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_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