Spaces:
Runtime error
Runtime error
File size: 11,510 Bytes
0102e16 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 |
from typing import List
from typing import Optional
from typing import Sequence
from typing import Tuple
from typing import Union
import logging
import torch
import torch.nn as nn
from torch.nn import functional as F
import numpy as np
from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask
from funasr_detach.models.transformer.layer_norm import LayerNorm
from funasr_detach.models.encoder.abs_encoder import AbsEncoder
import math
from funasr_detach.models.transformer.utils.repeat import repeat
class EncoderLayer(nn.Module):
def __init__(
self,
input_units,
num_units,
kernel_size=3,
activation="tanh",
stride=1,
include_batch_norm=False,
residual=False,
):
super().__init__()
left_padding = math.ceil((kernel_size - stride) / 2)
right_padding = kernel_size - stride - left_padding
self.conv_padding = nn.ConstantPad1d((left_padding, right_padding), 0.0)
self.conv1d = nn.Conv1d(
input_units,
num_units,
kernel_size,
stride,
)
self.activation = self.get_activation(activation)
if include_batch_norm:
self.bn = nn.BatchNorm1d(num_units, momentum=0.99, eps=1e-3)
self.residual = residual
self.include_batch_norm = include_batch_norm
self.input_units = input_units
self.num_units = num_units
self.stride = stride
@staticmethod
def get_activation(activation):
if activation == "tanh":
return nn.Tanh()
else:
return nn.ReLU()
def forward(self, xs_pad, ilens=None):
outputs = self.conv1d(self.conv_padding(xs_pad))
if self.residual and self.stride == 1 and self.input_units == self.num_units:
outputs = outputs + xs_pad
if self.include_batch_norm:
outputs = self.bn(outputs)
# add parenthesis for repeat module
return self.activation(outputs), ilens
class ConvEncoder(AbsEncoder):
"""
Author: Speech Lab of DAMO Academy, Alibaba Group
Convolution encoder in OpenNMT framework
"""
def __init__(
self,
num_layers,
input_units,
num_units,
kernel_size=3,
dropout_rate=0.3,
position_encoder=None,
activation="tanh",
auxiliary_states=True,
out_units=None,
out_norm=False,
out_residual=False,
include_batchnorm=False,
regularization_weight=0.0,
stride=1,
tf2torch_tensor_name_prefix_torch: str = "speaker_encoder",
tf2torch_tensor_name_prefix_tf: str = "EAND/speaker_encoder",
):
super().__init__()
self._output_size = num_units
self.num_layers = num_layers
self.input_units = input_units
self.num_units = num_units
self.kernel_size = kernel_size
self.dropout_rate = dropout_rate
self.position_encoder = position_encoder
self.out_units = out_units
self.auxiliary_states = auxiliary_states
self.out_norm = out_norm
self.activation = activation
self.out_residual = out_residual
self.include_batch_norm = include_batchnorm
self.regularization_weight = regularization_weight
self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
if isinstance(stride, int):
self.stride = [stride] * self.num_layers
else:
self.stride = stride
self.downsample_rate = 1
for s in self.stride:
self.downsample_rate *= s
self.dropout = nn.Dropout(dropout_rate)
self.cnn_a = repeat(
self.num_layers,
lambda lnum: EncoderLayer(
input_units if lnum == 0 else num_units,
num_units,
kernel_size,
activation,
self.stride[lnum],
include_batchnorm,
residual=True if lnum > 0 else False,
),
)
if self.out_units is not None:
left_padding = math.ceil((kernel_size - stride) / 2)
right_padding = kernel_size - stride - left_padding
self.out_padding = nn.ConstantPad1d((left_padding, right_padding), 0.0)
self.conv_out = nn.Conv1d(
num_units,
out_units,
kernel_size,
)
if self.out_norm:
self.after_norm = LayerNorm(out_units)
def output_size(self) -> int:
return self.num_units
def forward(
self,
xs_pad: torch.Tensor,
ilens: torch.Tensor,
prev_states: torch.Tensor = None,
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
inputs = xs_pad
if self.position_encoder is not None:
inputs = self.position_encoder(inputs)
if self.dropout_rate > 0:
inputs = self.dropout(inputs)
outputs, _ = self.cnn_a(inputs.transpose(1, 2), ilens)
if self.out_units is not None:
outputs = self.conv_out(self.out_padding(outputs))
outputs = outputs.transpose(1, 2)
if self.out_norm:
outputs = self.after_norm(outputs)
if self.out_residual:
outputs = outputs + inputs
return outputs, ilens, None
def gen_tf2torch_map_dict(self):
tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf
map_dict_local = {
# torch: conv1d.weight in "out_channel in_channel kernel_size"
# tf : conv1d.weight in "kernel_size in_channel out_channel"
# torch: linear.weight in "out_channel in_channel"
# tf : dense.weight in "in_channel out_channel"
"{}.cnn_a.0.conv1d.weight".format(tensor_name_prefix_torch): {
"name": "{}/cnn_a/conv1d/kernel".format(tensor_name_prefix_tf),
"squeeze": None,
"transpose": (2, 1, 0),
},
"{}.cnn_a.0.conv1d.bias".format(tensor_name_prefix_torch): {
"name": "{}/cnn_a/conv1d/bias".format(tensor_name_prefix_tf),
"squeeze": None,
"transpose": None,
},
"{}.cnn_a.layeridx.conv1d.weight".format(tensor_name_prefix_torch): {
"name": "{}/cnn_a/conv1d_layeridx/kernel".format(tensor_name_prefix_tf),
"squeeze": None,
"transpose": (2, 1, 0),
},
"{}.cnn_a.layeridx.conv1d.bias".format(tensor_name_prefix_torch): {
"name": "{}/cnn_a/conv1d_layeridx/bias".format(tensor_name_prefix_tf),
"squeeze": None,
"transpose": None,
},
}
if self.out_units is not None:
# add output layer
map_dict_local.update(
{
"{}.conv_out.weight".format(tensor_name_prefix_torch): {
"name": "{}/cnn_a/conv1d_{}/kernel".format(
tensor_name_prefix_tf, self.num_layers
),
"squeeze": None,
"transpose": (2, 1, 0),
}, # tf: (1, 256, 256) -> torch: (256, 256, 1)
"{}.conv_out.bias".format(tensor_name_prefix_torch): {
"name": "{}/cnn_a/conv1d_{}/bias".format(
tensor_name_prefix_tf, self.num_layers
),
"squeeze": None,
"transpose": None,
}, # tf: (256,) -> torch: (256,)
}
)
return map_dict_local
def convert_tf2torch(
self,
var_dict_tf,
var_dict_torch,
):
map_dict = self.gen_tf2torch_map_dict()
var_dict_torch_update = dict()
for name in sorted(var_dict_torch.keys(), reverse=False):
if name.startswith(self.tf2torch_tensor_name_prefix_torch):
# process special (first and last) layers
if name in map_dict:
name_tf = map_dict[name]["name"]
data_tf = var_dict_tf[name_tf]
if map_dict[name]["squeeze"] is not None:
data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"])
if map_dict[name]["transpose"] is not None:
data_tf = np.transpose(data_tf, map_dict[name]["transpose"])
data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
assert (
var_dict_torch[name].size() == data_tf.size()
), "{}, {}, {} != {}".format(
name, name_tf, var_dict_torch[name].size(), data_tf.size()
)
var_dict_torch_update[name] = data_tf
logging.info(
"torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape
)
)
# process general layers
else:
# self.tf2torch_tensor_name_prefix_torch may include ".", solve this case
names = name.replace(
self.tf2torch_tensor_name_prefix_torch, "todo"
).split(".")
layeridx = int(names[2])
name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
if name_q in map_dict.keys():
name_v = map_dict[name_q]["name"]
name_tf = name_v.replace("layeridx", "{}".format(layeridx))
data_tf = var_dict_tf[name_tf]
if map_dict[name_q]["squeeze"] is not None:
data_tf = np.squeeze(
data_tf, axis=map_dict[name_q]["squeeze"]
)
if map_dict[name_q]["transpose"] is not None:
data_tf = np.transpose(
data_tf, map_dict[name_q]["transpose"]
)
data_tf = (
torch.from_numpy(data_tf).type(torch.float32).to("cpu")
)
assert (
var_dict_torch[name].size() == data_tf.size()
), "{}, {}, {} != {}".format(
name, name_tf, var_dict_torch[name].size(), data_tf.size()
)
var_dict_torch_update[name] = data_tf
logging.info(
"torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
name,
data_tf.size(),
name_tf,
var_dict_tf[name_tf].shape,
)
)
else:
logging.warning("{} is missed from tf checkpoint".format(name))
return var_dict_torch_update
|