[AL-2351] Print a transform summary if ignore_errors=True

deeplake/core/transform/test_transform.py

@@ -1682,3 +1682,56 @@ def upload(sample_in, ds, class_names):
         )
         assert e.index == 20
         assert e.sample == 10
+
+
+def test_transform_summary(local_ds, capsys):
+    @deeplake.compute
+    def upload(sample_in, sample_out):
+        sample_out.images.append(sample_in)
+
+    with local_ds as ds:
+        ds.create_tensor("images", htype="image", sample_compression="jpg")
+
+    samples = (
+        ["bad_sample"]
+        + [np.random.randint(0, 255, (10, 10), dtype=np.uint8) for _ in range(8)]
+        + ["bad_sample"]
+    ) * 2
+
+    upload().eval(
+        samples,
+        ds,
+        num_workers=TRANSFORM_TEST_NUM_WORKERS,
+        progressbar=False,
+        ignore_errors=True,
+    )
+
+    captured = capsys.readouterr()
+    assert captured.out == (
+        "No. of samples successfully processed: 16 (80.0%)\n"
+        "No. of samples skipped: 4 (20.0%)\n"
+    )
+
+    samples = [np.random.randint(0, 255, (10, 10), dtype=np.uint8) for _ in range(8)]
+    upload().eval(
+        samples,
+        ds,
+        num_workers=TRANSFORM_TEST_NUM_WORKERS,
+        progressbar=False,
+        ignore_errors=True,
+    )
+
+    captured = capsys.readouterr()
+    assert captured.out == (
+        "No. of samples successfully processed: 8 (100.0%)\n"
+        "No. of samples skipped: 0 (0.0%)\n"
+    )
+
+    # no summary if ignore_errors=False
+    samples = [np.random.randint(0, 255, (10, 10), dtype=np.uint8) for _ in range(8)]
+    upload().eval(
+        samples, ds, num_workers=TRANSFORM_TEST_NUM_WORKERS, progressbar=False
+    )
+
+    captured = capsys.readouterr()
+    assert captured.out == ""

deeplake/core/transform/transform.py

@@ -25,6 +25,7 @@
     store_data_slice_with_pbar,
     check_checkpoint_interval,
     len_data_in,
+    transform_summary,
 )
 from deeplake.util.encoder import merge_all_meta_info
 from deeplake.util.exceptions import (
@@ -464,6 +465,9 @@ def run(
                 verbose=progressbar,
             )
 
+        if ignore_errors:
+            transform_summary(data_in, result)
+
         for res in result["error"]:
             if res is not None:
                 raise res

deeplake/util/transform.py

@@ -261,9 +261,10 @@ def _transform_and_append_data_slice(
             )
             continue
 
-    if skipped_samples == n:
-        return False
-    return True
+    return {
+        "samples_skipped": skipped_samples,
+        "all_samples_skipped": skipped_samples == n,
+    }
 
 
 def _retrieve_memory_objects(all_chunk_engines):
@@ -335,7 +336,10 @@ def store_data_slice_with_pbar(pg_callback, transform_input: Tuple) -> Dict:
         cache_size=cache_size,
     )
 
-    ret = True
+    ret = {
+        "all_samples_skipped": False,
+        "samples_skipped": 0,
+    }
     err = None
     try:
         if extend_only:
@@ -366,7 +370,7 @@ def store_data_slice_with_pbar(pg_callback, transform_input: Tuple) -> Dict:
     finally:
         # retrieve relevant objects from memory
         meta = _retrieve_memory_objects(all_chunk_engines)
-        meta["all_samples_skipped"] = not ret
+        meta.update(ret)
         meta["error"] = err
         return meta
 
@@ -541,6 +545,18 @@ def len_data_in(data_in):
         return len(data_in)
 
 
+def transform_summary(data_in, result):
+    samples_skipped = sum(result["samples_skipped"])
+    successful = len_data_in(data_in) - samples_skipped
+    successful_percent = round((successful / len_data_in(data_in)) * 100, 2)
+    skipped_percent = round(100 - successful_percent, 2)
+
+    print(
+        "No. of samples successfully processed:", successful, f"({successful_percent}%)"
+    )
+    print("No. of samples skipped:", samples_skipped, f"({skipped_percent}%)")
+
+
 def create_slices(data_in, num_workers):
     size = math.ceil(len_data_in(data_in) / num_workers)