def main(args):
test_ds = MnistDataset(
args.test_image_file,
args.test_label_file,
transform=transforms.Compose([ToTensor()]),
)
test_loader = torch.utils.data.DataLoader(
test_ds,
batch_size=args.batch_size,
collate_fn=collate_fn,
shuffle=False,
)
model = Net().to(device)
model.load_state_dict(torch.load(args.checkpoint))
model.eval()
predicts = []
truths = []
with torch.no_grad():
for i, sample in enumerate(test_loader):
X, Y_true = sample["X"].to(device), sample["Y"].to(device)
output = model(X)
predicts.append(torch.argmax(output, dim=1))
truths.append(Y_true)
predicts = torch.cat(predicts, dim=0)
truths = torch.cat(truths, dim=0)
acc = torch.sum(torch.eq(predicts, truths))
print("Acc: {:.4f}".format(acc / len(predicts)))
model = Net()
# 加载模型
print(model)
if cpu:
checkpoint = torch.load('./weights/best_model-20200904.pth.tar',
map_location=lambda storage, loc: storage)
else:
checkpoint = torch.load('./weights/best_model-20200904.pth.tar')
#new_state_dict = OrderedDict()
# 用了nn.DataParallel的模型需要处理才能在cpu上使用
'''for k, v in checkpoint.items():
name = k[7:] # remove module.
new_state_dict[name] = v
model.load_state_dict(new_state_dict)'''
#model.load_state_dict(torch.load('./weights/best-8-24.pth.tar'))
model.load_state_dict(torch.load('./weights/best_model-20200904.pth.tar'))
model.eval()
model = model.to(device)
a = 0
b = 0
model = Net()
# 加载模型
print(model)
if cpu:
checkpoint = torch.load('./weights/chen_liveness_mobilenetv2.pth.tar',
map_location=lambda storage, loc: storage)
else:
checkpoint = torch.load('./weights/chen_liveness_mobilenetv2.pth.tar')
print('gpu')
new_state_dict = OrderedDict()
# 用了nn.DataParallel的模型需要处理才能在cpu上使用
'''for k, v in checkpoint.items():
name = k[7:] # remove module.
new_state_dict[name] = v
model.load_state_dict(new_state_dict)'''
model.load_state_dict(
torch.load('./weights/chen_liveness_mobilenetv2.pth.tar'))
model.eval()
model = model.to(device)
a = 0
b = 0
start_time = time.time()
# print('Pre-train from:', pre_path)
################################
################################
print('---------- Networks architecture -------------')
print_network(model)
print('----------------------------------------------')
if opt.resume:
if os.path.isfile(opt.resume):
print("=> loading checkpoint '{}'".format(opt.resume))
checkpoint = torch.load(opt.resume)
opt.start_epoch = checkpoint["epoch"] + 1
print(opt.start_epoch)
model.load_state_dict(checkpoint["model"].state_dict())
else:
print("=> no checkpoint found at '{}'".format(opt.resume))
if cuda:
model = model.cuda()
criterion = criterion.cuda()
optimizer = optim.Adam(model.parameters(),
lr=opt.lr,
betas=(0.9, 0.999),
eps=1e-8)
for epoch in range(opt.start_epoch, opt.nEpochs + 1):
train(model, epoch)
# time: 28/06/2017
import os
import torch
import torch.optim as optim
import torch.nn as nn
from torch.autograd import Variable
from config import MODEL_PATH, DATA_PATH
from models.model import transform, Net
from data import loadTrainData
trainset, trainloader = loadTrainData()
net = Net()
if os.path.exists(MODEL_PATH):
net.load_state_dict(torch.load(MODEL_PATH))
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(2): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs
inputs, labels = data
# wrap them in Variable
inputs, labels = Variable(inputs), Variable(labels)
# zero the parameter gradients
state_dict = torch.load(config["model"])
df = pd.read_csv('kaggle_template.csv')
features_test = np.load(
os.path.join(config["load_dir"] + '/_feature_test_assigment.npy'))
features_test_crop = np.load(
os.path.join(config["load_dir"] + '/_feature_test_assigment_crop.npy'))
if config["concatenate"]:
features_test = np.concatenate((features_test, features_test_crop), axis=1)
#Model
model = Net(features_test.shape[1])
model.load_state_dict(state_dict['model'])
model.eval()
if use_cuda:
print('Using GPU')
model.cuda()
else:
print('Using CPU')
for dossier, sous_dossiers, fichiers in os.walk(config["test_dir"]):
for num, fichier in enumerate(fichiers):
num_photo = df.loc[df['Id'] == fichier.split('.')[0]].index[0]
data = torch.tensor(features_test[num])
if use_cuda:
data = data.cuda()
output = model(data)
prout, pred = torch.max(output.data, 0)
