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
