| | |
| | | import torch |
| | | from torch.nn import functional as F |
| | | from funasr.models.encoder.abs_encoder import AbsEncoder |
| | | from typing import Tuple, Optional |
| | | from funasr.models.pooling.statistic_pooling import statistic_pooling, windowed_statistic_pooling |
| | | from funasr.models.encoder.abs_encoder import AbsEncoder |
| | | from collections import OrderedDict |
| | | import logging |
| | | import numpy as np |
| | |
| | | return xs_pad, ilens |
| | | |
| | | |
| | | class ResNet34(AbsEncoder): |
| | | class ResNet34(torch.nn.Module): |
| | | def __init__( |
| | | self, |
| | | input_size, |
| | |
| | | name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape |
| | | )) |
| | | else: |
| | | var_dict_torch_update[name] = torch.Tensor(map_dict[name]).type(torch.int64).to("cpu") |
| | | var_dict_torch_update[name] = torch.from_numpy(np.array(map_dict[name])).type(torch.int64).to("cpu") |
| | | logging.info("torch tensor: {}, manually assigning to: {}".format( |
| | | name, map_dict[name] |
| | | )) |