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168 rivejä
7.6 KiB
168 rivejä
7.6 KiB
import tensorflow as tf
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from tensorflow.contrib import slim
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from nets import vgg
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from utils.rpn_msr.anchor_target_layer import anchor_target_layer as anchor_target_layer_py
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def mean_image_subtraction(images, means=[123.68, 116.78, 103.94]):
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num_channels = images.get_shape().as_list()[-1]
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if len(means) != num_channels:
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raise ValueError('len(means) must match the number of channels')
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channels = tf.split(axis=3, num_or_size_splits=num_channels, value=images)
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for i in range(num_channels):
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channels[i] -= means[i]
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return tf.concat(axis=3, values=channels)
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def make_var(name, shape, initializer=None):
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return tf.get_variable(name, shape, initializer=initializer)
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def Bilstm(net, input_channel, hidden_unit_num, output_channel, scope_name):
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# width--->time step
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with tf.variable_scope(scope_name) as scope:
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shape = tf.shape(net)
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N, H, W, C = shape[0], shape[1], shape[2], shape[3]
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net = tf.reshape(net, [N * H, W, C])
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net.set_shape([None, None, input_channel])
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lstm_fw_cell = tf.contrib.rnn.LSTMCell(hidden_unit_num, state_is_tuple=True)
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lstm_bw_cell = tf.contrib.rnn.LSTMCell(hidden_unit_num, state_is_tuple=True)
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lstm_out, last_state = tf.nn.bidirectional_dynamic_rnn(lstm_fw_cell, lstm_bw_cell, net, dtype=tf.float32)
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lstm_out = tf.concat(lstm_out, axis=-1)
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lstm_out = tf.reshape(lstm_out, [N * H * W, 2 * hidden_unit_num])
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init_weights = tf.contrib.layers.variance_scaling_initializer(factor=0.01, mode='FAN_AVG', uniform=False)
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init_biases = tf.constant_initializer(0.0)
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weights = make_var('weights', [2 * hidden_unit_num, output_channel], init_weights)
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biases = make_var('biases', [output_channel], init_biases)
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outputs = tf.matmul(lstm_out, weights) + biases
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outputs = tf.reshape(outputs, [N, H, W, output_channel])
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return outputs
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def lstm_fc(net, input_channel, output_channel, scope_name):
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with tf.variable_scope(scope_name) as scope:
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shape = tf.shape(net)
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N, H, W, C = shape[0], shape[1], shape[2], shape[3]
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net = tf.reshape(net, [N * H * W, C])
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init_weights = tf.contrib.layers.variance_scaling_initializer(factor=0.01, mode='FAN_AVG', uniform=False)
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init_biases = tf.constant_initializer(0.0)
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weights = make_var('weights', [input_channel, output_channel], init_weights)
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biases = make_var('biases', [output_channel], init_biases)
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output = tf.matmul(net, weights) + biases
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output = tf.reshape(output, [N, H, W, output_channel])
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return output
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def model(image,language):#改
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image = mean_image_subtraction(image)
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with slim.arg_scope(vgg.vgg_arg_scope()):
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conv5_3 = vgg.vgg_16(image)
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rpn_conv = slim.conv2d(conv5_3, 512, 3)
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lstm_output = Bilstm(rpn_conv, 512, 128, 512, scope_name='BiLSTM')
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bbox_pred = lstm_fc(lstm_output, 512, 10 * 4, scope_name="bbox_pred")
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cls_pred = lstm_fc(lstm_output, 512, 10 * 3, scope_name="cls_pred")#改
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# transpose: (1, H, W, A x d) -> (1, H, WxA, d)
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cls_pred_shape = tf.shape(cls_pred)
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cls_pred_reshape = tf.reshape(cls_pred, [cls_pred_shape[0], cls_pred_shape[1], -1, 3])#改
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cls_pred_reshape_shape = tf.shape(cls_pred_reshape)
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cls_prob = tf.reshape(tf.nn.softmax(tf.reshape(cls_pred_reshape, [-1, cls_pred_reshape_shape[3]])),
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[-1, cls_pred_reshape_shape[1], cls_pred_reshape_shape[2], cls_pred_reshape_shape[3]],
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name="cls_prob")
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return bbox_pred, cls_pred, cls_prob
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def anchor_target_layer(cls_pred, bbox, im_info, scope_name):
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with tf.variable_scope(scope_name) as scope:
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# 'rpn_cls_score', 'gt_boxes', 'im_info'
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rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = \
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tf.py_func(anchor_target_layer_py,
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[cls_pred, bbox, im_info, [16, ], [16]],
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[tf.float32, tf.float32, tf.float32, tf.float32])
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rpn_labels = tf.convert_to_tensor(tf.cast(rpn_labels, tf.int32),
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name='rpn_labels')
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rpn_bbox_targets = tf.convert_to_tensor(rpn_bbox_targets,
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name='rpn_bbox_targets')
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rpn_bbox_inside_weights = tf.convert_to_tensor(rpn_bbox_inside_weights,
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name='rpn_bbox_inside_weights')
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rpn_bbox_outside_weights = tf.convert_to_tensor(rpn_bbox_outside_weights,
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name='rpn_bbox_outside_weights')
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return [rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights]
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def smooth_l1_dist(deltas, sigma2=9.0, name='smooth_l1_dist'):
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with tf.name_scope(name=name) as scope:
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deltas_abs = tf.abs(deltas)
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smoothL1_sign = tf.cast(tf.less(deltas_abs, 1.0 / sigma2), tf.float32)
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return tf.square(deltas) * 0.5 * sigma2 * smoothL1_sign + \
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(deltas_abs - 0.5 / sigma2) * tf.abs(smoothL1_sign - 1)
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def loss(bbox_pred, cls_pred, bbox, im_info):
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# rpn_labels : (HxWxA, 1), for each anchor, 0 denotes bg, 1 fg, -1 dontcare
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#rpn_bbox_targets: (HxWxA, 4), distances of the anchors to the gt_boxes(may contains some transform)
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# that are the regression objectives
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#rpn_bbox_inside_weights: (HxWxA, 4) weights of each boxes, mainly accepts hyper param in cfg
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#rpn_bbox_outside_weights: (HxWxA, 4) used to balance the fg/bg,
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# beacuse the numbers of bgs and fgs mays significiantly different
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rpn_data = anchor_target_layer(cls_pred, bbox, im_info, "anchor_target_layer")#改
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# classification loss
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# transpose: (1, H, W, A x d) -> (1, H, WxA, d)
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cls_pred_shape = tf.shape(cls_pred)
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cls_pred_reshape = tf.reshape(cls_pred, [cls_pred_shape[0], cls_pred_shape[1], -1, 3])#改
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rpn_cls_score = tf.reshape(cls_pred_reshape, [-1, 3])#改
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rpn_label = tf.reshape(rpn_data[0], [-1])
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# ignore_label(-1)
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fg_keep = tf.not_equal(rpn_label, -1)&tf.not_equal(rpn_label, 0)#改
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rpn_keep = tf.where(tf.not_equal(rpn_label, -1))
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rpn_cls_score = tf.gather(rpn_cls_score, rpn_keep)
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rpn_label = tf.gather(rpn_label, rpn_keep)
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rpn_cross_entropy_n = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=rpn_label, logits=rpn_cls_score)
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# box loss
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rpn_bbox_pred = bbox_pred
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rpn_bbox_targets = rpn_data[1]
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rpn_bbox_inside_weights = rpn_data[2]
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rpn_bbox_outside_weights = rpn_data[3]
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rpn_bbox_pred = tf.gather(tf.reshape(rpn_bbox_pred, [-1, 4]), rpn_keep) # shape (N, 4)
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rpn_bbox_targets = tf.gather(tf.reshape(rpn_bbox_targets, [-1, 4]), rpn_keep)
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rpn_bbox_inside_weights = tf.gather(tf.reshape(rpn_bbox_inside_weights, [-1, 4]), rpn_keep)
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rpn_bbox_outside_weights = tf.gather(tf.reshape(rpn_bbox_outside_weights, [-1, 4]), rpn_keep)
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rpn_loss_box_n = tf.reduce_sum(rpn_bbox_outside_weights * smooth_l1_dist(
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rpn_bbox_inside_weights * (rpn_bbox_pred - rpn_bbox_targets)), reduction_indices=[1])
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rpn_loss_box = tf.reduce_sum(rpn_loss_box_n) / (tf.reduce_sum(tf.cast(fg_keep, tf.float32)) + 1)
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rpn_cross_entropy = tf.reduce_mean(rpn_cross_entropy_n)
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model_loss = rpn_cross_entropy + rpn_loss_box
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regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
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total_loss = tf.add_n(regularization_losses) + model_loss
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tf.summary.scalar('model_loss', model_loss)
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tf.summary.scalar('total_loss', total_loss)
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tf.summary.scalar('rpn_cross_entropy', rpn_cross_entropy)
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tf.summary.scalar('rpn_loss_box', rpn_loss_box)
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return total_loss, model_loss, rpn_cross_entropy, rpn_loss_box
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