Skip to main content

FID and custom feature extractor

I would like to use a custom feature extractor to calculate FID

according to https://lightning.ai/docs/torchmetrics/stable/image/frechet_inception_distance.html I can use nn.Module for feature

What is wrong with the following code?



import torch
_ = torch.manual_seed(123)
from torchmetrics.image.fid import FrechetInceptionDistance
from torchvision.models import inception_v3


net = inception_v3()
checkpoint = torch.load('checkpoint.pt')
net.load_state_dict(checkpoint['state_dict'])
net.eval()

fid = FrechetInceptionDistance(feature=net)
# generate two slightly overlapping image intensity distributions
imgs_dist1 = torch.randint(0, 200, (100, 3, 299, 299), dtype=torch.uint8)
imgs_dist2 = torch.randint(100, 255, (100, 3, 299, 299), dtype=torch.uint8)
fid.update(imgs_dist1, real=True)
fid.update(imgs_dist2, real=False)
result = fid.compute()

print(result)


Traceback (most recent call last):
  File "foo.py", line 12, in <module>
    fid = FrechetInceptionDistance(feature=net)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Lib/site-packages/torchmetrics/image/fid.py", line 304, in __init__
    num_features = self.inception(dummy_image).shape[-1]
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Lib/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Lib/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Lib/site-packages/torchvision/models/inception.py", line 166, in forward
    x, aux = self._forward(x)
             ^^^^^^^^^^^^^^^^
  File "/Lib/site-packages/torchvision/models/inception.py", line 105, in _forward
    x = self.Conv2d_1a_3x3(x)
        ^^^^^^^^^^^^^^^^^^^^^
  File "/Lib/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Lib/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Lib/site-packages/torchvision/models/inception.py", line 405, in forward
    x = self.conv(x)
        ^^^^^^^^^^^^
  File "/Lib/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Lib/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Lib/site-packages/torch/nn/modules/conv.py", line 460, in forward
    return self._conv_forward(input, self.weight, self.bias)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Lib/site-packages/torch/nn/modules/conv.py", line 456, in _conv_forward
    return F.conv2d(input, weight, bias, self.stride,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: expected scalar type Byte but found Float

Process finished with exit code 1


source https://stackoverflow.com/questions/77675693/fid-and-custom-feature-extractor

Labels

Labels:

Comments

Post a Comment

Popular posts from this blog

How to show number of registered users in Laravel based on usertype?

i'm trying to display data from the database in the admin dashboard i used this: <?php use Illuminate\Support\Facades\DB; $users = DB::table('users')->count(); echo $users; ?> and i have successfully get the correct data from the database but what if i want to display a specific data for example in this user table there is "usertype" that specify if the user is normal user or admin i want to user the same code above but to display a specific usertype i tried this: <?php use Illuminate\Support\Facades\DB; $users = DB::table('users')->count()->WHERE usertype =admin; echo $users; ?> but it didn't work, what am i doing wrong? source https://stackoverflow.com/questions/68199726/how-to-show-number-of-registered-users-in-laravel-based-on-usertype
Post a Comment
Read more

Why is my reports service not connecting?

I am trying to pull some data from a Postgres database using Node.js and node-postures but I can't figure out why my service isn't connecting. my routes/index.js file: const express = require('express'); const router = express.Router(); const ordersCountController = require('../controllers/ordersCountController'); const ordersController = require('../controllers/ordersController'); const weeklyReportsController = require('../controllers/weeklyReportsController'); router.get('/orders_count', ordersCountController); router.get('/orders', ordersController); router.get('/weekly_reports', weeklyReportsController); module.exports = router; My controllers/weeklyReportsController.js file: const weeklyReportsService = require('../services/weeklyReportsService'); const weeklyReportsController = async (req, res) => { try { const data = await weeklyReportsService; res.json({data}) console...
Post a Comment
Read more

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...
Post a Comment
Read more