nn.ModuleList loses None objects inside it after scripting

WaterKnight: <h2>
<img src="https://discuss.pytorch.org/images/emoji/apple/bug.png?v=9" title=":bug:" class="emoji" alt=":bug:"> Bug</h2>

<h2>To Reproduce</h2>
<p>Executing torch.jit.script over my model is working however it returns a model that fails at runtime.</p>
<p>Looking deeply the nn.ModuleList is loosing None elements from the Modulelist.</p>
<p>Here, above I attach a code for reproducing the error:</p>
<pre><code class="lang-auto">import os
import sys
import torch.nn as nn
import torch.nn.functional as F
import torch
from torchvision import transforms
from PIL import Image


class TestBlock(nn.Module):
    def __init__(self):
        super(TestBlock, self).__init__()
        
        layers = []
        layers.append(None)
        layers.append(None)
        layers.append(nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1,
                               bias=False))
        self.layer = nn.ModuleList(layers)
        
    def forward(self,x):
        for aux in self.layer:
            print("ENTER")
            if aux is not None:
                x = aux(x)
                print("Not None")
        return x
</code></pre>
<p>Creating model and tracing it:</p>
<pre><code class="lang-auto">model=TestBlock()
traced_cell=torch.jit.script(model)
</code></pre>
<p>Testing model with an image:</p>
<pre><code class="lang-auto">img = Image.open("test.png")

my_transforms = transforms.Compose([transforms.Resize((1002,1002)),
                                    transforms.ToTensor(),
                                    transforms.Normalize(
                                                        [0.485, 0.456, 0.406],
                                                        [0.229, 0.224, 0.225])])
img_input= my_transforms(img).unsqueeze(0).cpu()

res=model(img_input)
</code></pre>
<p>This outputs the next:</p>
<pre><code class="lang-auto">ENTER
ENTER
ENTER
Not None
</code></pre>
<p>Traced version output:</p>
<pre><code class="lang-auto">res=traced_cell(img_input)
</code></pre>
<pre><code class="lang-auto">ENTER
Not None
</code></pre>
<h2>Expected behavior</h2>
<p>Get same output as original model</p>

ptrblck: <p>You are currently trying to script a tensor in:</p>
<pre><code class="lang-python">traced_cell=torch.jit.script(aux)
</code></pre>
<p>so I assume you want to pass <code>model</code> instead to the method?</p>
<p>Try to narrow down the issue, as your current code contains more than 700 lines of code.</p>

WaterKnight: <p>Sorry, I paste the wrong code. I am scripting the model with torch.jit.script(model).</p>

WaterKnight: <aside class="quote no-group" data-username="ptrblck" data-post="2" data-topic="83578">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/48/1823_2.png" class="avatar"> ptrblck:</div>
<blockquote>
<p>Try to narrow down the issue, as your current code contains more than 700 lines of code.</p>
</blockquote>
</aside>
<p>I reduced the model to 15lines producing the same error. Look at it again pls <a class="mention" href="/u/ptrblck">@ptrblck</a></p>

ptrblck: <p>That’s great. Thanks for reducing the code.</p>
<p>I would assume it’s on purpose for the JIT to remove no-ops from the graph, as they won’t do anything.<br>
What’s your use case that you need these <code>None</code> objects in an <code>nn.ModuleList</code>?<br>
As a workaround you could probably just add <code>nn.Identity()</code> modules instead of <code>None</code>s.</p>

WaterKnight: <aside class="quote no-group" data-username="ptrblck" data-post="6" data-topic="83578">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/48/1823_2.png" class="avatar"> ptrblck:</div>
<blockquote>
<p>What’s your use case that you need these <code>None</code> objects in an <code>nn.ModuleList</code> ?</p>
</blockquote>
</aside>
<p>This is not my defined Arch, this arch is <a href="https://github.com/HRNet/HRNet-Semantic-Segmentation/blob/pytorch-v1.1/lib/models/seg_hrnet.py" rel="noopener nofollow ugc">Microsoft HRNet</a>.</p>
<p>But if you look in his code they are using it to create a new list:</p>
<pre><code class="lang-auto">y_list = self.stage2(x_list)

        x_list = []
        for i in range(self.transition2):
            if self.transition2[i] is not None:
                x_list.append(self.transition2[i](y_list[-1]))
            else:
                x_list.append(y_list[i])
</code></pre>
<p><code>self.transition2</code> is the module list containing <code>None</code> objects</p>
<p>With you workaround C should change <code>is not None</code> with <code>not is isinstance(layer, nn.Identity())</code><br>
I am going to try your work-around and let you know!</p>

WaterKnight: <p>The previous approach work.</p>
<p>However, the model works fast with first image. If we pass an image again it never ends! In addition, TorchScript model is also slower at first image.</p>

ptrblck: <p>Thanks for the update and the code snippet.<br>
The <code>nn.Identity()</code> approach might work, but looks quite hacky given the new code snippet.</p>
<p>However, I’m not familiar with the model, so don’t know which approach would be best to make is scriptable and would suggest to create an issue in their GitHub.</p>
<aside class="quote no-group" data-username="WaterKnight" data-post="8" data-topic="83578">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/waterknight/48/54725_2.png" class="avatar"> WaterKnight:</div>
<blockquote>
<p>However, the model works fast with first image. If we pass an image again it never ends! In addition, TorchScript model is also slower at first image.</p>
</blockquote>
</aside>
<p>I understand that the eager model is working for a single iteration and hangs in the second one?<br>
While the scripted model is slower in the first iteration and works fine afterwards?</p>

WaterKnight: <aside class="quote no-group" data-username="ptrblck" data-post="9" data-topic="83578">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/48/1823_2.png" class="avatar"> ptrblck:</div>
<blockquote>
<p>I understand that the eager model is working for a single iteration and hangs in the second one?<br>
While the scripted model is slower in the first iteration and works fine afterwards?</p>
</blockquote>
</aside>
<p>The model works well in eager model. After scripting it,  the first iteration is like 6 seconds, second one around 3minutes and third one and go on like 1 second.</p>
<p>I think that it is being optimized. Is there any way of having pre optimizing in a Flask API Rest or disabling optimization? Or Saving optimized one so when it gets loaded is the optimized version?</p>

ptrblck: <p>You could try to use</p>
<pre><code class="lang-python">torch._C._jit_set_profiling_executor(False)
torch._C._jit_set_profiling_mode(False)
</code></pre>
<p>at the beginning of your script to disable the optimization.<br>
However, how if your Flask application is running longer than a couple of seconds, the startup time could probably be ignored.</p>

WaterKnight: <aside class="quote no-group" data-username="ptrblck" data-post="11" data-topic="83578">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/48/1823_2.png" class="avatar"> ptrblck:</div>
<blockquote>
<p>However, how if your Flask application is running longer than a couple of seconds, the startup time could probably be ignored.</p>
</blockquote>
</aside>
<p>I don’t understand it.</p>
<p>My Flask application has several models loaded. It makes inference over the same image with different models, the models names are given in a list.</p>

ptrblck: <p>Since the second iteration seems to run for 3 minutes, I would ask you to create an issue <a href="https://github.com/pytorch/pytorch/issues">here</a>, as it doesn’t seem right.</p>

ptrblck: <p>If the first iteration would be a warmup time (ignoring the 3 minutes, which seems to be a bug), then you would only pay the cost once. Every other time the prediction would use the optimized graph and should be fast.</p>

WaterKnight: <p>Ah okey. Thank you very much for the info and all your help in the forums!</p>
<p>What issue should I submit? Upload traced model or the model definition code?</p>
<p>I have an issue open for None objects dissapearing fron inside nn.ModuleList</p>

ptrblck: <p>If possible, a minimal code snippet, which is executable and shows the JIT behavior, where the second iteration takes 3 minutes, while the first one finishes in 6 seconds.</p>

WaterKnight: <p>I don’t know if I will be able to paste a minimal snippet. This post was created with the part of nn.ModuleList containing Nones.</p>
<p>I have tried to measure times again:</p>
<p>Withouth tracing:</p>
<pre><code class="lang-auto">CPU times: user 12.8 s, sys: 1.41 s, total: 14.2 s
Wall time: 2.32 s
</code></pre>
<p>Scripted First Iteration:</p>
<pre><code class="lang-auto">CPU times: user 15 s, sys: 1.77 s, total: 16.8 s
Wall time: 4.64 s
</code></pre>
<p>Scripted Second Iteration:</p>
<pre><code class="lang-auto">CPU times: user 5min 8s, sys: 1.14 s, total: 5min 9s
Wall time: 5min
</code></pre>
<p>Scripted Third Iteration:</p>
<pre><code class="lang-auto">CPU times: user 11 s, sys: 1.18 s, total: 12.2 s
Wall time: 2.02 s
</code></pre>

WaterKnight: <aside class="quote no-group" data-username="ptrblck" data-post="13" data-topic="83578">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/48/1823_2.png" class="avatar"> ptrblck:</div>
<blockquote>
<p>I would ask you to create an issue <a href="https://github.com/pytorch/pytorch/issues" rel="noopener nofollow ugc">here</a></p>
</blockquote>
</aside>
<p>Done.</p><aside class="onebox githubissue" data-onebox-src="https://github.com/pytorch/pytorch/issues/39438">
  <header class="source">

      <a href="https://github.com/pytorch/pytorch/issues/39438" target="_blank" rel="noopener nofollow ugc">github.com/pytorch/pytorch</a>
  </header>

  <article class="onebox-body">
    <div class="github-row">
  <div class="github-icon-container" title="Issue">
	  <svg width="60" height="60" class="github-icon" viewBox="0 0 14 16" aria-hidden="true"><path fill-rule="evenodd" d="M7 2.3c3.14 0 5.7 2.56 5.7 5.7s-2.56 5.7-5.7 5.7A5.71 5.71 0 0 1 1.3 8c0-3.14 2.56-5.7 5.7-5.7zM7 1C3.14 1 0 4.14 0 8s3.14 7 7 7 7-3.14 7-7-3.14-7-7-7zm1 3H6v5h2V4zm0 6H6v2h2v-2z"></path></svg>
  </div>

  <div class="github-info-container">
    <h4>
      <a href="https://github.com/pytorch/pytorch/issues/39438" target="_blank" rel="noopener nofollow ugc">[JIT] Scripted model second run cost 5 mins, after that 1-2 secs</a>
    </h4>

    <div class="github-info">
      <div class="date">
        opened <span class="discourse-local-date" data-format="ll" data-date="2020-06-03" data-time="10:44:06" data-timezone="UTC">10:44AM - 03 Jun 20 UTC</span>
      </div>

        <div class="date">
          closed <span class="discourse-local-date" data-format="ll" data-date="2020-06-10" data-time="21:43:57" data-timezone="UTC">09:43PM - 10 Jun 20 UTC</span>
        </div>

      <div class="user">
        <a href="https://github.com/WaterKnight1998" target="_blank" rel="noopener nofollow ugc">
          <img alt="WaterKnight1998" src="https://avatars.githubusercontent.com/u/41203448?v=4" class="onebox-avatar-inline" width="20" height="20">
          WaterKnight1998
        </a>
      </div>
    </div>

    <div class="labels">
        <span style="display:inline-block;margin-top:2px;background-color: #B8B8B8;padding: 2px;border-radius: 4px;color: #fff;margin-left: 3px;">
          oncall: jit
        </span>
        <span style="display:inline-block;margin-top:2px;background-color: #B8B8B8;padding: 2px;border-radius: 4px;color: #fff;margin-left: 3px;">
          triaged
        </span>
    </div>
  </div>
</div>

  <div class="github-row">
    <p class="github-body-container">## 🐛 Bug

This model uses an nn.ModuleList that used to contains `None` object<span class="show-more-container"><a href="" rel="noopener" class="show-more">…</a></span><span class="excerpt hidden">s. After scripting the model `None` disappear. I opened an issue for that error [here](https://github.com/pytorch/pytorch/issues/39309).

A workaround mentioned by @ptrblck was to use instead `nn.Identity()` instead of `None`. Owing to that fact I need to change `is not None` by `not isinstance(..., nn.Identity)`.

This solved the errors and the model gets converted to TorchScript now. However, the problem is that **second iteration is very slow, nearly 6 minutes**!

Here bellow i attach you the times:

Eager Mode: 
```
CPU times: user 12.8 s, sys: 1.41 s, total: 14.2 s
Wall time: 2.32 s
```

Scripted First Iteration:
```
CPU times: user 15 s, sys: 1.77 s, total: 16.8 s
Wall time: 4.64 s
```

Scripted Second Iteration:
```
CPU times: user 5min 8s, sys: 1.14 s, total: 5min 9s
Wall time: 5min
```

Scripted Third Iteration:
```
CPU times: user 11 s, sys: 1.18 s, total: 12.2 s
Wall time: 2.02 s
```
## To Reproduce

Here you have the model definition. It is very large, so jump to the end of the post if this doesn't interest you.
```
# this code is based on https://github.com/HRNet

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import logging
import functools

import numpy as np

import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F

from pathlib import Path


# For adding torch script support
from typing import List

BatchNorm2d = nn.BatchNorm2d
BN_MOMENTUM = 0.01
logger = logging.getLogger(__name__)

def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1,
                               bias=False)
        self.bn3 = BatchNorm2d(planes * self.expansion,
                               momentum=BN_MOMENTUM)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class HighResolutionModule(nn.Module):
    def __init__(self, num_branches, blocks, num_blocks, num_inchannels,
                 num_channels, fuse_method, multi_scale_output=True):
        super(HighResolutionModule, self).__init__()
        self._check_branches(
            num_branches, blocks, num_blocks, num_inchannels, num_channels)

        self.num_inchannels = num_inchannels
        self.fuse_method = fuse_method
        self.num_branches = num_branches

        self.multi_scale_output = multi_scale_output

        self.branches = self._make_branches(
            num_branches, blocks, num_blocks, num_channels)
        self.fuse_layers = self._make_fuse_layers()
        self.relu = nn.ReLU(inplace=True)

    def _check_branches(self, num_branches, blocks, num_blocks,
                        num_inchannels, num_channels):
        if num_branches != len(num_blocks):
            error_msg = 'NUM_BRANCHES({}) &lt;&gt; NUM_BLOCKS({})'.format(
                num_branches, len(num_blocks))
            logger.error(error_msg)
            raise ValueError(error_msg)

        if num_branches != len(num_channels):
            error_msg = 'NUM_BRANCHES({}) &lt;&gt; NUM_CHANNELS({})'.format(
                num_branches, len(num_channels))
            logger.error(error_msg)
            raise ValueError(error_msg)

        if num_branches != len(num_inchannels):
            error_msg = 'NUM_BRANCHES({}) &lt;&gt; NUM_INCHANNELS({})'.format(
                num_branches, len(num_inchannels))
            logger.error(error_msg)
            raise ValueError(error_msg)

    def _make_one_branch(self, branch_index, block, num_blocks, num_channels,
                         stride=1):
        downsample = None
        if stride != 1 or \
           self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.num_inchannels[branch_index],
                          num_channels[branch_index] * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                BatchNorm2d(num_channels[branch_index] * block.expansion,
                            momentum=BN_MOMENTUM),
            )

        layers = []
        layers.append(block(self.num_inchannels[branch_index],
                            num_channels[branch_index], stride, downsample))
        self.num_inchannels[branch_index] = \
            num_channels[branch_index] * block.expansion
        for i in range(1, num_blocks[branch_index]):
            layers.append(block(self.num_inchannels[branch_index],
                                num_channels[branch_index]))

        return nn.Sequential(*layers)

    def _make_branches(self, num_branches, block, num_blocks, num_channels):
        branches = []

        for i in range(num_branches):
            branches.append(
                self._make_one_branch(i, block, num_blocks, num_channels))

        return nn.ModuleList(branches)

    def _make_fuse_layers(self):
        if self.num_branches == 1:
            return None

        num_branches = self.num_branches
        num_inchannels = self.num_inchannels
        fuse_layers = []
        for i in range(num_branches if self.multi_scale_output else 1):
            fuse_layer = []
            for j in range(num_branches):
                if j &gt; i:
                    fuse_layer.append(nn.Sequential(
                        nn.Conv2d(num_inchannels[j],
                                  num_inchannels[i],
                                  1,
                                  1,
                                  0,
                                  bias=False),
                        BatchNorm2d(num_inchannels[i], momentum=BN_MOMENTUM)))
                elif j == i:
                    fuse_layer.append(nn.Identity())
                else:
                    conv3x3s = []
                    for k in range(i-j):
                        if k == i - j - 1:
                            num_outchannels_conv3x3 = num_inchannels[i]
                            conv3x3s.append(nn.Sequential(
                                nn.Conv2d(num_inchannels[j],
                                          num_outchannels_conv3x3,
                                          3, 2, 1, bias=False),
                                BatchNorm2d(num_outchannels_conv3x3, 
                                            momentum=BN_MOMENTUM)))
                        else:
                            num_outchannels_conv3x3 = num_inchannels[j]
                            conv3x3s.append(nn.Sequential(
                                nn.Conv2d(num_inchannels[j],
                                          num_outchannels_conv3x3,
                                          3, 2, 1, bias=False),
                                BatchNorm2d(num_outchannels_conv3x3,
                                            momentum=BN_MOMENTUM),
                                nn.ReLU(inplace=True)))
                    fuse_layer.append(nn.Sequential(*conv3x3s))
            fuse_layers.append(nn.ModuleList(fuse_layer))

        return nn.ModuleList(fuse_layers)

    def get_num_inchannels(self):
        return self.num_inchannels

    def forward(self, x: List[torch.Tensor]):
        if self.num_branches == 1:
            return [self.branches[0](x[0])]

        for i, branch in enumerate(self.branches):
            x[i] = branch(x[i])

        x_fuse = []
        for i, fuse_layer in enumerate(self.fuse_layers):
            y = x[0] if i == 0 else fuse_layer[0](x[0])
            for j, fuse_sub_layer in enumerate(fuse_layer):
                if j == 0 or j &gt; self.num_branches:
                    pass
                else:
                    if i == j:
                        y = y + x[j]
                    elif j &gt; i:
                        width_output = x[i].shape[-1]
                        height_output = x[i].shape[-2]
                        y = y + F.interpolate(
                            fuse_sub_layer(x[j]),
                            size=[height_output, width_output],
                            mode='bilinear')
                    else:
                        y = y + fuse_sub_layer(x[j])
            x_fuse.append(self.relu(y))
        return x_fuse


blocks_dict = {
    'BASIC': BasicBlock,
    'BOTTLENECK': Bottleneck
}


class HighResolutionNet(nn.Module):

    def __init__(self, config, nclass=2, **kwargs):
        super(HighResolutionNet, self).__init__()
        self.nclass=nclass
        # stem net
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1,
                               bias=False)
        self.bn1 = BatchNorm2d(64, momentum=BN_MOMENTUM)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1,
                               bias=False)
        self.bn2 = BatchNorm2d(64, momentum=BN_MOMENTUM)
        self.relu = nn.ReLU(inplace=True)
        
        self.stage1_cfg = config['STAGE1']
        num_channels = self.stage1_cfg['NUM_CHANNELS'][0]
        block = blocks_dict[self.stage1_cfg['BLOCK']]
        num_blocks = self.stage1_cfg['NUM_BLOCKS'][0]
        self.layer1 = self._make_layer(block, 64, num_channels, num_blocks)
        stage1_out_channel = block.expansion*num_channels

        self.stage2_cfg = config['STAGE2']

        num_channels = self.stage2_cfg['NUM_CHANNELS']
        block = blocks_dict[self.stage2_cfg['BLOCK']]
        num_channels = [
            num_channels[i] * block.expansion for i in range(len(num_channels))]
        self.transition1 = self._make_transition_layer(
            [stage1_out_channel], num_channels)
        self.stage2, pre_stage_channels = self._make_stage(
            self.stage2_cfg, num_channels)

        self.stage3_cfg = config['STAGE3']
        num_channels = self.stage3_cfg['NUM_CHANNELS']
        block = blocks_dict[self.stage3_cfg['BLOCK']]
        num_channels = [
            num_channels[i] * block.expansion for i in range(len(num_channels))]
        self.transition2 = self._make_transition_layer(
            pre_stage_channels, num_channels)
        self.stage3, pre_stage_channels = self._make_stage(
            self.stage3_cfg, num_channels)

        self.stage4_cfg = config['STAGE4']
        num_channels = self.stage4_cfg['NUM_CHANNELS']
        block = blocks_dict[self.stage4_cfg['BLOCK']]
        num_channels = [
            num_channels[i] * block.expansion for i in range(len(num_channels))]
        self.transition3 = self._make_transition_layer(
            pre_stage_channels, num_channels)
        self.stage4, pre_stage_channels = self._make_stage(
            self.stage4_cfg, num_channels, multi_scale_output=True)
        
        self.last_inp_channels = np.int(np.sum(pre_stage_channels))

        # self.last_layer = nn.Sequential(
        #     nn.Conv2d(
        #         in_channels=last_inp_channels,
        #         out_channels=last_inp_channels,
        #         kernel_size=1,
        #         stride=1,
        #         padding=0),
        #     BatchNorm2d(last_inp_channels, momentum=BN_MOMENTUM),
        #     nn.ReLU(inplace=True),
        #     nn.Conv2d(
        #         in_channels=last_inp_channels,
        #         out_channels=self.nclass,
        #         kernel_size=config["FINAL_CONV_KERNEL"],
        #         stride=1,
        #         padding=1 if config["FINAL_CONV_KERNEL"] == 3 else 0)
        # )

    def _make_transition_layer(
            self, num_channels_pre_layer, num_channels_cur_layer):
        num_branches_cur = len(num_channels_cur_layer)
        num_branches_pre = len(num_channels_pre_layer)

        transition_layers = []
        for i in range(num_branches_cur):
            if i &lt; num_branches_pre:
                if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
                    transition_layers.append(nn.Sequential(
                        nn.Conv2d(num_channels_pre_layer[i],
                                  num_channels_cur_layer[i],
                                  3,
                                  1,
                                  1,
                                  bias=False),
                        BatchNorm2d(
                            num_channels_cur_layer[i], momentum=BN_MOMENTUM),
                        nn.ReLU(inplace=True)))
                else:
                    transition_layers.append(nn.Identity())
            else:
                conv3x3s = []
                for j in range(i+1-num_branches_pre):
                    inchannels = num_channels_pre_layer[-1]
                    outchannels = num_channels_cur_layer[i] \
                        if j == i-num_branches_pre else inchannels
                    conv3x3s.append(nn.Sequential(
                        nn.Conv2d(
                            inchannels, outchannels, 3, 2, 1, bias=False),
                        BatchNorm2d(outchannels, momentum=BN_MOMENTUM),
                        nn.ReLU(inplace=True)))
                transition_layers.append(nn.Sequential(*conv3x3s))

        return nn.ModuleList(transition_layers)

    def _make_layer(self, block, inplanes, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
            )

        layers = []
        layers.append(block(inplanes, planes, stride, downsample))
        inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(inplanes, planes))

        return nn.Sequential(*layers)

    def _make_stage(self, layer_config, num_inchannels,
                    multi_scale_output=True):
        num_modules = layer_config['NUM_MODULES']
        num_branches = layer_config['NUM_BRANCHES']
        num_blocks = layer_config['NUM_BLOCKS']
        num_channels = layer_config['NUM_CHANNELS']
        block = blocks_dict[layer_config['BLOCK']]
        fuse_method = layer_config['FUSE_METHOD']

        modules = []
        for i in range(num_modules):
            # multi_scale_output is only used last module
            if not multi_scale_output and i == num_modules - 1:
                reset_multi_scale_output = False
            else:
                reset_multi_scale_output = True
            modules.append(
                HighResolutionModule(num_branches,
                                      block,
                                      num_blocks,
                                      num_inchannels,
                                      num_channels,
                                      fuse_method,
                                      reset_multi_scale_output)
            )
            num_inchannels = modules[-1].get_num_inchannels()

        # return nn.Sequential(*modules), num_inchannels
        return nn.ModuleList(modules), num_inchannels

    def forward(self, x):

        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.layer1(x)

        x_list = []
        for aux in self.transition1:
            if not isinstance(aux,nn.Identity):
                x_list.append(aux(x))
            else:
                x_list.append(x)
        #y_list = self.stage2(x_list)

        for aux in self.stage2:
            x_list = aux(x_list)
        y_list = x_list


        x_list = []
        for i, aux in enumerate(self.transition2):
            if not isinstance(aux,nn.Identity):
                x_list.append(aux(y_list[-1]))
            else:
                x_list.append(y_list[i])
        #y_list = self.stage3(x_list)
        for aux in self.stage3:
            x_list = aux(x_list)
        y_list = x_list

        x_list = []
        for i, aux in enumerate(self.transition3):
            if not isinstance(aux,nn.Identity):
                x_list.append(aux(y_list[-1]))
            else:
                x_list.append(y_list[i])
        #x = self.stage4(x_list)
        for aux in self.stage4:
            x_list = aux(x_list)
        x = x_list
        # Upsampling
        x0_h, x0_w = x[0].size(2), x[0].size(3)
        x1 = F.interpolate(x[1], size=(x0_h, x0_w), mode='bilinear')
        x2 = F.interpolate(x[2], size=(x0_h, x0_w), mode='bilinear')
        x3 = F.interpolate(x[3], size=(x0_h, x0_w), mode='bilinear')

        x = torch.cat([x[0], x1, x2, x3], 1)

        # x = self.last_layer(x)

        # #UpSample
        # x = F.interpolate(x, size=(ori_height, ori_width),
        #                 mode='bilinear') 
        return x

    def init_weights(self, pretrained='',):
        logger.info('=&gt; init weights from normal distribution')
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.normal_(m.weight, std=0.001)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
        if os.path.isfile(pretrained):
            pretrained_dict = torch.load(pretrained)
            logger.info('=&gt; loading pretrained model {}'.format(pretrained))
            model_dict = self.state_dict()
            pretrained_dict = {k: v for k, v in pretrained_dict.items()
                               if k in model_dict.keys()}
            #for k, _ in pretrained_dict.items():
            #    logger.info(
            #        '=&gt; loading {} pretrained model {}'.format(k, pretrained))
            model_dict.update(pretrained_dict)
            self.load_state_dict(model_dict)

class HRNet_Model(nn.Module):
    def __init__(self, config, nclass):
        super(HRNet_Model, self).__init__()
        self.backbone = HighResolutionNet(config,nclass)
        self.head = nn.Sequential(
            nn.Conv2d(
                in_channels=self.backbone.last_inp_channels,
                out_channels=self.backbone.last_inp_channels,
                kernel_size=1,
                stride=1,
                padding=0),
            BatchNorm2d(self.backbone.last_inp_channels, momentum=BN_MOMENTUM),
            nn.ReLU(inplace=True),
            nn.Conv2d(
                in_channels=self.backbone.last_inp_channels,
                out_channels=nclass,
                kernel_size=config["FINAL_CONV_KERNEL"],
                stride=1,
                padding=1 if config["FINAL_CONV_KERNEL"] == 3 else 0)
        )
    def forward(self, x):
        ori_height, ori_width =x.shape[2],x.shape[3]
        x = self.backbone(x)
        x = self.head(x)
        x = F.interpolate(x, size=(ori_height, ori_width),
                        mode='bilinear') 
        return x

architecture_config={
    "hrnet_w18_small_v1": {
        "FINAL_CONV_KERNEL": 1,
        "STAGE1": {
            "NUM_MODULES": 1,
            "NUM_BRANCHES": 1,
            "BLOCK": "BOTTLENECK",
            "NUM_BLOCKS": [1],
            "NUM_CHANNELS": [32],
            "FUSE_METHOD": "SUM"
        },
        "STAGE2": {
            "NUM_MODULES": 1,
            "NUM_BRANCHES": 2,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [2,2],
            "NUM_CHANNELS": [16,32],
            "FUSE_METHOD": "SUM"
        },
        "STAGE3": {
            "NUM_MODULES": 1,
            "NUM_BRANCHES": 3,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [2,2,2],
            "NUM_CHANNELS": [16,32,64],
            "FUSE_METHOD": "SUM"
        },
        "STAGE4": {
            "NUM_MODULES": 1,
            "NUM_BRANCHES": 4,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [2,2,2,2],
            "NUM_CHANNELS": [16,32,64,128],
            "FUSE_METHOD": "SUM"
        }
    },
    "hrnet_w18_small_v2": {
        "FINAL_CONV_KERNEL": 1,
        "STAGE1": {
            "NUM_MODULES": 1,
            "NUM_BRANCHES": 1,
            "BLOCK": "BOTTLENECK",
            "NUM_BLOCKS": [2],
            "NUM_CHANNELS": [64],
            "FUSE_METHOD": "SUM"
        },
        "STAGE2": {
            "NUM_MODULES": 1,
            "NUM_BRANCHES": 2,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [2,2],
            "NUM_CHANNELS": [18,36],
            "FUSE_METHOD": "SUM"
        },
        "STAGE3": {
            "NUM_MODULES": 3,
            "NUM_BRANCHES": 3,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [2,2,2],
            "NUM_CHANNELS": [18,36,72],
            "FUSE_METHOD": "SUM"
        },
        "STAGE4": {
            "NUM_MODULES": 2,
            "NUM_BRANCHES": 4,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [2,2,2,2],
            "NUM_CHANNELS": [18, 36, 72, 144],
            "FUSE_METHOD": "SUM"
        }
    },
    "hrnet_w18": {
        "FINAL_CONV_KERNEL": 1,
        "STAGE1": {
            "NUM_MODULES": 1,
            "NUM_BRANCHES": 1,
            "BLOCK": "BOTTLENECK",
            "NUM_BLOCKS": [4],
            "NUM_CHANNELS": [64],
            "FUSE_METHOD": "SUM"
        },
        "STAGE2": {
            "NUM_MODULES": 1,
            "NUM_BRANCHES": 2,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [4,4],
            "NUM_CHANNELS": [18,36],
            "FUSE_METHOD": "SUM"
        },
        "STAGE3": {
            "NUM_MODULES": 4,
            "NUM_BRANCHES": 3,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [4,4,4],
            "NUM_CHANNELS": [18,36,72],
            "FUSE_METHOD": "SUM"
        },
        "STAGE4": {
            "NUM_MODULES": 3,
            "NUM_BRANCHES": 4,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [4,4,4,4],
            "NUM_CHANNELS": [18, 36, 72, 144],
            "FUSE_METHOD": "SUM"
        }
    },
    "hrnet_w30": {
        "FINAL_CONV_KERNEL": 1,
        "STAGE1": {
            "NUM_MODULES": 1,
            "NUM_BRANCHES": 1,
            "BLOCK": "BOTTLENECK",
            "NUM_BLOCKS": [4],
            "NUM_CHANNELS": [64],
            "FUSE_METHOD": "SUM"
        },
        "STAGE2": {
            "NUM_MODULES": 1,
            "NUM_BRANCHES": 2,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [4,4],
            "NUM_CHANNELS": [30, 60],
            "FUSE_METHOD": "SUM"
        },
        "STAGE3": {
            "NUM_MODULES": 4,
            "NUM_BRANCHES": 3,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [4, 4, 4],
            "NUM_CHANNELS": [30, 60, 120],
            "FUSE_METHOD": "SUM"
        },
        "STAGE4": {
            "NUM_MODULES": 3,
            "NUM_BRANCHES": 4,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [4, 4, 4, 4],
            "NUM_CHANNELS": [30, 60, 120, 240],
            "FUSE_METHOD": "SUM"
        }
    },
    "hrnet_w32": {
        "FINAL_CONV_KERNEL": 1,
        "STAGE1": {
            "NUM_MODULES": 1,
            "NUM_BRANCHES": 1,
            "BLOCK": "BOTTLENECK",
            "NUM_BLOCKS": [4],
            "NUM_CHANNELS": [64],
            "FUSE_METHOD": "SUM"
        },
        "STAGE2": {
            "NUM_MODULES": 1,
            "NUM_BRANCHES": 2,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [4,4],
            "NUM_CHANNELS": [32, 64],
            "FUSE_METHOD": "SUM"
        },
        "STAGE3": {
            "NUM_MODULES": 4,
            "NUM_BRANCHES": 3,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [4, 4, 4],
            "NUM_CHANNELS": [32, 64, 128],
            "FUSE_METHOD": "SUM"
        },
        "STAGE4": {
            "NUM_MODULES": 3,
            "NUM_BRANCHES": 4,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [4, 4, 4, 4],
            "NUM_CHANNELS": [32, 64, 128, 256],
            "FUSE_METHOD": "SUM"
        }
    },
    "hrnet_w48": {
        "FINAL_CONV_KERNEL": 1,
        "STAGE1": {
            "NUM_MODULES": 1,
            "NUM_BRANCHES": 1,
            "BLOCK": "BOTTLENECK",
            "NUM_BLOCKS": [4],
            "NUM_CHANNELS": [64],
            "FUSE_METHOD": "SUM"
        },
        "STAGE2": {
            "NUM_MODULES": 1,
            "NUM_BRANCHES": 2,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [4,4],
            "NUM_CHANNELS": [48, 96],
            "FUSE_METHOD": "SUM"
        },
        "STAGE3": {
            "NUM_MODULES": 4,
            "NUM_BRANCHES": 3,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [4, 4, 4],
            "NUM_CHANNELS": [48, 96, 192],
            "FUSE_METHOD": "SUM"
        },
        "STAGE4": {
            "NUM_MODULES": 3,
            "NUM_BRANCHES": 4,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [4, 4, 4, 4],
            "NUM_CHANNELS": [48, 96, 192, 384],
            "FUSE_METHOD": "SUM"
        }
    }
}

def HRNet(nclass=2, backbone_name="hrnet_w18"):
    cfg=architecture_config[backbone_name]
    model = HRNet_Model(cfg,nclass)
    return model

```

For using it you need to: 

```
model=HRNet(nclass=2, backbone_name="hrnet_w30")
traced_cell=torch.jit.script(model)
```
Here you have the input needed:
```
img = PILImage.create("inputImage.png")
aux=Resize((1002,1002))(img)
my_transforms = transforms.Compose([transforms.ToTensor(),
                                                               transforms.Normalize(
                                                                                                  [0.485, 0.456, 0.406],
                                                                                                  [0.229, 0.224, 0.225])])
aux= my_transforms(aux).unsqueeze(0).cpu()
```
For inference:
```
res=model(aux)
res=traced_cell(aux)
```




## Expected behavior

The first run should be slower and the rest faster.

cc @suo</span></p>
  </div>

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