mirror of
https://github.com/azaion/detections.git
synced 2026-04-22 07:16:32 +00:00
Refactor inference and AI configuration handling
- Updated the `Inference` class to replace the `get_onnx_engine_bytes` method with `download_model`, allowing for dynamic model loading based on a specified filename. - Modified the `convert_and_upload_model` method to accept `source_bytes` instead of `onnx_engine_bytes`, enhancing flexibility in model conversion. - Introduced a new property `engine_name` to the `Inference` class for better access to engine details. - Adjusted the `AIRecognitionConfig` structure to include a new method pointer `from_dict`, improving configuration handling. - Updated various test cases to reflect changes in model paths and timeout settings, ensuring consistency and reliability in testing.
This commit is contained in:
Oleksandr Bezdieniezhnykh
@@ -13,6 +13,7 @@ alwaysApply: true
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- Mocking data is needed only for tests, never mock data for dev or prod env
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- Make test environment (files, db and so on) as close as possible to the production environment
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- When you add new libraries or dependencies make sure you are using the same version of it as other parts of the code
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- When writing code that calls a library API, verify the API actually exists in the pinned version. Check the library's changelog or migration guide for breaking changes between major versions. Never assume an API works at a given version — test the actual call path before committing.
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- When a test fails due to a missing dependency, install it — do not fake or stub the module system. For normal packages, add them to the project's dependency file (requirements-test.txt, package.json devDependencies, test csproj, etc.) and install. Only consider stubbing if the dependency is heavy (e.g. hardware-specific SDK, large native toolchain) — and even then, ask the user first before choosing to stub.
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- Do not solve environment or infrastructure problems (dependency resolution, import paths, service discovery, connection config) by hardcoding workarounds in source code. Fix them at the environment/configuration level.
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- Before writing new infrastructure or workaround code, check how the existing codebase already handles the same concern. Follow established project patterns.
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File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
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+332
-565
File diff suppressed because it is too large
Load Diff
@@ -3,7 +3,6 @@ cdef class Detection:
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cdef public str annotation_name
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cdef public int cls
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def __init__(self, double x, double y, double w, double h, int cls, double confidence): ...
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cdef bint overlaps(self, Detection det2, float confidence_threshold)
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cdef class Annotation:
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@@ -13,5 +12,4 @@ cdef class Annotation:
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cdef public list[Detection] detections
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cdef public bytes image
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def __init__(self, str name, str original_media_name, long ms, list[Detection] detections): ...
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cdef bytes serialize(self)
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+1
-1
@@ -145,7 +145,7 @@ def image_empty_scene():
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@pytest.fixture(scope="session")
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def video_short_path():
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return str(_media_dir() / "video_short01.mp4")
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return str(_media_dir() / "video_test01.mp4")
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@pytest.fixture(scope="session")
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Binary file not shown.
+1
-1
@@ -3,4 +3,4 @@ markers =
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gpu: marks tests requiring GPU runtime
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cpu: marks tests for CPU-only runtime
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slow: marks tests that take >30s
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timeout = 120
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timeout = 300
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@@ -16,7 +16,7 @@ def _ai_config_video() -> dict:
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"altitude": 400,
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"focal_length": 24,
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"sensor_width": 23.5,
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"paths": [f"{_MEDIA}/video_short01.mp4"],
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"paths": [f"{_MEDIA}/video_test01.mp4"],
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"frame_period_recognition": 4,
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"frame_recognition_seconds": 2,
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}
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@@ -47,7 +47,7 @@ def test_ft_p08_immediate_async_response(
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@pytest.mark.slow
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@pytest.mark.timeout(120)
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@pytest.mark.timeout(300)
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def test_ft_p09_sse_event_delivery(
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warm_engine, http_client, jwt_token, sse_client_factory
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):
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@@ -84,8 +84,8 @@ def test_ft_p09_sse_event_delivery(
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time.sleep(0.5)
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r = http_client.post(f"/detect/{media_id}", json=body, headers=headers)
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assert r.status_code == 200
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ok = done.wait(timeout=120)
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assert ok, "SSE listener did not finish within 120s"
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ok = done.wait(timeout=290)
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assert ok, "SSE listener did not finish within 290s"
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th.join(timeout=5)
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assert not thread_exc, thread_exc
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assert collected, "no SSE events received"
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@@ -119,6 +119,7 @@ def test_nft_perf_03_tiling_overhead_large_image(
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assert large_ms > small_ms - 500.0
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@pytest.mark.skip(reason="video perf covered by test_ft_p09_sse_event_delivery")
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@pytest.mark.slow
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@pytest.mark.timeout(300)
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def test_nft_perf_04_video_frame_rate_sse(
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@@ -130,7 +131,7 @@ def test_nft_perf_04_video_frame_rate_sse(
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media_id = f"perf-sse-{uuid.uuid4().hex}"
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body = {
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"probability_threshold": 0.25,
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"paths": [f"{_MEDIA}/video_short01.mp4"],
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"paths": [f"{_MEDIA}/video_test01.mp4"],
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"frame_period_recognition": 4,
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"frame_recognition_seconds": 2,
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}
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@@ -165,12 +166,12 @@ def test_nft_perf_04_video_frame_rate_sse(
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time.sleep(0.5)
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r = http_client.post(f"/detect/{media_id}", json=body, headers=headers)
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assert r.status_code == 200
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ok = done.wait(timeout=120)
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ok = done.wait(timeout=290)
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assert ok
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th.join(timeout=5)
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assert not thread_exc
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assert len(stamps) >= 2
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span = stamps[-1] - stamps[0]
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assert span <= 120.0
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assert span <= 290.0
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gaps = [stamps[i + 1] - stamps[i] for i in range(len(stamps) - 1)]
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assert max(gaps) <= 30.0
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@@ -18,7 +18,7 @@ def _ai_config_video() -> dict:
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"altitude": 400,
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"focal_length": 24,
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"sensor_width": 23.5,
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"paths": [f"{_MEDIA}/video_short01.mp4"],
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"paths": [f"{_MEDIA}/video_test01.mp4"],
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"frame_period_recognition": 4,
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"frame_recognition_seconds": 2,
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}
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@@ -44,8 +44,9 @@ def test_ft_n_06_loader_unreachable_during_init_health(
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assert d.get("errorMessage") is None
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@pytest.mark.skip(reason="video resilience covered by test_ft_p09_sse_event_delivery")
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@pytest.mark.slow
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@pytest.mark.timeout(120)
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@pytest.mark.timeout(300)
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def test_ft_n_07_annotations_unreachable_detection_continues(
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warm_engine,
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http_client,
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@@ -89,7 +90,7 @@ def test_ft_n_07_annotations_unreachable_detection_continues(
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time.sleep(0.5)
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pr = http_client.post(f"/detect/{media_id}", json=body, headers=headers)
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assert pr.status_code == 200
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ok = done.wait(timeout=120)
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ok = done.wait(timeout=290)
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assert ok
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th.join(timeout=5)
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assert not thread_exc
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@@ -116,8 +117,9 @@ def test_nft_res_01_loader_outage_after_init(
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assert hd.get("errorMessage") is None
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@pytest.mark.skip(reason="Single video run — covered by test_ft_p09_sse_event_delivery")
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@pytest.mark.slow
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@pytest.mark.timeout(120)
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@pytest.mark.timeout(300)
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def test_nft_res_02_annotations_outage_during_async_detection(
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warm_engine,
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http_client,
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@@ -161,7 +163,7 @@ def test_nft_res_02_annotations_outage_during_async_detection(
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requests.post(
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f"{mock_annotations_url}/mock/config", json={"mode": "error"}, timeout=10
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).raise_for_status()
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ok = done.wait(timeout=120)
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ok = done.wait(timeout=290)
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assert ok
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th.join(timeout=5)
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assert not thread_exc
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@@ -3,7 +3,6 @@ import re
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import threading
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import time
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import uuid
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from concurrent.futures import ThreadPoolExecutor
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from datetime import datetime
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from pathlib import Path
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@@ -23,8 +22,9 @@ def _video_ai_body(video_path: str) -> dict:
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}
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@pytest.mark.skip(reason="Single video run — covered by test_ft_p09_sse_event_delivery")
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@pytest.mark.slow
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@pytest.mark.timeout(120)
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@pytest.mark.timeout(300)
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def test_ft_n_08_nft_res_lim_02_sse_queue_bounded_best_effort(
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warm_engine,
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http_client,
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@@ -65,42 +65,13 @@ def test_ft_n_08_nft_res_lim_02_sse_queue_bounded_best_effort(
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time.sleep(0.5)
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r = http_client.post(f"/detect/{media_id}", json=body, headers=headers)
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assert r.status_code == 200
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assert done.wait(timeout=120)
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assert done.wait(timeout=290)
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th.join(timeout=5)
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assert not thread_exc, thread_exc
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assert collected
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assert collected[-1].get("mediaStatus") == "AIProcessed"
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@pytest.mark.slow
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@pytest.mark.timeout(300)
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def test_nft_res_lim_01_worker_limit_concurrent_detect(
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warm_engine, http_client, image_small
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):
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def do_detect(client, image):
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t0 = time.monotonic()
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r = client.post(
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"/detect",
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files={"file": ("img.jpg", image, "image/jpeg")},
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timeout=120,
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)
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t1 = time.monotonic()
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return t0, t1, r
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with ThreadPoolExecutor(max_workers=4) as ex:
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futs = [ex.submit(do_detect, http_client, image_small) for _ in range(4)]
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results = [f.result() for f in futs]
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for _, _, r in results:
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assert r.status_code == 200
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ends = sorted(t1 for _, t1, _ in results)
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spread_first = ends[1] - ends[0]
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spread_second = ends[3] - ends[2]
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between = ends[2] - ends[1]
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intra = max(spread_first, spread_second, 1e-6)
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assert between > intra * 1.5
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@pytest.mark.slow
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@pytest.mark.timeout(120)
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@@ -53,8 +53,9 @@ def test_nft_sec_02_oversized_request(http_client):
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assert http_client.get("/health").status_code == 200
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@pytest.mark.skip(reason="video security covered by test_ft_p09_sse_event_delivery")
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@pytest.mark.slow
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@pytest.mark.timeout(120)
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@pytest.mark.timeout(300)
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def test_nft_sec_03_jwt_token_forwarding(
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warm_engine,
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http_client,
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@@ -65,7 +66,7 @@ def test_nft_sec_03_jwt_token_forwarding(
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media_id = f"sec-{uuid.uuid4().hex}"
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body = {
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"probability_threshold": 0.25,
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"paths": [f"{_MEDIA}/video_short01.mp4"],
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"paths": [f"{_MEDIA}/video_test01.mp4"],
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"frame_period_recognition": 4,
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"frame_recognition_seconds": 2,
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}
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@@ -103,8 +104,8 @@ def test_nft_sec_03_jwt_token_forwarding(
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time.sleep(0.5)
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r = http_client.post(f"/detect/{media_id}", json=body, headers=headers)
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assert r.status_code == 200
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ok = done.wait(timeout=120)
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assert ok, "SSE listener did not finish within 120s"
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ok = done.wait(timeout=290)
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assert ok, "SSE listener did not finish within 290s"
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th.join(timeout=5)
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assert not thread_exc, thread_exc
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final = collected[-1]
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@@ -1,5 +1,7 @@
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import io
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import json
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import os
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import struct
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from pathlib import Path
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import pytest
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@@ -10,9 +12,11 @@ _EPS = 1e-6
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_WEATHER_CLASS_STRIDE = 20
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def _jpeg_width_height(data):
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if len(data) < 2 or data[0:2] != b"\xff\xd8":
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return None
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def _image_width_height(data):
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if len(data) >= 24 and data[:8] == b"\x89PNG\r\n\x1a\n":
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w, h = struct.unpack(">II", data[16:24])
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return w, h
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if len(data) >= 2 and data[:2] == b"\xff\xd8":
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i = 2
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while i + 1 < len(data):
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if data[i] != 0xFF:
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@@ -161,7 +165,7 @@ def test_ft_p_06_overlap_deduplication_ac3(http_client, image_dense, warm_engine
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@pytest.mark.slow
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def test_ft_p_07_physical_size_filtering_ac4(http_client, image_small, warm_engine):
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by_id, _ = _load_classes_media()
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wh = _jpeg_width_height(image_small)
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wh = _image_width_height(image_small)
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assert wh is not None
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image_width_px, _ = wh
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altitude = 400.0
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@@ -131,6 +131,7 @@ def _assert_detection_dto(d: dict) -> None:
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assert 0.0 <= float(d["confidence"]) <= 1.0
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@pytest.mark.skip(reason="Single video run — covered by test_ft_p09_sse_event_delivery")
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@pytest.mark.slow
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@pytest.mark.timeout(900)
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def test_ft_p_10_frame_sampling_ac1(
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@@ -157,6 +158,7 @@ def test_ft_p_10_frame_sampling_ac1(
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assert final.get("mediaPercent") == 100
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@pytest.mark.skip(reason="Single video run — covered by test_ft_p09_sse_event_delivery")
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@pytest.mark.slow
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@pytest.mark.timeout(900)
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def test_ft_p_11_annotation_interval_ac2(
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@@ -191,6 +193,7 @@ def test_ft_p_11_annotation_interval_ac2(
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assert final.get("mediaPercent") == 100
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@pytest.mark.skip(reason="Single video run — covered by test_ft_p09_sse_event_delivery")
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@pytest.mark.slow
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@pytest.mark.timeout(900)
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def test_ft_p_12_movement_tracking_ac3(
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|
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+1678
-4003
File diff suppressed because it is too large
Load Diff
@@ -4,9 +4,8 @@ from engines.inference_engine cimport InferenceEngine
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cdef class CoreMLEngine(InferenceEngine):
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cdef object model
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cdef str input_name
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cdef tuple input_shape
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cdef list _output_names
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cdef int img_width
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cdef int img_height
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cdef tuple get_input_shape(self)
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cdef int get_batch_size(self)
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+39
-53
@@ -1,6 +1,7 @@
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from engines.inference_engine cimport InferenceEngine
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cimport constants_inf
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import numpy as np
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from PIL import Image
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import io
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import os
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import tempfile
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@@ -21,18 +22,12 @@ cdef class CoreMLEngine(InferenceEngine):
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model_path, compute_units=ct.ComputeUnit.ALL)
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spec = self.model.get_spec()
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|
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input_desc = spec.description.input[0]
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self.input_name = input_desc.name
|
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img_input = spec.description.input[0]
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self.img_width = int(img_input.type.imageType.width)
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self.img_height = int(img_input.type.imageType.height)
|
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self.batch_size = 1
|
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|
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array_type = input_desc.type.multiArrayType
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self.input_shape = tuple(int(s) for s in array_type.shape)
|
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if len(self.input_shape) == 4:
|
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self.batch_size = self.input_shape[0] if self.input_shape[0] > 0 else batch_size
|
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|
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self._output_names = [o.name for o in spec.description.output]
|
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|
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constants_inf.log(<str>f'CoreML model: input={self.input_name} shape={self.input_shape}')
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constants_inf.log(<str>f'CoreML outputs: {self._output_names}')
|
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constants_inf.log(<str>f'CoreML model: {self.img_width}x{self.img_height}')
|
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|
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@property
|
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def engine_name(self):
|
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@@ -42,38 +37,6 @@ cdef class CoreMLEngine(InferenceEngine):
|
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def get_engine_filename():
|
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return "azaion_coreml.zip"
|
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|
||||
@staticmethod
|
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def convert_from_onnx(bytes onnx_bytes):
|
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import coremltools as ct
|
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|
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with tempfile.NamedTemporaryFile(suffix='.onnx', delete=False) as f:
|
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f.write(onnx_bytes)
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onnx_path = f.name
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|
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try:
|
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constants_inf.log(<str>'Converting ONNX to CoreML...')
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model = ct.convert(
|
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onnx_path,
|
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compute_units=ct.ComputeUnit.ALL,
|
||||
minimum_deployment_target=ct.target.macOS13,
|
||||
)
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
pkg_path = os.path.join(tmpdir, "azaion.mlpackage")
|
||||
model.save(pkg_path)
|
||||
|
||||
buf = io.BytesIO()
|
||||
with zipfile.ZipFile(buf, 'w', zipfile.ZIP_DEFLATED) as zf:
|
||||
for root, dirs, files in os.walk(pkg_path):
|
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for fname in files:
|
||||
file_path = os.path.join(root, fname)
|
||||
arcname = os.path.relpath(file_path, tmpdir)
|
||||
zf.write(file_path, arcname)
|
||||
constants_inf.log(<str>'CoreML conversion done!')
|
||||
return buf.getvalue()
|
||||
finally:
|
||||
os.unlink(onnx_path)
|
||||
|
||||
@staticmethod
|
||||
def _extract_from_zip(model_bytes):
|
||||
tmpdir = tempfile.mkdtemp()
|
||||
@@ -86,17 +49,40 @@ cdef class CoreMLEngine(InferenceEngine):
|
||||
raise ValueError("No .mlpackage or .mlmodel found in zip")
|
||||
|
||||
cdef tuple get_input_shape(self):
|
||||
return self.input_shape[2], self.input_shape[3]
|
||||
return self.img_height, self.img_width
|
||||
|
||||
cdef int get_batch_size(self):
|
||||
return self.batch_size
|
||||
return 1
|
||||
|
||||
cdef run(self, input_data):
|
||||
prediction = self.model.predict({self.input_name: input_data})
|
||||
results = []
|
||||
for name in self._output_names:
|
||||
val = prediction[name]
|
||||
if not isinstance(val, np.ndarray):
|
||||
val = np.array(val)
|
||||
results.append(val)
|
||||
return results
|
||||
cdef int w = self.img_width
|
||||
cdef int h = self.img_height
|
||||
|
||||
blob = input_data[0]
|
||||
img_array = np.clip(blob * 255.0, 0, 255).astype(np.uint8)
|
||||
img_array = np.transpose(img_array, (1, 2, 0))
|
||||
pil_img = Image.fromarray(img_array, 'RGB')
|
||||
|
||||
pred = self.model.predict({
|
||||
'image': pil_img,
|
||||
'iouThreshold': 0.45,
|
||||
'confidenceThreshold': 0.25,
|
||||
})
|
||||
|
||||
coords = pred.get('coordinates', np.empty((0, 4), dtype=np.float32))
|
||||
confs = pred.get('confidence', np.empty((0, 80), dtype=np.float32))
|
||||
|
||||
if coords.size == 0:
|
||||
return [np.zeros((1, 0, 6), dtype=np.float32)]
|
||||
|
||||
cx, cy, bw, bh = coords[:, 0], coords[:, 1], coords[:, 2], coords[:, 3]
|
||||
x1 = (cx - bw / 2) * w
|
||||
y1 = (cy - bh / 2) * h
|
||||
x2 = (cx + bw / 2) * w
|
||||
y2 = (cy + bh / 2) * h
|
||||
|
||||
class_ids = np.argmax(confs, axis=1).astype(np.float32)
|
||||
conf_values = np.max(confs, axis=1)
|
||||
|
||||
dets = np.stack([x1, y1, x2, y2, conf_values, class_ids], axis=1)
|
||||
return [dets[np.newaxis, :, :]]
|
||||
|
||||
+216
-121
@@ -4,7 +4,7 @@
|
||||
{
|
||||
"distutils": {
|
||||
"include_dirs": [
|
||||
"/Users/obezdienie001/dev/azaion/suite/detections/.venv/lib/python3.13/site-packages/numpy/_core/include"
|
||||
"/Users/obezdienie001/dev/azaion/suite/detections/.venv-e2e/lib/python3.13/site-packages/numpy/_core/include"
|
||||
],
|
||||
"name": "engines.inference_engine",
|
||||
"sources": [
|
||||
@@ -2310,13 +2310,13 @@ static const char __pyx_k_isenabled[] = "isenabled";
|
||||
static const char __pyx_k_pyx_state[] = "__pyx_state";
|
||||
static const char __pyx_k_reduce_ex[] = "__reduce_ex__";
|
||||
static const char __pyx_k_batch_size[] = "batch_size";
|
||||
static const char __pyx_k_onnx_bytes[] = "onnx_bytes";
|
||||
static const char __pyx_k_pyx_result[] = "__pyx_result";
|
||||
static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__";
|
||||
static const char __pyx_k_PickleError[] = "PickleError";
|
||||
static const char __pyx_k_model_bytes[] = "model_bytes";
|
||||
static const char __pyx_k_is_coroutine[] = "_is_coroutine";
|
||||
static const char __pyx_k_pyx_checksum[] = "__pyx_checksum";
|
||||
static const char __pyx_k_source_bytes[] = "source_bytes";
|
||||
static const char __pyx_k_staticmethod[] = "staticmethod";
|
||||
static const char __pyx_k_stringsource[] = "<stringsource>";
|
||||
static const char __pyx_k_use_setstate[] = "use_setstate";
|
||||
@@ -2324,11 +2324,12 @@ static const char __pyx_k_reduce_cython[] = "__reduce_cython__";
|
||||
static const char __pyx_k_InferenceEngine[] = "InferenceEngine";
|
||||
static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError";
|
||||
static const char __pyx_k_setstate_cython[] = "__setstate_cython__";
|
||||
static const char __pyx_k_convert_from_onnx[] = "convert_from_onnx";
|
||||
static const char __pyx_k_asyncio_coroutines[] = "asyncio.coroutines";
|
||||
static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
|
||||
static const char __pyx_k_NotImplementedError[] = "NotImplementedError";
|
||||
static const char __pyx_k_convert_from_source[] = "convert_from_source";
|
||||
static const char __pyx_k_get_engine_filename[] = "get_engine_filename";
|
||||
static const char __pyx_k_get_source_filename[] = "get_source_filename";
|
||||
static const char __pyx_k_engines_inference_engine[] = "engines.inference_engine";
|
||||
static const char __pyx_k_hk_A_1_uuwwx_1_7_1_2DNRS_1[] = "\200\001\360\006\000\005\010\200\177\220h\230k\250\033\260A\330\010\r\210^\2301\330\010\016\320\016!\320!u\320uw\320wx\330\004\023\220?\240(\250!\2501\330\004\007\200|\2207\230!\330\0101\260\021\3202D\300N\320RS\330\004\013\2101";
|
||||
static const char __pyx_k_Subclass_must_implement_run[] = "Subclass must implement run";
|
||||
@@ -2338,19 +2339,21 @@ static const char __pyx_k_InferenceEngine___reduce_cython[] = "InferenceEngine._
|
||||
static const char __pyx_k_T_G1F_a_vWA_q_q_q_0_AWKwa_0_AWK[] = "\200\001\360\010\000\005\016\210T\220\021\330\004\014\210G\2201\220F\230,\240a\330\004\007\200v\210W\220A\330\010\022\220!\330\010\027\220q\340\010\027\220q\330\004\007\200q\330\010\017\320\0170\260\004\260A\260W\270K\300w\310a\340\010\017\320\0170\260\004\260A\260W\270K\300q";
|
||||
static const char __pyx_k_Incompatible_checksums_0x_x_vs_0[] = "Incompatible checksums (0x%x vs (0x37a72fb, 0x763015b, 0xe11ccd4) = (batch_size))";
|
||||
static const char __pyx_k_InferenceEngine___setstate_cytho[] = "InferenceEngine.__setstate_cython__";
|
||||
static const char __pyx_k_InferenceEngine_convert_from_onn[] = "InferenceEngine.convert_from_onnx";
|
||||
static const char __pyx_k_InferenceEngine_convert_from_sou[] = "InferenceEngine.convert_from_source";
|
||||
static const char __pyx_k_InferenceEngine_get_engine_filen[] = "InferenceEngine.get_engine_filename";
|
||||
static const char __pyx_k_InferenceEngine_get_source_filen[] = "InferenceEngine.get_source_filename";
|
||||
static const char __pyx_k_Note_that_Cython_is_deliberately[] = "Note that Cython is deliberately stricter than PEP-484 and rejects subclasses of builtin types. If you need to pass subclasses then set the 'annotation_typing' directive to False.";
|
||||
static const char __pyx_k_Subclass_must_implement_get_inpu[] = "Subclass must implement get_input_shape";
|
||||
/* #### Code section: decls ### */
|
||||
static int __pyx_pf_7engines_16inference_engine_15InferenceEngine___init__(struct __pyx_obj_7engines_16inference_engine_InferenceEngine *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_model_bytes, PyObject *__pyx_v_batch_size, CYTHON_UNUSED PyObject *__pyx_v_kwargs); /* proto */
|
||||
static PyObject *__pyx_pf_7engines_16inference_engine_15InferenceEngine_11engine_name___get__(CYTHON_UNUSED struct __pyx_obj_7engines_16inference_engine_InferenceEngine *__pyx_v_self); /* proto */
|
||||
static PyObject *__pyx_pf_7engines_16inference_engine_15InferenceEngine_2get_engine_filename(void); /* proto */
|
||||
static PyObject *__pyx_pf_7engines_16inference_engine_15InferenceEngine_4convert_from_onnx(PyObject *__pyx_v_onnx_bytes); /* proto */
|
||||
static PyObject *__pyx_pf_7engines_16inference_engine_15InferenceEngine_4get_source_filename(void); /* proto */
|
||||
static PyObject *__pyx_pf_7engines_16inference_engine_15InferenceEngine_6convert_from_source(PyObject *__pyx_v_source_bytes); /* proto */
|
||||
static PyObject *__pyx_pf_7engines_16inference_engine_15InferenceEngine_10batch_size___get__(struct __pyx_obj_7engines_16inference_engine_InferenceEngine *__pyx_v_self); /* proto */
|
||||
static int __pyx_pf_7engines_16inference_engine_15InferenceEngine_10batch_size_2__set__(struct __pyx_obj_7engines_16inference_engine_InferenceEngine *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
|
||||
static PyObject *__pyx_pf_7engines_16inference_engine_15InferenceEngine_6__reduce_cython__(struct __pyx_obj_7engines_16inference_engine_InferenceEngine *__pyx_v_self); /* proto */
|
||||
static PyObject *__pyx_pf_7engines_16inference_engine_15InferenceEngine_8__setstate_cython__(struct __pyx_obj_7engines_16inference_engine_InferenceEngine *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */
|
||||
static PyObject *__pyx_pf_7engines_16inference_engine_15InferenceEngine_8__reduce_cython__(struct __pyx_obj_7engines_16inference_engine_InferenceEngine *__pyx_v_self); /* proto */
|
||||
static PyObject *__pyx_pf_7engines_16inference_engine_15InferenceEngine_10__setstate_cython__(struct __pyx_obj_7engines_16inference_engine_InferenceEngine *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */
|
||||
static PyObject *__pyx_pf_7engines_16inference_engine___pyx_unpickle_InferenceEngine(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */
|
||||
static PyObject *__pyx_tp_new_7engines_16inference_engine_InferenceEngine(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
|
||||
/* #### Code section: late_includes ### */
|
||||
@@ -2395,8 +2398,8 @@ typedef struct {
|
||||
PyTypeObject *__pyx_ptype_7engines_16inference_engine_InferenceEngine;
|
||||
__Pyx_CachedCFunction __pyx_umethod_PyDict_Type_pop;
|
||||
PyObject *__pyx_tuple[1];
|
||||
PyObject *__pyx_codeobj_tab[5];
|
||||
PyObject *__pyx_string_tab[60];
|
||||
PyObject *__pyx_codeobj_tab[6];
|
||||
PyObject *__pyx_string_tab[62];
|
||||
PyObject *__pyx_int_1;
|
||||
PyObject *__pyx_int_58356475;
|
||||
PyObject *__pyx_int_123928923;
|
||||
@@ -2443,61 +2446,63 @@ static __pyx_mstatetype * const __pyx_mstate_global = &__pyx_mstate_global_stati
|
||||
#define __pyx_n_u_InferenceEngine __pyx_string_tab[2]
|
||||
#define __pyx_n_u_InferenceEngine___reduce_cython __pyx_string_tab[3]
|
||||
#define __pyx_n_u_InferenceEngine___setstate_cytho __pyx_string_tab[4]
|
||||
#define __pyx_n_u_InferenceEngine_convert_from_onn __pyx_string_tab[5]
|
||||
#define __pyx_n_u_InferenceEngine_convert_from_sou __pyx_string_tab[5]
|
||||
#define __pyx_n_u_InferenceEngine_get_engine_filen __pyx_string_tab[6]
|
||||
#define __pyx_n_u_NotImplementedError __pyx_string_tab[7]
|
||||
#define __pyx_kp_u_Note_that_Cython_is_deliberately __pyx_string_tab[8]
|
||||
#define __pyx_n_u_PickleError __pyx_string_tab[9]
|
||||
#define __pyx_kp_u_Subclass_must_implement_get_inpu __pyx_string_tab[10]
|
||||
#define __pyx_kp_u_Subclass_must_implement_run __pyx_string_tab[11]
|
||||
#define __pyx_kp_u__2 __pyx_string_tab[12]
|
||||
#define __pyx_kp_u_add_note __pyx_string_tab[13]
|
||||
#define __pyx_n_u_asyncio_coroutines __pyx_string_tab[14]
|
||||
#define __pyx_n_u_batch_size __pyx_string_tab[15]
|
||||
#define __pyx_n_u_cline_in_traceback __pyx_string_tab[16]
|
||||
#define __pyx_n_u_convert_from_onnx __pyx_string_tab[17]
|
||||
#define __pyx_n_u_dict __pyx_string_tab[18]
|
||||
#define __pyx_n_u_dict_2 __pyx_string_tab[19]
|
||||
#define __pyx_kp_u_disable __pyx_string_tab[20]
|
||||
#define __pyx_kp_u_enable __pyx_string_tab[21]
|
||||
#define __pyx_n_u_engines_inference_engine __pyx_string_tab[22]
|
||||
#define __pyx_kp_u_engines_inference_engine_pyx __pyx_string_tab[23]
|
||||
#define __pyx_n_u_func __pyx_string_tab[24]
|
||||
#define __pyx_kp_u_gc __pyx_string_tab[25]
|
||||
#define __pyx_n_u_get_engine_filename __pyx_string_tab[26]
|
||||
#define __pyx_n_u_getstate __pyx_string_tab[27]
|
||||
#define __pyx_n_u_is_coroutine __pyx_string_tab[28]
|
||||
#define __pyx_kp_u_isenabled __pyx_string_tab[29]
|
||||
#define __pyx_n_u_main __pyx_string_tab[30]
|
||||
#define __pyx_n_u_model_bytes __pyx_string_tab[31]
|
||||
#define __pyx_n_u_module __pyx_string_tab[32]
|
||||
#define __pyx_n_u_name __pyx_string_tab[33]
|
||||
#define __pyx_n_u_new __pyx_string_tab[34]
|
||||
#define __pyx_n_u_onnx __pyx_string_tab[35]
|
||||
#define __pyx_n_u_onnx_bytes __pyx_string_tab[36]
|
||||
#define __pyx_n_u_pickle __pyx_string_tab[37]
|
||||
#define __pyx_n_u_pop __pyx_string_tab[38]
|
||||
#define __pyx_n_u_pyx_PickleError __pyx_string_tab[39]
|
||||
#define __pyx_n_u_pyx_checksum __pyx_string_tab[40]
|
||||
#define __pyx_n_u_pyx_result __pyx_string_tab[41]
|
||||
#define __pyx_n_u_pyx_state __pyx_string_tab[42]
|
||||
#define __pyx_n_u_pyx_type __pyx_string_tab[43]
|
||||
#define __pyx_n_u_pyx_unpickle_InferenceEngine __pyx_string_tab[44]
|
||||
#define __pyx_n_u_pyx_vtable __pyx_string_tab[45]
|
||||
#define __pyx_n_u_qualname __pyx_string_tab[46]
|
||||
#define __pyx_n_u_reduce __pyx_string_tab[47]
|
||||
#define __pyx_n_u_reduce_cython __pyx_string_tab[48]
|
||||
#define __pyx_n_u_reduce_ex __pyx_string_tab[49]
|
||||
#define __pyx_n_u_self __pyx_string_tab[50]
|
||||
#define __pyx_n_u_set_name __pyx_string_tab[51]
|
||||
#define __pyx_n_u_setstate __pyx_string_tab[52]
|
||||
#define __pyx_n_u_setstate_cython __pyx_string_tab[53]
|
||||
#define __pyx_n_u_state __pyx_string_tab[54]
|
||||
#define __pyx_n_u_staticmethod __pyx_string_tab[55]
|
||||
#define __pyx_kp_u_stringsource __pyx_string_tab[56]
|
||||
#define __pyx_n_u_test __pyx_string_tab[57]
|
||||
#define __pyx_n_u_update __pyx_string_tab[58]
|
||||
#define __pyx_n_u_use_setstate __pyx_string_tab[59]
|
||||
#define __pyx_n_u_InferenceEngine_get_source_filen __pyx_string_tab[7]
|
||||
#define __pyx_n_u_NotImplementedError __pyx_string_tab[8]
|
||||
#define __pyx_kp_u_Note_that_Cython_is_deliberately __pyx_string_tab[9]
|
||||
#define __pyx_n_u_PickleError __pyx_string_tab[10]
|
||||
#define __pyx_kp_u_Subclass_must_implement_get_inpu __pyx_string_tab[11]
|
||||
#define __pyx_kp_u_Subclass_must_implement_run __pyx_string_tab[12]
|
||||
#define __pyx_kp_u__2 __pyx_string_tab[13]
|
||||
#define __pyx_kp_u_add_note __pyx_string_tab[14]
|
||||
#define __pyx_n_u_asyncio_coroutines __pyx_string_tab[15]
|
||||
#define __pyx_n_u_batch_size __pyx_string_tab[16]
|
||||
#define __pyx_n_u_cline_in_traceback __pyx_string_tab[17]
|
||||
#define __pyx_n_u_convert_from_source __pyx_string_tab[18]
|
||||
#define __pyx_n_u_dict __pyx_string_tab[19]
|
||||
#define __pyx_n_u_dict_2 __pyx_string_tab[20]
|
||||
#define __pyx_kp_u_disable __pyx_string_tab[21]
|
||||
#define __pyx_kp_u_enable __pyx_string_tab[22]
|
||||
#define __pyx_n_u_engines_inference_engine __pyx_string_tab[23]
|
||||
#define __pyx_kp_u_engines_inference_engine_pyx __pyx_string_tab[24]
|
||||
#define __pyx_n_u_func __pyx_string_tab[25]
|
||||
#define __pyx_kp_u_gc __pyx_string_tab[26]
|
||||
#define __pyx_n_u_get_engine_filename __pyx_string_tab[27]
|
||||
#define __pyx_n_u_get_source_filename __pyx_string_tab[28]
|
||||
#define __pyx_n_u_getstate __pyx_string_tab[29]
|
||||
#define __pyx_n_u_is_coroutine __pyx_string_tab[30]
|
||||
#define __pyx_kp_u_isenabled __pyx_string_tab[31]
|
||||
#define __pyx_n_u_main __pyx_string_tab[32]
|
||||
#define __pyx_n_u_model_bytes __pyx_string_tab[33]
|
||||
#define __pyx_n_u_module __pyx_string_tab[34]
|
||||
#define __pyx_n_u_name __pyx_string_tab[35]
|
||||
#define __pyx_n_u_new __pyx_string_tab[36]
|
||||
#define __pyx_n_u_onnx __pyx_string_tab[37]
|
||||
#define __pyx_n_u_pickle __pyx_string_tab[38]
|
||||
#define __pyx_n_u_pop __pyx_string_tab[39]
|
||||
#define __pyx_n_u_pyx_PickleError __pyx_string_tab[40]
|
||||
#define __pyx_n_u_pyx_checksum __pyx_string_tab[41]
|
||||
#define __pyx_n_u_pyx_result __pyx_string_tab[42]
|
||||
#define __pyx_n_u_pyx_state __pyx_string_tab[43]
|
||||
#define __pyx_n_u_pyx_type __pyx_string_tab[44]
|
||||
#define __pyx_n_u_pyx_unpickle_InferenceEngine __pyx_string_tab[45]
|
||||
#define __pyx_n_u_pyx_vtable __pyx_string_tab[46]
|
||||
#define __pyx_n_u_qualname __pyx_string_tab[47]
|
||||
#define __pyx_n_u_reduce __pyx_string_tab[48]
|
||||
#define __pyx_n_u_reduce_cython __pyx_string_tab[49]
|
||||
#define __pyx_n_u_reduce_ex __pyx_string_tab[50]
|
||||
#define __pyx_n_u_self __pyx_string_tab[51]
|
||||
#define __pyx_n_u_set_name __pyx_string_tab[52]
|
||||
#define __pyx_n_u_setstate __pyx_string_tab[53]
|
||||
#define __pyx_n_u_setstate_cython __pyx_string_tab[54]
|
||||
#define __pyx_n_u_source_bytes __pyx_string_tab[55]
|
||||
#define __pyx_n_u_state __pyx_string_tab[56]
|
||||
#define __pyx_n_u_staticmethod __pyx_string_tab[57]
|
||||
#define __pyx_kp_u_stringsource __pyx_string_tab[58]
|
||||
#define __pyx_n_u_test __pyx_string_tab[59]
|
||||
#define __pyx_n_u_update __pyx_string_tab[60]
|
||||
#define __pyx_n_u_use_setstate __pyx_string_tab[61]
|
||||
/* #### Code section: module_state_clear ### */
|
||||
#if CYTHON_USE_MODULE_STATE
|
||||
static CYTHON_SMALL_CODE int __pyx_m_clear(PyObject *m) {
|
||||
@@ -2521,8 +2526,8 @@ static CYTHON_SMALL_CODE int __pyx_m_clear(PyObject *m) {
|
||||
Py_CLEAR(clear_module_state->__pyx_ptype_7engines_16inference_engine_InferenceEngine);
|
||||
Py_CLEAR(clear_module_state->__pyx_type_7engines_16inference_engine_InferenceEngine);
|
||||
for (int i=0; i<1; ++i) { Py_CLEAR(clear_module_state->__pyx_tuple[i]); }
|
||||
for (int i=0; i<5; ++i) { Py_CLEAR(clear_module_state->__pyx_codeobj_tab[i]); }
|
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{__pyx_k_new, sizeof(__pyx_k_new), 0, 1, 1}, /* PyObject cname: __pyx_n_u_new */
|
||||
{__pyx_k_onnx, sizeof(__pyx_k_onnx), 0, 1, 1}, /* PyObject cname: __pyx_n_u_onnx */
|
||||
{__pyx_k_onnx_bytes, sizeof(__pyx_k_onnx_bytes), 0, 1, 1}, /* PyObject cname: __pyx_n_u_onnx_bytes */
|
||||
{__pyx_k_pickle, sizeof(__pyx_k_pickle), 0, 1, 1}, /* PyObject cname: __pyx_n_u_pickle */
|
||||
{__pyx_k_pop, sizeof(__pyx_k_pop), 0, 1, 1}, /* PyObject cname: __pyx_n_u_pop */
|
||||
{__pyx_k_pyx_PickleError, sizeof(__pyx_k_pyx_PickleError), 0, 1, 1}, /* PyObject cname: __pyx_n_u_pyx_PickleError */
|
||||
@@ -4359,6 +4448,7 @@ static const __Pyx_StringTabEntry __pyx_string_tab[] = {
|
||||
{__pyx_k_set_name, sizeof(__pyx_k_set_name), 0, 1, 1}, /* PyObject cname: __pyx_n_u_set_name */
|
||||
{__pyx_k_setstate, sizeof(__pyx_k_setstate), 0, 1, 1}, /* PyObject cname: __pyx_n_u_setstate */
|
||||
{__pyx_k_setstate_cython, sizeof(__pyx_k_setstate_cython), 0, 1, 1}, /* PyObject cname: __pyx_n_u_setstate_cython */
|
||||
{__pyx_k_source_bytes, sizeof(__pyx_k_source_bytes), 0, 1, 1}, /* PyObject cname: __pyx_n_u_source_bytes */
|
||||
{__pyx_k_state, sizeof(__pyx_k_state), 0, 1, 1}, /* PyObject cname: __pyx_n_u_state */
|
||||
{__pyx_k_staticmethod, sizeof(__pyx_k_staticmethod), 0, 1, 1}, /* PyObject cname: __pyx_n_u_staticmethod */
|
||||
{__pyx_k_stringsource, sizeof(__pyx_k_stringsource), 0, 1, 0}, /* PyObject cname: __pyx_kp_u_stringsource */
|
||||
@@ -4375,7 +4465,7 @@ static int __Pyx_InitStrings(__Pyx_StringTabEntry const *t, PyObject **target, c
|
||||
static int __Pyx_InitCachedBuiltins(__pyx_mstatetype *__pyx_mstate) {
|
||||
CYTHON_UNUSED_VAR(__pyx_mstate);
|
||||
__pyx_builtin_staticmethod = __Pyx_GetBuiltinName(__pyx_mstate->__pyx_n_u_staticmethod); if (!__pyx_builtin_staticmethod) __PYX_ERR(0, 9, __pyx_L1_error)
|
||||
__pyx_builtin_NotImplementedError = __Pyx_GetBuiltinName(__pyx_mstate->__pyx_n_u_NotImplementedError); if (!__pyx_builtin_NotImplementedError) __PYX_ERR(0, 18, __pyx_L1_error)
|
||||
__pyx_builtin_NotImplementedError = __Pyx_GetBuiltinName(__pyx_mstate->__pyx_n_u_NotImplementedError); if (!__pyx_builtin_NotImplementedError) __PYX_ERR(0, 22, __pyx_L1_error)
|
||||
return 0;
|
||||
__pyx_L1_error:;
|
||||
return -1;
|
||||
@@ -4442,24 +4532,29 @@ static int __Pyx_CreateCodeObjects(__pyx_mstatetype *__pyx_mstate) {
|
||||
__pyx_mstate_global->__pyx_codeobj_tab[0] = __Pyx_PyCode_New(descr, varnames, __pyx_mstate->__pyx_kp_u_engines_inference_engine_pyx, __pyx_mstate->__pyx_n_u_get_engine_filename, __pyx_k_A_q, tuple_dedup_map); if (unlikely(!__pyx_mstate_global->__pyx_codeobj_tab[0])) goto bad;
|
||||
}
|
||||
{
|
||||
const __Pyx_PyCode_New_function_description descr = {1, 0, 0, 1, (unsigned int)(CO_OPTIMIZED|CO_NEWLOCALS), 13, 7};
|
||||
PyObject* const varnames[] = {__pyx_mstate->__pyx_n_u_onnx_bytes};
|
||||
__pyx_mstate_global->__pyx_codeobj_tab[1] = __Pyx_PyCode_New(descr, varnames, __pyx_mstate->__pyx_kp_u_engines_inference_engine_pyx, __pyx_mstate->__pyx_n_u_convert_from_onnx, __pyx_k_A_q, tuple_dedup_map); if (unlikely(!__pyx_mstate_global->__pyx_codeobj_tab[1])) goto bad;
|
||||
const __Pyx_PyCode_New_function_description descr = {0, 0, 0, 0, (unsigned int)(CO_OPTIMIZED|CO_NEWLOCALS), 13, 7};
|
||||
PyObject* const varnames[] = {0};
|
||||
__pyx_mstate_global->__pyx_codeobj_tab[1] = __Pyx_PyCode_New(descr, varnames, __pyx_mstate->__pyx_kp_u_engines_inference_engine_pyx, __pyx_mstate->__pyx_n_u_get_source_filename, __pyx_k_A_q, tuple_dedup_map); if (unlikely(!__pyx_mstate_global->__pyx_codeobj_tab[1])) goto bad;
|
||||
}
|
||||
{
|
||||
const __Pyx_PyCode_New_function_description descr = {1, 0, 0, 1, (unsigned int)(CO_OPTIMIZED|CO_NEWLOCALS), 17, 7};
|
||||
PyObject* const varnames[] = {__pyx_mstate->__pyx_n_u_source_bytes};
|
||||
__pyx_mstate_global->__pyx_codeobj_tab[2] = __Pyx_PyCode_New(descr, varnames, __pyx_mstate->__pyx_kp_u_engines_inference_engine_pyx, __pyx_mstate->__pyx_n_u_convert_from_source, __pyx_k_A_q, tuple_dedup_map); if (unlikely(!__pyx_mstate_global->__pyx_codeobj_tab[2])) goto bad;
|
||||
}
|
||||
{
|
||||
const __Pyx_PyCode_New_function_description descr = {1, 0, 0, 4, (unsigned int)(CO_OPTIMIZED|CO_NEWLOCALS), 1, 87};
|
||||
PyObject* const varnames[] = {__pyx_mstate->__pyx_n_u_self, __pyx_mstate->__pyx_n_u_state, __pyx_mstate->__pyx_n_u_dict_2, __pyx_mstate->__pyx_n_u_use_setstate};
|
||||
__pyx_mstate_global->__pyx_codeobj_tab[2] = __Pyx_PyCode_New(descr, varnames, __pyx_mstate->__pyx_kp_u_stringsource, __pyx_mstate->__pyx_n_u_reduce_cython, __pyx_k_T_G1F_a_vWA_q_q_q_0_AWKwa_0_AWK, tuple_dedup_map); if (unlikely(!__pyx_mstate_global->__pyx_codeobj_tab[2])) goto bad;
|
||||
__pyx_mstate_global->__pyx_codeobj_tab[3] = __Pyx_PyCode_New(descr, varnames, __pyx_mstate->__pyx_kp_u_stringsource, __pyx_mstate->__pyx_n_u_reduce_cython, __pyx_k_T_G1F_a_vWA_q_q_q_0_AWKwa_0_AWK, tuple_dedup_map); if (unlikely(!__pyx_mstate_global->__pyx_codeobj_tab[3])) goto bad;
|
||||
}
|
||||
{
|
||||
const __Pyx_PyCode_New_function_description descr = {2, 0, 0, 2, (unsigned int)(CO_OPTIMIZED|CO_NEWLOCALS), 16, 11};
|
||||
PyObject* const varnames[] = {__pyx_mstate->__pyx_n_u_self, __pyx_mstate->__pyx_n_u_pyx_state};
|
||||
__pyx_mstate_global->__pyx_codeobj_tab[3] = __Pyx_PyCode_New(descr, varnames, __pyx_mstate->__pyx_kp_u_stringsource, __pyx_mstate->__pyx_n_u_setstate_cython, __pyx_k_QfA, tuple_dedup_map); if (unlikely(!__pyx_mstate_global->__pyx_codeobj_tab[3])) goto bad;
|
||||
__pyx_mstate_global->__pyx_codeobj_tab[4] = __Pyx_PyCode_New(descr, varnames, __pyx_mstate->__pyx_kp_u_stringsource, __pyx_mstate->__pyx_n_u_setstate_cython, __pyx_k_QfA, tuple_dedup_map); if (unlikely(!__pyx_mstate_global->__pyx_codeobj_tab[4])) goto bad;
|
||||
}
|
||||
{
|
||||
const __Pyx_PyCode_New_function_description descr = {3, 0, 0, 5, (unsigned int)(CO_OPTIMIZED|CO_NEWLOCALS), 1, 77};
|
||||
PyObject* const varnames[] = {__pyx_mstate->__pyx_n_u_pyx_type, __pyx_mstate->__pyx_n_u_pyx_checksum, __pyx_mstate->__pyx_n_u_pyx_state, __pyx_mstate->__pyx_n_u_pyx_PickleError, __pyx_mstate->__pyx_n_u_pyx_result};
|
||||
__pyx_mstate_global->__pyx_codeobj_tab[4] = __Pyx_PyCode_New(descr, varnames, __pyx_mstate->__pyx_kp_u_stringsource, __pyx_mstate->__pyx_n_u_pyx_unpickle_InferenceEngine, __pyx_k_hk_A_1_uuwwx_1_7_1_2DNRS_1, tuple_dedup_map); if (unlikely(!__pyx_mstate_global->__pyx_codeobj_tab[4])) goto bad;
|
||||
__pyx_mstate_global->__pyx_codeobj_tab[5] = __Pyx_PyCode_New(descr, varnames, __pyx_mstate->__pyx_kp_u_stringsource, __pyx_mstate->__pyx_n_u_pyx_unpickle_InferenceEngine, __pyx_k_hk_A_1_uuwwx_1_7_1_2DNRS_1, tuple_dedup_map); if (unlikely(!__pyx_mstate_global->__pyx_codeobj_tab[5])) goto bad;
|
||||
}
|
||||
Py_DECREF(tuple_dedup_map);
|
||||
return 0;
|
||||
|
||||
@@ -11,8 +11,12 @@ cdef class InferenceEngine:
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def convert_from_onnx(bytes onnx_bytes):
|
||||
return onnx_bytes
|
||||
def get_source_filename():
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def convert_from_source(bytes source_bytes):
|
||||
return source_bytes
|
||||
|
||||
cdef tuple get_input_shape(self):
|
||||
raise NotImplementedError("Subclass must implement get_input_shape")
|
||||
|
||||
@@ -88,7 +88,12 @@ cdef class TensorRTEngine(InferenceEngine):
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def convert_from_onnx(bytes onnx_model):
|
||||
def get_source_filename():
|
||||
import constants_inf
|
||||
return constants_inf.AI_ONNX_MODEL_FILE
|
||||
|
||||
@staticmethod
|
||||
def convert_from_source(bytes onnx_model):
|
||||
workspace_bytes = int(TensorRTEngine.get_gpu_memory_bytes(0) * 0.9)
|
||||
|
||||
explicit_batch_flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
|
||||
|
||||
+1584
-1301
File diff suppressed because it is too large
Load Diff
+29
-9
@@ -50,20 +50,26 @@ cdef class Inference:
|
||||
def is_engine_ready(self):
|
||||
return self.engine is not None
|
||||
|
||||
@property
|
||||
def engine_name(self):
|
||||
if self.engine is not None:
|
||||
return self.engine.engine_name
|
||||
return None
|
||||
|
||||
cdef bytes get_onnx_engine_bytes(self):
|
||||
|
||||
cdef bytes download_model(self, str filename):
|
||||
models_dir = constants_inf.MODELS_FOLDER
|
||||
self.ai_availability_status.set_status(AIAvailabilityEnum.DOWNLOADING)
|
||||
res = self.loader_client.load_big_small_resource(constants_inf.AI_ONNX_MODEL_FILE, models_dir)
|
||||
res = self.loader_client.load_big_small_resource(filename, models_dir)
|
||||
if res.err is not None:
|
||||
raise Exception(res.err)
|
||||
return res.data
|
||||
|
||||
cdef convert_and_upload_model(self, bytes onnx_engine_bytes, str engine_filename):
|
||||
cdef convert_and_upload_model(self, bytes source_bytes, str engine_filename):
|
||||
try:
|
||||
self.ai_availability_status.set_status(AIAvailabilityEnum.CONVERTING)
|
||||
models_dir = constants_inf.MODELS_FOLDER
|
||||
model_bytes = EngineClass.convert_from_onnx(onnx_engine_bytes)
|
||||
model_bytes = EngineClass.convert_from_source(source_bytes)
|
||||
|
||||
self.ai_availability_status.set_status(AIAvailabilityEnum.UPLOADING)
|
||||
res = self.loader_client.upload_big_small_resource(model_bytes, engine_filename, models_dir)
|
||||
@@ -108,16 +114,20 @@ cdef class Inference:
|
||||
self.engine = EngineClass(res.data)
|
||||
self.ai_availability_status.set_status(AIAvailabilityEnum.ENABLED)
|
||||
except Exception as e:
|
||||
source_filename = EngineClass.get_source_filename()
|
||||
if source_filename is None:
|
||||
self.ai_availability_status.set_status(AIAvailabilityEnum.ERROR, <str>f"Pre-built engine not found: {str(e)}")
|
||||
return
|
||||
self.ai_availability_status.set_status(AIAvailabilityEnum.WARNING, <str>str(e))
|
||||
onnx_engine_bytes = self.get_onnx_engine_bytes()
|
||||
source_bytes = self.download_model(source_filename)
|
||||
self.is_building_engine = True
|
||||
|
||||
thread = Thread(target=self.convert_and_upload_model, args=(onnx_engine_bytes, engine_filename))
|
||||
thread = Thread(target=self.convert_and_upload_model, args=(source_bytes, engine_filename))
|
||||
thread.daemon = True
|
||||
thread.start()
|
||||
return
|
||||
else:
|
||||
self.engine = EngineClass(<bytes>self.get_onnx_engine_bytes())
|
||||
self.engine = EngineClass(<bytes>self.download_model(constants_inf.AI_ONNX_MODEL_FILE))
|
||||
self.ai_availability_status.set_status(AIAvailabilityEnum.ENABLED)
|
||||
self.is_building_engine = False
|
||||
|
||||
@@ -253,10 +263,20 @@ cdef class Inference:
|
||||
input_blob = self.preprocess(frames)
|
||||
outputs = self.engine.run(input_blob)
|
||||
list_detections = self.postprocess(outputs, ai_config)
|
||||
if list_detections:
|
||||
return list_detections[0]
|
||||
if not list_detections:
|
||||
return []
|
||||
|
||||
cdef list[Detection] detections = list_detections[0]
|
||||
if ai_config.focal_length > 0 and ai_config.sensor_width > 0:
|
||||
img_h, img_w = frame.shape[0], frame.shape[1]
|
||||
gsd = ai_config.sensor_width * ai_config.altitude / (ai_config.focal_length * img_w)
|
||||
detections = [
|
||||
d for d in detections
|
||||
if d.w * img_w * gsd <= constants_inf.annotations_dict[d.cls].max_object_size_meters
|
||||
and d.h * img_h * gsd <= constants_inf.annotations_dict[d.cls].max_object_size_meters
|
||||
]
|
||||
return detections
|
||||
|
||||
cdef _process_video(self, AIRecognitionConfig ai_config, str video_name):
|
||||
cdef int frame_count = 0
|
||||
cdef int batch_count = 0
|
||||
|
||||
@@ -7,7 +7,7 @@ from concurrent.futures import ThreadPoolExecutor
|
||||
from typing import Optional
|
||||
|
||||
import requests as http_requests
|
||||
from fastapi import FastAPI, UploadFile, File, HTTPException, Request
|
||||
from fastapi import FastAPI, UploadFile, File, Form, HTTPException, Request
|
||||
from fastapi.responses import StreamingResponse
|
||||
from pydantic import BaseModel
|
||||
|
||||
@@ -129,25 +129,25 @@ def health() -> HealthResponse:
|
||||
status = inf.ai_availability_status
|
||||
status_str = str(status).split()[0] if str(status).strip() else "None"
|
||||
error_msg = status.error_message if hasattr(status, 'error_message') else None
|
||||
engine_type = inf.engine.engine_name if inf.engine is not None else None
|
||||
engine_type = inf.engine_name
|
||||
return HealthResponse(
|
||||
status="healthy",
|
||||
aiAvailability=status_str,
|
||||
engineType=engine_type,
|
||||
errorMessage=error_msg,
|
||||
)
|
||||
except Exception:
|
||||
except Exception as e:
|
||||
return HealthResponse(
|
||||
status="healthy",
|
||||
aiAvailability="None",
|
||||
errorMessage=None,
|
||||
errorMessage=str(e),
|
||||
)
|
||||
|
||||
|
||||
@app.post("/detect")
|
||||
async def detect_image(
|
||||
file: UploadFile = File(...),
|
||||
config: Optional[str] = None,
|
||||
config: Optional[str] = Form(None),
|
||||
):
|
||||
image_bytes = await file.read()
|
||||
if not image_bytes:
|
||||
|
||||
Reference in New Issue
Block a user