From e97ebe68f3900407f9c4ce9a40e853d8a85c8dad Mon Sep 17 00:00:00 2001 From: Animatory Date: Tue, 2 May 2023 01:44:53 +0400 Subject: [PATCH 01/11] Update lightning compatibility --- environment.yml | 43 ++++++++--------- .../metric_manager/test_metric_manager_ddp.py | 2 +- torchok/callbacks/__init__.py | 3 +- torchok/callbacks/finalize_logger.py | 3 +- torchok/constructor/config_structure.py | 48 +++++++++---------- torchok/constructor/load.py | 2 +- torchok/losses/detection/mmdet_losses.py | 2 +- torchok/metrics/detection.py | 2 +- torchok/models/heads/detection/__init__.py | 7 +-- torchok/models/heads/detection/detr.py | 5 +- torchok/tasks/multihead_classification.py | 9 ++-- 11 files changed, 61 insertions(+), 65 deletions(-) diff --git a/environment.yml b/environment.yml index d1159081..3f6f2a7a 100644 --- a/environment.yml +++ b/environment.yml @@ -3,29 +3,28 @@ channels: - pytorch - conda-forge - defaults + - nvidia dependencies: - - python=3.9 - - pip=22.3.1 - - numpy=1.21.* + - python=3.10 + - pip==23.1.1 - faiss-cpu=1.7.2 - - mlflow=1.27.0 - - pandas=1.4.3 - - pillow=9.1.1 - - pytorch=1.12.1 - - torchvision=0.13.1 - - cudatoolkit=11.6 - - parameterized=0.8.1 + - mlflow=2.3.0 + - pytorch=2.0 + - torchvision + - pytorch-cuda=11.8 + - parameterized=0.9.0 + - pytorch-lightning=2.0.2 + - torchmetrics=0.11.4 - pip: - - pytorch-lightning==1.9.0 - - torchmetrics==0.11.0 - - onnx==1.12.0 - - onnxruntime-gpu==1.11.1 + - opencv-python==4.7.0.72 + - onnx==1.13.1 + - onnxruntime-gpu==1.14.1 - albumentations==1.3.0 - - hydra-core==1.2.0 - - opencv-python==4.6.0.* - - ranx==0.2.8 - - timm==0.6.12 - - mmdet==2.26.0 - - pymysql==1.0.2 -# install mmcv-full via -# `pip install mmcv-full==1.6.2 -f https://download.openmmlab.com/mmcv/dist/cu116/torch1.12/index.html` + - hydra-core==1.3.2 + - ranx==0.3.7 + - timm==0.6.13 + - pymysql==1.0.3 + - imagesize + - mmdet==3.0.0 +# install mmcv via +# `pip install mmcv==2.0.0 -f https://download.openmmlab.com/mmcv/dist/cu118/torch2.0/index.html` diff --git a/tests/base_tests/metrics/metric_manager/test_metric_manager_ddp.py b/tests/base_tests/metrics/metric_manager/test_metric_manager_ddp.py index 3821848f..24a7b11b 100644 --- a/tests/base_tests/metrics/metric_manager/test_metric_manager_ddp.py +++ b/tests/base_tests/metrics/metric_manager/test_metric_manager_ddp.py @@ -81,7 +81,7 @@ def run_model(metric_params: List[MetricParams]): trainer = Trainer( accelerator="cpu", strategy="ddp", - num_processes=2, + num_nodes=1, max_epochs=EPOCH, ) diff --git a/torchok/callbacks/__init__.py b/torchok/callbacks/__init__.py index 6e45a5db..990c0c9b 100644 --- a/torchok/callbacks/__init__.py +++ b/torchok/callbacks/__init__.py @@ -1,6 +1,6 @@ from pytorch_lightning.callbacks import (BackboneFinetuning, DeviceStatsMonitor, EarlyStopping, GradientAccumulationScheduler, LearningRateMonitor, ModelCheckpoint, - ModelPruning, ModelSummary, QuantizationAwareTraining, RichModelSummary, + ModelPruning, ModelSummary, RichModelSummary, RichProgressBar, StochasticWeightAveraging, Timer, TQDMProgressBar) import torchok.callbacks.finalize_logger @@ -21,4 +21,3 @@ CALLBACKS.register_class(StochasticWeightAveraging) CALLBACKS.register_class(Timer) CALLBACKS.register_class(TQDMProgressBar) -CALLBACKS.register_class(QuantizationAwareTraining) diff --git a/torchok/callbacks/finalize_logger.py b/torchok/callbacks/finalize_logger.py index f4ca0c7d..56867799 100644 --- a/torchok/callbacks/finalize_logger.py +++ b/torchok/callbacks/finalize_logger.py @@ -10,4 +10,5 @@ class FinalizeLogger(Callback): def on_exception(self, trainer, pl_module, outputs): # Need to save checkpoints for every exception not only isinstance(outputs, KeyboardInterrupt) status = 'KILLED' if type(outputs) == KeyboardInterrupt else 'FAILED' - trainer.logger.finalize(status) + if trainer.logger is not None: + trainer.logger.finalize(status) diff --git a/torchok/constructor/config_structure.py b/torchok/constructor/config_structure.py index 6471f17f..f884abec 100644 --- a/torchok/constructor/config_structure.py +++ b/torchok/constructor/config_structure.py @@ -134,17 +134,12 @@ class TaskParams: # Trainer parameters @dataclass class TrainerParams: - enable_checkpointing: bool = True - default_root_dir: Optional[str] = None - gradient_clip_val: Optional[float] = None - gradient_clip_algorithm: Optional[str] = None + accelerator: str = "auto" + strategy: str = "auto" + devices: str = "auto" num_nodes: int = 1 - enable_progress_bar: bool = True - overfit_batches: Any = 0.0 # Union[int, float] - track_grad_norm: Any = -1 # Union[int, float, str] - check_val_every_n_epoch: Optional[int] = 1 + precision: Any = 32 # Literal[64, 32, 16, "64", "32", "16", "bf16"] fast_dev_run: Any = False # Union[int, bool] - accumulate_grad_batches: Optional[Any] = None # Optional[Union[int, Dict[int, int]]] max_epochs: Optional[int] = None min_epochs: Optional[int] = None max_steps: int = -1 @@ -154,26 +149,27 @@ class TrainerParams: limit_val_batches: Optional[Any] = None # Optional[Union[int, float]] limit_test_batches: Optional[Any] = None # Optional[Union[int, float]] limit_predict_batches: Optional[Any] = None # Optional[Union[int, float]] + overfit_batches: Any = 0.0 # Union[int, float] val_check_interval: Optional[Any] = None # Optional[Union[int, float]] - log_every_n_steps: int = 50 - accelerator: Optional[str] = None - strategy: Optional[str] = None - sync_batchnorm: bool = False - precision: Any = 32 # Literal[64, 32, 16, "64", "32", "16", "bf16"] - enable_model_summary: bool = True - num_sanity_val_steps: int = 2 - resume_from_checkpoint: Optional[str] = None - profiler: Optional[str] = None + check_val_every_n_epoch: Optional[int] = 1 + num_sanity_val_steps: Optional[int] = None + log_every_n_steps: Optional[int] = None + enable_checkpointing: Optional[bool] = None + enable_progress_bar: Optional[bool] = None + enable_model_summary: Optional[bool] = None + accumulate_grad_batches: int = 1 + gradient_clip_val: Optional[float] = None + gradient_clip_algorithm: Optional[str] = None + deterministic: Optional[bool] = None benchmark: Optional[bool] = None - deterministic: Optional[bool] = None # Optional[Union[bool, _LITERAL_WARN]] - reload_dataloaders_every_n_epochs: int = 0 - auto_lr_find: bool = False # Union[bool, str] - replace_sampler_ddp: bool = True - detect_anomaly: bool = False - auto_scale_batch_size: bool = False # Union[str, bool] - move_metrics_to_cpu: bool = False - multiple_trainloader_mode: str = "max_size_cycle" inference_mode: bool = True + use_distributed_sampler: bool = True + profiler: Optional[str] = None + detect_anomaly: bool = False + barebones: bool = False + sync_batchnorm: bool = False + reload_dataloaders_every_n_epochs: int = 0 + default_root_dir: str = None # Logger diff --git a/torchok/constructor/load.py b/torchok/constructor/load.py index e73eb9c5..89c8eecc 100644 --- a/torchok/constructor/load.py +++ b/torchok/constructor/load.py @@ -4,7 +4,7 @@ import pytorch_lightning as pl import torch -from pytorch_lightning.utilities.cloud_io import load +from lightning_fabric.utilities.cloud_io import _load as load def load_state_dict(checkpoint_path: str, map_location: Optional[Union[str, Callable, torch.device]] = 'cpu'): diff --git a/torchok/losses/detection/mmdet_losses.py b/torchok/losses/detection/mmdet_losses.py index 16d1b15e..0d74fdab 100644 --- a/torchok/losses/detection/mmdet_losses.py +++ b/torchok/losses/detection/mmdet_losses.py @@ -1,6 +1,6 @@ import types -from mmdet.models.builder import LOSSES as MMLOSSES +from mmdet.registry import MODELS as MMLOSSES from torchok.constructor import LOSSES diff --git a/torchok/metrics/detection.py b/torchok/metrics/detection.py index 2e67a3fe..13bf1e4f 100644 --- a/torchok/metrics/detection.py +++ b/torchok/metrics/detection.py @@ -1,7 +1,7 @@ from typing import Dict, List import numpy as np -from mmdet.core.evaluation.mean_ap import eval_map +from mmdet.evaluation.functional import eval_map from torch import Tensor from torchmetrics import Metric diff --git a/torchok/models/heads/detection/__init__.py b/torchok/models/heads/detection/__init__.py index 03462f51..526bb997 100644 --- a/torchok/models/heads/detection/__init__.py +++ b/torchok/models/heads/detection/__init__.py @@ -1,6 +1,7 @@ import importlib has_mmcv = importlib.util.find_spec("mmcv") -if has_mmcv is not None: - import torchok.models.heads.detection.fcos - import torchok.models.heads.detection.detr +# ToDo: fix mmdet heads +# if has_mmcv is not None: +# import torchok.models.heads.detection.fcos +# import torchok.models.heads.detection.detr diff --git a/torchok/models/heads/detection/detr.py b/torchok/models/heads/detection/detr.py index b3d84c02..08340c4a 100644 --- a/torchok/models/heads/detection/detr.py +++ b/torchok/models/heads/detection/detr.py @@ -5,10 +5,9 @@ import torch import torch.nn.functional as F -from mmcv import ConfigDict +from mmcv.utils.config import ConfigDict from mmcv.cnn import build_activation_layer from mmcv.cnn.bricks.transformer import build_positional_encoding -from mmcv.runner import force_fp32 from mmdet.core import bbox_cxcywh_to_xyxy, build_assigner, build_sampler, reduce_mean from mmdet.models.dense_heads import detr_head from mmdet.models.dense_heads.anchor_free_head import AnchorFreeHead @@ -207,7 +206,6 @@ def get_fit_image_size(fit_size, image_size): def format_dict(head_output): return dict(zip(['all_cls_scores', 'all_bbox_preds'], head_output)) - @force_fp32(apply_to=('all_cls_scores', 'all_bbox_preds')) def loss(self, joint_loss: JointLoss, all_cls_scores: List[torch.Tensor], all_bbox_preds: List[torch.Tensor], @@ -292,7 +290,6 @@ def loss(self, joint_loss: JointLoss, tagged_loss_dict[k] += v return global_total_loss, tagged_loss_dict - @force_fp32(apply_to=('all_cls_scores', 'all_bbox_preds')) def get_bboxes(self, all_cls_scores: torch.Tensor, all_bbox_preds: torch.Tensor, diff --git a/torchok/tasks/multihead_classification.py b/torchok/tasks/multihead_classification.py index 7ca70a24..dd31b1df 100644 --- a/torchok/tasks/multihead_classification.py +++ b/torchok/tasks/multihead_classification.py @@ -1,4 +1,5 @@ from typing import Dict, List, Any +from collections import namedtuple import torch from omegaconf import DictConfig @@ -79,14 +80,16 @@ def __init__( self.heads[head_name] = head_type(in_channels=head_in_channels, **head['params']) self.target_mapping[head_name] = target - def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]: + self.head_tuple = namedtuple('HeadOutput', list(self.target_mapping.keys())) + + def forward(self, x: torch.Tensor) -> namedtuple: """Forward method. Args: x: torch.Tensor of shape `(B, C, H, W)`. Batch of input images. Returns: - Dict with string keys representing head name + Namedtuple with string keys representing head name and torch.Tensor values representing output of corresponding head. """ features = self.backbone(x) @@ -94,7 +97,7 @@ def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]: head_outputs = {} for head_name, head in self.heads.items(): head_outputs[head_name] = head(features) - return head_outputs + return self.head_tuple(**head_outputs) def forward_with_gt(self, batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: """Forward with ground truth labels. From fa574274d2df665cb4e059b6ab2f2d44cb6bad6b Mon Sep 17 00:00:00 2001 From: Animatory Date: Thu, 4 May 2023 13:09:59 +0400 Subject: [PATCH 02/11] Update lightning compatibility Add Swav --- environment.yml | 1 + torchok/constructor/config_structure.py | 1 - torchok/constructor/constructor.py | 4 +- .../data/datasets/representation/__init__.py | 1 + torchok/data/datasets/representation/swav.py | 34 ++++++ torchok/data/transforms/__init__.py | 1 + torchok/data/transforms/swav.py | 90 +++++++++++++++ torchok/data/transforms/utils.py | 105 ++++++++++++++++++ torchok/losses/__init__.py | 3 + torchok/losses/representation/__init__.py | 2 + .../heads/representation/linear_head.py | 20 ++++ torchok/tasks/__init__.py | 1 + torchok/tasks/base.py | 35 ++++-- torchok/tasks/self_supervised/__init__.py | 1 + torchok/tasks/self_supervised/swav.py | 100 +++++++++++++++++ 15 files changed, 385 insertions(+), 14 deletions(-) create mode 100644 torchok/data/datasets/representation/swav.py create mode 100644 torchok/data/transforms/swav.py create mode 100644 torchok/tasks/self_supervised/__init__.py create mode 100644 torchok/tasks/self_supervised/swav.py diff --git a/environment.yml b/environment.yml index 3f6f2a7a..92fc12b7 100644 --- a/environment.yml +++ b/environment.yml @@ -16,6 +16,7 @@ dependencies: - pytorch-lightning=2.0.2 - torchmetrics=0.11.4 - pip: + - lightly==1.4.3 - opencv-python==4.7.0.72 - onnx==1.13.1 - onnxruntime-gpu==1.14.1 diff --git a/torchok/constructor/config_structure.py b/torchok/constructor/config_structure.py index f884abec..83d8af99 100644 --- a/torchok/constructor/config_structure.py +++ b/torchok/constructor/config_structure.py @@ -169,7 +169,6 @@ class TrainerParams: barebones: bool = False sync_batchnorm: bool = False reload_dataloaders_every_n_epochs: int = 0 - default_root_dir: str = None # Logger diff --git a/torchok/constructor/constructor.py b/torchok/constructor/constructor.py index 2fd02815..14bf91f0 100644 --- a/torchok/constructor/constructor.py +++ b/torchok/constructor/constructor.py @@ -329,10 +329,8 @@ def _prepare_transforms_recursively(transforms: ListConfig) -> List[Union[A.Comp transform_name = transform_info.name transform_params = transform_info.get('params', dict()) - if transform_name in ['Compose', 'OneOf', 'SomeOf', 'PerChannel', 'Sequential']: + if 'transforms' in transform_params: transform = Constructor._prepare_base_compose(transform_name, **transform_params) - elif transform_name == 'OneOrOther': - raise ValueError('OneOrOther composition is currently not supported') else: transform = TRANSFORMS.get(transform_name)(**transform_params) diff --git a/torchok/data/datasets/representation/__init__.py b/torchok/data/datasets/representation/__init__.py index fae6c67a..21d0c3b0 100644 --- a/torchok/data/datasets/representation/__init__.py +++ b/torchok/data/datasets/representation/__init__.py @@ -1,2 +1,3 @@ import torchok.data.datasets.representation.validation import torchok.data.datasets.representation.unsupervised_contrastive_dataset +import torchok.data.datasets.representation.swav diff --git a/torchok/data/datasets/representation/swav.py b/torchok/data/datasets/representation/swav.py new file mode 100644 index 00000000..ab646012 --- /dev/null +++ b/torchok/data/datasets/representation/swav.py @@ -0,0 +1,34 @@ +from collections import defaultdict +from typing import Any, List, Dict + +import torch +from torch.utils.data._utils.collate import default_collate + +from torchok.constructor import DATASETS +from torchok.data.datasets.classification.classification import ImageClassificationDataset + + +@DATASETS.register_class +class SwaVDataset(ImageClassificationDataset): + + def __getitem__(self, idx: int) -> List[Dict[str, Any]]: + samples = self.get_raw(idx) + new_samples = [] + for i in range(len(samples['image'])): + view = {k: v[i] for k, v in samples.items()} + view = self._apply_transform(self.transform, view) + view['image'] = view['image'].type(torch.__dict__[self.input_dtype]) + new_samples.append(view) + + return new_samples + + def collate_fn(self, batch: List[List[Dict[str, Any]]]) -> Dict[str, List[torch.Tensor]]: + num_views = len(batch[0]) + new_batch = defaultdict(list) + + for j in range(num_views): + cross_batch_view = default_collate([sample_views[j] for sample_views in batch]) + for k, v in cross_batch_view.items(): + new_batch[k].append(v) + + return new_batch diff --git a/torchok/data/transforms/__init__.py b/torchok/data/transforms/__init__.py index 82c7b2a5..4c31544d 100644 --- a/torchok/data/transforms/__init__.py +++ b/torchok/data/transforms/__init__.py @@ -19,6 +19,7 @@ from torchok.constructor import TRANSFORMS from torchok.data.transforms import spatial from torchok.data.transforms import pixelwise +from torchok.data.transforms import swav TRANSFORMS.register_class(Compose) diff --git a/torchok/data/transforms/swav.py b/torchok/data/transforms/swav.py new file mode 100644 index 00000000..eee07984 --- /dev/null +++ b/torchok/data/transforms/swav.py @@ -0,0 +1,90 @@ +from typing import Tuple, Union, Optional, Dict + +import albumentations as alb + +from torchok.data.transforms.utils import MultiCrop +from torchok.constructor import TRANSFORMS + + +@TRANSFORMS.register_class +class SwaVTransform(MultiCrop): + """Implements the multi-crop transformations for SwaV. + + Args: + crop_sizes: Size of the input image in pixels for each crop category. + crop_counts: Number of crops for each crop category. + crop_min_scales: Min scales for each crop category. + crop_max_scales: Max_scales for each crop category. + hf_prob: Probability that horizontal flip is applied. + vf_prob: Probability that vertical flip is applied. + rr_prob: Probability that random rotation is applied. + rr_degrees: Range of degrees to select from for random rotation. + If rr_degrees is None, images randomly are rotated by 90 degrees zero or more times. + If rr_degrees is a (min, max) tuple, images rotated by a random angle in [min, max]. + If rr_degrees is a single number, images rotated by a random angle in [-rr_degrees, +rr_degrees]. + All rotations are counter-clockwise. + cj_prob: Probability that color jitter is applied. + cj_strength: Strength of the color jitter. + `cj_bright`, `cj_contrast`, `cj_sat`, and `cj_hue` are multiplied by this value. + cj_bright: How much to jitter brightness. + cj_contrast: How much to jitter contrast. + cj_sat: How much to jitter saturation. + cj_hue: How much to jitter hue. + random_gray_scale: Probability of conversion to grayscale. + gaussian_blur: Probability of Gaussian blur. + sigmas: Tuple of min and max value from which the std of the gaussian kernel is sampled. + Is ignored if `kernel_size` is set. + + """ + + def __init__( + self, + crop_sizes: Tuple[int, int] = (224, 96), + crop_counts: Tuple[int, int] = (2, 6), + crop_min_scales: Tuple[float, float] = (0.14, 0.05), + crop_max_scales: Tuple[float, float] = (1.0, 0.14), + hf_prob: float = 0.5, + vf_prob: float = 0.0, + rr_prob: float = 0.0, + rr_degrees: Union[None, int, Tuple[int, int]] = 0, + cj_prob: float = 0.8, + cj_strength: float = 1.0, + cj_bright: float = 0.8, + cj_contrast: float = 0.8, + cj_sat: float = 0.8, + cj_hue: float = 0.2, + random_gray_scale: float = 0.2, + gaussian_blur: float = 0.5, + sigmas: Tuple[float, float] = (0.1, 2), + bbox_params: Optional[Union[dict, "BboxParams"]] = None, + keypoint_params: Optional[Union[dict, "KeypointParams"]] = None, + additional_targets: Optional[Dict[str, str]] = None, + p: float = 1.0, + ): + + transforms = [ + alb.HorizontalFlip(p=hf_prob), + alb.VerticalFlip(p=vf_prob), + alb.RandomRotate90(p=rr_prob) if rr_degrees is None else alb.Rotate(limit=rr_degrees, p=rr_prob), + alb.ColorJitter( + brightness=cj_strength * cj_bright, + contrast=cj_strength * cj_contrast, + saturation=cj_strength * cj_sat, + hue=cj_strength * cj_hue, + p=cj_prob + ), + alb.ToGray(p=random_gray_scale), + alb.GaussianBlur(sigma_limit=sigmas, p=gaussian_blur) + ] + + super().__init__( + crop_sizes=crop_sizes, + crop_counts=crop_counts, + crop_min_scales=crop_min_scales, + crop_max_scales=crop_max_scales, + transforms=transforms, + bbox_params=bbox_params, + keypoint_params=keypoint_params, + additional_targets=additional_targets, + p=p, + ) diff --git a/torchok/data/transforms/utils.py b/torchok/data/transforms/utils.py index e69de29b..a473ed3d 100644 --- a/torchok/data/transforms/utils.py +++ b/torchok/data/transforms/utils.py @@ -0,0 +1,105 @@ +from typing import List, Dict, Any, Tuple, Optional, Union +from collections import defaultdict + +import albumentations as alb +from albumentations.core.composition import TransformsSeqType + +from torchok.constructor import TRANSFORMS + + +class MultiView(alb.Compose): + """Transforms an image into multiple views. + + Args: + branch_transforms: A sequence of transforms. Every transform creates a new view. + transforms: A sequence of transforms applied on output of every view. + bbox_params (BboxParams): Parameters for bounding boxes transforms + keypoint_params (KeypointParams): Parameters for keypoints transforms + additional_targets (dict): Dict with keys - new target name, values - old target name. ex: {'image2': 'image'} + p (float): probability of applying all list of transforms. Default: 1.0. + """ + + def __init__( + self, + branch_transforms: TransformsSeqType, + transforms: TransformsSeqType, + bbox_params: Optional[Union[dict, "BboxParams"]] = None, + keypoint_params: Optional[Union[dict, "KeypointParams"]] = None, + additional_targets: Optional[Dict[str, str]] = None, + p: float = 1.0, + ): + super().__init__(transforms, bbox_params, keypoint_params, additional_targets, p) + self.branch_transforms = branch_transforms + + def __call__(self, *args, force_apply: bool = False, **data) -> List[Dict[str, Any]]: + """Transforms an image into multiple views. + + Every transform in self.branch_transforms creates a new view. + """ + if args: + raise KeyError("You have to pass data to augmentations as named arguments, for example: aug(image=image)") + + result = defaultdict(list) + for transform in self.branch_transforms: + view = super(MultiView, self).__call__(**transform(**data)) + for k, v in view.items(): + result[k].append(v) + + return result + + +@TRANSFORMS.register_class +class MultiCrop(MultiView): + """Implements the multi-crop transformations. + + Args: + crop_sizes: Size of the input image in pixels for each crop category. + crop_counts: Number of crops for each crop category. + crop_min_scales: Min scales for each crop category. + crop_max_scales: Max_scales for each crop category. + transforms: Transforms which are applied to all crops. + bbox_params (BboxParams): Parameters for bounding boxes transforms + keypoint_params (KeypointParams): Parameters for keypoints transforms + additional_targets (dict): Dict with keys - new target name, values - old target name. ex: {'image2': 'image'} + p (float): probability of applying all list of transforms. Default: 1.0. + + """ + + def __init__( + self, + crop_sizes: Tuple[int], + crop_counts: Tuple[int], + crop_min_scales: Tuple[float], + crop_max_scales: Tuple[float], + transforms: TransformsSeqType, + bbox_params: Optional[Union[dict, "BboxParams"]] = None, + keypoint_params: Optional[Union[dict, "KeypointParams"]] = None, + additional_targets: Optional[Dict[str, str]] = None, + p: float = 1.0, + ): + if len(crop_sizes) != len(crop_counts): + raise ValueError( + "Length of crop_sizes and crop_counts must be equal but are" + f" {len(crop_sizes)} and {len(crop_counts)}." + ) + if len(crop_sizes) != len(crop_min_scales): + raise ValueError( + "Length of crop_sizes and crop_min_scales must be equal but are" + f" {len(crop_sizes)} and {len(crop_min_scales)}." + ) + if len(crop_sizes) != len(crop_min_scales): + raise ValueError( + "Length of crop_sizes and crop_max_scales must be equal but are" + f" {len(crop_sizes)} and {len(crop_min_scales)}." + ) + + crop_transforms = [] + for i, crop_size in enumerate(crop_sizes): + random_resized_crop = alb.RandomResizedCrop( + height=crop_size, + width=crop_size, + scale=(crop_min_scales[i], crop_max_scales[i]) + ) + + crop_transforms.extend([random_resized_crop] * crop_counts[i]) + super().__init__(crop_transforms, transforms, bbox_params, keypoint_params, additional_targets, p) diff --git a/torchok/losses/__init__.py b/torchok/losses/__init__.py index 14ec251d..8352f8f0 100644 --- a/torchok/losses/__init__.py +++ b/torchok/losses/__init__.py @@ -3,6 +3,7 @@ MultiLabelMarginLoss, MultiLabelSoftMarginLoss, MultiMarginLoss, NLLLoss, NLLLoss2d, PoissonNLLLoss, SmoothL1Loss, SoftMarginLoss, TripletMarginLoss, TripletMarginWithDistanceLoss) from torch.nn import Identity +from lightly.loss import SwaVLoss import torchok.losses.detection import torchok.losses.segmentation @@ -32,3 +33,5 @@ LOSSES.register_class(TripletMarginWithDistanceLoss) LOSSES.register_class(CTCLoss) LOSSES.register_class(Identity) + +LOSSES.register_class(SwaVLoss) diff --git a/torchok/losses/representation/__init__.py b/torchok/losses/representation/__init__.py index 0f4b8a88..4d76a9e5 100644 --- a/torchok/losses/representation/__init__.py +++ b/torchok/losses/representation/__init__.py @@ -1,2 +1,4 @@ import torchok.losses.representation.pairwise import torchok.losses.representation.unsupervised + + diff --git a/torchok/models/heads/representation/linear_head.py b/torchok/models/heads/representation/linear_head.py index 1ab86f96..52115d8b 100644 --- a/torchok/models/heads/representation/linear_head.py +++ b/torchok/models/heads/representation/linear_head.py @@ -1,15 +1,21 @@ from typing import Optional import torch.nn.functional as F +from lightly.models.modules import SwaVProjectionHead as LightlySwaVProjectionHead from torch import nn, Tensor from torchok.constructor import HEADS from torchok.models.base import BaseModel +from lightly.transforms.swav_transform import SwaVTransform +from lightly.data.multi_view_collate import MultiViewCollate +from lightly.data import LightlyDataset +from torchok.data.transforms.utils import MultiCrop @HEADS.register_class class LinearHead(BaseModel): """Linear Head""" + def __init__(self, in_channels, out_channels, drop_rate=0.0, bias=True, normalize=False): """Init LinearHead. Args: @@ -34,3 +40,17 @@ def forward(self, x: Tensor, targets: Optional[Tensor] = None) -> Tensor: x = F.normalize(x, p=2, dim=-1) return x + + +@HEADS.register_class +class SwaVProjectionHead(LightlySwaVProjectionHead): + """Projection head used for SwaV. + + [0]: SwAV, 2020, https://arxiv.org/abs/2006.09882 + """ + + def __init__(self, in_channels: int = 2048, hidden_channels: int = 2048, out_channels: int = 128): + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.out_channels = out_channels + super().__init__(input_dim=in_channels, hidden_dim=hidden_channels, output_dim=out_channels) diff --git a/torchok/tasks/__init__.py b/torchok/tasks/__init__.py index 292819ee..1909cb7c 100644 --- a/torchok/tasks/__init__.py +++ b/torchok/tasks/__init__.py @@ -4,6 +4,7 @@ import torchok.tasks.onnx import torchok.tasks.pairwise_task import torchok.tasks.segmentation +import torchok.tasks.self_supervised import torchok.tasks.simclr_task import torchok.tasks.triplet_task from torchok.tasks.base import BaseTask diff --git a/torchok/tasks/base.py b/torchok/tasks/base.py index f0769b9b..84ce4c27 100644 --- a/torchok/tasks/base.py +++ b/torchok/tasks/base.py @@ -5,6 +5,7 @@ import torch.nn as nn from omegaconf import DictConfig from pytorch_lightning import LightningModule +from torch import Tensor from torch.optim import Optimizer from torch.utils.data import DataLoader @@ -121,17 +122,18 @@ def on_predict_start(self) -> None: if self._hparams.task.load_checkpoint is not None: load_checkpoint(self, **self._hparams.task.load_checkpoint) - def training_step(self, batch: Dict[str, Union[torch.Tensor, int]], batch_idx: int) -> Dict[str, torch.Tensor]: + def training_step(self, batch: Dict[str, Union[Tensor, int]], batch_idx: int) -> Dict[str, Tensor]: """Complete training loop.""" output = self.forward_with_gt(batch) total_loss, tagged_loss_values = self.losses(**output) + # self.log("loss", total_loss, prog_bar=True, on_step=True) self.metrics_manager.update(Phase.TRAIN, **output) output_dict = {'loss': total_loss} output_dict.update(tagged_loss_values) return output_dict - def validation_step(self, batch: Dict[str, Union[torch.Tensor, int]], - batch_idx: int, dataloader_idx: int = 0) -> Dict[str, torch.Tensor]: + def validation_step(self, batch: Dict[str, Union[Tensor, int]], + batch_idx: int, dataloader_idx: int = 0) -> Dict[str, Tensor]: """Complete validation loop.""" output = self.forward_with_gt(batch) self.metrics_manager.update(Phase.VALID, dataloader_idx, **output) @@ -141,45 +143,58 @@ def validation_step(self, batch: Dict[str, Union[torch.Tensor, int]], if self._hparams.task.compute_loss_on_valid: total_loss, tagged_loss_values = self.losses(**output) output_dict = {'loss': total_loss} + # self.log("loss", total_loss, prog_bar=True, on_step=True) output_dict.update(tagged_loss_values) else: output_dict = {} return output_dict - def test_step(self, batch: Dict[str, torch.Tensor], batch_idx: int, dataloader_idx: int = 0) -> None: + def test_step(self, batch: Dict[str, Tensor], batch_idx: int, dataloader_idx: int = 0) -> None: """Complete test loop.""" output = self.forward_with_gt(batch) self.metrics_manager.update(Phase.TEST, dataloader_idx, **output) - def predict_step(self, batch: Dict[str, torch.Tensor], batch_idx: int) -> torch.Tensor: + def predict_step(self, batch: Dict[str, Tensor], batch_idx: int, dataloader_idx: int = 0) -> dict[str, Tensor]: """Complete predict loop.""" output = self.forward_with_gt(batch) return output - def training_step_end(self, outputs: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: + def on_train_batch_end( + self, + outputs: Dict[str, Tensor], + batch: Dict[str, Tensor], + batch_idx: int, + dataloader_idx: int = 0 + ) -> Dict[str, Tensor]: output_dict = {tag: value.mean() for tag, value in self.all_gather(outputs, sync_grads=True).items()} for tag, value in output_dict.items(): self.log(f'train/{tag}', value, on_step=False, on_epoch=True, batch_size=len(outputs)) return output_dict - def validation_step_end(self, outputs: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: + def on_validation_batch_end( + self, + outputs: Dict[str, Tensor], + batch: Dict[str, Tensor], + batch_idx: int, + dataloader_idx: int = 0 + ) -> Dict[str, Tensor]: output_dict = {tag: value.mean() for tag, value in self.all_gather(outputs).items()} for tag, value in output_dict.items(): self.log(f'valid/{tag}', value, on_step=False, on_epoch=True, batch_size=len(outputs)) return output_dict - def training_epoch_end(self, training_step_outputs: List[Dict[str, torch.Tensor]]) -> None: + def on_train_epoch_end(self) -> None: """It's calling at the end of the training epoch with the outputs of all training steps.""" self.log_dict(self.metrics_manager.on_epoch_end(Phase.TRAIN)) self.log('step', float(self.current_epoch), on_step=False, on_epoch=True) - def validation_epoch_end(self, valid_step_outputs: List[Dict[str, torch.Tensor]]) -> None: + def on_validation_epoch_end(self) -> None: """It's calling at the end of the validation epoch with the outputs of all validation steps.""" self.log_dict(self.metrics_manager.on_epoch_end(Phase.VALID)) self.log('step', float(self.current_epoch), on_step=False, on_epoch=True) - def test_epoch_end(self, test_step_outputs: List[Dict[str, torch.Tensor]]) -> None: + def on_test_epoch_end(self) -> None: """It's calling at the end of a test epoch with the output of all test steps.""" self.log_dict(self.metrics_manager.on_epoch_end(Phase.TEST)) diff --git a/torchok/tasks/self_supervised/__init__.py b/torchok/tasks/self_supervised/__init__.py new file mode 100644 index 00000000..0b24a92f --- /dev/null +++ b/torchok/tasks/self_supervised/__init__.py @@ -0,0 +1 @@ +import torchok.tasks.self_supervised.swav diff --git a/torchok/tasks/self_supervised/swav.py b/torchok/tasks/self_supervised/swav.py new file mode 100644 index 00000000..b9c687f8 --- /dev/null +++ b/torchok/tasks/self_supervised/swav.py @@ -0,0 +1,100 @@ +from typing import Dict, List +from omegaconf import DictConfig +from torch import Tensor, nn +from lightly.models.modules import SwaVPrototypes + +from torchok.constructor import TASKS +from torchok.tasks.classification import ClassificationTask + + +@TASKS.register_class +class SwaVTask(ClassificationTask): + """ + Task-agnostic part of the SimCLR v2 approach described in paper + Big Self-Supervised Models are Strong Semi-Supervised Learners: https://arxiv.org/abs/2006.10029 + + This task use `UnsupervisedContrastiveDataset` dataset. + """ + + def __init__( + self, + hparams: DictConfig, + backbone_name: str, + pooling_name: str = None, + head_name: str = None, + neck_name: str = None, + backbone_params: dict = None, + neck_params: dict = None, + pooling_params: dict = None, + head_params: dict = None, + inputs: dict = None, + n_prototypes: int = 3000, + num_highres_crops: int = 2, + num_lowres_crops: int = 6 + ): + """Init SimCLRTask. + + Args: + hparams: Hyperparameters that set in yaml file. + backbone_name: name of the backbone architecture in the BACKBONES registry. + pooling_name: name of the backbone architecture in the POOLINGS registry. + head_name: name of the neck architecture in the HEADS registry. + neck_name: if present, name of the head architecture in the NECKS registry. Otherwise, model will be created + without neck. + backbone_params: parameters for backbone constructor. + neck_params: parameters for neck constructor. `in_channels` will be set automatically based on backbone. + pooling_params: parameters for neck constructor. `in_channels` will be set automatically based on neck or + backbone if neck is absent. + head_params: parameters for head constructor. `in_channels` will be set automatically based on neck. + inputs: information about input model shapes and dtypes. + n_prototypes: Number of prototypes used in SwaV method. + """ + super().__init__( + hparams=hparams, + backbone_name=backbone_name, + pooling_name=pooling_name, + head_name=head_name, + neck_name=neck_name, + backbone_params=backbone_params, + neck_params=neck_params, + pooling_params=pooling_params, + head_params=head_params, + inputs=inputs, + ) + self.prototypes = SwaVPrototypes(self.head.out_channels, n_prototypes=n_prototypes) + self.num_highres_crops = num_highres_crops + self.num_lowres_crops = num_lowres_crops + + def forward(self, x): + x = self.backbone(x) + x = self.pooling(x) + x = self.head(x) + x = nn.functional.normalize(x, dim=1, p=2) + p = self.prototypes(x) + return p + + def forward_with_gt(self, batch: Dict[str, Tensor]) -> Dict[str, List[Tensor]]: + """Forward with ground truth labels. + + Args: + batch: Dictionary with the following keys and values: + + - `image` (List[torch.Tensor]): + list of tensor of shape `(B, C, H, W)`, each tensor contains cross-batch view of images. + + Returns: + Dictionary with the following keys and values + + - 'high_resolution': list of torch.Tensor of shape `(B, num_features)`, + representing embeddings per each image view in high resolution. + - 'low_resolution': list of torch.Tensor of shape `(B, num_classes)`, + representing logits per each image view in low resolution. + """ + self.prototypes.normalize() + crops = batch['image'] + multi_crop_features = [self.forward(crop) for crop in crops] + high_resolution = multi_crop_features[:self.num_highres_crops] + low_resolution = multi_crop_features[self.num_highres_crops:] + output = {"high_resolution": high_resolution, "low_resolution": low_resolution} + + return output From 6b47b151c4599430499e425ef93cc19a03f5195e Mon Sep 17 00:00:00 2001 From: Animatory Date: Thu, 4 May 2023 16:31:19 +0400 Subject: [PATCH 03/11] Fix for flake8 --- torchok/data/transforms/utils.py | 10 +++++----- torchok/losses/representation/__init__.py | 2 -- torchok/models/heads/representation/linear_head.py | 4 ---- torchok/tasks/base.py | 4 ++-- 4 files changed, 7 insertions(+), 13 deletions(-) diff --git a/torchok/data/transforms/utils.py b/torchok/data/transforms/utils.py index a473ed3d..f8c1001e 100644 --- a/torchok/data/transforms/utils.py +++ b/torchok/data/transforms/utils.py @@ -1,5 +1,5 @@ -from typing import List, Dict, Any, Tuple, Optional, Union from collections import defaultdict +from typing import List, Dict, Any, Tuple, Optional import albumentations as alb from albumentations.core.composition import TransformsSeqType @@ -23,8 +23,8 @@ def __init__( self, branch_transforms: TransformsSeqType, transforms: TransformsSeqType, - bbox_params: Optional[Union[dict, "BboxParams"]] = None, - keypoint_params: Optional[Union[dict, "KeypointParams"]] = None, + bbox_params: Optional[dict] = None, + keypoint_params: Optional[dict] = None, additional_targets: Optional[Dict[str, str]] = None, p: float = 1.0, ): @@ -72,8 +72,8 @@ def __init__( crop_min_scales: Tuple[float], crop_max_scales: Tuple[float], transforms: TransformsSeqType, - bbox_params: Optional[Union[dict, "BboxParams"]] = None, - keypoint_params: Optional[Union[dict, "KeypointParams"]] = None, + bbox_params: Optional[dict] = None, + keypoint_params: Optional[dict] = None, additional_targets: Optional[Dict[str, str]] = None, p: float = 1.0, ): diff --git a/torchok/losses/representation/__init__.py b/torchok/losses/representation/__init__.py index 4d76a9e5..0f4b8a88 100644 --- a/torchok/losses/representation/__init__.py +++ b/torchok/losses/representation/__init__.py @@ -1,4 +1,2 @@ import torchok.losses.representation.pairwise import torchok.losses.representation.unsupervised - - diff --git a/torchok/models/heads/representation/linear_head.py b/torchok/models/heads/representation/linear_head.py index 52115d8b..2f1e32f7 100644 --- a/torchok/models/heads/representation/linear_head.py +++ b/torchok/models/heads/representation/linear_head.py @@ -6,10 +6,6 @@ from torchok.constructor import HEADS from torchok.models.base import BaseModel -from lightly.transforms.swav_transform import SwaVTransform -from lightly.data.multi_view_collate import MultiViewCollate -from lightly.data import LightlyDataset -from torchok.data.transforms.utils import MultiCrop @HEADS.register_class diff --git a/torchok/tasks/base.py b/torchok/tasks/base.py index 84ce4c27..de31b54c 100644 --- a/torchok/tasks/base.py +++ b/torchok/tasks/base.py @@ -126,7 +126,7 @@ def training_step(self, batch: Dict[str, Union[Tensor, int]], batch_idx: int) -> """Complete training loop.""" output = self.forward_with_gt(batch) total_loss, tagged_loss_values = self.losses(**output) - # self.log("loss", total_loss, prog_bar=True, on_step=True) + self.log("loss", total_loss, prog_bar=True, on_step=True) self.metrics_manager.update(Phase.TRAIN, **output) output_dict = {'loss': total_loss} output_dict.update(tagged_loss_values) @@ -143,7 +143,7 @@ def validation_step(self, batch: Dict[str, Union[Tensor, int]], if self._hparams.task.compute_loss_on_valid: total_loss, tagged_loss_values = self.losses(**output) output_dict = {'loss': total_loss} - # self.log("loss", total_loss, prog_bar=True, on_step=True) + self.log("loss", total_loss, prog_bar=True, on_step=True) output_dict.update(tagged_loss_values) else: output_dict = {} From 9d7cc6b3375111a36cd848a4c7c07d8c6d253eba Mon Sep 17 00:00:00 2001 From: Animatory Date: Thu, 4 May 2023 16:56:08 +0400 Subject: [PATCH 04/11] Fix for flake8 --- torchok/data/transforms/swav.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/torchok/data/transforms/swav.py b/torchok/data/transforms/swav.py index eee07984..65be33e9 100644 --- a/torchok/data/transforms/swav.py +++ b/torchok/data/transforms/swav.py @@ -56,8 +56,8 @@ def __init__( random_gray_scale: float = 0.2, gaussian_blur: float = 0.5, sigmas: Tuple[float, float] = (0.1, 2), - bbox_params: Optional[Union[dict, "BboxParams"]] = None, - keypoint_params: Optional[Union[dict, "KeypointParams"]] = None, + bbox_params: Optional[dict] = None, + keypoint_params: Optional[dict] = None, additional_targets: Optional[Dict[str, str]] = None, p: float = 1.0, ): From da74972ea0477b45e04ff41e110eddb60dec06e4 Mon Sep 17 00:00:00 2001 From: Animatory Date: Thu, 4 May 2023 17:00:16 +0400 Subject: [PATCH 05/11] Add Cifar100 dataset --- torchok/data/datasets/examples/__init__.py | 2 +- .../examples/{cifar10.py => cifar.py} | 24 +++++++++++++++++++ 2 files changed, 25 insertions(+), 1 deletion(-) rename torchok/data/datasets/examples/{cifar10.py => cifar.py} (91%) diff --git a/torchok/data/datasets/examples/__init__.py b/torchok/data/datasets/examples/__init__.py index 08e5b57c..13eeac07 100644 --- a/torchok/data/datasets/examples/__init__.py +++ b/torchok/data/datasets/examples/__init__.py @@ -1,4 +1,4 @@ -import torchok.data.datasets.examples.cifar10 +import torchok.data.datasets.examples.cifar import torchok.data.datasets.examples.sop import torchok.data.datasets.examples.triplet_sop import torchok.data.datasets.examples.sweet_pepper diff --git a/torchok/data/datasets/examples/cifar10.py b/torchok/data/datasets/examples/cifar.py similarity index 91% rename from torchok/data/datasets/examples/cifar10.py rename to torchok/data/datasets/examples/cifar.py index 6733ba93..33115f40 100644 --- a/torchok/data/datasets/examples/cifar10.py +++ b/torchok/data/datasets/examples/cifar.py @@ -171,3 +171,27 @@ def _download(self) -> None: else: download_and_extract_archive(self.url, self.data_folder.as_posix(), filename=self.filename, md5=self.tgz_md5) + + +@DATASETS.register_class +class CIFAR100(CIFAR10): + """`CIFAR100 `_ Dataset. + + This is a subclass of the `CIFAR100` Dataset. + """ + base_folder = 'cifar-100-python' + url = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz" + filename = "cifar-100-python.tar.gz" + tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85' + train_list = [ + ['train', '16019d7e3df5f24257cddd939b257f8d'], + ] + + test_list = [ + ['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'], + ] + meta = { + 'filename': 'meta', + 'key': 'fine_label_names', + 'md5': '7973b15100ade9c7d40fb424638fde48', + } From d3a56861af85d259233ef5ba39342f6c25a8d4e0 Mon Sep 17 00:00:00 2001 From: Animatory Date: Fri, 5 May 2023 15:57:09 +0400 Subject: [PATCH 06/11] Update CI/CD --- .github/workflows/cd.yml | 2 +- .github/workflows/ci.yml | 2 +- poetry.lock | 4659 +++++++++++++++++++++----------------- pyproject.toml | 30 +- 4 files changed, 2629 insertions(+), 2064 deletions(-) diff --git a/.github/workflows/cd.yml b/.github/workflows/cd.yml index fea255ed..ecaeda08 100644 --- a/.github/workflows/cd.yml +++ b/.github/workflows/cd.yml @@ -62,7 +62,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ["3.7", "3.8", "3.9"] + python-version: ["3.8", "3.9", "3.10"] steps: #---------------------------------------------- # check-out repo and set-up python diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 8ad45505..d176677c 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -59,7 +59,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ["3.7", "3.8", "3.9"] + python-version: ["3.8", "3.9", "3.10"] steps: #---------------------------------------------- # check-out repo and set-up python diff --git a/poetry.lock b/poetry.lock index a0a46472..b904e7ee 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,21 +1,128 @@ +# This file is automatically @generated by 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-parameterized = "~0.8" -pillow = "~9.1" -python = ">=3.7,<3.11" -pytorch-lightning = "1.9.0" -ranx = "~0.2" -torch = "1.12.1" -torchvision = "0.13.1" -torchmetrics = "~0.11" +parameterized = "~0.9" +python = ">=3.8,<3.11" +pytorch-lightning = "~2.0" +ranx = "~0.3" timm = "~0.6" -mmdet = "2.26.0" +torch = "~2.0" +torchvision = "~0.15" +torchmetrics = "~0.11" [build-system] requires = ["poetry-core>=1.0.0"] From 4dfc7fa64ae9aab52c01ba42faabda71180eff12 Mon Sep 17 00:00:00 2001 From: Animatory Date: Fri, 5 May 2023 16:30:42 +0400 Subject: [PATCH 07/11] Update CI/CD --- poetry.lock | 24 +++++++++++++++++++++++- pyproject.toml | 1 + torchok/tasks/base.py | 2 +- 3 files changed, 25 insertions(+), 2 deletions(-) diff --git a/poetry.lock b/poetry.lock index b904e7ee..d741cf08 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2827,6 +2827,28 @@ dev = ["click", "doit (>=0.36.0)", "flake8", "mypy", "pycodestyle", "pydevtool", doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"] test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] +[[package]] +name = "seaborn" +version = "0.12.2" +description = "Statistical data visualization" +category = "main" +optional = false +python-versions = ">=3.7" +files = [ + {file = "seaborn-0.12.2-py3-none-any.whl", hash = "sha256:ebf15355a4dba46037dfd65b7350f014ceb1f13c05e814eda2c9f5fd731afc08"}, + {file = "seaborn-0.12.2.tar.gz", hash = "sha256:374645f36509d0dcab895cba5b47daf0586f77bfe3b36c97c607db7da5be0139"}, +] + +[package.dependencies] +matplotlib = ">=3.1,<3.6.1 || >3.6.1" +numpy = ">=1.17,<1.24.0 || >1.24.0" +pandas = ">=0.25" + +[package.extras] +dev = ["flake8", "flit", "mypy", "pandas-stubs", "pre-commit", "pytest", "pytest-cov", "pytest-xdist"] +docs = ["ipykernel", "nbconvert", "numpydoc", "pydata_sphinx_theme (==0.10.0rc2)", "pyyaml", "sphinx-copybutton", "sphinx-design", "sphinx-issues"] +stats = ["scipy (>=1.3)", "statsmodels (>=0.10)"] + [[package]] name = "setuptools" version = "67.7.2" @@ -3440,4 +3462,4 @@ files = [ [metadata] lock-version = "2.0" python-versions = ">=3.8,<3.11" -content-hash = "8801f2dd3afc2a57be5a5639ee5515f7f99aacc7701b2b4e80dbffe11fd150cc" +content-hash = "15ed5cba48c02dab0190d5f49be009de3572f532cb5d7d78d18cb4cdf1301a92" diff --git a/pyproject.toml b/pyproject.toml index 067992e9..10173be5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,6 +41,7 @@ parameterized = "~0.9" python = ">=3.8,<3.11" pytorch-lightning = "~2.0" ranx = "~0.3" +seaborn = "^0.12.2" timm = "~0.6" torch = "~2.0" torchvision = "~0.15" diff --git a/torchok/tasks/base.py b/torchok/tasks/base.py index de31b54c..340b181d 100644 --- a/torchok/tasks/base.py +++ b/torchok/tasks/base.py @@ -155,7 +155,7 @@ def test_step(self, batch: Dict[str, Tensor], batch_idx: int, dataloader_idx: in output = self.forward_with_gt(batch) self.metrics_manager.update(Phase.TEST, dataloader_idx, **output) - def predict_step(self, batch: Dict[str, Tensor], batch_idx: int, dataloader_idx: int = 0) -> dict[str, Tensor]: + def predict_step(self, batch: Dict[str, Tensor], batch_idx: int, dataloader_idx: int = 0) -> Dict[str, Tensor]: """Complete predict loop.""" output = self.forward_with_gt(batch) return output From a34e36ee17b656371da56bdeedd850739df9f73a Mon Sep 17 00:00:00 2001 From: Animatory Date: Thu, 11 May 2023 16:19:10 +0400 Subject: [PATCH 08/11] Add comments and some fixes --- torchok/data/datasets/representation/swav.py | 19 ++++++++++++++++++- torchok/data/transforms/swav.py | 12 ++++++------ torchok/data/transforms/utils.py | 4 ++-- torchok/tasks/self_supervised/swav.py | 13 +++++++------ 4 files changed, 33 insertions(+), 15 deletions(-) diff --git a/torchok/data/datasets/representation/swav.py b/torchok/data/datasets/representation/swav.py index ab646012..fd8eb923 100644 --- a/torchok/data/datasets/representation/swav.py +++ b/torchok/data/datasets/representation/swav.py @@ -15,7 +15,7 @@ def __getitem__(self, idx: int) -> List[Dict[str, Any]]: samples = self.get_raw(idx) new_samples = [] for i in range(len(samples['image'])): - view = {k: v[i] for k, v in samples.items()} + view = {name: views[i] for name, views in samples.items()} view = self._apply_transform(self.transform, view) view['image'] = view['image'].type(torch.__dict__[self.input_dtype]) new_samples.append(view) @@ -23,6 +23,23 @@ def __getitem__(self, idx: int) -> List[Dict[str, Any]]: return new_samples def collate_fn(self, batch: List[List[Dict[str, Any]]]) -> Dict[str, List[torch.Tensor]]: + """ + Construct batch from multiview samples + + Args: + batch: list of samples where each sample is a list of several views from the same image. + Example: sample_i = [view_1_i, ..., view_j_i, ..., view_m_i], where + m - number of views, view_j_i = {'image': crop_j_i, ...} and + crop_j_i is a j-th crop from i-th image in the dataset. + + Returns: + Dict where values are list of batches stacked along the view. + Example with the same notation as in Args: + batch = {'image': [batch_view_1, ..., batch_view_j, ..., batch_view_m], ...}, where + batch_view_j = torch.stack([view_j_1, view_j_2, ..., view_j_i, ..., view_j_n]) and + n - batch size. + """ + num_views = len(batch[0]) new_batch = defaultdict(list) diff --git a/torchok/data/transforms/swav.py b/torchok/data/transforms/swav.py index 65be33e9..72a12315 100644 --- a/torchok/data/transforms/swav.py +++ b/torchok/data/transforms/swav.py @@ -30,8 +30,8 @@ class SwaVTransform(MultiCrop): cj_contrast: How much to jitter contrast. cj_sat: How much to jitter saturation. cj_hue: How much to jitter hue. - random_gray_scale: Probability of conversion to grayscale. - gaussian_blur: Probability of Gaussian blur. + gray_prob: Probability of conversion to grayscale. + gb_prob: Probability of Gaussian blur. sigmas: Tuple of min and max value from which the std of the gaussian kernel is sampled. Is ignored if `kernel_size` is set. @@ -53,8 +53,8 @@ def __init__( cj_contrast: float = 0.8, cj_sat: float = 0.8, cj_hue: float = 0.2, - random_gray_scale: float = 0.2, - gaussian_blur: float = 0.5, + gray_prob: float = 0.2, + gb_prob: float = 0.5, sigmas: Tuple[float, float] = (0.1, 2), bbox_params: Optional[dict] = None, keypoint_params: Optional[dict] = None, @@ -73,8 +73,8 @@ def __init__( hue=cj_strength * cj_hue, p=cj_prob ), - alb.ToGray(p=random_gray_scale), - alb.GaussianBlur(sigma_limit=sigmas, p=gaussian_blur) + alb.ToGray(p=gray_prob), + alb.GaussianBlur(sigma_limit=sigmas, p=gb_prob) ] super().__init__( diff --git a/torchok/data/transforms/utils.py b/torchok/data/transforms/utils.py index f8c1001e..518ccb52 100644 --- a/torchok/data/transforms/utils.py +++ b/torchok/data/transforms/utils.py @@ -87,10 +87,10 @@ def __init__( "Length of crop_sizes and crop_min_scales must be equal but are" f" {len(crop_sizes)} and {len(crop_min_scales)}." ) - if len(crop_sizes) != len(crop_min_scales): + if len(crop_sizes) != len(crop_max_scales): raise ValueError( "Length of crop_sizes and crop_max_scales must be equal but are" - f" {len(crop_sizes)} and {len(crop_min_scales)}." + f" {len(crop_sizes)} and {len(crop_max_scales)}." ) crop_transforms = [] diff --git a/torchok/tasks/self_supervised/swav.py b/torchok/tasks/self_supervised/swav.py index b9c687f8..9583116d 100644 --- a/torchok/tasks/self_supervised/swav.py +++ b/torchok/tasks/self_supervised/swav.py @@ -73,7 +73,7 @@ def forward(self, x): p = self.prototypes(x) return p - def forward_with_gt(self, batch: Dict[str, Tensor]) -> Dict[str, List[Tensor]]: + def forward_with_gt(self, batch: Dict[str, List[Tensor]]) -> Dict[str, List[Tensor]]: """Forward with ground truth labels. Args: @@ -85,16 +85,17 @@ def forward_with_gt(self, batch: Dict[str, Tensor]) -> Dict[str, List[Tensor]]: Returns: Dictionary with the following keys and values - - 'high_resolution': list of torch.Tensor of shape `(B, num_features)`, + - 'high_resolution_features': list of torch.Tensor of shape `(B, num_features)`, representing embeddings per each image view in high resolution. - - 'low_resolution': list of torch.Tensor of shape `(B, num_classes)`, + - 'low_resolution_features': list of torch.Tensor of shape `(B, num_classes)`, representing logits per each image view in low resolution. """ self.prototypes.normalize() crops = batch['image'] multi_crop_features = [self.forward(crop) for crop in crops] - high_resolution = multi_crop_features[:self.num_highres_crops] - low_resolution = multi_crop_features[self.num_highres_crops:] - output = {"high_resolution": high_resolution, "low_resolution": low_resolution} + high_resolution_features = multi_crop_features[:self.num_highres_crops] + low_resolution_features = multi_crop_features[self.num_highres_crops:] + output = {"high_resolution_features": high_resolution_features, + "low_resolution_features": low_resolution_features} return output From dbc9e02132097e52f418fe79d545ace8cb9e4a84 Mon Sep 17 00:00:00 2001 From: Animatory Date: Thu, 11 May 2023 16:28:59 +0400 Subject: [PATCH 09/11] Fix typing --- torchok/data/transforms/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchok/data/transforms/utils.py b/torchok/data/transforms/utils.py index 518ccb52..c8d7c4c2 100644 --- a/torchok/data/transforms/utils.py +++ b/torchok/data/transforms/utils.py @@ -31,7 +31,7 @@ def __init__( super().__init__(transforms, bbox_params, keypoint_params, additional_targets, p) self.branch_transforms = branch_transforms - def __call__(self, *args, force_apply: bool = False, **data) -> List[Dict[str, Any]]: + def __call__(self, *args, force_apply: bool = False, **data) -> Dict[str, List[Any]]: """Transforms an image into multiple views. Every transform in self.branch_transforms creates a new view. From d3cda577bb39d1afa617e59e17858ef5dd579fac Mon Sep 17 00:00:00 2001 From: Animatory Date: Sun, 14 May 2023 16:06:03 +0400 Subject: [PATCH 10/11] Add config fow SwaV method --- examples/configs/self_supervised/swav.yaml | 116 +++++++++++++++++++++ 1 file changed, 116 insertions(+) create mode 100644 examples/configs/self_supervised/swav.yaml diff --git a/examples/configs/self_supervised/swav.yaml b/examples/configs/self_supervised/swav.yaml new file mode 100644 index 00000000..32f3af9b --- /dev/null +++ b/examples/configs/self_supervised/swav.yaml @@ -0,0 +1,116 @@ +task: + name: SwaVTask + params: + backbone_name: resnet18 + backbone_params: + pretrained: true + in_channels: 3 + pooling_name: Pooling + head_name: SwaVProjectionHead + head_params: + hidden_channels: 2048 + out_channels: 128 + n_prototypes: 3000 + num_highres_crops: &num_highres_crops 2 + num_lowres_crops: &num_lowres_crops 6 + inputs: + - shape: [3, &height 128, &width 128] + dtype: &input_dtype bfloat16 + +joint_loss: + losses: + - name: SwaVLoss + params: + temperature: 0.1 + sinkhorn_iterations: 3 + sinkhorn_epsilon: 0.05 + sinkhorn_gather_distributed: false + mapping: + high_resolution_outputs: high_resolution_features + low_resolution_outputs: low_resolution_features + +optimization: + - optimizer: + name: SGD + params: + lr: &base_lr 4.8 + weight_decay: 0.000001 + momentum: 0.9 + scheduler: + name: OneCycleLR + params: + max_lr: *base_lr + steps_per_epoch: 508 + epochs: &num_epochs 200 + pct_start: 0.05 + pl_params: + interval: step + + +data: + TRAIN: + - dataloader: + batch_size: 256 + num_workers: 8 + drop_last: false + shuffle: true + dataset: + name: SwaVDataset + params: + data_folder: &data_folder /home/ubuntu/workdir/experiments/imagenet100/data + annotation_path: data_train.csv + input_dtype: *input_dtype + rgba_layout_color: 0 + test_mode: true + augment: + - name: SwaVTransform + params: + crop_sizes: [*height, 96] + crop_counts: [*num_highres_crops, *num_lowres_crops] + transform: + - &normalize + name: Normalize + - &totensor + name: ToTensorV2 + +trainer: + accelerator: 'gpu' + max_epochs: *num_epochs + precision: bf16 + num_sanity_val_steps: 0 + limit_val_batches: 0 +# limit_train_batches: 10 + +seed_params: + seed: 42 + workers: true + +logger: + name: MLFlowLoggerX + experiment_name: imagenet100 + log_dir: '${oc.env:HOME}/${logger.experiment_name}/logs' + timestamp: '${now:%Y-%m-%d}/${now:%H-%M-%S}' + params: + run_name: resnet18_swav + tracking_uri: '${oc.env:DB_URI}' + artifact_location: '${oc.env:S3_ARTIFACT_BUCKET}' + +hydra: + run: + dir: &logs_dir '${logger.log_dir}/${logger.params.run_name}/${logger.timestamp}' + +callbacks: + - name: ModelCheckpoint + params: + dirpath: *logs_dir + monitor: train/loss + save_top_k: 1 + save_last: true + mode: min + save_weights_only: False + - name: FinalizeLogger + - name: TQDMProgressBar + params: + refresh_rate: 100 + +metrics: [] From 903c57c6473239296d1997c48725914f42c69112 Mon Sep 17 00:00:00 2001 From: Animatory Date: Sun, 14 May 2023 18:29:34 +0400 Subject: [PATCH 11/11] Update config fow SwaV method --- examples/configs/self_supervised/swav.yaml | 49 +++++++++++++++++++++- 1 file changed, 48 insertions(+), 1 deletion(-) diff --git a/examples/configs/self_supervised/swav.yaml b/examples/configs/self_supervised/swav.yaml index 32f3af9b..d3545e39 100644 --- a/examples/configs/self_supervised/swav.yaml +++ b/examples/configs/self_supervised/swav.yaml @@ -72,6 +72,54 @@ data: name: Normalize - &totensor name: ToTensorV2 + VALID: + - dataloader: + batch_size: 256 + num_workers: 8 + drop_last: false + shuffle: true + dataset: + name: ImageClassificationDataset + params: + data_folder: *data_folder + annotation_path: data_train.csv + input_dtype: *input_dtype + rgba_layout_color: 0 + transform: + - name: PadIfNeeded + params: + min_height: *height + min_width: *width + - name: CenterCrop + params: + height: *height + width: *width + - *normalize + - *totensor + TEST: + - dataloader: + batch_size: 256 + num_workers: 8 + drop_last: false + shuffle: true + dataset: + name: ImageClassificationDataset + params: + data_folder: *data_folder + annotation_path: data_valid.csv + input_dtype: *input_dtype + rgba_layout_color: 0 + transform: + - name: PadIfNeeded + params: + min_height: *height + min_width: *width + - name: CenterCrop + params: + height: *height + width: *width + - *normalize + - *totensor trainer: accelerator: 'gpu' @@ -79,7 +127,6 @@ trainer: precision: bf16 num_sanity_val_steps: 0 limit_val_batches: 0 -# limit_train_batches: 10 seed_params: seed: 42