From 68de6fcfc9725f1571c2f3beecbf8ccb6b952e15 Mon Sep 17 00:00:00 2001 From: Alex Parinov Date: Thu, 24 Dec 2020 10:42:51 +0300 Subject: [PATCH] Version 0.2.0 (#5) --- .github/workflows/ci.yml | 13 +- .github/workflows/publish_docker_image.yml | 23 ++++ autoalbument/__init__.py | 2 +- autoalbument/cli/search.py | 4 +- .../templates/classification/search.yaml.tmpl | 4 + .../classification/search_full.yaml.tmpl | 3 + .../semantic_segmentation/search.yaml.tmpl | 5 + .../search_full.yaml.tmpl | 4 + autoalbument/config/faster_autoaugment.py | 6 +- autoalbument/faster_autoaugment/models.py | 59 +++++++++ autoalbument/faster_autoaugment/search.py | 80 +++---------- docker/Dockerfile | 18 +++ docker/entrypoint.sh | 3 + examples/cifar10/dataset.py | 4 +- examples/cifar10/model.py | 113 ++++++++++++++++++ examples/cifar10/search.yaml | 34 ++++-- examples/pascal_voc/dataset.py | 4 +- examples/pascal_voc/search.yaml | 7 ++ setup.py | 4 +- tests/configs/classification/search.yaml | 1 + tests/test_faa_search.py | 4 +- .../configs/classification_1_1/dataset.py | 23 ++++ .../classification_1_1/expected_policy.json | 1 + .../configs/classification_1_1/search.yaml | 57 +++++++++ .../configs/classification_2_1/dataset.py | 23 ++++ .../classification_2_1/expected_policy.json | 1 + .../configs/classification_2_1/search.yaml | 58 +++++++++ .../configs/classification_3_1/dataset.py | 23 ++++ .../classification_3_1/expected_policy.json | 1 + .../configs/classification_3_1/search.yaml | 43 +++++++ .../semantic_segmentation_1_1/dataset.py | 23 ++++ .../expected_policy.json | 1 + .../semantic_segmentation_1_1/search.yaml | 66 ++++++++++ .../semantic_segmentation_2_1/dataset.py | 23 ++++ .../expected_policy.json | 1 + .../semantic_segmentation_2_1/search.yaml | 67 +++++++++++ .../semantic_segmentation_3_1/dataset.py | 23 ++++ .../expected_policy.json | 1 + .../semantic_segmentation_3_1/search.yaml | 50 ++++++++ tests_e2e/requirements.txt | 4 +- tests_e2e/run.sh | 6 + 41 files changed, 796 insertions(+), 94 deletions(-) create mode 100644 .github/workflows/publish_docker_image.yml create mode 100644 autoalbument/faster_autoaugment/models.py create mode 100644 docker/Dockerfile create mode 100755 docker/entrypoint.sh create mode 100644 examples/cifar10/model.py create mode 100644 tests_e2e/configs/classification_1_1/dataset.py create mode 100644 tests_e2e/configs/classification_1_1/expected_policy.json create mode 100644 tests_e2e/configs/classification_1_1/search.yaml create mode 100644 tests_e2e/configs/classification_2_1/dataset.py create mode 100644 tests_e2e/configs/classification_2_1/expected_policy.json create mode 100644 tests_e2e/configs/classification_2_1/search.yaml create mode 100644 tests_e2e/configs/classification_3_1/dataset.py create mode 100644 tests_e2e/configs/classification_3_1/expected_policy.json create mode 100644 tests_e2e/configs/classification_3_1/search.yaml create mode 100644 tests_e2e/configs/semantic_segmentation_1_1/dataset.py create mode 100644 tests_e2e/configs/semantic_segmentation_1_1/expected_policy.json create mode 100644 tests_e2e/configs/semantic_segmentation_1_1/search.yaml create mode 100644 tests_e2e/configs/semantic_segmentation_2_1/dataset.py create mode 100644 tests_e2e/configs/semantic_segmentation_2_1/expected_policy.json create mode 100644 tests_e2e/configs/semantic_segmentation_2_1/search.yaml create mode 100644 tests_e2e/configs/semantic_segmentation_3_1/dataset.py create mode 100644 tests_e2e/configs/semantic_segmentation_3_1/expected_policy.json create mode 100644 tests_e2e/configs/semantic_segmentation_3_1/search.yaml diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 09e12b1..d197a1d 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -19,8 +19,19 @@ jobs: python-version: ${{ matrix.python-version }} - name: Update pip run: python -m pip install --upgrade pip + + - name: Install PyTorch on Linux and Windows + if: > + matrix.operating-system == 'ubuntu-latest' || + matrix.operating-system == 'windows-latest' + run: > + pip install torch==1.7.1+cpu torchvision==0.8.2+cpu + -f https://download.pytorch.org/whl/torch_stable.html + - name: Install PyTorch on MacOS + if: matrix.operating-system == 'macos-latest' + run: pip install torch==1.7.1 torchvision==0.8.2 - name: Install dependencies - run: pip install -f https://download.pytorch.org/whl/torch_stable.html .[tests] + run: pip install .[tests] - name: Install linters run: pip install pydocstyle flake8 flake8-docstrings mypy - name: Run PyTest diff --git a/.github/workflows/publish_docker_image.yml b/.github/workflows/publish_docker_image.yml new file mode 100644 index 0000000..13f39e6 --- /dev/null +++ b/.github/workflows/publish_docker_image.yml @@ -0,0 +1,23 @@ +name: Publish Docker image +on: + release: + types: [published] + +jobs: + push_to_registry: + name: Push Docker image to GitHub Packages + runs-on: ubuntu-latest + steps: + - name: Check out the repo + uses: actions/checkout@v2 + - name: Login to Github Container Registry + run: echo ${{ secrets.CR_PAT }} | docker login ghcr.io -u ${{ github.actor }} --password-stdin + - name: Build image + run: | + docker build + -f docker/Dockerfile + --tag ghcr.io/albumentations-team/autoalbument:${{ github.event.release.tag_name }} + --tag ghcr.io/albumentations-team/autoalbument:latest + . + - name: Push image + run: docker push ghcr.io/albumentations-team/autoalbument diff --git a/autoalbument/__init__.py b/autoalbument/__init__.py index 485f44a..d3ec452 100644 --- a/autoalbument/__init__.py +++ b/autoalbument/__init__.py @@ -1 +1 @@ -__version__ = "0.1.1" +__version__ = "0.2.0" diff --git a/autoalbument/cli/search.py b/autoalbument/cli/search.py index f3b492b..6a1b595 100644 --- a/autoalbument/cli/search.py +++ b/autoalbument/cli/search.py @@ -8,7 +8,7 @@ from autoalbument.config.faster_autoaugment import FasterAutoAugmentSearchConfig from autoalbument.config.validation import validate_cfg -from autoalbument.faster_autoaugment.search import get_faa_seacher +from autoalbument.faster_autoaugment.search import get_faa_searcher from autoalbument.utils.hydra import get_config_dir OmegaConf.register_resolver("config_dir", get_config_dir) @@ -37,5 +37,5 @@ def main(cfg): print(get_prettified_cfg(cfg)) cwd = os.getcwd() print(f"Working directory: {cwd}") - faa_searcher = get_faa_seacher(cfg) + faa_searcher = get_faa_searcher(cfg) faa_searcher.search() diff --git a/autoalbument/cli/templates/classification/search.yaml.tmpl b/autoalbument/cli/templates/classification/search.yaml.tmpl index 5f7c98e..8eb9e53 100644 --- a/autoalbument/cli/templates/classification/search.yaml.tmpl +++ b/autoalbument/cli/templates/classification/search.yaml.tmpl @@ -32,7 +32,11 @@ policy_model: # Settings for Classification Model that is used for two purposes: # 1. As a model that performs classification of input images. # 2. As a Discriminator for Policy Model. + classification_model: + # By default, AutoAlbument uses an instance of `autoalbument.faster_autoaugment.models.ClassificationModel` + # as a classification model. This model takes three parameters: `num_classes`, `architecture` and `pretrained`. + _target_: autoalbument.faster_autoaugment.models.ClassificationModel # Number of classes in the dataset. The dataset implementation should return an integer in the range # [0, num_classes - 1] as a class label of an image. diff --git a/autoalbument/cli/templates/classification/search_full.yaml.tmpl b/autoalbument/cli/templates/classification/search_full.yaml.tmpl index 039ce84..aa0f174 100644 --- a/autoalbument/cli/templates/classification/search_full.yaml.tmpl +++ b/autoalbument/cli/templates/classification/search_full.yaml.tmpl @@ -45,6 +45,9 @@ policy_model: # 1. As a model that performs classification of input images. # 2. As a Discriminator for Policy Model. classification_model: + # By default, AutoAlbument uses an instance of `autoalbument.faster_autoaugment.models.ClassificationModel` + # as a classification model. This model takes three parameters: `num_classes`, `architecture` and `pretrained`. + _target_: autoalbument.faster_autoaugment.models.ClassificationModel # Number of classes in the dataset. The dataset implementation should return an integer in the range # [0, num_classes - 1] as a class label of an image. diff --git a/autoalbument/cli/templates/semantic_segmentation/search.yaml.tmpl b/autoalbument/cli/templates/semantic_segmentation/search.yaml.tmpl index d357451..53b1a0e 100644 --- a/autoalbument/cli/templates/semantic_segmentation/search.yaml.tmpl +++ b/autoalbument/cli/templates/semantic_segmentation/search.yaml.tmpl @@ -33,6 +33,10 @@ policy_model: # 1. As a model that performs semantic segmentation of input images. # 2. As a Discriminator for Policy Model. semantic_segmentation_model: + # By default, AutoAlbument uses an instance of `autoalbument.faster_autoaugment.models.SemanticSegmentationModel` as + # a semantic segmentation model. + # This model takes four parameters: `num_classes`, `architecture`, `encoder_architecture` and `pretrained`. + _target_: autoalbument.faster_autoaugment.models.SemanticSegmentationModel # The number of classes in the dataset. The dataset implementation should return a mask as a NumPy array with # the shape [height, width, num_classes]. In a case of binary segmentation you can set `num_classes` to 1. @@ -55,6 +59,7 @@ semantic_segmentation_model: # refer to https://github.com/qubvel/segmentation_models.pytorch#encoders- pretrained: True + data: # Class for the PyTorch Dataset and arguments to it. AutoAlbument will create an object of this class using # the `instantiate` method from Hydra - https://hydra.cc/docs/next/patterns/instantiate_objects/overview/. diff --git a/autoalbument/cli/templates/semantic_segmentation/search_full.yaml.tmpl b/autoalbument/cli/templates/semantic_segmentation/search_full.yaml.tmpl index 5a637ac..e408e5e 100644 --- a/autoalbument/cli/templates/semantic_segmentation/search_full.yaml.tmpl +++ b/autoalbument/cli/templates/semantic_segmentation/search_full.yaml.tmpl @@ -43,6 +43,10 @@ policy_model: # 1. As a model that performs semantic segmentation of input images. # 2. As a Discriminator for Policy Model. semantic_segmentation_model: + # By default, AutoAlbument uses an instance of `autoalbument.faster_autoaugment.models.SemanticSegmentationModel` as + # a semantic segmentation model. + # This model takes four parameters: `num_classes`, `architecture`, `encoder_architecture` and `pretrained`. + _target_: autoalbument.faster_autoaugment.models.SemanticSegmentationModel # The number of classes in the dataset. The dataset implementation should return a mask as a NumPy array with # the shape [height, width, num_classes]. In a case of binary segmentation you can set `num_classes` to 1. diff --git a/autoalbument/config/faster_autoaugment.py b/autoalbument/config/faster_autoaugment.py index 6b94cc5..482d006 100644 --- a/autoalbument/config/faster_autoaugment.py +++ b/autoalbument/config/faster_autoaugment.py @@ -46,14 +46,16 @@ class PolicyModelConfig: @dataclass class ClassificationModelConfig: - num_classes: int = MISSING + _target_: str = "autoalbument.faster_autoaugment.models.ClassificationModel" + num_classes: Optional[int] = None architecture: str = "resnet18" pretrained: bool = False @dataclass class SemanticSegmentationModelConfig: - num_classes: int = MISSING + _target_: str = "autoalbument.faster_autoaugment.models.SemanticSegmentationModel" + num_classes: Optional[int] = None architecture: str = "Unet" encoder_architecture: str = "resnet18" pretrained: bool = False diff --git a/autoalbument/faster_autoaugment/models.py b/autoalbument/faster_autoaugment/models.py new file mode 100644 index 0000000..1edfcfc --- /dev/null +++ b/autoalbument/faster_autoaugment/models.py @@ -0,0 +1,59 @@ +from typing import Tuple + +import segmentation_models_pytorch as smp +import timm +from torch import nn, Tensor +from torch.nn import Flatten + + +class BaseDiscriminator(nn.Module): + def __init__(self, *args, **kwargs): + super().__init__() + + def forward(self, input: Tensor) -> Tuple[Tensor, Tensor]: + raise NotImplementedError + + +class ClassificationModel(BaseDiscriminator): + def __init__(self, architecture, pretrained, num_classes): + super().__init__() + self.base_model = timm.create_model(architecture, pretrained=pretrained) + self.base_model.reset_classifier(num_classes) + self.classifier = self.base_model.get_classifier() + num_features = self.classifier.in_features + self.discriminator = nn.Sequential( + nn.Linear(num_features, num_features), nn.ReLU(), nn.Linear(num_features, 1) + ) + + def forward(self, input: Tensor) -> Tuple[Tensor, Tensor]: + x = self.base_model.forward_features(input) + x = self.base_model.global_pool(x).flatten(1) + return self.classifier(x), self.discriminator(x).view(-1) + + +class SemanticSegmentationModel(BaseDiscriminator): + def __init__(self, architecture, encoder_architecture, num_classes, pretrained): + super().__init__() + model = getattr(smp, architecture) + + self.base_model = model( + encoder_architecture, encoder_weights=self._get_encoder_weights(pretrained), classes=num_classes + ) + num_features = self.base_model.encoder.out_channels[-1] + self.base_model.classification_head = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + Flatten(), + nn.Linear(num_features, num_features), + nn.ReLU(), + nn.Linear(num_features, 1), + ) + + @staticmethod + def _get_encoder_weights(pretrained): + if isinstance(pretrained, bool): + return "imagenet" if pretrained else None + return pretrained + + def forward(self, input: Tensor) -> Tuple[Tensor, Tensor]: + mask, discriminator_output = self.base_model(input) + return mask, discriminator_output.view(-1) diff --git a/autoalbument/faster_autoaugment/search.py b/autoalbument/faster_autoaugment/search.py index e0b1e21..72e8714 100644 --- a/autoalbument/faster_autoaugment/search.py +++ b/autoalbument/faster_autoaugment/search.py @@ -10,69 +10,22 @@ from typing import Tuple import albumentations as A -import timm import torch from albumentations.pytorch import ToTensorV2 from hydra.utils import instantiate from torch import Tensor, nn -from torch.nn import Flatten from torch.utils.tensorboard import SummaryWriter from tqdm import tqdm from autoalbument.faster_autoaugment.metrics import get_average_parameter_change +from autoalbument.faster_autoaugment.models import ClassificationModel, SemanticSegmentationModel from autoalbument.faster_autoaugment.utils import MAX_VALUES_BY_INPUT_DTYPE, get_dataset_cls, MetricTracker, set_seed from autoalbument.utils.files import symlink from autoalbument.faster_autoaugment.policy import Policy -import segmentation_models_pytorch as smp log = logging.getLogger(__name__) -class ClassificationDiscriminator(nn.Module): - def __init__(self, architecture, pretrained, num_classes): - super().__init__() - self.base_model = timm.create_model(architecture, pretrained=pretrained) - self.base_model.reset_classifier(num_classes) - self.classifier = self.base_model.get_classifier() - num_features = self.classifier.in_features - self.discriminator = nn.Sequential( - nn.Linear(num_features, num_features), nn.ReLU(), nn.Linear(num_features, 1) - ) - - def forward(self, input: Tensor) -> Tuple[Tensor, Tensor]: - x = self.base_model.forward_features(input) - x = self.base_model.global_pool(x).flatten(1) - return self.classifier(x), self.discriminator(x).view(-1) - - -class SegmentationDiscriminator(nn.Module): - def __init__(self, architecture, encoder_architecture, num_classes, pretrained): - super().__init__() - model = getattr(smp, architecture) - - self.base_model = model( - encoder_architecture, encoder_weights=self._get_encoder_weights(pretrained), classes=num_classes - ) - num_features = self.base_model.encoder.out_channels[-1] - self.base_model.classification_head = nn.Sequential( - nn.AdaptiveAvgPool2d(1), - Flatten(), - nn.Linear(num_features, num_features), - nn.ReLU(), - nn.Linear(num_features, 1), - ) - - @staticmethod - def _get_encoder_weights(pretrained): - if isinstance(pretrained, bool): - return "imagenet" if pretrained else None - return pretrained - - def forward(self, input: Tensor) -> Tuple[Tensor, Tensor]: - mask, discriminator_output = self.base_model(input) - return mask, discriminator_output.view(-1) - - class FasterAutoAugmentBase: def __init__(self, cfg): self.cfg = cfg @@ -187,9 +140,15 @@ def create_optimizers(self): "policy": policy_optimizer, } - def create_main_model(self): + def get_main_model_cfg(self): raise NotImplementedError + def create_main_model(self): + model_cfg = self.get_main_model_cfg() + main_model = instantiate(model_cfg) + main_model = main_model.to(self.cfg.device) + return main_model + def create_policy_model(self): policy_model_cfg = self.cfg.policy_model normalization_cfg = self.cfg.data.normalization @@ -239,6 +198,7 @@ def wgan_loss( loss = self.cfg.policy_model.task_factor * self.loss(output, n_target) loss.backward(retain_graph=True) d_n_loss = n_output.mean() + d_n_loss.backward(-ones) with torch.no_grad(): @@ -247,6 +207,7 @@ def wgan_loss( _, a_output = self.models["main"](augmented) d_a_loss = a_output.mean() + d_a_loss.backward(ones) gp = self.cfg.policy_model.gp_factor * self.gradient_penalty(n_input, augmented) gp.backward() @@ -392,12 +353,8 @@ def search(self): class FAAClassification(FasterAutoAugmentBase): - def create_main_model(self): - model_cfg = self.cfg.classification_model - main_model = ClassificationDiscriminator( - model_cfg.architecture, num_classes=model_cfg.num_classes, pretrained=model_cfg.pretrained - ).to(self.cfg.device) - return main_model + def get_main_model_cfg(self): + return self.cfg.classification_model def create_loss(self): return nn.CrossEntropyLoss().to(self.cfg.device) @@ -408,15 +365,8 @@ def policy_forward_for_policy_train(self, a_input, a_target): class FAASemanticSegmentation(FasterAutoAugmentBase): - def create_main_model(self): - model_cfg = self.cfg.semantic_segmentation_model - main_model = SegmentationDiscriminator( - model_cfg.architecture, - encoder_architecture=model_cfg.encoder_architecture, - num_classes=model_cfg.num_classes, - pretrained=model_cfg.pretrained, - ).to(self.cfg.device) - return main_model + def get_main_model_cfg(self): + return self.cfg.semantic_segmentation_model def create_loss(self): return nn.BCEWithLogitsLoss().to(self.cfg.device) @@ -426,7 +376,7 @@ def policy_forward_for_policy_train(self, a_input, a_target): return output["image_batch"], output["mask_batch"] -def get_faa_seacher(cfg): +def get_faa_searcher(cfg): task = cfg.task if task == "semantic_segmentation": return FAASemanticSegmentation(cfg) diff --git a/docker/Dockerfile b/docker/Dockerfile new file mode 100644 index 0000000..f4090eb --- /dev/null +++ b/docker/Dockerfile @@ -0,0 +1,18 @@ +FROM pytorch/pytorch:1.7.0-cuda11.0-cudnn8-runtime + +RUN apt-get update && apt-get install -y \ + libgl1-mesa-glx \ + libglib2.0-0 \ + && rm -rf /var/lib/apt/lists/* + +RUN useradd --create-home --shell /bin/bash --no-log-init autoalbument +USER autoalbument +ENV PATH="/home/autoalbument/.local/bin:${PATH}" +WORKDIR /opt/autoalbument +COPY . . +RUN pip install --no-cache-dir . +COPY docker/entrypoint.sh entrypoint.sh + +WORKDIR /autoalbument + +ENTRYPOINT ["/opt/autoalbument/entrypoint.sh"] diff --git a/docker/entrypoint.sh b/docker/entrypoint.sh new file mode 100755 index 0000000..0388413 --- /dev/null +++ b/docker/entrypoint.sh @@ -0,0 +1,3 @@ +#!/bin/bash + +autoalbument-search --config-dir /config "$@" diff --git a/examples/cifar10/dataset.py b/examples/cifar10/dataset.py index 929d92c..61ec597 100644 --- a/examples/cifar10/dataset.py +++ b/examples/cifar10/dataset.py @@ -2,8 +2,8 @@ class SearchDataset(torchvision.datasets.CIFAR10): - def __init__(self, transform=None): - super().__init__(root="~/data/cifar10", train=True, download=True, transform=transform) + def __init__(self, root="~/data/cifar10", train=True, download=True, transform=None): + super().__init__(root=root, train=train, download=download, transform=transform) def __getitem__(self, index): image, label = self.data[index], self.targets[index] diff --git a/examples/cifar10/model.py b/examples/cifar10/model.py new file mode 100644 index 0000000..d91d939 --- /dev/null +++ b/examples/cifar10/model.py @@ -0,0 +1,113 @@ +# ResNet models for CIFAR10 are taken from https://github.com/kuangliu/pytorch-cifar + +import torch.nn as nn +import torch.nn.functional as F + +from autoalbument.faster_autoaugment.models import BaseDiscriminator + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_planes, planes, stride=1): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * planes), + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, in_planes, planes, stride=1): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(self.expansion * planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * planes), + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = F.relu(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + + +class ResNet(nn.Module): + def __init__(self, block, num_blocks, num_classes=10): + super(ResNet, self).__init__() + self.in_planes = 64 + + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + self.linear = nn.Linear(512 * block.expansion, num_classes) + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward_features(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + out = F.avg_pool2d(out, 4) + out = out.view(out.size(0), -1) + return out + + def forward_classifier(self, x): + out = self.linear(x) + return out + + +def ResNet18(): + return ResNet(BasicBlock, [2, 2, 2, 2]) + + +class Cifar10ClassificationModel(BaseDiscriminator): + def __init__(self, *args, **kwargs): + super().__init__() + self.base_model = ResNet18() + num_features = self.base_model.linear.in_features + self.discriminator = nn.Sequential( + nn.Linear(num_features, num_features), nn.ReLU(), nn.Linear(num_features, 1) + ) + + def forward(self, input): + x = self.base_model.forward_features(input) + return self.base_model.forward_classifier(x), self.discriminator(x).view(-1) diff --git a/examples/cifar10/search.yaml b/examples/cifar10/search.yaml index 90f6da7..069ce15 100644 --- a/examples/cifar10/search.yaml +++ b/examples/cifar10/search.yaml @@ -41,24 +41,29 @@ policy_model: # and increase the searching time. operation_count: 4 - # Settings for Classification Model that is used for two purposes: # 1. As a model that performs classification of input images. # 2. As a Discriminator for Policy Model. classification_model: + # A custom classification model is used. This model is defined inside the `model.py` file which is located + # in the same directory with `search.yaml` and `dataset.py`. + _target_: model.Cifar10ClassificationModel - # Number of classes in the dataset. The dataset implementation should return an integer in the range - # [0, num_classes - 1] as a class label of an image. - num_classes: 10 - - # The architecture of Classification Model. AutoAlbument uses models from - # https://github.com/rwightman/pytorch-image-models/. Please refer to its documentation to get a list of available - # models - https://rwightman.github.io/pytorch-image-models/#list-models-with-pretrained-weights. - architecture: resnet18 - - # Boolean flag that indicates whether the selected model architecture should load pretrained weights or use randomly - # initialized weights. - pretrained: False + # # As an alternative, you could use a built-in AutoAldbument model using the following config: + # # _target_: autoalbument.faster_autoaugment.models.ClassificationModel + # + # # Number of classes in the dataset. The dataset implementation should return an integer in the range + # # [0, num_classes - 1] as a class label of an image. + # num_classes: 10 + # + # # The architecture of Classification Model. AutoAlbument uses models from + # # https://github.com/rwightman/pytorch-image-models/. Please refer to its documentation to get a list of available + # # models - https://rwightman.github.io/pytorch-image-models/#list-models-with-pretrained-weights. + # architecture: resnet18 + # + # # Boolean flag that indicates whether the selected model architecture should load pretrained weights or use randomly + # # initialized weights. + # pretrained: False data: # Class for the PyTorch Dataset and arguments to it. AutoAlbument will create an object of this class using @@ -83,6 +88,9 @@ data: # dataset: _target_: dataset.SearchDataset + root: "~/data/cifar10" + train: True + download: True # The data type of input images. Two values are supported: # - uint8. In that case, all input images should be NumPy arrays with the np.uint8 data type and values in the range diff --git a/examples/pascal_voc/dataset.py b/examples/pascal_voc/dataset.py index 14a02ad..38cbd0b 100644 --- a/examples/pascal_voc/dataset.py +++ b/examples/pascal_voc/dataset.py @@ -54,8 +54,8 @@ class SearchDataset(VOCSegmentation): - def __init__(self, image_set="train", transform=None): - super().__init__(root="~/data/pascal_voc", image_set=image_set, download=True, transform=transform) + def __init__(self, root="~/data/pascal_voc", image_set="train", download=True, transform=None): + super().__init__(root=root, image_set=image_set, download=download, transform=transform) @staticmethod def _convert_to_segmentation_mask(mask): diff --git a/examples/pascal_voc/search.yaml b/examples/pascal_voc/search.yaml index 64c2d3e..4af448b 100644 --- a/examples/pascal_voc/search.yaml +++ b/examples/pascal_voc/search.yaml @@ -45,6 +45,10 @@ policy_model: # 1. As a model that performs semantic segmentation of input images. # 2. As a Discriminator for Policy Model. semantic_segmentation_model: + # By default, AutoAlbument uses an instance of `autoalbument.faster_autoaugment.models.SemanticSegmentationModel` as + # a semantic segmentation model. + # This model takes four parameters: `num_classes`, `architecture`, `encoder_architecture` and `pretrained`. + _target_: autoalbument.faster_autoaugment.models.SemanticSegmentationModel # The number of classes in the dataset. The dataset implementation should return a mask as a NumPy array with # the shape [height, width, num_classes]. In a case of binary segmentation you can set `num_classes` to 1. @@ -91,6 +95,9 @@ data: # dataset: _target_: dataset.SearchDataset + root: "~/data/pascal_voc" + image_set: "train" + download: True # The data type of input images. Two values are supported: # - uint8. In that case, all input images should be NumPy arrays with the np.uint8 data type and values in the range diff --git a/setup.py b/setup.py index 1e96ce2..b0a9376 100644 --- a/setup.py +++ b/setup.py @@ -31,8 +31,8 @@ def get_long_description(): "albumentations>=0.5.1", "torch>=1.6.0", "hydra-core>=1.0", - "timm==0.1.20", # This version is required for segmentation-models-pytorch - "segmentation-models-pytorch", + "timm>=0.3.2", # This version is required for segmentation-models-pytorch + "segmentation-models-pytorch>=0.1.3", "tqdm", "click", "colorama", diff --git a/tests/configs/classification/search.yaml b/tests/configs/classification/search.yaml index 66f0b8d..7524b76 100644 --- a/tests/configs/classification/search.yaml +++ b/tests/configs/classification/search.yaml @@ -10,6 +10,7 @@ policy_model: classification_model: + _target_: autoalbument.faster_autoaugment.models.ClassificationModel num_classes: 10 architecture: resnet18 pretrained: False diff --git a/tests/test_faa_search.py b/tests/test_faa_search.py index 98439c5..d738f3a 100644 --- a/tests/test_faa_search.py +++ b/tests/test_faa_search.py @@ -3,7 +3,7 @@ from hydra.experimental import initialize, compose, initialize_config_dir -from autoalbument.faster_autoaugment.search import get_faa_seacher +from autoalbument.faster_autoaugment.search import get_faa_searcher def test_dataloader_drops_last(tmpdir) -> None: @@ -13,5 +13,5 @@ def test_dataloader_drops_last(tmpdir) -> None: os.environ["AUTOALBUMENT_CONFIG_DIR"] = str((Path(__file__).parent / config_path).resolve()) os.chdir(tmpdir) cfg = compose(config_name="search", overrides=["data.dataloader.batch_size=12"]) - faa_searcher = get_faa_seacher(cfg) + faa_searcher = get_faa_searcher(cfg) faa_searcher.search() diff --git a/tests_e2e/configs/classification_1_1/dataset.py b/tests_e2e/configs/classification_1_1/dataset.py new file mode 100644 index 0000000..c56f702 --- /dev/null +++ b/tests_e2e/configs/classification_1_1/dataset.py @@ -0,0 +1,23 @@ +import os + +import numpy as np +import torch.utils.data + + +class SearchDataset(torch.utils.data.Dataset): + def __init__(self, transform=None): + self.transform = transform + + def __len__(self): + return int(os.environ.get("AUTOALBUMENT_TEST_DATASET_LENGTH", 16)) + + def __getitem__(self, index): + np.random.seed(index) + image = np.random.randint(low=0, high=256, size=(32, 32, 3), dtype=np.uint8) + label = np.random.randint(low=0, high=10, dtype=np.int64) + + if self.transform is not None: + transformed = self.transform(image=image) + image = transformed["image"] + + return image, label diff --git a/tests_e2e/configs/classification_1_1/expected_policy.json b/tests_e2e/configs/classification_1_1/expected_policy.json new file mode 100644 index 0000000..c9f5cd6 --- /dev/null +++ b/tests_e2e/configs/classification_1_1/expected_policy.json @@ -0,0 +1 @@ +{"__version__": "0.5.1", "transform": {"__class_fullname__": "albumentations.core.composition.Compose", "p": 1.0, "transforms": 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+ task_factor: 0.1 + gp_factor: 10 + temperature: 0.05 + num_sub_policies: 10 + num_chunks: 8 + operation_count: 4 + +classification_model: + _target_: autoalbument.faster_autoaugment.models.ClassificationModel + num_classes: 10 + architecture: resnet18 + pretrained: False + +data: + dataset_file: dataset.py + input_dtype: uint8 + preprocessing: null + normalization: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + + dataloader: + _target_: torch.utils.data.DataLoader + batch_size: 16 + shuffle: True + num_workers: 0 + pin_memory: True + drop_last: True + +optim: + epochs: 1 + main: + _target_: torch.optim.Adam + lr: 1e-3 + betas: [0, 0.999] + policy: + _target_: torch.optim.Adam + lr: 1e-3 + betas: [0, 0.999] + +device: cpu + +cudnn_benchmark: True +save_checkpoints: False +checkpoint_path: null +tensorboard_logs_dir: null + +seed: 42 + +hydra: + run: + dir: ${config_dir:}/outputs diff --git a/tests_e2e/configs/classification_2_1/dataset.py b/tests_e2e/configs/classification_2_1/dataset.py new file mode 100644 index 0000000..c56f702 --- /dev/null +++ b/tests_e2e/configs/classification_2_1/dataset.py @@ -0,0 +1,23 @@ +import os + +import numpy as np +import torch.utils.data + + +class SearchDataset(torch.utils.data.Dataset): + def __init__(self, transform=None): + self.transform = transform + + def __len__(self): + return int(os.environ.get("AUTOALBUMENT_TEST_DATASET_LENGTH", 16)) + + def __getitem__(self, index): + np.random.seed(index) + image = np.random.randint(low=0, high=256, size=(32, 32, 3), dtype=np.uint8) + label = np.random.randint(low=0, high=10, dtype=np.int64) + + if self.transform is not None: + transformed = self.transform(image=image) + image = transformed["image"] + + return image, label diff --git a/tests_e2e/configs/classification_2_1/expected_policy.json b/tests_e2e/configs/classification_2_1/expected_policy.json new file mode 100644 index 0000000..c9f5cd6 --- /dev/null +++ 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+ + +classification_model: + num_classes: 10 + architecture: resnet18 + pretrained: False + +data: + dataset: + _target_: dataset.SearchDataset + input_dtype: uint8 + preprocessing: null + normalization: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + + dataloader: + _target_: torch.utils.data.DataLoader + batch_size: 16 + shuffle: True + num_workers: 0 + pin_memory: True + drop_last: True + +optim: + epochs: 1 + main: + _target_: torch.optim.Adam + lr: 1e-3 + betas: [0, 0.999] + policy: + _target_: torch.optim.Adam + lr: 1e-3 + betas: [0, 0.999] + +device: cpu + +cudnn_benchmark: True +save_checkpoints: False +checkpoint_path: null +tensorboard_logs_dir: null + +seed: 42 + +hydra: + run: + dir: ${config_dir:}/outputs diff --git a/tests_e2e/configs/classification_3_1/dataset.py b/tests_e2e/configs/classification_3_1/dataset.py new file mode 100644 index 0000000..c56f702 --- /dev/null +++ b/tests_e2e/configs/classification_3_1/dataset.py @@ -0,0 +1,23 @@ +import os + +import numpy as np +import torch.utils.data + + +class SearchDataset(torch.utils.data.Dataset): + def __init__(self, transform=None): + self.transform = transform + + def __len__(self): + return int(os.environ.get("AUTOALBUMENT_TEST_DATASET_LENGTH", 16)) + + def __getitem__(self, index): + np.random.seed(index) + image = np.random.randint(low=0, high=256, size=(32, 32, 3), dtype=np.uint8) + label = np.random.randint(low=0, high=10, dtype=np.int64) + + if self.transform is not None: + transformed = self.transform(image=image) + image = transformed["image"] + + return image, label diff --git a/tests_e2e/configs/classification_3_1/expected_policy.json b/tests_e2e/configs/classification_3_1/expected_policy.json new file mode 100644 index 0000000..c9f5cd6 --- /dev/null +++ b/tests_e2e/configs/classification_3_1/expected_policy.json @@ -0,0 +1 @@ +{"__version__": "0.5.1", "transform": {"__class_fullname__": "albumentations.core.composition.Compose", "p": 1.0, "transforms": 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+ num_sub_policies: 10 + num_chunks: 8 + +classification_model: + _target_: autoalbument.faster_autoaugment.models.ClassificationModel + num_classes: 10 + architecture: resnet18 + pretrained: False + +data: + dataset: + _target_: dataset.SearchDataset + + preprocessing: null + + dataloader: + _target_: torch.utils.data.DataLoader + batch_size: 16 + num_workers: 0 + +optim: + epochs: 1 + main: + _target_: torch.optim.Adam + lr: 1e-3 + betas: [0, 0.999] + policy: + _target_: torch.optim.Adam + lr: 1e-3 + betas: [0, 0.999] + +device: cpu + +seed: 42 + +hydra: + run: + dir: ${config_dir:}/outputs diff --git a/tests_e2e/configs/semantic_segmentation_1_1/dataset.py b/tests_e2e/configs/semantic_segmentation_1_1/dataset.py new file mode 100644 index 0000000..12a583f --- /dev/null +++ b/tests_e2e/configs/semantic_segmentation_1_1/dataset.py @@ -0,0 +1,23 @@ +import os + +import numpy as np +import torch.utils.data + + +class SearchDataset(torch.utils.data.Dataset): + def __init__(self, transform=None): + self.transform = transform + + def __len__(self): + return int(os.environ.get("AUTOALBUMENT_TEST_DATASET_LENGTH", 16)) + + def __getitem__(self, index): + np.random.seed(index) + image = np.random.randint(low=0, high=256, size=(32, 32, 3), dtype=np.uint8) + mask = np.random.uniform(low=0.0, high=1.0, size=(32, 32, 10)).astype(np.float32) + if self.transform is not None: + transformed = self.transform(image=image, mask=mask) + image = transformed["image"] + mask = transformed["mask"] + + return image, mask diff --git a/tests_e2e/configs/semantic_segmentation_1_1/expected_policy.json b/tests_e2e/configs/semantic_segmentation_1_1/expected_policy.json new file mode 100644 index 0000000..d97d8a5 --- /dev/null +++ b/tests_e2e/configs/semantic_segmentation_1_1/expected_policy.json @@ -0,0 +1 @@ +{"__version__": "0.5.1", "transform": {"__class_fullname__": "albumentations.core.composition.Compose", "p": 1.0, "transforms": [{"__class_fullname__": 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"min_height": 1, "min_width": 1, "fill_value": 0, "mask_fill_value": null}, {"__class_fullname__": "albumentations.core.transforms_interface.NoOp", "always_apply": false, "p": 0.09071567919115131}]}]}]}, {"__class_fullname__": "albumentations.augmentations.transforms.Normalize", "always_apply": false, "p": 1.0, "mean": [0.48500001430511475, 0.4560000002384186, 0.4059999883174896], "std": [0.2290000021457672, 0.2240000069141388, 0.22499999403953552], "max_pixel_value": 255}, {"__class_fullname__": "albumentations.pytorch.transforms.ToTensorV2", "always_apply": true, "p": 1.0, "transpose_mask": true}], "bbox_params": null, "keypoint_params": null, "additional_targets": {}}} diff --git a/tests_e2e/configs/semantic_segmentation_1_1/search.yaml b/tests_e2e/configs/semantic_segmentation_1_1/search.yaml new file mode 100644 index 0000000..5a7e1dd --- /dev/null +++ b/tests_e2e/configs/semantic_segmentation_1_1/search.yaml @@ -0,0 +1,66 @@ +# @package _global_ + +task: semantic_segmentation + +policy_model: + task_factor: 0.1 + gp_factor: 10 + temperature: 0.05 + num_sub_policies: 10 + num_chunks: 4 + operation_count: 4 + + +semantic_segmentation_model: + _target_: autoalbument.faster_autoaugment.models.SemanticSegmentationModel + num_classes: 10 + architecture: Unet + encoder_architecture: resnet18 + pretrained: False + + +data: + dataset_file: dataset.py + input_dtype: uint8 + preprocessing: + - Resize: + height: 128 + width: 128 + + normalization: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + + dataloader: + _target_: torch.utils.data.DataLoader + batch_size: 16 + shuffle: True + num_workers: 0 + pin_memory: True + drop_last: True + +optim: + epochs: 1 + main: + _target_: torch.optim.Adam + lr: 1e-3 + betas: [0, 0.999] + + policy: + _target_: torch.optim.Adam + lr: 1e-3 + betas: [0, 0.999] + +device: cpu + +cudnn_benchmark: True + +save_checkpoints: False +checkpoint_path: null +tensorboard_logs_dir: null + +seed: 42 + +hydra: + run: + dir: ${config_dir:}/outputs diff --git a/tests_e2e/configs/semantic_segmentation_2_1/dataset.py b/tests_e2e/configs/semantic_segmentation_2_1/dataset.py new file mode 100644 index 0000000..12a583f --- /dev/null +++ b/tests_e2e/configs/semantic_segmentation_2_1/dataset.py @@ -0,0 +1,23 @@ +import os + +import numpy as np +import torch.utils.data + + +class SearchDataset(torch.utils.data.Dataset): + def __init__(self, transform=None): + self.transform = transform + + def __len__(self): + return int(os.environ.get("AUTOALBUMENT_TEST_DATASET_LENGTH", 16)) + + def __getitem__(self, index): + np.random.seed(index) + image = np.random.randint(low=0, high=256, size=(32, 32, 3), dtype=np.uint8) + mask = np.random.uniform(low=0.0, high=1.0, size=(32, 32, 10)).astype(np.float32) + if self.transform is not None: + transformed = self.transform(image=image, mask=mask) + image = transformed["image"] + mask = transformed["mask"] + + return image, mask diff --git a/tests_e2e/configs/semantic_segmentation_2_1/expected_policy.json b/tests_e2e/configs/semantic_segmentation_2_1/expected_policy.json new file mode 100644 index 0000000..d97d8a5 --- /dev/null +++ b/tests_e2e/configs/semantic_segmentation_2_1/expected_policy.json @@ -0,0 +1 @@ +{"__version__": "0.5.1", "transform": {"__class_fullname__": "albumentations.core.composition.Compose", "p": 1.0, "transforms": [{"__class_fullname__": "albumentations.augmentations.transforms.Resize", "always_apply": false, "p": 1, "height": 128, "width": 128, "interpolation": 1}, {"__class_fullname__": "albumentations.core.composition.OneOf", "p": 1, "transforms": [{"__class_fullname__": "albumentations.core.composition.Sequential", "p": 0.1, "transforms": [{"__class_fullname__": "albumentations.core.composition.OneOf", "p": 1, "transforms": [{"__class_fullname__": "albumentations.augmentations.transforms.RGBShift", "always_apply": false, "p": 0.011674547732012153, "r_shift_limit": [163, 163], "g_shift_limit": 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"keypoint_params": null, "additional_targets": {}}} diff --git a/tests_e2e/configs/semantic_segmentation_2_1/search.yaml b/tests_e2e/configs/semantic_segmentation_2_1/search.yaml new file mode 100644 index 0000000..2080851 --- /dev/null +++ b/tests_e2e/configs/semantic_segmentation_2_1/search.yaml @@ -0,0 +1,67 @@ +# @package _global_ + +task: semantic_segmentation + +policy_model: + task_factor: 0.1 + gp_factor: 10 + temperature: 0.05 + num_sub_policies: 10 + num_chunks: 4 + operation_count: 4 + + +semantic_segmentation_model: + _target_: autoalbument.faster_autoaugment.models.SemanticSegmentationModel + num_classes: 10 + architecture: Unet + encoder_architecture: resnet18 + pretrained: False + + +data: + dataset: + _target_: dataset.SearchDataset + input_dtype: uint8 + preprocessing: + - Resize: + height: 128 + width: 128 + + normalization: + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + + dataloader: + _target_: torch.utils.data.DataLoader + batch_size: 16 + shuffle: True + num_workers: 0 + pin_memory: True + drop_last: True + +optim: + epochs: 1 + main: + _target_: torch.optim.Adam + lr: 1e-3 + betas: [0, 0.999] + + policy: + _target_: torch.optim.Adam + lr: 1e-3 + betas: [0, 0.999] + +device: cpu + +cudnn_benchmark: True + +save_checkpoints: False +checkpoint_path: null +tensorboard_logs_dir: null + +seed: 42 + +hydra: + run: + dir: ${config_dir:}/outputs diff --git a/tests_e2e/configs/semantic_segmentation_3_1/dataset.py b/tests_e2e/configs/semantic_segmentation_3_1/dataset.py new file mode 100644 index 0000000..12a583f --- /dev/null +++ b/tests_e2e/configs/semantic_segmentation_3_1/dataset.py @@ -0,0 +1,23 @@ +import os + +import numpy as np +import torch.utils.data + + +class SearchDataset(torch.utils.data.Dataset): + def __init__(self, transform=None): + self.transform = transform + + def __len__(self): + return int(os.environ.get("AUTOALBUMENT_TEST_DATASET_LENGTH", 16)) + + def __getitem__(self, index): + np.random.seed(index) + image = np.random.randint(low=0, high=256, size=(32, 32, 3), dtype=np.uint8) + mask = np.random.uniform(low=0.0, high=1.0, size=(32, 32, 10)).astype(np.float32) + if self.transform is not None: + transformed = self.transform(image=image, mask=mask) + image = transformed["image"] + mask = transformed["mask"] + + return image, mask diff --git a/tests_e2e/configs/semantic_segmentation_3_1/expected_policy.json b/tests_e2e/configs/semantic_segmentation_3_1/expected_policy.json new file mode 100644 index 0000000..d97d8a5 --- /dev/null +++ b/tests_e2e/configs/semantic_segmentation_3_1/expected_policy.json @@ -0,0 +1 @@ +{"__version__": "0.5.1", "transform": {"__class_fullname__": "albumentations.core.composition.Compose", "p": 1.0, "transforms": [{"__class_fullname__": "albumentations.augmentations.transforms.Resize", "always_apply": false, "p": 1, "height": 128, "width": 128, "interpolation": 1}, {"__class_fullname__": "albumentations.core.composition.OneOf", "p": 1, "transforms": 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"keypoint_params": null, "additional_targets": {}}} diff --git a/tests_e2e/configs/semantic_segmentation_3_1/search.yaml b/tests_e2e/configs/semantic_segmentation_3_1/search.yaml new file mode 100644 index 0000000..2fe8fa0 --- /dev/null +++ b/tests_e2e/configs/semantic_segmentation_3_1/search.yaml @@ -0,0 +1,50 @@ +# @package _global_ + +task: semantic_segmentation + +policy_model: + num_sub_policies: 10 + num_chunks: 4 + + +semantic_segmentation_model: + _target_: autoalbument.faster_autoaugment.models.SemanticSegmentationModel + num_classes: 10 + architecture: Unet + encoder_architecture: resnet18 + pretrained: False + + +data: + dataset: + _target_: dataset.SearchDataset + input_dtype: uint8 + preprocessing: + - Resize: + height: 128 + width: 128 + + dataloader: + _target_: torch.utils.data.DataLoader + batch_size: 16 + num_workers: 0 + +optim: + epochs: 1 + main: + _target_: torch.optim.Adam + lr: 1e-3 + betas: [0, 0.999] + + policy: + _target_: torch.optim.Adam + lr: 1e-3 + betas: [0, 0.999] + +device: cpu + +seed: 42 + +hydra: + run: + dir: ${config_dir:}/outputs diff --git a/tests_e2e/requirements.txt b/tests_e2e/requirements.txt index f7b13f3..d304dfd 100644 --- a/tests_e2e/requirements.txt +++ b/tests_e2e/requirements.txt @@ -43,13 +43,13 @@ requests-oauthlib==1.3.0 rsa==4.6 scikit-image==0.17.2 scipy==1.5.4 -segmentation-models-pytorch==0.1.2 +segmentation-models-pytorch==0.1.3 Shapely==1.7.1 six==1.15.0 tensorboard==2.4.0 tensorboard-plugin-wit==1.7.0 tifffile==2020.10.1 -timm==0.1.20 +timm==0.3.2 torch==1.7.0+cpu torchvision==0.8.1+cpu tqdm==4.51.0 diff --git a/tests_e2e/run.sh b/tests_e2e/run.sh index 7a0cbff..447242b 100755 --- a/tests_e2e/run.sh +++ b/tests_e2e/run.sh @@ -56,10 +56,16 @@ test_search_from_merged_create () { test_simple_search classification_1 test_simple_search classification_2 test_simple_search classification_3 +test_simple_search classification_1_1 +test_simple_search classification_2_1 +test_simple_search classification_3_1 test_simple_search semantic_segmentation_1 test_simple_search semantic_segmentation_2 test_simple_search semantic_segmentation_3 +test_simple_search semantic_segmentation_1_1 +test_simple_search semantic_segmentation_2_1 +test_simple_search semantic_segmentation_3_1 test_search_from_merged_create classification_from_create_1 classification test_search_from_merged_create semantic_segmentation_from_create_1 semantic_segmentation