From b258fb60c30c32c336454f851a77985704dadc5c Mon Sep 17 00:00:00 2001 From: Xinyue Li Date: Wed, 30 Aug 2023 15:59:42 -0400 Subject: [PATCH] add data augmentation and dc2 psf/deconv --- bliss/conf/base_config.yaml | 4 +- bliss/data_augmentation.py | 161 ++++++++---------- bliss/encoder.py | 33 ++-- .../DC2_galaxy_psf_params.ipynb | 160 +++++++++++++++++ .../merged_catalog/merged_catalog_100000.pkl | Bin 3353177 -> 0 bytes .../merged_catalog_bright_bright_r.pkl | Bin 2191912 -> 0 bytes tests/test_dc2.py | 22 ++- 7 files changed, 263 insertions(+), 117 deletions(-) create mode 100644 case_studies/dc2_merged_catalog/DC2_galaxy_psf_params.ipynb delete mode 100644 data/dc2/merged_catalog/merged_catalog_100000.pkl delete mode 100644 data/dc2/merged_catalog/merged_catalog_bright_bright_r.pkl diff --git a/bliss/conf/base_config.yaml b/bliss/conf/base_config.yaml index 4d7c3bc70..d4c655d83 100644 --- a/bliss/conf/base_config.yaml +++ b/bliss/conf/base_config.yaml @@ -84,9 +84,7 @@ encoder: concat_psf_params: false log_transform: false rolling_z_score: true - data_augmentation: - do_data_augmentation: true - epoch_start: 1 + do_data_augmentation: false architecture: # this architecture is based on yolov5l.yaml, see # https://github.com/ultralytics/yolov5/blob/master/models/yolov5l.yaml diff --git a/bliss/data_augmentation.py b/bliss/data_augmentation.py index 838be8740..04fbb5a5e 100644 --- a/bliss/data_augmentation.py +++ b/bliss/data_augmentation.py @@ -1,117 +1,98 @@ import random -import numpy as np -import torchvision as TF +import torch +from torchvision.transforms import functional as TF from bliss.catalog import TileCatalog -def augment_data(tile_catalog, image, deconv_image=None): +def augment_data(tile_catalog, image): origin_tile = TileCatalog(4, tile_catalog) origin_full = origin_tile.to_full_params() + aug_full, aug_image = origin_full, image - num_transform_list = [1, 2, 3] - num_transform = random.choices(num_transform_list, weights=[0.7, 0.2, 0.1], k=1)[0] - transform_list = ["vflip", "hflip", "rotate90", "rotate180", "rotate270", "shift"] - - aug_method = np.random.choice( - transform_list, num_transform, p=(0.1, 0.1, 0.1, 0.1, 0.1, 0.5), replace=False - ) - - aug_image, aug_tile, aug_deconv = origin_full, image, deconv_image - for i in aug_method: - if i == "vflip": - aug_image, aug_tile, aug_deconv = aug_vflip(aug_image, aug_tile, aug_deconv) - if i == "hflip": - aug_image, aug_tile, aug_deconv = aug_hflip(aug_image, aug_tile, aug_deconv) - if i == "rotate90": - aug_image, aug_tile, aug_deconv = aug_rotate90(aug_image, aug_tile, aug_deconv) - if i == "rotate180": - aug_image, aug_tile, aug_deconv = aug_rotate180(aug_image, aug_tile, aug_deconv) - if i == "rotate270": - aug_image, aug_tile, aug_deconv = aug_rotate270(aug_image, aug_tile, aug_deconv) - if i == "shift": - aug_image, aug_tile, aug_deconv = aug_shift(aug_image, aug_tile, aug_deconv) - - return aug_image, aug_tile, aug_deconv - - -def aug_vflip(origin_full, image, deconv_image): - aug_image = TF.transforms.functional.vflip(image) - aug_deconv = None - if deconv_image is not None: - aug_deconv = TF.transforms.functional.vflip(deconv_image) - image_size = image.size(2) + rotate_list = [None, aug_rotate90, aug_rotate180, aug_rotate270] + flip_list = [None, aug_vflip] + rotate_choice = random.choice(rotate_list) + flip_choice = random.choice(flip_list) + + if rotate_choice is not None: + aug_image, aug_full = rotate_choice(aug_full, aug_image) + if flip_choice is not None: + aug_image, aug_full = flip_choice(aug_full, aug_image) + + aug_image, aug_full = aug_shift(aug_full, aug_image) + aug_tile = aug_full.to_tile_params(4, 4).get_brightest_source_per_tile().to_dict() + return aug_image, aug_tile + + +def aug_vflip(origin_full, image): + aug_image = TF.vflip(image) + image_size = image.size(3) origin_full["plocs"][:, :, 0] = image_size - origin_full["plocs"][:, :, 0] - 1 - aug_tile = origin_full.to_tile_params(4, 4).get_brightest_source_per_tile().to_dict() - return aug_image, aug_tile, aug_deconv - - -def aug_hflip(origin_full, image, deconv_image): - aug_image = TF.transforms.functional.hflip(image) - aug_deconv = None - if deconv_image is not None: - aug_deconv = TF.transforms.functional.hflip(deconv_image) - image_size = image.size(2) - origin_full["plocs"][:, :, 1] = image_size - origin_full["plocs"][:, :, 1] - 1 - aug_tile = origin_full.to_tile_params(4, 4).get_brightest_source_per_tile().to_dict() - return aug_image, aug_tile, aug_deconv - - -def aug_rotate90(origin_full, image, deconv_image): - aug_image = TF.transforms.functional.rotate(image, 90) - aug_deconv = None - if deconv_image is not None: - aug_deconv = TF.transforms.functional.rotate(deconv_image, 90) - image_size = image.size(2) + return aug_image, origin_full + + +def aug_rotate90(origin_full, image): + num_channel = image.size(2) + aug_images = [] + for i in range(num_channel): + rotated_slice = TF.rotate(image[:, :, i, :, :], 90) + aug_images.append(rotated_slice) + aug_image = torch.stack(aug_images, dim=2) + + image_size = image.size(3) plocs = origin_full["plocs"].clone() origin_full["plocs"][:, :, 1] = plocs[:, :, 0] origin_full["plocs"][:, :, 0] = image_size - plocs[:, :, 1] - 1 - aug_tile = origin_full.to_tile_params(4, 4).get_brightest_source_per_tile().to_dict() - return aug_image, aug_tile, aug_deconv + return aug_image, origin_full -def aug_rotate180(origin_full, image, deconv_image): - aug_image = TF.transforms.functional.rotate(image, 180) - aug_deconv = None - if deconv_image is not None: - aug_deconv = TF.transforms.functional.rotate(deconv_image, 180) - image_size = image.size(2) +def aug_rotate180(origin_full, image): + num_channel = image.size(2) + aug_images = [] + for i in range(num_channel): + rotated_slice = TF.rotate(image[:, :, i, :, :], 180) + aug_images.append(rotated_slice) + aug_image = torch.stack(aug_images, dim=2) + + image_size = image.size(3) plocs = origin_full["plocs"].clone() origin_full["plocs"][:, :, 1] = image_size - plocs[:, :, 1] - 1 origin_full["plocs"][:, :, 0] = image_size - plocs[:, :, 0] - 1 - aug_tile = origin_full.to_tile_params(4, 4).get_brightest_source_per_tile().to_dict() - return aug_image, aug_tile, aug_deconv + return aug_image, origin_full + +def aug_rotate270(origin_full, image): + num_channel = image.size(2) + aug_images = [] + for i in range(num_channel): + rotated_slice = TF.rotate(image[:, :, i, :, :], 270) + aug_images.append(rotated_slice) + aug_image = torch.stack(aug_images, dim=2) -def aug_rotate270(origin_full, image, deconv_image): - aug_image = TF.transforms.functional.rotate(image, 270) - aug_deconv = None - if deconv_image is not None: - aug_deconv = TF.transforms.functional.rotate(deconv_image, 270) - image_size = image.size(2) + image_size = image.size(3) plocs = origin_full["plocs"].clone() origin_full["plocs"][:, :, 1] = image_size - plocs[:, :, 0] - 1 origin_full["plocs"][:, :, 0] = plocs[:, :, 1] - aug_tile = origin_full.to_tile_params(4, 4).get_brightest_source_per_tile().to_dict() - return aug_image, aug_tile, aug_deconv - - -def aug_shift(origin_full, image, deconv_image): - shift_x = random.randint(0, 3) - shift_y = random.randint(0, 3) - shift_xy = (shift_x, shift_y) - image_size = image.size(2) - aug_deconv = None - pad_image = TF.transforms.functional.pad(image, shift_xy, padding_mode="reflect") - if deconv_image is not None: - pad_deconv = TF.transforms.functional.pad(deconv_image, shift_xy, padding_mode="reflect") - aug_deconv = pad_deconv[:, :, :image_size, :image_size] - - aug_image = pad_image[:, :, :image_size, :image_size] + return aug_image, origin_full + + +def aug_shift(origin_full, image): + shift_x = random.randint(-1, 2) + shift_y = random.randint(-1, 2) + image_size = image.size(3) + image_lim = [2 - shift_x, 2 - shift_x + image_size, 2 - shift_y, 2 - shift_y + image_size] + + num_channel = image.size(2) + aug_images = [] + for i in range(num_channel): + pad_slice = TF.pad(image[:, :, i, :, :], (2, 2), padding_mode="reflect") + aug_images.append(pad_slice[:, :, image_lim[0] : image_lim[1], image_lim[2] : image_lim[3]]) + aug_image = torch.stack(aug_images, dim=2) + plocs = origin_full["plocs"].clone() origin_full["plocs"][:, :, 1] = plocs[:, :, 1] + shift_x origin_full["plocs"][:, :, 0] = plocs[:, :, 0] + shift_y - aug_tile = origin_full.to_tile_params(4, 4).get_brightest_source_per_tile().to_dict() - return aug_image, aug_tile, aug_deconv + return aug_image, origin_full diff --git a/bliss/encoder.py b/bliss/encoder.py index 14fe63f3d..95f8380f8 100644 --- a/bliss/encoder.py +++ b/bliss/encoder.py @@ -49,7 +49,7 @@ def __init__( optimizer_params: Optional[dict] = None, scheduler_params: Optional[dict] = None, input_transform_params: Optional[dict] = None, - data_augmentation: Optional[dict] = None, + do_data_augmentation: bool = False, ): """Initializes DetectionEncoder. @@ -65,8 +65,7 @@ def __init__( scheduler_params: arguments passed to the learning rate scheduler input_transform_params: used for determining what channels to use as input (e.g. deconvolution, concatenate PSF parameters, z-score inputs, etc.) - data_augmentation: used for determining whether or not do data augmentation and the - data augmentation start point (a.g. after 1 epoch) + do_data_augmentation: used for determining whether or not do data augmentation """ super().__init__() self.save_hyperparameters() @@ -81,7 +80,7 @@ def __init__( self.optimizer_params = optimizer_params self.scheduler_params = scheduler_params if scheduler_params else {"milestones": []} self.input_transform_params = input_transform_params - self.data_augmentation = data_augmentation + self.do_data_augmentation = do_data_augmentation transform_enabled = ( "log_transform" in self.input_transform_params @@ -334,23 +333,26 @@ def _get_loss(self, pred: Dict[str, Distribution], true_tile_cat: TileCatalog): return loss_with_components - def _generic_step(self, batch, logging_name, log_metrics=False, plot_images=False): - do_data_augmentation = self.data_augmentation.get("do_data_augmentation") - data_augmentation_start = self.current_epoch >= self.data_augmentation.get("epoch_start") - - if data_augmentation_start and (logging_name == "train") and do_data_augmentation: - deconv_image = None + def _generic_step( + self, batch, logging_name, do_data_augmentation=False, log_metrics=False, plot_images=False + ): + if do_data_augmentation: + imgs = batch["images"][:, self.bands].unsqueeze(2) # add extra dim for 5d input + bgs = batch["background"][:, self.bands].unsqueeze(2) + aug_input_images = [imgs, bgs] if self.input_transform_params.get("use_deconv_channel"): assert ( "deconvolution" in batch ), "use_deconv_channel specified but deconvolution not present in data" - deconv_image = batch["deconvolution"] + aug_input_images.append(batch["background"][:, self.bands].unsqueeze(2)) + aug_input_image = torch.cat(aug_input_images, dim=2) - image, tile, deconv = augment_data(batch["tile_catalog"], batch["images"], deconv_image) - batch["images"] = image + aug_output_image, tile = augment_data(batch["tile_catalog"], aug_input_image) + batch["images"] = aug_output_image[:, :, 0, :, :] + batch["background"] = aug_output_image[:, :, 1, :, :] batch["tile_catalog"] = tile if self.input_transform_params.get("use_deconv_channel"): - batch["deconvolution"] = deconv + batch["deconvolution"] = aug_output_image[:, :, 2, :, :] batch_size = batch["images"].size(0) pred = self.encode_batch(batch) @@ -407,7 +409,8 @@ def _generic_step(self, batch, logging_name, log_metrics=False, plot_images=Fals def training_step(self, batch, batch_idx, optimizer_idx=0): """Training step (pytorch lightning).""" - return self._generic_step(batch, "train") + do_data_augmentation = self.do_data_augmentation + return self._generic_step(batch, "train", do_data_augmentation=do_data_augmentation) def validation_step(self, batch, batch_idx): """Pytorch lightning method.""" diff --git a/case_studies/dc2_merged_catalog/DC2_galaxy_psf_params.ipynb b/case_studies/dc2_merged_catalog/DC2_galaxy_psf_params.ipynb new file mode 100644 index 000000000..a1a47158f --- /dev/null +++ b/case_studies/dc2_merged_catalog/DC2_galaxy_psf_params.ipynb @@ -0,0 +1,160 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook present the steps to generatea merged catalog files. The reason that we need to generate such files is that:\n", + "\n", + "1. Bliss needs input parameters that contains psf, fluxes, location, source type, galaxy related parameters, but the truth catalog in DC2 does not provide galaxy parameters or psf\n", + "2. To get those parameter, we need to use CosmoDC2 dataset and truth-match table, where the galaxy parameters and psf arestores.\n", + "3. To save the time for multiple merging when loading the data, we store the merged dataset into corresponding merged_catalog_{}.pkl files\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Catalog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use [GCRCatalogs](https://data.lsstdesc.org/doc/install_gcr) package to load the dc2 truth catalog and CosomoDC2.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import GCRCatalogs\n", + "truth_cat = GCRCatalogs.load_catalog('desc_dc2_run2.2i_dr6_truth') \n", + "data = truth_cat.get_quantities([\n", + " \"id\", \"match_objectId\", \"cosmodc2_id\", \"ra\", \"dec\", \"truth_type\", \n", + " \"flux_g\", \"flux_i\", \"flux_r\", \"flux_u\", \"flux_y\", \"flux_z\"\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "config_overwrite = dict(\n", + " catalog_root_dir='/data/scratch/lxinyue/cosmoDC2'\n", + ")\n", + "\n", + "galaxy_cat = GCRCatalogs.load_catalog('desc_cosmodc2', config_overwrite)\n", + "galaxy = galaxy_cat.get_quantities([\n", + " \"galaxy_id\", \"position_angle_true\", \"size_minor_disk_true\", \n", + " \"size_disk_true\", \"size_minor_bulge_true\", \n", + " \"size_bulge_true\", \"bulge_to_total_ratio_i\"\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import GCRCatalogs\n", + "match_cat = GCRCatalogs.load_catalog('desc_dc2_run2.2i_dr6_object_with_truth_match')\n", + "psf_params = match_cat.get_quantities([\n", + " \"objectId\", \"IxxPSF_pixel_g\", \"IxxPSF_pixel_z\", \n", + " \"IxxPSF_pixel_r\", \"IxxPSF_pixel_i\", \"IxxPSF_pixel_u\", \n", + " \"IxxPSF_pixel_y\", \"IyyPSF_pixel_g\", \"IyyPSF_pixel_z\", \n", + " \"IyyPSF_pixel_r\", \"IyyPSF_pixel_i\", \"IyyPSF_pixel_u\", \n", + " \"IyyPSF_pixel_y\", \"IxyPSF_pixel_g\", \"IxyPSF_pixel_z\", \n", + " \"IxyPSF_pixel_r\", \"IxyPSF_pixel_i\", \"IxyPSF_pixel_u\", \n", + " \"IxyPSF_pixel_y\", \"psf_fwhm_g\", \"psf_fwhm_z\", \"psf_fwhm_r\",\n", + " \"psf_fwhm_i\", \"psf_fwhm_u\", \"psf_fwhm_y\"\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Merge Catalog " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "df_data = pd.DataFrame(data)\n", + "df_galaxy = pd.DataFrame(galaxy)\n", + "df_psf = pd.DataFrame(psf_params)\n", + "merge_data = df_data.merge(\n", + " df_galaxy, \n", + " left_on = \"cosmodc2_id\", \n", + " right_on = \"galaxy_id\", \n", + " how = \"left\" \n", + ")\n", + "merge_data_fill = merge_data.fillna(-1)\n", + "data_psf = merge_data_fill.merge(\n", + " df_psf, \n", + " left_on = \"match_objectId\", \n", + " right_on = \"objectId\", \n", + " how = \"left\" \n", + ")\n", + "\n", + "# filter the object by flix\n", + "bright_merge = data_psf[data_psf['flux_r'] > 100]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Save to File" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "# create a pickle file \n", + "f = bright_merge.to_pickle(\"merged_catalog_psf_100.pkl\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bliss-deblender-IlvvpPLR-py3.10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/data/dc2/merged_catalog/merged_catalog_100000.pkl b/data/dc2/merged_catalog/merged_catalog_100000.pkl deleted file mode 100644 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