Datasets:

echodict
/

NeMo

# 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

	# 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