def reconstruct_input_roicnn(images, 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_5x5_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_5x5_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=2, kwidth=2)
pool_tensor_shape.append(pool_tensor.get_shape().as_list())
switches.append(switches_tmp)
if l == layer_num:
with tf.variable_scope('toplayer' + str(l)) as scope:
# Set top layer activations based on maximum activations
max_act_feat = tf.Variable(tf.zeros([pool_tensor_shape[l][3]*pool_tensor_shape[l][1]*pool_tensor_shape[l][2]]), name='max_act_feat')
max_act_feat = tf.assign(max_act_feat, tf.zeros([pool_tensor_shape[l][3]*pool_tensor_shape[l][1]*pool_tensor_shape[l][2]]))
max_features_tmp = tf.scatter_update(max_act_feat, max_ind_pl + filter_num * pool_tensor_shape[l][1] * pool_tensor_shape[l][2], max_act_pl)
max_features_tmp2 = tf.reshape(max_features_tmp, [pool_tensor_shape[l][3], pool_tensor_shape[l][1],pool_tensor_shape[l][2]])
max_features_tmp3 = tf.transpose(max_features_tmp2, [1, 2, 0])
max_feature = tf.expand_dims(max_features_tmp3, 0)
deconv_tensor = max_feature
# Deconvolution network
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=2, kwidth=2)
deconv_tensor = rsvp_quick_deconv.deconv_5x5_unfilter \
(unpool_tensor, conv_tensor_input_shape[l], 'conv' + str(l))
# if l == 0:
# deconv_tensor = rsvp_quick_deconv.deconv_5x5_unfilter(unpool_tensor, conv_tensor_input_shape[l], 'conv0')
# else:
# deconv_tensor = rsvp_quick_deconv.deconv_5x5_unfilter \
# (unpool_tensor, conv_tensor_input_shape[l], 'conv' + str(l))
returnTensors = []
returnTensors.extend([max_act_feat])
returnTensors.extend([max_feature])
returnTensors.extend([deconv_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 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_cvcnn(images, max_feature, keep_prob, layer_num, filter_num, max_act_pl, max_ind_pl, layer, feat=[2, 4]):
switches = []
pool_tensor_shape = []
conv_tensor_input_shape = []
pool_tensors = []
deconv_tensors = []
unpool_tensors = []
unpool_resize_tensors = []
conv_tensors = []
for l in range(0, layer_num + 1):
if l == 0:
conv_tensor = rsvp_quick_deconv.deconv_5x5_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_5x5_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())
conv_tensors.append(conv_tensor)
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())
pool_tensors.append(pool_tensor)
switches.append(switches_tmp)
# deconv_tensor = max_feature
if (layer_num == 1):
logits, layer1, layer2 = rsvp_quick_deconv.deconv_fully_connected_1layer(pool_tensor, keep_prob)
deconv_tensor = max_feature
for l in range(layer_num, -1, -1):
unpool_tensor, unpool_resize_tensor = rsvp_quick_deconv.deconv_unpooling_n_filter(deconv_tensor , switches[l], 'pool' + str(l), kheight=poolh, kwidth=poolw)
unpool_resize_tensors.append(unpool_resize_tensor)
unpool_tensors.append(unpool_tensor)
if l == 0:
deconv_tensor = rsvp_quick_deconv.deconv_5x5_unfilter(unpool_tensor, conv_tensor_input_shape[l], 'conv0')
else:
deconv_tensor = rsvp_quick_deconv.deconv_5x5_unfilter \
(unpool_tensor, conv_tensor_input_shape[l], 'conv' + str(l))
deconv_tensors.append(deconv_tensor)
returnTensors = []
#returnTensors.extend([max_act_val] )
#returnTensors.extend([max_ind_val])
returnTensors.extend(deconv_tensors)
returnTensors.extend(pool_tensors)
returnTensors.extend(switches)
returnTensors.extend(unpool_tensors)
returnTensors.extend(unpool_resize_tensors)
returnTensors.extend(conv_tensors)
if (layer_num == 1):
returnTensors.extend([logits, layer1, layer2])
return returnTensors
def reconstruct_input_lasso_cvcnn(images, max_feature, keep_prob, layer_num, filter_num, max_act_pl, max_ind_pl, layer, feat=[2, 4]):
switches = []
pool_tensor_shape = []
conv_tensor_input_shape = []
pool_tensors = []
deconv_tensors = []
unpool_tensors = []
unpool_resize_tensors = []
conv_tensors = []
for l in range(0, layer_num + 1):
if l == 0:
conv_tensor = rsvp_quick_deconv.deconv_5x5_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_5x5_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())
conv_tensors.append(conv_tensor)
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())
pool_tensors.append(pool_tensor)
switches.append(switches_tmp)
# deconv_tensor = max_feature
if (layer_num == 1):
logits, layer1, layer2 = rsvp_quick_deconv.deconv_fully_connected_1layer(pool_tensor, keep_prob)
deconv_tensor = max_feature
for l in range(layer_num, -1, -1):
unpool_tensor, unpool_resize_tensor = rsvp_quick_deconv.deconv_unpooling_n_filter(deconv_tensor , switches[l], 'pool' + str(l), kheight=poolh, kwidth=poolw)
unpool_resize_tensors.append(unpool_resize_tensor)
unpool_tensors.append(unpool_tensor)
if l == 0:
deconv_tensor = rsvp_quick_deconv.deconv_5x5_unfilter(unpool_tensor, conv_tensor_input_shape[l], 'conv0')
else:
deconv_tensor = rsvp_quick_deconv.deconv_5x5_unfilter \
(unpool_tensor, conv_tensor_input_shape[l], 'conv' + str(l))
deconv_tensors.append(deconv_tensor)
returnTensors = []
#returnTensors.extend([max_act_val] )
#returnTensors.extend([max_ind_val])
returnTensors.extend(deconv_tensors)
returnTensors.extend(pool_tensors)
returnTensors.extend(switches)
returnTensors.extend(unpool_tensors)
returnTensors.extend(unpool_resize_tensors)
returnTensors.extend(conv_tensors)
if (layer_num == 1):
returnTensors.extend([logits, layer1, layer2])
return returnTensors
def reconstruct_input_roicnn(images,
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_5x5_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_5x5_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=2, kwidth=2)
pool_tensor_shape.append(pool_tensor.get_shape().as_list())
switches.append(switches_tmp)
if l == layer_num:
with tf.variable_scope('toplayer' + str(l)) as scope:
# Set top layer activations based on maximum activations
max_act_feat = tf.Variable(tf.zeros([
pool_tensor_shape[l][3] * pool_tensor_shape[l][1] *
pool_tensor_shape[l][2]
]),
name='max_act_feat')
max_act_feat = tf.assign(
max_act_feat,
tf.zeros([
pool_tensor_shape[l][3] * pool_tensor_shape[l][1] *
pool_tensor_shape[l][2]
]))
max_features_tmp = tf.scatter_update(
max_act_feat, max_ind_pl + filter_num *
pool_tensor_shape[l][1] * pool_tensor_shape[l][2],
max_act_pl)
max_features_tmp2 = tf.reshape(max_features_tmp, [
pool_tensor_shape[l][3], pool_tensor_shape[l][1],
pool_tensor_shape[l][2]
])
max_features_tmp3 = tf.transpose(max_features_tmp2, [1, 2, 0])
max_feature = tf.expand_dims(max_features_tmp3, 0)
deconv_tensor = max_feature
# Deconvolution network
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=2, kwidth=2)
deconv_tensor = rsvp_quick_deconv.deconv_5x5_unfilter \
(unpool_tensor, conv_tensor_input_shape[l], 'conv' + str(l))
# if l == 0:
# deconv_tensor = rsvp_quick_deconv.deconv_5x5_unfilter(unpool_tensor, conv_tensor_input_shape[l], 'conv0')
# else:
# deconv_tensor = rsvp_quick_deconv.deconv_5x5_unfilter \
# (unpool_tensor, conv_tensor_input_shape[l], 'conv' + str(l))
returnTensors = []
returnTensors.extend([max_act_feat])
returnTensors.extend([max_feature])
returnTensors.extend([deconv_tensor])
returnTensors.extend(switches)
return returnTensors
