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6ffdc93 35b51f6 6ffdc93 35b51f6 6ffdc93 35b51f6 6ffdc93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | import os
import gdown
import torch
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import time
import modules.model as model
# Download model if not available
modelsave_name = 'model_parameters.pt'
if os.path.isfile(modelsave_name) == False:
url = 'https://drive.google.com/file/d/1_mYn2LrhG080Xvt26tWBtJ8U_0F2E1-s/view?usp=sharing'
gdown.download(url, output=modelsave_name, fuzzy=True)
# Set device
if torch.backends.mps.is_available():
device = torch.device('mps')
device_name = 'Apple Silicon GPU'
elif torch.cuda.is_available():
device = torch.device('cuda')
device_name = 'CUDA'
else:
device = torch.device('cpu')
device_name = 'CPU'
torch.set_default_device(device)
print(f'\nDevice: {device_name}')
# Make model and load parameters
resnet = model.resnetModel_128()
resnet.load_state_dict(torch.load(modelsave_name, map_location=device))
resnet.eval()
imsize = 128
classes = ('Female', 'Male')
loader = transforms.Compose([
transforms.Resize([imsize, imsize]),
transforms.Grayscale(1),
transforms.ToTensor(),
transforms.Normalize(0, 1)
])
my_dataset = datasets.ImageFolder(
root='images/',
transform=loader
)
my_dataset_loader = DataLoader(
my_dataset,
batch_size=min(len(my_dataset), 10),
generator=torch.Generator(device=device)
)
# Make predictions
start_time = time.time()
with torch.no_grad():
for i, (X, y) in enumerate(my_dataset_loader):
X = X.to(device)
y_pred = resnet.forward(X)
predicted = torch.max(y_pred.data,1)[1]
for j in range(len(X)):
print(f'\nImage: {my_dataset.imgs[j][0]}')
print(f'Prediction: {classes[predicted[j]]}')
print(f'Actual: {classes[y[j]]}')
print(f'{classes[0]} weight: {y_pred[j][0]}')
print(f'{classes[1]} weight: {y_pred[j][1]}')
end_time = time.time()
avg_inference_time = (end_time - start_time)/len(my_dataset)
print(f'\nAverage inference time: {avg_inference_time} seconds per image\n') |