File size: 2,233 Bytes
827d9ec |
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 |
"""Utility functions for initializing weights and biases."""
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import torch
def _calculate_fan(linear_weight_shape, fan="fan_in"):
fan_out, fan_in = linear_weight_shape
if fan == "fan_in":
f = fan_in
elif fan == "fan_out":
f = fan_out
elif fan == "fan_avg":
f = (fan_in + fan_out) / 2
else:
raise ValueError("Invalid fan option")
return f
def trunc_normal_init_(weights, scale=1.0, fan="fan_in"):
shape = weights.shape
f = _calculate_fan(shape, fan)
scale = scale / max(1, f)
std = math.sqrt(scale)
with torch.no_grad():
torch.nn.init.trunc_normal_(weights, mean=0.0, std=std, a=-2 * std, b=2 * std)
def lecun_normal_init_(weights):
trunc_normal_init_(weights, scale=1.0)
def he_normal_init_(weights):
trunc_normal_init_(weights, scale=2.0)
def glorot_uniform_init_(weights):
torch.nn.init.xavier_uniform_(weights, gain=1)
def final_init_(weights):
with torch.no_grad():
weights.fill_(0.0)
def gating_init_(weights):
with torch.no_grad():
weights.fill_(0.0)
def bias_init_zero_(bias):
with torch.no_grad():
bias.fill_(0.0)
def bias_init_one_(bias):
with torch.no_grad():
bias.fill_(1.0)
def normal_init_(weights):
torch.nn.init.kaiming_normal_(weights, nonlinearity="linear")
def ipa_point_weights_init_(weights):
with torch.no_grad():
softplus_inverse_1 = 0.541324854612918
weights.fill_(softplus_inverse_1)
|