test: add tailor plus tuner integration test

tests/integration/keras/test_tail_and_tune.py

@@ -0,0 +1,32 @@
+import pytest
+import tensorflow as tf
+
+from finetuner import fit
+
+
+@pytest.fixture
+def embed_model():
+ return tf.keras.Sequential(
+ [
+ tf.keras.layers.Flatten(input_shape=(128,)),
+ tf.keras.layers.Dense(256, activation='relu'),
+ tf.keras.layers.Dense(128, activation='relu'),
+ tf.keras.layers.Dense(64, activation='relu'),
+ tf.keras.layers.Dense(32),
+ ]
+ )
+
+
+def test_tail_and_tune(embed_model, create_easy_data):
+ data, _ = create_easy_data(10, 128, 1000)
+ rv = fit(
+ model=embed_model,
+ train_data=data,
+ epochs=5,
+ to_embedding_model=True,
+ input_size=128,
+ output_dim=16,
+ layer_name='dense_2',
+ )
+ assert rv['loss']['train']
+ assert rv['metric']['train']

tests/integration/paddle/test_tail_and_tune.py

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+import pytest
+import paddle.nn as nn
+
+from finetuner import fit
+
+
+@pytest.fixture
+def embed_model():
+ return nn.Sequential(
+ nn.Flatten(),
+ nn.Linear(in_features=128, out_features=256),
+ nn.ReLU(),
+ nn.Linear(in_features=256, out_features=128),
+ nn.ReLU(),
+ nn.Linear(in_features=128, out_features=64),
+ nn.ReLU(),
+ nn.Linear(in_features=64, out_features=32),
+ )
+
+
+def test_tail_and_tune(embed_model, create_easy_data):
+ data, _ = create_easy_data(10, 128, 1000)
+ rv = fit(
+ model=embed_model,
+ train_data=data,
+ epochs=5,
+ to_embedding_model=True,
+ input_size=(128,),
+ output_dim=16,
+ layer_name='linear_4',
+ )
+ assert rv['loss']['train']
+ assert rv['metric']['train']

tests/integration/torch/test_tail_and_tune.py

@@ -0,0 +1,33 @@
+import pytest
+import torch.nn as nn
+
+from finetuner import fit
+
+
+@pytest.fixture
+def embed_model():
+ return nn.Sequential(
+ nn.Flatten(),
+ nn.Linear(in_features=128, out_features=256),
+ nn.ReLU(),
+ nn.Linear(in_features=256, out_features=128),
+ nn.ReLU(),
+ nn.Linear(in_features=128, out_features=64),
+ nn.ReLU(),
+ nn.Linear(in_features=64, out_features=32),
+ )
+
+
+def test_tail_and_tune(embed_model, create_easy_data):
+ data, _ = create_easy_data(10, 128, 1000)
+ rv = fit(
+ model=embed_model,
+ train_data=data,
+ epochs=5,
+ to_embedding_model=True,
+ input_size=(128,),
+ output_dim=16,
+ layer_name='linear_4',
+ )
+ assert rv['loss']['train']
+ assert rv['metric']['train']