public Graph BuildGraph()
{
var graph = new Graph().as_default();
tf_with(tf.name_scope("define_input"), scope =>
{
input_data = tf.placeholder(dtype: tf.float32, name: "input_data");
label_sbbox = tf.placeholder(dtype: tf.float32, name: "label_sbbox");
label_mbbox = tf.placeholder(dtype: tf.float32, name: "label_mbbox");
label_lbbox = tf.placeholder(dtype: tf.float32, name: "label_lbbox");
true_sbboxes = tf.placeholder(dtype: tf.float32, name: "sbboxes");
true_mbboxes = tf.placeholder(dtype: tf.float32, name: "mbboxes");
true_lbboxes = tf.placeholder(dtype: tf.float32, name: "lbboxes");
trainable = tf.placeholder(dtype: tf.@bool, name: "training");
});
tf_with(tf.name_scope("define_loss"), scope =>
{
model = new YOLOv3(cfg, input_data, trainable);
net_var = tf.global_variables();
(giou_loss, conf_loss, prob_loss) = model.compute_loss(
label_sbbox, label_mbbox, label_lbbox,
true_sbboxes, true_mbboxes, true_lbboxes);
});
tf_with(tf.name_scope("define_weight_decay"), scope =>
{
var moving_ave = tf.train.ExponentialMovingAverage(moving_ave_decay).apply((RefVariable[])tf.trainable_variables());
});
return(graph);
}
public Graph BuildGraph()
{
var graph = new Graph().as_default();
tf_with(tf.name_scope("define_input"), scope =>
{
input_data = tf.placeholder(dtype: tf.float32, name: "input_data");
label_sbbox = tf.placeholder(dtype: tf.float32, name: "label_sbbox");
label_mbbox = tf.placeholder(dtype: tf.float32, name: "label_mbbox");
label_lbbox = tf.placeholder(dtype: tf.float32, name: "label_lbbox");
true_sbboxes = tf.placeholder(dtype: tf.float32, name: "sbboxes");
true_mbboxes = tf.placeholder(dtype: tf.float32, name: "mbboxes");
true_lbboxes = tf.placeholder(dtype: tf.float32, name: "lbboxes");
trainable = tf.placeholder(dtype: tf.@bool, name: "training");
});
tf_with(tf.name_scope("define_loss"), scope =>
{
model = new YOLOv3(cfg, input_data, trainable);
net_var = tf.global_variables();
(giou_loss, conf_loss, prob_loss) = model.compute_loss(
label_sbbox, label_mbbox, label_lbbox,
true_sbboxes, true_mbboxes, true_lbboxes);
loss = giou_loss + conf_loss + prob_loss;
});
Tensor global_step_update = null;
tf_with(tf.name_scope("learn_rate"), scope =>
{
global_step = tf.Variable(1.0, dtype: tf.float64, trainable: false, name: "global_step");
var warmup_steps = tf.constant(warmup_periods * steps_per_period,
dtype: tf.float64, name: "warmup_steps");
var train_steps = tf.constant((first_stage_epochs + second_stage_epochs) * steps_per_period,
dtype: tf.float64, name: "train_steps");
learn_rate = tf.cond(
pred: global_step < warmup_steps,
true_fn: delegate
{
return(global_step / warmup_steps * learn_rate_init);
},
false_fn: delegate
{
return(learn_rate_end + 0.5 * (learn_rate_init - learn_rate_end) *
(1 + tf.cos(
(global_step - warmup_steps) / (train_steps - warmup_steps) * Math.PI)));
}
);
global_step_update = tf.assign_add(global_step, 1.0f);
});
Operation moving_ave = null;
tf_with(tf.name_scope("define_weight_decay"), scope =>
{
var emv = tf.train.ExponentialMovingAverage(moving_ave_decay);
var vars = tf.trainable_variables().Select(x => (RefVariable)x).ToArray();
moving_ave = emv.apply(vars);
});
tf_with(tf.name_scope("define_first_stage_train"), scope =>
{
});
return(graph);
}
public Graph BuildGraph()
{
var graph = new Graph().as_default();
tf_with(tf.name_scope("define_input"), scope =>
{
input_data = tf.placeholder(dtype: tf.float32, name: "input_data");
label_sbbox = tf.placeholder(dtype: tf.float32, name: "label_sbbox");
label_mbbox = tf.placeholder(dtype: tf.float32, name: "label_mbbox");
label_lbbox = tf.placeholder(dtype: tf.float32, name: "label_lbbox");
true_sbboxes = tf.placeholder(dtype: tf.float32, name: "sbboxes");
true_mbboxes = tf.placeholder(dtype: tf.float32, name: "mbboxes");
true_lbboxes = tf.placeholder(dtype: tf.float32, name: "lbboxes");
trainable = tf.placeholder(dtype: tf.@bool, name: "training");
});
tf_with(tf.name_scope("define_loss"), scope =>
{
model = new YOLOv3(cfg, input_data, trainable);
net_var = tf.global_variables();
(giou_loss, conf_loss, prob_loss) = model.compute_loss(
label_sbbox, label_mbbox, label_lbbox,
true_sbboxes, true_mbboxes, true_lbboxes);
loss = giou_loss + conf_loss + prob_loss;
});
Tensor global_step_update = null;
tf_with(tf.name_scope("learn_rate"), scope =>
{
global_step = tf.Variable(1.0, dtype: tf.float64, trainable: false, name: "global_step");
var warmup_steps = tf.constant(warmup_periods * steps_per_period,
dtype: tf.float64, name: "warmup_steps");
var train_steps = tf.constant((first_stage_epochs + second_stage_epochs) * steps_per_period,
dtype: tf.float64, name: "train_steps");
learn_rate = tf.cond(
pred: global_step < warmup_steps,
true_fn: delegate
{
return(global_step / warmup_steps * learn_rate_init);
},
false_fn: delegate
{
return(learn_rate_end + 0.5 * (learn_rate_init - learn_rate_end) *
(1 + tf.cos(
(global_step - warmup_steps) / (train_steps - warmup_steps) * Math.PI)));
}
);
global_step_update = tf.assign_add(global_step, 1.0f);
});
Operation moving_ave = null;
tf_with(tf.name_scope("define_weight_decay"), scope =>
{
var emv = tf.train.ExponentialMovingAverage(moving_ave_decay);
var vars = tf.trainable_variables().Select(x => (RefVariable)x).ToArray();
moving_ave = emv.apply(vars);
});
tf_with(tf.name_scope("define_first_stage_train"), scope =>
{
first_stage_trainable_var_list = new List <RefVariable>();
foreach (var var in tf.trainable_variables())
{
var var_name = var.op.name;
var var_name_mess = var_name.Split('/');
if (new[] { "conv_sbbox", "conv_mbbox", "conv_lbbox" }.Contains(var_name_mess[0]))
{
first_stage_trainable_var_list.Add(var as RefVariable);
}
}
var adam = tf.train.AdamOptimizer(learn_rate);
var first_stage_optimizer = adam.minimize(loss, var_list: first_stage_trainable_var_list);
tf_with(tf.control_dependencies(tf.get_collection <Operation>(tf.GraphKeys.UPDATE_OPS).ToArray()), delegate
{
tf_with(tf.control_dependencies(new ITensorOrOperation[] { first_stage_optimizer, global_step_update }), delegate
{
tf_with(tf.control_dependencies(new[] { moving_ave }), delegate
{
train_op_with_frozen_variables = tf.no_op();
});
});
});
});
tf_with(tf.name_scope("define_second_stage_train"), delegate
{
var second_stage_trainable_var_list = tf.trainable_variables().Select(x => x as RefVariable).ToList();
var adam = tf.train.AdamOptimizer(learn_rate);
var second_stage_optimizer = adam.minimize(loss, var_list: second_stage_trainable_var_list);
tf_with(tf.control_dependencies(tf.get_collection <Operation>(tf.GraphKeys.UPDATE_OPS).ToArray()), delegate
{
tf_with(tf.control_dependencies(new ITensorOrOperation[] { second_stage_optimizer, global_step_update }), delegate
{
tf_with(tf.control_dependencies(new[] { moving_ave }), delegate
{
train_op_with_all_variables = tf.no_op();
});
});
});
});
tf_with(tf.name_scope("loader_and_saver"), delegate
{
loader = tf.train.Saver(net_var);
saver = tf.train.Saver(tf.global_variables(), max_to_keep: 10);
});
tf_with(tf.name_scope("summary"), delegate
{
tf.summary.scalar("learn_rate", learn_rate);
tf.summary.scalar("giou_loss", giou_loss);
tf.summary.scalar("conf_loss", conf_loss);
tf.summary.scalar("prob_loss", prob_loss);
tf.summary.scalar("total_loss", loss);
});
return(graph);
}