Suspicious unused loop iteration variable (#320)

* Fix keyword argument error * Add download option * Fix raise for Value error in mini-imagenet * Remove a couple of used variables * Remove a couple of used variables * Update CHANGELOG.md Co-authored-by: Séb Arnold <[email protected]>
learnables · Mar 15, 2022 · df3c329 · df3c329
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commit df3c329
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -19,7 +19,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
-* example for `detach_module`. ([Nimish Sanghi](https://github.com/nsanghi))
+* Example for `detach_module`. ([Nimish Sanghi](https://github.com/nsanghi))
+* Loading duplicate FGVC Aircraft images.
 
 ## v0.1.7
 

diff --git a/examples/rl/dist_promp.py b/examples/rl/dist_promp.py
@@ -166,7 +166,7 @@ def make_env():
         print('adaptation_reward', adaptation_reward)
 
         # ProMP meta-optimization
-        for ppo_step in tqdm(range(ppo_steps), leave=False, desc='Optim'):
+        for _ in tqdm(range(ppo_steps), leave=False, desc='Optim'):
             promp_loss = 0.0
             kl_total = 0.0
             for task_replays, old_policies in zip(iteration_replays, iteration_policies):

diff --git a/examples/rl/promp.py b/examples/rl/promp.py
@@ -148,7 +148,7 @@ def make_env():
         print('adaptation_reward', adaptation_reward)
 
         # ProMP meta-optimization
-        for ppo_step in tqdm(range(ppo_steps), leave=False, desc='Optim'):
+        for _ in tqdm(range(ppo_steps), leave=False, desc='Optim'):
             promp_loss = 0.0
             kl_total = 0.0
             for task_replays, old_policies in zip(iteration_replays, iteration_policies):

diff --git a/examples/text/news_topic_classification.py b/examples/text/news_topic_classification.py
@@ -136,15 +136,15 @@ def main(lr=0.005, maml_lr=0.01, iterations=1000, ways=5, shots=1, tps=32, fas=5
     tqdm_bar = tqdm(range(iterations))
 
     accs = []
-    for iteration in tqdm_bar:
+    for _ in tqdm_bar:
         iteration_error = 0.0
         iteration_acc = 0.0
         for _ in range(tps):
             learner = meta_model.clone()
             train_task, valid_task = train_gen.sample(), validation_gen.sample()
 
             # Fast Adaptation
-            for step in range(fas):
+            for _ in range(fas):
                 train_error, _ = compute_loss(train_task, roberta, device, learner, loss_func, batch=shots * ways)
                 learner.adapt(train_error)
 

diff --git a/learn2learn/vision/datasets/fgvc_aircraft.py b/learn2learn/vision/datasets/fgvc_aircraft.py
@@ -153,7 +153,7 @@ def download(self):
                         'images_family_test.txt']
         images_labels = []
         for family in family_names:
-            with open(os.path.join(data_path, DATA_DIR, family_names[0]), 'r') as family_file:
+            with open(os.path.join(data_path, DATA_DIR, family), 'r') as family_file:
                 for line in family_file.readlines():
                     image, label = line.split(' ', 1)
                     images_labels.append((image.strip(), label.strip()))