def predict():
model = Model(ResNet(predict=True))
model.compile(torch.optim.SGD(model.model.parameters(),
lr=0.001,
momentum=0.9,
weight_decay=1e-4),
ContrastiveLoss(),
metric=None,
device='cuda')
model.load_weights(
'/home/palm/PycharmProjects/seven2/snapshots/pairs/5/epoch_1_0.012463876953125.pth'
)
model.model.eval()
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform = transforms.Compose(
[transforms.Resize((224, 224)),
transforms.ToTensor(), normalize])
target_path = '/home/palm/PycharmProjects/seven/images/test6/train'
query_path = '/home/palm/PycharmProjects/seven/images/cropped6/train'
cache_path = '/home/palm/PycharmProjects/seven/caches'
cache_dict = {}
predicted_dict = {}
correct = 0
count = 0
with torch.no_grad():
for target_image_folder in os.listdir(target_path):
if target_image_folder not in os.listdir(query_path):
continue
predicted_dict[target_image_folder] = {}
for target_image_path in os.listdir(
os.path.join(target_path, target_image_folder)):
count += 1
target = os.path.join(target_path, target_image_folder,
target_image_path)
target_image_ori = Image.open(target)
target_image = transform(target_image_ori)
x = torch.zeros((1, 3, 224, 224))
x[0] = target_image
target_features = model.model._forward_impl(x.cuda())
minimum = (float('inf'), 0)
for query_folder in os.listdir(query_path):
for query_image_path in os.listdir(
os.path.join(query_path, query_folder)):
query = os.path.join(query_path, query_folder,
query_image_path)
cache_dict, query_features = memory_cache(
cache_dict, model.model, query,
os.path.join(cache_path, query_folder,
query_image_path + '.pth'), transform)
y = LSHash.euclidean_dist(
target_features.cpu().numpy()[0],
query_features.cpu().numpy()[0])
if y < minimum[0]:
minimum = (y, query_folder)
print(*minimum, target_image_folder)
predicted_dict[target_image_folder][
target_image_path] = minimum[1]
if minimum[1] == target_image_folder:
correct += 1
print(count / correct)
pk.dump(predicted_dict, open('cls_eval.pk', 'wb'))
import cv2
import tensorflow as tf
import keras
import shutil
import time
gpu_options = tf.GPUOptions(allow_growth=True)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
keras.backend.set_session(sess)
if __name__ == '__main__':
model = Model(ResNet(predict=True))
model.compile(torch.optim.SGD(model.model.parameters(),
lr=0.001,
momentum=0.9,
weight_decay=1e-4),
ContrastiveLoss(),
metric=None,
device='cuda')
model.load_weights(
'/home/palm/PycharmProjects/seven2/snapshots/pairs/4/epoch_0_0.016697616640688282.pth'
)
model.model.eval()
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform = transforms.Compose(
[transforms.Resize((224, 224)),
transforms.ToTensor(), normalize])
labels_to_names = {0: 'obj'}
prediction_model = models.load_model(
y_pred = nn.Sigmoid()(y_pred.float())
return nn.BCELoss()(y_pred, y_true)
if __name__ == '__main__':
try:
os.listdir('/root')
rootpath = '/root/palm/DATA/'
except PermissionError:
rootpath = '/home/palm/PycharmProjects/DATA/'
name = 'cifar10'
root = os.path.join(rootpath, name)
model = Model(ResNet())
sgd = SGD(model.model.parameters(), 0.01, 0.9)
model.compile(optimizer=sgd,
loss=SparceBCELoss(),
metric=model.categorical_accuracy(),
device='cuda')
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])