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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
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