Backport ONNX fix in #4475 to release branch (#4533)

Backport changes in #4475 to release branch to allow using ML-Agents 1.0.x with Barracuda 1.1.x.

com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs

             var heightBp = shape[0];
             var widthBp = shape[1];
             var pixelBp = shape[2];
-            var heightT = tensorProxy.shape[1];
-            var widthT = tensorProxy.shape[2];
-            var pixelT = tensorProxy.shape[3];
+            var heightT = tensorProxy.Height;
+            var widthT = tensorProxy.Width;
+            var pixelT = tensorProxy.Channels;
             if ((widthBp != widthT) || (heightBp != heightT) || (pixelBp != pixelT))
             {
                 return $"The visual Observation of the model does not match. " +

com.unity.ml-agents/Runtime/Inference/TensorProxy.cs

         public Type DataType => k_TypeMap[valueType];
         public long[] shape;
         public Tensor data;
+
+        public long Height
+        {
+            get { return shape.Length == 4 ? shape[1] : shape[5]; }
+        }
+
+        public long Width
+        {
+            get { return shape.Length == 4 ? shape[2] : shape[6]; }
+        }
+
+        public long Channels
+        {
+            get { return shape.Length == 4 ? shape[3] : shape[7]; }
+        }
     }
 
     internal static class TensorUtils
             tensor.data?.Dispose();
             tensor.shape[0] = batch;
 
-            if (tensor.shape.Length == 4)
+            if (tensor.shape.Length == 4 || tensor.shape.Length == 8)
-                        (int)tensor.shape[1],
-                        (int)tensor.shape[2],
-                        (int)tensor.shape[3]));
+                        (int)tensor.Height,
+                        (int)tensor.Width,
+                        (int)tensor.Channels));
             }
             else
             {

com.unity.ml-agents/Tests/Editor/TensorUtilsTest.cs

 {
     public class TensorUtilsTest
     {
+        [TestCase(4, TestName = "TestResizeTensor_4D")]
+        public void TestResizeTensor(int dimension)
+        {
+            var alloc = new TensorCachingAllocator();
+            var height = 64;
+            var width = 84;
+            var channels = 3;
+
+            // Set shape to {1, ..., height, width, channels}
+            // For 8D, the ... are all 1's
+            var shape = new long[dimension];
+            for (var i = 0; i < dimension; i++)
+            {
+                shape[i] = 1;
+            }
+
+            shape[dimension - 3] = height;
+            shape[dimension - 2] = width;
+            shape[dimension - 1] = channels;
+
+            var intShape = new int[dimension];
+            for (var i = 0; i < dimension; i++)
+            {
+                intShape[i] = (int)shape[i];
+            }
+
+            var tensorProxy = new TensorProxy
+            {
+                valueType = TensorProxy.TensorType.Integer,
+                data = new Tensor(intShape),
+                shape = shape,
+            };
+
+            // These should be invariant after the resize.
+            Assert.AreEqual(height, tensorProxy.data.shape.height);
+            Assert.AreEqual(width, tensorProxy.data.shape.width);
+            Assert.AreEqual(channels, tensorProxy.data.shape.channels);
+
+            TensorUtils.ResizeTensor(tensorProxy, 42, alloc);
+
+            Assert.AreEqual(height, tensorProxy.shape[dimension - 3]);
+            Assert.AreEqual(width, tensorProxy.shape[dimension - 2]);
+            Assert.AreEqual(channels, tensorProxy.shape[dimension - 1]);
+
+            Assert.AreEqual(height, tensorProxy.data.shape.height);
+            Assert.AreEqual(width, tensorProxy.data.shape.width);
+            Assert.AreEqual(channels, tensorProxy.data.shape.channels);
+
+            alloc.Dispose();
+        }
+
         [Test]
         public void RandomNormalTestTensorInt()
         {