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models.py
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models.py
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import math
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn.initializer import Normal, Constant
normal_ = lambda x, mean=0, std=1: Normal(mean, std)(x)
zeros_ = Constant(value=0.)
ones_ = Constant(value=1.)
class _DenseLayer(nn.Layer):
def __init__(self, num_input_features, growth_rate, bottleneck_width, drop_rate):
super(_DenseLayer, self).__init__()
growth_rate = int(growth_rate / 2)
inter_channel = int(growth_rate * bottleneck_width / 4) * 4
if inter_channel > num_input_features / 2:
inter_channel = int(num_input_features / 8) * 4
print('adjust inter_channel to ', inter_channel)
self.branch1a = BasicConv2d(
num_input_features, inter_channel, kernel_size=1)
self.branch1b = BasicConv2d(
inter_channel, growth_rate, kernel_size=3, padding=1)
self.branch2a = BasicConv2d(
num_input_features, inter_channel, kernel_size=1)
self.branch2b = BasicConv2d(
inter_channel, growth_rate, kernel_size=3, padding=1)
self.branch2c = BasicConv2d(
growth_rate, growth_rate, kernel_size=3, padding=1)
def forward(self, x):
branch1 = self.branch1a(x)
branch1 = self.branch1b(branch1)
branch2 = self.branch2a(x)
branch2 = self.branch2b(branch2)
branch2 = self.branch2c(branch2)
return paddle.concat([x, branch1, branch2], 1)
class _DenseBlock(nn.Sequential):
def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):
super(_DenseBlock, self).__init__()
for i in range(num_layers):
layer = _DenseLayer(num_input_features + i *
growth_rate, growth_rate, bn_size, drop_rate)
setattr(self, 'denselayer%d' % (i + 1), layer)
class _StemBlock(nn.Layer):
def __init__(self, num_input_channels, num_init_features):
super(_StemBlock, self).__init__()
num_stem_features = int(num_init_features/2)
self.stem1 = BasicConv2d(
num_input_channels, num_init_features, kernel_size=3, stride=2, padding=1)
self.stem2a = BasicConv2d(
num_init_features, num_stem_features, kernel_size=1, stride=1, padding=0)
self.stem2b = BasicConv2d(
num_stem_features, num_init_features, kernel_size=3, stride=2, padding=1)
self.stem3 = BasicConv2d(
2*num_init_features, num_init_features, kernel_size=1, stride=1, padding=0)
self.pool = nn.MaxPool2D(kernel_size=2, stride=2)
def forward(self, x):
out = self.stem1(x)
branch2 = self.stem2a(out)
branch2 = self.stem2b(branch2)
branch1 = self.pool(out)
out = paddle.concat([branch1, branch2], 1)
out = self.stem3(out)
return out
class BasicConv2d(nn.Layer):
def __init__(self, in_channels, out_channels, activation=True, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2D(in_channels, out_channels,
bias_attr=False, **kwargs)
self.norm = nn.BatchNorm2D(out_channels)
self.activation = activation
def forward(self, x):
x = self.conv(x)
x = self.norm(x)
if self.activation:
return F.relu(x)
else:
return x
class PeleeNet(nn.Layer):
r"""PeleeNet model class, based on
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf> and
"Pelee: A Real-Time Object Detection System on Mobile Devices" <https://arxiv.org/pdf/1608.06993.pdf>`
Args:
growth_rate (int or list of 4 ints) - how many filters to add each layer (`k` in paper)
block_config (list of 4 ints) - how many layers in each pooling block
num_init_features (int) - the number of filters to learn in the first convolution layer
bottleneck_width (int or list of 4 ints) - multiplicative factor for number of bottle neck layers
(i.e. bn_size * k features in the bottleneck layer)
drop_rate (float) - dropout rate after each dense layer
num_classes (int) - number of classification classes
"""
def __init__(self, growth_rate=32, block_config=[3, 4, 8, 6],
num_init_features=32, bottleneck_width=[1, 2, 4, 4],
drop_rate=0.05, num_classes=1000):
super(PeleeNet, self).__init__()
self.features = nn.Sequential(*[
('stemblock', _StemBlock(3, num_init_features)),
])
if type(growth_rate) is list:
growth_rates = growth_rate
assert len(growth_rates) == 4, \
'The growth rate must be the list and the size must be 4'
else:
growth_rates = [growth_rate] * 4
if type(bottleneck_width) is list:
bottleneck_widths = bottleneck_width
assert len(bottleneck_widths) == 4, \
'The bottleneck width must be the list and the size must be 4'
else:
bottleneck_widths = [bottleneck_width] * 4
# Each denseblock
num_features = num_init_features
for i, num_layers in enumerate(block_config):
block = _DenseBlock(num_layers=num_layers,
num_input_features=num_features,
bn_size=bottleneck_widths[i],
growth_rate=growth_rates[i],
drop_rate=drop_rate)
setattr(self.features, 'denseblock%d' % (i + 1), block)
num_features = num_features + num_layers * growth_rates[i]
setattr(self.features, 'transition%d' % (i + 1), BasicConv2d(
num_features, num_features, kernel_size=1, stride=1, padding=0))
if i != len(block_config) - 1:
setattr(self.features, 'transition%d_pool' %
(i + 1), nn.AvgPool2D(kernel_size=2, stride=2))
num_features = num_features
# Linear layer
self.classifier = nn.Linear(num_features, num_classes)
self.drop_rate = drop_rate
self.apply(self._initialize_weights)
def forward(self, x):
features = self.features(x)
out = F.avg_pool2d(features, kernel_size=features.shape[2:4]).flatten(1)
if self.drop_rate > 0:
out = F.dropout(out, p=self.drop_rate, training=self.training)
out = self.classifier(out)
return out
def _initialize_weights(self, m):
if isinstance(m, nn.Conv2D):
n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
normal_(m.weight, std=math.sqrt(2. / n))
if m.bias is not None:
zeros_(m.bias)
elif isinstance(m, nn.BatchNorm2D):
ones_(m.weight)
zeros_(m.bias)
elif isinstance(m, nn.Linear):
normal_(m.weight, std=0.01)
zeros_(m.bias)
def peleenet(**kwargs):
model = PeleeNet()
return model