cosmetic improvements to README

README.md

@@ -17,32 +17,44 @@
 
 To install
 <span style="color: #4D4352; font-family: 'arial narrow', arial, sans-serif;">
-quanda
-</span>:
+quanda</span>:
 
 ```setup
 pip install git+https://github.com/dilyabareeva/quanda.git
 ```
 
 ## Usage
 
-An excerpt from `tutorials/usage_testing.py`:
-```python
+Excerpts from `tutorials/usage_testing.py`:
+
+<details>
+<summary><b><big>Step 1. Import library components</big></b></summary>
 
+```python
 from src.explainers.wrappers.captum_influence import captum_similarity_explain
 from src.metrics.localization.identical_class import IdenticalClass
 from src.metrics.randomization.model_randomization import (
     ModelRandomizationMetric,
 )
 from src.metrics.unnamed.top_k_overlap import TopKOverlap
+```
+
+<details>
 
-# define explanation parameters
+<summary><b><big>Step 2. Define explanation parameters</big></b></summary>
+
+```python
 explain = captum_similarity_explain
 explain_fn_kwargs = {"layers": "avgpool"}
 model_id = "default_model_id"
 cache_dir = "./cache"
+```
+
+<details>
 
-# initialize metrics
+<summary><b><big>Step 3. Initialize metrics</big></b></summary>
+
+```python
 model_rand = ModelRandomizationMetric(
     model=model,
     train_dataset=train_set,
@@ -58,13 +70,17 @@ model_rand = ModelRandomizationMetric(
 id_class = IdenticalClass(model=model, train_dataset=train_set, device=DEVICE)
 
 top_k = TopKOverlap(model=model, train_dataset=train_set, top_k=1, device="cpu")
+```
+
+<details>
+<summary><b><big>Step 4. Iterate over test set and feed tensor batches first to explain, then to metric</big></b></summary>
 
-# iterate over test set and feed tensor batches first to explain, then to metric
+```python
 for i, (data, target) in enumerate(tqdm(test_loader)):
     data, target = data.to(DEVICE), target.to(DEVICE)
 
     # some metrics have an explain_update() method in addition to update():
-    model_rand.update(data)
+    model_rand.explain_update(data)
 
     # metrics that do not generate explanations only have an update() method:
     tda = explain(
@@ -79,7 +95,4 @@ for i, (data, target) in enumerate(tqdm(test_loader)):
     model_rand.update(data, tda)
     id_class.update(target, tda)
     top_k.update(target)
-
-
-
 ```