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pytorch-image-models/timm/models/pnasnet.py

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"""
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 __future__ import print_function, division, absolute_import
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
from .helpers import load_pretrained
from .adaptive_avgmax_pool import SelectAdaptivePool2d
_models = ['pnasnet5large']
__all__ = ['PNASNet5Large'] + _models
default_cfgs = {
'pnasnet5large': {
'url': 'http://data.lip6.fr/cadene/pretrainedmodels/pnasnet5large-bf079911.pth',
'input_size': (3, 331, 331),
'pool_size': (11, 11),
'crop_pct': 0.875,
'interpolation': 'bicubic',
'mean': (0.5, 0.5, 0.5),
'std': (0.5, 0.5, 0.5),
'num_classes': 1001,
'first_conv': 'conv_0.conv',
'classifier': 'last_linear',
},
}
class MaxPool(nn.Module):
def __init__(self, kernel_size, stride=1, padding=1, zero_pad=False):
super(MaxPool, self).__init__()
self.zero_pad = nn.ZeroPad2d((1, 0, 1, 0)) if zero_pad else None
self.pool = nn.MaxPool2d(kernel_size, stride=stride, padding=padding)
def forward(self, x):
if self.zero_pad:
x = self.zero_pad(x)
x = self.pool(x)
if self.zero_pad:
x = x[:, :, 1:, 1:]
return x
class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, dw_kernel_size, dw_stride,
dw_padding):
super(SeparableConv2d, self).__init__()
self.depthwise_conv2d = nn.Conv2d(in_channels, in_channels,
kernel_size=dw_kernel_size,
stride=dw_stride, padding=dw_padding,
groups=in_channels, bias=False)
self.pointwise_conv2d = nn.Conv2d(in_channels, out_channels,
kernel_size=1, bias=False)
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, zero_pad=False):
super(BranchSeparables, self).__init__()
padding = kernel_size // 2
middle_channels = out_channels if stem_cell else in_channels
self.zero_pad = nn.ZeroPad2d((1, 0, 1, 0)) if zero_pad else None
self.relu_1 = nn.ReLU()
self.separable_1 = SeparableConv2d(in_channels, middle_channels,
kernel_size, dw_stride=stride,
dw_padding=padding)
self.bn_sep_1 = nn.BatchNorm2d(middle_channels, eps=0.001)
self.relu_2 = nn.ReLU()
self.separable_2 = SeparableConv2d(middle_channels, out_channels,
kernel_size, dw_stride=1,
dw_padding=padding)
self.bn_sep_2 = nn.BatchNorm2d(out_channels, eps=0.001)
def forward(self, x):
x = self.relu_1(x)
if self.zero_pad:
x = self.zero_pad(x)
x = self.separable_1(x)
if self.zero_pad:
x = x[:, :, 1:, 1:].contiguous()
x = self.bn_sep_1(x)
x = self.relu_2(x)
x = self.separable_2(x)
x = self.bn_sep_2(x)
return x
class ReluConvBn(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1):
super(ReluConvBn, self).__init__()
self.relu = nn.ReLU()
self.conv = nn.Conv2d(in_channels, out_channels,
kernel_size=kernel_size, stride=stride,
bias=False)
self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
def forward(self, x):
x = self.relu(x)
x = self.conv(x)
x = self.bn(x)
return x
class FactorizedReduction(nn.Module):
def __init__(self, in_channels, out_channels):
super(FactorizedReduction, self).__init__()
self.relu = nn.ReLU()
self.path_1 = nn.Sequential(OrderedDict([
('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)),
('conv', nn.Conv2d(in_channels, out_channels // 2,
kernel_size=1, bias=False)),
]))
self.path_2 = nn.Sequential(OrderedDict([
('pad', nn.ZeroPad2d((0, 1, 0, 1))),
('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)),
('conv', nn.Conv2d(in_channels, out_channels // 2,
kernel_size=1, bias=False)),
]))
self.final_path_bn = nn.BatchNorm2d(out_channels, eps=0.001)
def forward(self, x):
x = self.relu(x)
x_path1 = self.path_1(x)
x_path2 = self.path_2.pad(x)
x_path2 = x_path2[:, :, 1:, 1:]
x_path2 = self.path_2.avgpool(x_path2)
x_path2 = self.path_2.conv(x_path2)
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:
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_channels_left, out_channels_left, in_channels_right,
out_channels_right):
super(CellStem0, self).__init__()
self.conv_1x1 = ReluConvBn(in_channels_right, out_channels_right,
kernel_size=1)
self.comb_iter_0_left = BranchSeparables(in_channels_left,
out_channels_left,
kernel_size=5, stride=2,
stem_cell=True)
self.comb_iter_0_right = nn.Sequential(OrderedDict([
('max_pool', MaxPool(3, stride=2)),
('conv', nn.Conv2d(in_channels_left, out_channels_left,
kernel_size=1, bias=False)),
('bn', nn.BatchNorm2d(out_channels_left, eps=0.001)),
]))
self.comb_iter_1_left = BranchSeparables(out_channels_right,
out_channels_right,
kernel_size=7, stride=2)
self.comb_iter_1_right = MaxPool(3, stride=2)
self.comb_iter_2_left = BranchSeparables(out_channels_right,
out_channels_right,
kernel_size=5, stride=2)
self.comb_iter_2_right = BranchSeparables(out_channels_right,
out_channels_right,
kernel_size=3, stride=2)
self.comb_iter_3_left = BranchSeparables(out_channels_right,
out_channels_right,
kernel_size=3)
self.comb_iter_3_right = MaxPool(3, stride=2)
self.comb_iter_4_left = BranchSeparables(in_channels_right,
out_channels_right,
kernel_size=3, stride=2,
stem_cell=True)
self.comb_iter_4_right = ReluConvBn(out_channels_right,
out_channels_right,
kernel_size=1, stride=2)
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_channels_left, out_channels_left, in_channels_right,
out_channels_right, is_reduction=False, zero_pad=False,
match_prev_layer_dimensions=False):
super(Cell, self).__init__()
# If `is_reduction` is set to `True` stride 2 is used for
# convolutional 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_dimensions
if match_prev_layer_dimensions:
self.conv_prev_1x1 = FactorizedReduction(in_channels_left,
out_channels_left)
else:
self.conv_prev_1x1 = ReluConvBn(in_channels_left,
out_channels_left, kernel_size=1)
self.conv_1x1 = ReluConvBn(in_channels_right, out_channels_right,
kernel_size=1)
self.comb_iter_0_left = BranchSeparables(out_channels_left,
out_channels_left,
kernel_size=5, stride=stride,
zero_pad=zero_pad)
self.comb_iter_0_right = MaxPool(3, stride=stride, zero_pad=zero_pad)
self.comb_iter_1_left = BranchSeparables(out_channels_right,
out_channels_right,
kernel_size=7, stride=stride,
zero_pad=zero_pad)
self.comb_iter_1_right = MaxPool(3, stride=stride, zero_pad=zero_pad)
self.comb_iter_2_left = BranchSeparables(out_channels_right,
out_channels_right,
kernel_size=5, stride=stride,
zero_pad=zero_pad)
self.comb_iter_2_right = BranchSeparables(out_channels_right,
out_channels_right,
kernel_size=3, stride=stride,
zero_pad=zero_pad)
self.comb_iter_3_left = BranchSeparables(out_channels_right,
out_channels_right,
kernel_size=3)
self.comb_iter_3_right = MaxPool(3, stride=stride, zero_pad=zero_pad)
self.comb_iter_4_left = BranchSeparables(out_channels_left,
out_channels_left,
kernel_size=3, stride=stride,
zero_pad=zero_pad)
if is_reduction:
self.comb_iter_4_right = ReluConvBn(out_channels_right,
out_channels_right,
kernel_size=1, stride=stride)
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=1001, in_chans=3, drop_rate=0.5, global_pool='avg'):
super(PNASNet5Large, self).__init__()
self.num_classes = num_classes
self.num_features = 4320
self.drop_rate = drop_rate
self.conv_0 = nn.Sequential(OrderedDict([
('conv', nn.Conv2d(in_chans, 96, kernel_size=3, stride=2, bias=False)),
('bn', nn.BatchNorm2d(96, eps=0.001))
]))
self.cell_stem_0 = CellStem0(in_channels_left=96, out_channels_left=54,
in_channels_right=96,
out_channels_right=54)
self.cell_stem_1 = Cell(in_channels_left=96, out_channels_left=108,
in_channels_right=270, out_channels_right=108,
match_prev_layer_dimensions=True,
is_reduction=True)
self.cell_0 = Cell(in_channels_left=270, out_channels_left=216,
in_channels_right=540, out_channels_right=216,
match_prev_layer_dimensions=True)
self.cell_1 = Cell(in_channels_left=540, out_channels_left=216,
in_channels_right=1080, out_channels_right=216)
self.cell_2 = Cell(in_channels_left=1080, out_channels_left=216,
in_channels_right=1080, out_channels_right=216)
self.cell_3 = Cell(in_channels_left=1080, out_channels_left=216,
in_channels_right=1080, out_channels_right=216)
self.cell_4 = Cell(in_channels_left=1080, out_channels_left=432,
in_channels_right=1080, out_channels_right=432,
is_reduction=True, zero_pad=True)
self.cell_5 = Cell(in_channels_left=1080, out_channels_left=432,
in_channels_right=2160, out_channels_right=432,
match_prev_layer_dimensions=True)
self.cell_6 = Cell(in_channels_left=2160, out_channels_left=432,
in_channels_right=2160, out_channels_right=432)
self.cell_7 = Cell(in_channels_left=2160, out_channels_left=432,
in_channels_right=2160, out_channels_right=432)
self.cell_8 = Cell(in_channels_left=2160, out_channels_left=864,
in_channels_right=2160, out_channels_right=864,
is_reduction=True)
self.cell_9 = Cell(in_channels_left=2160, out_channels_left=864,
in_channels_right=4320, out_channels_right=864,
match_prev_layer_dimensions=True)
self.cell_10 = Cell(in_channels_left=4320, out_channels_left=864,
in_channels_right=4320, out_channels_right=864)
self.cell_11 = Cell(in_channels_left=4320, out_channels_left=864,
in_channels_right=4320, out_channels_right=864)
self.relu = nn.ReLU()
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.last_linear = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
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 = SelectAdaptivePool2d(pool_type=global_pool)
del self.last_linear
if num_classes:
self.last_linear = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
else:
self.last_linear = None
def forward_features(self, x, pool=True):
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.relu(x_cell_11)
if pool:
x = self.global_pool(x)
x = x.view(x.size(0), -1)
return x
def forward(self, input):
x = self.forward_features(input)
if self.drop_rate > 0:
x = F.dropout(x, self.drop_rate, training=self.training)
x = self.last_linear(x)
return x
def pnasnet5large(pretrained='imagenet', num_classes=1000, in_chans=3, **kwargs):
r"""PNASNet-5 model architecture from the
`"Progressive Neural Architecture Search"
<https://arxiv.org/abs/1712.00559>`_ paper.
"""
default_cfg = default_cfgs['pnasnet5large']
model = PNASNet5Large(num_classes=1000, in_chans=in_chans, **kwargs)
model.default_cfg = default_cfg
if pretrained:
load_pretrained(model, default_cfg, num_classes, in_chans)
return model