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inference.py
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inference.py
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import argparse
from spherenet import OmniMNIST, OmniFashionMNIST
from spherenet import SphereConv2D, SphereMaxPool2D
import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
from torchvision import datasets, transforms
class SphereNet(nn.Module):
def __init__(self):
super(SphereNet, self).__init__()
self.conv1 = SphereConv2D(1, 32, stride=1)
self.pool1 = SphereMaxPool2D(stride=2)
self.conv2 = SphereConv2D(32, 64, stride=1)
self.pool2 = SphereMaxPool2D(stride=2)
self.fc = nn.Linear(14400, 10)
def forward(self, x):
x = F.relu(self.pool1(self.conv1(x)))
x = F.relu(self.pool2(self.conv2(x)))
x = x.view(-1, 14400) # flatten, [B, C, H, W) -> (B, C*H*W)
x = self.fc(x)
return x
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
self.fc = nn.Linear(64 * 13 * 13, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2(x), 2))
x = x.view(-1, 64 * 13 * 13) # flatten, [B, C, H, W) -> (B, C*H*W)
x = self.fc(x)
return x
class NetMNIST(nn.Module):
def __init__(self):
super(NetMNIST, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
self.fc = nn.Linear(64 * 5 * 5, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2(x), 2))
x = x.view(-1, 64 * 5 * 5) # flatten, [B, C, H, W) -> (B, C*H*W)
x = self.fc(x)
return x
def test(args, model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
if data.dim() == 3:
data = data.unsqueeze(1) # (B, H, W) -> (B, C, H, W)
output = model(data)
test_loss += F.cross_entropy(output, target).item() # sum up batch loss
pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
def main():
# Training settings
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--data', type=str, default='MNIST',
help='dataset for training, options={"FashionMNIST", "MNIST"}')
parser.add_argument('--batch-size', type=int, default=128, metavar='N',
help='input batch size for training')
parser.add_argument('--test-batch-size', type=int, default=128, metavar='N',
help='input batch size for testing')
parser.add_argument('--epochs', type=int, default=100, metavar='N',
help='number of epochs to train')
parser.add_argument('--optimizer', type=str, default='adam',
help='optimizer, options={"adam, sgd"}')
parser.add_argument('--lr', type=float, default=1e-4, metavar='LR',
help='learning rate')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed')
parser.add_argument('--log-interval', type=int, default=1, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save-interval', type=int, default=1, metavar='N',
help='how many epochs to wait before saving model weights')
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device('cuda' if use_cuda else 'cpu')
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
np.random.seed(args.seed)
if args.data == 'FashionMNIST':
test_dataset = OmniFashionMNIST(fov=120, flip=True, h_rotate=True, v_rotate=True, img_std=255, train=False,
fix_aug=True)
elif args.data == 'MNIST':
test_dataset = OmniMNIST(fov=120, flip=True, h_rotate=True, v_rotate=True, train=False, fix_aug=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.test_batch_size, shuffle=False, **kwargs)
load_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
perspective_test_dataset = datasets.MNIST('datas/MNIST', train=False, download=True, transform=load_transform)
perspective_test_loader = torch.utils.data.DataLoader(perspective_test_dataset, batch_size=args.test_batch_size,
shuffle=False, **kwargs)
perspective_model = NetMNIST()
perspective_model_state_dict = torch.load(
'/home/iago/workspace/SphereNet-pytorch/datas/models/model_perspective.pkl')
perspective_model.load_state_dict(perspective_model_state_dict)
perspective_model = perspective_model.to(device).eval()
sphere_model = SphereNet()
sphere_state_dict = torch.load('/home/iago/workspace/SphereNet-pytorch/datas/models/sphere_model.pkl')
sphere_model.load_state_dict(sphere_state_dict)
sphere_model = sphere_model.to(device).eval()
model = Net()
model_state_dict = torch.load('/home/iago/workspace/SphereNet-pytorch/datas/models/model.pkl')
model.load_state_dict(model_state_dict)
model = model.to(device).eval()
# SphereCNN
print('{} Sphere CNN {}'.format('=' * 10, '=' * 10))
test(args, sphere_model, device, test_loader)
# Conventional CNN
print('{} Conventional CNN {}'.format('=' * 10, '=' * 10))
test(args, model, device, test_loader)
# Perspective CNN
print('{} Perspective CNN {}'.format('=' * 10, '=' * 10))
test(args, perspective_model, device, perspective_test_loader)
if __name__ == '__main__':
main()