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522645f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 | import torch
import torch.nn.functional as F
import torchvision
from torch import nn
from torchvision import transforms
from scripts.dynamic.blocks import *
class Normalize:
def __init__(self, opt, expected_values, variance):
self.n_channels = opt.input_channel
self.expected_values = expected_values
self.variance = variance
assert self.n_channels == len(self.expected_values)
def __call__(self, x):
x_clone = x.clone()
for channel in range(self.n_channels):
x_clone[:, channel] = (x[:, channel] - self.expected_values[channel]) / self.variance[channel]
return x_clone
class Denormalize:
def __init__(self, opt, expected_values, variance):
self.n_channels = opt.input_channel
self.expected_values = expected_values
self.variance = variance
assert self.n_channels == len(self.expected_values)
def __call__(self, x):
x_clone = x.clone()
for channel in range(self.n_channels):
x_clone[:, channel] = x[:, channel] * self.variance[channel] + self.expected_values[channel]
return x_clone
# ---------------------------- Generators ----------------------------#
class Generator(nn.Sequential):
def __init__(self, opt, out_channels=None):
super(Generator, self).__init__()
if opt.dataset == "mnist":
channel_init = 16
steps = 2
else:
channel_init = 32
steps = 3
channel_current = opt.input_channel
channel_next = channel_init
for step in range(steps):
self.add_module("convblock_down_{}".format(2 * step), Conv2dBlock(channel_current, channel_next))
self.add_module("convblock_down_{}".format(2 * step + 1), Conv2dBlock(channel_next, channel_next))
self.add_module("downsample_{}".format(step), DownSampleBlock())
if step < steps - 1:
channel_current = channel_next
channel_next *= 2
self.add_module("convblock_middle", Conv2dBlock(channel_next, channel_next))
channel_current = channel_next
channel_next = channel_current // 2
for step in range(steps):
self.add_module("upsample_{}".format(step), UpSampleBlock())
self.add_module("convblock_up_{}".format(2 * step), Conv2dBlock(channel_current, channel_current))
if step == steps - 1:
self.add_module(
"convblock_up_{}".format(2 * step + 1), Conv2dBlock(channel_current, channel_next, relu=False)
)
else:
self.add_module("convblock_up_{}".format(2 * step + 1), Conv2dBlock(channel_current, channel_next))
channel_current = channel_next
channel_next = channel_next // 2
if step == steps - 2:
if out_channels is None:
channel_next = opt.input_channel
else:
channel_next = out_channels
self._EPSILON = 1e-7
self._normalizer = self._get_normalize(opt)
self._denormalizer = self._get_denormalize(opt)
def _get_denormalize(self, opt):
if opt.dataset == "cifar10":
denormalizer = Denormalize(opt, [0.4914, 0.4822, 0.4465], [0.247, 0.243, 0.261])
elif opt.dataset == "mnist":
denormalizer = Denormalize(opt, [0.5], [0.5])
elif opt.dataset == "gtsrb":
denormalizer = None
else:
raise Exception("Invalid dataset")
return denormalizer
def _get_normalize(self, opt):
if opt.dataset == "cifar10":
normalizer = Normalize(opt, [0.4914, 0.4822, 0.4465], [0.247, 0.243, 0.261])
elif opt.dataset == "mnist":
normalizer = Normalize(opt, [0.5], [0.5])
elif opt.dataset == "gtsrb":
normalizer = None
else:
raise Exception("Invalid dataset")
return normalizer
def forward(self, x):
for module in self.children():
x = module(x)
x = nn.Tanh()(x) / (2 + self._EPSILON) + 0.5
return x
def normalize_pattern(self, x):
if self._normalizer:
x = self._normalizer(x)
return x
def denormalize_pattern(self, x):
if self._denormalizer:
x = self._denormalizer(x)
return x
def threshold(self, x):
return nn.Tanh()(x * 20 - 10) / (2 + self._EPSILON) + 0.5
# ---------------------------- Classifiers ----------------------------#
class NetC_MNIST(nn.Module):
def __init__(self):
super(NetC_MNIST, self).__init__()
self.conv1 = nn.Conv2d(1, 32, (5, 5), 1, 0)
self.relu2 = nn.ReLU(inplace=True)
self.dropout3 = nn.Dropout(0.1)
self.maxpool4 = nn.MaxPool2d((2, 2))
self.conv5 = nn.Conv2d(32, 64, (5, 5), 1, 0)
self.relu6 = nn.ReLU(inplace=True)
self.dropout7 = nn.Dropout(0.1)
self.maxpool5 = nn.MaxPool2d((2, 2))
self.flatten = nn.Flatten()
self.linear6 = nn.Linear(64 * 4 * 4, 512)
self.relu7 = nn.ReLU(inplace=True)
self.dropout8 = nn.Dropout(0.1)
self.linear9 = nn.Linear(512, 10)
def forward(self, x):
for module in self.children():
x = module(x)
return x |