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PaddleRS/paddlers/models/ppgan/metrics/inception.py

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Add ppgan
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# code was heavily based on https://github.com/pytorch/vision/blob/main/torchvision/models/inception.py
# BSD 3-Clause License
# Copyright (c) Soumith Chintala 2016
import math
import paddle
import paddle.nn as nn
from paddle.nn import Conv2D, AvgPool2D, MaxPool2D, BatchNorm, Linear, AdaptiveAvgPool2D
__all__ = ['InceptionV3']
class InceptionV3(nn.Layer):
DEFAULT_BLOCK_INDEX = 3
BLOCK_INDEX_BY_DIM = {
64: 0, # First max pooling features
192: 1, # Second max pooling featurs
768: 2, # Pre-aux classifier features
2048: 3 # Final average pooling features
}
def __init__(self,
output_blocks=[DEFAULT_BLOCK_INDEX],
class_dim=1000,
aux_logits=False,
resize_input=True,
normalize_input=True):
super(InceptionV3, self).__init__()
self.resize_input = resize_input
self.normalize_input = normalize_input
self.output_blocks = sorted(output_blocks)
self.last_needed_block = max(output_blocks)
self.class_dim = class_dim
self.aux_logits = aux_logits
assert self.last_needed_block <= 3, 'Last possible output block index is 3'
self.blocks = []
self.Conv2d_1a_3x3 = ConvBNLayer(3,
32,
3,
stride=2,
name='Conv2d_1a_3x3')
self.Conv2d_2a_3x3 = ConvBNLayer(32, 32, 3, name='Conv2d_2a_3x3')
self.Conv2d_2b_3x3 = ConvBNLayer(32,
64,
3,
padding=1,
name='Conv2d_2b_3x3')
self.maxpool1 = MaxPool2D(kernel_size=3, stride=2)
block0 = [
self.Conv2d_1a_3x3, self.Conv2d_2a_3x3, self.Conv2d_2b_3x3,
self.maxpool1
]
self.blocks.append(nn.Sequential(*block0))
### block1
if self.last_needed_block >= 1:
self.Conv2d_3b_1x1 = ConvBNLayer(64, 80, 1, name='Conv2d_3b_1x1')
self.Conv2d_4a_3x3 = ConvBNLayer(80, 192, 3, name='Conv2d_4a_3x3')
self.maxpool2 = MaxPool2D(kernel_size=3, stride=2)
block1 = [self.Conv2d_3b_1x1, self.Conv2d_4a_3x3, self.maxpool2]
self.blocks.append(nn.Sequential(*block1))
### block2
### Mixed_5b 5c 5d
if self.last_needed_block >= 2:
self.Mixed_5b = Fid_inceptionA(192,
pool_features=32,
name='Mixed_5b')
self.Mixed_5c = Fid_inceptionA(256,
pool_features=64,
name='Mixed_5c')
self.Mixed_5d = Fid_inceptionA(288,
pool_features=64,
name='Mixed_5d')
### Mixed_6
self.Mixed_6a = InceptionB(288, name='Mixed_6a')
self.Mixed_6b = Fid_inceptionC(768, c7=128, name='Mixed_6b')
self.Mixed_6c = Fid_inceptionC(768, c7=160, name='Mixed_6c')
self.Mixed_6d = Fid_inceptionC(768, c7=160, name='Mixed_6d')
self.Mixed_6e = Fid_inceptionC(768, c7=192, name='Mixed_6e')
block2 = [
self.Mixed_5b, self.Mixed_5c, self.Mixed_5d, self.Mixed_6a,
self.Mixed_6b, self.Mixed_6c, self.Mixed_6d, self.Mixed_6e
]
self.blocks.append(nn.Sequential(*block2))
if self.aux_logits:
self.AuxLogits = InceptionAux(768, self.class_dim, name='AuxLogits')
### block3
### Mixed_7
if self.last_needed_block >= 3:
self.Mixed_7a = InceptionD(768, name='Mixed_7a')
self.Mixed_7b = Fid_inceptionE_1(1280, name='Mixed_7b')
self.Mixed_7c = Fid_inceptionE_2(2048, name='Mixed_7c')
self.avgpool = AdaptiveAvgPool2D(output_size=1)
block3 = [self.Mixed_7a, self.Mixed_7b, self.Mixed_7c, self.avgpool]
self.blocks.append(nn.Sequential(*block3))
def forward(self, x):
out = []
aux = None
if self.resize_input:
x = nn.functional.interpolate(x,
size=[299, 299],
mode='bilinear',
align_corners=False,
align_mode=0)
if self.normalize_input:
x = x * 2 - 1
for idx, block in enumerate(self.blocks):
x = block(x)
if self.aux_logits and (idx == 2):
aux = self.AuxLogits(x)
if idx in self.output_blocks:
out.append(x)
if idx == self.last_needed_block:
break
return out, aux
class InceptionA(nn.Layer):
def __init__(self, in_channels, pool_features, name=None):
super(InceptionA, self).__init__()
self.branch1x1 = ConvBNLayer(in_channels,
64,
1,
name=name + '.branch1x1')
self.branch5x5_1 = ConvBNLayer(in_channels,
48,
1,
name=name + '.branch5x5_1')
self.branch5x5_2 = ConvBNLayer(48,
64,
5,
padding=2,
name=name + '.branch5x5_2')
self.branch3x3dbl_1 = ConvBNLayer(in_channels,
64,
1,
name=name + '.branch3x3dbl_1')
self.branch3x3dbl_2 = ConvBNLayer(64,
96,
3,
padding=1,
name=name + '.branch3x3dbl_2')
self.branch3x3dbl_3 = ConvBNLayer(96,
96,
3,
padding=1,
name=name + '.branch3x3dbl_3')
self.branch_pool0 = AvgPool2D(kernel_size=3,
stride=1,
padding=1,
exclusive=True)
self.branch_pool = ConvBNLayer(in_channels,
pool_features,
1,
name=name + '.branch_pool')
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch5x5 = self.branch5x5_1(x)
branch5x5 = self.branch5x5_2(branch5x5)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
branch_pool = self.branch_pool0(x)
branch_pool = self.branch_pool(branch_pool)
return paddle.concat(
[branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
class InceptionB(nn.Layer):
def __init__(self, in_channels, name=None):
super(InceptionB, self).__init__()
self.branch3x3 = ConvBNLayer(in_channels,
384,
3,
stride=2,
name=name + '.branch3x3')
self.branch3x3dbl_1 = ConvBNLayer(in_channels,
64,
1,
name=name + '.branch3x3dbl_1')
self.branch3x3dbl_2 = ConvBNLayer(64,
96,
3,
padding=1,
name=name + '.branch3x3dbl_2')
self.branch3x3dbl_3 = ConvBNLayer(96,
96,
3,
stride=2,
name=name + '.branch3x3dbl_3')
self.branch_pool = MaxPool2D(kernel_size=3, stride=2)
def forward(self, x):
branch3x3 = self.branch3x3(x)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
branch_pool = self.branch_pool(x)
return paddle.concat([branch3x3, branch3x3dbl, branch_pool],
axis=1)
class InceptionC(nn.Layer):
def __init__(self, in_channels, c7, name=None):
super(InceptionC, self).__init__()
self.branch1x1 = ConvBNLayer(in_channels,
192,
1,
name=name + '.branch1x1')
self.branch7x7_1 = ConvBNLayer(in_channels,
c7,
1,
name=name + '.branch7x7_1')
self.branch7x7_2 = ConvBNLayer(c7,
c7, (1, 7),
padding=(0, 3),
name=name + '.branch7x7_2')
self.branch7x7_3 = ConvBNLayer(c7,
192, (7, 1),
padding=(3, 0),
name=name + '.branch7x7_3')
self.branch7x7dbl_1 = ConvBNLayer(in_channels,
c7,
1,
name=name + '.branch7x7dbl_1')
self.branch7x7dbl_2 = ConvBNLayer(c7,
c7, (7, 1),
padding=(3, 0),
name=name + '.branch7x7dbl_2')
self.branch7x7dbl_3 = ConvBNLayer(c7,
c7, (1, 7),
padding=(0, 3),
name=name + '.branch7x7dbl_3')
self.branch7x7dbl_4 = ConvBNLayer(c7,
c7, (7, 1),
padding=(3, 0),
name=name + '.branch7x7dbl_4')
self.branch7x7dbl_5 = ConvBNLayer(c7,
192, (1, 7),
padding=(0, 3),
name=name + '.branch7x7dbl_5')
self.branch_pool0 = AvgPool2D(kernel_size=3,
stride=1,
padding=1,
exclusive=True)
self.branch_pool = ConvBNLayer(in_channels,
192,
1,
name=name + '.branch_pool')
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch7x7 = self.branch7x7_1(x)
branch7x7 = self.branch7x7_2(branch7x7)
branch7x7 = self.branch7x7_3(branch7x7)
branch7x7dbl = self.branch7x7dbl_1(x)
branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
branch_pool = self.branch_pool0(x)
branch_pool = self.branch_pool(branch_pool)
return paddle.concat(
[branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
class InceptionD(nn.Layer):
def __init__(self, in_channels, name=None):
super(InceptionD, self).__init__()
self.branch3x3_1 = ConvBNLayer(in_channels,
192,
1,
name=name + '.branch3x3_1')
self.branch3x3_2 = ConvBNLayer(192,
320,
3,
stride=2,
name=name + '.branch3x3_2')
self.branch7x7x3_1 = ConvBNLayer(in_channels,
192,
1,
name=name + '.branch7x7x3_1')
self.branch7x7x3_2 = ConvBNLayer(192,
192, (1, 7),
padding=(0, 3),
name=name + '.branch7x7x3_2')
self.branch7x7x3_3 = ConvBNLayer(192,
192, (7, 1),
padding=(3, 0),
name=name + '.branch7x7x3_3')
self.branch7x7x3_4 = ConvBNLayer(192,
192,
3,
stride=2,
name=name + '.branch7x7x3_4')
self.branch_pool = MaxPool2D(kernel_size=3, stride=2)
def forward(self, x):
branch3x3 = self.branch3x3_1(x)
branch3x3 = self.branch3x3_2(branch3x3)
branch7x7x3 = self.branch7x7x3_1(x)
branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
branch7x7x3 = self.branch7x7x3_4(branch7x7x3)
branch_pool = self.branch_pool(x)
return paddle.concat([branch3x3, branch7x7x3, branch_pool],
axis=1)
class InceptionE(nn.Layer):
def __init__(self, in_channels, name=None):
super(InceptionE, self).__init__()
self.branch1x1 = ConvBNLayer(in_channels,
320,
1,
name=name + '.branch1x1')
self.branch3x3_1 = ConvBNLayer(in_channels,
384,
1,
name=name + '.branch3x3_1')
self.branch3x3_2a = ConvBNLayer(384,
384, (1, 3),
padding=(0, 1),
name=name + '.branch3x3_2a')
self.branch3x3_2b = ConvBNLayer(384,
384, (3, 1),
padding=(1, 0),
name=name + '.branch3x3_2b')
self.branch3x3dbl_1 = ConvBNLayer(in_channels,
448,
1,
name=name + '.branch3x3dbl_1')
self.branch3x3dbl_2 = ConvBNLayer(448,
384,
3,
padding=1,
name=name + '.branch3x3dbl_2')
self.branch3x3dbl_3a = ConvBNLayer(384,
384, (1, 3),
padding=(0, 1),
name=name + '.branch3x3dbl_3a')
self.branch3x3dbl_3b = ConvBNLayer(384,
384, (3, 1),
padding=(1, 0),
name=name + '.branch3x3dbl_3b')
self.branch_pool0 = AvgPool2D(kernel_size=3,
stride=1,
padding=1,
exclusive=True)
self.branch_pool = ConvBNLayer(in_channels,
192,
1,
name=name + '.branch_pool')
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3_1 = self.branch3x3_1(x)
branch3x3_2a = self.branch3x3_2a(branch3x3_1)
branch3x3_2b = self.branch3x3_2b(branch3x3_1)
branch3x3 = paddle.concat([branch3x3_2a, branch3x3_2b], axis=1)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl_3a = self.branch3x3dbl_3a(branch3x3dbl)
branch3x3dbl_3b = self.branch3x3dbl_3b(branch3x3dbl)
branch3x3dbl = paddle.concat([branch3x3dbl_3a, branch3x3dbl_3b],
axis=1)
branch_pool = self.branch_pool0(x)
branch_pool = self.branch_pool(branch_pool)
return paddle.concat(
[branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
class InceptionAux(nn.Layer):
def __init__(self, in_channels, num_classes, name=None):
super(InceptionAux, self).__init__()
self.num_classes = num_classes
self.pool0 = AvgPool2D(kernel_size=5, stride=3)
self.conv0 = ConvBNLayer(in_channels, 128, 1, name=name + '.conv0')
self.conv1 = ConvBNLayer(128, 768, 5, name=name + '.conv1')
self.pool1 = AvgPool2D(global_pooling=True)
def forward(self, x):
x = self.pool0(x)
x = self.conv0(x)
x = self.conv1(x)
x = self.pool1(x)
x = paddle.flatten(x, axis=1)
x = paddle.static.nn.fc(x, size=self.num_classes)
return x
class Fid_inceptionA(nn.Layer):
""" FID block in inception v3
"""
def __init__(self, in_channels, pool_features, name=None):
super(Fid_inceptionA, self).__init__()
self.branch1x1 = ConvBNLayer(in_channels,
64,
1,
name=name + '.branch1x1')
self.branch5x5_1 = ConvBNLayer(in_channels,
48,
1,
name=name + '.branch5x5_1')
self.branch5x5_2 = ConvBNLayer(48,
64,
5,
padding=2,
name=name + '.branch5x5_2')
self.branch3x3dbl_1 = ConvBNLayer(in_channels,
64,
1,
name=name + '.branch3x3dbl_1')
self.branch3x3dbl_2 = ConvBNLayer(64,
96,
3,
padding=1,
name=name + '.branch3x3dbl_2')
self.branch3x3dbl_3 = ConvBNLayer(96,
96,
3,
padding=1,
name=name + '.branch3x3dbl_3')
self.branch_pool0 = AvgPool2D(kernel_size=3,
stride=1,
padding=1,
exclusive=True)
self.branch_pool = ConvBNLayer(in_channels,
pool_features,
1,
name=name + '.branch_pool')
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch5x5 = self.branch5x5_1(x)
branch5x5 = self.branch5x5_2(branch5x5)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
branch_pool = self.branch_pool0(x)
branch_pool = self.branch_pool(branch_pool)
return paddle.concat(
[branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
class Fid_inceptionC(nn.Layer):
""" FID block in inception v3
"""
def __init__(self, in_channels, c7, name=None):
super(Fid_inceptionC, self).__init__()
self.branch1x1 = ConvBNLayer(in_channels,
192,
1,
name=name + '.branch1x1')
self.branch7x7_1 = ConvBNLayer(in_channels,
c7,
1,
name=name + '.branch7x7_1')
self.branch7x7_2 = ConvBNLayer(c7,
c7, (1, 7),
padding=(0, 3),
name=name + '.branch7x7_2')
self.branch7x7_3 = ConvBNLayer(c7,
192, (7, 1),
padding=(3, 0),
name=name + '.branch7x7_3')
self.branch7x7dbl_1 = ConvBNLayer(in_channels,
c7,
1,
name=name + '.branch7x7dbl_1')
self.branch7x7dbl_2 = ConvBNLayer(c7,
c7, (7, 1),
padding=(3, 0),
name=name + '.branch7x7dbl_2')
self.branch7x7dbl_3 = ConvBNLayer(c7,
c7, (1, 7),
padding=(0, 3),
name=name + '.branch7x7dbl_3')
self.branch7x7dbl_4 = ConvBNLayer(c7,
c7, (7, 1),
padding=(3, 0),
name=name + '.branch7x7dbl_4')
self.branch7x7dbl_5 = ConvBNLayer(c7,
192, (1, 7),
padding=(0, 3),
name=name + '.branch7x7dbl_5')
self.branch_pool0 = AvgPool2D(kernel_size=3,
stride=1,
padding=1,
exclusive=True)
self.branch_pool = ConvBNLayer(in_channels,
192,
1,
name=name + '.branch_pool')
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch7x7 = self.branch7x7_1(x)
branch7x7 = self.branch7x7_2(branch7x7)
branch7x7 = self.branch7x7_3(branch7x7)
branch7x7dbl = self.branch7x7dbl_1(x)
branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
branch_pool = self.branch_pool0(x)
branch_pool = self.branch_pool(branch_pool)
return paddle.concat(
[branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
class Fid_inceptionE_1(nn.Layer):
""" FID block in inception v3
"""
def __init__(self, in_channels, name=None):
super(Fid_inceptionE_1, self).__init__()
self.branch1x1 = ConvBNLayer(in_channels,
320,
1,
name=name + '.branch1x1')
self.branch3x3_1 = ConvBNLayer(in_channels,
384,
1,
name=name + '.branch3x3_1')
self.branch3x3_2a = ConvBNLayer(384,
384, (1, 3),
padding=(0, 1),
name=name + '.branch3x3_2a')
self.branch3x3_2b = ConvBNLayer(384,
384, (3, 1),
padding=(1, 0),
name=name + '.branch3x3_2b')
self.branch3x3dbl_1 = ConvBNLayer(in_channels,
448,
1,
name=name + '.branch3x3dbl_1')
self.branch3x3dbl_2 = ConvBNLayer(448,
384,
3,
padding=1,
name=name + '.branch3x3dbl_2')
self.branch3x3dbl_3a = ConvBNLayer(384,
384, (1, 3),
padding=(0, 1),
name=name + '.branch3x3dbl_3a')
self.branch3x3dbl_3b = ConvBNLayer(384,
384, (3, 1),
padding=(1, 0),
name=name + '.branch3x3dbl_3b')
self.branch_pool0 = AvgPool2D(kernel_size=3,
stride=1,
padding=1,
exclusive=True)
self.branch_pool = ConvBNLayer(in_channels,
192,
1,
name=name + '.branch_pool')
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3_1 = self.branch3x3_1(x)
branch3x3_2a = self.branch3x3_2a(branch3x3_1)
branch3x3_2b = self.branch3x3_2b(branch3x3_1)
branch3x3 = paddle.concat([branch3x3_2a, branch3x3_2b], axis=1)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl_3a = self.branch3x3dbl_3a(branch3x3dbl)
branch3x3dbl_3b = self.branch3x3dbl_3b(branch3x3dbl)
branch3x3dbl = paddle.concat([branch3x3dbl_3a, branch3x3dbl_3b],
axis=1)
branch_pool = self.branch_pool0(x)
branch_pool = self.branch_pool(branch_pool)
return paddle.concat(
[branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
class Fid_inceptionE_2(nn.Layer):
""" FID block in inception v3
"""
def __init__(self, in_channels, name=None):
super(Fid_inceptionE_2, self).__init__()
self.branch1x1 = ConvBNLayer(in_channels,
320,
1,
name=name + '.branch1x1')
self.branch3x3_1 = ConvBNLayer(in_channels,
384,
1,
name=name + '.branch3x3_1')
self.branch3x3_2a = ConvBNLayer(384,
384, (1, 3),
padding=(0, 1),
name=name + '.branch3x3_2a')
self.branch3x3_2b = ConvBNLayer(384,
384, (3, 1),
padding=(1, 0),
name=name + '.branch3x3_2b')
self.branch3x3dbl_1 = ConvBNLayer(in_channels,
448,
1,
name=name + '.branch3x3dbl_1')
self.branch3x3dbl_2 = ConvBNLayer(448,
384,
3,
padding=1,
name=name + '.branch3x3dbl_2')
self.branch3x3dbl_3a = ConvBNLayer(384,
384, (1, 3),
padding=(0, 1),
name=name + '.branch3x3dbl_3a')
self.branch3x3dbl_3b = ConvBNLayer(384,
384, (3, 1),
padding=(1, 0),
name=name + '.branch3x3dbl_3b')
### same with paper
self.branch_pool0 = MaxPool2D(kernel_size=3,
stride=1,
padding=1)
self.branch_pool = ConvBNLayer(in_channels,
192,
1,
name=name + '.branch_pool')
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3_1 = self.branch3x3_1(x)
branch3x3_2a = self.branch3x3_2a(branch3x3_1)
branch3x3_2b = self.branch3x3_2b(branch3x3_1)
branch3x3 = paddle.concat([branch3x3_2a, branch3x3_2b], axis=1)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl_3a = self.branch3x3dbl_3a(branch3x3dbl)
branch3x3dbl_3b = self.branch3x3dbl_3b(branch3x3dbl)
branch3x3dbl = paddle.concat([branch3x3dbl_3a, branch3x3dbl_3b],
axis=1)
branch_pool = self.branch_pool0(x)
branch_pool = self.branch_pool(branch_pool)
return paddle.concat(
[branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
class ConvBNLayer(nn.Layer):
def __init__(self,
in_channels,
num_filters,
filter_size,
stride=1,
padding=0,
groups=1,
act='relu',
name=None):
super(ConvBNLayer, self).__init__()
self.conv = Conv2D(in_channels=in_channels,
out_channels=num_filters,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
weight_attr=paddle.ParamAttr(name=name + ".conv.weight"),
bias_attr=False)
self.bn = BatchNorm(num_filters,
act=act,
epsilon=0.001,
param_attr=paddle.ParamAttr(name=name + ".bn.weight"),
bias_attr=paddle.ParamAttr(name=name + ".bn.bias"),
moving_mean_name=name + '.bn.running_mean',
moving_variance_name=name + '.bn.running_var')
def forward(self, inputs):
y = self.conv(inputs)
y = self.bn(y)
return y