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# Copyright (c) 2020, NVIDIA CORPORATION. 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 os
import tempfile
import numpy as np
import pytest
import torch
from omegaconf import DictConfig, OmegaConf
from nemo.collections.asr.models import EncDecCTCModel
try:
from eff.cookbooks import NeMoCookbook
_EFF_PRESENT_ = True
except ImportError:
_EFF_PRESENT_ = False
# A decorator marking the EFF requirement.
requires_eff = pytest.mark.skipif(not _EFF_PRESENT_, reason="Export File Format library required to run test")
@pytest.fixture()
def asr_model():
preprocessor = {'cls': 'nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor', 'params': dict({})}
encoder = {
'cls': 'nemo.collections.asr.modules.ConvASREncoder',
'params': {
'feat_in': 64,
'activation': 'relu',
'conv_mask': True,
'jasper': [
{
'filters': 1024,
'repeat': 1,
'kernel': [1],
'stride': [1],
'dilation': [1],
'dropout': 0.0,
'residual': False,
'separable': True,
'se': True,
'se_context_size': -1,
}
],
},
}
decoder = {
'cls': 'nemo.collections.asr.modules.ConvASRDecoder',
'params': {
'feat_in': 1024,
'num_classes': 28,
'vocabulary': [
' ',
'a',
'b',
'c',
'd',
'e',
'f',
'g',
'h',
'i',
'j',
'k',
'l',
'm',
'n',
'o',
'p',
'q',
'r',
's',
't',
'u',
'v',
'w',
'x',
'y',
'z',
"'",
],
},
}
modelConfig = DictConfig(
{'preprocessor': DictConfig(preprocessor), 'encoder': DictConfig(encoder), 'decoder': DictConfig(decoder)}
)
model_instance = EncDecCTCModel(cfg=modelConfig)
return model_instance
class TestFileIO:
@pytest.mark.unit
def test_to_from_config_file(self, asr_model):
"""" Test makes sure that the second instance created with the same configuration (BUT NOT checkpoint)
has different weights. """
with tempfile.NamedTemporaryFile() as fp:
yaml_filename = fp.name
asr_model.to_config_file(path2yaml_file=yaml_filename)
next_instance = EncDecCTCModel.from_config_file(path2yaml_file=yaml_filename)
assert isinstance(next_instance, EncDecCTCModel)
assert len(next_instance.decoder.vocabulary) == 28
assert asr_model.num_weights == next_instance.num_weights
w1 = asr_model.encoder.encoder[0].mconv[0].conv.weight.data.detach().cpu().numpy()
w2 = next_instance.encoder.encoder[0].mconv[0].conv.weight.data.detach().cpu().numpy()
assert not np.array_equal(w1, w2)
@pytest.mark.unit
def test_save_restore_from_nemo_file(self, asr_model):
"""" Test makes sure that the second instance created from the same configuration AND checkpoint
has the same weights. """
with tempfile.NamedTemporaryFile() as fp:
filename = fp.name
# Save model (with random artifact).
with tempfile.NamedTemporaryFile() as artifact:
asr_model.register_artifact(config_path="abc", src=artifact.name)
asr_model.save_to(save_path=filename)
# Restore the model.
asr_model2 = EncDecCTCModel.restore_from(restore_path=filename)
assert len(asr_model.decoder.vocabulary) == len(asr_model2.decoder.vocabulary)
assert asr_model.num_weights == asr_model2.num_weights
w1 = asr_model.encoder.encoder[0].mconv[0].conv.weight.data.detach().cpu().numpy()
w2 = asr_model2.encoder.encoder[0].mconv[0].conv.weight.data.detach().cpu().numpy()
assert np.array_equal(w1, w2)
@requires_eff
@pytest.mark.unit
def test_eff_save_restore_from_nemo_file_encrypted(self, asr_model):
"""" Test makes sure that after encrypted save-restore the model has the same weights. """
with tempfile.NamedTemporaryFile() as fp:
filename = fp.name
# Set key - use checkpoint encryption.
NeMoCookbook.set_encryption_key("test_key")
# Save model (with random artifact).
with tempfile.NamedTemporaryFile() as artifact:
asr_model.register_artifact(config_path="abc", src=artifact.name)
asr_model.save_to(save_path=filename)
# Try to restore the encrypted archive (weights) without the encryption key.
NeMoCookbook.set_encryption_key(None)
with pytest.raises(PermissionError):
# Restore the model.
asr_model2 = EncDecCTCModel.restore_from(restore_path=filename)
# Restore the model.
NeMoCookbook.set_encryption_key("test_key")
asr_model3 = EncDecCTCModel.restore_from(restore_path=filename)
# Reset encryption so it won't mess up with other save/restore.
NeMoCookbook.set_encryption_key(None)
assert asr_model.num_weights == asr_model3.num_weights
@pytest.mark.unit
def test_save_restore_from_nemo_file_with_override(self, asr_model, tmpdir):
"""" Test makes sure that the second instance created from the same configuration AND checkpoint
has the same weights.
Args:
tmpdir: fixture providing a temporary directory unique to the test invocation.
"""
# Name of the archive in tmp folder.
filename = os.path.join(tmpdir, "eff.nemo")
# Get path where the command is executed - the artifacts will be "retrieved" there.
# (original .nemo behavior)
cwd = os.getcwd()
with tempfile.NamedTemporaryFile(mode='a+') as conf_fp:
# Create a "random artifact".
with tempfile.NamedTemporaryFile(mode="w", delete=False) as artifact:
artifact.write("magic content 42")
# Remember the filename of the artifact.
_, artifact_filename = os.path.split(artifact.name)
# Add artifact to model.
asr_model.register_artifact(config_path="abc", src=artifact.name)
# Save model (with "random artifact").
asr_model.save_to(save_path=filename)
# Modify config slightly
cfg = asr_model.cfg
cfg.encoder.activation = 'swish'
yaml_cfg = OmegaConf.to_yaml(cfg)
conf_fp.write(yaml_cfg)
conf_fp.seek(0)
# Restore the model.
asr_model2 = EncDecCTCModel.restore_from(restore_path=filename, override_config_path=conf_fp.name)
assert len(asr_model.decoder.vocabulary) == len(asr_model2.decoder.vocabulary)
assert asr_model.num_weights == asr_model2.num_weights
w1 = asr_model.encoder.encoder[0].mconv[0].conv.weight.data.detach().cpu().numpy()
w2 = asr_model2.encoder.encoder[0].mconv[0].conv.weight.data.detach().cpu().numpy()
assert np.array_equal(w1, w2)
assert asr_model2.cfg.encoder.activation == 'swish'
@pytest.mark.unit
def test_save_model_level_pt_ckpt(self, asr_model):
with tempfile.TemporaryDirectory() as ckpt_dir:
nemo_file = os.path.join(ckpt_dir, 'asr.nemo')
asr_model.save_to(nemo_file)
# Save model level PT checkpoint
asr_model.extract_state_dict_from(nemo_file, ckpt_dir)
ckpt_path = os.path.join(ckpt_dir, 'model_weights.ckpt')
assert os.path.exists(ckpt_path)
# Restore the model.
asr_model2 = EncDecCTCModel.restore_from(restore_path=nemo_file)
assert len(asr_model.decoder.vocabulary) == len(asr_model2.decoder.vocabulary)
assert asr_model.num_weights == asr_model2.num_weights
# Change weights values
asr_model2.encoder.encoder[0].mconv[0].conv.weight.data += 1.0
w1 = asr_model.encoder.encoder[0].mconv[0].conv.weight.data.detach().cpu().numpy()
w2 = asr_model2.encoder.encoder[0].mconv[0].conv.weight.data.detach().cpu().numpy()
assert not np.array_equal(w1, w2)
# Restore from checkpoint
asr_model2.load_state_dict(torch.load(ckpt_path))
w1 = asr_model.encoder.encoder[0].mconv[0].conv.weight.data.detach().cpu().numpy()
w2 = asr_model2.encoder.encoder[0].mconv[0].conv.weight.data.detach().cpu().numpy()
assert np.array_equal(w1, w2)
@pytest.mark.unit
def test_save_module_level_pt_ckpt(self, asr_model):
with tempfile.TemporaryDirectory() as ckpt_dir:
nemo_file = os.path.join(ckpt_dir, 'asr.nemo')
asr_model.save_to(nemo_file)
# Save model level PT checkpoint
asr_model.extract_state_dict_from(nemo_file, ckpt_dir, split_by_module=True)
encoder_path = os.path.join(ckpt_dir, 'encoder.ckpt')
decoder_path = os.path.join(ckpt_dir, 'decoder.ckpt')
preprocessor_path = os.path.join(ckpt_dir, 'preprocessor.ckpt')
assert os.path.exists(encoder_path)
assert os.path.exists(decoder_path)
assert os.path.exists(preprocessor_path)
# Restore the model.
asr_model2 = EncDecCTCModel.restore_from(restore_path=nemo_file)
assert len(asr_model.decoder.vocabulary) == len(asr_model2.decoder.vocabulary)
assert asr_model.num_weights == asr_model2.num_weights
# Change weights values
asr_model2.encoder.encoder[0].mconv[0].conv.weight.data += 1.0
w1 = asr_model.encoder.encoder[0].mconv[0].conv.weight.data.detach().cpu().numpy()
w2 = asr_model2.encoder.encoder[0].mconv[0].conv.weight.data.detach().cpu().numpy()
assert not np.array_equal(w1, w2)
# Restore from checkpoint
asr_model2.encoder.load_state_dict(torch.load(encoder_path))
w1 = asr_model.encoder.encoder[0].mconv[0].conv.weight.data.detach().cpu().numpy()
w2 = asr_model2.encoder.encoder[0].mconv[0].conv.weight.data.detach().cpu().numpy()
assert np.array_equal(w1, w2)
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