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)), ])