_combine_histograms histogram_with_output_range = torch.zeros((Nbins * downsample_rate), device=orig_hist.device) RuntimeError: Trying to create tensor with negative dimension -4398046511104: [-4398046511104]

Shisho_Sama: <p>Hello everyone.<br>
This is a followup question concerning  this <a href="https://discuss.pytorch.org/t/how-can-i-incorporate-prelu-in-a-quantized-model/89080">one</a><br>
The issue is everything goes just fine expect at some point in time, this weird error occurs when running this specific block! :</p>
<pre><code class="lang-python">class SEBlock(nn.Module):
    def __init__(self, channel, reduction=16):
        super().__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.mult_xy = nn.quantized.FloatFunctional()

        self.fc = nn.Sequential(
                                nn.Linear(channel, channel // reduction),
                                nn.PReLU(),
                                # nn.ReLU(),
                                nn.Linear(channel // reduction, channel),
                                nn.Sigmoid()
                                )
        self.fc1 = self.fc[0]
        self.prelu = self.fc[1]
        self.fc2 = self.fc[2]
        self.sigmoid = self.fc[3]
        self.prelu_q = PReLU_Quantized(self.prelu)

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        # y = self.fc(y).view(b, c, 1, 1)
        y = self.fc1(y)
        y = self.prelu_q(y)
        y = self.fc2(y)
        y = self.sigmoid(y).view(b, c, 1, 1)

        # out = x*y 
        out = self.mult_xy.mul(x, y)
        return out

</code></pre>
<p>It runs several times fine, but at some point it fails with the following error message :</p>
<pre><code class="lang-python">Traceback (most recent call last):
  File "d:\Codes\org\python\Quantization\quantizer.py", line 248, in &lt;module&gt;
    quantize_test()
  File "d:\Codes\org\python\Quantization\quantizer.py", line 230, in quantize_test
    evaluate(model, dtloader, neval_batches=num_calibration_batches)
  File "d:\Codes\org\python\Quantization\quantizer.py", line 145, in evaluate
    features = model(image.unsqueeze(0))
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "d:\codes\org\python\FV\quantized_models.py", line 473, in forward
    x = self.layer3(x)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\container.py", line 100, in forward
    input = module(input)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "d:\codes\org\python\FV\quantized_models.py", line 387, in forward
    out = self.se(out)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "d:\codes\org\python\FV\quantized_models.py", line 345, in forward
    y = self.prelu_q(y)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "d:\codes\org\python\FV\quantized_models.py", line 221, in forward
    inputs = self.quantized_op.add(tmax, weight_min_res).unsqueeze(0)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\quantized\modules\functional_modules.py", line 43, in add
    r = self.activation_post_process(r)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\quantization\observer.py", line 833, in forward
    self.bins)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\quantization\observer.py", line 789, in _combine_histograms
    histogram_with_output_range = torch.zeros((Nbins * downsample_rate), device=orig_hist.device)
RuntimeError: Trying to create tensor with negative dimension -4398046511104: [-4398046511104]
</code></pre>
<p>what am I missing here ?</p>
<p>Any help is geatly appreciated</p>

albanD: <p>Hi,</p>
<p>Can you check what are the inputs to the add operation at <code>"d:\codes\org\python\FV\quantized_models.py", line 221</code><br>
It looks like it is not handling these properly.</p>
<p>If you could give us a set of inputs that reproduces this issue so that we can reproduce on our side, that would be very helpful!</p>

Shisho_Sama: <p>Hi,<br>
This is the latest error I get (after updating the PReLU_Quantized (link to implementation is <a href="https://discuss.pytorch.org/t/how-can-i-incorporate-prelu-in-a-quantized-model/89080/4">here</a> by the way):<br>
The inputs are included in the log below (as <strong>X</strong>) and the error only happens in <strong>SEBlock</strong> module which its definition is also given below all other modules that use the <code>PReLU_Quantzied</code> module run fine except <strong>SEBlock</strong>!:</p>
<pre><code class="lang-python">Size (MB): 89.297826
QConfig(activation=functools.partial(&lt;class 'torch.quantization.observer.HistogramObserver'&gt;, reduce_range=True), weight=functools.partial(&lt;class 'torch.quantization.observer.PerChannelMinMaxObserver'&gt;, dtype=torch.qint8, qscheme=torch.per_channel_symmetric))
Post Training Quantization Prepare: Inserting Observers

 Inverted Residual Block:After observer insertion

 Conv2d(
  3, 64, kernel_size=(3, 3), stride=(1, 1)
  (activation_post_process): HistogramObserver()
)
&lt;inside se forward:&gt;
X: tensor([[-1.5691, -0.7516, -0.7360, -0.6458]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 3.6605e-01,  3.3855e+00, -5.0032e-19, -9.0280e-19]])
Traceback (most recent call last):
  File "d:\Codes\org\python\Quantization\quantizer.py", line 266, in &lt;module&gt;
    quantize_test()
  File "d:\Codes\org\python\Quantization\quantizer.py", line 248, in quantize_test
    evaluate(model, dtloader, neval_batches=num_calibration_batches)
  File "d:\Codes\org\python\Quantization\quantizer.py", line 152, in evaluate
    features = model(image.unsqueeze(0))
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "d:\codes\org\python\FV\quantized_models.py", line 576, in forward
    x = self.layer1(x)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\container.py", line 100, in forward
    input = module(input)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "d:\codes\org\python\FV\quantized_models.py", line 489, in forward
    out = self.se(out)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "d:\codes\org\python\FV\quantized_models.py", line 447, in forward
    y = self.prelu_q(y)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "d:\codes\org\python\FV\quantized_models.py", line 322, in forward
    inputs = self.quantized_op.add(torch.relu(inputs), weight_min_res)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\quantized\modules\functional_modules.py", line 43, in add
    r = self.activation_post_process(r)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\quantization\observer.py", line 833, in forward
    self.bins)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\quantization\observer.py", line 789, in _combine_histograms
    histogram_with_output_range = torch.zeros((Nbins * downsample_rate), device=orig_hist.device)
RuntimeError: Trying to create tensor with negative dimension -4398046511104: [-4398046511104]
</code></pre>
<p>and this is how the SE block looks like :</p>
<pre><code class="lang-python">class SEBlock(nn.Module):
    def __init__(self, channel, reduction=16):
        super().__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.mult_xy = nn.quantized.FloatFunctional()

        self.fc = nn.Sequential(
                                nn.Linear(channel, channel // reduction),
                                nn.PReLU(),
                                # nn.ReLU(),
                                nn.Linear(channel // reduction, channel),
                                nn.Sigmoid()
                                )
        self.fc1 = self.fc[0]
        self.prelu = self.fc[1]
        self.fc2 = self.fc[2]
        self.sigmoid = self.fc[3]
        self.prelu_q = PReLU_Quantized(self.prelu)

    def forward(self, x):
        print(f'&lt;inside se forward:&gt;')
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        # y = self.fc(y).view(b, c, 1, 1)
        y = self.fc1(y)
        print(f'X: {y}')
        y = self.prelu_q(y)
        y = self.fc2(y)
        y = self.sigmoid(y).view(b, c, 1, 1)
        print('--------------------------')
        # out = x*y 
        out = self.mult_xy.mul(x, y)
        return out
</code></pre>
<p>amd  this is the output when I use PReLU instead of PReLU_Quantized in the SE block only (all other instance of PReLU is replaced with PReLU_Quantized in other modulels of ResNet) :</p>
<details>
<summary>
Summary</summary>
<pre><code class="lang-python">Size (MB): 89.29209
QConfig(activation=functools.partial(&lt;class 'torch.quantization.observer.HistogramObserver'&gt;, reduce_range=True), weight=functools.partial(&lt;class 'torch.quantization.observer.PerChannelMinMaxObserver'&gt;, dtype=torch.qint8, qscheme=torch.per_channel_symmetric))
Post Training Quantization Prepare: Inserting Observers

 Inverted Residual Block:After observer insertion

 Conv2d(
  3, 64, kernel_size=(3, 3), stride=(1, 1)
  (activation_post_process): HistogramObserver()
)
&lt;inside se forward:&gt;
X: tensor([[-1.5691, -0.7516, -0.7360, -0.6458]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 3.6605e-01,  3.3855e+00, -5.0032e-19, -9.0280e-19]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.0513, -0.0656, -0.4529,  0.0653, -0.4762, -0.6304, -1.5043, -0.9484]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 4.8730,  1.6650, -0.5135, -0.6811, -0.0392, -0.4689, -0.1496,  0.0717]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.8759, -0.8886, -1.3295, -0.5375,  0.7598, -0.8526, -1.9066,  0.0985,
         -0.1461, -0.5857,  0.1513, -0.3050,  0.1955, -0.8470,  0.4528,  0.9689]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.6184e+00, -2.2714e-18,  2.8052e+00,  1.0378e+01,  4.6361e-05,
          1.0644e+01,  1.4302e-02,  2.6143e-02,  2.4926e-05,  6.2237e+00,
          8.8411e-05,  6.4360e+00,  3.3530e+00,  3.9302e-05,  8.1652e+00,
          8.7950e-07]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 9.1687e+00,  3.1469e+00, -1.1788e+01,  4.9410e-02,  1.7272e+00,
         -3.0913e+00,  1.1572e+00, -6.7104e+00,  1.1371e+01,  4.8926e+00,
         -1.3102e+00, -4.9774e+00, -4.1444e+00, -6.3367e-01, -1.5672e+00,
          4.2629e+00,  3.2491e+00, -4.6632e+00,  5.9241e-01, -2.4883e+00,
          5.2599e+00, -7.1710e+00,  4.7197e+00,  7.2724e+00, -2.3363e+00,
         -2.2564e+00,  5.4431e+00, -2.2832e-12,  1.9732e+00,  1.1682e+00,
          6.1555e+00,  6.3574e+00]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.2785e-01,  1.1057e+00,  3.1581e-07,  9.7595e-01,  9.7386e-03,
          8.4260e-07,  2.4243e-01,  2.1749e+00,  4.5704e-01,  2.9307e+00,
          3.2384e+00,  2.6099e+00,  1.7640e-01,  4.3206e-04,  9.9380e-18,
          1.3450e-11,  1.5721e-09,  2.7632e-07,  3.6721e-04,  2.1237e-07,
          1.8839e-10,  1.8423e-02,  1.8514e-13,  4.3584e+00,  1.0972e-01,
          7.5909e-03,  4.3828e-02,  2.9285e-02,  8.3840e-07, -2.6420e-19,
          3.6933e-01,  1.0561e+00]])
--------------------------
0-feature dims: torch.Size([1, 512])
&lt;inside se forward:&gt;
X: tensor([[-1.5517, -0.8007, -0.7286, -0.6478]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 5.0945e-01,  3.2514e+00, -5.2950e-19, -9.1256e-19]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.0556, -0.1015, -0.4792,  0.0956, -0.4782, -0.6346, -1.4946, -0.9745]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 4.8254,  1.6459, -0.4613, -0.6462, -0.0376, -0.4217, -0.0865,  0.0773]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.8807, -0.8899, -1.3275, -0.5305,  0.7527, -0.8557, -1.9068,  0.1042,
         -0.1444, -0.5798,  0.1493, -0.3055,  0.1952, -0.8383,  0.4532,  0.9664]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.6193e+00, -2.2732e-18,  2.8069e+00,  1.0384e+01,  4.6389e-05,
          1.0650e+01,  1.4310e-02,  2.6159e-02,  2.4941e-05,  6.2275e+00,
          8.8464e-05,  6.4398e+00,  3.3551e+00,  3.9326e-05,  8.1701e+00,
          8.8003e-07]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 9.1444e+00,  3.1584e+00, -1.1794e+01,  4.9510e-02,  1.7366e+00,
         -3.0976e+00,  1.1594e+00, -6.7127e+00,  1.1380e+01,  4.9035e+00,
         -1.3231e+00, -4.9740e+00, -4.1439e+00, -6.3774e-01, -1.5777e+00,
          4.2655e+00,  3.2341e+00, -4.6753e+00,  6.1677e-01, -2.4898e+00,
          5.2556e+00, -7.1508e+00,  4.7271e+00,  7.2643e+00, -2.3301e+00,
         -2.2546e+00,  5.4412e+00, -2.2872e-12,  1.9668e+00,  1.1764e+00,
          6.1590e+00,  6.3575e+00]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.2778e-01,  1.1051e+00,  3.1564e-07,  9.7544e-01,  9.7335e-03,
          8.4216e-07,  2.4230e-01,  2.1737e+00,  4.5681e-01,  2.9292e+00,
          3.2367e+00,  2.6086e+00,  1.7631e-01,  4.3183e-04,  9.9393e-18,
          1.3443e-11,  1.5713e-09,  2.7617e-07,  3.6702e-04,  2.1226e-07,
          1.8829e-10,  1.8414e-02,  1.8504e-13,  4.3561e+00,  1.0967e-01,
          7.5869e-03,  4.3805e-02,  2.9270e-02,  8.3797e-07, -2.6259e-19,
          3.6914e-01,  1.0555e+00]])
--------------------------
1-feature dims: torch.Size([1, 512])
&lt;inside se forward:&gt;
X: tensor([[-1.6008, -0.7627, -0.7418, -0.6562]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 4.6180e-01,  3.2969e+00, -5.1091e-19, -8.5673e-19]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.0860, -0.0888, -0.4410,  0.0515, -0.4853, -0.6203, -1.4854, -0.9521]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 4.8713,  1.6702, -0.5249, -0.6848, -0.0393, -0.4817, -0.1603,  0.0686]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.8888, -0.8991, -1.3308, -0.5351,  0.7626, -0.8547, -1.9075,  0.1075,
         -0.1457, -0.5770,  0.1518, -0.3068,  0.2023, -0.8418,  0.4610,  0.9654]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.6180e+00, -2.2720e-18,  2.8046e+00,  1.0376e+01,  4.6351e-05,
          1.0642e+01,  1.4299e-02,  2.6138e-02,  2.4921e-05,  6.2225e+00,
          8.8393e-05,  6.4347e+00,  3.3524e+00,  3.9294e-05,  8.1636e+00,
          8.7932e-07]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 9.1925e+00,  3.1589e+00, -1.1792e+01,  4.9472e-02,  1.7246e+00,
         -3.0884e+00,  1.1586e+00, -6.7112e+00,  1.1375e+01,  4.8954e+00,
         -1.3047e+00, -4.9715e+00, -4.1392e+00, -6.4653e-01, -1.5772e+00,
          4.2795e+00,  3.2537e+00, -4.6607e+00,  5.9939e-01, -2.4853e+00,
          5.2615e+00, -7.1921e+00,  4.7311e+00,  7.2626e+00, -2.3221e+00,
         -2.2574e+00,  5.4390e+00, -2.2799e-12,  1.9636e+00,  1.1820e+00,
          6.1593e+00,  6.3554e+00]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.2775e-01,  1.1048e+00,  3.1557e-07,  9.7521e-01,  9.7312e-03,
          8.4196e-07,  2.4225e-01,  2.1732e+00,  4.5670e-01,  2.9285e+00,
          3.2360e+00,  2.6079e+00,  1.7627e-01,  4.3173e-04,  9.9377e-18,
          1.3440e-11,  1.5710e-09,  2.7611e-07,  3.6693e-04,  2.1221e-07,
          1.8825e-10,  1.8409e-02,  1.8500e-13,  4.3551e+00,  1.0964e-01,
          7.5851e-03,  4.3795e-02,  2.9263e-02,  8.3777e-07, -2.6075e-19,
          3.6905e-01,  1.0553e+00]])
--------------------------
2-feature dims: torch.Size([1, 512])
&lt;inside se forward:&gt;
X: tensor([[-1.5790, -0.8100, -0.7292, -0.6440]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 5.0116e-01,  3.2659e+00, -5.2126e-19, -8.5920e-19]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.0427, -0.0929, -0.4953,  0.0674, -0.4784, -0.6115, -1.4972, -0.9645]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 4.8374,  1.6551, -0.4788, -0.6555, -0.0380, -0.4393, -0.1045,  0.0742]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.8727, -0.8932, -1.3280, -0.5371,  0.7591, -0.8533, -1.8998,  0.1003,
         -0.1452, -0.5813,  0.1475, -0.3055,  0.2016, -0.8411,  0.4535,  0.9559]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.6189e+00, -2.2717e-18,  2.8060e+00,  1.0381e+01,  4.6375e-05,
          1.0647e+01,  1.4306e-02,  2.6151e-02,  2.4933e-05,  6.2256e+00,
          8.8438e-05,  6.4379e+00,  3.3541e+00,  3.9314e-05,  8.1676e+00,
          8.7976e-07]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 9.1427e+00,  3.1480e+00, -1.1763e+01,  4.9449e-02,  1.7342e+00,
         -3.0890e+00,  1.1581e+00, -6.7127e+00,  1.1348e+01,  4.8951e+00,
         -1.3154e+00, -4.9691e+00, -4.1414e+00, -6.4151e-01, -1.5783e+00,
          4.2688e+00,  3.2439e+00, -4.6649e+00,  6.0231e-01, -2.4855e+00,
          5.2647e+00, -7.1494e+00,  4.7290e+00,  7.2520e+00, -2.3288e+00,
         -2.2466e+00,  5.4410e+00, -2.2847e-12,  1.9777e+00,  1.1817e+00,
          6.1588e+00,  6.3552e+00]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.2778e-01,  1.1050e+00,  3.1563e-07,  9.7541e-01,  9.7331e-03,
          8.4213e-07,  2.4229e-01,  2.1736e+00,  4.5679e-01,  2.9291e+00,
          3.2366e+00,  2.6084e+00,  1.7631e-01,  4.3181e-04,  9.9368e-18,
          1.3443e-11,  1.5713e-09,  2.7616e-07,  3.6700e-04,  2.1225e-07,
          1.8828e-10,  1.8413e-02,  1.8503e-13,  4.3559e+00,  1.0966e-01,
          7.5866e-03,  4.3804e-02,  2.9269e-02,  8.3793e-07, -2.6231e-19,
          3.6912e-01,  1.0555e+00]])
--------------------------
3-feature dims: torch.Size([1, 512])
&lt;inside se forward:&gt;
X: tensor([[-1.6226, -0.7605, -0.6854, -0.5836]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 2.6039e-01,  3.4835e+00, -4.8167e-19, -8.5980e-19]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.0699, -0.0526, -0.4319, -0.0069, -0.4890, -0.6087, -1.4835, -0.9184]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 4.8828,  1.6724, -0.5539, -0.7054, -0.0402, -0.5061, -0.2002,  0.0661]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.8790, -0.8969, -1.3365, -0.5384,  0.7664, -0.8571, -1.9043,  0.1059,
         -0.1459, -0.5847,  0.1542, -0.3094,  0.2076, -0.8439,  0.4567,  0.9642]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.6174e+00, -2.2710e-18,  2.8035e+00,  1.0371e+01,  4.6333e-05,
          1.0638e+01,  1.4293e-02,  2.6128e-02,  2.4911e-05,  6.2200e+00,
          8.8358e-05,  6.4321e+00,  3.3510e+00,  3.9279e-05,  8.1603e+00,
          8.7897e-07]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 9.1583e+00,  3.1523e+00, -1.1765e+01,  4.9511e-02,  1.7292e+00,
         -3.0851e+00,  1.1595e+00, -6.7154e+00,  1.1350e+01,  4.9005e+00,
         -1.3040e+00, -4.9675e+00, -4.1433e+00, -6.3643e-01, -1.5745e+00,
          4.2669e+00,  3.2492e+00, -4.6569e+00,  6.0002e-01, -2.4789e+00,
          5.2519e+00, -7.1619e+00,  4.7275e+00,  7.2465e+00, -2.3229e+00,
         -2.2525e+00,  5.4448e+00, -2.2806e-12,  1.9732e+00,  1.1739e+00,
          6.1550e+00,  6.3576e+00]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.2778e-01,  1.1050e+00,  3.1563e-07,  9.7540e-01,  9.7331e-03,
          8.4212e-07,  2.4229e-01,  2.1736e+00,  4.5679e-01,  2.9291e+00,
          3.2366e+00,  2.6084e+00,  1.7630e-01,  4.3181e-04,  9.9369e-18,
          1.3443e-11,  1.5712e-09,  2.7616e-07,  3.6700e-04,  2.1225e-07,
          1.8828e-10,  1.8413e-02,  1.8503e-13,  4.3559e+00,  1.0966e-01,
          7.5866e-03,  4.3803e-02,  2.9269e-02,  8.3793e-07, -2.6177e-19,
          3.6912e-01,  1.0555e+00]])
--------------------------
4-feature dims: torch.Size([1, 512])
&lt;inside se forward:&gt;
X: tensor([[-1.5559, -0.7016, -0.7545, -0.6793]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 4.6992e-01,  3.2951e+00, -5.1868e-19, -8.9299e-19]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.0106, -0.0831, -0.5151,  0.0650, -0.4869, -0.6094, -1.5116, -0.9355]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 4.8588,  1.6723, -0.4774, -0.6520, -0.0379, -0.4428, -0.0917,  0.0721]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.8655, -0.8893, -1.3313, -0.5367,  0.7590, -0.8533, -1.9023,  0.1008,
         -0.1428, -0.5834,  0.1448, -0.3016,  0.2040, -0.8361,  0.4534,  0.9494]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.6194e+00, -2.2728e-18,  2.8070e+00,  1.0384e+01,  4.6391e-05,
          1.0651e+01,  1.4311e-02,  2.6160e-02,  2.4942e-05,  6.2277e+00,
          8.8468e-05,  6.4401e+00,  3.3552e+00,  3.9328e-05,  8.1704e+00,
          8.8006e-07]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 9.1170e+00,  3.1500e+00, -1.1769e+01,  4.9446e-02,  1.7362e+00,
         -3.0951e+00,  1.1581e+00, -6.7183e+00,  1.1354e+01,  4.8964e+00,
         -1.3110e+00, -4.9689e+00, -4.1461e+00, -6.4890e-01, -1.5875e+00,
          4.2782e+00,  3.2361e+00, -4.6685e+00,  6.0150e-01, -2.4799e+00,
          5.2726e+00, -7.1287e+00,  4.7384e+00,  7.2532e+00, -2.3235e+00,
         -2.2367e+00,  5.4463e+00, -2.2915e-12,  1.9780e+00,  1.1893e+00,
          6.1668e+00,  6.3629e+00]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.2774e-01,  1.1047e+00,  3.1554e-07,  9.7513e-01,  9.7304e-03,
          8.4189e-07,  2.4223e-01,  2.1730e+00,  4.5666e-01,  2.9283e+00,
          3.2357e+00,  2.6077e+00,  1.7626e-01,  4.3169e-04,  9.9353e-18,
          1.3439e-11,  1.5708e-09,  2.7609e-07,  3.6690e-04,  2.1219e-07,
          1.8823e-10,  1.8408e-02,  1.8498e-13,  4.3547e+00,  1.0963e-01,
          7.5845e-03,  4.3792e-02,  2.9261e-02,  8.3770e-07, -2.6081e-19,
          3.6902e-01,  1.0552e+00]])
--------------------------
5-feature dims: torch.Size([1, 512])
&lt;inside se forward:&gt;
X: tensor([[-1.5922, -0.7833, -0.8099, -0.7581]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 6.0425e-01,  3.1537e+00, -5.2917e-19, -8.2412e-19]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.0295, -0.1079, -0.5239,  0.1099, -0.4906, -0.6187, -1.5178, -0.9515]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 4.9047,  1.7059, -0.4654, -0.6338, -0.0371, -0.4419, -0.0531,  0.0689]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.8792, -0.8972, -1.3274, -0.5352,  0.7649, -0.8542, -1.9078,  0.1055,
         -0.1455, -0.5737,  0.1437, -0.3026,  0.2050, -0.8408,  0.4609,  0.9527]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.6192e+00, -2.2734e-18,  2.8065e+00,  1.0383e+01,  4.6383e-05,
          1.0649e+01,  1.4309e-02,  2.6156e-02,  2.4938e-05,  6.2268e+00,
          8.8454e-05,  6.4391e+00,  3.3547e+00,  3.9321e-05,  8.1692e+00,
          8.7993e-07]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 9.1462e+00,  3.1594e+00, -1.1777e+01,  4.9445e-02,  1.7250e+00,
         -3.0903e+00,  1.1580e+00, -6.6971e+00,  1.1362e+01,  4.8978e+00,
         -1.3202e+00, -4.9701e+00, -4.1377e+00, -6.3982e-01, -1.5717e+00,
          4.2688e+00,  3.2314e+00, -4.6666e+00,  6.1283e-01, -2.4762e+00,
          5.2739e+00, -7.1517e+00,  4.7211e+00,  7.2673e+00, -2.3338e+00,
         -2.2474e+00,  5.4291e+00, -2.2837e-12,  1.9676e+00,  1.1787e+00,
          6.1559e+00,  6.3495e+00]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.2772e-01,  1.1046e+00,  3.1549e-07,  9.7498e-01,  9.7289e-03,
          8.4176e-07,  2.4219e-01,  2.1727e+00,  4.5659e-01,  2.9278e+00,
          3.2352e+00,  2.6073e+00,  1.7623e-01,  4.3163e-04,  9.9370e-18,
          1.3437e-11,  1.5706e-09,  2.7604e-07,  3.6684e-04,  2.1216e-07,
          1.8820e-10,  1.8405e-02,  1.8495e-13,  4.3541e+00,  1.0962e-01,
          7.5833e-03,  4.3785e-02,  2.9256e-02,  8.3757e-07, -2.6052e-19,
          3.6896e-01,  1.0550e+00]])
--------------------------
6-feature dims: torch.Size([1, 512])
&lt;inside se forward:&gt;
X: tensor([[-1.5156, -0.5839, -0.7718, -0.6881]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 6.3789e-01,  3.1470e+00, -5.4607e-19, -8.8140e-19]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.0068, -0.1239, -0.5419,  0.1311, -0.4739, -0.6220, -1.5159, -1.0039]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 4.7764,  1.6289, -0.3860, -0.5940, -0.0352, -0.3554,  0.0103,  0.0848]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.8759, -0.8883, -1.3219, -0.5339,  0.7527, -0.8555, -1.9051,  0.0963,
         -0.1418, -0.5765,  0.1501, -0.2970,  0.1911, -0.8370,  0.4527,  0.9548]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.6203e+00, -2.2739e-18,  2.8086e+00,  1.0390e+01,  4.6417e-05,
          1.0657e+01,  1.4319e-02,  2.6175e-02,  2.4956e-05,  6.2312e+00,
          8.8518e-05,  6.4437e+00,  3.3571e+00,  3.9350e-05,  8.1750e+00,
          8.8056e-07]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 9.1541e+00,  3.1531e+00, -1.1772e+01,  4.9404e-02,  1.7326e+00,
         -3.0931e+00,  1.1571e+00, -6.6943e+00,  1.1357e+01,  4.8937e+00,
         -1.3274e+00, -4.9758e+00, -4.1305e+00, -6.4647e-01, -1.5764e+00,
          4.2726e+00,  3.2396e+00, -4.6719e+00,  6.0704e-01, -2.4865e+00,
          5.2721e+00, -7.1595e+00,  4.7218e+00,  7.2695e+00, -2.3445e+00,
         -2.2482e+00,  5.4221e+00, -2.2827e-12,  1.9751e+00,  1.1886e+00,
          6.1566e+00,  6.3400e+00]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.2782e-01,  1.1054e+00,  3.1574e-07,  9.7576e-01,  9.7366e-03,
          8.4243e-07,  2.4238e-01,  2.1744e+00,  4.5695e-01,  2.9301e+00,
          3.2378e+00,  2.6094e+00,  1.7637e-01,  4.3197e-04,  9.9450e-18,
          1.3448e-11,  1.5718e-09,  2.7626e-07,  3.6714e-04,  2.1232e-07,
          1.8835e-10,  1.8419e-02,  1.8510e-13,  4.3575e+00,  1.0970e-01,
          7.5893e-03,  4.3819e-02,  2.9279e-02,  8.3823e-07, -2.6201e-19,
          3.6925e-01,  1.0558e+00]])
--------------------------
7-feature dims: torch.Size([1, 512])
&lt;inside se forward:&gt;
X: tensor([[-1.5567, -0.7524, -0.7620, -0.6805]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 5.3279e-01,  3.2445e+00, -5.2411e-19, -8.5973e-19]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.0248, -0.1011, -0.5172,  0.0823, -0.4737, -0.6192, -1.4961, -0.9762]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 4.8410,  1.6705, -0.4254, -0.6104, -0.0360, -0.4001, -0.0183,  0.0759]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.8740, -0.8943, -1.3243, -0.5337,  0.7550, -0.8610, -1.9063,  0.1108,
         -0.1408, -0.5770,  0.1506, -0.3089,  0.1984, -0.8347,  0.4544,  0.9591]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.6191e+00, -2.2732e-18,  2.8065e+00,  1.0383e+01,  4.6383e-05,
          1.0649e+01,  1.4309e-02,  2.6156e-02,  2.4938e-05,  6.2267e+00,
          8.8453e-05,  6.4390e+00,  3.3546e+00,  3.9321e-05,  8.1691e+00,
          8.7992e-07]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 9.1553e+00,  3.1582e+00, -1.1776e+01,  4.9516e-02,  1.7335e+00,
         -3.0909e+00,  1.1595e+00, -6.7080e+00,  1.1362e+01,  4.9002e+00,
         -1.3237e+00, -4.9679e+00, -4.1376e+00, -6.4026e-01, -1.5758e+00,
          4.2652e+00,  3.2360e+00, -4.6691e+00,  6.1957e-01, -2.4899e+00,
          5.2536e+00, -7.1605e+00,  4.7257e+00,  7.2488e+00, -2.3271e+00,
         -2.2548e+00,  5.4335e+00, -2.2811e-12,  1.9611e+00,  1.1809e+00,
          6.1551e+00,  6.3494e+00]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.2783e-01,  1.1055e+00,  3.1575e-07,  9.7577e-01,  9.7367e-03,
          8.4244e-07,  2.4238e-01,  2.1744e+00,  4.5696e-01,  2.9302e+00,
          3.2378e+00,  2.6094e+00,  1.7637e-01,  4.3197e-04,  9.9456e-18,
          1.3448e-11,  1.5718e-09,  2.7626e-07,  3.6714e-04,  2.1233e-07,
          1.8835e-10,  1.8420e-02,  1.8510e-13,  4.3576e+00,  1.0970e-01,
          7.5894e-03,  4.3820e-02,  2.9280e-02,  8.3824e-07, -2.6233e-19,
          3.6926e-01,  1.0559e+00]])
--------------------------
8-feature dims: torch.Size([1, 512])
&lt;inside se forward:&gt;
X: tensor([[-1.6060, -0.9100, -0.7711, -0.7195]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 5.6481e-01,  3.2033e+00, -5.2471e-19, -8.0308e-19]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.0948, -0.1106, -0.4654,  0.0768, -0.5028, -0.6202, -1.4778, -0.9581]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 4.9064,  1.6963, -0.5052, -0.6644, -0.0385, -0.4721, -0.1160,  0.0672]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.8868, -0.8981, -1.3322, -0.5298,  0.7566, -0.8556, -1.9039,  0.1134,
         -0.1447, -0.5744,  0.1480, -0.3113,  0.2017, -0.8359,  0.4564,  0.9658]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.6177e+00, -2.2718e-18,  2.8040e+00,  1.0373e+01,  4.6342e-05,
          1.0640e+01,  1.4296e-02,  2.6133e-02,  2.4916e-05,  6.2212e+00,
          8.8375e-05,  6.4333e+00,  3.3517e+00,  3.9286e-05,  8.1619e+00,
          8.7914e-07]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 9.1471e+00,  3.1531e+00, -1.1779e+01,  4.9447e-02,  1.7370e+00,
         -3.0912e+00,  1.1580e+00, -6.7101e+00,  1.1363e+01,  4.9010e+00,
         -1.3083e+00, -4.9699e+00, -4.1370e+00, -6.3986e-01, -1.5794e+00,
          4.2680e+00,  3.2415e+00, -4.6646e+00,  6.0562e-01, -2.4862e+00,
          5.2591e+00, -7.1519e+00,  4.7275e+00,  7.2529e+00, -2.3203e+00,
         -2.2537e+00,  5.4380e+00, -2.2843e-12,  1.9685e+00,  1.1793e+00,
          6.1543e+00,  6.3497e+00]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.2776e-01,  1.1049e+00,  3.1558e-07,  9.7525e-01,  9.7316e-03,
          8.4199e-07,  2.4226e-01,  2.1733e+00,  4.5672e-01,  2.9286e+00,
          3.2361e+00,  2.6080e+00,  1.7628e-01,  4.3175e-04,  9.9388e-18,
          1.3441e-11,  1.5710e-09,  2.7612e-07,  3.6695e-04,  2.1221e-07,
          1.8825e-10,  1.8410e-02,  1.8500e-13,  4.3553e+00,  1.0965e-01,
          7.5854e-03,  4.3797e-02,  2.9264e-02,  8.3780e-07, -2.6109e-19,
          3.6906e-01,  1.0553e+00]])
--------------------------
9-feature dims: torch.Size([1, 512])
Post Training Quantization: Calibration done
C:\Users\User\Anaconda3\Lib\site-packages\torch\quantization\observer.py:845: UserWarning: must run observer before calling calculate_qparams.
     Returning default scale and zero point
  Returning default scale and zero point "
Post Training Quantization: Convert done

 Inverted Residual Block: After fusion and quantization, note fused modules:

 QuantizedConv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), scale=0.011990774422883987, zero_point=80)
Size of model after quantization
Size (MB): 24.397458
</code></pre>
</details>

supriyar: <p>Which version of PyTorch are you currently using? We recently fixed a bug in the histogram observer that should be available in 1.6. You can also use nightlies to see if it fixes your issue.</p>

Shisho_Sama: <p>Hi, I’m using 1.5.0!<br>
Ok, I’ll give that a try and report back</p>

Shisho_Sama: <p>Updated to the latest nighly(<code>1.7.0.dev20200714+cpu</code> and <code>torchvision-0.8.0.dev20200714+cpu</code>) just now , it got a bit further, but ultimately crashed with the same error :</p>
<pre><code class="lang-python">Size (MB): 89.322487
QConfig(activation=functools.partial(&lt;class 'torch.quantization.observer.HistogramObserver'&gt;, reduce_range=True), weight=functools.partial(&lt;class 'torch.quantization.observer.PerChannelMinMaxObserver'&gt;, dtype=torch.qint8, qscheme=torch.per_channel_symmetric))
Post Training Quantization Prepare: Inserting Observers

 Inverted Residual Block:After observer insertion

 Conv2d(
  3, 64, kernel_size=(3, 3), stride=(1, 1)
  (activation_post_process): HistogramObserver()
)
&lt;inside se forward:&gt;
X: tensor([[-1.5691, -0.7516, -0.7360, -0.6458]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 3.6604e-01,  3.3855e+00, -5.0032e-19, -9.0280e-19]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.0513, -0.0656, -0.4529,  0.0653, -0.4762, -0.6304, -1.5043, -0.9484]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 4.8730,  1.6650, -0.5135, -0.6811, -0.0392, -0.4689, -0.1496,  0.0717]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[-1.8759, -0.8886, -1.3295, -0.5375,  0.7598, -0.8526, -1.9066,  0.0985,
         -0.1461, -0.5857,  0.1513, -0.3050,  0.1955, -0.8470,  0.4528,  0.9689]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.6184e+00, -2.2714e-18,  2.8052e+00,  1.0378e+01,  4.6361e-05,
          1.0644e+01,  1.4302e-02,  2.6143e-02,  2.4926e-05,  6.2237e+00,
          8.8411e-05,  6.4360e+00,  3.3530e+00,  3.9302e-05,  8.1652e+00,
          8.7950e-07]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 9.1687e+00,  3.1469e+00, -1.1788e+01,  4.9410e-02,  1.7272e+00,
         -3.0913e+00,  1.1572e+00, -6.7104e+00,  1.1371e+01,  4.8926e+00,
         -1.3102e+00, -4.9773e+00, -4.1444e+00, -6.3367e-01, -1.5672e+00,
          4.2629e+00,  3.2491e+00, -4.6632e+00,  5.9241e-01, -2.4883e+00,
          5.2599e+00, -7.1710e+00,  4.7197e+00,  7.2724e+00, -2.3363e+00,
         -2.2564e+00,  5.4431e+00, -2.2832e-12,  1.9732e+00,  1.1682e+00,
          6.1555e+00,  6.3574e+00]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 1.2785e-01,  1.1057e+00,  3.1581e-07,  9.7595e-01,  9.7386e-03,
          8.4260e-07,  2.4243e-01,  2.1749e+00,  4.5704e-01,  2.9307e+00,
          3.2384e+00,  2.6099e+00,  1.7640e-01,  4.3206e-04,  9.9380e-18,
          1.3450e-11,  1.5721e-09,  2.7632e-07,  3.6721e-04,  2.1237e-07,
          1.8839e-10,  1.8423e-02,  1.8514e-13,  4.3584e+00,  1.0972e-01,
          7.5909e-03,  4.3828e-02,  2.9285e-02,  8.3840e-07, -2.6420e-19,
          3.6933e-01,  1.0561e+00]])
--------------------------
0-feature dims: torch.Size([1, 512])
&lt;inside se forward:&gt;
X: tensor([[-1.5517, -0.8007, -0.7286, -0.6478]])
--------------------------
&lt;inside se forward:&gt;
X: tensor([[ 5.0945e-01,  3.2514e+00, -5.2950e-19, -9.1256e-19]])
Traceback (most recent call last):
  File "d:\Codes\org\python\Quantization\quantizer.py", line 266, in &lt;module&gt;
    quantize_test()
  File "d:\Codes\org\python\Quantization\quantizer.py", line 248, in quantize_test
    evaluate(model, dtloader, neval_batches=num_calibration_batches)
  File "d:\Codes\org\python\Quantization\quantizer.py", line 152, in evaluate
    features = model(image.unsqueeze(0))
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 726, in _call_impl
    result = self.forward(*input, **kwargs)
  File "d:\codes\org\python\FV\quantized_models.py", line 576, in forward
    x = self.layer1(x)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 726, in _call_impl
    result = self.forward(*input, **kwargs)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\container.py", line 117, in forward
    input = module(input)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 726, in _call_impl
    result = self.forward(*input, **kwargs)
  File "d:\codes\org\python\FV\quantized_models.py", line 489, in forward
    out = self.se(out)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 726, in _call_impl
    result = self.forward(*input, **kwargs)
  File "d:\codes\org\python\FV\quantized_models.py", line 447, in forward
    y = self.prelu_q(y)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 726, in _call_impl
    result = self.forward(*input, **kwargs)
  File "d:\codes\org\python\FV\quantized_models.py", line 322, in forward
    inputs = self.quantized_op.add(torch.relu(inputs), weight_min_res)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\quantized\modules\functional_modules.py", line 46, in add
    r = self.activation_post_process(r)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\nn\modules\module.py", line 726, in _call_impl
    result = self.forward(*input, **kwargs)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\quantization\observer.py", line 862, in forward
    self.bins)
  File "C:\Users\User\Anaconda3\Lib\site-packages\torch\quantization\observer.py", line 813, in _combine_histograms
    histogram_with_output_range = torch.zeros((Nbins * downsample_rate), device=orig_hist.device)
RuntimeError: Trying to create tensor with negative dimension -4398046511104: [-4398046511104]

</code></pre>

Shisho_Sama: <p><a class="mention" href="/u/supriyar">@supriyar</a> Any ideas whats the problem here?<br>
its greatly appreciated</p>

supriyar: <p>The initial error was due to the histogram observer getting a tensor with same values or all zero values. But since that was fixed I am not quite sure of the cause of this error.<br>
Could you provide a small repro for us to take a look? Along with the input tensor data for which this error shows up. Thanks!</p>

Shisho_Sama: <p>seems updating to 1.7 solved this issue! Hoever, the Unimplemented type and native bn related issues are still present.<br>
I created a minimal self contained example with Resnet18 and a simple 2 layered Network from quantizing the model to testing it using fake data.<br>
By setting the  two variables at the top (use_relu, disable_single_bn) you can see different behaviors(most of the code is biolerplates and resnet18 definitions)<br>
you are free to test this both with the <code>ResNet18</code> or the <code>SimpleNetwork</code>:</p>
<pre><code class="lang-python">import os
from os.path import abspath, dirname, join
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision.datasets import FakeData
import torchvision.transforms as transforms
from torch.quantization import fuse_modules
use_relu = False
disable_single_bns = False

class PReLU_Quantized(nn.Module):
    def __init__(self, prelu_object):
        super().__init__()
        self.prelu_weight = prelu_object.weight
        self.weight = self.prelu_weight
        self.quantized_op = nn.quantized.FloatFunctional()
        self.quant = torch.quantization.QuantStub()
        self.dequant = torch.quantization.DeQuantStub()

    def forward(self, inputs):
        # inputs = max(0, inputs) + alpha * min(0, inputs) 
        # this is how we do it 
        # pos = torch.relu(inputs)
        # neg = -alpha * torch.relu(-inputs)
        # res3 = pos + neg
        self.weight = self.quant(self.weight)
        weight_min_res = self.quantized_op.mul(-self.weight, torch.relu(-inputs))
        inputs = self.quantized_op.add(torch.relu(inputs), weight_min_res)
        inputs = self.dequant(inputs)
        self.weight = self.dequant(self.weight)
        return inputs

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().__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride
        self.add_relu = torch.nn.quantized.FloatFunctional()

    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)
        out = self.add_relu.add_relu(out, residual)
        return out

    def fuse_model(self):
        torch.quantization.fuse_modules(self, [['conv1', 'bn1', 'relu'],
                                               ['conv2', 'bn2']], inplace=True)
        if self.downsample:
            torch.quantization.fuse_modules(self.downsample, ['0', '1'], inplace=True)

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 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu1 = nn.ReLU(inplace=False)
        self.relu2 = nn.ReLU(inplace=False)
        self.downsample = downsample
        self.stride = stride
        self.skip_add_relu = nn.quantized.FloatFunctional()

    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu1(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu2(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)
        out = self.skip_add_relu.add_relu(out, residual)
        return out

    def fuse_model(self):
        fuse_modules(self, [['conv1', 'bn1', 'relu1'],
                            ['conv2', 'bn2', 'relu2'],
                            ['conv3', 'bn3']], inplace=True)
        if self.downsample:
            torch.quantization.fuse_modules(self.downsample, ['0', '1'], inplace=True)

class SEBlock(nn.Module):
    def __init__(self, channel, reduction=16):
        super().__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.mult_xy = nn.quantized.FloatFunctional()
        self.fc = nn.Sequential(nn.Linear(channel, channel // reduction),
                                nn.PReLU(),
                                nn.Linear(channel // reduction, channel),
                                nn.Sigmoid())
        self.fc1 = self.fc[0]
        self.prelu = self.fc[1]
        self.fc2 = self.fc[2]
        self.sigmoid = self.fc[3]
        self.prelu_q = PReLU_Quantized(self.prelu)
        if use_relu:
            self.prelu_q_or_relu = torch.relu
        else:
            self.prelu_q_or_relu = self.prelu_q

    def forward(self, x):
        # print(f'&lt;inside se forward:&gt;')
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        # y = self.fc(y).view(b, c, 1, 1)
        y = self.fc1(y)
        y = self.prelu_q_or_relu(y)
        y = self.fc2(y)
        y = self.sigmoid(y).view(b, c, 1, 1)
        # print('--------------------------')
        # out = x*y 
        out = self.mult_xy.mul(x, y)
        return out

class IRBlock(nn.Module):
    expansion = 1
    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
        super().__init__()
        self.bn0 = nn.BatchNorm2d(inplanes)
        if disable_single_bns:
            self.bn0_or_identity = torch.nn.Identity()
        else:
            self.bn0_or_identity = self.bn0

        self.conv1 = conv3x3(inplanes, inplanes)
        self.bn1 = nn.BatchNorm2d(inplanes)
        self.prelu = nn.PReLU()
        self.prelu_q = PReLU_Quantized(self.prelu)
        
        if use_relu:
            self.prelu_q_or_relu = torch.relu
        else:
            self.prelu_q_or_relu = self.prelu_q

        self.conv2 = conv3x3(inplanes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride
        self.use_se = use_se
        # if self.use_se:
        self.se = SEBlock(planes)
        self.add_residual = nn.quantized.FloatFunctional()

    def forward(self, x):
        residual = x
        # TODO:
        # this needs to be quantized as well!
        out = self.bn0_or_identity(x)

        out = self.conv1(out)
        out = self.bn1(out)
        # out = self.prelu(out)
        out = self.prelu_q_or_relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        if self.use_se:
            out = self.se(out)
        if self.downsample is not None:
            residual = self.downsample(x)
        # out += residual
        # out = self.prelu(out)
        out = self.prelu_q_or_relu(out)
        # we may need to change prelu into relu and instead of add, use add_relu here
        out = self.add_residual.add(out, residual)
        return out

    def fuse_model(self):
        fuse_modules(self, [# ['bn0'],
                            ['conv1', 'bn1'],
                            ['conv2', 'bn2']], inplace=True)
        if self.downsample:
            torch.quantization.fuse_modules(self.downsample, ['0', '1'], inplace=True)

class ResNet(nn.Module):

    def __init__(self, block, layers, use_se=True):
        self.inplanes = 64
        self.use_se = use_se
        super().__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.prelu = nn.PReLU()
        self.prelu_q = PReLU_Quantized(self.prelu)
        # This is to only get rid of the unimplemented CPUQuantization type error
        # when we use PReLU_Quantized during test time
        if use_relu:
            self.prelu_q_or_relu = torch.relu
        else:
             self.prelu_q_or_relu = self.prelu_q

        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.bn2 = nn.BatchNorm2d(512)
        # This is to get around the single BatchNorms not getting fused and thus causing 
        # a RuntimeError: Could not run 'aten::native_batch_norm' with arguments from the 'QuantizedCPU' backend.
        # 'aten::native_batch_norm' is only available for these backends: [CPU, MkldnnCPU, BackendSelect, Named, Autograd, Profiler, Tracer, Autocast, Batched].
        # during test time
        if disable_single_bns:
            self.bn2_or_identity = torch.nn.Identity()
        else:
            self.bn2_or_identity = self.bn2

        self.dropout = nn.Dropout()
        self.fc = nn.Linear(512 * 7 * 7, 512)
        self.bn3 = nn.BatchNorm1d(512)
        if disable_single_bns:
            self.bn3_or_identity = torch.nn.Identity()
        else:
            self.bn3_or_identity = self.bn3
        self.quant = torch.quantization.QuantStub()
        self.dequant = torch.quantization.DeQuantStub()

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.xavier_normal_(m.weight)
            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.xavier_normal_(m.weight)
                nn.init.constant_(m.bias, 0)

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

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

        return nn.Sequential(*layers)

    def forward(self, x):
        
        x = self.quant(x)
        x = self.conv1(x)
        # TODO: single bn needs to be fused
        x = self.bn1(x)

        # x = self.prelu(x)
        x = self.prelu_q_or_relu(x)

        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.bn2_or_identity(x)
        x = self.dropout(x)
        # x = x.view(x.size(0), -1)
        x = x.reshape(x.size(0), -1)
        x = self.fc(x)
        # TODO: single bn needs to be fused
        x = self.bn3_or_identity(x)
        x = self.dequant(x)
        return x

    def fuse_model(self):
        r"""Fuse conv/bn/relu modules in resnet models
        Fuse conv+bn+relu/ Conv+relu/conv+Bn modules to prepare for quantization.
        Model is modified in place.  Note that this operation does not change numerics
        and the model after modification is in floating point
        """
        fuse_modules(self, ['conv1', 'bn1'], inplace=True)
        for m in self.modules():
            if type(m) == Bottleneck or type(m) == BasicBlock or type(m) == IRBlock:
                m.fuse_model()

class SimpleNetwork(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=10, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(10)
        self.relu1 = nn.ReLU()

        self.prelu_q = PReLU_Quantized(nn.PReLU())
        self.bn = nn.BatchNorm2d(10)

        self.prelu_q_or_relu = torch.relu if use_relu else self.prelu_q
        self.bn_or_identity = nn.Identity() if disable_single_bns else self.bn    

        self.quant = torch.quantization.QuantStub()
        self.dequant = torch.quantization.DeQuantStub()
    
    def forward(self, x):
        x = self.quant(x)

        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)

        x = self.prelu_q_or_relu(x)
        x = self.bn_or_identity(x)

        x = self.dequant(x)
        return x

def resnet18(use_se=True, **kwargs):
    return ResNet(IRBlock, [2, 2, 2, 2], use_se=use_se, **kwargs)

def print_size_of_model(model):
    torch.save(model.state_dict(), "temp.p")
    print('Size (MB):', os.path.getsize("temp.p")/1e6)
    os.remove('temp.p')

def evaluate(model, data_loader, eval_batches):
    model.eval()
    with torch.no_grad():
        for i, (image, target) in enumerate(data_loader):
            features = model(image)
            print(f'{i})feature dims: {features.shape}')
            if i &gt;= eval_batches:
                return

def load_quantized(model, quantized_checkpoint_file_path):
    model.eval()
    if type(model) == ResNet:
        model.fuse_model()
    # Specify quantization configuration
    model.qconfig = torch.quantization.get_default_qconfig('fbgemm')
    torch.quantization.prepare(model, inplace=True)
    # Convert to quantized model
    torch.quantization.convert(model, inplace=True)
    checkpoint = torch.load(quantized_checkpoint_file_path, map_location=torch.device('cpu'))
    model.load_state_dict(checkpoint)
    print_size_of_model(model)
    return model

def test_the_model(model, dtloader):
    current_dir = abspath(dirname(__file__))
    model = load_quantized(model, join(current_dir, 'data', 'model_quantized_jit.pth'))
    model.eval()
    img, _ = next(iter(dtloader))
    embd1 = model(img)

def quantize_model(model, dtloader):
    calibration_batches = 10 
    saved_model_dir = 'data'
    scripted_quantized_model_file = 'model_quantized_jit.pth'
    # model = resnet18()
    model.eval()
    if type(model) == ResNet:
        model.fuse_model()
    print_size_of_model(model)
    model.qconfig = torch.quantization.get_default_qconfig('fbgemm')
    print(model.qconfig)
    torch.quantization.prepare(model, inplace=True)

    print(f'Model after fusion(prepared): {model}')

    # Calibrate first
    print('Post Training Quantization Prepare: Inserting Observers')
    print('\n Inverted Residual Block:After observer insertion \n\n', model.conv1)

    # Calibrate with the training set
    evaluate(model, dtloader, eval_batches=calibration_batches)
    print('Post Training Quantization: Calibration done')

    # Convert to quantized model
    torch.quantization.convert(model, inplace=True)
    print('Post Training Quantization: Convert done')
    print('\n Inverted Residual Block: After fusion and quantization, note fused modules: \n\n', model.conv1)

    print("Size of model after quantization")
    print_size_of_model(model)
    script = torch.jit.script(model)
    path_tosave = join(dirname(abspath(__file__)), saved_model_dir, scripted_quantized_model_file)
    print(f'path to save: {path_tosave}')
    with open(path_tosave, 'wb') as f:
        torch.save(model.state_dict(), f)

    print(f'model after quantization (prepared and converted:) {model}')
    # torch.jit.save(script, path_tosave)

dataset = FakeData(1000, image_size=(3, 112, 112), num_classes=5, transform=transforms.ToTensor())
data_loader = DataLoader(dataset, batch_size=1)

# quantize the model 
model = resnet18()
# model = SimpleNetwork()
quantize_model(model, data_loader)

# and load and test the quantized model
model = resnet18()
# model = SimpleNetwork()
test_the_model(model, data_loader)

</code></pre>