test: add tailor plus tuner integration test (#124)

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

@@ -0,0 +1,33 @@
+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']