Hello, Im facing a memory leak and I can't find out why. I am simply looping through a lot of images and it gradually fills all my memory, this is my setup: Version facenet-pytorch==2.0.1

mtcnn = MTCNN(image_size=64, keep_all=True)
resnet = InceptionResnetV1(pretrained='vggface2').eval()

for nth, img_path in enumerate(img_paths):
    img = Image.open(img_path.resolve())
    boxes, probs = mtcnn.detect(img)

I know it's not strictly related to facenet-pytorch library, but I do hope that may be you or others can give me an help.

My objective is to use the following class which simply uses your excellent work:

from facenet_pytorch import MTCNN, InceptionResnetV1
import torch
from torch.utils.data import DataLoader
from torchvision import datasets
import numpy as np
import pandas as pd
import os

workers = 0 if os.name == 'nt' else 4

def collate_fn(x):
    return x[0]

def describe(x):
    print("Type: {}".format(x.type()))
    print("Shape/size: {}".format(x.shape))
    print("Values: \n{}".format(x))

class GetFaceEmbedding(torch.nn.Module):
    def __init__(self):
        super(GetFaceEmbedding, self).__init__()

    @classmethod
    def getFaceEmbedding(self, imagePath):

        device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
        print('Running on device: {}'.format(device))

        mtcnn = MTCNN(
            image_size=160, margin=0, min_face_size=20,
            thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True,
            device=device
        )
        resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device)
        dataset = datasets.ImageFolder(imagePath)
        dataset.idx_to_class = {i:c for c, i in dataset.class_to_idx.items()}
        loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=workers)
        aligned = []
        names = []
        for x, y in loader:
            x_aligned, prob = mtcnn(x, return_prob=True)
            if x_aligned is not None:
                print('Face detected with probability: {:8f}'.format(prob))
                aligned.append(x_aligned)
                names.append(dataset.idx_to_class[y])
        aligned = torch.stack(aligned).to(device)
        embeddings = resnet(aligned).detach().cpu()
        return embeddings

With python it works fine:

(venv373) (base) [email protected]:~/PyTorchMatters/facenet_pytorch/examples$ python3 .  
/getFaceEmbedding-01.py 
Running on device: cpu
Face detected with probability: 0.999430
Type: torch.FloatTensor
Shape/size: torch.Size([1, 512])
Values: 
tensor([[ 3.6307e-02, -8.8092e-02, -3.5002e-02, -8.2932e-02,  1.9032e-02,
          2.3228e-02,  2.4253e-02, -3.7844e-02, -6.8906e-02,  2.0351e-02,
         -6.7093e-02,  3.6181e-02, -2.5933e-02, -6.0015e-02,  2.6653e-02,
          9.4335e-02, -2.9241e-02, -2.8357e-02,  7.2207e-02, -3.7747e-02,
          6.3515e-03, -3.0220e-02, -2.4530e-02,  1.0004e-01,  6.6520e-02,
          ....
          3.2497e-02,  2.3421e-02, -5.3921e-02,  1.9589e-02, -2.8655e-03,
          1.3474e-02, -2.2743e-02,  3.2976e-02, -5.6658e-02,  2.0837e-02,
        -4.7152e-02, -6.5534e-02]])

Following the indications found here: https://pytorch.org/tutorials/advanced/cpp_export.html

I added to getFaceEmbedding.py these lines:

my_module = GetFaceEmbedding()
sm = torch.jit.script(my_module)
sm.save("annotated_get_face_embedding.pt")

I then saved the serialized file:

(venv373) (base) [email protected]:~/PyTorchMatters/facenet_pytorch/examples$ python3 
./getFaceEmbedding.py

-rw-r--r-- 1 marco marco 1,4K mar 19 18:52 annotated_get_face_embedding.pt

And created this cpp file:

(venv373) (base) [email protected]:~/PyTorchMatters/facenet_pytorch/examples$ nano 
faceEmbedding.cpp

#include <torch/script.h>
#include <iostream>
#include <memory>
#include <filesystem>

int main(int argc, const char* argv[]) {
    //if(argc != 3) {
    //    std::cerr << "usage:usage: faceEmbedding <path-to-exported-script-module> <path-to-
   // image-file> \n";
    //    return -1;
    //}

  torch::jit::script::Module module;
  try {
      // Deserialize the ScriptModule from a file using torch::jit::load().
      module = torch::jit::load(argv[1]);
      std::filesystem::path imgPath = argv[2];

      // Execute the model and turn its output into a tensor
      at::Tensor output = module.getFaceEmbedding(imgPath).ToTensor();
  }
  catch (const c10::Error& e) {
      std::cerr << "error loading the model\n";
      return -1;
  }
  std::cout << "ok\n";
} // end of main() function

But during the compilation phase I get this error :

(venv373) (base) [email protected]:~/PyTorchMatters/facenet_pytorch/examples$ mkdir build
(venv373) (base) [email protected]:~/PyTorchMatters/facenet_pytorch/examples$ cd build
(venv373) (base) [email protected]:~/PyTorchMatters/facenet_pytorch/examples/build$ cmake 
-DCMAKE_PREFIX_PATH=/home/marco/PyTorchMatters/libtorch ..
-- The C compiler identification is GNU 9.2.1
-- The CXX compiler identification is GNU 9.2.1
-- Check for working C compiler: /usr/bin/cc
-- Check for working C compiler: /usr/bin/cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Check for working CXX compiler: /usr/bin/c++
-- Check for working CXX compiler: /usr/bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Looking for pthread.h
-- Looking for pthread.h - found
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Failed
-- Looking for pthread_create in pthreads
-- Looking for pthread_create in pthreads - not found
-- Looking for pthread_create in pthread
-- Looking for pthread_create in pthread - found
-- Found Threads: TRUE  
-- Found torch: /home/marco/PyTorchMatters/libtorch/lib/libtorch.so  
-- Configuring done
-- Generating done
-- Build files have been written to: /home/marco/PyTorchMatters/facenet_pytorch/examples/build

(venv373) (base) [email protected]:~/PyTorchMatters/facenet_pytorch/examples/build$ cmake --build 
. 
--config Release
Scanning dependencies of target faceEmbedding
[ 50%] Building CXX object CMakeFiles/faceEmbedding.dir/faceEmbedding.cpp.o
    /home/marco/PyTorchMatters/facenet_pytorch/examples/faceEmbedding.cpp: In function ‘int 
main(int, const char**)’:
/home/marco/PyTorchMatters/facenet_pytorch/examples/faceEmbedding.cpp:20:34: error: ‘struct 
torch::jit::script::Module’ has no member named ‘getFaceEmbedding’
   20 |       at::Tensor output = module.getFaceEmbedding(imgPath).ToTensor();
      |                                  ^~~~~~~~~~~~~~~~
CMakeFiles/faceEmbedding.dir/build.make:62: recipe for target 'CMakeFiles/faceEmbedding.dir
/faceEmbedding.cpp.o' failed
make[2]: *** [CMakeFiles/faceEmbedding.dir/faceEmbedding.cpp.o] Error 1
CMakeFiles/Makefile2:75: recipe for target 'CMakeFiles/faceEmbedding.dir/all' failed
make[1]: *** [CMakeFiles/faceEmbedding.dir/all] Error 2
Makefile:83: recipe for target 'all' failed
make: *** [all] Error 2

How to solve the problem? Looking forward to your kind help. Marco

since the internet is too slow to download and frequently disconnected.

I could get the pretained model files some other way.

so how should I set it up locally?

Thank you!

thanks for this repo , i've a problem , how to upload my own dataset for resnet? does inception resnet with resnet are the same thing ?! im new to nn thanks for your advice ..

I'm using this code to fine-tune the model but I'm getting very poor training accuracy, could you please point out what could be the issue:

# define mtcnn
mtcnn = MTCNN(
    image_size=160, margin=0, min_face_size=20,
    thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True,
    device=device
)

# load data
dataset = datasets.ImageFolder(data_dir, transform=transforms.Resize((512, 512)))

dataset.samples = [
    (p, p.replace(data_dir, data_dir + '_cropped'))
        for p, _ in dataset.samples
]

loader = DataLoader(
    dataset,
    num_workers=workers,
    batch_size=batch_size,
    collate_fn=training.collate_pil
)

print('applying mtcnn...')

# apply mtcnn
for i, (x, y) in enumerate(loader):
    mtcnn(x, save_path=y)
    print('\rBatch {} of {}'.format(i + 1, len(loader)), end='')
    
# Remove mtcnn to reduce GPU memory usage
del mtcnn

print('')
print('starting training...')

print(len(dataset.class_to_idx))

resnet = InceptionResnetV1(
    classify=False,
    pretrained='vggface2',
    num_classes=len(dataset.class_to_idx)
).to(device)

optimizer = optim.Adam(resnet.parameters(), lr=0.001)
scheduler = MultiStepLR(optimizer, [5, 10])

trans = transforms.Compose([
    np.float32,
    transforms.ToTensor(),
    fixed_image_standardization
])

dataset = datasets.ImageFolder(data_dir + '_cropped', transform=trans)
img_inds = np.arange(len(dataset))
np.random.shuffle(img_inds)
train_inds = img_inds[:int(0.8 * len(img_inds))]
val_inds = img_inds[int(0.8 * len(img_inds)):]

train_loader = DataLoader(
    dataset,
    num_workers=workers,
    batch_size=batch_size,
    sampler=SubsetRandomSampler(train_inds)
)

val_loader = DataLoader(
    dataset,
    num_workers=workers,
    batch_size=batch_size,
    sampler=SubsetRandomSampler(val_inds)
)

loss_fn = torch.nn.CrossEntropyLoss()
metrics = {
    'fps': training.BatchTimer(),
    'acc': training.accuracy
}

writer = SummaryWriter()
writer.iteration, writer.interval = 0, 10

print('\n\nInitial')
print('-' * 10)
resnet.eval()
training.pass_epoch(
    resnet, loss_fn, val_loader,
    batch_metrics=metrics, show_running=True, device=device,
    writer=writer
)

for epoch in range(epochs):
    print('\nEpoch {}/{}'.format(epoch + 1, epochs))
    print('-' * 10)

    resnet.train()
    training.pass_epoch(
        resnet, loss_fn, train_loader, optimizer, scheduler,
        batch_metrics=metrics, show_running=True, device=device,
        writer=writer
    )

    resnet.eval()
    training.pass_epoch(
        resnet, loss_fn, val_loader,
        batch_metrics=metrics, show_running=True, device=device,
        writer=writer
    )

writer.close()

# save trained model
torch.save(resnet.state_dict(), trained_model)

and this is the accuracy I'm getting after 8 epochs:

Initial

Valid | 1/1 | loss: 6.2234 | fps: 1.4201 | acc: 0.0000

Epoch 1/8

Train | 1/1 | loss: 6.2504 | fps: 0.8825 | acc: 0.0000
Valid | 1/1 | loss: 6.2631 | fps: 1.5254 | acc: 0.0000

Epoch 2/8

Train | 1/1 | loss: 6.1771 | fps: 1.3746 | acc: 0.0000
Valid | 1/1 | loss: 6.2687 | fps: 1.3014 | acc: 0.0000

Epoch 3/8

Train | 1/1 | loss: 6.1627 | fps: 1.0087 | acc: 0.2500
Valid | 1/1 | loss: 6.2471 | fps: 1.5138 | acc: 0.0000

Epoch 4/8

Train | 1/1 | loss: 6.1570 | fps: 1.0297 | acc: 0.2500
Valid | 1/1 | loss: 6.2371 | fps: 1.8226 | acc: 0.0000

Epoch 5/8

Train | 1/1 | loss: 6.1445 | fps: 0.8727 | acc: 0.5000
Valid | 1/1 | loss: 6.2335 | fps: 0.9244 | acc: 0.0000

Epoch 6/8

Train | 1/1 | loss: 6.1274 | fps: 1.1550 | acc: 0.5000
Valid | 1/1 | loss: 6.2234 | fps: 1.6978 | acc: 0.0000

Epoch 7/8

Train | 1/1 | loss: 6.1252 | fps: 1.4416 | acc: 0.2500
Valid | 1/1 | loss: 6.1999 | fps: 1.7895 | acc: 0.0000

Epoch 8/8

Train | 1/1 | loss: 6.1179 | fps: 1.4245 | acc: 0.5000
Valid | 1/1 | loss: 6.1874 | fps: 1.6070 | acc: 0.0000

Hi! This repo is one of the best libraries to perform the task. I have been using your package for the last few weeks on a daily basis without encountering any issues. However, since the last few hours (3 July 2020 GMT+5:30), upon instantiating the resnet model with pretrained models (both, vggface2 and casia-webface) in the following way: resnet = InceptionResnetV1(pretrained='vggface2') The following error is being generated on every run:

---------------------------------------------------------------------------
UnpicklingError                           Traceback (most recent call last)
<ipython-input-76-a829445a43a7> in <module>()
----> 1     resnet = InceptionResnetV1(pretrained='vggface2')

3 frames
/usr/local/lib/python3.6/dist-packages/facenet_pytorch/models/inception_resnet_v1.py in __init__(self, pretrained, classify, num_classes, dropout_prob, device)
    259 
    260         if pretrained is not None:
--> 261             load_weights(self, pretrained)
    262 
    263         if self.num_classes is not None:

/usr/local/lib/python3.6/dist-packages/facenet_pytorch/models/inception_resnet_v1.py in load_weights(mdl, name)
    334             with open(cached_file, 'wb') as f:
    335                 f.write(r.content)
--> 336         state_dict.update(torch.load(cached_file))
    337 
    338     mdl.load_state_dict(state_dict)

/usr/local/lib/python3.6/dist-packages/torch/serialization.py in load(f, map_location, pickle_module, **pickle_load_args)
    591                     return torch.jit.load(f)
    592                 return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
--> 593         return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
    594 
    595 

/usr/local/lib/python3.6/dist-packages/torch/serialization.py in _legacy_load(f, map_location, pickle_module, **pickle_load_args)
    761             "functionality.".format(type(f)))
    762 
--> 763     magic_number = pickle_module.load(f, **pickle_load_args)
    764     if magic_number != MAGIC_NUMBER:
    765         raise RuntimeError("Invalid magic number; corrupt file?")

UnpicklingError: invalid load key, '<'.

My guess is that the pretrained model files were changed/corrupted. Please have a look at it.

Hi Tim, I would like to use this facenet as a feature extractor, i.e., delete the last few FC layers. My Code model = InceptionResnetV1(pretrained='vggface2', num_classes=8, classify=True).to(device) print(model) Result of last few layers: (avgpool_1a): AdaptiveAvgPool2d(output_size=1) (dropout): Dropout(p=0.6, inplace=False) (last_linear): Linear(in_features=1792, out_features=512, bias=False) (last_bn): BatchNorm1d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True) (logits): Linear(in_features=512, out_features=8, bias=True) )' Then, I delete the last FC and BN, so I do the following model = nn.Sequential(*list(model.children())[:-2]) print("After Delete") print(model) Result of last few layers: (13): AdaptiveAvgPool2d(output_size=1) (14): Dropout(p=0.6, inplace=False) (15): Linear(in_features=1792, out_features=512, bias=False)

Yes. This architecture is what I want.

But, if I want to use this pretrained weighted to get a 512d output, then I have the following error: File "/home/xxx/anaconda3/envs/face_env/lib/python3.7/site/packages/torch/nn/functional.py", line 1371, in linear output = input.matmul(weight.t()) RuntimeError: size mismatch, m1: [3584 x 1], m2: [1792 x 512] at /opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/THC/generic/THCTensorMathBlas.cu:273

The repo currently only includes models (MTCNN and InceptionResnetV1). It would be good to add code for updating pretrained models given new data or different training hyperparameters.

I get this error when I read the image with PIL and even cv2. Traceback (most recent call last): File "preprocessing_siwm.py", line 81, in <module> mtcnn_caller(new_testset_path, image_folder+'/',image) File "preprocessing_siwm.py", line 49, in mtcnn_caller img_cropped = mtcnn(img, save_path=path+'spoof/'+image+'/'+image+'.png') File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 722, in _call_impl result = self.forward(*input, **kwargs) File "/usr/local/lib/python3.6/dist-packages/facenet_pytorch/models/mtcnn.py", line 262, in forward batch_boxes, batch_probs, batch_points, img, method=self.selection_method File "/usr/local/lib/python3.6/dist-packages/facenet_pytorch/models/mtcnn.py", line 408, in select_boxes if len(boxes) == 0: TypeError: len() of unsized object I tried checking if the image existed using if img is not None: But that doesn't help. Please guide me if I'm missing on something. Thanks a lot.

It was not possible to pass the mtcnn forward method a batch of np ndarrays images (4D images) stacked on the first dimension, even if described as possible in the documentation and working with the detection methods already implemented. I propose a little change to make it possible. I have tested it against a batch of np ndarray images, a list of ndarrays images, a list of PIL images, single PIL and np ndarrays images and it is working. Great package, thank you!

Thanks @timesler for the great work! I was trying to fine-tune on a small dataset and noticed that the network overfits very quickly. I think it's useful to train only the last linear layer ("logits" in your code). This worked fine for me:

optimizer = optim.Adam(net.module.logits.parameters())

Unrelated-ly, the pre-whiten transform may be screwing up the identification a bit as it normalizes each face using its own mean & std-dev. In one of my test videos, an African guy is identified as a white guy. So using a standard pre-processing for all faces may be a good idea. I see there's a related pull-request. Maybe I will experiment a bit myself next week.

Thanks again.

Problem: muti error occurred when I try to run the example from help(MTCNN) on my MacBook Air M1. Env: macOS Monterey 12.6 MacBook Air(M1, 2020) 8GB RAM iBoot 7459.141.1

conda 4.13.0 Python 3.8.13 # packages in environment at /opt/homebrew/anaconda3/envs/pytorch: # # Name Version Build Channel bzip2 1.0.8 h3422bc3_4 conda-forge ca-certificates 2022.9.24 h4653dfc_0 conda-forge certifi 2022.9.24 pypi_0 pypi charset-normalizer 2.1.1 pypi_0 pypi facenet-pytorch 2.5.2 pypi_0 pypi idna 3.4 pypi_0 pypi libffi 3.4.2 h3422bc3_5 conda-forge libsqlite 3.39.4 h76d750c_0 conda-forge libzlib 1.2.12 h03a7124_4 conda-forge ncurses 6.3 h07bb92c_1 conda-forge numpy 1.23.4 pypi_0 pypi opencv-python 4.6.0.66 pypi_0 pypi openssl 3.0.5 h03a7124_2 conda-forge pillow 9.2.0 pypi_0 pypi pip 22.2.2 pyhd8ed1ab_0 conda-forge python 3.8.13 hd3575e6_0_cpython conda-forge readline 8.1.2 h46ed386_0 conda-forge requests 2.28.1 pypi_0 pypi setuptools 65.4.1 pyhd8ed1ab_0 conda-forge sqlite 3.39.4 h2229b38_0 conda-forge tk 8.6.12 he1e0b03_0 conda-forge torch 1.14.0.dev20221012 pypi_0 pypi torchvision 0.15.0.dev20221012 pypi_0 pypi typing-extensions 4.4.0 pypi_0 pypi urllib3 1.26.12 pypi_0 pypi wheel 0.37.1 pyhd8ed1ab_0 conda-forge xz 5.2.6 h57fd34a_0 conda-forge

Result: (pytorch) Running on device: mps /opt/homebrew/anaconda3/envs/pytorch/lib/python3.8/site-packages/facenet_pytorch/models/utils/detect_face.py:210: UserWarning: The operator 'aten::nonzero' is not currently supported on the MPS backend and will fall back to run on the CPU. This may have performance implications. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/mps/MPSFallback.mm:11.) mask_inds = mask.nonzero() Traceback (most recent call last): File "facenetMTCNN_example.py", line 13, in <module> boxes, probs, points = mtcnn.detect(img, landmarks=True) File "/opt/homebrew/anaconda3/envs/pytorch/lib/python3.8/site-packages/facenet_pytorch/models/mtcnn.py", line 313, in detect batch_boxes, batch_points = detect_face( File "/opt/homebrew/anaconda3/envs/pytorch/lib/python3.8/site-packages/facenet_pytorch/models/utils/detect_face.py", line 79, in detect_face pick = batched_nms(boxes_scale[:, :4], boxes_scale[:, 4], image_inds_scale, 0.5) File "/opt/homebrew/anaconda3/envs/pytorch/lib/python3.8/site-packages/torchvision/ops/boxes.py", line 75, in batched_nms return _batched_nms_coordinate_trick(boxes, scores, idxs, iou_threshold) File "/opt/homebrew/anaconda3/envs/pytorch/lib/python3.8/site-packages/torch/jit/_trace.py", line 1136, in wrapper return fn(*args, **kwargs) File "/opt/homebrew/anaconda3/envs/pytorch/lib/python3.8/site-packages/torchvision/ops/boxes.py", line 94, in _batched_nms_coordinate_trick keep = nms(boxes_for_nms, scores, iou_threshold) File "/opt/homebrew/anaconda3/envs/pytorch/lib/python3.8/site-packages/torchvision/ops/boxes.py", line 41, in nms return torch.ops.torchvision.nms(boxes, scores, iou_threshold) File "/opt/homebrew/anaconda3/envs/pytorch/lib/python3.8/site-packages/torch/_ops.py", line 442, in __call__ return self._op(*args, **kwargs or {}) NotImplementedError: The operator 'torchvision::nms' is not currently implemented for the MPS device. If you want this op to be added in priority during the prototype phase of this feature, please comment on https://github.com/pytorch/pytorch/issues/77764. As a temporary fix, you can set the environment variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op. WARNING: this will be slower than running natively on MPS.

Here's the code: from PIL import Image, ImageDraw from facenet_pytorch import MTCNN, extract_face import cv2 import torch

device = 'mps' if torch.backends.mps.is_available() and torch.backends.mps.is_built() else 'cpu' print("Running on device: {}".format(device))

#cap = cv2.VideoCapture(0) img = Image.open('./photos/Chris/16871.jpg')

mtcnn = MTCNN(keep_all=True, device=device) boxes, probs, points = mtcnn.detect(img, landmarks=True) print(boxes, probs, points)

img_draw = img.copy() draw = ImageDraw.Draw(img_draw) for i, (box, point) in enumerate(zip(boxes, points)):     draw.rectangle(box.tolist(), width=5)     for p in point:         draw.rectangle((p - 10).tolist() + (p + 10).tolist(), width=10)     extract_face(img, box, save_path='detected_face_{}.png'.format(i)) img_draw.save('annotated_faces.png')

Has anybody successfully converted the MTCNN to ONNX? Keep getting the error:

~\Anaconda3\envs\facenet\lib\site-packages\facenet_pytorch\models\utils\detect_face.py in detect_face(imgs, minsize, pnet, rnet, onet, threshold, factor, device)
     81         offset += boxes_scale.shape[0]
     82 
---> 83     boxes = torch.cat(boxes, dim=0)
     84     image_inds = torch.cat(image_inds, dim=0)
     85 

RuntimeError: torch.cat(): expected a non-empty list of Tensors

my input is torch.Size([1, 3, 160, 160]). Why is the output dimensions torch.Size([1, 1792, 3, 3]) and not 512?

I initialize the model like this - resnet = InceptionResnetV1('vggface2').eval()

Running the following code produces an "Unexpected end of file" error:

resnet = InceptionResnetV1(pretrained='vggface2').eval()

Traceback (most recent call last): File "<pyshell#4>", line 1, in resnet = InceptionResnetV1(pretrained='vggface2').eval() File "C:\Users\AVezey\AppData\Local\Programs\Python\Python310\lib\site-packages\facenet_pytorch\models\inception_resnet_v1.py", line 262, in init load_weights(self, pretrained) File "C:\Users\AVezey\AppData\Local\Programs\Python\Python310\lib\site-packages\facenet_pytorch\models\inception_resnet_v1.py", line 329, in load_weights state_dict = torch.load(cached_file) File "C:\Users\AVezey\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\serialization.py", line 713, in load return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args) File "C:\Users\AVezey\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\serialization.py", line 938, in _legacy_load typed_storage._storage._set_from_file( RuntimeError: unexpected EOF, expected 386827 more bytes. The file might be corrupted.

Numpy is throwing deprecation warnings for creating arrays from nested sequences on line 183 of detect_face.py and on lines 339, 340, 341 of mtcnn.py when running the example training script. The fix is to pass 'dtype=object' as a parameter when creating the ndarray. E.g., on line 339 of mtcnn.py np.array(boxes) becomes np.array(boxes, dtype=object)