User Profile

Coremltools: 6.2.0 When I run coreml model in python result is good: {'var_840': array([[-8.15439941e+02, 2.88793579e+02, -3.83110474e+02, -8.95208740e+02, -3.53131561e+02, -3.65339783e+02, -4.94590851e+02, 6.24686813e+01, -5.92614822e+01, -9.67470627e+01, -4.30247498e+02, -9.27047348e+01, 2.19661942e+01, -2.96691345e+02, -4.26566772e+02........ But when I run on xcode so result look like: [-inf,inf,nan,-inf,nan,nan,nan,nan,nan,-inf,-inf,-inf,-inf,-inf,-inf,nan,-inf,-inf,nan,-inf,nan,nan,-inf,nan,-inf,-inf,-inf,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,-inf,nan,nan,nan,nan,-inf,nan,-inf ....... Step1: Convert Resnet50 to coreml: import torch import torchvision # Load a pre-trained version of MobileNetV2 model. torch_model = torchvision.models.resnet50(pretrained=True) # Set the model in evaluation mode. torch_model.eval() # Trace the model with random data. example_input = torch.rand(1, 3, 224, 224) traced_model = torch.jit.trace(torch_model, example_input) out = traced_model(example_input) # Download class labels in ImageNetLabel.txt. # Set the image scale and bias for input image preprocessing. import coremltools as ct image_input = ct.ImageType(shape=example_input.shape, ) # Using image_input in the inputs parameter: # Convert to Core ML using the Unified Conversion API. model = ct.convert( traced_model, inputs=[image_input], compute_units=ct.ComputeUnit.CPU_ONLY, ) # Save the converted model. model.save("resnettest.mlmodel") # Print a confirmation message. print('model converted and saved') Step2: Test model coreml in python: import coremltools as ct import PIL import numpy as np # Load the model model = ct.models.MLModel('/Users/ngoclh/Downloads/resnettest.mlmodel') print(model) img_path = "/Users/ngoclh/gitlocal/DetectCirtochApp/DetectCirtochApp/resources/image.jpg" img = PIL.Image.open(img_path) img = img.resize([224, 224], PIL.Image.ANTIALIAS) coreml_out_dict = model.predict({"input_1" : img}) print(coreml_out_dict) Step3: Test coreml model in Xcode: func getFeature() { do { let deepLab = try VGG_emb.init() //mobilenet_emb.init()//cirtorch_emb.init() let image = UIImage(named: "image.jpg") let pixBuf = image!.pixelBuffer(width: 224, height: 224)! guard let output = try? deepLab.prediction(input_1: pixBuf) else { return } let names = output.featureNames print("ngoc names: ", names) for name in names { let feature = output.featureValue(for: name) print("ngoc feature: ", feature) } } catch { print(error) } }