| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748 |
- import io
- from collections import OrderedDict
- import numpy as np
- def statistic_model_parameters(model, prefix=None):
- var_dict = model.state_dict()
- numel = 0
- for i, key in enumerate(sorted(list([x for x in var_dict.keys() if "num_batches_tracked" not in x]))):
- if prefix is None or key.startswith(prefix):
- numel += var_dict[key].numel()
- return numel
- def int2vec(x, vec_dim=8, dtype=np.int32):
- b = ('{:0' + str(vec_dim) + 'b}').format(x)
- # little-endian order: lower bit first
- return (np.array(list(b)[::-1]) == '1').astype(dtype)
- def seq2arr(seq, vec_dim=8):
- return np.row_stack([int2vec(int(x), vec_dim) for x in seq])
- def load_scp_as_dict(scp_path, value_type='str', kv_sep=" "):
- with io.open(scp_path, 'r', encoding='utf-8') as f:
- ret_dict = OrderedDict()
- for one_line in f.readlines():
- one_line = one_line.strip()
- pos = one_line.find(kv_sep)
- key, value = one_line[:pos], one_line[pos + 1:]
- if value_type == 'list':
- value = value.split(' ')
- ret_dict[key] = value
- return ret_dict
- def load_scp_as_list(scp_path, value_type='str', kv_sep=" "):
- with io.open(scp_path, 'r', encoding='utf8') as f:
- ret_dict = []
- for one_line in f.readlines():
- one_line = one_line.strip()
- pos = one_line.find(kv_sep)
- key, value = one_line[:pos], one_line[pos + 1:]
- if value_type == 'list':
- value = value.split(' ')
- ret_dict.append((key, value))
- return ret_dict
|
