Fix for stuck Linux build: Move pytest to Windows (#652)
Also renamed many build legs so that they can be found more easily in the UI.
This commit is contained in:
Anton Schwaighofer
GitHub
Родитель
b8fe0eb2f1
Коммит
a6b15166b7
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@ -6,7 +6,8 @@ name: Check Changelog
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on:
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pull_request:
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jobs:
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check:
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check_changelog:
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name: Check Changelog
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runs-on: ubuntu-latest
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if: ${{ contains(github.event.pull_request.labels.*.name, 'no changelog needed') == 0 }}
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steps:
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@ -10,8 +10,8 @@ on:
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- cron: '45 4 * * 1'
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jobs:
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analyze:
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name: Analyze
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codeql_analyze:
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name: CodeQL Analyze
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runs-on: ubuntu-latest
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strategy:
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@ -6,7 +6,8 @@ on:
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[opened, edited, deleted, closed, reopened, labeled, unlabeled, assigned]
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jobs:
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alert:
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issues_to_ado:
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name: Sync issues with Azure DevOps
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runs-on: ubuntu-latest
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steps:
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- uses: danhellem/github-actions-issue-to-work-item@master
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@ -7,7 +7,8 @@ on:
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pull_request:
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jobs:
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linux:
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flake_mypy_helloworld_linux:
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name: Flake8, MyPy, HelloWorld on Linux
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v2
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@ -53,7 +54,8 @@ jobs:
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PYTHONPATH: ${{ github.workspace }}
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if: always()
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windows:
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hello_world_windows:
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name: HelloWorld on Windows
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runs-on: windows-latest
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steps:
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- uses: actions/checkout@v2
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@ -119,6 +119,7 @@ in inference-only runs when using lightning containers.
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- ([#628](https://github.com/microsoft/InnerEye-DeepLearning/pull/628)) SSL SimCLR using the wrong LR schedule when running on multiple nodes
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- ([#638](https://github.com/microsoft/InnerEye-DeepLearning/pull/638)) SimClr cosine LR scheduler was using wrong length information when using with long linear head datasets
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- ([#612](https://github.com/microsoft/InnerEye-DeepLearning/pull/612)) SSL online evaluator was not doing distributed training
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- ([#652](https://github.com/microsoft/InnerEye-DeepLearning/pull/652)) Run pytest build on Windows after Linux agent version upgrade
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### Removed
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@ -2,12 +2,11 @@
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
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# ------------------------------------------------------------------------------------------
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import logging
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from pathlib import Path
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from typing import Any, Dict, Union, Optional
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import pandas as pd
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from cucim import CuImage
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from health_ml.utils import box_utils
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from monai.config import KeysCollection
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from monai.data.image_reader import ImageReader, WSIReader
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@ -15,6 +14,11 @@ from monai.transforms import MapTransform
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from InnerEye.ML.Histopathology.datasets.base_dataset import SlidesDataset
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try:
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from cucim import CuImage
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except:
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logging.warning("cucim library not available, code may fail.")
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class PandaDataset(SlidesDataset):
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"""Dataset class for loading files from the PANDA challenge dataset.
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@ -48,6 +52,7 @@ class PandaDataset(SlidesDataset):
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# MONAI's convention is that dictionary transforms have a 'd' suffix in the class name
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class ReadImaged(MapTransform):
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"""Basic transform to read image files."""
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def __init__(self, reader: ImageReader, keys: KeysCollection,
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allow_missing_keys: bool = False, **kwargs: Any) -> None:
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super().__init__(keys, allow_missing_keys=allow_missing_keys)
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@ -71,6 +76,7 @@ class LoadPandaROId(MapTransform):
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- `'level'` (int): chosen magnification level
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- `'scale'` (float): corresponding scale, loaded from the file
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"""
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def __init__(self, reader: WSIReader, image_key: str = 'image', mask_key: str = 'mask',
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level: int = 0, margin: int = 0, **kwargs: Any) -> None:
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"""
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@ -88,7 +94,7 @@ class LoadPandaROId(MapTransform):
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self.margin = margin
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self.kwargs = kwargs
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def _get_bounding_box(self, mask_obj: CuImage) -> box_utils.Box:
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def _get_bounding_box(self, mask_obj: 'CuImage') -> box_utils.Box:
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# Estimate bounding box at the lowest resolution (i.e. highest level)
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highest_level = mask_obj.resolutions['level_count'] - 1
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scale = mask_obj.resolutions['level_downsamples'][highest_level]
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@ -1,14 +1,19 @@
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import logging
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from typing import Dict, Optional, Tuple
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import numpy as np
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import skimage.filters
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from cucim import CuImage
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from health_ml.utils import box_utils
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from monai.data.image_reader import WSIReader
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from monai.transforms import MapTransform
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from InnerEye.ML.Histopathology.utils.naming import SlideKey
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try:
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from cucim import CuImage
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except:
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logging.warning("cucim library not available, code may fail.")
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def get_luminance(slide: np.ndarray) -> np.ndarray:
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"""Compute a grayscale version of the input slide.
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@ -35,7 +40,7 @@ def segment_foreground(slide: np.ndarray, threshold: Optional[float] = None) \
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return luminance < threshold, threshold
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def load_slide_at_level(reader: WSIReader, slide_obj: CuImage, level: int) -> np.ndarray:
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def load_slide_at_level(reader: WSIReader, slide_obj: 'CuImage', level: int) -> np.ndarray:
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"""Load full slide array at the given magnification level.
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This is a manual workaround for a MONAI bug (https://github.com/Project-MONAI/MONAI/issues/3415)
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@ -60,6 +65,7 @@ class LoadROId(MapTransform):
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- `SlideKey.SCALE` (float): corresponding scale, loaded from the file
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- `SlideKey.FOREGROUND_THRESHOLD` (float): threshold used to segment the foreground
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"""
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def __init__(self, reader: WSIReader, image_key: str = SlideKey.IMAGE, level: int = 0,
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margin: int = 0, foreground_threshold: Optional[float] = None) -> None:
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"""
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@ -77,7 +83,7 @@ class LoadROId(MapTransform):
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self.margin = margin
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self.foreground_threshold = foreground_threshold
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def _get_bounding_box(self, slide_obj: CuImage) -> Tuple[box_utils.Box, float]:
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def _get_bounding_box(self, slide_obj: 'CuImage') -> Tuple[box_utils.Box, float]:
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# Estimate bounding box at the lowest resolution (i.e. highest level)
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highest_level = slide_obj.resolutions['level_count'] - 1
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scale = slide_obj.resolutions['level_downsamples'][highest_level]
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@ -88,6 +94,7 @@ class LoadROId(MapTransform):
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return bbox, threshold
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def __call__(self, data: Dict) -> Dict:
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from cucim import CuImage
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image_obj: CuImage = self.reader.read(data[self.image_key])
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level0_bbox, threshold = self._get_bounding_box(image_obj)
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@ -9,6 +9,7 @@ import pytest
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from InnerEye.Azure.azure_config import AzureConfig
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from InnerEye.Azure.azure_runner import create_dataset_configs
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from InnerEye.Common.common_util import is_linux
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from InnerEye.ML.deep_learning_config import DatasetParams
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from Tests.ML.util import get_default_azure_config
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@ -65,8 +66,10 @@ def test_dataset_consumption2() -> None:
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assert datasets[1].name == "2"
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assert datasets[0].local_folder == Path("l1")
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assert datasets[1].local_folder == Path("l2")
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assert datasets[0].target_folder == PosixPath("mp1")
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assert datasets[1].target_folder == PosixPath("mp2")
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if is_linux():
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# PosixPath cannot be instantiated on Windows
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assert datasets[0].target_folder == PosixPath("mp1")
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assert datasets[1].target_folder == PosixPath("mp2")
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def test_dataset_consumption3() -> None:
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@ -7,6 +7,7 @@ import os
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from typing import Callable, Dict, List, Type # noqa
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import pytest
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import torch
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from torch import Tensor, argmax, nn, rand, randint, randn, round, stack, allclose
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from torchvision.models import resnet18
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@ -29,7 +30,7 @@ from InnerEye.ML.Histopathology.datasets.default_paths import (
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)
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from InnerEye.ML.Histopathology.models.deepmil import DeepMILModule
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from InnerEye.ML.Histopathology.models.encoders import ImageNetEncoder, TileEncoder
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from InnerEye.ML.Histopathology.utils.naming import ResultsKey
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from InnerEye.ML.Histopathology.utils.naming import MetricsKey, ResultsKey
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def get_supervised_imagenet_encoder() -> TileEncoder:
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@ -38,10 +39,10 @@ def get_supervised_imagenet_encoder() -> TileEncoder:
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@pytest.mark.parametrize("n_classes", [1, 3])
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@pytest.mark.parametrize("pooling_layer", [AttentionLayer, GatedAttentionLayer])
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@pytest.mark.parametrize("batch_size", [1, 15])
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@pytest.mark.parametrize("max_bag_size", [1, 7])
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@pytest.mark.parametrize("pool_hidden_dim", [1, 5])
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@pytest.mark.parametrize("pool_out_dim", [1, 6])
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@pytest.mark.parametrize("batch_size", [1, 2])
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@pytest.mark.parametrize("max_bag_size", [1, 3])
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@pytest.mark.parametrize("pool_hidden_dim", [1, 4])
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@pytest.mark.parametrize("pool_out_dim", [1, 5])
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def test_lightningmodule(
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n_classes: int,
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pooling_layer: Callable[[int, int, int], nn.Module],
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@ -108,9 +109,12 @@ def test_lightningmodule(
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assert preds.shape[0] == batch_size
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for metric_name, metric_object in module.train_metrics.items():
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if (batch_size > 1) or (not metric_name == "auroc"):
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if metric_name == MetricsKey.CONF_MATRIX or metric_name == MetricsKey.AUROC:
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continue
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if batch_size > 1:
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score = metric_object(preds.view(-1, 1), bag_labels.view(-1, 1))
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assert score >= 0 and score <= 1
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assert torch.all(score >= 0)
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assert torch.all(score <= 1)
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def move_batch_to_expected_device(batch: Dict[str, List], use_gpu: bool) -> Dict:
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@ -2,21 +2,24 @@ from typing import Optional
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import numpy as np
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import pytest
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from cucim import CuImage
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from monai.data.image_reader import WSIReader
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from InnerEye.Common.common_util import is_windows
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from InnerEye.Common.fixed_paths_for_tests import tests_root_directory
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from InnerEye.ML.Histopathology.preprocessing.tiling import tile_array_2d
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from InnerEye.ML.Histopathology.preprocessing.loading import LoadROId, get_luminance, load_slide_at_level, segment_foreground
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from InnerEye.ML.Histopathology.preprocessing.loading import (LoadROId, get_luminance, load_slide_at_level,
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segment_foreground)
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from InnerEye.ML.Histopathology.utils.naming import SlideKey
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from Tests.ML.histopathology.datasets.test_slides_dataset import MockSlidesDataset
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TEST_IMAGE_PATH = str(tests_root_directory("ML/histopathology/test_data/panda_wsi_example.tiff"))
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@pytest.mark.skipif(is_windows(), reason="cucim package is not available on Windows")
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def test_load_slide() -> None:
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level = 2
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reader = WSIReader('cuCIM')
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from cucim import CuImage
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slide_obj: CuImage = reader.read(TEST_IMAGE_PATH)
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dims = slide_obj.resolutions['level_dimensions'][level][::-1]
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@ -39,9 +42,11 @@ def test_load_slide() -> None:
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assert np.array_equiv(larger_slide[:, :, dims[1]:], empty_fill_value)
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@pytest.mark.skipif(is_windows(), reason="cucim package is not available on Windows")
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def test_get_luminance() -> None:
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level = 2 # here we only need to test at a single resolution
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reader = WSIReader('cuCIM')
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from cucim import CuImage
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slide_obj: CuImage = reader.read(TEST_IMAGE_PATH)
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slide = load_slide_at_level(reader, slide_obj, level)
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@ -61,9 +66,11 @@ def test_get_luminance() -> None:
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assert np.array_equal(slide_luminance_tiles.squeeze(1), tiles_luminance)
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@pytest.mark.skipif(is_windows(), reason="cucim package is not available on Windows")
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def test_segment_foreground() -> None:
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level = 2 # here we only need to test at a single resolution
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reader = WSIReader('cuCIM')
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from cucim import CuImage
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slide_obj: CuImage = reader.read(TEST_IMAGE_PATH)
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slide = load_slide_at_level(reader, slide_obj, level)
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@ -95,11 +102,13 @@ def test_segment_foreground() -> None:
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@pytest.mark.parametrize('level', [1, 2])
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@pytest.mark.parametrize('foreground_threshold', [None, 215])
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@pytest.mark.skipif(is_windows(), reason="cucim package is not available on Windows")
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def test_get_bounding_box(level: int, foreground_threshold: Optional[float]) -> None:
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margin = 0
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reader = WSIReader('cuCIM')
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loader = LoadROId(reader, image_key=SlideKey.IMAGE, level=level, margin=margin,
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foreground_threshold=foreground_threshold)
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from cucim import CuImage
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slide_obj: CuImage = reader.read(TEST_IMAGE_PATH)
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level0_bbox, _ = loader._get_bounding_box(slide_obj)
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@ -130,6 +139,7 @@ def test_get_bounding_box(level: int, foreground_threshold: Optional[float]) ->
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@pytest.mark.parametrize('level', [1, 2])
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@pytest.mark.parametrize('margin', [0, 42])
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@pytest.mark.parametrize('foreground_threshold', [None, 215])
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@pytest.mark.skipif(is_windows(), reason="cucim package is not available on Windows")
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def test_load_roi(level: int, margin: int, foreground_threshold: Optional[float]) -> None:
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dataset = MockSlidesDataset()
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sample = dataset[0]
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|
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@ -13,7 +13,9 @@ import matplotlib
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from torch.functional import Tensor
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import pytest
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from InnerEye.ML.Histopathology.utils.metrics_utils import plot_scores_hist, select_k_tiles, plot_slide, plot_heatmap_overlay, plot_normalized_confusion_matrix
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from InnerEye.Common.common_util import is_windows
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from InnerEye.ML.Histopathology.utils.metrics_utils import plot_scores_hist, select_k_tiles, plot_slide, \
|
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plot_heatmap_overlay, plot_normalized_confusion_matrix
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from InnerEye.ML.Histopathology.utils.naming import ResultsKey
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from InnerEye.ML.Histopathology.utils.heatmap_utils import location_selected_tiles
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from InnerEye.Common.fixed_paths_for_tests import full_ml_test_data_path
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@ -44,22 +46,23 @@ test_dict = {ResultsKey.SLIDE_ID: [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4,
|
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ResultsKey.PROB: [Tensor([0.5]), Tensor([0.7]), Tensor([0.4]), Tensor([1.0])],
|
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ResultsKey.TRUE_LABEL: [0, 1, 1, 1],
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ResultsKey.BAG_ATTN:
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[Tensor([[0.1, 0.0, 0.2, 0.15]]),
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[Tensor([[0.1, 0.0, 0.2, 0.15]]),
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Tensor([[0.10, 0.18, 0.15, 0.13]]),
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Tensor([[0.25, 0.23, 0.20, 0.21]]),
|
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Tensor([[0.33, 0.31, 0.37, 0.35]])],
|
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ResultsKey.TILE_X:
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[Tensor([200, 200, 424, 424]),
|
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[Tensor([200, 200, 424, 424]),
|
||||
Tensor([200, 200, 424, 424]),
|
||||
Tensor([200, 200, 424, 424]),
|
||||
Tensor([200, 200, 424, 424]),
|
||||
Tensor([200, 200, 424, 424])],
|
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ResultsKey.TILE_Y:
|
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[Tensor([200, 424, 200, 424]),
|
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ResultsKey.TILE_Y:
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[Tensor([200, 424, 200, 424]),
|
||||
Tensor([200, 200, 424, 424]),
|
||||
Tensor([200, 200, 424, 424]),
|
||||
Tensor([200, 200, 424, 424]),
|
||||
Tensor([200, 200, 424, 424])]
|
||||
}
|
||||
|
||||
|
||||
def test_select_k_tiles() -> None:
|
||||
top_tn = select_k_tiles(test_dict, n_slides=1, label=0, n_tiles=2, select=('lowest_pred', 'highest_att'))
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||||
assert_equal_lists(top_tn, [(1, 0.5, [3, 4], [Tensor([0.2]), Tensor([0.15])])])
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||||
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@ -67,16 +70,24 @@ def test_select_k_tiles() -> None:
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|||
nslides = 2
|
||||
ntiles = 2
|
||||
top_fn = select_k_tiles(test_dict, n_slides=nslides, label=1, n_tiles=ntiles, select=('lowest_pred', 'highest_att'))
|
||||
bottom_fn = select_k_tiles(test_dict, n_slides=nslides, label=1, n_tiles=ntiles, select=('lowest_pred', 'lowest_att'))
|
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assert_equal_lists(top_fn, [(3, 0.4, [1, 2], [Tensor([0.25]), Tensor([0.23])]), (2, 0.7, [2, 3], [Tensor([0.18]), Tensor([0.15])])])
|
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assert_equal_lists(bottom_fn, [(3, 0.4, [3, 4], [Tensor([0.20]), Tensor([0.21])]), (2, 0.7, [1, 4], [Tensor([0.10]), Tensor([0.13])])])
|
||||
bottom_fn = select_k_tiles(test_dict, n_slides=nslides, label=1, n_tiles=ntiles,
|
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select=('lowest_pred', 'lowest_att'))
|
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assert_equal_lists(top_fn, [(3, 0.4, [1, 2], [Tensor([0.25]), Tensor([0.23])]),
|
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(2, 0.7, [2, 3], [Tensor([0.18]), Tensor([0.15])])])
|
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assert_equal_lists(bottom_fn, [(3, 0.4, [3, 4], [Tensor([0.20]), Tensor([0.21])]),
|
||||
(2, 0.7, [1, 4], [Tensor([0.10]), Tensor([0.13])])])
|
||||
|
||||
top_tp = select_k_tiles(test_dict, n_slides=nslides, label=1, n_tiles=ntiles, select=('highest_pred', 'highest_att'))
|
||||
bottom_tp = select_k_tiles(test_dict, n_slides=nslides, label=1, n_tiles=ntiles, select=('highest_pred', 'lowest_att'))
|
||||
assert_equal_lists(top_tp, [(4, 1.0, [3, 4], [Tensor([0.37]), Tensor([0.35])]), (2, 0.7, [2, 3], [Tensor([0.18]), Tensor([0.15])])])
|
||||
assert_equal_lists(bottom_tp, [(4, 1.0, [2, 1], [Tensor([0.31]), Tensor([0.33])]), (2, 0.7, [1, 4], [Tensor([0.10]), Tensor([0.13])])])
|
||||
top_tp = select_k_tiles(test_dict, n_slides=nslides, label=1, n_tiles=ntiles,
|
||||
select=('highest_pred', 'highest_att'))
|
||||
bottom_tp = select_k_tiles(test_dict, n_slides=nslides, label=1, n_tiles=ntiles,
|
||||
select=('highest_pred', 'lowest_att'))
|
||||
assert_equal_lists(top_tp, [(4, 1.0, [3, 4], [Tensor([0.37]), Tensor([0.35])]),
|
||||
(2, 0.7, [2, 3], [Tensor([0.18]), Tensor([0.15])])])
|
||||
assert_equal_lists(bottom_tp, [(4, 1.0, [2, 1], [Tensor([0.31]), Tensor([0.33])]),
|
||||
(2, 0.7, [1, 4], [Tensor([0.10]), Tensor([0.13])])])
|
||||
|
||||
|
||||
@pytest.mark.skipif(is_windows(), reason="Rendering is different on Windows")
|
||||
def test_plot_scores_hist(test_output_dirs: OutputFolderForTests) -> None:
|
||||
fig = plot_scores_hist(test_dict)
|
||||
assert isinstance(fig, matplotlib.figure.Figure)
|
||||
|
|
@ -104,16 +115,17 @@ def test_plot_slide(test_output_dirs: OutputFolderForTests, scale: int) -> None:
|
|||
assert_binary_files_match(file, expected)
|
||||
|
||||
|
||||
@pytest.mark.skipif(is_windows(), reason="Rendering is different on Windows")
|
||||
def test_plot_heatmap_overlay(test_output_dirs: OutputFolderForTests) -> None:
|
||||
set_random_seed(0)
|
||||
slide_image = np.random.rand(3, 1000, 2000)
|
||||
location_bbox = [100, 100]
|
||||
slide = 1
|
||||
slide = 1
|
||||
tile_size = 224
|
||||
level = 0
|
||||
fig = plot_heatmap_overlay(slide=slide, # type: ignore
|
||||
fig = plot_heatmap_overlay(slide=slide, # type: ignore
|
||||
slide_image=slide_image,
|
||||
results=test_dict, # type: ignore
|
||||
results=test_dict, # type: ignore
|
||||
location_bbox=location_bbox,
|
||||
tile_size=tile_size,
|
||||
level=level)
|
||||
|
|
@ -128,15 +140,16 @@ def test_plot_heatmap_overlay(test_output_dirs: OutputFolderForTests) -> None:
|
|||
|
||||
|
||||
@pytest.mark.parametrize("n_classes", [1, 3])
|
||||
@pytest.mark.skipif(is_windows(), reason="Rendering is different on Windows")
|
||||
def test_plot_normalized_confusion_matrix(test_output_dirs: OutputFolderForTests, n_classes: int) -> None:
|
||||
set_random_seed(0)
|
||||
if n_classes > 1:
|
||||
cm = np.random.randint(1, 1000, size=(n_classes, n_classes))
|
||||
class_names = [str(i) for i in range(n_classes)]
|
||||
else:
|
||||
cm = np.random.randint(1, 1000, size=(n_classes+1, n_classes+1))
|
||||
class_names = [str(i) for i in range(n_classes+1)]
|
||||
cm_n = cm/cm.sum(axis=1, keepdims=True)
|
||||
cm = np.random.randint(1, 1000, size=(n_classes + 1, n_classes + 1))
|
||||
class_names = [str(i) for i in range(n_classes + 1)]
|
||||
cm_n = cm / cm.sum(axis=1, keepdims=True)
|
||||
assert (cm_n <= 1).all()
|
||||
|
||||
fig = plot_normalized_confusion_matrix(cm=cm_n, class_names=class_names)
|
||||
|
|
@ -153,26 +166,27 @@ def test_plot_normalized_confusion_matrix(test_output_dirs: OutputFolderForTests
|
|||
@pytest.mark.parametrize("level", [0, 1, 2])
|
||||
def test_location_selected_tiles(level: int) -> None:
|
||||
set_random_seed(0)
|
||||
slide = 1
|
||||
slide = 1
|
||||
location_bbox = [100, 100]
|
||||
slide_image = np.random.rand(3, 1000, 2000)
|
||||
|
||||
coords = []
|
||||
slide_ids = [item[0] for item in test_dict[ResultsKey.SLIDE_ID]] # type: ignore
|
||||
slide_ids = [item[0] for item in test_dict[ResultsKey.SLIDE_ID]] # type: ignore
|
||||
slide_idx = slide_ids.index(slide)
|
||||
for tile_idx in range(len(test_dict[ResultsKey.IMAGE_PATH][slide_idx])): # type: ignore
|
||||
tile_coords = np.transpose(np.array([test_dict[ResultsKey.TILE_X][slide_idx][tile_idx].cpu().numpy(), # type: ignore
|
||||
test_dict[ResultsKey.TILE_Y][slide_idx][tile_idx].cpu().numpy()])) # type: ignore
|
||||
for tile_idx in range(len(test_dict[ResultsKey.IMAGE_PATH][slide_idx])): # type: ignore
|
||||
tile_coords = np.transpose(
|
||||
np.array([test_dict[ResultsKey.TILE_X][slide_idx][tile_idx].cpu().numpy(), # type: ignore
|
||||
test_dict[ResultsKey.TILE_Y][slide_idx][tile_idx].cpu().numpy()])) # type: ignore
|
||||
coords.append(tile_coords)
|
||||
|
||||
coords = np.array(coords)
|
||||
tile_coords_transformed = location_selected_tiles(tile_coords=coords,
|
||||
location_bbox=location_bbox,
|
||||
level=level)
|
||||
tile_coords_transformed = location_selected_tiles(tile_coords=coords,
|
||||
location_bbox=location_bbox,
|
||||
level=level)
|
||||
tile_xs, tile_ys = tile_coords_transformed.T
|
||||
level_dict = {0: 1, 1: 4, 2: 16}
|
||||
factor = level_dict[level]
|
||||
assert min(tile_xs) >= 0
|
||||
assert max(tile_xs) <= slide_image.shape[2]//factor
|
||||
assert min(tile_ys) >= 0
|
||||
assert max(tile_ys) <= slide_image.shape[1]//factor
|
||||
assert min(tile_xs) >= 0
|
||||
assert max(tile_xs) <= slide_image.shape[2] // factor
|
||||
assert min(tile_ys) >= 0
|
||||
assert max(tile_ys) <= slide_image.shape[1] // factor
|
||||
|
|
|
|||
|
|
@ -634,7 +634,24 @@ def test_simclr_dataloader_type() -> None:
|
|||
""" This test checks if the transform pipeline of a SSL job can handle different
|
||||
data types coming from the dataloader.
|
||||
"""
|
||||
def check_types_in_dataloader(dataloader: CombinedLoader) -> None:
|
||||
# TODO: Once the pytorch lightning bug is fixed the following test can be removed.
|
||||
# The training and val loader will be both CombinedLoaders
|
||||
def check_types_in_train_dataloader(dataloader: dict) -> None:
|
||||
for i, batch in enumerate(dataloader[SSLDataModuleType.ENCODER]):
|
||||
assert isinstance(batch[0][0], torch.Tensor)
|
||||
assert isinstance(batch[0][1], torch.Tensor)
|
||||
assert isinstance(batch[1], torch.Tensor)
|
||||
if i == 1:
|
||||
break
|
||||
|
||||
for i, batch in enumerate(dataloader[SSLDataModuleType.LINEAR_HEAD]):
|
||||
assert isinstance(batch[0], torch.Tensor)
|
||||
assert isinstance(batch[1], torch.Tensor)
|
||||
assert isinstance(batch[2], torch.Tensor)
|
||||
if i == 1:
|
||||
break
|
||||
|
||||
def check_types_in_val_dataloader(dataloader: CombinedLoader) -> None:
|
||||
for i, batch in enumerate(dataloader):
|
||||
assert isinstance(batch[SSLDataModuleType.ENCODER][0][0], torch.Tensor)
|
||||
assert isinstance(batch[SSLDataModuleType.ENCODER][0][1], torch.Tensor)
|
||||
|
|
@ -646,8 +663,8 @@ def test_simclr_dataloader_type() -> None:
|
|||
break
|
||||
|
||||
def check_types_in_train_and_val(data: CombinedDataModule) -> None:
|
||||
check_types_in_dataloader(data.train_dataloader())
|
||||
check_types_in_dataloader(data.val_dataloader())
|
||||
check_types_in_train_dataloader(data.train_dataloader())
|
||||
check_types_in_val_dataloader(data.val_dataloader())
|
||||
|
||||
container = DummySimCLR()
|
||||
container.setup()
|
||||
|
|
|
|||
|
|
@ -23,15 +23,29 @@ jobs:
|
|||
steps:
|
||||
- template: cancel_aml_jobs.yml
|
||||
|
||||
- job: Windows
|
||||
- job: CredScan_ComponentGov
|
||||
pool:
|
||||
vmImage: 'windows-2019'
|
||||
steps:
|
||||
- template: build_windows.yaml
|
||||
|
||||
- job: Linux
|
||||
# Run jobs that only build the environment. These jobs have a high chance of succeeding and filling the build
|
||||
# cache. Pytest, etc legs will only fill the cache if they succeed.
|
||||
- job: CreateCondaEnvCache_Windows
|
||||
pool:
|
||||
vmImage: 'ubuntu-20.04'
|
||||
vmImage: 'windows-2019'
|
||||
steps:
|
||||
- template: inner_eye_env.yml
|
||||
|
||||
- job: CreateCondaEnvAndCache_Linux
|
||||
pool:
|
||||
vmImage: 'ubuntu-18.04'
|
||||
steps:
|
||||
- template: inner_eye_env.yml
|
||||
|
||||
- job: PyTest
|
||||
pool:
|
||||
vmImage: 'windows-2019'
|
||||
steps:
|
||||
- template: build.yaml
|
||||
|
||||
|
|
|
|||
|
|
@ -5,23 +5,31 @@ steps:
|
|||
|
||||
- template: prepare_conda.yml
|
||||
|
||||
- bash: echo "##vso[task.setvariable variable=conda_env_dir]/usr/share/miniconda/envs"
|
||||
displayName: "Set the Conda environment folder (Linux)"
|
||||
condition: eq(variables['Agent.OS'], 'Linux')
|
||||
|
||||
- bash: echo "##vso[task.setvariable variable=conda_env_dir]C:/Miniconda/envs"
|
||||
displayName: "Set the Conda environment folder(Windows)"
|
||||
condition: eq(variables['Agent.OS'], 'Windows_NT')
|
||||
|
||||
- bash: echo $(conda_env_dir)
|
||||
displayName: 'Printing Conda environment folder'
|
||||
|
||||
# https://docs.microsoft.com/en-us/azure/devops/pipelines/release/caching?view=azure-devops#pythonanaconda
|
||||
- task: Cache@2
|
||||
displayName: Use cached Conda environment
|
||||
inputs:
|
||||
# Beware of changing the cache key or path independently, safest to change in sync
|
||||
key: 'usr_share_miniconda_envs | "$(Agent.OS)" | environment.yml'
|
||||
path: /usr/share/miniconda/envs
|
||||
key: 'conda_env | "$(Agent.OS)" | environment.yml'
|
||||
cacheHitVar: CONDA_CACHE_RESTORED
|
||||
path: $(conda_env_dir)
|
||||
|
||||
- script: conda env create --file environment.yml
|
||||
- bash: conda env create --file environment.yml
|
||||
displayName: Create Anaconda environment
|
||||
failOnStderr: false # Conda env create does not have an option to suppress warnings generated in wheel.py
|
||||
condition: eq(variables.CONDA_CACHE_RESTORED, 'false')
|
||||
|
||||
- script: source activate InnerEye
|
||||
displayName: Check if InnerEye is present
|
||||
|
||||
- bash: |
|
||||
source activate InnerEye
|
||||
which python
|
||||
|
|
@ -29,3 +37,7 @@ steps:
|
|||
pip freeze
|
||||
failOnStderr: false
|
||||
displayName: Print package list and Conda info
|
||||
condition: succeededOrFailed()
|
||||
|
||||
- bash: source activate InnerEye
|
||||
displayName: Check if InnerEye environment is present
|
||||
|
|
|
|||
|
|
@ -1,6 +1,9 @@
|
|||
steps:
|
||||
- bash: |
|
||||
subdir=bin
|
||||
if [ $(Agent.OS) = 'Windows_NT' ]
|
||||
then subdir=Scripts
|
||||
else subdir=bin
|
||||
fi
|
||||
echo "Adding this directory to PATH: $CONDA/$subdir"
|
||||
echo "##vso[task.prependpath]$CONDA/$subdir"
|
||||
displayName: Add conda to PATH
|
||||
|
|
@ -9,4 +12,4 @@ steps:
|
|||
- bash: |
|
||||
sudo chown -R $USER /usr/share/miniconda
|
||||
condition: and(succeeded(), eq( variables['Agent.OS'], 'Linux' ))
|
||||
displayName: Take ownership of conda installation
|
||||
displayName: Take ownership of conda installation (Linux only)
|
||||
|
|
|
|||
Загрузка…
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