File size: 1,817 Bytes
b386992 | 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 | # Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 pytest
import torch
from nemo.collections.tts.modules import submodules
@pytest.mark.unit
def test_conditional_layer_norm():
# NLP Example
batch, sentence_length, embedding_dim = 20, 5, 10
embedding = torch.randn(batch, sentence_length, embedding_dim)
ln = torch.nn.LayerNorm(embedding_dim)
cln = submodules.ConditionalLayerNorm(embedding_dim)
assert torch.all(ln(embedding) == cln(embedding))
weight = torch.nn.Parameter(torch.randn(embedding_dim))
bias = torch.nn.Parameter(torch.randn(embedding_dim))
ln.weight, ln.bias = weight, bias
cln.weight, cln.bias = weight, bias
assert torch.all(ln(embedding) == cln(embedding)) # Simulate trained weights
# Image Example
N, C, H, W = 20, 5, 10, 10
image = torch.randn(N, C, H, W)
ln = torch.nn.LayerNorm([C, H, W])
cln = submodules.ConditionalLayerNorm([C, H, W])
assert torch.all(ln(image) == cln(image))
weight = torch.nn.Parameter(torch.randn(C, H, W))
bias = torch.nn.Parameter(torch.randn(C, H, W))
ln.weight, ln.bias = weight, bias
cln.weight, cln.bias = weight, bias
assert torch.all(ln(image) == cln(image)) # Simulate trained weights
|