def compute_last_conv_layer_cvcnn(images, cur_image_num, keep_prob, layer=2, feat=[2, 4]):
for l in range(0, layer):
if l == 0:
conv_tensor = rsvp_quick_deconv.deconv_5x5_filter(images, 'conv0', in_feat=1, out_feat=feat[0])
else:
conv_tensor = rsvp_quick_deconv.deconv_5x5_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())
logits, layer1, layer2 = rsvp_quick_deconv.deconv_fully_connected_1layer(pool_tensor, keep_prob)
returnTensors = []
returnTensors.extend([pool_tensor])
returnTensors.extend([layer2])
returnTensors.extend([logits])
return returnTensors
def compute_last_conv_layer_cvcnn(images, cur_image_num, keep_prob, layer=2, feat=[2, 4]):
for l in range(0, layer):
if l == 0:
conv_tensor = rsvp_quick_deconv.deconv_5x5_filter(images, 'conv0', in_feat=1, out_feat=feat[0])
else:
conv_tensor = rsvp_quick_deconv.deconv_5x5_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())
logits, layer1, layer2 = rsvp_quick_deconv.deconv_fully_connected_1layer(pool_tensor, keep_prob)
returnTensors = []
returnTensors.extend([pool_tensor])
returnTensors.extend([layer2])
returnTensors.extend([logits])
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