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MnasnetEager.py
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MnasnetEager.py
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import os
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers, regularizers, activations
class Mnasnet(tf.keras.Model):
def __init__(self, num_classes, alpha=1, **kwargs):
super(Mnasnet, self).__init__(**kwargs)
self.blocks = []
self.conv_bn_initial = Conv_BN(filters=32*alpha, kernel_size=3, strides=2)
# Frist block (non-identity) Conv+ DepthwiseConv
self.conv1_block1 = depthwiseConv(depth_multiplier=1, kernel_size=3, strides=1)
self.bn1_block1 = layers.BatchNormalization(epsilon=1e-3, momentum=0.999)
self.relu1_block1 = layers.ReLU(max_value=6)
self.conv_bn_block_1 = Conv_BN(filters=16*alpha, kernel_size=1, strides=1)
# MBConv3 3x3
self.blocks.append(MBConv_idskip(input_filters=16*alpha, filters=24, kernel_size=3, strides=2,
filters_multiplier=3, alpha=alpha))
self.blocks.append(MBConv_idskip(input_filters=24*alpha, filters=24, kernel_size=3, strides=1,
filters_multiplier=3, alpha=alpha))
self.blocks.append(MBConv_idskip(input_filters=24*alpha, filters=24, kernel_size=3, strides=1,
filters_multiplier=3, alpha=alpha))
# MBConv3 5x5
self.blocks.append(MBConv_idskip(input_filters=24*alpha, filters=40, kernel_size=5, strides=2,
filters_multiplier=3, alpha=alpha))
self.blocks.append(MBConv_idskip(input_filters=40*alpha, filters=40, kernel_size=5, strides=1,
filters_multiplier=3, alpha=alpha))
self.blocks.append(MBConv_idskip(input_filters=40*alpha, filters=40, kernel_size=5, strides=1,
filters_multiplier=3, alpha=alpha))
# MBConv6 5x5
self.blocks.append(MBConv_idskip(input_filters=40*alpha, filters=80, kernel_size=5, strides=2,
filters_multiplier=6, alpha=alpha))
self.blocks.append(MBConv_idskip(input_filters=80*alpha, filters=80, kernel_size=5, strides=1,
filters_multiplier=6, alpha=alpha))
self.blocks.append(MBConv_idskip(input_filters=80*alpha, filters=80, kernel_size=5, strides=1,
filters_multiplier=6, alpha=alpha))
# MBConv6 3x3
self.blocks.append(MBConv_idskip(input_filters=80*alpha, filters=96, kernel_size=3, strides=1,
filters_multiplier=6, alpha=alpha))
self.blocks.append(MBConv_idskip(input_filters=96*alpha, filters=96, kernel_size=3, strides=1,
filters_multiplier=6, alpha=alpha))
# MBConv6 5x5
self.blocks.append(MBConv_idskip(input_filters=96*alpha, filters=192, kernel_size=5, strides=2,
filters_multiplier=6, alpha=alpha))
self.blocks.append(MBConv_idskip(input_filters=192*alpha, filters=192, kernel_size=5, strides=1,
filters_multiplier=6, alpha=alpha))
self.blocks.append(MBConv_idskip(input_filters=192*alpha, filters=192, kernel_size=5, strides=1,
filters_multiplier=6, alpha=alpha))
self.blocks.append(MBConv_idskip(input_filters=192*alpha, filters=192, kernel_size=5, strides=1,
filters_multiplier=6, alpha=alpha))
# MBConv6 3x3
self.blocks.append(MBConv_idskip(input_filters=192*alpha, filters=320, kernel_size=3, strides=1,
filters_multiplier=6, alpha=alpha))
# Last convolution
self.conv_bn_last = Conv_BN(filters=1152*alpha, kernel_size=1, strides=1)
# Pool + FC
self.avg_pool = layers.GlobalAveragePooling2D()
self.fc = layers.Dense(num_classes)
def call(self, inputs, training=None, mask=None):
out = self.conv_bn_initial(inputs, training=training)
out = self.conv1_block1(out)
out = self.bn1_block1(out, training=training)
out = self.relu1_block1(out)
out = self.conv_bn_block_1(out, training=training)
# forward pass through all the blocks
for block in self.blocks:
out = block(out, training=training)
out = self.conv_bn_last(out, training=training)
out = self.avg_pool(out)
out = self.fc(out)
'''
You could return several outputs, even intermediate outputs
'''
return out
class MBConv_idskip(tf.keras.Model):
def __init__(self, input_filters, filters, kernel_size, strides=1, filters_multiplier=1, alpha=1):
super(MBConv_idskip, self).__init__()
self.filters = filters
self.kernel_size = kernel_size
self.strides = strides
self.filters_multiplier = filters_multiplier
self.alpha = alpha
self.depthwise_conv_filters = _make_divisible(input_filters)
self.pointwise_conv_filters = _make_divisible(filters * alpha)
#conv1
self.conv_bn1 = Conv_BN(filters=self.depthwise_conv_filters*filters_multiplier, kernel_size=1, strides=1)
#depthwiseconv2
self.depthwise_conv = depthwiseConv(depth_multiplier=1, kernel_size=kernel_size, strides=strides)
self.bn = layers.BatchNormalization(epsilon=1e-3, momentum=0.999)
self.relu = layers.ReLU(max_value=6)
#conv3
self.conv_bn2 = Conv_BN(filters=self.pointwise_conv_filters, kernel_size=1, strides=1)
def call(self, inputs, training=None):
x = self.conv_bn1(inputs, training=training)
x = self.depthwise_conv(x)
x = self.bn(x, training=training)
x = self.relu(x)
x = self.conv_bn2(x, training=training, activation=False)
# Residual/Identity connection if possible
if self.strides==1 and x.shape[3] == inputs.shape[3]:
return layers.add([inputs, x])
else:
return x
class Conv_BN(tf.keras.Model):
def __init__(self, filters, kernel_size, strides=1):
super(Conv_BN, self).__init__()
self.filters = filters
self.kernel_size = kernel_size
self.strides = strides
self.conv = conv(filters=filters, kernel_size=kernel_size, strides=strides)
self.bn = layers.BatchNormalization(epsilon=1e-3, momentum=0.999)
self.relu = layers.ReLU(max_value=6)
def call(self, inputs, training=None, activation=True):
x = self.conv(inputs)
x = self.bn(x, training=training)
if activation:
x = self.relu(x)
return x
# convolution
def conv(filters, kernel_size, strides=1, dilation_rate=1, use_bias=False):
return layers.Conv2D(filters, kernel_size, strides=strides, padding='same', use_bias=use_bias,
kernel_regularizer=regularizers.l2(l=0.0003), dilation_rate=dilation_rate)
# Depthwise convolution
def depthwiseConv(kernel_size, strides=1, depth_multiplier=1, dilation_rate=1, use_bias=False):
return layers.DepthwiseConv2D(kernel_size, strides=strides, depth_multiplier=depth_multiplier,
padding='same', use_bias=use_bias, kernel_regularizer=regularizers.l2(l=0.0003),
dilation_rate=dilation_rate)
# dilation_rate
# This function is taken from the original tf repo.
# It ensures that all layers have a channel number that is divisible by 8
# It can be seen here:
# https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
def _make_divisible(v, divisor=8, min_value=None):
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v