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arch.py
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arch.py
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# This is part of the demo source code for the paper:
# Esteves, C., Allen-Blanchette, C., Zhou, X. and Daniilidis, K., 2017. Polar Transformer Networks. arXiv preprint arXiv:1709.01889. http://arxiv.org/abs/1709.01889v1
# GRASP Laboratory - University of Pennsylvania
# http://github.com/daniilidis-group/polar-transformer-networks
import os
import numpy as np
import tensorflow as tf
from tflearn.layers.core import input_data
from tflearn.layers.estimator import regression
from tflearn.layers.conv import conv_2d, global_avg_pool
import tflearn
import layers
def get_input_layer(flags):
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
curr = input_data(shape=[None, flags.inres, flags.inres, flags.inchan],
name='input',
data_preprocessing=img_prep)
if flags.rts_aug:
w, h = curr.get_shape().as_list()[1:3]
a = -flags.rts_aug_ang + 2*flags.rts_aug_ang*tf.random_uniform([flags.bs])
a *= np.pi/180
# centralize rot/scale
y = ((w - 1) - (tf.cos(a)*(w-1) - tf.sin(a)*(h-1))) / 2.0
x = ((h - 1) - (tf.sin(a)*(w-1) + tf.cos(a)*(h-1))) / 2.0
transforms = tf.transpose(tf.stack([tf.cos(a), tf.sin(a), x,
-tf.sin(a), tf.cos(a), y,
tf.zeros(flags.bs), tf.zeros(flags.bs)]))
return tf.cond(tflearn.get_training_mode(),
lambda: tf.contrib.image.transform(curr, transforms),
lambda: curr)
else:
return curr
def finalize_get_model(net, flags):
net['gap'], curr = dup(global_avg_pool(net['conv_final'], name='gap'))
net['final'] = regression(curr,
optimizer='adam',
learning_rate=flags.lr,
batch_size=flags.bs,
loss='softmax_categorical_crossentropy',
name='target',
n_classes=flags.nc,
shuffle_batches=True)
model = tflearn.DNN(net['final'],
tensorboard_verbose=0,
tensorboard_dir=flags.logdir,
best_checkpoint_path=os.path.join(flags.logdir,
flags.run_id,
flags.run_id),
best_val_accuracy=flags.acc_save)
model.net_dict = net
model.flags = flags
return model
def polar_transformer_network_layers(flags):
net = {}
net['input'] = get_input_layer(flags)
regr = pt_regressor(net['input'], flags)
for k, v in regr.items():
net[k] = v
log = True if flags.polarmode == 'log' else False
net['polar'], curr = dup(layers.polar_transformer(net['input'],
net['polar_origin'],
(flags.inres, flags.inres),
log=log,
radius_factor=flags.polar_rf)[..., 0][..., np.newaxis])
# classifier network
cl = conv_from_flags(curr, flags)
for k, v in cl.items():
net[k] = v
return net
def polar_transformer_network(flags):
net = polar_transformer_network_layers(flags)
model = finalize_get_model(net, flags)
return model
def pt_regressor(layer_in, flags):
net, curr = pt_regressor_conv(layer_in, flags)
net['ptreg_in'] = layer_in
dims = curr.get_shape().as_list()
weights_init = 'zeros'
bias_init = tf.ones([1])
# 1x1 conv, no BN, no ReLU on final heatmap
net['ptreg_out'], curr = dup(conv_2d(curr, 1, 1, activation='linear',
weights_init=weights_init,
bias_init=bias_init,
padding=flags.pad,
name='ptreg_out'))
# take the centroid of the feature map
s = tf.shape(curr)
# compute xc, yc from -1 to 1
xc = tf.tile(tf.linspace(-1., 1., s[2])[np.newaxis, ...],
(s[1], 1))
yc = tf.transpose(xc)
net['po_j'] = (tf.reduce_sum(curr[..., 0]*xc[np.newaxis, ...], axis=(1, 2)) /
tf.reduce_sum(curr[..., 0], axis=(1, 2)))
net['po_i'] = (tf.reduce_sum(curr[..., 0]*yc[np.newaxis, ...], axis=(1, 2)) /
tf.reduce_sum(curr[..., 0], axis=(1, 2)))
net['polar_origin'] = tf.stack([net['po_j'], net['po_i']], axis=1)
# origin augmentation
if flags.ptreg_aug > 0:
dim = layer_in.get_shape().as_list()[1]
shift = tf.cond(tflearn.get_training_mode(),
lambda: 1./dim * tf.random_uniform([flags.bs, 2],
minval=-flags.ptreg_aug,
maxval=flags.ptreg_aug),
lambda: tf.zeros([flags.bs, 2]))
net['polar_origin'] += shift
return net
def pt_regressor_conv(layer_in, flags):
""" Return standard convolutional polar transform origin regressor """
nfilters = [int(x) for x in flags.ptreg_nfilters.split(",")]
strides = [int(x) for x in flags.ptreg_strides.split(",")]
weights_init = flags.weights_init
# first block is always conv_bn_relu
name1st = 'ptreg_conv0'
first = layers.conv_bn_relu(layer_in, nfilters[0], 3, name1st,
strides=strides[0],
padding=flags.pad,
weight_decay=flags.weight_decay,
weights_init=weights_init,
activation=flags.activation)
nfilters = nfilters[1:]
strides = strides[1:]
net, curr = layers.conv_sequence(first,
nfilters,
strides,
block_fun=layers.conv_bn_relu,
pad=flags.pad,
weight_decay=flags.weight_decay,
weights_init=weights_init,
name_prefix='ptreg_',
activation=flags.activation)
net[name1st] = first
return net, curr
def conv_from_flags(layer_in, flags):
""" Build series of strided convolutional layers from flags.nfilters and flags.strides. """
nfilters = [int(x) for x in flags.nfilters.split(",")]
strides = [int(x) for x in flags.strides.split(",")]
block_fun = layers.conv_bn_relu
net = {}
# first block is always conv_bn_relu
if flags.pad_wrap:
pad = 'wrap'
else:
pad = flags.pad
net['conv0'], curr = dup(layers.conv_bn_relu(layer_in, nfilters[0], flags.filter_size, 'conv0',
strides=strides[0],
padding=pad,
weight_decay=flags.weight_decay,
weights_init=flags.weights_init,
activation=flags.activation))
nfilters = nfilters[1:]
strides = strides[1:]
seq, curr = layers.conv_sequence(curr, nfilters, strides, block_fun,
pad=pad,
weight_decay=flags.weight_decay,
activation=flags.activation,
filter_size=flags.filter_size)
for k, v in seq.items():
net[k] = v
net = finalize_conv_from_flags(net, curr, flags)
return net
def finalize_conv_from_flags(net, curr, flags):
if flags.pad_wrap:
curr = layers.wrap_pad_rows(curr)
pad = 'valid'
# final layer is linear
name = 'conv_final'
net[name] = conv_2d(curr, flags.nc, flags.filter_size,
activation='linear', name=name, padding=pad)
return net
def dup(x):
""" Return two references for input; useful when creating NNs and storing references to layers """
return [x, x]