate=0.,
global_pool='avg',
pad_type='',
):
super(PNASNet5Large, self).__init__()
self.num_classes = num_classes
self.num_features = 4320
assert output_stride == 32
self.conv_0 = ConvNormAct(
in_chans, 96, kernel_size=3, stride=2, padding=0,
norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.1), apply_act=False)
self.cell_stem_0 = CellStem0(
in_chs_left=96, out_chs_left=54, in_chs_right=96, out_chs_right=54, pad_type=pad_type)
self.cell_stem_1 = Cell(
in_chs_left=96, out_chs_left=108, in_chs_right=270, out_chs_right=108, pad_type=pad_type,
match_prev_layer_dims=True, is_reduction=True)
self.cell_0 = Cell(
in_chs_left=270, out_chs_left=216, in_chs_right=540, out_chs_right=216, pad_type=pad_type,
match_prev_layer_dims=True)
self.cell_1 = Cell(
in_chs_left=540, out_chs_left=216, in_chs_right=1080, out_chs_right=216, pad_type=pad_type)
self.cell_2 = Cell(
in_chs_left=1080, out_chs_left=216, in_chs_right=1080, out_chs_right=216, pad_type=pad_type)
self.cell_3 = Cell(
in_chs_left=1080, out_chs_left=216, in_chs_right=1080, out_chs_right=216, pad_type=pad_type)
self.cell_4 = Cell(
in_chs_left=1080, out_chs_left=432, in_chs_right=1080, out_chs_right=432, pad_type=pad_type,
is_reduction=True)
self.cell_5 = Cell(
in_chs_left=1080, out_chs_left=432, in_chs_right=2160, out_chs_right=432, pad_type=pad_type,
match_prev_layer_dims=True)
self.cell_6 = Cell(
in_chs_left=2160, out_chs_left=432, in_chs_right=2160, out_chs_right=432, pad_type=pad_type)
self.cell_7 = Cell(
in_chs_left=2160, out_chs_left=432, in_chs_right=2160, out_chs_right=432, pad_type=pad_type)
self.cell_8 = Cell(
in_chs_left=2160, out_chs_left=864, in_chs_right=2160, out_chs_right=864, pad_type=pad_type,
is_reduction=True)
self.cell_9 = Cell(
in_chs_left=2160, out_chs_left=864, in_chs_right=4320, out_chs_right=864, pad_type=pad_type,
match_prev_layer_dims=True)
self.cell_10 = Cell(
in_chs_left=4320, out_chs_left=864, in_chs_right=4320, out_chs_right=864, pad_type=pad_type)
self.cell_11 = Cell(
in_chs_left=4320, out_chs_left=864, in_chs_right=4320, out_chs_right=864, pad_type=pad_type)
self.act = nn.ReLU()
self.feature_info = [
dict(num_chs=96, reduction=2, module='conv_0'),
dict(num_chs=270, reduction=4, module='cell_stem_1.conv_1x1.act'),
dict(num_chs=1080, reduction=8, module='cell_4.conv_1x1.act'),
dict(num_chs=2160, reduction=16, module='cell_8.conv_1x1.act'),
dict(num_chs=4320, reduction=32, module='act'),
]
self.global_pool, self.head_drop, self.last_linear = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool, drop_rate=drop_rate)
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(stem=r'^conv_0|cell_stem_[01]', blocks=r'^cell_(\d+)')
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
@torch.jit.ignore
def get_classifier(self):
return self.last_linear
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.last_linear = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x):
x_conv_0 = self.conv_0(x)
x_stem_0 = self.cell_stem_0(x_conv_0)
x_stem_1 = self.cell_stem_1(x_conv_0, x_stem_0)
x_cell_0 = self.cell_0(x_stem_0, x_stem_1)
x_cell_1 = self.cell_1(x_stem_1, x_cell_0)
x_cell_2 = self.cell_2(x_cell_0, x_cell_1)
x_cell_3 = self.cell_3(x_cell_1, x_cell_2)
x_cell_4 = self.cell_4(x_cell_2, x_cell_3)
x_cell_5 = self.cell_5(x_cell_3, x_cell_4)
x_cell_6 = self.cell_6(x_cell_4, x_cell_5)
x_cell_7 = self.cell_7(x_cell_5, x_cell_6)
x_cell_8 = self.cell_8(x_cell_6, x_cell_7)
x_cell_9 = self.cell_9(x_cell_7, x_cell_8)
x_cell_10 = self.cell_10(x_cell_8, x_cell_9)
x_cell_11 = self.cell_11(x_cell_9, x_cell_10)
x = self.act(x_cell_11)
return x
def forward_head(self, x, pre_logits: bool = False):
x = self.global_pool(x)
x = self.head_drop(x)
return x if pre_logits else self.last_linear(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_pnasnet(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
PNASNet5Large,
variant,
pretrained,
feature_cfg=dict(feature_cls='hook', no_rewrite=True), # not possible to re-write this model
**kwargs,
)
default_cfgs = generate_default_cfgs({
'pnasnet5large.tf_in1k': {
'hf_hub_id': 'timm/',
'input_size': (3, 331, 331),
'pool_size': (11, 11),
'crop_pct': 0.911,
'interpolation': 'bicubic',
'mean': (0.5, 0.5, 0.5),
'std': (0.5, 0.5, 0.5),
'num_classes': 1000,
'first_conv': 'conv_0.conv',
'classifier': 'last_linear',
},
})
@register_model
def pnasnet5large(pretrained=False, **kwargs) -> PNASNet5Large:
r"""PNASNet-5 model architecture from the
`"Progressive Neural Architecture Search"
<https://arxiv.org/abs/1712.00559>`_ paper.
"""
model_kwargs = dict(pad_type='same', **kwargs)
return _create_pnasnet('pnasnet5large', pretrained, **model_kwargs)