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I specifically give the model the shape of (1, 3, 1024, 1024), but for some reason, CoreML thinks it's 2 channels instead of 3. The pytorch model is based on this - LINK The "local.pth" model to be specific. My CoreML conversion code is attached below. #from networks.drn_seg import DRNSeg import coremltools as ct import coremltools.proto.FeatureTypes_pb2 as ft import io from PIL import Image from torchvision import transforms import math import torch import torch.nn as nn from networks.drn import drn_c_26 import torchvision from torchvision.io import read_image def fill_up_weights(up): w = up.weight.data f = math.ceil(w.size(2) / 2) c = (2 * f - 1 - f % 2) / (2. * f) for i in range(w.size(2)): for j in range(w.size(3)): w[0, 0, i, j] = \ (1 - math.fabs(i / f - c)) * (1 - math.fabs(j / f - c)) for c in range(1, w.size(0)): w[c, 0, :, :] = w[0, 0, :, :] class DRNSeg(nn.Module): def __init__(self): super(DRNSeg, self).__init__() classes=2 pretrained_drn=None pretrained_model=None use_torch_up=False model = drn_c_26(pretrained=pretrained_drn) self.base = nn.Sequential(*list(model.children())[:-2]) if pretrained_model: self.load_pretrained(pretrained_model) self.seg = nn.Conv2d(model.out_dim, classes, kernel_size=1, bias=True) m = self.seg n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) m.bias.data.zero_() if use_torch_up: self.up = nn.UpsamplingBilinear2d(scale_factor=8) else: up = nn.ConvTranspose2d(classes, classes, 16, stride=8, padding=4, output_padding=0, groups=classes, bias=False) fill_up_weights(up) up.weight.requires_grad = False self.up = up def forward(self, x): x = self.base(x) x = self.seg(x) y = self.up(x) return y def optim_parameters(self, memo=None): for param in self.base.parameters(): yield param for param in self.seg.parameters(): yield param def load_pretrained(self, pretrained_model): print("loading the pretrained drn model from %s" % pretrained_model) state_dict = torch.load(pretrained_model, map_location='cpu') if hasattr(state_dict, '_metadata'): del state_dict._metadata # filter out unnecessary keys pretrained_dict = state_dict['model'] pretrained_dict = {k[5:]: v for k, v in pretrained_dict.items() if k.split('.')[0] == 'base'} # load the pretrained state dict self.base.load_state_dict(pretrained_dict) #img = read_image('test.jpg') model_path = ("modelphotoshop.pth") device = 'cpu' model_flow = DRNSeg() model_flow.load_state_dict(torch.load(model_path, map_location=device)) model_flow.to(device) model_flow.eval() example_input = torch.randn([1,3,1024,1024]) traced_model = torch.jit.trace(model_flow, example_input) traced_model.save("modelphotoshop.pt") shape = (1,3, 1024, 1024) mlmodel = ct.convert(traced_model, convert_to="mlprogram", inputs=[ct.ImageType(name="colorImage", shape=shape, color_layout=ct.colorlayout.RGB,)], outputs=[ct.ImageType(name="colorOutput", color_layout=ct.colorlayout.RGB,)], ) mlmodel.save("Model.mlmodel")

HI everyone, is there like a sample project with a sound classifier model I can use? or is there a tutorial somewhere of building a sound classifier app? Thanks