YACWC

.gitignore

@@ -0,0 +1,9 @@
+*.mp4
+*.engine
+*.pt
+*.onnx
+*.trt
+*.whl
+*.npy
+*.npz
+runs/

compare.py

@@ -1,6 +1,9 @@
-import numpy as np
 import json
-datum = np.load('dump.npz.npy')
+
+import numpy as np
+
+#datum = np.load('dump.npz.npy')
+datum = np.load('dump_so400m.npy')
 
 
 with open('dump.json','r') as rr:
@@ -29,25 +32,15 @@ def cosine_sim(emb_in_1, emb_in_2):
 
 
 arr_in_deepstream = np.asarray([y for _,y in emb_dict.items()])
-
 normed = np.divide(datum.T, np.linalg.norm(datum, axis=1)).T
 print('_________________________')
 
+
+print(len(emb_dict))
+print(len(datum))
 for fr, emb in emb_dict.items():
     emb1 = np.linalg.norm(emb)
     emb2 = np.linalg.norm(datum[fr])
-#    print( cosine_sim(emb, datum[fr]))
+    print( cosine_sim(emb, datum[fr]))
 
 
-print('Deepstream and Actual norm')    
-print(np.max(np.dot(arr_in_deepstream, normed.T),axis=1))
-
-print('_________________________')
-for dat in datum:
-#    print(cosine_sim(dat, datum[0]))
-     pass
-
-
-#print(cosine_sim(datum[fr], datum[fr+1]))
-
-#print(cosine_sim(emb_dict[fr], emb_dict[fr+1]))

deepstream_obj_det.py

@@ -380,6 +380,7 @@ def run_inference(file_path):
 
     nugget_detector.link(fakesink1)
     nugget_embedder.link(fakesink2)
+    print(get_pipeline_string(pipeline))
 
     
     # create an event loop and feed gstreamer bus mesages to it

deepstream_obj_det_pre_queue.py

@@ -0,0 +1,545 @@
+import sys
+
+sys.path.append("/opt/nvidia/deepstream/deepstream/sources/deepstream_python_apps/apps")
+import os
+
+import gi
+
+gi.require_version("Gst", "1.0")
+import argparse
+import ctypes
+from functools import partial
+
+import numpy as np
+import pyds
+from common.bus_call import bus_call
+from common.platform_info import PlatformInfo
+from CommonCode.settings import LogColorize, get_logger
+from gi.repository import GLib, Gst
+
+pfm = LogColorize.watch_and_fix_permissions
+
+logger = get_logger(
+    __name__,
+    "/var/log/ml_vision_logs/00_watch_and_fix_permissions",
+    stdout=True,
+    systemd=False,
+)
+Gst.debug_set_default_threshold(Gst.DebugLevel.ERROR)
+os.environ.pop("DISPLAY", ":0")
+target_width_detect = 1280
+target_height_detect = 720
+os.environ["GST_DEBUG_DUMP_DOT_DIR"] = "/tmp"
+os.putenv("GST_DEBUG_DUMP_DIR_DIR", "/tmp")
+
+target_width_embed = 512
+target_height_embed = 512
+
+MUXER_BATCH_TIMEOUT_USEC = 1000000
+
+
+def print_pipeline_structure(pipeline):
+    """
+    Recursively prints elements in the pipeline and their properties.
+    """
+    if not isinstance(pipeline, Gst.Pipeline):
+        print("Not a valid GStreamer pipeline.")
+        return
+
+    def _print_element_properties(element, indent=0):
+        spaces = " " * indent
+        print(
+            spaces
+            + f"Element: {element.get_name()} (Type: {element.get_factory().get_name()})"
+        )
+
+        # Print its properties
+        for prop in element.list_properties():
+            try:
+                val = element.get_property(prop.name)
+                if val != prop.default_value:  # Display only non-default properties
+                    print(spaces + f"  - {prop.name}: {val}")
+            except:
+                pass
+
+    def _print_pipeline_structure(element, indent=0):
+        spaces = " " * indent
+        children = element.children if hasattr(element, "children") else []
+
+        if len(children) > 0:
+            print(spaces + f"[{element.get_name()}]")
+            for child in children:
+                _print_pipeline_structure(child, indent + 2)
+        else:
+            _print_element_properties(element, indent)
+
+    print("\nPipeline Structure:")
+    print("===================")
+    _print_pipeline_structure(pipeline)
+    print("===================\n")
+
+
+def get_detailed_pipeline_string(pipeline):
+    """Generate a more detailed pipeline string with properties"""
+    if not isinstance(pipeline, Gst.Pipeline):
+        return None
+
+    def get_element_string(element):
+        # Get element factory name
+        factory = element.get_factory()
+        if factory:
+            element_str = factory.get_name()
+        else:
+            element_str = element.get_name()
+
+        # Add properties
+        props = []
+        for prop in element.list_properties():
+            # Skip some properties that are typically not set in command line
+            if prop.name in ("name", "parent"):
+                continue
+
+            try:
+                val = element.get_property(prop.name)
+                if val is not None and val != prop.default_value:
+                    # Format value appropriately based on type
+                    if isinstance(val, str):
+                        props.append(f'{prop.name}="{val}"')
+                    elif isinstance(val, bool):
+                        props.append(f"{prop.name}={str(val).lower()}")
+                    else:
+                        props.append(f"{prop.name}={val}")
+            except:
+                # Skip properties that can't be read
+                pass
+
+        if props:
+            element_str += " " + " ".join(props)
+
+        return element_str
+
+    result = []
+
+    # Simple approach - just gets top-level elements
+    iterator = pipeline.iterate_elements()
+    while True:
+        ret, element = iterator.next()
+        if ret != Gst.IteratorResult.OK:
+            break
+        result.append(get_element_string(element))
+
+    return " ! ".join(result)
+
+
+def embed_results_probe(pad, info, u_data, list_add, frame_num=0):
+    gst_buffer = info.get_buffer()
+    print("HEY I AM PROBING EMBEDDINGS")
+    if not gst_buffer:
+        print("Unable to get GstBuffer ")
+        return
+    batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(gst_buffer))
+    l_frame = batch_meta.frame_meta_list
+
+    while l_frame is not None:
+        try:
+            # Note that l_frame.data needs a cast to pyds.NvDsFrameMeta
+            # The casting also keeps ownership of the underlying memory
+            # in the C code, so the Python garbage collector will leave
+            # it alone.
+            frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
+        except StopIteration:
+            break
+
+        frame_number = frame_meta.frame_num
+
+        l_user = frame_meta.frame_user_meta_list
+        while l_user is not None:
+            try:
+                # Note that l_user.data needs a cast to pyds.NvDsUserMeta
+                # The casting also keeps ownership of the underlying memory
+                # in the C code, so the Python garbage collector will leave
+                # it alone.
+                user_meta = pyds.NvDsUserMeta.cast(l_user.data)
+            except StopIteration:
+                break
+
+            if (
+                user_meta.base_meta.meta_type
+                != pyds.NvDsMetaType.NVDSINFER_TENSOR_OUTPUT_META
+            ):
+                continue
+
+            tensor_meta = pyds.NvDsInferTensorMeta.cast(user_meta.user_meta_data)
+
+            # Boxes in the tensor meta should be in network resolution which is
+            # found in tensor_meta.network_info. Use this info to scale boxes to
+            # the input frame resolution.
+            layers_info = []
+            if True:
+                for i in range(tensor_meta.num_output_layers):
+                    layer = pyds.get_nvds_LayerInfo(tensor_meta, i)
+                    if layer.layerName == "output":
+
+                        ptr = ctypes.cast(
+                            pyds.get_ptr(layer.buffer), ctypes.POINTER(ctypes.c_float)
+                        )
+                        num_elements = layer.inferDims.numElements
+                        v = list(np.ctypeslib.as_array(ptr, shape=(num_elements,)))
+                        v = [float(x) for x in v]
+
+                        list_add.append({"frame_number": frame_number, "vector": v})
+
+                try:
+                    l_user = l_user.next
+                except StopIteration:
+                    break
+
+        try:
+            # indicate inference is performed on the frame
+            frame_meta.bInferDone = True
+            l_frame = l_frame.next
+        except StopIteration:
+            break
+
+    return Gst.PadProbeReturn.OK
+
+
+def detector_results_probe(pad, info, u_data, list_add, frame_num=0):
+    frame_number = 0
+    num_rects = 0
+    got_fps = False
+    print("HEY I AM PROBING DETECTIONS")
+    gst_buffer = info.get_buffer()
+    if not gst_buffer:
+        print("Unable to get GstBuffer ")
+        return
+
+    batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(gst_buffer))
+    l_frame = batch_meta.frame_meta_list
+
+    while l_frame is not None:
+        try:
+            frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
+        except StopIteration:
+            break
+
+        frame_number = frame_meta.frame_num
+        l_obj = frame_meta.obj_meta_list
+        num_rects = frame_meta.num_obj_meta
+
+        l_user = frame_meta.frame_user_meta_list
+
+        while l_obj is not None:
+            try:
+                # Casting l_obj.data to pyds.NvDsObjectMeta
+                obj_meta = pyds.NvDsObjectMeta.cast(l_obj.data)
+            except StopIteration:
+                break
+
+            # param_extract = ['left','top','width','height']
+            # strc = ''
+            # for param in param_extract:
+            #     strc+=str(getattr(obj_meta.rect_params, param))
+            #     strc+=' '
+
+            #           target_width
+            #            target_height
+            score = obj_meta.confidence
+            label = obj_meta.obj_label
+            left = obj_meta.rect_params.left
+            top = obj_meta.rect_params.top
+            width = obj_meta.rect_params.width
+            height = obj_meta.rect_params.height
+            frame_number = frame_number
+            class_id = obj_meta.class_id
+
+            d_add = {
+                "score": score,
+                "label": label,
+                "left": left,
+                "top": top,
+                "width": width,
+                "height": height,
+                "frame_number": frame_number,
+                "class_id": class_id,
+            }
+            list_add.append(d_add)
+            print(frame_number, label, score)
+            if frame_number % 100 == 0:
+                str_pr = "FRAME_PROGRESS: " + pfm(
+                    str(frame_number) + "/" + str(frame_num)
+                )
+                logger.info(str_pr)
+
+            try:
+                l_obj = l_obj.next
+            except StopIteration:
+                break
+
+        # Update frame rate through this probe
+        stream_index = "stream{0}".format(frame_meta.pad_index)
+
+        try:
+            l_frame = l_frame.next
+        except StopIteration:
+            break
+
+    return Gst.PadProbeReturn.OK
+
+
+def cb_newpad(decodebin, decoder_src_pad, data):
+    caps = decoder_src_pad.get_current_caps()
+    if not caps:
+        caps = decoder_src_pad.query_caps()
+    gststruct = caps.get_structure(0)
+    gstname = gststruct.get_name()
+    source_bin = data
+    features = caps.get_features(0)
+
+    # Need to check if the pad created by the decodebin is for video and not
+    # audio.
+    print("gstname=", gstname)
+    if gstname.find("video") != -1:
+        # Link the decodebin pad only if decodebin has picked nvidia
+        # decoder plugin nvdec_*. We do this by checking if the pad caps contain
+        # NVMM memory features.
+        print("features=", features)
+        if features.contains("memory:NVMM"):
+            # Get the source bin ghost pad
+            bin_ghost_pad = source_bin.get_static_pad("src")
+            if not bin_ghost_pad.set_target(decoder_src_pad):
+                sys.stderr.write(
+                    "Failed to link decoder src pad to source bin ghost pad\n"
+                )
+        else:
+            sys.stderr.write(" Error: Decodebin did not pick nvidia decoder plugin.\n")
+
+
+def decodebin_child_added(child_proxy, Object, name, user_data):
+    print("Decodebin child added:", name, "\n")
+    if name.find("decodebin") != -1:
+        Object.connect("child-added", decodebin_child_added, user_data)
+
+    if "source" in name:
+        source_element = child_proxy.get_by_name("source")
+        if source_element.find_property("drop-on-latency") != None:
+            Object.set_property("drop-on-latency", True)
+
+
+def create_source_bin(uri):
+
+    bin_name = "source-bin-any-format"
+
+    nbin = Gst.Bin.new(bin_name)
+    if not nbin:
+        sys.stderr.write(" Unable to create source bin \n")
+
+    uri_decode_bin = Gst.ElementFactory.make("uridecodebin", "uri-decode-bin")
+    if not uri_decode_bin:
+        sys.stderr.write(" Unable to create uri decode bin \n")
+
+    uri_decode_bin.set_property("uri", uri)
+
+    uri_decode_bin.connect("pad-added", cb_newpad, nbin)
+    uri_decode_bin.connect("child-added", decodebin_child_added, nbin)
+
+    Gst.Bin.add(nbin, uri_decode_bin)
+    bin_pad = nbin.add_pad(Gst.GhostPad.new_no_target("src", Gst.PadDirection.SRC))
+    if not bin_pad:
+        sys.stderr.write(" Failed to add ghost pad in source bin \n")
+        return None
+    return nbin
+
+
+# def run_inference(file_path):
+if True:
+    file_path = "/home/thebears/local/source/short.mp4"
+    os.environ.pop("DISPLAY", None)
+    if not file_path.startswith("file://"):
+        file_path = "file://" + file_path
+
+    platform_info = PlatformInfo()
+    Gst.init(None)
+
+    pipeline = Gst.Pipeline()
+
+    source_file = create_source_bin(file_path)
+
+    tee = Gst.ElementFactory.make("tee", "nvsink-tee")
+
+    # DETECT
+    queue_detect = Gst.ElementFactory.make("queue", "nvtee-detect")
+
+    streammux_detect = Gst.ElementFactory.make("nvstreammux", "Stream-muxer-detector")
+    streammux_detect.set_property("width", target_width_detect)
+    streammux_detect.set_property("height", target_height_detect)
+    streammux_detect.set_property("batched-push-timeout", MUXER_BATCH_TIMEOUT_USEC)
+    streammux_detect.set_property("enable-padding", 1)
+    streammux_detect.set_property("batch-size", 4)
+
+    nugget_detector = Gst.ElementFactory.make("nvinfer", "primary-inference")
+    nugget_detector.set_property(
+        "config-file-path",
+        "/home/thebears/DeepStream-Yolo/config_infer_primary_yoloV7.txt",
+    )
+
+    fakesink_detect = Gst.ElementFactory.make("fakesink", "fakesink")
+    fakesink_detect.set_property("enable-last-sample", 0)
+    fakesink_detect.set_property("sync", 0)
+
+    # EMBED
+
+    queue_embed = Gst.ElementFactory.make("queue", "nvtee-que-embed")
+
+    streammux_embed = Gst.ElementFactory.make("nvstreammux", "Stream-muxer-embed")
+    streammux_embed.set_property("width", target_width_embed)
+    streammux_embed.set_property("height", target_height_embed)
+    streammux_embed.set_property("batched-push-timeout", MUXER_BATCH_TIMEOUT_USEC)
+    streammux_embed.set_property("enable-padding", 0)
+    streammux_embed.set_property("batch-size", 1)
+
+    nugget_embed = Gst.ElementFactory.make("nvinfer", "primary-inference")
+    nugget_embed.set_property(
+        "config-file-path", "/home/thebears/DeepStream-Yolo/embedder.txt"
+    )
+    #    nugget_embed.set_property('config-file-path', "/home/thebears/DeepStream-Yolo/config_infer_primary_yoloV7.txt")
+
+    fakesink_embed = Gst.ElementFactory.make("fakesink", "fakesink2")
+    fakesink_embed.set_property("enable-last-sample", 0)
+    fakesink_embed.set_property("sync", 0)
+
+    # LINKING
+
+    # Ensure NVMM caps with a capsfilter
+    #    capsfilter = Gst.ElementFactory.make("capsfilter", "capsfilter")
+    #    capsfilter.set_property("caps", Gst.Caps.from_string("video/x-raw(memory:NVMM), format=NV12"))
+    #    pipeline.add(capsfilter)
+
+    pipeline.add(source_file)
+    pipeline.add(tee)
+
+    nvvidconv = Gst.ElementFactory.make("nvvidconv", "nvvidconv")
+    pipeline.add(nvvidconv)
+
+    source_file.link(nvvidconv)
+    #  nvvidconv.link(capsfilter)
+    #   capsfilter.link(tee)
+    nvvidconv.link(tee)
+
+    if False:
+        pipeline.add(queue_detect)
+        pipeline.add(streammux_detect)
+        pipeline.add(nugget_detector)
+        pipeline.add(fakesink_detect)
+
+        tee.get_request_pad("src_%u").link(queue_detect.get_static_pad("sink"))
+        queue_detect.get_static_pad("src").link(
+            streammux_detect.get_request_pad("sink_0")
+        )
+        streammux_detect.link(nugget_detector)
+        nugget_detector.link(fakesink_detect)
+
+    os.environ["GST_DEBUG_DUMP_DOT_DIR"] = "/tmp"
+    os.putenv("GST_DEBUG_DUMP_DIR_DIR", "/tmp")
+
+    if True:
+
+        pipeline.add(queue_embed)
+        pipeline.add(streammux_embed)
+        pipeline.add(nugget_embed)
+        pipeline.add(fakesink_embed)
+
+        tee.get_request_pad("src_%u").link(queue_embed.get_static_pad("sink"))
+        queue_embed.get_static_pad("src").link(
+            streammux_embed.get_request_pad("sink_0")
+        )
+        streammux_embed.link(nugget_embed)
+        nugget_embed.link(fakesink_embed)
+
+    Gst.debug_bin_to_dot_file(pipeline, Gst.DebugGraphDetails.ALL, "pipeline")
+    print_pipeline_structure(pipeline)
+    cmd = f"/usr/bin/ffprobe -v error -select_streams v:0 -count_packets -show_entries stream=nb_read_packets -of csv=p=0 {file_path}"  # /srv/ftp/railing/2025/02/28/railing_00_20250228115800.mp4
+    try:
+        frames = int(os.popen(cmd).read().strip())
+    except:
+        frames = 0
+
+    logger.info(f"TOTAL_FRAMES: {frames}")
+
+    embed_list = list()
+
+    Gst.debug_bin_to_dot_file(pipeline, Gst.DebugGraphDetails.ALL, "pipeline_structure")
+    embed_results = partial(embed_results_probe, list_add=embed_list, frame_num=frames)
+    nugget_embed.get_static_pad("src").add_probe(
+        Gst.PadProbeType.BUFFER, embed_results, 0
+    )
+
+    Gst.debug_bin_to_dot_file(
+        pipeline,
+        Gst.DebugGraphDetails.ALL,
+        "/home/thebears/local/source/pipeline_structure",
+    )
+
+    detector_list = list()
+
+    detector_results = partial(
+        detector_results_probe, list_add=detector_list, frame_num=frames
+    )
+    nugget_detector.get_static_pad("src").add_probe(
+        Gst.PadProbeType.BUFFER, detector_results, 0
+    )
+
+    print("AFTER SETTING STATIC PADS")
+
+    def get_pipeline_string(pipeline):
+        if not isinstance(pipeline, Gst.Pipeline):
+            return None
+
+        elements = []
+        iterator = pipeline.iterate_elements()
+        while True:
+            result, element = iterator.next()
+            if result != Gst.IteratorResult.OK:
+                break
+            elements.append(element.get_name())
+
+        return " ! ".join(elements)
+
+    Gst.debug_bin_to_dot_file(pipeline, Gst.DebugGraphDetails.ALL, "pipeline_structure")
+
+    # create an event loop and feed gstreamer bus mesages to it
+    loop = GLib.MainLoop()
+    bus = pipeline.get_bus()
+    bus.add_signal_watch()
+    bus.connect("message", bus_call, loop)
+
+    # start play back and listen to events
+    print("Starting pipeline \n")
+    pipeline.set_state(Gst.State.PLAYING)
+    try:
+        loop.run()
+    except:
+        pass
+    # cleanup
+    pipeline.set_state(Gst.State.NULL)
+
+    #    return  detector_list, embed_list\\
+    out = [detector_list, embed_list ]
+    import json
+    with open("dump.json", "w") as ff:
+        json.dump([out[0], out[1]], ff)
+    sys.exit()
+
+
+if __name__ == "__main__":
+    cpath = sys.argv[1]
+    if cpath.endswith("-i"):
+        cpath = "/home/thebears/local/source/short.mp4"
+
+    if not cpath.startswith("file"):
+        cpath = os.path.abspath(cpath)
+
+    out = run_inference(cpath)
+
+    import json
+

min_repro.py

@@ -0,0 +1,336 @@
+import io
+
+import tensorrt as trt
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class AttentionUsingScaledDotProduct(nn.Module):
+    """
+    An alternative implementation of the Attention layer using `F.scaled_dot_product_attention`, which is ~50% faster,
+    but doesn't compile correctly when using TensorRT v10.
+    """
+
+    def __init__(
+        self,
+        dim,
+        num_heads=8,
+        qkv_bias=False,
+        qk_scale=None,
+        attn_drop=0.0,
+        proj_drop=0.0,
+        attn_head_dim=None,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        if attn_head_dim is not None:
+            head_dim = attn_head_dim
+        all_head_dim = head_dim * self.num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
+        if qkv_bias:
+            self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
+            self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
+        else:
+            self.q_bias = None
+            self.v_bias = None
+
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(all_head_dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv_bias = None
+        if self.q_bias is not None:
+            qkv_bias = torch.cat(
+                (
+                    self.q_bias,
+                    torch.zeros_like(self.v_bias, requires_grad=False),
+                    self.v_bias,
+                )
+            )
+        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+        qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)
+
+        x = F.scaled_dot_product_attention(
+            q,
+            k,
+            v,
+            dropout_p=self.attn_drop.p if self.training else 0.0,
+            scale=self.scale,
+        )
+
+        x = x.transpose(1, 2).reshape(B, N, -1)
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class ExplicitAttention(nn.Module):
+    """
+    The explicit, original version of the Attention layer from the VideoMAEv2 codebase.
+    """
+
+    def __init__(
+        self,
+        dim,
+        num_heads=8,
+        qkv_bias=False,
+        qk_scale=None,
+        attn_drop=0.0,
+        proj_drop=0.0,
+        attn_head_dim=None,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        if attn_head_dim is not None:
+            head_dim = attn_head_dim
+        all_head_dim = head_dim * self.num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
+        if qkv_bias:
+            self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
+            self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
+        else:
+            self.q_bias = None
+            self.v_bias = None
+
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(all_head_dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv_bias = None
+        if self.q_bias is not None:
+            qkv_bias = torch.cat(
+                (
+                    self.q_bias,
+                    torch.zeros_like(self.v_bias, requires_grad=False),
+                    self.v_bias,
+                )
+            )
+        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+        qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)
+
+        q = q * self.scale
+        attn = q @ k.transpose(-2, -1)
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+
+        return x
+
+
+class AttentionUsingMHAForward(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_heads=8,
+        qkv_bias=False,
+        qk_scale=None,
+        attn_drop=0.0,
+        proj_drop=0.0,
+        attn_head_dim=None,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        if attn_head_dim is not None:
+            head_dim = attn_head_dim
+        all_head_dim = head_dim * self.num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
+        if qkv_bias:
+            self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
+            self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
+        else:
+            self.q_bias = None
+            self.v_bias = None
+
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(all_head_dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv_bias = None
+        if self.q_bias is not None:
+            qkv_bias = torch.cat(
+                (
+                    self.q_bias,
+                    torch.zeros_like(self.v_bias, requires_grad=False),
+                    self.v_bias,
+                )
+            )
+        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+        qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)
+
+        # MHA expects [sequence, batch, embed_dim].
+        x_t = x.transpose(0, 1)  # => [N, B, C]
+
+        attn_out, _ = F.multi_head_attention_forward(
+            x_t,
+            x_t,
+            x_t,
+            embed_dim_to_check=C,
+            num_heads=self.num_heads,
+            # Since use_separate_proj_weight=False (default), then according to the docs:
+            # "in_proj_weight will be used, which is a combination of q_proj_weight, k_proj_weight, v_proj_weight."
+            in_proj_weight=self.qkv.weight,
+            in_proj_bias=qkv_bias,
+            bias_k=None,
+            bias_v=None,
+            add_zero_attn=False,
+            dropout_p=self.attn_drop.p,
+            out_proj_weight=self.proj.weight,
+            out_proj_bias=self.proj.bias,
+            training=self.training,
+            key_padding_mask=None,
+            need_weights=False,
+            attn_mask=None,
+        )
+
+        # Transpose back to [B, N, C].
+        x = attn_out.transpose(0, 1)
+
+        return x
+
+
+def onnx_to_trt(onnx_bytes: bytes) -> bytes:
+    TRT_LOGGER = trt.Logger(trt.Logger.INFO)
+    builder = trt.Builder(TRT_LOGGER)
+
+    network = builder.create_network()
+    parser = trt.OnnxParser(network, TRT_LOGGER)
+
+    parser.parse(onnx_bytes)
+
+    config = builder.create_builder_config()
+    config.builder_optimization_level = 0
+
+    engine = builder.build_serialized_network(network, config)
+
+    return engine
+
+
+def build_trt_module(model, x):
+    onnx_bytes = io.BytesIO()
+
+    torch.onnx.export(
+        model,
+        (x,),
+        onnx_bytes,
+        export_params=True,
+        opset_version=17,
+        do_constant_folding=True,
+        input_names=["x"],
+        output_names=["y"],
+    )
+
+    trt_engine = onnx_to_trt(onnx_bytes.getvalue())
+    return trt_engine
+
+
+
+#@torch.inference_mode()
+#def main():
+with torch.no_grad():
+    torch.manual_seed(0)
+
+    EMB_DIM = 384
+    x = torch.rand((6, 1568, EMB_DIM))
+
+    explicit_attention = ExplicitAttention(EMB_DIM)
+    sdpa = AttentionUsingScaledDotProduct(EMB_DIM)
+    mha_fwd = AttentionUsingMHAForward(EMB_DIM)
+
+    # Use the same params for all.
+    sdpa.load_state_dict(explicit_attention.state_dict())
+    mha_fwd.load_state_dict(explicit_attention.state_dict())
+
+    sdpa_torch_y = sdpa(x)
+    explicit_attention_torch_y = explicit_attention(x)
+    mha_fwd_torch_y = mha_fwd(x)
+
+    print(
+        "Torch: [explicit<->sdpa] Is allclose?",
+        sdpa_torch_y.allclose(explicit_attention_torch_y, atol=0.0001),
+    )
+    print(
+        "Torch: [explicit<->mha_fwd] Is allclose?",
+        mha_fwd_torch_y.allclose(explicit_attention_torch_y, atol=0.0001),
+    )
+    print(
+        "Torch: [explicit<->sdpa] Total difference:",
+        (sdpa_torch_y - explicit_attention_torch_y).abs().sum(),
+    )
+    print(
+        "Torch: [explicit<->mha_fwd] Total difference:",
+        (mha_fwd_torch_y - explicit_attention_torch_y).abs().sum(),
+    )
+    assert sdpa_torch_y.allclose(explicit_attention_torch_y, atol=0.0001), "Precheck"
+    assert mha_fwd_torch_y.allclose(explicit_attention_torch_y, atol=0.0001), "Precheck"
+# %%
+    
+    explicit_attention_trt = build_trt_module(explicit_attention, x)
+    with open('explicit_attention_trt.trt','wb') as ea:
+        ea.write(explicit_attention_trt)
+
+    sdpa_trt_model = build_trt_module(sdpa, x)
+    with open('sdpa_trt.trt','wb') as ea:
+        ea.write(sdpa_trt_model)
+
+    mha_fwd_trt_model = build_trt_module(mha_fwd, x)
+    with open('mha_trt.trt','wb') as ea:
+        ea.write(mha_fwd_trt_model)
+# %%
+
+
+
+# %%
+    explicit_attention_y = explicit_attention_trt(x.cuda())
+    sdpa_y = sdpa_trt_model(x.cuda())
+    mha_fwd_y = mha_fwd_trt_model(x.cuda())
+
+    print(
+        "TRT: [explicit<->sdpa] Is allclose?",
+        sdpa_y.allclose(explicit_attention_y, atol=0.0001),
+    )
+    print(
+        "TRT: [explicit<->sdpa] Total difference:",
+        (sdpa_y - explicit_attention_y).abs().sum(),
+    )
+
+    print(
+        "TRT: [explicit<->mha_fwd] Is allclose?",
+        mha_fwd_y.allclose(explicit_attention_y, atol=0.0001),
+    )
+    print(
+        "TRT: [explicit<->mha_fwd] Total difference:",
+        (mha_fwd_y - explicit_attention_y).abs().sum(),
+    )
+
+    print("TRT: Explicit Attention:", explicit_attention_y[0, 0, :32])
+    print("TRT: Scaled Dot Product Attention:", sdpa_y[0, 0, :32])
+    print("TRT: MHA Forward:", mha_fwd_y[0, 0, :32])
+
+
+if __name__ == "__main__":
+    main()

ml_run.py

@@ -0,0 +1,8 @@
+from model_runner import ModelRunner
+mr = ModelRunner()
+# %%
+mr.init_model_det()
+mr.init_model_clip()
+# %%
+scored_results = mr.score_video('/home/thebears/source/ml_code/short.mp4')
+print(scored_results)

model_runner.py

@@ -0,0 +1,310 @@
+import sys
+
+sys.path.insert(0, "/home/thebears/source/models/yolov7")
+import time
+import base64 as b64
+from datetime import datetime
+import cv2
+import numpy as np
+import json
+from pymediainfo import MediaInfo
+import inspect
+import open_clip
+import sys
+import torch
+import yaml
+from models.experimental import attempt_load
+from utils.general import check_img_size, non_max_suppression
+from torchvision import transforms
+import torch.nn.functional as F
+import os
+device = torch.device("cuda")
+
+
+# %%
+class ModelRunner:
+    def __init__(self):
+        self.pretrained_name = "webli"
+        self.model_name = "ViT-SO400M-16-SigLIP2-512"
+        self.det_root_path = "/home/thebears/source/model_weights"
+
+    def init_model_clip(self):
+        if hasattr(self, 'clip_preprocess'):
+            return
+
+
+        model_name = self.model_name
+        pretrained_name = self.pretrained_name
+
+        clip_model, _, clip_preprocess_og = open_clip.create_model_and_transforms(
+            model_name, pretrained=pretrained_name
+        )
+        tokenizer = open_clip.get_tokenizer("hf-hub:timm/" + model_name)
+        clip_model = clip_model.half().to(device)
+        clip_dtype = next(clip_model.parameters()).dtype
+        clip_img_size = clip_preprocess_og.transforms[0].size
+        clip_model.encode_image(
+                torch.rand(1, 3, *clip_img_size, dtype=clip_dtype, device=device))
+        clip_preprocess = transforms.Compose(
+            [clip_preprocess_og.transforms[x] for x in [0, 3]]
+        )
+        self.clip_model = clip_model
+        self.clip_preprocess_og = clip_preprocess_og
+        self.clip_tokenizer = tokenizer
+        self.clip_dtype = clip_dtype
+        self.clip_img_size = clip_img_size
+        self.clip_preprocess = clip_preprocess
+
+    def init_model_det(self):
+        if hasattr(self, 'det_model'):
+            return 
+        
+        det_root_path = self.det_root_path
+        det_model_weights_root = os.path.join(det_root_path, "yolov7")
+        det_model_weights_path = os.path.join(det_model_weights_root, "best.pt")
+        det_data_yaml_path = os.path.join(det_model_weights_root, "inaturalist.yaml")
+        det_model = attempt_load(det_model_weights_path, map_location=device)
+        det_model = det_model.half().to(device)
+
+        det_dtype = next(det_model.parameters()).dtype
+        det_imgsz = 1280
+        det_stride = int(det_model.stride.max())
+        det_imgsz = check_img_size(det_imgsz, s=det_stride)
+        _ = det_model(
+            torch.zeros(1, 3, det_imgsz, det_imgsz, dtype=det_dtype).to(device)
+        )
+
+        with open(det_data_yaml_path, "r") as ff:
+            det_model_info = yaml.safe_load(ff)
+            det_labels = det_model_info["names"]
+
+        self.det_dtype = det_dtype
+        self.det_imgsz = det_imgsz
+        self.det_stride = det_stride
+        self.det_model_info = det_model_info
+        self.det_labels = det_labels
+        self.det_model = det_model
+
+    def get_det_vid_preprocessor(self, vid_h, vid_w):
+        if not hasattr(self, "_det_vid_preprocessors"):
+            self._det_vid_preprocessors = dict()
+            self.curr_det_vid_preprocessor = None
+        dict_key = (vid_h, vid_w)
+        det_stride = self.det_stride
+        if dict_key in self._det_vid_preprocessors:
+            self.curr_det_vid_preprocessor = self._det_vid_preprocessors[dict_key]
+            return self.curr_det_vid_preprocessor
+
+        target_max = self.det_imgsz
+
+        if vid_h > vid_w:
+            target_h = target_max
+            target_w = target_max * vid_w / vid_h
+        elif vid_h == vid_w:
+            target_h = target_max
+            target_w = target_max
+        elif vid_h < vid_w:
+            target_h = target_max * vid_h / vid_w
+            target_w = target_max
+
+        target_h = int(target_h)
+        target_w = int(target_w)
+
+        pad_amt = [None, None, None, None]
+        if target_w % det_stride != 0:
+            off = det_stride - target_w % det_stride
+            new_w = target_w + off
+            pad_diff = new_w - target_w
+            pad_left = round(pad_diff / 2)
+            pad_right = pad_diff - pad_left
+            pad_amt[0] = pad_left
+            pad_amt[2] = pad_right
+        else:
+            pad_amt[0] = 0
+            pad_amt[2] = 0
+
+        if target_h % det_stride != 0:
+            off = det_stride - target_h % det_stride
+            new_h = target_h + off
+            pad_diff = new_h - target_h
+            pad_up = round(pad_diff / 2)
+            pad_down = pad_diff - pad_up
+            pad_amt[1] = pad_up
+            pad_amt[3] = pad_down
+        else:
+            pad_amt[1] = 0
+            pad_amt[3] = 0
+
+        det_vid_preprocess = transforms.Compose(
+            [transforms.Resize((target_h, target_w)), transforms.Pad(pad_amt, fill=127)]
+        )
+        
+        self.target_h = target_h
+        self.target_w = target_w
+        self.pad_amt = pad_amt
+        
+        self._det_vid_preprocessors[dict_key] = det_vid_preprocess
+        self.curr_det_vid_preprocessor = self._det_vid_preprocessors[dict_key]
+        return self.curr_det_vid_preprocessor
+
+    def score_frames_det(self, array_score, det_vid_preprocess=None):
+        det_model = self.det_model
+        if det_vid_preprocess is None:
+            det_vid_preprocess = self.curr_det_vid_preprocessor
+
+        frame_numbers = [x[0] for x in array_score]
+        frame_values = [x[1] for x in array_score]
+        frame_as_tensor = (
+            torch.from_numpy(np.stack(frame_values)[:, :, :, 0:3])
+            .to(torch.float16)
+            .to(device)
+            .permute([0, 3, 1, 2])
+        )
+
+        with torch.no_grad():
+            frame_for_model = det_vid_preprocess(frame_as_tensor).div(255)[
+                :, [2, 1, 0], :, :
+            ]
+            det_preds = det_model(frame_for_model)[0]
+            det_pred_post_nms = non_max_suppression(det_preds, 0.25, 0.5)
+            det_cpu_pred = [x.detach().cpu().numpy() for x in det_pred_post_nms]
+
+        return {"det": det_cpu_pred, "fr#": frame_numbers}
+
+    def score_frames_clip(self, clip_array_score):
+        frame_numbers = [x[0] for x in clip_array_score]
+        frame_values = [x[1] for x in clip_array_score]
+        frame_as_tensor = (
+            torch.from_numpy(np.stack(frame_values)[:, :, :, 0:3])
+            .to(torch.float16)
+            .to(device)
+            .permute([0, 3, 1, 2])
+        )
+
+        with torch.no_grad():
+            frame_for_clip = self.clip_preprocess(frame_as_tensor[:, [0, 1, 2], :, :])
+            clip_pred = self.clip_model.encode_image(frame_for_clip).detach().cpu().numpy()
+
+        return {"clip": clip_pred, "fr#": frame_numbers}
+
+    def get_video_info(self, file_path):
+        file_info = MediaInfo.parse(file_path)
+        video_info = None
+        frame_count = 0
+        if len(file_info.video_tracks) > 0:
+            video_info = file_info.video_tracks[0]
+
+        video_info.frame_count = int(video_info.frame_count)
+        return video_info
+
+    def score_video(self, file_to_score, batch_size = 6, clip_interval = 10):
+        video_info = self.get_video_info(file_to_score)
+        vid_decoder = "h264parse"
+        if video_info.format.lower() == "HEVC".lower():
+            vid_decoder = "h265parse"
+
+        gst_cmd = "filesrc location={file_to_score} ! qtdemux name=demux demux.video_0 ! queue ! {vid_decoder} ! nvv4l2decoder ! nvvidconv ! videoscale method=1 add-borders=false ! video/x-raw,width=1280,height=1280 ! appsink sync=false".format(
+            file_to_score=file_to_score, vid_decoder=vid_decoder
+        )
+        cap_handle = cv2.VideoCapture(gst_cmd, cv2.CAP_GSTREAMER)
+        vid_h = video_info.height
+        vid_w = video_info.width
+
+        vid_preprocessor = self.get_det_vid_preprocessor(vid_h, vid_w)
+        target_w = self.target_w
+        target_h = self.target_h
+        pad_amt = self.pad_amt
+
+
+        array_score = list()
+        final_output = dict()
+        final_output["start_score_time"] = time.time()
+        final_output["num_frames"] = video_info.frame_count
+        st = time.time()
+        frame_numbers = list()
+        det_results = list()
+        clip_results = list()
+        clip_frame_numbers = list()
+
+        clip_array = list()
+
+        for i in range(video_info.frame_count):
+            success, frame_matrix = cap_handle.read()
+
+            if not success:
+                break
+
+            array_score.append((i, frame_matrix))
+
+            if len(array_score) >= batch_size:
+                score_result = self.score_frames_det(array_score, det_vid_preprocess = vid_preprocessor)
+                det_results.extend(score_result["det"])
+                frame_numbers.extend(score_result["fr#"])
+                array_score = list()
+
+            if not (i % clip_interval):
+                clip_score_result = self.score_frames_clip([(i, frame_matrix)])
+                clip_results.extend(clip_score_result["clip"])
+                clip_frame_numbers.extend(clip_score_result["fr#"])
+
+
+        if len(array_score) > 0:
+            score_result = self.score_frames_det(array_score, det_vid_preprocess = vid_preprocessor)
+            det_results.extend(score_result["det"])
+            frame_numbers.extend(score_result["fr#"])
+
+        cap_handle.release()
+
+
+        final_output["end_score_time"] = time.time()
+        final_output["video"] = {
+            "w": vid_w,
+            "h": vid_h,
+            "path": file_to_score,
+            "target_w": target_w,
+            "target_h": target_h,
+            "pad_amt": pad_amt,
+        }
+
+        try:
+            final_output["scoring_fps"] = final_output["num_frames"] / (
+                final_output["end_score_time"] - final_output["start_score_time"]
+            )
+        except Exception as e:
+            pass
+
+        final_output["scores"] = list()
+
+
+        clip_results_as_np = np.asarray(clip_results)
+
+        for frame_number, frame in zip(frame_numbers, det_results):
+            cframe_dict = dict()
+            cframe_dict["frame"] = frame_number
+            cframe_dict["detections"] = list()
+
+            for det in frame:
+                data = dict()
+                data["coords"] = [float(x) for x in list(det[0:4])]
+                data["score"] = float(det[4])
+                data["idx"] = int(det[5])
+
+                try:
+                    data["name"] = det_labels[data["idx"]]
+                except:
+                    data["name"] = "Code failed"
+
+                cframe_dict["detections"].append(data)
+
+            final_output["scores"].append(cframe_dict)
+
+        emb_dict = dict()
+
+        emb_dict["frame_numbers"] = clip_frame_numbers
+        emb_dict["array_size"] = clip_results_as_np.shape
+        emb_dict["array_dtype"] = str(clip_results_as_np.dtype)
+        emb_dict["array_binary"] = b64.b64encode(clip_results_as_np).decode()
+
+        final_output["embeds"] = emb_dict
+
+        return final_output

models/try_decode.py

@@ -0,0 +1,171 @@
+import time
+
+import cv2
+import numpy
+import numpy as np
+import onnxruntime as rt
+import open_clip
+import pycuda.autoinit
+import pycuda.driver as cuda
+import tensorrt as trt
+import torch
+import os
+from cuda import cuda as ccuda
+from cuda import cudart
+
+cmd = "filesrc location=/home/thebears/local/source/full.mp4 ! qtdemux name=demux demux.video_0 ! queue ! h265parse ! nvv4l2decoder ! nvvidconv ! videoscale method=1 add-borders=false ! video/x-raw,width=1280,height=1280 ! appsink sync=false"
+
+cap = cv2.VideoCapture(cmd, cv2.CAP_GSTREAMER)
+
+st = time.time()
+fr = 0
+
+
+arrays_to_score = list()
+array = list()
+while True:
+    good, frf = cap.read()
+    fr += 1
+    print(good, fr)
+    if not good:
+        break
+
+
+    array.append(frf)
+
+    if len(array) > 8:
+        arrays_to_score.append(torch.from_numpy(np.asarray(array)))
+        array = list()
+        break
+
+
+if len(array) > 0:
+    arrays_to_score.append(torch.from_numpy(np.asarray(array)))
+
+
+et = time.time()
+
+print(et - st, fr / (st - et))
+
+
+
+
+
+# %%
+pretrained_name = "webli"
+model_name = "ViT-L-16-SigLIP2-512"
+
+#model_name, pretrained_name = ('ViT-B-16-quickgelu', 'openai')
+
+model, _, preprocess = open_clip.create_model_and_transforms(
+    model_name, pretrained=pretrained_name
+)
+
+
+# %%
+with torch.no_grad():
+    et = time.time()
+    if True:
+#        tensor_raw = arrays_to_score[0][0,:,:,0:3][None,:,:,:]
+#        tensor_perm = tensor_raw.permute([0, 3, 1, 2]).to(torch.float32) / 255
+        tensor_perm = torch.rand(1,3,512,512)
+        tensor_reshaped = preprocess.transforms[0](tensor_perm)
+        tensor_mean = preprocess.transforms[-1](tensor_reshaped)
+    else:
+        tensor_raw = torch.concat(arrays_to_score)[0:4, :, :, 0:3]
+        tensor_perm = tensor_raw.permute([0, 3, 1, 2]).to(torch.float32) / 255
+        tensor_reshaped = preprocess.transforms[1](preprocess.transforms[0](tensor_perm))
+        tensor_mean = preprocess.transforms[-1](tensor_reshaped)
+    imp = model.encode_image(tensor_mean)
+    st = time.time()
+#    print((st - et) / tensor_raw.shape[0], tensor_raw.shape[0]/(st - et) )
+
+    from_model_on_gpu = imp.cpu().numpy()
+
+# %%
+
+# %%
+#ONNX_FILE_PATH = "/home/thebears/local/source/engine_siglip2_512.onnx"
+#ONNX_FILE_PATH = "/home/thebears/local/source/engine_small.onnx"
+ONNX_FILE_PATH = "in_docker.onnx"
+torch.onnx.export(
+    model.visual,
+    tensor_mean,
+    ONNX_FILE_PATH,
+    input_names=["input"],
+    output_names=["output"],
+)
+# %%
+X_test = tensor_mean.cpu().numpy()
+sess = rt.InferenceSession(
+    ONNX_FILE_PATH, providers=rt.get_available_providers())
+input_name = sess.get_inputs()[0].name
+pred_onx = sess.run(None, {input_name: X_test.astype(numpy.float32)})[0]
+print(pred_onx)
+
+def norm(v):
+    return np.divide(v.T,np.linalg.norm(v,axis=1)).T
+
+print(np.dot(norm(pred_onx), norm(from_model_on_gpu).T))
+
+# %%
+TRT_LOGGER = trt.Logger()
+def build_engine_from_onnx(onnx_file_path, use_fp16=True):
+    """
+    Convert ONNX model to TensorRT engine with FP16 precision
+
+    Args:
+        onnx_file_path: Path to ONNX model
+        use_fp16: Boolean to enable FP16 mode
+
+    Returns:
+        TensorRT engine
+    """
+    # Logger to capture info/warning/errors
+    TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
+
+    # Create builder and network
+    builder = trt.Builder(TRT_LOGGER)
+    network = builder.create_network(
+        1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
+    )
+
+    # Create ONNX parser and parse model
+    parser = trt.OnnxParser(network, TRT_LOGGER)
+    with open(onnx_file_path, "rb") as model:
+        if not parser.parse(model.read()):
+            for error in range(parser.num_errors):
+                print(f"ONNX parse error: {parser.get_error(error)}")
+            raise ValueError(f"Failed to parse ONNX file: {onnx_file_path}")
+
+    # Create optimization config
+    config = builder.create_builder_config()
+    config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 20)  # 1 MiB
+    # Enable FP16 precision if requested and if the GPU supports it
+    if use_fp16:
+        if builder.platform_has_fast_fp16:
+#            config.set_flag(trt.BuilderFlag.FP16)
+            print("FP16 enabled successfully")
+        else:
+            print("Warning: GPU doesn't support fast FP16, using FP32 instead")
+
+    # Set optimization profile for dynamic shapes (if needed)
+    # If you have dynamic shapes, you'd need to set min/opt/max dimensions
+
+    # Build and serialize engine
+    engine = builder.build_serialized_network(network, config)
+
+    return engine
+
+
+
+ENGINE_FILE_PATH = os.path.splitext(ONNX_FILE_PATH)[0]+'.trt'
+
+
+engine = build_engine_from_onnx(ONNX_FILE_PATH, use_fp16=False)
+
+with open(ENGINE_FILE_PATH, "wb") as f:
+    f.write(engine)
+
+
+# %%

new_temp.py

@@ -0,0 +1,79 @@
+    
+    
+import gi
+gi.require_version('Gst', '1.0')
+#gi.require_version('GstNv', '1.0')
+from gi.repository import Gst, GObject
+
+# Initialize GStreamer
+Gst.init(None)
+
+def main():
+    # Create a pipeline
+    pipeline = Gst.Pipeline()
+
+    # Source element: File or Camera
+    source_bin = Gst.ElementFactory.make("filesrc", "file-source")
+
+    source_bin.set_property("location", '/opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.mp4')    
+    
+    decodebin = Gst.ElementFactory.make("decodebin", "decode-bin")
+    source_bin.link(decodebin)
+
+    # Queue 1 for decodebin output
+    queue1 = Gst.ElementFactory.make("queue", "queue1")
+    pipeline.add(queue1)
+
+    # Splitter: Tee Element
+    tee = Gst.ElementFactory.make("tee", "splitter")
+    pipeline.add(tee)
+
+    # Resizer branch
+    queue_resizer = Gst.ElementFactory.make("queue", "resizer-queue")
+
+
+    resizer = Gst.ElementFactory.make("nvvideoconvert", "video-converter")
+    capsfilter = Gst.ElementFactory.make("capsfilter", "caps-filter")
+    capsfilter.set_property("caps", Gst.Caps.from_string("video/x-raw(memory:NVMM), width=300, height=300"))
+
+    # Model inference element
+    nvinfer = Gst.ElementFactory.make("nvinfer", "primary-inference")
+    nvinfer.set_property("config-file-path", "path_to_config_file.txt")
+
+    # Create a sink for visualizing output
+    fakesink_detect = Gst.ElementFactory.make("fakesink","fakesink")
+    fakesink_detect.set_property('enable-last-sample', 0)
+    fakesink_detect.set_property('sync', 0)
+    sink = fakesink_detect
+
+    # Add and link all elements (Resizer Branch)
+    for element in [queue_resizer, resizer, capsfilter, nvinfer, sink]:
+        pipeline.add(element)
+
+    decodebin.connect("pad-added", lambda decode, src_pad: src_pad.link(queue1.get_static_pad("sink")))
+    queue1.link(tee)
+
+    # Link the tee to the resizer pipeline
+    tee_src_pad1 = tee.get_request_pad("src_%u")
+    queue_resizer_sink_pad = queue_resizer.get_static_pad("sink")
+    tee_src_pad1.link(queue_resizer_sink_pad)
+
+    queue_resizer.link(resizer)
+    resizer.link(capsfilter)
+    capsfilter.link(nvinfer)
+    nvinfer.link(sink)
+
+    # Start the pipeline
+    pipeline.set_state(Gst.State.PLAYING)
+
+    # Run a main loop to listen for EOS or error
+    loop = GObject.MainLoop()
+    try:
+        loop.run()
+    except KeyboardInterrupt:
+        pass
+
+    pipeline.set_state(Gst.State.NULL)
+
+if __name__ == "__main__":
+    main()

new_test.py

@@ -0,0 +1,228 @@
+import sys
+import gi
+gi.require_version('Gst', '1.0')
+from gi.repository import GObject, Gst, GLib
+
+# Initialize GStreamer
+Gst.init(None)
+
+def create_source_bin(uri, source_id):
+    # Create a source bin element
+    bin_name = "source-bin-%02d" % source_id
+    source_bin = Gst.Bin.new(bin_name)
+    if not source_bin:
+        sys.stderr.write("Unable to create source bin\n")
+        return None
+
+    # Create elements
+    uri_decode_bin = Gst.ElementFactory.make("uridecodebin", "uri-decode-bin")
+    if not uri_decode_bin:
+        sys.stderr.write("Unable to create uri decode bin\n")
+        return None
+
+    # Set properties
+    uri_decode_bin.set_property("uri", uri)
+
+    # Add the URI decode bin to the source bin
+    source_bin.add(uri_decode_bin)
+
+    # Add pad probe for data flow
+    pad = Gst.Ghost.Pad.new_no_target("src", Gst.PadDirection.SRC)
+    if not pad:
+        sys.stderr.write("Failed to add ghost pad in source bin\n")
+        return None
+    source_bin.add_pad(pad)
+
+    # Connect to the "pad-added" signal of the decoder
+    uri_decode_bin.connect("pad-added", cb_newpad, source_bin)
+    return source_bin
+
+def cb_newpad(decoder, pad, source_bin):
+    # Check the media type of the pad
+    pad_caps = pad.get_current_caps()
+    gst_struct = pad_caps.get_structure(0)
+    pad_type = gst_struct.get_name()
+    
+    if pad_type.startswith("video"):
+        # Link the decoder to the ghost pad
+        sink_pad = source_bin.get_static_pad("src")
+        if not sink_pad.is_linked():
+            pad.link(sink_pad)
+
+def main():
+    # Create the main pipeline
+    pipeline = Gst.Pipeline()
+    
+    # Create the source bin
+    source_bin = create_source_bin("file:///path/to/video.mp4", 0)
+    
+    # Create a tee element to split the stream
+    tee = Gst.ElementFactory.make("tee", "tee")
+    
+    # Create two streammux elements
+    streammux1 = Gst.ElementFactory.make("nvstreammux", "stream-muxer1")
+    streammux2 = Gst.ElementFactory.make("nvstreammux", "stream-muxer2")
+    
+    # Set streammux properties
+    streammux1.set_property("width", 1920)
+    streammux1.set_property("height", 1080)
+    streammux1.set_property("batch-size", 1)
+    streammux1.set_property("batched-push-timeout", 4000000)
+    
+    streammux2.set_property("width", 1280)
+    streammux2.set_property("height", 720)
+    streammux2.set_property("batch-size", 1)
+    streammux2.set_property("batched-push-timeout", 4000000)
+    
+    # Create queue elements for each branch
+    queue1 = Gst.ElementFactory.make("queue", "queue1")
+    queue2 = Gst.ElementFactory.make("queue", "queue2")
+    
+    # Add all elements to the pipeline
+    pipeline.add(source_bin)
+    pipeline.add(tee)
+    pipeline.add(queue1)
+    pipeline.add(queue2)
+    pipeline.add(streammux1)
+    pipeline.add(streammux2)
+    
+    # Link the source bin to the tee
+    source_bin.link(tee)
+    
+    # Link tee to the first queue and then to streammux1
+    tee_pad1 = tee.get_request_pad("src_%u")
+    queue1_pad = queue1.get_static_pad("sink")
+    tee_pad1.link(queue1_pad)
+    
+    # Link the queue1 to streammux1
+    sinkpad1 = streammux1.get_request_pad("sink_0")
+    srcpad1 = queue1.get_static_pad("src")
+    srcpad1.link(sinkpad1)
+    
+    # Link tee to the second queue and then to streammux2
+    tee_pad2 = tee.get_request_pad("src_%u")
+    queue2_pad = queue2.get_static_pad("sink")
+    tee_pad2.link(queue2_pad)
+    
+    # Link the queue2 to streammux2
+    sinkpad2 = streammux2.get_request_pad("sink_0")
+    srcpad2 = queue2.get_static_pad("src")
+    srcpad2.link(sinkpad2)
+    
+    # Continue building your pipeline with the two streammux outputs
+    # For example, you can add nvinfer, nvtracker, nvvideoconvert, etc.
+    
+    # Start the pipeline
+    pipeline.set_state(Gst.State.PLAYING)
+    
+    # Run the main loop
+    loop = GLib.MainLoop()
+    loop.run()
+    
+    # Clean up
+    pipeline.set_state(Gst.State.NULL)
+
+if __name__ == "__main__":
+    main()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+import gi
+gi.require_version('Gst', '1.0')
+from gi.repository import Gst, GObject
+
+def main():
+    # Initialize GStreamer
+    Gst.init(None)
+
+    # Create the pipeline
+    pipeline = Gst.Pipeline()
+
+    # Create a source bin (e.g., decodebin from a file source or cameras)
+    source_bin = create_source_bin("file://path/to/your/video")
+    pipeline.add(source_bin)
+
+    # Create the tee element
+    tee = Gst.ElementFactory.make("tee", "tee")
+    pipeline.add(tee)
+
+    # Create two nvstreammux elements
+    streammux1 = Gst.ElementFactory.make("nvstreammux", "streammux1")
+    streammux1.set_property("batch-size", 1)
+    pipeline.add(streammux1)
+
+    streammux2 = Gst.ElementFactory.make("nvstreammux", "streammux2")
+    streammux2.set_property("batch-size", 1)
+    pipeline.add(streammux2)
+
+    # Request pads from the tee element
+    tee_src_pad_1 = tee.get_request_pad("src_%u")
+    tee_src_pad_2 = tee.get_request_pad("src_%u")
+
+    # Create two queues for each branch
+    queue1 = Gst.ElementFactory.make("queue", "queue1")
+    queue2 = Gst.ElementFactory.make("queue", "queue2")
+    pipeline.add(queue1)
+    pipeline.add(queue2)
+
+    # Link tee to the queues
+    tee_src_pad_1.link(queue1.get_static_pad("sink"))
+    tee_src_pad_2.link(queue2.get_static_pad("sink"))
+
+    # Link queue1 -> streammux1 and queue2 -> streammux2
+    queue1.link(streammux1)
+    queue2.link(streammux2)
+
+    # Add other elements (e.g., processing, display sinks) after each streammux as required.
+    # For simplicity, you can just add a fakesink at the end of each branch for now. 
+
+    fake_sink_1 = Gst.ElementFactory.make("fakesink", "fakesink1")
+    fake_sink_2 = Gst.ElementFactory.make("fakesink2")
+    pipeline.add(fake_sink_1)
+    pipeline.add(fake_sink_2)
+    streammux1.link(fake_sink_1)
+    streammux2.link(fake_sink_2)
+
+    # Connect the source bin to the tee
+    source_bin_src_pad = source_bin.get_static_pad("src")
+    tee_sink_pad = tee.get_static_pad("sink")
+    source_bin_src_pad.link(tee_sink_pad)
+
+    # Start the pipeline
+    pipeline.set_state(Gst.State.PLAYING)
+
+    # Run the pipeline
+    loop = GObject.MainLoop()
+    try:
+        loop.run()
+    except KeyboardInterrupt:
+        print("Exiting...")
+        pipeline.set_state(Gst.State.NULL)
+
+
+def create_source_bin(uri):
+    # Create the source bin (file or live source)
+    uridecodebin = Gst.ElementFactory.make("uridecodebin", "source-bin")
+    uridecodebin.set_property("uri", uri)
+
+    bin_pad = uridecodebin.get_static_pad("src")
+
+    return uridecodebin
+
+
+if __name__ == "__main__":
+    main()

output

@@ -0,0 +1,817 @@
+Reading package lists...
+Building dependency tree...
+Reading state information...
+Calculating upgrade...
+The following packages were automatically installed and are no longer required:
+  gdal-data libarmadillo10 libarpack2 libavcodec58 libavfilter7 libavformat58
+  libavutil56 libblosc1 libcfitsio9 libcharls2 libfreexl1 libfyba0 libgdal30
+  libgdcm-dev libgdcm3.0 libgeos-c1v5 libgeos3.10.2 libgeotiff5 libgl2ps1.4
+  libgphoto2-dev libhdf4-0-alt libkmlbase1 libkmldom1 libkmlengine1
+  libminizip1 libmysqlclient21 libnetcdf19 libodbc2 libodbcinst2 libogdi4.1
+  libopencv-calib3d4.5d libopencv-contrib4.5d libopencv-dnn4.5d
+  libopencv-features2d4.5d libopencv-flann4.5d libopencv-highgui4.5d
+  libopencv-imgcodecs4.5d libopencv-imgproc4.5d libopencv-ml4.5d
+  libopencv-objdetect4.5d libopencv-photo4.5d libopencv-shape4.5d
+  libopencv-stitching4.5d libopencv-superres4.5d libopencv-video4.5d
+  libopencv-videoio4.5d libopencv-videostab4.5d libopencv-viz4.5d
+  libpostproc55 libpq5 libproj22 librttopo1 libsocket++1 libspatialite7
+  libssh-gcrypt-4 libsuperlu5 libswresample3 libswscale5 liburiparser1
+  libvdpau1 libvtk9.1 libxerces-c3.2 mysql-common proj-data unixodbc-common
+Use 'sudo apt autoremove' to remove them.
+The following packages will be REMOVED:
+  libopencv-calib3d-dev libopencv-contrib-dev libopencv-core-dev
+  libopencv-dnn-dev libopencv-features2d-dev libopencv-flann-dev
+  libopencv-highgui-dev libopencv-imgcodecs-dev libopencv-imgproc-dev
+  libopencv-ml-dev libopencv-objdetect-dev libopencv-photo-dev
+  libopencv-shape-dev libopencv-stitching-dev libopencv-superres-dev
+  libopencv-video-dev libopencv-videoio-dev libopencv-videostab-dev
+  libopencv-viz-dev
+The following NEW packages will be installed:
+  libcudnn9-headers-cuda-12 ubuntu-pro-client ubuntu-pro-client-l10n
+The following packages will be upgraded:
+  apport apport-gtk apt bind9-host bind9-libs binutils
+  binutils-aarch64-linux-gnu binutils-common bluez bluez-obexd containerd.io
+  cryptsetup-bin cuda-cccl-12-6 cuda-crt-12-6 cuda-cudart-12-6
+  cuda-cudart-dev-12-6 cuda-cuobjdump-12-6 cuda-cupti-12-6 cuda-cupti-dev-12-6
+  cuda-cuxxfilt-12-6 cuda-documentation-12-6 cuda-driver-dev-12-6
+  cuda-gdb-12-6 cuda-nvcc-12-6 cuda-nvdisasm-12-6 cuda-nvml-dev-12-6
+  cuda-nvprune-12-6 cuda-nvrtc-12-6 cuda-nvrtc-dev-12-6 cuda-nvtx-12-6
+  cuda-nvvm-12-6 cuda-profiler-api-12-6 cuda-sanitizer-12-6
+  cuda-toolkit-12-6-config-common cuda-toolkit-12-config-common
+  cuda-toolkit-config-common dirmngr distro-info-data dmeventd dmsetup
+  dns-root-data docker-buildx-plugin docker-ce docker-ce-cli
+  docker-ce-rootless-extras docker-compose-plugin ethtool fonts-opensymbol
+  ghostscript ghostscript-x gir1.2-gstreamer-1.0 gir1.2-javascriptcoregtk-4.0
+  gir1.2-nm-1.0 gir1.2-packagekitglib-1.0 gir1.2-soup-2.4 gir1.2-webkit2-4.0
+  git-man gnome-shell gnome-shell-common gnupg gnupg-l10n gnupg-utils gnupg2
+  gpg gpg-agent gpg-wks-client gpg-wks-server gpgconf gpgsm gpgv
+  gstreamer1.0-alsa gstreamer1.0-gl gstreamer1.0-gtk3 gstreamer1.0-packagekit
+  gstreamer1.0-plugins-base gstreamer1.0-plugins-base-apps
+  gstreamer1.0-pulseaudio gstreamer1.0-x initramfs-tools initramfs-tools-bin
+  initramfs-tools-core ldap-utils libabsl20210324 libaom3 libapt-pkg6.0
+  libbinutils libbluetooth3 libc-bin libc-dev-bin libc6 libc6-dbg libc6-dev
+  libcap2 libcap2-bin libcryptsetup12 libctf-nobfd0 libctf0 libcublas-12-6
+  libcublas-dev-12-6 libcudla-12-6 libcudla-dev-12-6 libcudnn9-cuda-12
+  libcudnn9-dev-cuda-12 libcudnn9-samples libcufft-12-6 libcufft-dev-12-6
+  libcurand-12-6 libcurand-dev-12-6 libcurl3-gnutls libcusolver-12-6
+  libcusolver-dev-12-6 libcusparse-12-6 libcusparse-dev-12-6 libcusparselt-dev
+  libcusparselt0 libdebuginfod-common libdebuginfod1 libdevmapper-event1.02.1
+  libdevmapper1.02.1 libdw-dev libdw1 libegl-mesa0 libelf-dev libelf1
+  libexpat1 libexpat1-dev libfreetype-dev libfreetype6 libfreetype6-dev
+  libgbm-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri libglapi-mesa
+  libglib2.0-0 libglib2.0-bin libglib2.0-data libglib2.0-dev
+  libglib2.0-dev-bin libglib2.0-doc libglib2.0-tests libglx-mesa0
+  libgnutls-dane0 libgnutls-openssl27 libgnutls28-dev libgnutls30
+  libgnutlsxx28 libgs9 libgs9-common libgssapi-krb5-2 libgstreamer1.0-0
+  libgstreamer1.0-dev libiniparser1 libipa-hbac0 libjavascriptcoregtk-4.0-18
+  libk5crypto3 libkrb5-3 libkrb5support0 libldap-2.5-0 liblvm2cmd2.03
+  libmalcontent-0-0 libmbim-glib4 libmbim-proxy libmysqlclient21
+  libnautilus-extension1a libnm0 libnss-sss libnss-systemd libnvfatbin-12-6
+  libnvfatbin-dev-12-6 libnvidia-container-tools libnvidia-container1
+  libnvinfer-bin libnvinfer-dev libnvinfer-dispatch-dev libnvinfer-dispatch10
+  libnvinfer-headers-dev libnvinfer-headers-plugin-dev libnvinfer-lean-dev
+  libnvinfer-lean10 libnvinfer-plugin-dev libnvinfer-plugin10
+  libnvinfer-samples libnvinfer-vc-plugin-dev libnvinfer-vc-plugin10
+  libnvinfer10 libnvjitlink-12-6 libnvjitlink-dev-12-6 libnvonnxparsers-dev
+  libnvonnxparsers10 libopencv-dev libpackagekit-glib2-18 libpam-modules
+  libpam-modules-bin libpam-runtime libpam-sss libpam-systemd libpam0g
+  libperl5.34 libpoppler-glib8 libpoppler118 libpq5 libpython2.7
+  libpython2.7-minimal libpython2.7-stdlib libpython3.10 libpython3.10-dev
+  libpython3.10-minimal libpython3.10-stdlib librados2 libraptor2-0 libraw20
+  librbd1 libreoffice-base-core libreoffice-calc libreoffice-common
+  libreoffice-core libreoffice-draw libreoffice-gnome libreoffice-gtk3
+  libreoffice-impress libreoffice-math libreoffice-pdfimport
+  libreoffice-style-breeze libreoffice-style-colibre
+  libreoffice-style-elementary libreoffice-style-yaru libreoffice-writer
+  libseccomp2 libsndfile1 libsnmp-base libsnmp40 libsoup-gnome2.4-1
+  libsoup2.4-1 libsoup2.4-common libsqlite3-0 libsss-certmap0 libsss-idmap0
+  libsss-nss-idmap0 libsystemd-dev libsystemd0 libtasn1-6 libtasn1-6-dev
+  libudev-dev libudev1 libunbound8 libuno-cppu3 libuno-cppuhelpergcc3-3
+  libuno-purpenvhelpergcc3-3 libuno-sal3 libuno-salhelpergcc3-3 libvpx7
+  libwebkit2gtk-4.0-37 libxml2 libxml2-dev libxslt1.1 libyelp0 linux-base
+  linux-firmware linux-libc-dev locales lvm2 nautilus nautilus-data net-tools
+  network-manager network-manager-config-connectivity-ubuntu
+  nvidia-container-toolkit nvidia-container-toolkit-base openssh-client
+  openssh-server openssh-sftp-server openssl packagekit packagekit-tools
+  pci.ids perl perl-base perl-modules-5.34 poppler-utils python2.7
+  python2.7-minimal python3-apport python3-libnvinfer python3-libnvinfer-dev
+  python3-libnvinfer-dispatch python3-libnvinfer-lean python3-paramiko
+  python3-pkg-resources python3-problem-report python3-protobuf
+  python3-requests python3-setuptools python3-setuptools-whl python3-sss
+  python3-uno python3-update-manager python3.10 python3.10-dev
+  python3.10-minimal python3.10-venv rsync snapd sssd sssd-ad sssd-ad-common
+  sssd-common sssd-ipa sssd-krb5 sssd-krb5-common sssd-ldap sssd-proxy systemd
+  systemd-journal-remote systemd-oomd systemd-sysv systemd-timesyncd tensorrt
+  tensorrt-libs thunderbird thunderbird-gnome-support tzdata
+  ubuntu-advantage-tools udev uno-libs-private update-manager
+  update-manager-core ure vim vim-common vim-runtime wireless-regdb
+  wpasupplicant xserver-common xserver-xephyr xserver-xorg-core
+  xserver-xorg-legacy xwayland xxd yelp yelp-xsl
+341 upgraded, 3 newly installed, 19 to remove and 0 not upgraded.
+Inst libperl5.34 [5.34.0-3ubuntu1.3] (5.34.0-3ubuntu1.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [perl:arm64 ]
+Inst perl [5.34.0-3ubuntu1.3] (5.34.0-3ubuntu1.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst perl-base [5.34.0-3ubuntu1.3] (5.34.0-3ubuntu1.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Conf perl-base (5.34.0-3ubuntu1.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst perl-modules-5.34 [5.34.0-3ubuntu1.3] (5.34.0-3ubuntu1.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst libc6-dbg [2.35-0ubuntu3.8] (2.35-0ubuntu3.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libc6-dev [2.35-0ubuntu3.8] (2.35-0ubuntu3.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libc-dev-bin [2.35-0ubuntu3.8] (2.35-0ubuntu3.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst linux-libc-dev [5.15.0-126.136] (5.15.0-141.151 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libc6 [2.35-0ubuntu3.8] (2.35-0ubuntu3.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf libc6 (2.35-0ubuntu3.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libc-bin [2.35-0ubuntu3.8] (2.35-0ubuntu3.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf libc-bin (2.35-0ubuntu3.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libsystemd-dev [249.11-0ubuntu3.12] (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libnss-systemd [249.11-0ubuntu3.12] (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst xserver-common [2:21.1.4-2ubuntu1.7~22.04.12] (2:21.1.4-2ubuntu1.7~22.04.14 Ubuntu:22.04/jammy-updates [all]) []
+Inst xserver-xorg-legacy [2:21.1.4-2ubuntu1.7~22.04.12] (2:21.1.4-2ubuntu1.7~22.04.14 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst libudev-dev [249.11-0ubuntu3.12] (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libsystemd0 [249.11-0ubuntu3.12] (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [systemd:arm64 ]
+Conf libsystemd0 (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [systemd:arm64 ]
+Inst xserver-xorg-core [2:21.1.4-2ubuntu1.7~22.04.12] (2:21.1.4-2ubuntu1.7~22.04.14 Ubuntu:22.04/jammy-updates [arm64]) [systemd:arm64 ]
+Inst systemd-timesyncd [249.11-0ubuntu3.12] (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [systemd:arm64 ]
+Inst systemd-sysv [249.11-0ubuntu3.12] (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [systemd:arm64 ]
+Inst systemd-oomd [249.11-0ubuntu3.12] (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [systemd:arm64 ]
+Inst systemd-journal-remote [249.11-0ubuntu3.12] (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [systemd:arm64 ]
+Inst libpam-systemd [249.11-0ubuntu3.12] (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [systemd:arm64 ]
+Inst systemd [249.11-0ubuntu3.12] (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst bluez [5.64-0ubuntu1.3] (5.64-0ubuntu1.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst udev [249.11-0ubuntu3.12] (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libudev1 [249.11-0ubuntu3.12] (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf libudev1 (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libglib2.0-dev [2.72.4-0ubuntu2.4] (2.72.4-0ubuntu2.5 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libglib2.0-dev-bin [2.72.4-0ubuntu2.4] (2.72.4-0ubuntu2.5 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libglib2.0-data [2.72.4-0ubuntu2.4] (2.72.4-0ubuntu2.5 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all]) []
+Inst libelf-dev [0.186-1build1] (0.186-1ubuntu0.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libdw-dev [0.186-1build1] (0.186-1ubuntu0.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libdebuginfod-common [0.186-1build1] (0.186-1ubuntu0.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all]) []
+Inst libgnutls28-dev [3.7.3-4ubuntu1.5] (3.7.3-4ubuntu1.6 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libgnutls-openssl27 [3.7.3-4ubuntu1.5] (3.7.3-4ubuntu1.6 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libgnutlsxx28 [3.7.3-4ubuntu1.5] (3.7.3-4ubuntu1.6 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libtasn1-6-dev [4.18.0-4build1] (4.18.0-4ubuntu0.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libtasn1-6 [4.18.0-4build1] (4.18.0-4ubuntu0.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Conf libtasn1-6 (4.18.0-4ubuntu0.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libunbound8 [1.13.1-1ubuntu5.8] (1.13.1-1ubuntu5.10 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst libgnutls-dane0 [3.7.3-4ubuntu1.5] (3.7.3-4ubuntu1.6 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libgnutls30 [3.7.3-4ubuntu1.5] (3.7.3-4ubuntu1.6 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Conf libgnutls30 (3.7.3-4ubuntu1.6 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libk5crypto3 [1.19.2-2ubuntu0.4] (1.19.2-2ubuntu0.7 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Conf libk5crypto3 (1.19.2-2ubuntu0.7 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libkrb5support0 [1.19.2-2ubuntu0.4] (1.19.2-2ubuntu0.7 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [libkrb5-3:arm64 ]
+Conf libkrb5support0 (1.19.2-2ubuntu0.7 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [libkrb5-3:arm64 ]
+Inst libkrb5-3 [1.19.2-2ubuntu0.4] (1.19.2-2ubuntu0.7 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [libgssapi-krb5-2:arm64 ]
+Conf libkrb5-3 (1.19.2-2ubuntu0.7 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [libgssapi-krb5-2:arm64 ]
+Inst libgssapi-krb5-2 [1.19.2-2ubuntu0.4] (1.19.2-2ubuntu0.7 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Conf libgssapi-krb5-2 (1.19.2-2ubuntu0.7 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst ldap-utils [2.5.18+dfsg-0ubuntu0.22.04.2] (2.5.19+dfsg-0ubuntu0.22.04.1 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst libldap-2.5-0 [2.5.18+dfsg-0ubuntu0.22.04.2] (2.5.19+dfsg-0ubuntu0.22.04.1 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst libcurl3-gnutls [7.81.0-1ubuntu1.19] (7.81.0-1ubuntu1.20 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libdebuginfod1 [0.186-1build1] (0.186-1ubuntu0.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libdw1 [0.186-1build1] (0.186-1ubuntu0.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libelf1 [0.186-1build1] (0.186-1ubuntu0.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libglib2.0-bin [2.72.4-0ubuntu2.4] (2.72.4-0ubuntu2.5 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst python3.10-dev [3.10.12-1~22.04.8] (3.10.12-1~22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libexpat1-dev [2.4.7-1ubuntu0.4] (2.4.7-1ubuntu0.6 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libexpat1 [2.4.7-1ubuntu0.4] (2.4.7-1ubuntu0.6 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libpython3.10-dev [3.10.12-1~22.04.8] (3.10.12-1~22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libpython3.10 [3.10.12-1~22.04.8] (3.10.12-1~22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libsqlite3-0 [3.37.2-2ubuntu0.3] (3.37.2-2ubuntu0.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst python3.10-venv [3.10.12-1~22.04.8] (3.10.12-1~22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst python3.10 [3.10.12-1~22.04.8] (3.10.12-1~22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libpython3.10-stdlib [3.10.12-1~22.04.8] (3.10.12-1~22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst python3.10-minimal [3.10.12-1~22.04.8] (3.10.12-1~22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libpython3.10-minimal [3.10.12-1~22.04.8] (3.10.12-1~22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst python3-setuptools-whl [59.6.0-1.2ubuntu0.22.04.2] (59.6.0-1.2ubuntu0.22.04.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all]) []
+Inst libglib2.0-tests [2.72.4-0ubuntu2.4] (2.72.4-0ubuntu2.5 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libglib2.0-0 [2.72.4-0ubuntu2.4] (2.72.4-0ubuntu2.5 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libcap2 [1:2.44-1ubuntu0.22.04.1] (1:2.44-1ubuntu0.22.04.2 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf libcap2 (1:2.44-1ubuntu0.22.04.2 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libgl1-mesa-dri [23.2.1-1ubuntu3.1~22.04.2] (23.2.1-1ubuntu3.1~22.04.3 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst libglx-mesa0 [23.2.1-1ubuntu3.1~22.04.2] (23.2.1-1ubuntu3.1~22.04.3 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst libegl-mesa0 [23.2.1-1ubuntu3.1~22.04.2] (23.2.1-1ubuntu3.1~22.04.3 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst libglapi-mesa [23.2.1-1ubuntu3.1~22.04.2] (23.2.1-1ubuntu3.1~22.04.3 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst libgbm-dev [23.2.1-1ubuntu3.1~22.04.2] (23.2.1-1ubuntu3.1~22.04.3 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst libgbm1 [23.2.1-1ubuntu3.1~22.04.2] (23.2.1-1ubuntu3.1~22.04.3 Ubuntu:22.04/jammy-updates [arm64])
+Inst libpam0g [1.4.0-11ubuntu2.4] (1.4.0-11ubuntu2.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf libpam0g (1.4.0-11ubuntu2.5 Ubuntu:22.04/jammy-updates [arm64])
+Inst libpam-modules-bin [1.4.0-11ubuntu2.4] (1.4.0-11ubuntu2.5 Ubuntu:22.04/jammy-updates [arm64]) [libpam-modules:arm64 on libpam-modules-bin:arm64] [libpam-modules:arm64 ]
+Conf libpam-modules-bin (1.4.0-11ubuntu2.5 Ubuntu:22.04/jammy-updates [arm64]) [libpam-modules:arm64 ]
+Inst libpam-modules [1.4.0-11ubuntu2.4] (1.4.0-11ubuntu2.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf libpam-modules (1.4.0-11ubuntu2.5 Ubuntu:22.04/jammy-updates [arm64])
+Inst libpam-runtime [1.4.0-11ubuntu2.4] (1.4.0-11ubuntu2.5 Ubuntu:22.04/jammy-updates [all])
+Conf libpam-runtime (1.4.0-11ubuntu2.5 Ubuntu:22.04/jammy-updates [all])
+Inst libdevmapper1.02.1 [2:1.02.175-2.1ubuntu4] (2:1.02.175-2.1ubuntu5 Ubuntu:22.04/jammy-updates [arm64])
+Inst libcryptsetup12 [2:2.4.3-1ubuntu1.2] (2:2.4.3-1ubuntu1.3 Ubuntu:22.04/jammy-updates [arm64])
+Inst libseccomp2 [2.5.3-2ubuntu2] (2.5.3-2ubuntu3~22.04.1 Ubuntu:22.04/jammy-updates [arm64])
+Conf libseccomp2 (2.5.3-2ubuntu3~22.04.1 Ubuntu:22.04/jammy-updates [arm64])
+Inst libapt-pkg6.0 [2.4.13] (2.4.14 Ubuntu:22.04/jammy-updates [arm64])
+Conf libapt-pkg6.0 (2.4.14 Ubuntu:22.04/jammy-updates [arm64])
+Inst gpg-wks-client [2.2.27-3ubuntu2.1] (2.2.27-3ubuntu2.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [gnupg:arm64 ]
+Inst dirmngr [2.2.27-3ubuntu2.1] (2.2.27-3ubuntu2.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [gnupg:arm64 ]
+Inst gpg-wks-server [2.2.27-3ubuntu2.1] (2.2.27-3ubuntu2.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [gnupg:arm64 ]
+Inst gnupg-utils [2.2.27-3ubuntu2.1] (2.2.27-3ubuntu2.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [gnupg:arm64 ]
+Inst gpg-agent [2.2.27-3ubuntu2.1] (2.2.27-3ubuntu2.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [gnupg:arm64 ]
+Inst gpg [2.2.27-3ubuntu2.1] (2.2.27-3ubuntu2.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [gnupg:arm64 ]
+Inst gpgconf [2.2.27-3ubuntu2.1] (2.2.27-3ubuntu2.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [gnupg:arm64 gpgsm:arm64 ]
+Inst gnupg-l10n [2.2.27-3ubuntu2.1] (2.2.27-3ubuntu2.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all]) [gnupg:arm64 gpgsm:arm64 ]
+Inst gnupg [2.2.27-3ubuntu2.1] (2.2.27-3ubuntu2.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all]) [gpgsm:arm64 ]
+Inst gpgsm [2.2.27-3ubuntu2.1] (2.2.27-3ubuntu2.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst gpgv [2.2.27-3ubuntu2.1] (2.2.27-3ubuntu2.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf gpgv (2.2.27-3ubuntu2.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst apt [2.4.13] (2.4.14 Ubuntu:22.04/jammy-updates [arm64])
+Conf apt (2.4.14 Ubuntu:22.04/jammy-updates [arm64])
+Remv libopencv-videostab-dev [4.5.4+dfsg-9ubuntu4] [libopencv-contrib-dev:arm64 libopencv-dev:arm64 ]
+Remv libopencv-calib3d-dev [4.5.4+dfsg-9ubuntu4] [libopencv-contrib-dev:arm64 libopencv-stitching-dev:arm64 libopencv-dev:arm64 ]
+Remv libopencv-stitching-dev [4.5.4+dfsg-9ubuntu4] [libopencv-contrib-dev:arm64 libopencv-dev:arm64 ]
+Remv libopencv-objdetect-dev [4.5.4+dfsg-9ubuntu4] [libopencv-contrib-dev:arm64 libopencv-dev:arm64 ]
+Remv libopencv-ml-dev [4.5.4+dfsg-9ubuntu4] [libopencv-contrib-dev:arm64 libopencv-dev:arm64 libopencv-features2d-dev:arm64 ]
+Remv libopencv-core-dev [4.5.4+dfsg-9ubuntu4] [libopencv-contrib-dev:arm64 libopencv-viz-dev:arm64 libopencv-dev:arm64 libopencv-features2d-dev:arm64 libopencv-dnn-dev:arm64 libopencv-flann-dev:arm64 libopencv-imgproc-dev:arm64 ]
+Remv libopencv-dnn-dev [4.5.4+dfsg-9ubuntu4] [libopencv-contrib-dev:arm64 libopencv-viz-dev:arm64 libopencv-dev:arm64 libopencv-features2d-dev:arm64 libopencv-flann-dev:arm64 libopencv-imgproc-dev:arm64 ]
+Remv libopencv-features2d-dev [4.5.4+dfsg-9ubuntu4] [libopencv-contrib-dev:arm64 libopencv-viz-dev:arm64 libopencv-dev:arm64 libopencv-flann-dev:arm64 libopencv-imgproc-dev:arm64 ]
+Remv libopencv-flann-dev [4.5.4+dfsg-9ubuntu4] [libopencv-contrib-dev:arm64 libopencv-viz-dev:arm64 libopencv-dev:arm64 libopencv-imgproc-dev:arm64 ]
+Remv libopencv-highgui-dev [4.5.4+dfsg-9ubuntu4] [libopencv-contrib-dev:arm64 libopencv-viz-dev:arm64 libopencv-dev:arm64 libopencv-imgproc-dev:arm64 ]
+Remv libopencv-superres-dev [4.5.4+dfsg-9ubuntu4] [libopencv-contrib-dev:arm64 libopencv-viz-dev:arm64 libopencv-dev:arm64 libopencv-imgproc-dev:arm64 ]
+Remv libopencv-videoio-dev [4.5.4+dfsg-9ubuntu4] [libopencv-contrib-dev:arm64 libopencv-viz-dev:arm64 libopencv-dev:arm64 libopencv-imgproc-dev:arm64 ]
+Remv libopencv-imgcodecs-dev [4.5.4+dfsg-9ubuntu4] [libopencv-contrib-dev:arm64 libopencv-viz-dev:arm64 libopencv-dev:arm64 libopencv-imgproc-dev:arm64 ]
+Remv libopencv-shape-dev [4.5.4+dfsg-9ubuntu4] [libopencv-contrib-dev:arm64 libopencv-viz-dev:arm64 libopencv-dev:arm64 libopencv-imgproc-dev:arm64 ]
+Remv libopencv-video-dev [4.5.4+dfsg-9ubuntu4] [libopencv-contrib-dev:arm64 libopencv-viz-dev:arm64 libopencv-dev:arm64 libopencv-imgproc-dev:arm64 ]
+Remv libopencv-imgproc-dev [4.5.4+dfsg-9ubuntu4] [libopencv-contrib-dev:arm64 libopencv-viz-dev:arm64 libopencv-dev:arm64 libopencv-photo-dev:arm64 ]
+Remv libopencv-photo-dev [4.5.4+dfsg-9ubuntu4] [libopencv-contrib-dev:arm64 libopencv-viz-dev:arm64 libopencv-dev:arm64 ]
+Inst libopencv-dev [4.5.4+dfsg-9ubuntu4] (4.8.0-1-g6371ee1 L4T Jetson r36.4:stable [arm64]) [libopencv-contrib-dev:arm64 libopencv-viz-dev:arm64 ]
+Remv libopencv-contrib-dev [4.5.4+dfsg-9ubuntu4] [libopencv-viz-dev:arm64 ]
+Remv libopencv-viz-dev [4.5.4+dfsg-9ubuntu4]
+Inst rsync [3.2.7-0ubuntu0.22.04.2] (3.2.7-0ubuntu0.22.04.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst docker-ce-cli [5:27.5.1-1~ubuntu.22.04~jammy] (5:28.2.2-1~ubuntu.22.04~jammy Docker CE:jammy [arm64])
+Inst containerd.io [1.7.25-1] (1.7.27-1 Docker CE:jammy [arm64])
+Inst docker-ce [5:27.5.1-1~ubuntu.22.04~jammy] (5:28.2.2-1~ubuntu.22.04~jammy Docker CE:jammy [arm64])
+Inst libdevmapper-event1.02.1 [2:1.02.175-2.1ubuntu4] (2:1.02.175-2.1ubuntu5 Ubuntu:22.04/jammy-updates [arm64])
+Inst dmsetup [2:1.02.175-2.1ubuntu4] (2:1.02.175-2.1ubuntu5 Ubuntu:22.04/jammy-updates [arm64])
+Inst liblvm2cmd2.03 [2.03.11-2.1ubuntu4] (2.03.11-2.1ubuntu5 Ubuntu:22.04/jammy-updates [arm64])
+Inst dmeventd [2:1.02.175-2.1ubuntu4] (2:1.02.175-2.1ubuntu5 Ubuntu:22.04/jammy-updates [arm64])
+Inst lvm2 [2.03.11-2.1ubuntu4] (2.03.11-2.1ubuntu5 Ubuntu:22.04/jammy-updates [arm64])
+Inst openssh-sftp-server [1:8.9p1-3ubuntu0.10] (1:8.9p1-3ubuntu0.13 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst openssh-server [1:8.9p1-3ubuntu0.10] (1:8.9p1-3ubuntu0.13 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst openssh-client [1:8.9p1-3ubuntu0.10] (1:8.9p1-3ubuntu0.13 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libpython2.7 [2.7.18-13ubuntu1.4] (2.7.18-13ubuntu1.5 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst python2.7 [2.7.18-13ubuntu1.4] (2.7.18-13ubuntu1.5 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libpython2.7-stdlib [2.7.18-13ubuntu1.4] (2.7.18-13ubuntu1.5 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst python2.7-minimal [2.7.18-13ubuntu1.4] (2.7.18-13ubuntu1.5 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libpython2.7-minimal [2.7.18-13ubuntu1.4] (2.7.18-13ubuntu1.5 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst distro-info-data [0.52ubuntu0.8] (0.52ubuntu0.9 Ubuntu:22.04/jammy-updates [all])
+Inst libcap2-bin [1:2.44-1ubuntu0.22.04.1] (1:2.44-1ubuntu0.22.04.2 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libxml2-dev [2.9.13+dfsg-1ubuntu0.4] (2.9.13+dfsg-1ubuntu0.7 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libxml2 [2.9.13+dfsg-1ubuntu0.4] (2.9.13+dfsg-1ubuntu0.7 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst locales [2.35-0ubuntu3.8] (2.35-0ubuntu3.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst openssl [3.0.2-0ubuntu1.18] (3.0.2-0ubuntu1.19 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst python3-setuptools [59.6.0-1.2ubuntu0.22.04.2] (59.6.0-1.2ubuntu0.22.04.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all]) []
+Inst python3-pkg-resources [59.6.0-1.2ubuntu0.22.04.2] (59.6.0-1.2ubuntu0.22.04.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst tzdata [2024a-0ubuntu0.22.04.1] (2025b-0ubuntu0.22.04.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst ubuntu-advantage-tools [30~22.04] (35.1ubuntu0~22.04 Ubuntu:22.04/jammy-updates [all]) []
+Inst ubuntu-pro-client (35.1ubuntu0~22.04 Ubuntu:22.04/jammy-updates [arm64])
+Inst ubuntu-pro-client-l10n (35.1ubuntu0~22.04 Ubuntu:22.04/jammy-updates [arm64])
+Inst vim [2:8.2.3995-1ubuntu2.21] (2:8.2.3995-1ubuntu2.24 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst vim-runtime [2:8.2.3995-1ubuntu2.21] (2:8.2.3995-1ubuntu2.24 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all]) []
+Inst xxd [2:8.2.3995-1ubuntu2.21] (2:8.2.3995-1ubuntu2.24 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst vim-common [2:8.2.3995-1ubuntu2.21] (2:8.2.3995-1ubuntu2.24 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst bind9-host [1:9.18.28-0ubuntu0.22.04.1] (1:9.18.30-0ubuntu0.22.04.2 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst bind9-libs [1:9.18.28-0ubuntu0.22.04.1] (1:9.18.30-0ubuntu0.22.04.2 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst pci.ids [0.0~2022.01.22-1] (0.0~2022.01.22-1ubuntu0.1 Ubuntu:22.04/jammy-updates [all])
+Inst gnome-shell [42.9-0ubuntu2.2] (42.9-0ubuntu2.3 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst gnome-shell-common [42.9-0ubuntu2.2] (42.9-0ubuntu2.3 Ubuntu:22.04/jammy-updates [all])
+Inst libgstreamer1.0-dev [1.20.3-0ubuntu1] (1.20.3-0ubuntu1.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libgstreamer1.0-0 [1.20.3-0ubuntu1] (1.20.3-0ubuntu1.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst gir1.2-gstreamer-1.0 [1.20.3-0ubuntu1] (1.20.3-0ubuntu1.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libbluetooth3 [5.64-0ubuntu1.3] (5.64-0ubuntu1.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst network-manager [1.36.6-0ubuntu2] (1.36.6-0ubuntu2.1 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst libnm0 [1.36.6-0ubuntu2] (1.36.6-0ubuntu2.1 Ubuntu:22.04/jammy-updates [arm64])
+Inst gir1.2-nm-1.0 [1.36.6-0ubuntu2] (1.36.6-0ubuntu2.1 Ubuntu:22.04/jammy-updates [arm64])
+Inst libsoup2.4-common [2.74.2-3ubuntu0.1] (2.74.2-3ubuntu0.5 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst gir1.2-soup-2.4 [2.74.2-3ubuntu0.1] (2.74.2-3ubuntu0.5 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libsoup-gnome2.4-1 [2.74.2-3ubuntu0.1] (2.74.2-3ubuntu0.5 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libsoup2.4-1 [2.74.2-3ubuntu0.1] (2.74.2-3ubuntu0.5 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst gir1.2-webkit2-4.0 [2.46.3-0ubuntu0.22.04.1] (2.48.3-0ubuntu0.22.04.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst gir1.2-javascriptcoregtk-4.0 [2.46.3-0ubuntu0.22.04.1] (2.48.3-0ubuntu0.22.04.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libwebkit2gtk-4.0-37 [2.46.3-0ubuntu0.22.04.1] (2.48.3-0ubuntu0.22.04.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libjavascriptcoregtk-4.0-18 [2.46.3-0ubuntu0.22.04.1] (2.48.3-0ubuntu0.22.04.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst gstreamer1.0-plugins-base [1.20.1-1ubuntu0.2] (1.20.1-1ubuntu0.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libfreetype-dev [2.11.1+dfsg-1ubuntu0.2] (2.11.1+dfsg-1ubuntu0.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [libfreetype6-dev:arm64 ]
+Inst libfreetype6-dev [2.11.1+dfsg-1ubuntu0.2] (2.11.1+dfsg-1ubuntu0.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libfreetype6 [2.11.1+dfsg-1ubuntu0.2] (2.11.1+dfsg-1ubuntu0.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libxslt1.1 [1.1.34-4ubuntu0.22.04.1] (1.1.34-4ubuntu0.22.04.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst python3-update-manager [1:22.04.21] (1:22.04.22 Ubuntu:22.04/jammy-updates [all]) [update-manager-core:arm64 ]
+Inst update-manager-core [1:22.04.21] (1:22.04.22 Ubuntu:22.04/jammy-updates [all]) [update-manager:arm64 ]
+Inst update-manager [1:22.04.21] (1:22.04.22 Ubuntu:22.04/jammy-updates [all])
+Inst python3-problem-report [2.20.11-0ubuntu82.6] (2.20.11-0ubuntu82.8 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst python3-apport [2.20.11-0ubuntu82.6] (2.20.11-0ubuntu82.8 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst apport [2.20.11-0ubuntu82.6] (2.20.11-0ubuntu82.8 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst apport-gtk [2.20.11-0ubuntu82.6] (2.20.11-0ubuntu82.8 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst libctf0 [2.38-4ubuntu2.6] (2.38-4ubuntu2.8 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libctf-nobfd0 [2.38-4ubuntu2.6] (2.38-4ubuntu2.8 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst binutils-aarch64-linux-gnu [2.38-4ubuntu2.6] (2.38-4ubuntu2.8 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [binutils:arm64 ]
+Inst libbinutils [2.38-4ubuntu2.6] (2.38-4ubuntu2.8 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [binutils:arm64 ]
+Inst binutils [2.38-4ubuntu2.6] (2.38-4ubuntu2.8 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst binutils-common [2.38-4ubuntu2.6] (2.38-4ubuntu2.8 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst bluez-obexd [5.64-0ubuntu1.3] (5.64-0ubuntu1.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst cryptsetup-bin [2:2.4.3-1ubuntu1.2] (2:2.4.3-1ubuntu1.3 Ubuntu:22.04/jammy-updates [arm64])
+Inst cuda-cccl-12-6 [12.6.37-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-toolkit-config-common [12.6.68-1] (12.9.79-1 NVIDIA CUDA:developer.download.nvidia.com [all])
+Inst cuda-toolkit-12-config-common [12.6.68-1] (12.9.79-1 NVIDIA CUDA:developer.download.nvidia.com [all])
+Inst cuda-toolkit-12-6-config-common [12.6.68-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [all])
+Inst cuda-cudart-12-6 [12.6.68-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-driver-dev-12-6 [12.6.68-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-cudart-dev-12-6 [12.6.68-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-nvcc-12-6 [12.6.68-1] (12.6.85-1 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst cuda-nvvm-12-6 [12.6.68-1] (12.6.85-1 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst cuda-crt-12-6 [12.6.68-1] (12.6.85-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-cuobjdump-12-6 [12.6.68-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-cupti-12-6 [12.6.68-1] (12.6.80-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-cupti-dev-12-6 [12.6.68-1] (12.6.80-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-cuxxfilt-12-6 [12.6.68-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-documentation-12-6 [12.6.68-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-nvdisasm-12-6 [12.6.68-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-gdb-12-6 [12.6.68-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-nvml-dev-12-6 [12.6.68-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-nvprune-12-6 [12.6.68-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-nvrtc-12-6 [12.6.68-1] (12.6.85-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-nvrtc-dev-12-6 [12.6.68-1] (12.6.85-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-nvtx-12-6 [12.6.68-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-profiler-api-12-6 [12.6.68-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst cuda-sanitizer-12-6 [12.6.68-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst dns-root-data [2023112702~ubuntu0.22.04.1] (2024071801~ubuntu0.22.04.1 Ubuntu:22.04/jammy-updates [all])
+Inst docker-buildx-plugin [0.20.0-1~ubuntu.22.04~jammy] (0.24.0-1~ubuntu.22.04~jammy Docker CE:jammy [arm64])
+Inst docker-ce-rootless-extras [5:27.5.1-1~ubuntu.22.04~jammy] (5:28.2.2-1~ubuntu.22.04~jammy Docker CE:jammy [arm64])
+Inst docker-compose-plugin [2.32.4-1~ubuntu.22.04~jammy] (2.36.2-1~ubuntu.22.04~jammy Docker CE:jammy [arm64])
+Inst ethtool [1:5.16-1ubuntu0.1] (1:5.16-1ubuntu0.2 Ubuntu:22.04/jammy-updates [arm64])
+Inst fonts-opensymbol [2:102.12+LibO7.3.7-0ubuntu0.22.04.7] (2:102.12+LibO7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
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+Inst ghostscript [9.55.0~dfsg1-0ubuntu5.10] (9.55.0~dfsg1-0ubuntu5.11 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libgs9 [9.55.0~dfsg1-0ubuntu5.10] (9.55.0~dfsg1-0ubuntu5.11 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libgs9-common [9.55.0~dfsg1-0ubuntu5.10] (9.55.0~dfsg1-0ubuntu5.11 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst libpackagekit-glib2-18 [1.2.5-2ubuntu2] (1.2.5-2ubuntu3 Ubuntu:22.04/jammy-updates [arm64])
+Inst gir1.2-packagekitglib-1.0 [1.2.5-2ubuntu2] (1.2.5-2ubuntu3 Ubuntu:22.04/jammy-updates [arm64])
+Inst git-man [1:2.34.1-1ubuntu1.11] (1:2.34.1-1ubuntu1.12 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
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+Inst libcudla-dev-12-6 [12.6.68-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
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+Inst libcudnn9-samples [9.3.0.75-1] (9.10.2.21-1 NVIDIA CUDA:developer.download.nvidia.com [all])
+Inst libcufft-12-6 [11.2.6.59-1] (11.3.0.4-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst libcufft-dev-12-6 [11.2.6.59-1] (11.3.0.4-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst libcurand-12-6 [10.3.7.68-1] (10.3.7.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst libcurand-dev-12-6 [10.3.7.68-1] (10.3.7.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst libcusolver-12-6 [11.6.4.69-1] (11.7.1.2-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst libcusolver-dev-12-6 [11.6.4.69-1] (11.7.1.2-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst libcusparse-12-6 [12.5.3.3-1] (12.5.4.2-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst libcusparse-dev-12-6 [12.5.3.3-1] (12.5.4.2-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst libcusparselt0 [0.7.0.0-1] (0.7.1.0-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst libcusparselt-dev [0.7.0.0-1] (0.7.1.0-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst libglib2.0-doc [2.72.4-0ubuntu2.4] (2.72.4-0ubuntu2.5 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst libiniparser1 [4.1-4ubuntu4.1] (4.1-4ubuntu4.2 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libmalcontent-0-0 [0.10.4-1] (0.10.4-1ubuntu0.22.04 Ubuntu:22.04/jammy-updates [arm64])
+Inst libmbim-proxy [1.28.0-1~ubuntu20.04.1] (1.28.0-1~ubuntu20.04.2 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst libmbim-glib4 [1.28.0-1~ubuntu20.04.1] (1.28.0-1~ubuntu20.04.2 Ubuntu:22.04/jammy-updates [arm64])
+Inst libmysqlclient21 [8.0.40-0ubuntu0.22.04.1] (8.0.42-0ubuntu0.22.04.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst nautilus [1:42.6-0ubuntu1] (1:42.6-0ubuntu2 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst nautilus-data [1:42.6-0ubuntu1] (1:42.6-0ubuntu2 Ubuntu:22.04/jammy-updates [all]) []
+Inst libnautilus-extension1a [1:42.6-0ubuntu1] (1:42.6-0ubuntu2 Ubuntu:22.04/jammy-updates [arm64])
+Inst libnvfatbin-12-6 [12.6.68-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst libnvfatbin-dev-12-6 [12.6.68-1] (12.6.77-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst libnvidia-container1 [1.16.2-1] (1.17.8-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst libnvidia-container-tools [1.16.2-1] (1.17.8-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst tensorrt [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst libnvinfer-bin [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst tensorrt-libs [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst python3-libnvinfer-dev [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst python3-libnvinfer [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst libnvinfer-samples [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [all]) []
+Inst libnvonnxparsers-dev [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst libnvonnxparsers10 [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst libnvinfer-vc-plugin-dev [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst libnvinfer-headers-plugin-dev [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) [libnvinfer-plugin-dev:arm64 ]
+Inst libnvinfer-plugin-dev [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst libnvinfer-lean-dev [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst libnvinfer-dispatch-dev [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst libnvinfer-headers-dev [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) [libnvinfer-dev:arm64 ]
+Inst libnvinfer-dev [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst python3-libnvinfer-dispatch [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst libnvinfer-dispatch10 [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst libnvinfer-vc-plugin10 [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst libnvinfer-plugin10 [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst python3-libnvinfer-lean [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst libnvinfer-lean10 [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst libnvinfer10 [10.3.0.30-1+cuda12.5] (10.7.0.23-1+cuda12.6 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst libnvjitlink-12-6 [12.6.68-1] (12.6.85-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst libnvjitlink-dev-12-6 [12.6.68-1] (12.6.85-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst poppler-utils [22.02.0-2ubuntu0.5] (22.02.0-2ubuntu0.8 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libpoppler-glib8 [22.02.0-2ubuntu0.5] (22.02.0-2ubuntu0.8 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libpoppler118 [22.02.0-2ubuntu0.5] (22.02.0-2ubuntu0.8 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libpq5 [14.15-0ubuntu0.22.04.1] (14.18-0ubuntu0.22.04.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst librbd1 [17.2.7-0ubuntu0.22.04.1] (17.2.7-0ubuntu0.22.04.2 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst librados2 [17.2.7-0ubuntu0.22.04.1] (17.2.7-0ubuntu0.22.04.2 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libraptor2-0 [2.0.15-0ubuntu4] (2.0.15-0ubuntu4.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libraw20 [0.20.2-2ubuntu2.22.04.1] (0.20.2-2ubuntu2.22.04.2 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libuno-sal3 [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libuno-salhelpergcc3-3 [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libuno-cppu3 [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libuno-cppuhelpergcc3-3 [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst ure [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst uno-libs-private [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libuno-purpenvhelpergcc3-3 [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libreoffice-calc [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libreoffice-impress [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libreoffice-draw [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libreoffice-math [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libreoffice-common [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all]) []
+Inst libreoffice-gnome [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libreoffice-gtk3 [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libreoffice-base-core [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [libreoffice-writer:arm64 ]
+Inst python3-uno [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [libreoffice-writer:arm64 ]
+Inst libreoffice-writer [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libreoffice-core [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libreoffice-style-colibre [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst libreoffice-style-breeze [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst libreoffice-style-elementary [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst libreoffice-style-yaru [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst libsndfile1 [1.0.31-2ubuntu0.1] (1.0.31-2ubuntu0.2 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst libsnmp-base [5.9.1+dfsg-1ubuntu2.6] (5.9.1+dfsg-1ubuntu2.8 Ubuntu:22.04/jammy-updates [all])
+Inst libsnmp40 [5.9.1+dfsg-1ubuntu2.6] (5.9.1+dfsg-1ubuntu2.8 Ubuntu:22.04/jammy-updates [arm64])
+Inst libvpx7 [1.11.0-2ubuntu2.3] (1.11.0-2ubuntu2.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst yelp-xsl [42.0-1] (42.0-1ubuntu0.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst yelp [42.1-1] (42.1-1ubuntu0.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) []
+Inst libyelp0 [42.1-1] (42.1-1ubuntu0.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst linux-firmware [20220329.git681281e4-0ubuntu3.36] (20220329.git681281e4-0ubuntu3.37 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst net-tools [1.60+git20181103.0eebece-1ubuntu5] (1.60+git20181103.0eebece-1ubuntu5.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst network-manager-config-connectivity-ubuntu [1.36.6-0ubuntu2] (1.36.6-0ubuntu2.1 Ubuntu:22.04/jammy-updates [all])
+Inst nvidia-container-toolkit [1.16.2-1] (1.17.8-1 NVIDIA CUDA:developer.download.nvidia.com [arm64]) []
+Inst nvidia-container-toolkit-base [1.16.2-1] (1.17.8-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Inst python3-paramiko [2.9.3-0ubuntu1.2] (2.9.3-0ubuntu1.3 Ubuntu:22.04/jammy-updates [all])
+Inst python3-protobuf [3.12.4-1ubuntu7.22.04.1] (3.12.4-1ubuntu7.22.04.2 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst python3-requests [2.25.1+dfsg-2ubuntu0.1] (2.25.1+dfsg-2ubuntu0.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst snapd [2.66.1+22.04] (2.67.1+22.04 Ubuntu:22.04/jammy-updates [arm64])
+Inst thunderbird [1:115.16.0+build2-0ubuntu0.22.04.1] (1:115.18.0+build1-0ubuntu0.22.04.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64]) [thunderbird-gnome-support:arm64 ]
+Inst thunderbird-gnome-support [1:115.16.0+build2-0ubuntu0.22.04.1] (1:115.18.0+build1-0ubuntu0.22.04.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst wireless-regdb [2022.06.06-0ubuntu1~22.04.1] (2024.10.07-0ubuntu1~22.04.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst wpasupplicant [2:2.10-6ubuntu2.1] (2:2.10-6ubuntu2.2 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Inst xserver-xephyr [2:21.1.4-2ubuntu1.7~22.04.12] (2:21.1.4-2ubuntu1.7~22.04.14 Ubuntu:22.04/jammy-updates [arm64])
+Inst xwayland [2:22.1.1-1ubuntu0.14] (2:22.1.1-1ubuntu0.18 Ubuntu:22.04/jammy-updates [arm64])
+Inst gnupg2 [2.2.27-3ubuntu2.1] (2.2.27-3ubuntu2.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Inst libgl1-mesa-dev [23.2.1-1ubuntu3.1~22.04.2] (23.2.1-1ubuntu3.1~22.04.3 Ubuntu:22.04/jammy-updates [arm64])
+Inst sssd [2.6.3-1ubuntu3.4] (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst python3-sss [2.6.3-1ubuntu3.4] (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst sssd-proxy [2.6.3-1ubuntu3.4] (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst sssd-krb5 [2.6.3-1ubuntu3.4] (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst sssd-ad [2.6.3-1ubuntu3.4] (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst sssd-ldap [2.6.3-1ubuntu3.4] (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst sssd-ipa [2.6.3-1ubuntu3.4] (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst sssd-krb5-common [2.6.3-1ubuntu3.4] (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst sssd-ad-common [2.6.3-1ubuntu3.4] (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst sssd-common [2.6.3-1ubuntu3.4] (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst libnss-sss [2.6.3-1ubuntu3.4] (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst libpam-sss [2.6.3-1ubuntu3.4] (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst libsss-certmap0 [2.6.3-1ubuntu3.4] (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst libsss-nss-idmap0 [2.6.3-1ubuntu3.4] (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst libsss-idmap0 [2.6.3-1ubuntu3.4] (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64]) []
+Inst libipa-hbac0 [2.6.3-1ubuntu3.4] (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64])
+Inst libreoffice-pdfimport [1:7.3.7-0ubuntu0.22.04.7] (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Conf libperl5.34 (5.34.0-3ubuntu1.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf perl (5.34.0-3ubuntu1.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf perl-modules-5.34 (5.34.0-3ubuntu1.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Conf libc6-dbg (2.35-0ubuntu3.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf libc6-dev (2.35-0ubuntu3.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf libc-dev-bin (2.35-0ubuntu3.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf linux-libc-dev (5.15.0-141.151 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf libsystemd-dev (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf libnss-systemd (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf xserver-common (2:21.1.4-2ubuntu1.7~22.04.14 Ubuntu:22.04/jammy-updates [all])
+Conf xserver-xorg-legacy (2:21.1.4-2ubuntu1.7~22.04.14 Ubuntu:22.04/jammy-updates [arm64])
+Conf libudev-dev (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf xserver-xorg-core (2:21.1.4-2ubuntu1.7~22.04.14 Ubuntu:22.04/jammy-updates [arm64])
+Conf systemd-timesyncd (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf systemd-sysv (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf systemd-oomd (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf systemd-journal-remote (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf libpam-systemd (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf systemd (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf bluez (5.64-0ubuntu1.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf udev (249.11-0ubuntu3.16 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
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+Conf uno-libs-private (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf libuno-purpenvhelpergcc3-3 (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
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+Conf libreoffice-impress (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf libreoffice-draw (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf libreoffice-math (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
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+Conf libreoffice-base-core (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf python3-uno (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf libreoffice-writer (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf libreoffice-core (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf libreoffice-style-colibre (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Conf libreoffice-style-breeze (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
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+Conf libreoffice-style-yaru (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
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+Conf libvpx7 (1.11.0-2ubuntu2.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf yelp-xsl (42.0-1ubuntu0.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
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+Conf libyelp0 (42.1-1ubuntu0.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf linux-firmware (20220329.git681281e4-0ubuntu3.37 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Conf net-tools (1.60+git20181103.0eebece-1ubuntu5.4 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf network-manager-config-connectivity-ubuntu (1.36.6-0ubuntu2.1 Ubuntu:22.04/jammy-updates [all])
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+Conf nvidia-container-toolkit-base (1.17.8-1 NVIDIA CUDA:developer.download.nvidia.com [arm64])
+Conf python3-paramiko (2.9.3-0ubuntu1.3 Ubuntu:22.04/jammy-updates [all])
+Conf python3-protobuf (3.12.4-1ubuntu7.22.04.2 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf python3-requests (2.25.1+dfsg-2ubuntu0.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
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+Conf thunderbird (1:115.18.0+build1-0ubuntu0.22.04.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf thunderbird-gnome-support (1:115.18.0+build1-0ubuntu0.22.04.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf wireless-regdb (2024.10.07-0ubuntu1~22.04.1 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Conf wpasupplicant (2:2.10-6ubuntu2.2 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [arm64])
+Conf xserver-xephyr (2:21.1.4-2ubuntu1.7~22.04.14 Ubuntu:22.04/jammy-updates [arm64])
+Conf xwayland (2:22.1.1-1ubuntu0.18 Ubuntu:22.04/jammy-updates [arm64])
+Conf gnupg2 (2.2.27-3ubuntu2.3 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])
+Conf libgl1-mesa-dev (23.2.1-1ubuntu3.1~22.04.3 Ubuntu:22.04/jammy-updates [arm64])
+Conf sssd (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf python3-sss (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf sssd-proxy (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf sssd-krb5 (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf sssd-ad (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf sssd-ldap (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf sssd-ipa (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf sssd-krb5-common (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf sssd-ad-common (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf sssd-common (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf libnss-sss (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf libpam-sss (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf libsss-certmap0 (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf libsss-nss-idmap0 (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf libsss-idmap0 (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf libipa-hbac0 (2.6.3-1ubuntu3.5 Ubuntu:22.04/jammy-updates [arm64])
+Conf libreoffice-pdfimport (1:7.3.7-0ubuntu0.22.04.10 Ubuntu:22.04/jammy-updates, Ubuntu:22.04/jammy-security [all])

random_stuff.py

@@ -0,0 +1,253 @@
+def element_to_string(element, include_props=True):
+    """Convert a GStreamer element to string with properties"""
+    factory_name = element.get_factory().get_name()
+    result = factory_name
+    
+    if include_props:
+        # Get interesting properties
+        for prop in element.list_properties():
+            # Skip default values
+            if element.get_property(prop.name) != prop.default_value:
+                value = element.get_property(prop.name)
+                result += f" {prop.name}={value}"
+    
+    # For containers like bins, recursively process children
+    if isinstance(element, Gst.Bin):
+        children = []
+        for child in element.iterate_elements():
+            children.append(element_to_string(child, include_props))
+        if children:
+            result += " ( " + " ! ".join(children) + " )"
+            
+    return result
+
+# Example usage
+#pipeline = Gst.parse_launch("videotestsrc pattern=ball ! videoconvert ! autovideosink")
+#print(element_to_string(pipeline))
+def pipeline_to_string(pipeline):
+    """Convert a GStreamer pipeline to string representation"""
+    result = []
+    
+    if isinstance(pipeline, Gst.Pipeline):
+        # Get top-level elements
+        iterator = pipeline.iterate_elements()
+        elements = list(iterator)
+        
+        for i, element in enumerate(elements):
+            # Get element factory name
+            factory_name = element.get_factory().get_name()
+            result.append(factory_name)
+            
+            # Add properties that differ from default
+            # (This is complex and incomplete)
+            
+            # Add separator if not the last element
+            if i < len(elements) - 1:
+                result.append("!")
+    
+    return " ".join(result)
+
+# Example usage
+
+
+
+import gi
+gi.require_version('Gst', '1.0')
+from gi.repository import Gst
+
+# Initialize GStreamer
+Gst.init(None)
+
+# Create a pipeline
+pipeline_description = "videotestsrc pattern=3 ! videoconvert ! autovideosink"
+pipeline = Gst.parse_launch(pipeline_description)
+
+def get_pipeline_string(element):
+    """Recursively convert elements of a pipeline to a pipeline string."""
+    if not element:
+        return ""
+
+    iterator = element.iterate_elements()
+    pipeline_parts = []
+
+    while True:
+        result, elem = iterator.next()
+        if result == Gst.IteratorResult.OK:
+            # Get element name
+            elem_name = elem.get_name()
+            elem_factory_name = elem.get_factory().get_name()
+
+            # Fetch element properties
+            properties = elem.list_properties()
+            prop_strings = []
+            for prop in properties:
+                try:
+                    prop_value = elem.get_property(prop.name)
+                    prop_strings.append(f'{prop.name}={prop_value}')
+                except Exception:
+                    pass  # Skip properties that cannot be retrieved
+
+            # Build string representation of the element
+            if prop_strings:
+                element_string = f'{elem_factory_name} {",".join(prop_strings)}'
+            else:
+                element_string = elem_factory_name
+
+            pipeline_parts.append(element_string)
+        elif result != Gst.IteratorResult.OK:
+            break
+
+    return " ! ".join(pipeline_parts)
+
+# Convert pipeline to a string
+pipeline_string = get_pipeline_string(pipeline)
+print(f"Pipeline String: {pipeline_string}")
+import gi
+gi.require_version('Gst', '1.0')
+from gi.repository import Gst
+
+# Initialize GStreamer
+Gst.init(None)
+
+# Create a pipeline
+pipeline_description = "videotestsrc pattern=3 ! videoconvert ! autovideosink"
+pipeline = Gst.parse_launch(pipeline_description)
+
+def get_pipeline_string(element):
+    """Recursively convert elements of a pipeline to a pipeline string."""
+    if not element:
+        return ""
+
+    iterator = element.iterate_elements()
+    pipeline_parts = []
+
+    while True:
+        result, elem = iterator.next()
+        if result == Gst.IteratorResult.OK:
+            # Get element name
+            elem_name = elem.get_name()
+            elem_factory_name = elem.get_factory().get_name()
+
+            # Fetch element properties
+            properties = elem.list_properties()
+            prop_strings = []
+            for prop in properties:
+                try:
+                    prop_value = elem.get_property(prop.name)
+                    prop_strings.append(f'{prop.name}={prop_value}')
+                except Exception:
+                    pass  # Skip properties that cannot be retrieved
+
+            # Build string representation of the element
+            if prop_strings:
+                element_string = f'{elem_factory_name} {",".join(prop_strings)}'
+            else:
+                element_string = elem_factory_name
+
+            pipeline_parts.append(element_string)
+        elif result != Gst.IteratorResult.OK:
+            break
+
+    return " ! ".join(pipeline_parts)
+
+# Convert pipeline to a string
+pipeline_string = get_pipeline_string(pipeline)
+print(f"Pipeline String: {pipeline_string}")
+import gi
+gi.require_version('Gst', '1.0')
+from gi.repository import Gst
+
+# Initialize GStreamer
+Gst.init(None)
+
+# Create a pipeline
+pipeline_description = "videotestsrc pattern=3 ! videoconvert ! autovideosink"
+pipeline = Gst.parse_launch(pipeline_description)
+
+def get_pipeline_string(element):
+    """Recursively convert elements of a pipeline to a pipeline string."""
+    if not element:
+        return ""
+
+    iterator = element.iterate_elements()
+    pipeline_parts = []
+
+    while True:
+        result, elem = iterator.next()
+        if result == Gst.IteratorResult.OK:
+            # Get element name
+            elem_name = elem.get_name()
+            elem_factory_name = elem.get_factory().get_name()
+
+            # Fetch element properties
+            properties = elem.list_properties()
+            prop_strings = []
+            for prop in properties:
+                try:
+                    prop_value = elem.get_property(prop.name)
+                    prop_strings.append(f'{prop.name}={prop_value}')
+                except Exception:
+                    pass  # Skip properties that cannot be retrieved
+
+            # Build string representation of the element
+            if prop_strings:
+                element_string = f'{elem_factory_name} {",".join(prop_strings)}'
+            else:
+                element_string = elem_factory_name
+
+            pipeline_parts.append(element_string)
+        elif result != Gst.IteratorResult.OK:
+            break
+
+    return " ! ".join(pipeline_parts)
+
+# Convert pipeline to a string
+pipeline_string = get_pipeline_string(pipeline)
+print(f"Pipeline String: {pipeline_string}")
+import gi
+gi.require_version('Gst', '1.0')
+from gi.repository import Gst
+
+# Initialize GStreamer
+Gst.init(None)
+
+# Create a pipeline
+pipeline_description = "videotestsrc pattern=3 ! videoconvert ! autovideosink"
+pipeline = Gst.parse_launch(pipeline_description)
+
+def get_pipeline_string(element):
+    """Recursively convert elements of a pipeline to a pipeline string."""
+    if not element:
+        return ""
+
+    iterator = element.iterate_elements()
+    pipeline_parts = []
+
+    while True:
+        result, elem = iterator.next()
+        if result == Gst.IteratorResult.OK:
+            # Get element name
+            elem_name = elem.get_name()
+            elem_factory_name = elem.get_factory().get_name()
+
+            # Fetch element properties
+            properties = elem.list_properties()
+            prop_strings = []
+            for prop in properties:
+                try:
+                    prop_value = elem.get_property(prop.name)
+                    prop_strings.append(f'{prop.name}={prop_value}')
+                except Exception:
+                    pass  # Skip properties that cannot be retrieved
+
+            # Build string representation of the element
+            if prop_strings:
+                element_string = f'{elem_factory_name} {",".join(prop_strings)}'
+            else:
+                element_string = elem_factory_name
+
+            pipeline_parts.append(element_string)
+        elif result != Gst.IteratorResult.OK:
+            break
+
+    return " ! ".join(pipeline_parts)

try_decode.py

@@ -1,24 +1,29 @@
+import time
+from datetime import datetime
 
 import cv2
+import numpy
+import numpy as np
+import onnxruntime as rt
+import open_clip
+import pycuda.autoinit
+import pycuda.driver as cuda
+import tensorrt as trt
+import torch
+from cuda import cuda as ccuda
+from cuda import cudart
 
-#cap = cv2.VideoCapture("rtspsrc location=rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mov ! application/x-rtp, media=video ! rtph264depay ! h264parse ! nvv4l2decoder ! nvvidconv ! video/x-raw, format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink", cv2.CAP_GSTREAMER)
-
-
-
-#cmd = 'filesrc location=/home/thebears/local/source/reduced.mp4 ! qtdemux name=demux demux.video_0 ! queue ! h264parse ! nvv4l2decoder ! nvvidconv ! appsink'
-
-cmd = 'filesrc location=/home/thebears/local/source/full.mp4 ! qtdemux name=demux demux.video_0 ! queue ! h265parse ! nvv4l2decoder ! nvvidconv ! appsink sync=false'
-#cmd = 'filesrc location=/home/thebears/local/source/full.mp4 ! qtdemux name=demux demux.video_0 ! h265parse ! avdec_h265 ! videoconvert ! appsink'
-
-
-#cmd = 'videotestsrc ! autovideosink'
+cmd = "filesrc location=/home/thebears/local/source/short.mp4 ! qtdemux name=demux demux.video_0 ! queue ! h265parse ! nvv4l2decoder ! nvvidconv ! videoscale method=1 add-borders=false ! video/x-raw,width=1280,height=1280 ! appsink sync=false"
 
 cap = cv2.VideoCapture(cmd, cv2.CAP_GSTREAMER)
 
-
-import time
 st = time.time()
 fr = 0
+
+
+arrays_to_score = list()
+imgs = list()
+array = list()
 while True:
     good, frf = cap.read()
     fr += 1
@@ -27,7 +32,169 @@ while True:
         break
 
 
+    array.append(frf)
+    imgs.append(frf)
+    if len(array) > 8:
+        arrays_to_score.append(torch.from_numpy(np.asarray(array)))
+        array = list()
+
+
+
+if len(array) > 0:
+    arrays_to_score.append(torch.from_numpy(np.asarray(array)))
+
+
 et = time.time()
 
 print(et - st, fr / (st - et))
+# %%
 
+from datetime import datetime
+
+pretrained_name = "webli"
+#model_name = "ViT-L-16-SigLIP2-512"
+model_name = 'ViT-SO400M-16-SigLIP2-512'
+rt_dir ='/home/thebears/local/source/models/'
+
+os.makedirs(rt_dir, exist_ok=True)
+fname = model_name.replace('-','_').lower() + '_'+datetime.now().strftime('%Y%m%d')
+ONNX_FILE_PATH=os.path.join(rt_dir, fname + '.onnx')
+ENGINE_FILE_PATH = os.path.splitext(ONNX_FILE_PATH)[0]+'.engine'
+
+# %%
+
+model, _, preprocess = open_clip.create_model_and_transforms(
+    model_name, pretrained=pretrained_name
+)
+
+
+# %%
+
+model_gpu = model.cuda()
+scores = list()
+all_means = list()
+with torch.no_grad():
+    for fr_num, img in enumerate(imgs):
+
+        tensor_raw = torch.tensor(img[None,:,:,0:3])
+        tensor_perm = tensor_raw.permute([0, 3, 1, 2]).to(torch.float32) / 255
+        tensor_reshaped = preprocess.transforms[0](tensor_perm)
+        tensor_mean = preprocess.transforms[-1](tensor_reshaped)
+        all_means.append(tensor_mean)
+        imp = model_gpu.encode_image(tensor_mean.cuda())
+
+        print(fr_num)
+        scores.append((fr_num, imp.detach().cpu().numpy()))
+# %%
+np.save('dump_so400m',np.concatenate([x[1] for x in scores]))
+
+
+# %%
+with torch.no_grad():
+    et = time.time()
+
+    if True:
+
+        tensor_raw = torch.concat(arrays_to_score)[0:4, :, :, 0:3]
+        tensor_perm = tensor_raw.permute([0, 3, 1, 2]).to(torch.float32) / 255
+        tensor_reshaped = preprocess.transforms[0](tensor_perm)
+        tensor_mean = preprocess.transforms[-1](tensor_reshaped)
+    else:
+        tensor_raw = torch.concat(arrays_to_score)[0, :, :, 0:3]
+        tensor_perm = tensor_raw.permute([0, 3, 1, 2]).to(torch.float32) / 255
+        tensor_reshaped = preprocess.transforms[1](preprocess.transforms[0](tensor_perm))
+        tensor_mean = preprocess.transforms[-1](tensor_reshaped)
+    #imp = model.encode_image(tensor_mean)
+    imp = model_gpu.encode_image(tensor_mean.cuda())
+    st = time.time()
+    print((st - et) / tensor_raw.shape[0], tensor_raw.shape[0]/(st - et) )
+    
+from_model_on_gpu = imp.detach().cpu().numpy()
+
+
+
+
+# %%
+
+
+torch.onnx.export(
+    model.visual.cuda(),
+    tensor_mean.cuda(),
+    ONNX_FILE_PATH,
+    input_names=["input"],
+    output_names=["output"],
+)
+
+X_test = tensor_mean.cpu().numpy()
+sess = rt.InferenceSession(
+    ONNX_FILE_PATH, providers=rt.get_available_providers())
+input_name = sess.get_inputs()[0].name
+pred_onx = sess.run(None, {input_name: X_test.astype(numpy.float32)})[0]
+print(pred_onx)
+
+def norm(v):
+    return np.divide(v.T,np.linalg.norm(v,axis=1)).T
+
+print(np.dot(norm(pred_onx), norm(from_model_on_gpu).T))
+
+
+TRT_LOGGER = trt.Logger()
+def build_engine_from_onnx(onnx_file_path, use_fp16=True):
+    """
+    Convert ONNX model to TensorRT engine with FP16 precision
+
+    Args:
+        onnx_file_path: Path to ONNX model
+        use_fp16: Boolean to enable FP16 mode
+
+    Returns:
+        TensorRT engine
+    """
+    # Logger to capture info/warning/errors
+    TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
+
+    # Create builder and network
+    builder = trt.Builder(TRT_LOGGER)
+    network = builder.create_network(
+        1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
+    )
+
+    # Create ONNX parser and parse model
+    parser = trt.OnnxParser(network, TRT_LOGGER)
+    with open(onnx_file_path, "rb") as model:
+        if not parser.parse(model.read()):
+            for error in range(parser.num_errors):
+                print(f"ONNX parse error: {parser.get_error(error)}")
+            raise ValueError(f"Failed to parse ONNX file: {onnx_file_path}")
+
+    # Create optimization config
+    config = builder.create_builder_config()
+    config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 20)  # 1 MiB
+    # Enable FP16 precision if requested and if the GPU supports it
+    if use_fp16:
+        if builder.platform_has_fast_fp16:
+            config.set_flag(trt.BuilderFlag.FP16)
+            print("FP16 enabled successfully")
+        else:
+            print("Warning: GPU doesn't support fast FP16, using FP32 instead")
+
+    # Set optimization profile for dynamic shapes (if needed)
+    # If you have dynamic shapes, you'd need to set min/opt/max dimensions
+
+    # Build and serialize engine
+    engine = builder.build_serialized_network(network, config)
+
+    return engine
+
+
+
+
+
+
+engine = build_engine_from_onnx(ONNX_FILE_PATH, use_fp16=True)
+
+with open(ENGINE_FILE_PATH, "wb") as f:
+    f.write(engine)
+
+
+# %%