Finish backbone.darknet53() for YOLOv3. #360

test/TensorFlowNET.Examples/ImageProcessing/YOLO/YOLOv3.cs

@@ -58,6 +58,18 @@ namespace TensorFlowNET.Examples.ImageProcessing.YOLO
         {
             Tensor route_1, route_2;
             (route_1, route_2, input_data) = backbone.darknet53(input_data, trainable);
+            input_data = common.convolutional(input_data, new[] { 1, 1, 1024, 512 }, trainable, "conv52");
+            input_data = common.convolutional(input_data, new[] { 3, 3, 512, 1024 }, trainable, "conv53");
+            input_data = common.convolutional(input_data, new[] { 1, 1, 1024, 512 }, trainable, "conv54");
+            input_data = common.convolutional(input_data, new[] { 3, 3, 512, 1024 }, trainable, "conv55");
+            input_data = common.convolutional(input_data, new[] { 1, 1, 1024, 512 }, trainable, "conv56");
+
+            var conv_lobj_branch = common.convolutional(input_data, new[] { 3, 3, 512, 1024 }, trainable, name: "conv_lobj_branch");
+            var conv_lbbox = common.convolutional(conv_lobj_branch, new[] { 1, 1, 1024, 3 * (num_class + 5) },
+                                          trainable: trainable, name: "conv_lbbox", activate: false, bn: false);
+
+            input_data = common.convolutional(input_data, new[] { 1, 1, 512, 256 }, trainable, "conv57");
+            input_data = common.upsample(input_data, name: "upsample0", method: upsample_method);
 
             return (conv_lbbox, conv_mbbox, conv_sbbox);
         }

test/TensorFlowNET.Examples/ImageProcessing/YOLO/backbone.cs

@@ -18,8 +18,31 @@ namespace TensorFlowNET.Examples.ImageProcessing.YOLO
                 foreach (var i in range(1))
                     input_data = common.residual_block(input_data, 64, 32, 64, trainable: trainable, name: $"residual{i + 0}");
 
+                input_data = common.convolutional(input_data, filters_shape: new[] { 3, 3, 64, 128 },
+                                          trainable: trainable, name: "conv4", downsample: true);
+
+                foreach (var i in range(2))
+                    input_data = common.residual_block(input_data, 128, 64, 128, trainable: trainable, name: $"residual{i + 1}");
+
+                input_data = common.convolutional(input_data, filters_shape: new[] { 3, 3, 128, 256 },
+                                          trainable: trainable, name: "conv9", downsample: true);
+
+                foreach (var i in range(8))
+                    input_data = common.residual_block(input_data, 256, 128, 256, trainable: trainable, name: $"residual{i + 3}");
+
                 var route_1 = input_data;
+                input_data = common.convolutional(input_data, filters_shape: new int[] { 3, 3, 256, 512 },
+                                          trainable: trainable, name: "conv26", downsample: true);
+
+                foreach (var i in range(8))
+                    input_data = common.residual_block(input_data, 512, 256, 512, trainable: trainable, name: $"residual{i + 11}");
+
                 var route_2 = input_data;
+                input_data = common.convolutional(input_data, filters_shape: new[] { 3, 3, 512, 1024 },
+                                          trainable: trainable, name: "conv43", downsample: true);
+
+                foreach (var i in range(4))
+                    input_data = common.residual_block(input_data, 1024, 512, 1024, trainable: trainable, name: $"residual{i + 19}");
 
                 return (route_1, route_2, input_data);
             });

test/TensorFlowNET.Examples/ImageProcessing/YOLO/common.cs

@@ -1,5 +1,7 @@
 using System;
 using System.Collections.Generic;
+using System.Diagnostics;
+using System.Linq;
 using System.Text;
 using Tensorflow;
 using static Tensorflow.Binding;
@@ -22,7 +24,8 @@ namespace TensorFlowNET.Examples.ImageProcessing.YOLO
                     (int pad_h, int pad_w) = ((int)Math.Floor((filters_shape[0] - 2) / 2.0f) + 1, (int)Math.Floor((filters_shape[1] - 2) / 2.0f) + 1);
                     var paddings = tf.constant(new int[,] { { 0, 0 }, { pad_h, pad_h }, { pad_w, pad_w }, { 0, 0 } });
                     input_data = tf.pad(input_data, paddings, "CONSTANT");
-                    throw new NotImplementedException("");
+                    strides = new[] { 1, 2, 2, 1 };
+                    padding = "VALID";
                 }
                 else
                 {
@@ -44,7 +47,9 @@ namespace TensorFlowNET.Examples.ImageProcessing.YOLO
                 }
                 else
                 {
-                    throw new NotImplementedException("");
+                    var bias = tf.get_variable(name: "bias", shape: filters_shape.Last(), trainable: true,
+                       dtype: tf.float32, initializer: tf.constant_initializer(0.0f));
+                    conv = tf.nn.bias_add(conv, bias);
                 }
 
                 if (activate)
@@ -54,6 +59,22 @@ namespace TensorFlowNET.Examples.ImageProcessing.YOLO
             });
         }
 
+        public static Tensor upsample(Tensor input_data, string name, string method = "deconv")
+        {
+            Debug.Assert(new[] { "resize", "deconv" }.Contains(method));
+            Tensor output = null;
+            if (method == "resize")
+            {
+
+            }
+            else if(method == "deconv")
+            {
+
+            }
+
+            return output;
+        }
+
         public static Tensor residual_block(Tensor input_data, int input_channel, int filter_num1, 
             int filter_num2, Tensor trainable, string name)
         {