Add semantic script, trainer (#16834)

* Add first draft

* Improve script and README

* Improve README

* Apply suggestions from code review

* Improve script, add link to resulting model

* Add corresponding test

* Adjust learning rate

examples/pytorch/test_pytorch_examples.py

@@ -19,7 +19,6 @@ import json
 import logging
 import os
 import sys
-import unittest
 from unittest.mock import patch
 
 import torch
@@ -45,6 +44,7 @@ SRC_DIRS = [
         "audio-classification",
         "speech-pretraining",
         "image-pretraining",
+        "semantic-segmentation",
     ]
 ]
 sys.path.extend(SRC_DIRS)
@@ -60,6 +60,7 @@ if SRC_DIRS is not None:
     import run_mlm
     import run_ner
     import run_qa as run_squad
+    import run_semantic_segmentation
     import run_seq2seq_qa as run_squad_seq2seq
     import run_speech_recognition_ctc
     import run_speech_recognition_seq2seq
@@ -386,7 +387,6 @@ class ExamplesTests(TestCasePlus):
             result = get_results(tmp_dir)
             self.assertGreaterEqual(result["eval_bleu"], 30)
 
-    @unittest.skip("This is currently broken.")
     def test_run_image_classification(self):
         tmp_dir = self.get_auto_remove_tmp_dir()
         testargs = f"""
@@ -534,7 +534,6 @@ class ExamplesTests(TestCasePlus):
             model = Wav2Vec2ForPreTraining.from_pretrained(tmp_dir)
             self.assertIsNotNone(model)
 
-    @unittest.skip("This is currently broken.")
     def test_run_vit_mae_pretraining(self):
         tmp_dir = self.get_auto_remove_tmp_dir()
         testargs = f"""
@@ -562,3 +561,28 @@ class ExamplesTests(TestCasePlus):
             run_mae.main()
             model = ViTMAEForPreTraining.from_pretrained(tmp_dir)
             self.assertIsNotNone(model)
+
+    def test_run_semantic_segmentation(self):
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            run_semantic_segmentation.py
+            --output_dir {tmp_dir}
+            --dataset_name huggingface/semantic-segmentation-test-sample
+            --do_train
+            --do_eval
+            --remove_unused_columns False
+            --overwrite_output_dir True
+            --max_steps 10
+            --learning_rate=2e-4
+            --per_device_train_batch_size=2
+            --per_device_eval_batch_size=1
+            --seed 32
+        """.split()
+
+        if is_cuda_and_apex_available():
+            testargs.append("--fp16")
+
+        with patch.object(sys, "argv", testargs):
+            run_semantic_segmentation.main()
+            result = get_results(tmp_dir)
+            self.assertGreaterEqual(result["eval_overall_accuracy"], 0.1)