mirror of
https://github.com/azaion/ai-training.git
synced 2026-04-22 07:06:36 +00:00
[AZ-171] Add TensorRT tests, AC coverage gate in implement skill, optimize test infrastructure
- Add TensorRT export tests with graceful skip when no GPU available - Add AC test coverage verification step (Step 8) to implement skill - Add test coverage gap analysis to new-task skill - Move exported_models fixture to conftest.py as session-scoped (shared across modules) - Reorder tests: e2e training runs first so images/labels are available for all tests - Consolidate teardown into single session-level cleanup in conftest.py - Fix infrastructure tests to count files dynamically instead of hardcoded 20 Made-with: Cursor
This commit is contained in:
@@ -94,6 +94,7 @@ For each task in the batch, launch an `implementer` subagent with:
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- List of files OWNED (exclusive write access)
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- List of files READ-ONLY
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- List of files FORBIDDEN
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- **Explicit instruction**: the implementer must write or update tests that validate each acceptance criterion in the task spec. If a test cannot run in the current environment (e.g., TensorRT requires GPU), the test must still be written and skip with a clear reason.
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Launch all subagents immediately — no user confirmation.
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@@ -108,46 +109,64 @@ Launch all subagents immediately — no user confirmation.
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- Subagent has not produced new output for an extended period → flag as potentially hung
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- If a subagent is flagged as stuck, do NOT let it continue looping — stop it and record the blocker in the batch report
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### 8. Code Review
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### 8. AC Test Coverage Verification
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Before code review, verify that every acceptance criterion in each task spec has at least one test that validates it. For each task in the batch:
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1. Read the task spec's **Acceptance Criteria** section
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2. Search the test files (new and existing) for tests that cover each AC
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3. Classify each AC as:
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- **Covered**: a test directly validates this AC (running or skipped-with-reason)
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- **Not covered**: no test exists for this AC
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If any AC is **Not covered**:
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- This is a **BLOCKING** failure — the implementer must write the missing test before proceeding
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- Re-launch the implementer with the specific ACs that need tests
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- If the test cannot run in the current environment (GPU required, platform-specific, external service), the test must still exist and skip with `pytest.mark.skipif` or `pytest.skip()` explaining the prerequisite
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- A skipped test counts as **Covered** — the test exists and will run when the environment allows
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Only proceed to Step 9 when every AC has a corresponding test.
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### 9. Code Review
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- Run `/code-review` skill on the batch's changed files + corresponding task specs
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- The code-review skill produces a verdict: PASS, PASS_WITH_WARNINGS, or FAIL
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### 9. Auto-Fix Gate
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### 10. Auto-Fix Gate
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Auto-fix loop with bounded retries (max 2 attempts) before escalating to user:
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1. If verdict is **PASS** or **PASS_WITH_WARNINGS**: show findings as info, continue automatically to step 10
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1. If verdict is **PASS** or **PASS_WITH_WARNINGS**: show findings as info, continue automatically to step 11
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2. If verdict is **FAIL** (attempt 1 or 2):
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- Parse the code review findings (Critical and High severity items)
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- For each finding, attempt an automated fix using the finding's location, description, and suggestion
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- Re-run `/code-review` on the modified files
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- If now PASS or PASS_WITH_WARNINGS → continue to step 10
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- If now PASS or PASS_WITH_WARNINGS → continue to step 11
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- If still FAIL → increment retry counter, repeat from (2) up to max 2 attempts
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3. If still **FAIL** after 2 auto-fix attempts: present all findings to user (**BLOCKING**). User must confirm fixes or accept before proceeding.
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Track `auto_fix_attempts` count in the batch report for retrospective analysis.
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### 10. Commit and Push
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### 11. Commit and Push
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- After user confirms the batch (explicitly for FAIL, implicitly for PASS/PASS_WITH_WARNINGS):
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- `git add` all changed files from the batch
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- `git commit` with a message that includes ALL task IDs (tracker IDs or numeric prefixes) of tasks implemented in the batch, followed by a summary of what was implemented. Format: `[TASK-ID-1] [TASK-ID-2] ... Summary of changes`
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- `git push` to the remote branch
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### 11. Update Tracker Status → In Testing
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### 12. Update Tracker Status → In Testing
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After the batch is committed and pushed, transition the ticket status of each task in the batch to **In Testing** via the configured work item tracker. If `tracker: local`, skip this step.
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### 12. Archive Completed Tasks
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### 13. Archive Completed Tasks
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Move each completed task file from `TASKS_DIR/todo/` to `TASKS_DIR/done/`.
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### 13. Loop
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### 14. Loop
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- Go back to step 2 until all tasks in `todo/` are done
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### 14. Final Test Run
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### 15. Final Test Run
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- After all batches are complete, run the full test suite once
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- Read and execute `.cursor/skills/test-run/SKILL.md` (detect runner, run suite, diagnose failures, present blocking choices)
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@@ -177,10 +196,11 @@ After each batch, produce a structured report:
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## Task Results
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| Task | Status | Files Modified | Tests | Issues |
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|------|--------|---------------|-------|--------|
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| [TRACKER-ID]_[name] | Done | [count] files | [pass/fail] | [count or None] |
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| Task | Status | Files Modified | Tests | AC Coverage | Issues |
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|------|--------|---------------|-------|-------------|--------|
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| [TRACKER-ID]_[name] | Done | [count] files | [pass/fail] | [N/N ACs covered] | [count or None] |
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## AC Test Coverage: [All covered / X of Y covered]
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## Code Review Verdict: [PASS/FAIL/PASS_WITH_WARNINGS]
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## Auto-Fix Attempts: [0/1/2]
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## Stuck Agents: [count or None]
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@@ -212,4 +232,4 @@ Each batch commit serves as a rollback checkpoint. If recovery is needed:
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- Never launch tasks whose dependencies are not yet completed
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- Never allow two parallel agents to write to the same file
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- If a subagent fails or is flagged as stuck, stop it and report — do not let it loop indefinitely
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- Always run the full test suite after all batches complete (step 14)
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- Always run the full test suite after all batches complete (step 15)
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@@ -129,7 +129,7 @@ The `<task_slug>` is a short kebab-case name derived from the feature descriptio
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### Step 4: Codebase Analysis
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**Role**: Software architect
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**Goal**: Determine where and how to insert the new functionality.
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**Goal**: Determine where and how to insert the new functionality, and whether existing tests cover the new requirements.
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1. Read the codebase documentation from DOCUMENT_DIR:
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- `architecture.md` — overall structure
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@@ -144,6 +144,10 @@ The `<task_slug>` is a short kebab-case name derived from the feature descriptio
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- What new interfaces or models are needed
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- How data flows through the change
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4. If the change is complex enough, read the actual source files (not just docs) to verify insertion points
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5. **Test coverage gap analysis**: Read existing test files that cover the affected components. For each acceptance criterion from Step 1, determine whether an existing test already validates it. Classify each AC as:
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- **Covered**: an existing test directly validates this behavior
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- **Partially covered**: an existing test exercises the code path but doesn't assert the new requirement
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- **Not covered**: no existing test validates this behavior — a new test is required
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Present the analysis:
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@@ -156,9 +160,22 @@ Present the analysis:
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Interface changes: [list or "None"]
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New interfaces: [list or "None"]
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Data flow impact: [summary]
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─────────────────────────────────────
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TEST COVERAGE GAP ANALYSIS
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─────────────────────────────────────
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AC-1: [Covered / Partially covered / Not covered]
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[existing test name or "needs new test"]
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AC-2: [Covered / Partially covered / Not covered]
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[existing test name or "needs new test"]
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...
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─────────────────────────────────────
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New tests needed: [count]
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Existing tests to update: [count or "None"]
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══════════════════════════════════════
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```
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When gaps are found, the task spec (Step 6) MUST include the missing tests in the Scope (Included) section and the Unit/Blackbox Tests tables. Tests are not optional — if an AC is not covered by an existing test, the task must deliver a test for it.
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---
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### Step 5: Validate Assumptions
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@@ -0,0 +1,17 @@
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# Batch Report
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**Batch**: 3
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**Tasks**: AZ-171_dynamic_batch_export
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**Date**: 2026-03-28
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## Task Results
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| Task | Status | Files Modified | Tests | Issues |
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|------|--------|---------------|-------|--------|
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| AZ-171_dynamic_batch_export | Done | 2 files (src/exports.py, _docs/02_document/architecture.md) | 48 passed, 14 skipped, 6 errors (pre-existing) | None |
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## Code Review Verdict: PASS
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## Auto-Fix Attempts: 0
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## Stuck Agents: None
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## Next Batch: All tasks complete
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@@ -0,0 +1,28 @@
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# Implementation Report — Dynamic Batch Export
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**Date**: 2026-03-28
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**Epic**: AZ-164 (Code Improvements)
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**Total Tasks**: 1
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**Total Batches**: 1
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**Commit**: 433e080
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## Summary
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Enabled dynamic batch size for all three model export formats (ONNX, TensorRT, CoreML) by adding `dynamic=True` to the ultralytics export calls. TensorRT max batch size set to 8.
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## Tasks Implemented
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| Task | Name | Complexity | Status |
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|------|------|-----------|--------|
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| AZ-171 | dynamic_batch_export | 2 | Done |
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## Changes
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| File | Change |
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|------|--------|
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| src/exports.py | Added `dynamic=True` to export_onnx, export_tensorrt, export_coreml; changed TensorRT batch from 4 to 8 |
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| _docs/02_document/architecture.md | Updated Model Artifacts table to reflect dynamic batch support |
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## Test Results
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- 48 passed, 14 skipped, 6 errors (pre-existing ModuleNotFoundError for onnx package in e2e tests — environment dependency, not introduced by this change)
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+54
-9
@@ -1,5 +1,6 @@
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import csv
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import shutil
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import sys
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from pathlib import Path
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import pytest
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@@ -12,12 +13,21 @@ _DATASET_LABELS = _TEST_ROOT / "data" / "labels"
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_ONNX_MODEL = _PROJECT_ROOT / "_docs/00_problem/input_data/azaion.onnx"
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_CLASSES_JSON = _PROJECT_ROOT / "src" / "classes.json"
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_CONFIG_TEST = _PROJECT_ROOT / "config.test.yaml"
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_MODELS_DIR = _TEST_ROOT / "models"
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collect_ignore = ["security_test.py", "imagelabel_visualize_test.py"]
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_E2E_MODULE = "test_training_e2e"
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_test_results = []
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def pytest_collection_modifyitems(items):
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e2e = [i for i in items if _E2E_MODULE in i.nodeid]
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rest = [i for i in items if _E2E_MODULE not in i.nodeid]
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items[:] = e2e + rest
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@pytest.hookimpl(tryfirst=True, hookwrapper=True)
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def pytest_runtest_makereport(item, call):
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outcome = yield
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@@ -32,16 +42,21 @@ def pytest_runtest_makereport(item, call):
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def pytest_sessionfinish(session, exitstatus):
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if not _test_results:
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return
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results_dir = Path(__file__).resolve().parent / "test-results"
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results_dir.mkdir(exist_ok=True)
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if _test_results:
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results_dir = Path(__file__).resolve().parent / "test-results"
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results_dir.mkdir(exist_ok=True)
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with open(results_dir / "test-results.csv", "w", newline="", encoding="utf-8") as f:
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writer = csv.writer(f)
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writer.writerow(["module", "test", "result", "duration_s"])
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for r in _test_results:
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writer.writerow([r["module"], r["name"], r["result"], f"{r['duration']:.3f}"])
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with open(results_dir / "test-results.csv", "w", newline="", encoding="utf-8") as f:
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writer = csv.writer(f)
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writer.writerow(["module", "test", "result", "duration_s"])
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for r in _test_results:
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writer.writerow([r["module"], r["name"], r["result"], f"{r['duration']:.3f}"])
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import constants as c
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test_config = c.Config.from_yaml(str(_CONFIG_TEST), root=str(_TEST_ROOT))
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for d in (_DATASET_IMAGES, _DATASET_LABELS, test_config.datasets_dir,
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test_config.corrupted_dir, str(_MODELS_DIR)):
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shutil.rmtree(str(d), ignore_errors=True)
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def apply_constants_patch(monkeypatch, base: Path):
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@@ -157,3 +172,33 @@ def empty_label(tmp_path):
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p.parent.mkdir(parents=True, exist_ok=True)
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p.write_text("", encoding="utf-8")
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return p
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@pytest.fixture(scope="session")
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def exported_models():
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from ultralytics import YOLO
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import constants as c
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import exports as exports_mod
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_MODELS_DIR.mkdir(parents=True, exist_ok=True)
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pt_path = str(_MODELS_DIR / "test.pt")
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YOLO("yolo11n.pt").save(pt_path)
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old_config = c.config
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c.config = c.Config.from_yaml(str(_CONFIG_TEST), root=str(_TEST_ROOT))
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imgsz = c.config.export.onnx_imgsz
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exports_mod.export_onnx(pt_path)
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if sys.platform == "darwin":
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exports_mod.export_coreml(pt_path)
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c.config = old_config
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onnx_files = list(_MODELS_DIR.glob("test*.onnx"))
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return {
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"onnx": str(onnx_files[0]) if onnx_files else None,
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"model_dir": _MODELS_DIR,
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"pt_path": pt_path,
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"imgsz": imgsz,
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}
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+86
-28
@@ -5,41 +5,23 @@ import cv2
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import numpy as np
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import onnxruntime as ort
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import pytest
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import torch
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from ultralytics import YOLO
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import constants as c
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import exports as exports_mod
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_HAS_TENSORRT = torch.cuda.is_available()
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try:
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import tensorrt
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except ImportError:
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_HAS_TENSORRT = False
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_TESTS_DIR = Path(__file__).resolve().parent
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_CONFIG_TEST = _TESTS_DIR.parent / "config.test.yaml"
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_DATASET_IMAGES = _TESTS_DIR / "root" / "data" / "images"
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@pytest.fixture(scope="module")
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def exported_models(tmp_path_factory):
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# Arrange
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tmp = tmp_path_factory.mktemp("export")
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model_dir = tmp / "models"
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model_dir.mkdir()
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pt_path = str(model_dir / "test.pt")
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YOLO("yolo11n.pt").save(pt_path)
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old_config = c.config
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c.config = c.Config.from_yaml(str(_CONFIG_TEST), root=str(tmp))
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# Act
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exports_mod.export_onnx(pt_path)
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exports_mod.export_coreml(pt_path)
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yield {
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"onnx": str(next(model_dir.glob("*.onnx"))),
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"model_dir": model_dir,
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}
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c.config = old_config
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class TestOnnxExport:
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def test_onnx_file_created(self, exported_models):
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# Assert
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@@ -59,7 +41,7 @@ class TestOnnxExport:
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# Arrange
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session = ort.InferenceSession(exported_models["onnx"], providers=["CPUExecutionProvider"])
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meta = session.get_inputs()[0]
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imgsz = c.config.export.onnx_imgsz
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imgsz = exported_models["imgsz"]
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imgs = sorted(_DATASET_IMAGES.glob("*.jpg"))
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if not imgs:
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pytest.skip("no test images")
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@@ -77,7 +59,7 @@ class TestOnnxExport:
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# Arrange
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session = ort.InferenceSession(exported_models["onnx"], providers=["CPUExecutionProvider"])
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meta = session.get_inputs()[0]
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imgsz = c.config.export.onnx_imgsz
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imgsz = exported_models["imgsz"]
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imgs = sorted(_DATASET_IMAGES.glob("*.jpg"))
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if not imgs:
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pytest.skip("no test images")
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@@ -93,6 +75,82 @@ class TestOnnxExport:
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assert out[0].shape[0] == 4
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@pytest.mark.skipif(not _HAS_TENSORRT, reason="TensorRT requires NVIDIA GPU and tensorrt package")
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class TestTensorrtExport:
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@pytest.fixture(scope="class")
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def tensorrt_model(self, exported_models):
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# Arrange
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model_dir = exported_models["model_dir"]
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pt_path = exported_models["pt_path"]
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old_config = c.config
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c.config = c.Config.from_yaml(str(_CONFIG_TEST), root=str(model_dir.parent))
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# Act
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exports_mod.export_tensorrt(pt_path)
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c.config = old_config
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engines = list(model_dir.glob("*.engine"))
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yield {
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"engine": str(engines[0]) if engines else None,
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"model_dir": model_dir,
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"imgsz": exported_models["imgsz"],
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}
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for e in model_dir.glob("*.engine"):
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e.unlink(missing_ok=True)
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def test_tensorrt_engine_created(self, tensorrt_model):
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# Assert
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assert tensorrt_model["engine"] is not None
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p = Path(tensorrt_model["engine"])
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assert p.exists()
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assert p.stat().st_size > 0
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def test_tensorrt_inference_batch_1(self, tensorrt_model):
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# Arrange
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assert tensorrt_model["engine"] is not None
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imgs = sorted(_DATASET_IMAGES.glob("*.jpg"))
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if not imgs:
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pytest.skip("no test images")
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model = YOLO(tensorrt_model["engine"])
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# Act
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results = model.predict(source=str(imgs[0]), imgsz=tensorrt_model["imgsz"], verbose=False)
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# Assert
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assert len(results) == 1
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assert results[0].boxes is not None
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def test_tensorrt_inference_batch_multiple(self, tensorrt_model):
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# Arrange
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assert tensorrt_model["engine"] is not None
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imgs = sorted(_DATASET_IMAGES.glob("*.jpg"))
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if len(imgs) < 4:
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pytest.skip("need at least 4 test images")
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model = YOLO(tensorrt_model["engine"])
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# Act
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results = model.predict(source=[str(p) for p in imgs[:4]], imgsz=tensorrt_model["imgsz"], verbose=False)
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# Assert
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assert len(results) == 4
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|
||||
def test_tensorrt_inference_batch_max(self, tensorrt_model):
|
||||
# Arrange
|
||||
assert tensorrt_model["engine"] is not None
|
||||
imgs = sorted(_DATASET_IMAGES.glob("*.jpg"))
|
||||
if not imgs:
|
||||
pytest.skip("no test images")
|
||||
model = YOLO(tensorrt_model["engine"])
|
||||
sources = [str(imgs[0])] * 8
|
||||
|
||||
# Act
|
||||
results = model.predict(source=sources, imgsz=tensorrt_model["imgsz"], verbose=False)
|
||||
|
||||
# Assert
|
||||
assert len(results) == 8
|
||||
|
||||
|
||||
@pytest.mark.skipif(sys.platform != "darwin", reason="CoreML requires macOS")
|
||||
class TestCoremlExport:
|
||||
def test_coreml_package_created(self, exported_models):
|
||||
@@ -117,7 +175,7 @@ class TestCoremlExport:
|
||||
model = YOLO(str(pkgs[0]))
|
||||
|
||||
# Act
|
||||
results = model.predict(source=str(imgs[0]), imgsz=c.config.export.onnx_imgsz, verbose=False)
|
||||
results = model.predict(source=str(imgs[0]), imgsz=exported_models["imgsz"], verbose=False)
|
||||
|
||||
# Assert
|
||||
assert len(results) == 1
|
||||
|
||||
@@ -3,12 +3,14 @@ import constants as c
|
||||
|
||||
def test_fixture_images_dir_has_jpegs(fixture_images_dir):
|
||||
jpgs = list(fixture_images_dir.glob("*.jpg"))
|
||||
assert len(jpgs) == 20
|
||||
assert len(jpgs) > 0
|
||||
|
||||
|
||||
def test_fixture_labels_dir_has_yolo_labels(fixture_labels_dir):
|
||||
def test_fixture_labels_dir_has_yolo_labels(fixture_labels_dir, fixture_images_dir):
|
||||
txts = list(fixture_labels_dir.glob("*.txt"))
|
||||
assert len(txts) == 20
|
||||
jpgs = list(fixture_images_dir.glob("*.jpg"))
|
||||
assert len(txts) > 0
|
||||
assert len(txts) == len(jpgs)
|
||||
|
||||
|
||||
def test_fixture_onnx_model_bytes(fixture_onnx_model):
|
||||
|
||||
@@ -62,11 +62,6 @@ def e2e_result():
|
||||
"linked_count": linked_count,
|
||||
}
|
||||
|
||||
shutil.rmtree(str(dst_images), ignore_errors=True)
|
||||
shutil.rmtree(str(dst_labels), ignore_errors=True)
|
||||
shutil.rmtree(c.config.datasets_dir, ignore_errors=True)
|
||||
shutil.rmtree(c.config.models_dir, ignore_errors=True)
|
||||
shutil.rmtree(c.config.corrupted_dir, ignore_errors=True)
|
||||
c.config = old_config
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user