Matlab doesn't import standard python modules, when importing a custom python module

I'm importing a python module by using py.importlib.import_modulndule). Now at the top of my my_module.py I'm importing a few python modules necessary for my functions. Despite having changed my default python version within Matlab to the correct virtual environment, Matlab fails to import the modules. Here is my python script

 __future__ import print_function, division
import os
import sys
import time
import argparse
import numpy as np
import torch
import torchvision
import random
import itertools
import torchvision.models as models
import matplotlib
matplotlib.use('agg')
from matplotlib.pyplot import imread
from torchvision import datasets, transforms

def get_patch(img, x_center, y_centre, legth):
  #Load image

    image=imread(img)

    return image[x_center:x_center+legth,y_centre:y_centre+legth,:]

    model_path='./PytorchModel/checkpoint_epoch_60.pt'

    model=Encoder('resnet18',False)
    model.load_state_dict(torch.load(model_path,map_location='cpu'))

    Transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])])
    image1=get_patch(im1, center1[0], center1[1], 100)
    image1=Transform(image1)
    image2=get_patch(im2, center2[0], center2[1], 100)
    image2=Transform(image2)

    data=torch.stack((image1,image2),dim=0)

    model.eval()

    with torch.no_grad():

        vectors=model(data)
        y_coord= vectors[:,1] #Extract y coordinates
        x_coord = vectors[:,0] #Extract x coordinates

        angles=np.degrees(torch.atan2(y_coord,x_coord).numpy())
        relative_angle=convert_to_convetion(angles[1]-angles[0])

    return relative_angle

def convert_to_convetion(input):
  """
  Coverts all anlges to convecntion used by atan2
  """

    input[input<180]=input[input<180]+360
    input[input>180]=input[input>180]-360

    return input

class Encoder(nn.Module):
  """
  Encoder to 2-dimnesional space
  """
  def __init__(self,model_type,pretrained):
      super(Encoder, self).__init__()

        if model_type=='resnet18':
            pretrained=models.resnet18(pretrained=pretrained)
        elif model_type=='resnet34':
            pretrained=models.resnet34(pretrained=pretrained)
        elif model_type=='resnet50':
            pretrained=models.resnet50(pretrained=pretrained)
  #Replace maxpool layer with convolutional layers
        pretrained.maxpool=nn.Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)

        #Replace AvgPool2d witth AdaptiveAvgPool2d

        pretrained.avgpool=nn.AdaptiveAvgPool2d(1) 

        #Remove the last  fc layer anx call in encoder

        self.encoder= nn.Sequential(*list(pretrained.children())[:-1], 
                     nn.ReLU(),
                     nn.Conv2d(512,256,1),
                     nn.ReLU(),
                     nn.Conv2d(256,2,1)) 

    def forward(self,x):
        return self.encoder(x)

And here is the error I get:

Error using _init_><module> (line 80) Python Error: RuntimeError: stoi: no conversion

Error in alexnet><module> (line 1) import torch.nn as nn

Error in _init_><module> (line 1) from .alexnet import *

Error in _init_><module> (line 1) from torchvision import models

Error in my_module><module> (line 10) import torchvision

Error in importlib_call_with_frames_removed (line 219)

Error in <frozen importlib>exec_module (line 678)

Error in importlib_load_unlocked (line 665)

Error in importlib_find_and_load_unlocked (line 955)

Error in importlib_find_and_load (line 971)

Error in importlib_gcd_import (line 994)

Error in _init_>import_module (line 126) return _bootstrap._gcd_import(name[level:], package, level)

[Sachin Meena]

Which system are you using? I am working on Windows 10, MATLAB R2018a, Python 3.7 64 bit. I had to make a few changes to your imports, but I am able to import this module in MATLAB. I had to change the following 2 import statements.

from __future__ import print_fucntion, division
import torch.nn as nn

Answer 1

[Sean de Wolski]

Please also note that the ability to use resnet and train/transfer deep networks is available in MATLAB in the Neural Network/Deep Learning Toolbox.

This could save you from having to interact with any of the python pieces.