Converted Model Preview Issues in Xcode

Hello!

I have a TrackNet model that I have converted to CoreML (.mlpackage) using coremltools, and the conversion process appears to go smoothly as I get the .mlpackage file I am looking for with the weights and model.mlmodel file in the folder. However, when I drag it into Xcode, it just shows up as 4 script tags (as pictured) instead of the model "interface" that is typically expected. I initially was concerned that my model was not compatible with CoreML, but upon logging the conversions, everything seems to be converted properly.

I have some code that may be relevant in debugging this issue: How I use the model:

model = BallTrackerNet() # this is the model architecture which will be referenced later
device = self.device # cpu
model.load_state_dict(torch.load("models/balltrackerbest.pt", map_location=device)) # balltrackerbest is the weights
model = model.to(device)
model.eval()

Here is the BallTrackerNet() model itself:

import torch.nn as nn
import torch
 
class ConvBlock(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=3, pad=1, stride=1, bias=True):
        super().__init__()
        self.block = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=pad, bias=bias),
            nn.ReLU(),
            nn.BatchNorm2d(out_channels)
        )
 
    def forward(self, x):
        return self.block(x)
    
class BallTrackerNet(nn.Module):
    def __init__(self, out_channels=256):
        super().__init__()
        self.out_channels = out_channels
 
        self.conv1 = ConvBlock(in_channels=9, out_channels=64)
        self.conv2 = ConvBlock(in_channels=64, out_channels=64)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3 = ConvBlock(in_channels=64, out_channels=128)
        self.conv4 = ConvBlock(in_channels=128, out_channels=128)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv5 = ConvBlock(in_channels=128, out_channels=256)
        self.conv6 = ConvBlock(in_channels=256, out_channels=256)
        self.conv7 = ConvBlock(in_channels=256, out_channels=256)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv8 = ConvBlock(in_channels=256, out_channels=512)
        self.conv9 = ConvBlock(in_channels=512, out_channels=512)
        self.conv10 = ConvBlock(in_channels=512, out_channels=512)
        self.ups1 = nn.Upsample(scale_factor=2)
        self.conv11 = ConvBlock(in_channels=512, out_channels=256)
        self.conv12 = ConvBlock(in_channels=256, out_channels=256)
        self.conv13 = ConvBlock(in_channels=256, out_channels=256)
        self.ups2 = nn.Upsample(scale_factor=2)
        self.conv14 = ConvBlock(in_channels=256, out_channels=128)
        self.conv15 = ConvBlock(in_channels=128, out_channels=128)
        self.ups3 = nn.Upsample(scale_factor=2)
        self.conv16 = ConvBlock(in_channels=128, out_channels=64)
        self.conv17 = ConvBlock(in_channels=64, out_channels=64)
        self.conv18 = ConvBlock(in_channels=64, out_channels=self.out_channels)
 
        self.softmax = nn.Softmax(dim=1)
        self._init_weights()
                  
    def forward(self, x, testing=False): 
        batch_size = x.size(0)
        x = self.conv1(x)
        x = self.conv2(x)    
        x = self.pool1(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.pool2(x)
        x = self.conv5(x)
        x = self.conv6(x)
        x = self.conv7(x)
        x = self.pool3(x)
        x = self.conv8(x)
        x = self.conv9(x)
        x = self.conv10(x)
        x = self.ups1(x)
        x = self.conv11(x)
        x = self.conv12(x)
        x = self.conv13(x)
        x = self.ups2(x)
        x = self.conv14(x)
        x = self.conv15(x)
        x = self.ups3(x)
        x = self.conv16(x)
        x = self.conv17(x)
        x = self.conv18(x)
        # x = self.softmax(x)
        out = x.reshape(batch_size, self.out_channels, -1)
        if testing:
            out = self.softmax(out)
        return out                       
    
    def _init_weights(self):
        for module in self.modules():
            if isinstance(module, nn.Conv2d):
                nn.init.uniform_(module.weight, -0.05, 0.05)
                if module.bias is not None:
                    nn.init.constant_(module.bias, 0)
 
            elif isinstance(module, nn.BatchNorm2d):
                nn.init.constant_(module.weight, 1)
                nn.init.constant_(module.bias, 0)

Here is also the meta data of my model:

[
  {
    "metadataOutputVersion" : "3.0",
    "storagePrecision" : "Float16",
    "outputSchema" : [
      {
        "hasShapeFlexibility" : "0",
        "isOptional" : "0",
        "dataType" : "Float32",
        "formattedType" : "MultiArray (Float32 1 × 256 × 230400)",
        "shortDescription" : "",
        "shape" : "[1, 256, 230400]",
        "name" : "var_462",
        "type" : "MultiArray"
      }
    ],
    "modelParameters" : [
 
    ],
    "specificationVersion" : 6,
    "mlProgramOperationTypeHistogram" : {
      "Cast" : 2,
      "Conv" : 18,
      "Relu" : 18,
      "BatchNorm" : 18,
      "Reshape" : 1,
      "UpsampleNearestNeighbor" : 3,
      "MaxPool" : 3
    },
    "computePrecision" : "Mixed (Float16, Float32, Int32)",
    "isUpdatable" : "0",
    "availability" : {
      "macOS" : "12.0",
      "tvOS" : "15.0",
      "visionOS" : "1.0",
      "watchOS" : "8.0",
      "iOS" : "15.0",
      "macCatalyst" : "15.0"
    },
    "modelType" : {
      "name" : "MLModelType_mlProgram"
    },
    "userDefinedMetadata" : {
      "com.github.apple.coremltools.source_dialect" : "TorchScript",
      "com.github.apple.coremltools.source" : "torch==2.5.1",
      "com.github.apple.coremltools.version" : "8.1"
    },
    "inputSchema" : [
      {
        "hasShapeFlexibility" : "0",
        "isOptional" : "0",
        "dataType" : "Float32",
        "formattedType" : "MultiArray (Float32 1 × 9 × 360 × 640)",
        "shortDescription" : "",
        "shape" : "[1, 9, 360, 640]",
        "name" : "input_frames",
        "type" : "MultiArray"
      }
    ],
    "generatedClassName" : "BallTracker",
    "method" : "predict"
  }
]

I have been struggling with this conversion for almost 2 weeks now so any help, ideas or pointers would be greatly appreciated! Let me know if any other information would be helpful to see as well.

Thanks!

Michael