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(
'/home/palm/PycharmProjects/seven2/snapshots/infer_model_temp.h5')
query_path = '/home/palm/PycharmProjects/seven/images/cropped5/train'
cache_path = '/home/palm/PycharmProjects/seven/caches'
cache_dict = {}
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'))
cropped_image = cv2.copyMakeBorder(cropped_image, p, p, 0, 0, cv2.BORDER_CONSTANT)
else:
p = ((y2 - y1) - (x2 - x1)) // 2
cropped_image = cv2.copyMakeBorder(cropped_image, 0, 0, p, p, cv2.BORDER_CONSTANT)
return cropped_image
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/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])
labels_to_names = [x.split(',')[0] for x in open('/home/palm/PycharmProjects/seven2/anns/classes.csv').read().split('\n')[:-1]]
prediction_model = models.load_model('/home/palm/PycharmProjects/seven2/snapshots/infer_model_temp.h5')
names_to_labels = {}
for x in open('/home/palm/PycharmProjects/seven2/anns/classes.csv').read().split('\n')[:-1]:
names_to_labels[x.split(',')[0]] = int(x.split(',')[1])
query_path = '/home/palm/PycharmProjects/seven/images/cropped7/train'
cache_path = '/home/palm/PycharmProjects/seven/caches'
cache_dict = {}
""" This is a hot function, hence some optimizations are made. """
diff = np.array(x) - y
return np.sqrt(np.dot(diff, diff))
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/3/epoch_0_0.03454810580774366.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])
lsh = LSHash(hash_size=16, input_dim=1024, num_hashtables=5)
cache_folder = '/home/palm/PycharmProjects/seven/caches'
with torch.no_grad():
target_image_ori = Image.open(
'/home/palm/PycharmProjects/seven/images/cropped2/unknown/obj/0_036.jpg'
)
def __str__(self):
return 'acc()'
if __name__ == '__main__':
save_no = len(os.listdir('./snapshots/pairs'))
impath = '/home/palm/PycharmProjects/seven/images/cropped3/train'
model = Model(ResNet(zero_init_residual=False))
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('./snapshots/base.pth', load_opt=False)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_datagen = DirectorySiameseLoader(
impath,
transforms.Compose([
transforms.Resize(256),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.ToTensor(), normalize
]))
train_generator = train_datagen.get_dset(8, 1)
os.makedirs(f'./snapshots/pairs/{save_no}', exist_ok=True)