Create TARGETS for captum/_utils (#1250)

Summary:
Pull Request resolved: #1250

Create separate TARGETS files for different part of Captum project. Start with a relatively simple one: captum/_utils.

Mostly TARGETS file change, with more exception to correct import, split test helper function to separate file etc.

Reviewed By: cyrjano

Differential Revision: D55091069

fbshipit-source-id: 83cbd8a632c8ba71d60d14859bbc549f7ae7b511

Author: yucu

captum/_utils/av.py

@@ -8,7 +8,7 @@
 
 import captum._utils.common as common
 import torch
-from captum.attr import LayerActivation
+from captum.attr._core.layer.layer_activation import LayerActivation
 from torch import Tensor
 from torch.nn import Module
 from torch.utils.data import DataLoader, Dataset

captum/_utils/models/__init__.py

@@ -1,25 +1,5 @@
-from captum._utils.models.linear_model import (
-    LinearModel,
-    SGDLasso,
-    SGDLinearModel,
-    SGDLinearRegression,
-    SGDRidge,
-    SkLearnLasso,
-    SkLearnLinearModel,
-    SkLearnLinearRegression,
-    SkLearnRidge,
-)
 from captum._utils.models.model import Model
 
 __all__ = [
     "Model",
-    "LinearModel",
-    "SGDLinearModel",
-    "SGDLasso",
-    "SGDRidge",
-    "SGDLinearRegression",
-    "SkLearnLinearModel",
-    "SkLearnLasso",
-    "SkLearnRidge",
-    "SkLearnLinearRegression",
 ]

tests/utils/evaluate_linear_model.py

@@ -0,0 +1,52 @@
+# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
+from typing import cast, Dict
+
+import torch
+from torch import Tensor
+
+
+def evaluate(test_data, classifier) -> Dict[str, Tensor]:
+    classifier.eval()
+
+    l1_loss = 0.0
+    l2_loss = 0.0
+    n = 0
+    l2_losses = []
+    with torch.no_grad():
+        for data in test_data:
+            if len(data) == 2:
+                x, y = data
+                w = None
+            else:
+                x, y, w = data
+
+            out = classifier(x)
+
+            y = y.view(x.shape[0], -1)
+            assert y.shape == out.shape
+
+            if w is None:
+                l1_loss += (out - y).abs().sum(0).to(dtype=torch.float64)
+                l2_loss += ((out - y) ** 2).sum(0).to(dtype=torch.float64)
+                l2_losses.append(((out - y) ** 2).to(dtype=torch.float64))
+            else:
+                l1_loss += (
+                    (w.view(-1, 1) * (out - y)).abs().sum(0).to(dtype=torch.float64)
+                )
+                l2_loss += (
+                    (w.view(-1, 1) * ((out - y) ** 2)).sum(0).to(dtype=torch.float64)
+                )
+                l2_losses.append(
+                    (w.view(-1, 1) * ((out - y) ** 2)).to(dtype=torch.float64)
+                )
+
+            n += x.shape[0]
+
+    l2_losses = torch.cat(l2_losses, dim=0)
+    assert n > 0
+
+    # just to double check
+    assert ((l2_losses.mean(0) - l2_loss / n).abs() <= 0.1).all()
+
+    classifier.train()
+    return {"l1": cast(Tensor, l1_loss / n), "l2": cast(Tensor, l2_loss / n)}

tests/utils/models/linear_models/_test_linear_classifier.py

@@ -6,7 +6,7 @@
 import numpy as np
 import sklearn.datasets as datasets
 import torch
-from tests.utils.test_linear_model import _evaluate
+from tests.utils.evaluate_linear_model import evaluate
 from torch.utils.data import DataLoader, TensorDataset
 
 
@@ -80,11 +80,11 @@ def compare_to_sk_learn(
         alpha=alpha,
     )
 
-    sklearn_stats.update(_evaluate(val_loader, sklearn_classifier))
-    pytorch_stats.update(_evaluate(val_loader, pytorch_classifier))
+    sklearn_stats.update(evaluate(val_loader, sklearn_classifier))
+    pytorch_stats.update(evaluate(val_loader, pytorch_classifier))
 
-    train_stats_pytorch = _evaluate(train_loader, pytorch_classifier)
-    train_stats_sklearn = _evaluate(train_loader, sklearn_classifier)
+    train_stats_pytorch = evaluate(train_loader, pytorch_classifier)
+    train_stats_sklearn = evaluate(train_loader, sklearn_classifier)
 
     o_pytorch = {"l2": train_stats_pytorch["l2"]}
     o_sklearn = {"l2": train_stats_sklearn["l2"]}

tests/utils/test_linear_model.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 
-from typing import cast, Dict, Optional, Union
+from typing import Optional, Union
 
 import torch
 from captum._utils.models.linear_model.model import (
@@ -10,56 +10,10 @@
 )
 from tests.helpers import BaseTest
 from tests.helpers.basic import assertTensorAlmostEqual
+from tests.utils.evaluate_linear_model import evaluate
 from torch import Tensor
 
 
-def _evaluate(test_data, classifier) -> Dict[str, Tensor]:
-    classifier.eval()
-
-    l1_loss = 0.0
-    l2_loss = 0.0
-    n = 0
-    l2_losses = []
-    with torch.no_grad():
-        for data in test_data:
-            if len(data) == 2:
-                x, y = data
-                w = None
-            else:
-                x, y, w = data
-
-            out = classifier(x)
-
-            y = y.view(x.shape[0], -1)
-            assert y.shape == out.shape
-
-            if w is None:
-                l1_loss += (out - y).abs().sum(0).to(dtype=torch.float64)
-                l2_loss += ((out - y) ** 2).sum(0).to(dtype=torch.float64)
-                l2_losses.append(((out - y) ** 2).to(dtype=torch.float64))
-            else:
-                l1_loss += (
-                    (w.view(-1, 1) * (out - y)).abs().sum(0).to(dtype=torch.float64)
-                )
-                l2_loss += (
-                    (w.view(-1, 1) * ((out - y) ** 2)).sum(0).to(dtype=torch.float64)
-                )
-                l2_losses.append(
-                    (w.view(-1, 1) * ((out - y) ** 2)).to(dtype=torch.float64)
-                )
-
-            n += x.shape[0]
-
-    l2_losses = torch.cat(l2_losses, dim=0)
-    assert n > 0
-
-    # just to double check
-    assert ((l2_losses.mean(0) - l2_loss / n).abs() <= 0.1).all()
-
-    classifier.train()
-    return {"l1": cast(Tensor, l1_loss / n), "l2": cast(Tensor, l2_loss / n)}
-
-
 class TestLinearModel(BaseTest):
     MAX_POINTS: int = 3
 
@@ -100,7 +54,7 @@ def train_and_compare(
 
         self.assertTrue(model.bias() is not None if bias else model.bias() is None)
 
-        l2_loss = _evaluate(train_loader, model)["l2"]
+        l2_loss = evaluate(train_loader, model)["l2"]
 
         if objective == "lasso":
             reg = model.representation().norm(p=1).view_as(l2_loss)