Owaner/CodeComputeX · Datasets at Hugging Face

code stringlengths 17 6.64M
def _make_cross_attention_qkv(d, db, input, keys_input, output, num_heads=8, key_dim=64, value_dim=64, ff_init=("variance_scaling_initializer(mode='fan_in', distribution='uniform', scale=%s)" % 1.0)): d[(output + '_query0')] = {'class': 'linear', 'activation': None, 'with_bias': False, 'from': [input], 'n_out': (...
def generic_add_lsh_attention_layer(d, queries_input, keys_input, values_input, output, *, query_time_axis, key_time_axis, num_heads=8, num_rounds=1, key_dim=64, value_dim=64, dropout=0.0, num_hashes, query_chunk_size, key_chunk_size, key_chunks_before=None, key_chunks_after=None, hash_init=("variance_scaling_initial...
def add_lsh_self_attention_layer(d, input, output, inside_rec_layer=True, past_only=None, time_axis=None, *, num_heads=8, num_rounds=1, key_dim=64, value_dim=64, dropout=0.0, num_hashes, chunk_size, chunks_before=None, chunks_after=None, ff_init=("variance_scaling_initializer(mode='fan_in', distribution='uniform', sc...
def add_lsh_cross_attention_layer(d, db, input, keys_input, output, query_time_axis=None, key_time_axis=None, *, num_heads=8, num_rounds=1, key_dim=64, value_dim=64, dropout=0.0, num_hashes, key_chunk_size, query_chunk_size, key_chunks_before=None, key_chunks_after=None, ff_init=("variance_scaling_initializer(mode='f...
def add_full_lsh_cross_attention_layer(d, db, input, keys_input, output, query_time_axis=None, key_time_axis=None, num_heads=8, key_dim=64, value_dim=64, dropout=0.0, ff_init=("variance_scaling_initializer(mode='fan_in', distribution='uniform', scale=%s)" % 1.0), num_hashes=14, num_rounds=1, mask_current_value=float(...
def add_vanilla_self_attention_layer(d, input, output, inside_rec_layer=True, past_only=None, time_axis=None, num_heads=8, key_dim=64, value_dim=64, dropout=0.0, ff_init=("variance_scaling_initializer(mode='fan_in', distribution='uniform', scale=%s)" % 1.0), share_key_query=False, normalize_keys=None, mask_current=Fa...
def add_vanilla_cross_attention_layer(d, db, input, keys_input, output, query_time_axis=None, key_time_axis=None, num_heads=8, key_dim=64, value_dim=64, dropout=0.0, ff_init=("variance_scaling_initializer(mode='fan_in', distribution='uniform', scale=%s)" % 1.0)): '\n Add a cross-attention layer.\n\n :param dict...
class DataLoader(object): '\n Only load data file and information file.\n ' @staticmethod def parse_data_args(parser): '\n data loader related command line arguments parser\n :param parser:\n :return:\n ' parser.add_argument('--path', type=str, default='....
class DataProcessor(object): data_columns = ['X'] @staticmethod def parse_dp_args(parser): '\n parse data processor related command line arguments\n ' parser.add_argument('--test_neg_n', type=int, default=10, help='Negative sample num for each instance in test/validation set...
class HisDataProcessor(DataProcessor): data_columns = ['X', global_p.C_HISTORY, global_p.C_HISTORY_LENGTH] @staticmethod def parse_dp_args(parser): '\n parse data processor related arguments\n ' parser.add_argument('--max_his', type=int, default=(- 1), help='Max history leng...
class ProLogicRecDP(HisDataProcessor): data_columns = ['X', global_p.C_HISTORY, global_p.C_HISTORY_POS_TAG, global_p.C_HISTORY_LENGTH] def format_data_dict(self, df): '\n 除了常规的uid,iid,label,user、item、context特征外，还需处理历史交互\n :param df: 训练、验证、测试df\n :return:\n ' his_li...
def main(): init_parser = argparse.ArgumentParser(description='Model') init_parser.add_argument('--rank', type=int, default=1, help='1=ranking, 0=rating/click') init_parser.add_argument('--data_loader', type=str, default='DataLoader', help='Choose data_loader') init_parser.add_argument('--model_name',...
class BaseModel(torch.nn.Module): '\n Base model, the following methods need to be overridden.\n parse_model_args,\n __init__,\n _init_weights,\n predict,\n forward,\n ' append_id = False include_id = True include_user_features = True include_item_features = True include_c...
class BaseRunner(object): @staticmethod def parse_runner_args(parser): '\n 跑模型的命令行参数\n :param parser:\n :return:\n ' parser.add_argument('--load', type=int, default=0, help='Whether load model and continue to train') parser.add_argument('--epoch', type=int,...
class ProLogicRunner(BaseRunner): @staticmethod def parse_runner_args(parser): '\n 跑模型的命令行参数\n :param parser:\n :return:\n ' parser.add_argument('--load', type=int, default=0, help='Whether load model and continue to train') parser.add_argument('--epoch', t...
def random_split_data(all_data_file, dataset_name, vt_ratio=0.1, u_f=None, i_f=None): '\n 随机切分已经生成的数据集文件 .all.csv -> .train.csv,.validation.csv,.test.csv\n :param all_data_file: 数据预处理完的文件 .all.csv\n :param dataset_name: 给数据集起个名字\n :param vt_ratio: 验证集合测试集比例\n :param u_f: 用户特征文件 .user.csv\n ...
def leave_out_by_time(all_data_file, dataset_name, leave_n=1, warm_n=5, u_f=None, i_f=None): '\n Split train/validation/test by timestamp.\n By default, the interactions in all_data_file are already sorted by timestamp.\n :param all_data_file: preprocessed dataset file *.all.csv，which is sorted by timest...
def group_user_interactions_csv(in_csv, out_csv, label='label', sep='\t'): print('group_user_interactions_csv', out_csv) all_data = pd.read_csv(in_csv, sep=sep) group_inters = group_user_interactions_df(in_df=all_data, label=label) group_inters.to_csv(out_csv, sep=sep, index=False) return group_in...
def group_user_interactions_df(in_df, label='label', seq_sep=','): all_data = in_df if (label in all_data.columns): all_data = all_data[(all_data[label] > 0)] (uids, inters) = ([], []) for (name, group) in all_data.groupby('uid'): uids.append(name) inters.append(seq_sep.join(gr...
def mean_reciprocal_rank(rs): "Score is reciprocal of the rank of the first relevant item\n First element is 'rank 1'. Relevance is binary (nonzero is relevant).\n Example from http://en.wikipedia.org/wiki/Mean_reciprocal_rank\n >>> rs = [[0, 0, 1], [0, 1, 0], [1, 0, 0]]\n >>> mean_reciprocal_rank(rs...
def r_precision(r): 'Score is precision after all relevant documents have been retrieved\n Relevance is binary (nonzero is relevant).\n >>> r = [0, 0, 1]\n >>> r_precision(r)\n 0.33333333333333331\n >>> r = [0, 1, 0]\n >>> r_precision(r)\n 0.5\n >>> r = [1, 0, 0]\n >>> r_precision(r)\n ...
def precision_at_k(r, k): 'Score is precision @ k\n Relevance is binary (nonzero is relevant).\n >>> r = [0, 0, 1]\n >>> precision_at_k(r, 1)\n 0.0\n >>> precision_at_k(r, 2)\n 0.0\n >>> precision_at_k(r, 3)\n 0.33333333333333331\n >>> precision_at_k(r, 4)\n Traceback (most recent ca...
def average_precision(r): 'Score is average precision (area under PR curve)\n Relevance is binary (nonzero is relevant).\n >>> r = [1, 1, 0, 1, 0, 1, 0, 0, 0, 1]\n >>> delta_r = 1. / sum(r)\n >>> sum([sum(r[:x + 1]) / (x + 1.) * delta_r for x, y in enumerate(r) if y])\n 0.7833333333333333\n >>> ...
def mean_average_precision(rs): 'Score is mean average precision\n Relevance is binary (nonzero is relevant).\n >>> rs = [[1, 1, 0, 1, 0, 1, 0, 0, 0, 1]]\n >>> mean_average_precision(rs)\n 0.78333333333333333\n >>> rs = [[1, 1, 0, 1, 0, 1, 0, 0, 0, 1], [0]]\n >>> mean_average_precision(rs)\n ...
def dcg_at_k(r, k, method=0): 'Score is discounted cumulative gain (dcg)\n Relevance is positive real values. Can use binary\n as the previous methods.\n Example from\n http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf\n >>> r = [3, 2, 3, 0, 0, 1, 2, 2, 3, 0]\n >>> dc...
def ndcg_at_k(r, k, method=0): 'Score is normalized discounted cumulative gain (ndcg)\n Relevance is positive real values. Can use binary\n as the previous methods.\n Example from\n http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf\n >>> r = [3, 2, 3, 0, 0, 1, 2, 2, 3, 0...
def parse_global_args(parser): parser.add_argument('--gpu', type=str, default='0', help='Set CUDA_VISIBLE_DEVICES') parser.add_argument('--verbose', type=int, default=logging.INFO, help='Logging Level, 0, 10, ..., 50') parser.add_argument('--log_file', type=str, default='../log/log.txt', help='Logging fil...
def balance_data(data): pos_indexes = np.where((data['Y'] == 1))[0] copy_num = int(((len(data['Y']) - len(pos_indexes)) / len(pos_indexes))) if (copy_num > 1): copy_indexes = np.tile(pos_indexes, copy_num) sample_index = np.concatenate([np.arange(0, len(data['Y'])), copy_indexes]) ...
def input_data_is_list(data): if ((type(data) is list) or (type(data) is tuple)): print('input_data_is_list') new_data = {} for k in data[0]: new_data[k] = np.concatenate([d[k] for d in data]) return new_data return data
def format_metric(metric): '\n convert output into string\n :param metric:\n :return:\n ' if ((type(metric) is not tuple) and (type(metric) is not list)): metric = [metric] format_str = [] if ((type(metric) is tuple) or (type(metric) is list)): for m in metric: ...
def shuffle_in_unison_scary(data): rng_state = np.random.get_state() for d in data: np.random.set_state(rng_state) np.random.shuffle(data[d]) return data
def best_result(metric, results_list): if ((type(metric) is list) or (type(metric) is tuple)): metric = metric[0] if (metric in LOWER_METRIC_LIST): return min(results_list) return max(results_list)
def strictly_increasing(l): return all(((x < y) for (x, y) in zip(l, l[1:])))
def strictly_decreasing(l): return all(((x > y) for (x, y) in zip(l, l[1:])))
def non_increasing(l): return all(((x >= y) for (x, y) in zip(l, l[1:])))
def non_decreasing(l): return all(((x <= y) for (x, y) in zip(l, l[1:])))
def monotonic(l): return (non_increasing(l) or non_decreasing(l))
def numpy_to_torch(d): t = torch.from_numpy(d) if (torch.cuda.device_count() > 0): t = t.cuda() return t
def conv3x3(in_planes, out_planes, stride=1): '3x3 convolution with padding' return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 ...
class ResNet(nn.Module): def __init__(self, block, layers, embedding_size=64): self.inplanes = 64 super(ResNet, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) ...
def resnet18(pretrained=False, kwargs): 'Constructs a ResNet-18 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n ' model = ResNet(BasicBlock, [2, 2, 2], kwargs) if pretrained: state = model.state_dict() loaded_state_dict = model_zoo....
def main(): global args, best_acc args = parser.parse_args() args.cuda = ((not args.no_cuda) and torch.cuda.is_available()) torch.manual_seed(args.seed) if args.cuda: torch.cuda.manual_seed(args.seed) if args.visdom: global plotter plotter = VisdomLinePlotter(env_name=a...
def train(train_loader, tnet, criterion, optimizer, epoch): losses = AverageMeter() accs = AverageMeter() emb_norms = AverageMeter() mask_norms = AverageMeter() tnet.train() for (batch_idx, (data1, data2, data3, c)) in enumerate(train_loader): if args.cuda: (data1, data2, d...
def test(test_loader, tnet, criterion, epoch): losses = AverageMeter() accs = AverageMeter() accs_cs = {} for condition in conditions: accs_cs[condition] = AverageMeter() tnet.eval() tnet.embeddingnet.eval() tnet.embeddingnet.embeddingnet.eval() for (batch_idx, (data1, data2, d...
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'): 'Saves checkpoint to disk' directory = ('runs/%s/' % args.name) if (not os.path.exists(directory)): os.makedirs(directory) filename = (directory + filename) torch.save(state, filename) if is_best: shutil.copyfi...
class VisdomLinePlotter(object): 'Plots to Visdom' def __init__(self, env_name='main'): self.viz = Visdom() self.env = env_name self.plots = {} def plot(self, var_name, split_name, x, y, env=None): if (env is not None): print_env = env else: ...
class AverageMeter(object): 'Computes and stores the average and current value' def __init__(self): self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += (val...
def adjust_learning_rate(optimizer, epoch): 'Sets the learning rate to the initial LR decayed by 10 every 30 epochs' lr = (args.lr * ((1 - 0.015) ** epoch)) if args.visdom: plotter.plot('lr', 'learning rate', epoch, lr) for param_group in optimizer.param_groups: param_group['lr'] = lr
def accuracy(dista, distb): margin = 0 pred = ((dista - distb) - margin).cpu().data return (((pred > 0).sum() * 1.0) / dista.size()[0])
def accuracy_id(dista, distb, c, c_id): margin = 0 pred = ((dista - distb) - margin).cpu().data return ((((pred > 0) * (c.cpu().data == c_id)).sum() * 1.0) / (c.cpu().data == c_id).sum())
def default_image_loader(path): return Image.open(path).convert('RGB')
class TripletImageLoader(torch.utils.data.Dataset): def __init__(self, root, base_path, filenames_filename, conditions, split, n_triplets, transform=None, loader=default_image_loader): " filenames_filename: A text file with each line containing the path to an image e.g.,\n images/class1/sa...
def get_params(p): with open(p, 'r') as f: return yaml.safe_load(f)
def generate_launch_description(): logger = LaunchConfiguration('log_level') share_dir = get_package_share_directory('online_fgo') xacro_path = os.path.join(share_dir, 'config/aachen_lc', 'dienstwagen.urdf.xacro') config_common_path = LaunchConfiguration('config_common_path') default_config_common...
def get_params(p): with open(p, 'r') as f: return yaml.safe_load(f)
def generate_launch_description(): logger = LaunchConfiguration('log_level') share_dir = get_package_share_directory('online_fgo') xacro_path = os.path.join(share_dir, 'config/aachen_tc', 'dienstwagen.urdf.xacro') config_common_path = LaunchConfiguration('config_common_path') default_config_common...
def get_params(p): with open(p, 'r') as f: return yaml.safe_load(f)
def generate_launch_description(): logger = LaunchConfiguration('log_level') share_dir = get_package_share_directory('online_fgo') xacro_path = os.path.join(share_dir, 'config/boreas', 'car_boreas.urdf.xacro') config_common_path = LaunchConfiguration('config_common_path') default_config_common = o...
def get_params(p): with open(p, 'r') as f: return yaml.safe_load(f)
def generate_launch_description(): logger = LaunchConfiguration('log_level') share_dir = get_package_share_directory('online_fgo') xacro_path = os.path.join(share_dir, 'config/urbanloco_ca', 'car_ca.urdf.xacro') config_common_path = LaunchConfiguration('config_common_path') default_config_common =...
def get_params(p): with open(p, 'r') as f: return yaml.safe_load(f)
def generate_launch_description(): logger = LaunchConfiguration('log_level') share_dir = get_package_share_directory('online_fgo') xacro_path = os.path.join(share_dir, 'config/deuschland_lc', 'dienstwagen.urdf.xacro') config_common_path = LaunchConfiguration('config_common_path') default_config_co...
def get_params(p): with open(p, 'r') as f: return yaml.safe_load(f)
def generate_launch_description(): logger = LaunchConfiguration('log_level') share_dir = get_package_share_directory('online_fgo') xacro_path = os.path.join(share_dir, 'config/deuschland_lc', 'dienstwagen.urdf.xacro') config_common_path = LaunchConfiguration('config_common_path') default_config_co...
def get_params(p): with open(p, 'r') as f: return yaml.safe_load(f)
def generate_launch_description(): logger = LaunchConfiguration('log_level') share_dir = get_package_share_directory('online_fgo') xacro_path = os.path.join(share_dir, 'config/urbanloco_hk', 'car_ca.urdf.xacro') config_common_path = LaunchConfiguration('config_common_path') default_config_common =...
def get_params(p): with open(p, 'r') as f: return yaml.safe_load(f)
def generate_launch_description(): logger = LaunchConfiguration('log_level') share_dir = get_package_share_directory('online_fgo') config_common_path = LaunchConfiguration('config_common_path') default_config_common = os.path.join(get_package_share_directory('online_fgo'), 'config/shipping', 'common.y...
def get_params(p): with open(p, 'r') as f: return yaml.safe_load(f)
def generate_launch_description(): logger = LaunchConfiguration('log_level') share_dir = get_package_share_directory('online_fgo') xacro_path = os.path.join(share_dir, 'config/aachen_lc', 'dienstwagen.urdf.xacro') config_common_path = LaunchConfiguration('config_common_path') default_config_common...
def get_params(p): with open(p, 'r') as f: return yaml.safe_load(f)
def generate_launch_description(): logger = LaunchConfiguration('log_level') share_dir = get_package_share_directory('online_fgo') xacro_path = os.path.join(share_dir, 'config/aachen_tc', 'dienstwagen.urdf.xacro') config_common_path = LaunchConfiguration('config_common_path') default_config_common...
def get_params(p): with open(p, 'r') as f: return yaml.safe_load(f)
def generate_launch_description(): logger = LaunchConfiguration('log_level') share_dir = get_package_share_directory('online_fgo') xacro_path = os.path.join(share_dir, 'config/boreas', 'car_boreas.urdf.xacro') config_common_path = LaunchConfiguration('config_common_path') default_config_common = o...
def get_params(p): with open(p, 'r') as f: return yaml.safe_load(f)
def generate_launch_description(): logger = LaunchConfiguration('log_level') share_dir = get_package_share_directory('online_fgo') xacro_path = os.path.join(share_dir, 'config/urbanloco_ca', 'car_ca.urdf.xacro') config_common_path = LaunchConfiguration('config_common_path') default_config_common =...
def get_params(p): with open(p, 'r') as f: return yaml.safe_load(f)
def generate_launch_description(): logger = LaunchConfiguration('log_level') share_dir = get_package_share_directory('online_fgo') xacro_path = os.path.join(share_dir, 'config/deuschland_lc', 'dienstwagen.urdf.xacro') config_common_path = LaunchConfiguration('config_common_path') default_config_co...
def get_params(p): with open(p, 'r') as f: return yaml.safe_load(f)
def generate_launch_description(): logger = LaunchConfiguration('log_level') share_dir = get_package_share_directory('online_fgo') xacro_path = os.path.join(share_dir, 'config/deuschland_lc', 'dienstwagen.urdf.xacro') config_common_path = LaunchConfiguration('config_common_path') default_config_co...
def get_params(p): with open(p, 'r') as f: return yaml.safe_load(f)
def generate_launch_description(): logger = LaunchConfiguration('log_level') share_dir = get_package_share_directory('online_fgo') xacro_path = os.path.join(share_dir, 'config/urbanloco_hk', 'car_ca.urdf.xacro') config_common_path = LaunchConfiguration('config_common_path') default_config_common =...
def get_params(p): with open(p, 'r') as f: return yaml.safe_load(f)
def generate_launch_description(): logger = LaunchConfiguration('log_level') share_dir = get_package_share_directory('online_fgo') config_common_path = LaunchConfiguration('config_common_path') default_config_common = os.path.join(get_package_share_directory('online_fgo'), 'config/shipping', 'common.y...
class Config(): def __init__(self): root = self.Scope('') for (k, v) in FLAGS.__dict__['__flags'].iteritems(): root[k] = v self.stack = [root] def iteritems(self): return self.to_dict().iteritems() def to_dict(self): self._pop_stale() out = {}...
def inputs(dataset, batch_size=None, num_preprocess_threads=None): 'Generate batches of ImageNet images for evaluation.\n\n Use this function as the inputs for evaluating a network.\n\n Note that some (minimal) image preprocessing occurs during evaluation\n including central cropping and resizing of the image ...
def decode_jpeg(image_buffer, scope=None): 'Decode a JPEG string into one 3-D float image Tensor.\n\n Args:\n image_buffer: scalar string Tensor.\n scope: Optional scope for op_scope.\n Returns:\n 3-D float Tensor with values ranging from [0, 1).\n ' with tf.op_scope([image_buffer], scope, 'decode...
def distort_color(image, thread_id=0, scope=None): 'Distort the color of the image.\n\n Each color distortion is non-commutative and thus ordering of the color ops\n matters. Ideally we would randomly permute the ordering of the color ops.\n Rather then adding that level of complication, we select a distinct o...
def distort_image(image, height, width, bbox, thread_id=0, scope=None): 'Distort one image for training a network.\n\n Distorting images provides a useful technique for augmenting the data\n set during training in order to make the network invariant to aspects\n of the image that do not effect the label.\n\n ...
def eval_image(image, height, width, scope=None): 'Prepare one image for evaluation.\n\n Args:\n image: 3-D float Tensor\n height: integer\n width: integer\n scope: Optional scope for op_scope.\n Returns:\n 3-D float Tensor of prepared image.\n ' with tf.op_scope([image, height, width], scop...
def image_preprocessing(image_buffer, bbox, train, thread_id=0): 'Decode and preprocess one image for evaluation or training.\n\n Args:\n image_buffer: JPEG encoded string Tensor\n bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]\n where each coordinate is [0, 1) and the coordi...
def parse_example_proto(example_serialized): "Parses an Example proto containing a training example of an image.\n\n The output of the build_image_data.py image preprocessing script is a dataset\n containing serialized Example protocol buffers. Each Example proto contains\n the following fields:\n\n image/h...
def batch_inputs(dataset, batch_size, train, num_preprocess_threads=None, num_readers=1): 'Contruct batches of training or evaluation examples from the image dataset.\n\n Args:\n dataset: instance of Dataset class specifying the dataset.\n See dataset.py for details.\n batch_size: integer\n train: ...
def inference(x, is_training, num_classes=1000, num_blocks=[3, 4, 6, 3], use_bias=False, bottleneck=True): c = Config() c['bottleneck'] = bottleneck c['is_training'] = tf.convert_to_tensor(is_training, dtype='bool', name='is_training') c['ksize'] = 3 c['stride'] = 1 c['use_bias'] = use_bias ...
def inference_small(x, is_training, num_blocks=3, use_bias=False, num_classes=10): c = Config() c['is_training'] = tf.convert_to_tensor(is_training, dtype='bool', name='is_training') c['use_bias'] = use_bias c['fc_units_out'] = num_classes c['num_blocks'] = num_blocks c['num_classes'] = num_cl...
def inference_small_config(x, c): c['bottleneck'] = False c['ksize'] = 3 c['stride'] = 1 with tf.variable_scope('scale1'): c['conv_filters_out'] = 16 c['block_filters_internal'] = 16 c['stack_stride'] = 1 x = conv(x, c) x = bn(x, c) x = activation(x) ...
def _imagenet_preprocess(rgb): 'Changes RGB [0,1] valued image to BGR [0,255] with mean subtracted.' (red, green, blue) = tf.split(3, 3, (rgb * 255.0)) bgr = tf.concat(3, [blue, green, red]) bgr -= IMAGENET_MEAN_BGR return bgr
def loss(logits, labels): cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels) cross_entropy_mean = tf.reduce_mean(cross_entropy) regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) loss_ = tf.add_n(([cross_entropy_mean] + regularization_losses)) ...
def stack(x, c): for n in range(c['num_blocks']): s = (c['stack_stride'] if (n == 0) else 1) c['block_stride'] = s with tf.variable_scope(('block%d' % (n + 1))): x = block(x, c) return x

def _make_cross_attention_qkv(d, db, input, keys_input, output, num_heads=8, key_dim=64, value_dim=64, ff_init=("variance_scaling_initializer(mode='fan_in', distribution='uniform', scale=%s)" % 1.0)): d[(output + '_query0')] = {'class': 'linear', 'activation': None, 'with_bias': False, 'from': [input], 'n_out': (...

def generic_add_lsh_attention_layer(d, queries_input, keys_input, values_input, output, *, query_time_axis, key_time_axis, num_heads=8, num_rounds=1, key_dim=64, value_dim=64, dropout=0.0, num_hashes, query_chunk_size, key_chunk_size, key_chunks_before=None, key_chunks_after=None, hash_init=("variance_scaling_initial...

def add_lsh_self_attention_layer(d, input, output, inside_rec_layer=True, past_only=None, time_axis=None, *, num_heads=8, num_rounds=1, key_dim=64, value_dim=64, dropout=0.0, num_hashes, chunk_size, chunks_before=None, chunks_after=None, ff_init=("variance_scaling_initializer(mode='fan_in', distribution='uniform', sc...

def add_lsh_cross_attention_layer(d, db, input, keys_input, output, query_time_axis=None, key_time_axis=None, *, num_heads=8, num_rounds=1, key_dim=64, value_dim=64, dropout=0.0, num_hashes, key_chunk_size, query_chunk_size, key_chunks_before=None, key_chunks_after=None, ff_init=("variance_scaling_initializer(mode='f...

def add_full_lsh_cross_attention_layer(d, db, input, keys_input, output, query_time_axis=None, key_time_axis=None, num_heads=8, key_dim=64, value_dim=64, dropout=0.0, ff_init=("variance_scaling_initializer(mode='fan_in', distribution='uniform', scale=%s)" % 1.0), num_hashes=14, num_rounds=1, mask_current_value=float(...

def add_vanilla_self_attention_layer(d, input, output, inside_rec_layer=True, past_only=None, time_axis=None, num_heads=8, key_dim=64, value_dim=64, dropout=0.0, ff_init=("variance_scaling_initializer(mode='fan_in', distribution='uniform', scale=%s)" % 1.0), share_key_query=False, normalize_keys=None, mask_current=Fa...

def add_vanilla_cross_attention_layer(d, db, input, keys_input, output, query_time_axis=None, key_time_axis=None, num_heads=8, key_dim=64, value_dim=64, dropout=0.0, ff_init=("variance_scaling_initializer(mode='fan_in', distribution='uniform', scale=%s)" % 1.0)): '\n Add a cross-attention layer.\n\n :param dict...

class DataLoader(object): '\n Only load data file and information file.\n ' @staticmethod def parse_data_args(parser): '\n data loader related command line arguments parser\n :param parser:\n :return:\n ' parser.add_argument('--path', type=str, default='....

class DataProcessor(object): data_columns = ['X'] @staticmethod def parse_dp_args(parser): '\n parse data processor related command line arguments\n ' parser.add_argument('--test_neg_n', type=int, default=10, help='Negative sample num for each instance in test/validation set...

class HisDataProcessor(DataProcessor): data_columns = ['X', global_p.C_HISTORY, global_p.C_HISTORY_LENGTH] @staticmethod def parse_dp_args(parser): '\n parse data processor related arguments\n ' parser.add_argument('--max_his', type=int, default=(- 1), help='Max history leng...

class ProLogicRecDP(HisDataProcessor): data_columns = ['X', global_p.C_HISTORY, global_p.C_HISTORY_POS_TAG, global_p.C_HISTORY_LENGTH] def format_data_dict(self, df): '\n 除了常规的uid,iid,label,user、item、context特征外，还需处理历史交互\n :param df: 训练、验证、测试df\n :return:\n ' his_li...

def main(): init_parser = argparse.ArgumentParser(description='Model') init_parser.add_argument('--rank', type=int, default=1, help='1=ranking, 0=rating/click') init_parser.add_argument('--data_loader', type=str, default='DataLoader', help='Choose data_loader') init_parser.add_argument('--model_name',...

class BaseModel(torch.nn.Module): '\n Base model, the following methods need to be overridden.\n parse_model_args,\n __init__,\n _init_weights,\n predict,\n forward,\n ' append_id = False include_id = True include_user_features = True include_item_features = True include_c...

class BaseRunner(object): @staticmethod def parse_runner_args(parser): '\n 跑模型的命令行参数\n :param parser:\n :return:\n ' parser.add_argument('--load', type=int, default=0, help='Whether load model and continue to train') parser.add_argument('--epoch', type=int,...

class ProLogicRunner(BaseRunner): @staticmethod def parse_runner_args(parser): '\n 跑模型的命令行参数\n :param parser:\n :return:\n ' parser.add_argument('--load', type=int, default=0, help='Whether load model and continue to train') parser.add_argument('--epoch', t...

def random_split_data(all_data_file, dataset_name, vt_ratio=0.1, u_f=None, i_f=None): '\n 随机切分已经生成的数据集文件 *.all.csv -> *.train.csv,*.validation.csv,*.test.csv\n :param all_data_file: 数据预处理完的文件 *.all.csv\n :param dataset_name: 给数据集起个名字\n :param vt_ratio: 验证集合测试集比例\n :param u_f: 用户特征文件 *.user.csv\n ...

def leave_out_by_time(all_data_file, dataset_name, leave_n=1, warm_n=5, u_f=None, i_f=None): '\n Split train/validation/test by timestamp.\n By default, the interactions in all_data_file are already sorted by timestamp.\n :param all_data_file: preprocessed dataset file *.all.csv，which is sorted by timest...

def group_user_interactions_csv(in_csv, out_csv, label='label', sep='\t'): print('group_user_interactions_csv', out_csv) all_data = pd.read_csv(in_csv, sep=sep) group_inters = group_user_interactions_df(in_df=all_data, label=label) group_inters.to_csv(out_csv, sep=sep, index=False) return group_in...

def group_user_interactions_df(in_df, label='label', seq_sep=','): all_data = in_df if (label in all_data.columns): all_data = all_data[(all_data[label] > 0)] (uids, inters) = ([], []) for (name, group) in all_data.groupby('uid'): uids.append(name) inters.append(seq_sep.join(gr...

def mean_reciprocal_rank(rs): "Score is reciprocal of the rank of the first relevant item\n First element is 'rank 1'. Relevance is binary (nonzero is relevant).\n Example from http://en.wikipedia.org/wiki/Mean_reciprocal_rank\n >>> rs = [[0, 0, 1], [0, 1, 0], [1, 0, 0]]\n >>> mean_reciprocal_rank(rs...

def r_precision(r): 'Score is precision after all relevant documents have been retrieved\n Relevance is binary (nonzero is relevant).\n >>> r = [0, 0, 1]\n >>> r_precision(r)\n 0.33333333333333331\n >>> r = [0, 1, 0]\n >>> r_precision(r)\n 0.5\n >>> r = [1, 0, 0]\n >>> r_precision(r)\n ...

def precision_at_k(r, k): 'Score is precision @ k\n Relevance is binary (nonzero is relevant).\n >>> r = [0, 0, 1]\n >>> precision_at_k(r, 1)\n 0.0\n >>> precision_at_k(r, 2)\n 0.0\n >>> precision_at_k(r, 3)\n 0.33333333333333331\n >>> precision_at_k(r, 4)\n Traceback (most recent ca...

def average_precision(r): 'Score is average precision (area under PR curve)\n Relevance is binary (nonzero is relevant).\n >>> r = [1, 1, 0, 1, 0, 1, 0, 0, 0, 1]\n >>> delta_r = 1. / sum(r)\n >>> sum([sum(r[:x + 1]) / (x + 1.) * delta_r for x, y in enumerate(r) if y])\n 0.7833333333333333\n >>> ...

def mean_average_precision(rs): 'Score is mean average precision\n Relevance is binary (nonzero is relevant).\n >>> rs = [[1, 1, 0, 1, 0, 1, 0, 0, 0, 1]]\n >>> mean_average_precision(rs)\n 0.78333333333333333\n >>> rs = [[1, 1, 0, 1, 0, 1, 0, 0, 0, 1], [0]]\n >>> mean_average_precision(rs)\n ...

def dcg_at_k(r, k, method=0): 'Score is discounted cumulative gain (dcg)\n Relevance is positive real values. Can use binary\n as the previous methods.\n Example from\n http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf\n >>> r = [3, 2, 3, 0, 0, 1, 2, 2, 3, 0]\n >>> dc...

def ndcg_at_k(r, k, method=0): 'Score is normalized discounted cumulative gain (ndcg)\n Relevance is positive real values. Can use binary\n as the previous methods.\n Example from\n http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf\n >>> r = [3, 2, 3, 0, 0, 1, 2, 2, 3, 0...

def parse_global_args(parser): parser.add_argument('--gpu', type=str, default='0', help='Set CUDA_VISIBLE_DEVICES') parser.add_argument('--verbose', type=int, default=logging.INFO, help='Logging Level, 0, 10, ..., 50') parser.add_argument('--log_file', type=str, default='../log/log.txt', help='Logging fil...

def balance_data(data): pos_indexes = np.where((data['Y'] == 1))[0] copy_num = int(((len(data['Y']) - len(pos_indexes)) / len(pos_indexes))) if (copy_num > 1): copy_indexes = np.tile(pos_indexes, copy_num) sample_index = np.concatenate([np.arange(0, len(data['Y'])), copy_indexes]) ...

def input_data_is_list(data): if ((type(data) is list) or (type(data) is tuple)): print('input_data_is_list') new_data = {} for k in data[0]: new_data[k] = np.concatenate([d[k] for d in data]) return new_data return data

def format_metric(metric): '\n convert output into string\n :param metric:\n :return:\n ' if ((type(metric) is not tuple) and (type(metric) is not list)): metric = [metric] format_str = [] if ((type(metric) is tuple) or (type(metric) is list)): for m in metric: ...

def shuffle_in_unison_scary(data): rng_state = np.random.get_state() for d in data: np.random.set_state(rng_state) np.random.shuffle(data[d]) return data

def best_result(metric, results_list): if ((type(metric) is list) or (type(metric) is tuple)): metric = metric[0] if (metric in LOWER_METRIC_LIST): return min(results_list) return max(results_list)

def strictly_increasing(l): return all(((x < y) for (x, y) in zip(l, l[1:])))

def strictly_decreasing(l): return all(((x > y) for (x, y) in zip(l, l[1:])))

def non_increasing(l): return all(((x >= y) for (x, y) in zip(l, l[1:])))

def non_decreasing(l): return all(((x <= y) for (x, y) in zip(l, l[1:])))

def monotonic(l): return (non_increasing(l) or non_decreasing(l))

def numpy_to_torch(d): t = torch.from_numpy(d) if (torch.cuda.device_count() > 0): t = t.cuda() return t

def conv3x3(in_planes, out_planes, stride=1): '3x3 convolution with padding' return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)

class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 ...

class ResNet(nn.Module): def __init__(self, block, layers, embedding_size=64): self.inplanes = 64 super(ResNet, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) ...

def resnet18(pretrained=False, **kwargs): 'Constructs a ResNet-18 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n ' model = ResNet(BasicBlock, [2, 2, 2], **kwargs) if pretrained: state = model.state_dict() loaded_state_dict = model_zoo....

def main(): global args, best_acc args = parser.parse_args() args.cuda = ((not args.no_cuda) and torch.cuda.is_available()) torch.manual_seed(args.seed) if args.cuda: torch.cuda.manual_seed(args.seed) if args.visdom: global plotter plotter = VisdomLinePlotter(env_name=a...

def train(train_loader, tnet, criterion, optimizer, epoch): losses = AverageMeter() accs = AverageMeter() emb_norms = AverageMeter() mask_norms = AverageMeter() tnet.train() for (batch_idx, (data1, data2, data3, c)) in enumerate(train_loader): if args.cuda: (data1, data2, d...

def test(test_loader, tnet, criterion, epoch): losses = AverageMeter() accs = AverageMeter() accs_cs = {} for condition in conditions: accs_cs[condition] = AverageMeter() tnet.eval() tnet.embeddingnet.eval() tnet.embeddingnet.embeddingnet.eval() for (batch_idx, (data1, data2, d...

def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'): 'Saves checkpoint to disk' directory = ('runs/%s/' % args.name) if (not os.path.exists(directory)): os.makedirs(directory) filename = (directory + filename) torch.save(state, filename) if is_best: shutil.copyfi...

class VisdomLinePlotter(object): 'Plots to Visdom' def __init__(self, env_name='main'): self.viz = Visdom() self.env = env_name self.plots = {} def plot(self, var_name, split_name, x, y, env=None): if (env is not None): print_env = env else: ...

class AverageMeter(object): 'Computes and stores the average and current value' def __init__(self): self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += (val...

def adjust_learning_rate(optimizer, epoch): 'Sets the learning rate to the initial LR decayed by 10 every 30 epochs' lr = (args.lr * ((1 - 0.015) ** epoch)) if args.visdom: plotter.plot('lr', 'learning rate', epoch, lr) for param_group in optimizer.param_groups: param_group['lr'] = lr

def accuracy(dista, distb): margin = 0 pred = ((dista - distb) - margin).cpu().data return (((pred > 0).sum() * 1.0) / dista.size()[0])

def accuracy_id(dista, distb, c, c_id): margin = 0 pred = ((dista - distb) - margin).cpu().data return ((((pred > 0) * (c.cpu().data == c_id)).sum() * 1.0) / (c.cpu().data == c_id).sum())

def default_image_loader(path): return Image.open(path).convert('RGB')

class TripletImageLoader(torch.utils.data.Dataset): def __init__(self, root, base_path, filenames_filename, conditions, split, n_triplets, transform=None, loader=default_image_loader): " filenames_filename: A text file with each line containing the path to an image e.g.,\n images/class1/sa...