def test_get_cache_model_noncache_models():
language_models_params = {'awd_lstm_lm_1150': 'awd_lstm_lm_1150_wikitext-2-45d6df33.params',
'awd_lstm_lm_600': 'awd_lstm_lm_600_wikitext-2-7894a046.params',
'standard_lstm_lm_200': 'standard_lstm_lm_200_wikitext-2-700b532d.params',
'standard_lstm_lm_650': 'standard_lstm_lm_650_wikitext-2-14041667.params',
'standard_lstm_lm_1500': 'standard_lstm_lm_1500_wikitext-2-d572ce71.params'}
datasets = ['wikitext-2']
for name in language_models_params.keys():
for dataset_name in datasets:
_, vocab = get_text_model(name=name, dataset_name=dataset_name, pretrained=True)
ntokens = len(vocab)
cache_cell_0 = get_cache_model(name, dataset_name, window=1, theta=0.6,
lambdas=0.2, root='tests/data/model/')
print(cache_cell_0)
model, _ = get_text_model(name=name, dataset_name=dataset_name, pretrained=True)
cache_cell_1 = CacheCell(model, ntokens, window=1, theta=0.6, lambdas=0.2)
cache_cell_1.load_params('tests/data/model/' + language_models_params.get(name))
print(cache_cell_1)
outs0, word_history0, cache_history0, hidden0 = \
cache_cell_0(mx.nd.arange(10).reshape(10, 1), mx.nd.arange(10).reshape(10, 1), None, None)
outs1, word_history1, cache_history1, hidden1 = \
cache_cell_1(mx.nd.arange(10).reshape(10, 1), mx.nd.arange(10).reshape(10, 1), None, None)
assert outs0.shape == outs1.shape, outs0.shape
assert len(word_history0) == len(word_history1), len(word_history0)
assert len(cache_history0) == len(cache_history1), len(cache_history0)
assert len(hidden0) == len(hidden1), len(hidden0)
def test_text_models():
val = nlp.data.WikiText2(segment='val', root='tests/data/wikitext-2')
val_freq = get_frequencies(val)
vocab = nlp.Vocab(val_freq)
text_models = [
'standard_lstm_lm_200', 'standard_lstm_lm_650',
'standard_lstm_lm_1500', 'awd_lstm_lm_1150', 'awd_lstm_lm_600'
]
pretrained_to_test = {
'standard_lstm_lm_1500': 'wikitext-2',
'standard_lstm_lm_650': 'wikitext-2',
'standard_lstm_lm_200': 'wikitext-2',
'awd_lstm_lm_1150': 'wikitext-2',
'awd_lstm_lm_600': 'wikitext-2'
}
for model_name in text_models:
eprint('testing forward for %s' % model_name)
pretrained_dataset = pretrained_to_test.get(model_name)
model, _ = get_text_model(model_name,
vocab=vocab,
dataset_name=pretrained_dataset,
pretrained=pretrained_dataset is not None,
root='tests/data/model/')
print(model)
if not pretrained_dataset:
model.collect_params().initialize()
output, state = model(mx.nd.arange(330).reshape(33, 10))
output.wait_to_read()
def _test_pretrained_big_text_models():
text_models = ['big_rnn_lm_2048_512']
pretrained_to_test = {'big_rnn_lm_2048_512': 'gbw'}
for model_name in text_models:
eprint('testing forward for %s' % model_name)
pretrained_dataset = pretrained_to_test.get(model_name)
model, _ = get_text_model(model_name, dataset_name=pretrained_dataset,
pretrained=True, root='tests/data/model/')
print(model)
batch_size = 10
hidden = model.begin_state(batch_size=batch_size, func=mx.nd.zeros)
output, state = model(mx.nd.arange(330).reshape((33, 10)), hidden)
output.wait_to_read()
def test_big_text_models():
# use a small vocabulary for testing
val = nlp.data.WikiText2(segment='val', root='tests/data/wikitext-2')
val_freq = get_frequencies(val)
vocab = nlp.Vocab(val_freq)
text_models = ['big_rnn_lm_2048_512']
for model_name in text_models:
eprint('testing forward for %s' % model_name)
model, _ = get_text_model(model_name, vocab=vocab, root='tests/data/model/')
print(model)
model.collect_params().initialize()
batch_size = 10
hidden = model.begin_state(batch_size=batch_size, func=mx.nd.zeros)
output, state = model(mx.nd.arange(330).reshape((33, 10)), hidden)
output.wait_to_read()
def test_text_models():
val = nlp.data.WikiText2(segment='val', root='tests/data/wikitext-2')
val_freq = get_frequencies(val)
vocab = nlp.Vocab(val_freq)
text_models = ['standard_lstm_lm_200', 'standard_lstm_lm_650', 'standard_lstm_lm_1500', 'awd_lstm_lm_1150', 'awd_lstm_lm_600']
pretrained_to_test = {'standard_lstm_lm_1500': 'wikitext-2', 'standard_lstm_lm_650': 'wikitext-2', 'standard_lstm_lm_200': 'wikitext-2', 'awd_lstm_lm_1150': 'wikitext-2', 'awd_lstm_lm_600': 'wikitext-2'}
for model_name in text_models:
eprint('testing forward for %s' % model_name)
pretrained_dataset = pretrained_to_test.get(model_name)
model, _ = get_text_model(model_name, vocab=vocab, dataset_name=pretrained_dataset,
pretrained=pretrained_dataset is not None, root='tests/data/model/')
print(model)
if not pretrained_dataset:
model.collect_params().initialize()
output, state = model(mx.nd.arange(330).reshape(33, 10))
output.wait_to_read()