Add image classification script, no trainer (#16727)

* Add first draft

* Improve README and run fixup

* Make script aligned with other scripts, improve README

* Improve script and add test

* Remove print statement

* Apply suggestions from code review

* Add num_labels to make test pass

* Improve README

examples/pytorch/test_accelerate_examples.py

@@ -52,6 +52,7 @@ sys.path.extend(SRC_DIRS)
 if SRC_DIRS is not None:
     import run_clm_no_trainer
     import run_glue_no_trainer
+    import run_image_classification_no_trainer
     import run_mlm_no_trainer
     import run_ner_no_trainer
     import run_qa_no_trainer as run_squad_no_trainer
@@ -321,3 +322,25 @@ class ExamplesTestsNoTrainer(TestCasePlus):
             run_semantic_segmentation_no_trainer.main()
             result = get_results(tmp_dir)
             self.assertGreaterEqual(result["eval_overall_accuracy"], 0.10)
+
+    def test_run_image_classification_no_trainer(self):
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            run_image_classification_no_trainer.py
+            --dataset_name huggingface/image-classification-test-sample
+            --output_dir {tmp_dir}
+            --num_warmup_steps=8
+            --learning_rate=3e-3
+            --per_device_train_batch_size=2
+            --per_device_eval_batch_size=1
+            --checkpointing_steps epoch
+            --with_tracking
+            --seed 42
+        """.split()
+
+        with patch.object(sys, "argv", testargs):
+            run_image_classification_no_trainer.main()
+            result = get_results(tmp_dir)
+            self.assertGreaterEqual(result["eval_accuracy"], 0.50)
+            self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
+            self.assertTrue(os.path.exists(os.path.join(tmp_dir, "image_classification_no_trainer")))