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# Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Utility functions for setting up the CMP graph.
"""
import os, numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.contrib import slim
from tensorflow.contrib.slim import arg_scope
import logging
from src import utils
import src.file_utils as fu
from tfcode import tf_utils
resnet_v2 = tf_utils.resnet_v2
custom_residual_block = tf_utils.custom_residual_block
def value_iteration_network(
fr, num_iters, val_neurons, action_neurons, kernel_size, share_wts=False,
name='vin', wt_decay=0.0001, activation_fn=None, shape_aware=False):
"""
Constructs a Value Iteration Network, convolutions and max pooling across
channels.
Input:
fr: NxWxHxC
val_neurons: Number of channels for maintaining the value.
action_neurons: Computes action_neurons * val_neurons at each iteration to
max pool over.
Output:
value image: NxHxWx(val_neurons)
"""
init_var = np.sqrt(2.0/(kernel_size**2)/(val_neurons*action_neurons))
vals = []
with tf.variable_scope(name) as varscope:
if shape_aware == False:
fr_shape = tf.unstack(tf.shape(fr))
val_shape = tf.stack(fr_shape[:-1] + [val_neurons])
val = tf.zeros(val_shape, name='val_init')
else:
val = tf.expand_dims(tf.zeros_like(fr[:,:,:,0]), dim=-1) * \
tf.constant(0., dtype=tf.float32, shape=[1,1,1,val_neurons])
val_shape = tf.shape(val)
vals.append(val)
for i in range(num_iters):
if share_wts:
# The first Value Iteration maybe special, so it can have its own
# paramterss.
scope = 'conv'
if i == 0: scope = 'conv_0'
if i > 1: varscope.reuse_variables()
else:
scope = 'conv_{:d}'.format(i)
val = slim.conv2d(tf.concat([val, fr], 3, name='concat_{:d}'.format(i)),
num_outputs=action_neurons*val_neurons,
kernel_size=kernel_size, stride=1, activation_fn=activation_fn,
scope=scope, normalizer_fn=None,
weights_regularizer=slim.l2_regularizer(wt_decay),
weights_initializer=tf.random_normal_initializer(stddev=init_var),
biases_initializer=tf.zeros_initializer())
val = tf.reshape(val, [-1, action_neurons*val_neurons, 1, 1],
name='re_{:d}'.format(i))
val = slim.max_pool2d(val, kernel_size=[action_neurons,1],
stride=[action_neurons,1], padding='VALID',
scope='val_{:d}'.format(i))
val = tf.reshape(val, val_shape, name='unre_{:d}'.format(i))
vals.append(val)
return val, vals
def rotate_preds(loc_on_map, relative_theta, map_size, preds,
output_valid_mask):
with tf.name_scope('rotate'):
flow_op = tf_utils.get_flow(loc_on_map, relative_theta, map_size=map_size)
if type(preds) != list:
rotated_preds, valid_mask_warps = tf_utils.dense_resample(preds, flow_op,
output_valid_mask)
else:
rotated_preds = [] ;valid_mask_warps = []
for pred in preds:
rotated_pred, valid_mask_warp = tf_utils.dense_resample(pred, flow_op,
output_valid_mask)
rotated_preds.append(rotated_pred)
valid_mask_warps.append(valid_mask_warp)
return rotated_preds, valid_mask_warps
def get_visual_frustum(map_size, shape_like, expand_dims=[0,0]):
with tf.name_scope('visual_frustum'):
l = np.tril(np.ones(map_size)) ;l = l + l[:,::-1]
l = (l == 2).astype(np.float32)
for e in expand_dims:
l = np.expand_dims(l, axis=e)
confs_probs = tf.constant(l, dtype=tf.float32)
confs_probs = tf.ones_like(shape_like, dtype=tf.float32) * confs_probs
return confs_probs
def deconv(x, is_training, wt_decay, neurons, strides, layers_per_block,
kernel_size, conv_fn, name, offset=0):
"""Generates a up sampling network with residual connections.
"""
batch_norm_param = {'center': True, 'scale': True,
'activation_fn': tf.nn.relu,
'is_training': is_training}
outs = []
for i, (neuron, stride) in enumerate(zip(neurons, strides)):
for s in range(layers_per_block):
scope = '{:s}_{:d}_{:d}'.format(name, i+1+offset,s+1)
x = custom_residual_block(x, neuron, kernel_size, stride, scope,
is_training, wt_decay, use_residual=True,
residual_stride_conv=True, conv_fn=conv_fn,
batch_norm_param=batch_norm_param)
stride = 1
outs.append((x,True))
return x, outs
def fr_v2(x, output_neurons, inside_neurons, is_training, name='fr',
wt_decay=0.0001, stride=1, updates_collections=tf.GraphKeys.UPDATE_OPS):
"""Performs fusion of information between the map and the reward map.
Inputs
x: NxHxWxC1
Outputs
fr map: NxHxWx(output_neurons)
"""
if type(stride) != list:
stride = [stride]
with slim.arg_scope(resnet_v2.resnet_utils.resnet_arg_scope(
is_training=is_training, weight_decay=wt_decay)):
with slim.arg_scope([slim.batch_norm], updates_collections=updates_collections) as arg_sc:
# Change the updates_collections for the conv normalizer_params to None
for i in range(len(arg_sc.keys())):
if 'convolution' in arg_sc.keys()[i]:
arg_sc.values()[i]['normalizer_params']['updates_collections'] = updates_collections
with slim.arg_scope(arg_sc):
bottleneck = resnet_v2.bottleneck
blocks = []
for i, s in enumerate(stride):
b = resnet_v2.resnet_utils.Block(
'block{:d}'.format(i + 1), bottleneck, [{
'depth': output_neurons,
'depth_bottleneck': inside_neurons,
'stride': stride[i]
}])
blocks.append(b)
x, outs = resnet_v2.resnet_v2(x, blocks, num_classes=None, global_pool=False,
output_stride=None, include_root_block=False,
reuse=False, scope=name)
return x, outs
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