Merge pull request #2945 from activeloopai/av_13

changes to work with av 13.0.0

deeplake/core/compression.py

@@ -961,14 +961,9 @@ def _decompress_video(
     container, vstream = _open_video(file)
     nframes, height, width, _ = _read_metadata_from_vstream(container, vstream)[0]
 
-    if start is None:
-        start = 0
-
-    if stop is None:
-        stop = nframes
-
-    if step is None:
-        step = 1
+    start = start or 0
+    stop = stop or nframes
+    step = step or 1
 
     nframes = math.ceil((stop - start) / step)
 
@@ -977,22 +972,20 @@ def _decompress_video(
     seek_target = _frame_to_stamp(start, vstream)
     step_time = _frame_to_stamp(step, vstream)
 
-    gop_size = (
-        vstream.codec_context.gop_size
-    )  # gop size is distance (in frames) between 2 I-frames
-    if step > gop_size:
-        step_seeking = True
-    else:
-        step_seeking = False
+    gop_size = 1
+    if vstream.codec_context.is_encoder:
+        gop_size = vstream.codec_context.gop_size
+
+    step_seeking = step > gop_size
 
     seekable = True
     try:
         container.seek(seek_target, stream=vstream)
     except av.error.FFmpegError:
         seekable = False
-        container, vstream = _open_video(file)  # try again but this time don't seek
+        container, vstream = _open_video(file)  # Retry without seeking
         warning(
-            "Cannot seek. Possibly a corrupted video file. Retrying with seeking disabled..."
+            "Cannot seek. Possibly a corrupted video file. Retrying without seeking."
         )
 
     i = 0
@@ -1005,7 +998,6 @@ def _decompress_video(
                 seek_target += step_time
                 if step_seeking and seekable:
                     container.seek(seek_target, stream=vstream)
-
         if i == nframes:
             break
 
@@ -1031,25 +1023,20 @@ def _read_timestamps(
     step_time = _frame_to_stamp(step, vstream)
 
     stamps = []
-    if vstream.duration is None:
-        time_base = 1 / av.time_base  # type: ignore
-    else:
-        time_base = vstream.time_base.numerator / vstream.time_base.denominator
+    time_base = vstream.time_base.numerator / vstream.time_base.denominator
 
-    gop_size = (
-        vstream.codec_context.gop_size
-    )  # gop size is distance (in frames) between 2 I-frames
-    if step > gop_size:
-        step_seeking = True
-    else:
-        step_seeking = False
+    gop_size = 1
+    if vstream.codec_context.is_encoder:
+        gop_size = vstream.codec_context.gop_size
+
+    step_seeking = step > gop_size
 
     seekable = True
     try:
         container.seek(seek_target, stream=vstream)
     except av.error.FFmpegError:
         seekable = False
-        container, vstream = _open_video(file)  # try again but this time don't seek
+        container, vstream = _open_video(file)  # Retry without seeking
         warning(
             "Cannot seek. Possibly a corrupted video file. Retrying with seeking disabled..."
         )
@@ -1070,6 +1057,7 @@ def _read_timestamps(
     # need to sort because when demuxing, frames are in order of dts (decoder timestamp)
     # we need it in order of pts (presentation timestamp)
     stamps.sort()
+
     stamps_arr = np.zeros((nframes,), dtype=np.float32)
     stamps_arr[: len(stamps)] = stamps
 

deeplake/core/vectorstore/deep_memory/test_deepmemory.py

@@ -461,7 +461,7 @@ def test_deepmemory_evaluate_without_embedding_function(
     questions_embeddings_and_relevances,
     hub_cloud_dev_token,
 ):
-    corpus, _, _, query_path = corpus_query_relevances_copy
+    corpus, _, _, _ = corpus_query_relevances_copy
     (
         questions_embeddings,
         question_relevances,
@@ -618,7 +618,7 @@ def test_unsupported_deepmemory_users(local_ds):
 def test_deepmemory_list_jobs_with_no_jobs(
     corpus_query_relevances_copy, hub_cloud_dev_token
 ):
-    corpus, queries, relevances, _ = corpus_query_relevances_copy
+    corpus, _, _, _ = corpus_query_relevances_copy
 
     db = VectorStore(
         path=corpus,
@@ -749,7 +749,7 @@ def test_deepmemory_train_with_embedding_function_specified_in_constructor_shoul
         embedding_function=embedding_fn,
     )
 
-    job_id = db.deep_memory.train(
+    _ = db.deep_memory.train(
         queries=queries,
         relevance=relevances,
     )
@@ -776,13 +776,14 @@ def test_deepmemory_evaluate_with_embedding_function_specified_in_constructor_sh
         runtime={"tensor_db": True},
         token=hub_cloud_dev_token,
         embedding_function=embedding_fn,
+        read_only=True,
     )
 
     queries = queries_vs.dataset[:10].text.data()["value"]
     relevance = queries_vs.dataset[:10].metadata.data()["value"]
     relevance = [rel["relevance"] for rel in relevance]
 
-    recall = db.deep_memory.evaluate(
+    _ = db.deep_memory.evaluate(
         queries=queries,
         relevance=relevance,
     )

deeplake/requirements/common.txt

@@ -11,7 +11,7 @@ pathos
 humbug>=0.3.1
 tqdm
 lz4
-av>=8.1.0,<=12.3.0; python_version >= '3.7' or sys_platform != 'win32'
+av>=8.1.0; python_version >= '3.7' or sys_platform != 'win32'
 pydicom
 IPython
 flask

deeplake/requirements/plugins.txt

@@ -10,4 +10,4 @@ mmdet==2.28.1; platform_system == "Linux" and python_version >= "3.7"
 mmsegmentation==0.30.0; platform_system == "Linux" and python_version >= "3.7"
 mmengine
 pandas
-av==12.3.0
+av