def compute_last_conv_layer_roicnn(images, cur_image_num, layer=2, feat=[2, 4]):
for l in range(0, layer):
if l == 0:
conv_tensor = rsvp_quick_deconv.deconv_local_st5_filter(images, 'conv0', in_feat=1, out_feat=feat[0])
else:
conv_tensor = rsvp_quick_deconv.deconv_local_st5_filter \
(pool_tensor, 'conv' + str(l), in_feat=feat[l - 1], out_feat=feat[l])
with tf.variable_scope('layer' + str(l)) as scope:
pool_tensor, _ = rsvp_quick_deconv.deconv_pooling_n_filter(conv_tensor, 'pool' + str(l), kheight=poolh, kwidth=poolw)
pool_tensor_shape.append(pool_tensor.get_shape().as_list())
returnTensors = []
returnTensors.extend([pool_tensor])
return returnTensors
def compute_last_conv_layer_roicnn(images, cur_image_num, layer=2, feat=[2, 4]):
for l in range(0, layer):
if l == 0:
conv_tensor = rsvp_quick_deconv.deconv_local_st5_filter(images, 'conv0', in_feat=1, out_feat=feat[0])
else:
conv_tensor = rsvp_quick_deconv.deconv_local_st5_filter \
(pool_tensor, 'conv' + str(l), in_feat=feat[l - 1], out_feat=feat[l])
with tf.variable_scope('layer' + str(l)) as scope:
pool_tensor, _ = rsvp_quick_deconv.deconv_pooling_n_filter(conv_tensor, 'pool' + str(l), kheight=poolh, kwidth=poolw)
pool_tensor_shape.append(pool_tensor.get_shape().as_list())
returnTensors = []
returnTensors.extend([pool_tensor])
return returnTensors
def reconstruct_input_lasso_roicnn(images, max_feature, layer_num, filter_num, max_act_pl, max_ind_pl, layer, feat=[2, 4]):
switches = []
pool_tensor_shape = []
conv_tensor_input_shape = []
for l in range(0, layer_num + 1):
if l == 0:
conv_tensor = rsvp_quick_deconv.deconv_local_st5_filter(images, 'conv0', in_feat=1, out_feat=feat[0])
conv_tensor_input_shape.append(images.get_shape().as_list())
else:
conv_tensor = rsvp_quick_deconv.deconv_local_st5_filter \
(pool_tensor, 'conv' + str(l), in_feat=feat[l - 1], out_feat=feat[l])
conv_tensor_input_shape.append(pool_tensor.get_shape().as_list())
pool_tensor, switches_tmp = rsvp_quick_deconv.deconv_pooling_n_filter(conv_tensor, 'pool' + str(l), kheight=poolh, kwidth=poolw)
pool_tensor_shape.append(pool_tensor.get_shape().as_list())
switches.append(switches_tmp)
deconv_tensor = max_feature
for l in range(layer_num, -1, -1):
unpool_tensor = rsvp_quick_deconv.deconv_unpooling_n_filter(deconv_tensor , switches[l], 'pool' + str(l), kheight=poolh, kwidth=poolw)
if l == 0:
deconv_tensor = rsvp_quick_deconv.deconv_local_st5_unfilter(unpool_tensor, conv_tensor_input_shape[l], 'conv0')
else:
deconv_tensor = rsvp_quick_deconv.deconv_local_st5_unfilter \
(unpool_tensor, conv_tensor_input_shape[l], 'conv' + str(l))
returnTensors = []
#returnTensors.extend([max_act_val] )
#returnTensors.extend([max_ind_val])
returnTensors.extend([deconv_tensor])
returnTensors.extend([pool_tensor])
returnTensors.extend(switches)
return returnTensors
def reconstruct_input_lasso_roicnn(images, max_feature, layer_num, filter_num, max_act_pl, max_ind_pl, layer, feat=[2, 4]):
switches = []
pool_tensor_shape = []
conv_tensor_input_shape = []
for l in range(0, layer_num + 1):
if l == 0:
conv_tensor = rsvp_quick_deconv.deconv_local_st5_filter(images, 'conv0', in_feat=1, out_feat=feat[0])
conv_tensor_input_shape.append(images.get_shape().as_list())
else:
conv_tensor = rsvp_quick_deconv.deconv_local_st5_filter \
(pool_tensor, 'conv' + str(l), in_feat=feat[l - 1], out_feat=feat[l])
conv_tensor_input_shape.append(pool_tensor.get_shape().as_list())
pool_tensor, switches_tmp = rsvp_quick_deconv.deconv_pooling_n_filter(conv_tensor, 'pool' + str(l), kheight=poolh, kwidth=poolw)
pool_tensor_shape.append(pool_tensor.get_shape().as_list())
switches.append(switches_tmp)
deconv_tensor = max_feature
for l in range(layer_num, -1, -1):
unpool_tensor = rsvp_quick_deconv.deconv_unpooling_n_filter(deconv_tensor , switches[l], 'pool' + str(l), kheight=poolh, kwidth=poolw)
if l == 0:
deconv_tensor = rsvp_quick_deconv.deconv_local_st5_unfilter(unpool_tensor, conv_tensor_input_shape[l], 'conv0')
else:
deconv_tensor = rsvp_quick_deconv.deconv_local_st5_unfilter \
(unpool_tensor, conv_tensor_input_shape[l], 'conv' + str(l))
returnTensors = []
#returnTensors.extend([max_act_val] )
#returnTensors.extend([max_ind_val])
returnTensors.extend([deconv_tensor])
returnTensors.extend([pool_tensor])
returnTensors.extend(switches)
return returnTensors
def find_max_activation_roicnn(images, cur_image_num, layer=2, feat=[2, 4]):
#global pool_tensor_shape, switches_batch, max_activation, max_ind, max_image_ind, cur_activation, cur_ind, cur_image_ind, max_acitvations_threshold
#pool_tensor_shape = []
#switches_batch -- Current maximum activations for feature on one image over all layers
#max_activation -- Global maximum activations for feature on all images over all layers
#max_ind -- Global maximum activations for feature on all images over all layers
#max_image_ind -- Max images generate switches
#cur_activation -- Current maximum activations for feature on one image over all layers
#cur_ind -- Current maximum activations indicies for feature on one image over all layers
#cur_image_ind -- Current image
#max_acitvations_threshold -- a threshold for discarding maximum activations
for l in range(0, layer):
if l == 0:
conv_tensor = rsvp_quick_deconv.deconv_local_st5_filter(images, 'conv0', in_feat=1, out_feat=feat[0])
else:
conv_tensor = rsvp_quick_deconv.deconv_local_st5_filter \
(pool_tensor, 'conv' + str(l), in_feat=feat[l - 1], out_feat=feat[l])
with tf.variable_scope('layer' + str(l)) as scope:
pool_tensor, _ = rsvp_quick_deconv.deconv_pooling_n_filter(conv_tensor, 'pool' + str(l), kheight=poolh, kwidth=poolw)
pool_tensor_shape.append(pool_tensor.get_shape().as_list())
# Initialize variables
num_filters = pool_tensor_shape[l][3]
max_acitvations_threshold.append(tf.Variable(tf.fill([num_filters], 10e+20 ),
name='max_acitvations_threshold'))
max_activation.append(tf.Variable(-10e+20 * tf.ones([num_filters]), name='max_activation'))
max_ind.append(tf.Variable(tf.fill([num_filters], tf.constant(-1, dtype=tf.int64)), name='max_ind'))
max_image_ind.append(tf.Variable(tf.fill([num_filters], tf.constant(-1, dtype=tf.int64)), name='max_image_ind'))
#max_activation.append(tf.Variable(tf.random_uniform(shape=[num_filters], minval=0.0001, maxval=0.0002), name='max_activation'))
#cur_activation.append(tf.Variable(tf.zeros([num_filters]), name='cur_activation'))
#cur_ind.append(tf.Variable(tf.fill([num_filters], tf.constant(-1, dtype=tf.int64)), name='cur_ind')) #tf.Variable(tf.constant(-1, shape=[num_filters]))
#cur_image_ind.append(tf.Variable(tf.fill([num_filters], tf.cast(-1, dtype=tf.int64)), name='cur_image_ind'))
#selection.append(tf.Variable(tf.fill([num_filters], tf.constant(False)), name='selection'))
#max_threshold.append(tf.Variable(tf.zeros([1,pool_tensor_shape[l][1], pool_tensor_shape[l][2], pool_tensor_shape[l][3]]), name='max_threshold'))
#max_threshold.append(tf.Variable(tf.fill([num_filters], tf.constant(-1, dtype=tf.int64)), name='max_ind'))
cur_image_ind.append(tf.fill([pool_tensor_shape[l][3]], cur_image_num))
max_acitvations_threshold_tmp1 = tf.expand_dims(max_acitvations_threshold[l], 0)
max_acitvations_threshold_tmp2 = tf.expand_dims(max_acitvations_threshold_tmp1, 0)
max_acitvations_threshold_tmp3 = tf.expand_dims(max_acitvations_threshold_tmp2, 0)
max_threshold.append(tf.tile(max_acitvations_threshold_tmp3, [1,pool_tensor_shape[l][1], pool_tensor_shape[l][2], 1]))
pool_tensor2 = tf.select(max_threshold[l] >= pool_tensor, pool_tensor, -10e+20 * tf.ones_like(pool_tensor))
cur_activation.append(tf.reduce_max(pool_tensor2, [0, 1, 2]))
pool_tensor2 = tf.transpose(pool_tensor2, [3, 1, 2, 0])
pool_tensor3 = tf.reshape(pool_tensor2, [pool_tensor_shape[l][3], pool_tensor_shape[l][1] * pool_tensor_shape[l][2]])
cur_ind.append(tf.argmax(pool_tensor3, 1))
selection.append(tf.logical_and(cur_activation[l] > max_activation[l],
max_acitvations_threshold[l] > cur_activation[l]))
updated_max_ind = tf.select(selection[l], cur_ind[l], max_ind[l])
update1.append(tf.assign(max_ind[l], updated_max_ind))
updated_max_image_ind = tf.select(selection[l], cur_image_ind[l], max_image_ind[l])
update2.append(tf.assign(max_image_ind[l], updated_max_image_ind))
updated_max_activations = tf.select(selection[l], cur_activation[l], max_activation[l])
update3.append(tf.assign(max_activation[l], updated_max_activations))
returnTensors = []
returnTensors.extend(cur_activation)
returnTensors.extend(selection)
returnTensors.extend(update1)
returnTensors.extend(update2)
returnTensors.extend(update3)
returnTensors.extend(max_acitvations_threshold)
returnTensors.extend(pool_tensor)
returnTensors.extend(max_ind)
returnTensors.extend(max_image_ind)
returnTensors.extend(max_activation)
#returnTensors.extend(cur_image_ind)
#returnTensors.extend(cur_activation)
#returnTensors.extend(cur_ind)
#returnTensors.extend(max_ind)
#returnTensors.extend(max_threshold)
#returnTensors.extend([pool_tensor])
#returnTensors.extend([pool_tensor2])
#returnTensors.extend(max_acitvations_threshold)
#returnTensors.extend([pool_tensor3])
update_max_threshold(layer=2)
clear_variables(layer=2)
return returnTensors
def find_max_activation_roicnn(images, cur_image_num, layer=2, feat=[2, 4]):
#global pool_tensor_shape, switches_batch, max_activation, max_ind, max_image_ind, cur_activation, cur_ind, cur_image_ind, max_acitvations_threshold
#pool_tensor_shape = []
#switches_batch -- Current maximum activations for feature on one image over all layers
#max_activation -- Global maximum activations for feature on all images over all layers
#max_ind -- Global maximum activations for feature on all images over all layers
#max_image_ind -- Max images generate switches
#cur_activation -- Current maximum activations for feature on one image over all layers
#cur_ind -- Current maximum activations indicies for feature on one image over all layers
#cur_image_ind -- Current image
#max_acitvations_threshold -- a threshold for discarding maximum activations
for l in range(0, layer):
if l == 0:
conv_tensor = rsvp_quick_deconv.deconv_local_st5_filter(images, 'conv0', in_feat=1, out_feat=feat[0])
else:
conv_tensor = rsvp_quick_deconv.deconv_local_st5_filter \
(pool_tensor, 'conv' + str(l), in_feat=feat[l - 1], out_feat=feat[l])
with tf.variable_scope('layer' + str(l)) as scope:
pool_tensor, _ = rsvp_quick_deconv.deconv_pooling_n_filter(conv_tensor, 'pool' + str(l), kheight=poolh, kwidth=poolw)
pool_tensor_shape.append(pool_tensor.get_shape().as_list())
# Initialize variables
num_filters = pool_tensor_shape[l][3]
max_acitvations_threshold.append(tf.Variable(tf.fill([num_filters], 10e+20 ),
name='max_acitvations_threshold'))
max_activation.append(tf.Variable(-10e+20 * tf.ones([num_filters]), name='max_activation'))
max_ind.append(tf.Variable(tf.fill([num_filters], tf.constant(-1, dtype=tf.int64)), name='max_ind'))
max_image_ind.append(tf.Variable(tf.fill([num_filters], tf.constant(-1, dtype=tf.int64)), name='max_image_ind'))
#max_activation.append(tf.Variable(tf.random_uniform(shape=[num_filters], minval=0.0001, maxval=0.0002), name='max_activation'))
#cur_activation.append(tf.Variable(tf.zeros([num_filters]), name='cur_activation'))
#cur_ind.append(tf.Variable(tf.fill([num_filters], tf.constant(-1, dtype=tf.int64)), name='cur_ind')) #tf.Variable(tf.constant(-1, shape=[num_filters]))
#cur_image_ind.append(tf.Variable(tf.fill([num_filters], tf.cast(-1, dtype=tf.int64)), name='cur_image_ind'))
#selection.append(tf.Variable(tf.fill([num_filters], tf.constant(False)), name='selection'))
#max_threshold.append(tf.Variable(tf.zeros([1,pool_tensor_shape[l][1], pool_tensor_shape[l][2], pool_tensor_shape[l][3]]), name='max_threshold'))
#max_threshold.append(tf.Variable(tf.fill([num_filters], tf.constant(-1, dtype=tf.int64)), name='max_ind'))
cur_image_ind.append(tf.fill([pool_tensor_shape[l][3]], cur_image_num))
max_acitvations_threshold_tmp1 = tf.expand_dims(max_acitvations_threshold[l], 0)
max_acitvations_threshold_tmp2 = tf.expand_dims(max_acitvations_threshold_tmp1, 0)
max_acitvations_threshold_tmp3 = tf.expand_dims(max_acitvations_threshold_tmp2, 0)
max_threshold.append(tf.tile(max_acitvations_threshold_tmp3, [1,pool_tensor_shape[l][1], pool_tensor_shape[l][2], 1]))
pool_tensor2 = tf.select(max_threshold[l] >= pool_tensor, pool_tensor, -10e+20 * tf.ones_like(pool_tensor))
cur_activation.append(tf.reduce_max(pool_tensor2, [0, 1, 2]))
pool_tensor2 = tf.transpose(pool_tensor2, [3, 1, 2, 0])
pool_tensor3 = tf.reshape(pool_tensor2, [pool_tensor_shape[l][3], pool_tensor_shape[l][1] * pool_tensor_shape[l][2]])
cur_ind.append(tf.argmax(pool_tensor3, 1))
selection.append(tf.logical_and(cur_activation[l] > max_activation[l],
max_acitvations_threshold[l] > cur_activation[l]))
updated_max_ind = tf.select(selection[l], cur_ind[l], max_ind[l])
update1.append(tf.assign(max_ind[l], updated_max_ind))
updated_max_image_ind = tf.select(selection[l], cur_image_ind[l], max_image_ind[l])
update2.append(tf.assign(max_image_ind[l], updated_max_image_ind))
updated_max_activations = tf.select(selection[l], cur_activation[l], max_activation[l])
update3.append(tf.assign(max_activation[l], updated_max_activations))
returnTensors = []
returnTensors.extend(cur_activation)
returnTensors.extend(selection)
returnTensors.extend(update1)
returnTensors.extend(update2)
returnTensors.extend(update3)
returnTensors.extend(max_acitvations_threshold)
returnTensors.extend(pool_tensor)
returnTensors.extend(max_ind)
returnTensors.extend(max_image_ind)
returnTensors.extend(max_activation)
#returnTensors.extend(cur_image_ind)
#returnTensors.extend(cur_activation)
#returnTensors.extend(cur_ind)
#returnTensors.extend(max_ind)
#returnTensors.extend(max_threshold)
#returnTensors.extend([pool_tensor])
#returnTensors.extend([pool_tensor2])
#returnTensors.extend(max_acitvations_threshold)
#returnTensors.extend([pool_tensor3])
update_max_threshold(layer=2)
clear_variables(layer=2)
return returnTensors
