From 8eb282c37120c650633587d0ab0c22ace9cc783a Mon Sep 17 00:00:00 2001 From: Caleb Spradlin <72508718+cssprad1@users.noreply.github.com> Date: Fri, 21 Jun 2024 09:56:57 -0400 Subject: [PATCH 01/12] Update requirements.txt --- requirements/requirements.txt | 2 ++ 1 file changed, 2 insertions(+) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 028ca75..a5d7614 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -27,4 +27,6 @@ termcolor numba segmentation-models-pytorch joblib +timm +deepspeed GDAL>=3.3.0 From 970706fd1548452e1a40327ee17f96f4b7584af1 Mon Sep 17 00:00:00 2001 From: Caleb Spradlin <72508718+cssprad1@users.noreply.github.com> Date: Fri, 21 Jun 2024 09:57:13 -0400 Subject: [PATCH 02/12] Update requirements-test.txt --- requirements/requirements-test.txt | 2 ++ 1 file changed, 2 insertions(+) diff --git a/requirements/requirements-test.txt b/requirements/requirements-test.txt index 462d3e4..d35fb47 100644 --- a/requirements/requirements-test.txt +++ b/requirements/requirements-test.txt @@ -26,3 +26,5 @@ yacs termcolor numba segmentation-models-pytorch +timm +deepspeed From 8b6fee0fba5b70068c657956f4328c5e2e8fb728 Mon Sep 17 00:00:00 2001 From: cssprad1 Date: Fri, 21 Jun 2024 11:25:22 -0400 Subject: [PATCH 03/12] updated hf model --- notebooks/satvision-toa-reconstruction.ipynb | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/notebooks/satvision-toa-reconstruction.ipynb b/notebooks/satvision-toa-reconstruction.ipynb index b7a9dde..1e06719 100644 --- a/notebooks/satvision-toa-reconstruction.ipynb +++ b/notebooks/satvision-toa-reconstruction.ipynb @@ -86,13 +86,15 @@ "source": [ "### Clone model ckpt from huggingface\n", "\n", + "### Model repo: https://huggingface.co/nasa-cisto-data-science-group/satvision-toa-huge-patch8-window8-128\n", + "\n", "```bash\n", "# On prism/explore\n", "module load git-lfs\n", "\n", "git lfs install\n", "\n", - "git clone git clone git@hf.co:nasa-cisto-data-science-group/satvision-toa-huge-patch8-window12-192\n", + "git clone git clone git@hf.co:nasa-cisto-data-science-group/satvision-toa-huge-patch8-window8-128\n", "```\n", "\n", "Note: If using git w/ ssh, make sure you have ssh keys enabled to clone using ssh auth.\n", @@ -123,8 +125,8 @@ "metadata": {}, "outputs": [], "source": [ - "MODEL_PATH: str = '../../satvision-toa-huge-patch8-window12-192/mp_rank_00_model_states.pt'\n", - "CONFIG_PATH: str = '../../satvision-toa-huge-patch8-window12-192/mim_pretrain_swinv2_satvision_huge_192_window12_100ep.yaml'\n", + "MODEL_PATH: str = '../../satvision-toa-huge-patch8-window8-128/mp_rank_00_model_states.pt'\n", + "CONFIG_PATH: str = '../../satvision-toa-huge-patch8-window8-128/mim_pretrain_swinv2_satvision_huge_128_window8_patch8_100ep.yaml'\n", "\n", "OUTPUT: str = '.'\n", "TAG: str = 'satvision-huge-toa-reconstruction'\n", @@ -419,7 +421,7 @@ "metadata": {}, "outputs": [], "source": [ - "pdf_path = '../../satvision-toa-reconstruction-pdf-huge-patch-8-04.30.pdf'\n", + "pdf_path = '../../satvision-toa-reconstruction-pdf-huge-patch-8-06.21.pdf'\n", "rgb_index = [0, 2, 1] # Indices of [Red band, Blue band, Green band]\n", "\n", "plot_export_pdf(pdf_path, inputs, outputs, masks, rgb_index)" From 3eba08af1e3e83c1c4968e6b61583ad4d6664349 Mon Sep 17 00:00:00 2001 From: Caleb Spradlin <72508718+cssprad1@users.noreply.github.com> Date: Fri, 21 Jun 2024 11:27:20 -0400 Subject: [PATCH 04/12] Update environment_gpu.yml --- requirements/environment_gpu.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/requirements/environment_gpu.yml b/requirements/environment_gpu.yml index 696e866..1f972cd 100644 --- a/requirements/environment_gpu.yml +++ b/requirements/environment_gpu.yml @@ -40,4 +40,6 @@ dependencies: - termcolor - numba - joblib + - timm + - deepspeed - segmentation-models-pytorch From 182f7a734e98bc7fc0a2f9503ddc9901df4950e1 Mon Sep 17 00:00:00 2001 From: cssprad1 Date: Fri, 12 Jul 2024 08:06:34 -0400 Subject: [PATCH 05/12] removed vanilla file --- .../pipelines/finetuning/finetune_vanilla.py | 438 ------------------ 1 file changed, 438 deletions(-) delete mode 100644 pytorch_caney/pipelines/finetuning/finetune_vanilla.py diff --git a/pytorch_caney/pipelines/finetuning/finetune_vanilla.py b/pytorch_caney/pipelines/finetuning/finetune_vanilla.py deleted file mode 100644 index 72ade94..0000000 --- a/pytorch_caney/pipelines/finetuning/finetune_vanilla.py +++ /dev/null @@ -1,438 +0,0 @@ -from pytorch_caney.models.build import build_model - -from pytorch_caney.data.datamodules.finetune_datamodule \ - import build_finetune_dataloaders - -from pytorch_caney.training.mim_utils \ - import build_optimizer, save_checkpoint, reduce_tensor - -from pytorch_caney.config import get_config -from pytorch_caney.loss.build import build_loss -from pytorch_caney.lr_scheduler import build_scheduler, setup_scaled_lr -from pytorch_caney.ptc_logging import create_logger -from pytorch_caney.training.mim_utils import get_grad_norm - -import argparse -import datetime -import joblib -import numpy as np -import os -import time - -import torch -import torch.cuda.amp as amp -import torch.backends.cudnn as cudnn -import torch.distributed as dist - -from timm.utils import AverageMeter - - -def parse_args(): - """ - Parse command-line arguments - """ - - parser = argparse.ArgumentParser( - 'pytorch-caney finetuning', - add_help=False) - - parser.add_argument( - '--cfg', - type=str, - required=True, - metavar="FILE", - help='path to config file') - - parser.add_argument( - "--data-paths", - nargs='+', - required=True, - help="paths where dataset is stored") - - parser.add_argument( - '--dataset', - type=str, - required=True, - help='Dataset to use') - - parser.add_argument( - '--pretrained', - type=str, - help='path to pre-trained model') - - parser.add_argument( - '--batch-size', - type=int, - help="batch size for single GPU") - - parser.add_argument( - '--resume', - help='resume from checkpoint') - - parser.add_argument( - '--accumulation-steps', - type=int, - help="gradient accumulation steps") - - parser.add_argument( - '--use-checkpoint', - action='store_true', - help="whether to use gradient checkpointing to save memory") - - parser.add_argument( - '--enable-amp', - action='store_true') - - parser.add_argument( - '--disable-amp', - action='store_false', - dest='enable_amp') - - parser.set_defaults(enable_amp=True) - - parser.add_argument( - '--output', - default='output', - type=str, - metavar='PATH', - help='root of output folder, the full path is ' + - '// (default: output)') - - parser.add_argument( - '--tag', - help='tag of experiment') - - args = parser.parse_args() - - config = get_config(args) - - return args, config - - -def train(config, - dataloader_train, - dataloader_val, - model, - model_wo_ddp, - optimizer, - lr_scheduler, - scaler, - criterion): - """ - Start fine-tuning a specific model and dataset. - - Args: - config: config object - dataloader_train: training pytorch dataloader - dataloader_val: validation pytorch dataloader - model: model to pre-train - model_wo_ddp: model to pre-train that is not the DDP version - optimizer: pytorch optimizer - lr_scheduler: learning-rate scheduler - scaler: loss scaler - criterion: loss function to use for fine-tuning - """ - - loss = validate(config, model, dataloader_val, criterion) - - logger.info(f'Model validation loss: {loss:.3f}%') - - logger.info("Start fine-tuning") - - start_time = time.time() - - for epoch in range(config.TRAIN.START_EPOCH, config.TRAIN.EPOCHS): - - dataloader_train.sampler.set_epoch(epoch) - - execute_one_epoch(config, model, dataloader_train, - optimizer, criterion, epoch, lr_scheduler, scaler) - - loss = validate(config, model, dataloader_val, criterion) - - logger.info(f'Model validation loss: {loss:.3f}%') - - if dist.get_rank() == 0 and \ - (epoch % config.SAVE_FREQ == 0 or - epoch == (config.TRAIN.EPOCHS - 1)): - - save_checkpoint(config, epoch, model_wo_ddp, 0., - optimizer, lr_scheduler, scaler, logger) - - total_time = time.time() - start_time - - total_time_str = str(datetime.timedelta(seconds=int(total_time))) - - logger.info('Training time {}'.format(total_time_str)) - - -def execute_one_epoch(config, - model, - dataloader, - optimizer, - criterion, - epoch, - lr_scheduler, - scaler): - """ - Execute training iterations on a single epoch. - - Args: - config: config object - model: model to pre-train - dataloader: dataloader to use - optimizer: pytorch optimizer - epoch: int epoch number - lr_scheduler: learning-rate scheduler - scaler: loss scaler - """ - model.train() - - optimizer.zero_grad() - - num_steps = len(dataloader) - - # Set up logging meters - batch_time = AverageMeter() - data_time = AverageMeter() - loss_meter = AverageMeter() - norm_meter = AverageMeter() - loss_scale_meter = AverageMeter() - - start = time.time() - end = time.time() - for idx, (samples, targets) in enumerate(dataloader): - - data_time.update(time.time() - start) - - samples = samples.cuda(non_blocking=True) - targets = targets.cuda(non_blocking=True) - - samples = samples.to(torch.bfloat16) - - with amp.autocast(enabled=config.ENABLE_AMP): - logits = model(samples) - - loss = criterion(logits, targets) - loss = loss / config.TRAIN.ACCUMULATION_STEPS - - scaler.scale(loss).backward() - - grad_norm = get_grad_norm(model.parameters()) - - if (idx + 1) % config.TRAIN.ACCUMULATION_STEPS == 0: - optimizer.zero_grad() - lr_scheduler.step_update((epoch * num_steps + idx) // config.TRAIN.ACCUMULATION_STEPS) - - loss_scale_value = scaler.state_dict()["scale"] - - torch.cuda.synchronize() - - loss_meter.update(loss.item(), targets.size(0)) - norm_meter.update(grad_norm) - loss_scale_meter.update(loss_scale_value) - batch_time.update(time.time() - end) - end = time.time() - - if idx % config.PRINT_FREQ == 0: - lr = optimizer.param_groups[0]['lr'] - memory_used = torch.cuda.max_memory_allocated() / (1024.0 * 1024.0) - etas = batch_time.avg * (num_steps - idx) - logger.info( - f'Train: [{epoch}/{config.TRAIN.EPOCHS}][{idx}/{num_steps}]\t' - f'eta {datetime.timedelta(seconds=int(etas))} lr {lr:.6f}\t' - f'time {batch_time.val:.4f} ({batch_time.avg:.4f})\t' - f'data_time {data_time.val:.4f} ({data_time.avg:.4f})\t' - f'loss {loss_meter.val:.4f} ({loss_meter.avg:.4f})\t' - f'grad_norm {norm_meter.val:.4f} ({norm_meter.avg:.4f})\t' - f'loss_scale {loss_scale_meter.val:.4f}' + - f' ({loss_scale_meter.avg:.4f})\t' - f'mem {memory_used:.0f}MB') - - epoch_time = time.time() - start - logger.info( - f"EPOCH {epoch} training takes " + - f"{datetime.timedelta(seconds=int(epoch_time))}") - - -@torch.no_grad() -def validate(config, model, dataloader, criterion): - """Validation function which given a model and validation loader - performs a validation run and returns the average loss according - to the criterion. - - Args: - config: config object - model: pytorch model to validate - dataloader: pytorch validation loader - criterion: pytorch-friendly loss function - - Returns: - loss_meter.avg: average of the loss throught the validation - iterations - """ - - model.eval() - - batch_time = AverageMeter() - - loss_meter = AverageMeter() - - end = time.time() - - for idx, (images, target) in enumerate(dataloader): - - images = images.cuda(non_blocking=True) - - target = target.cuda(non_blocking=True) - - images = images.to(torch.bfloat16) - - # compute output - with amp.autocast(enabled=config.ENABLE_AMP): - output = model(images) - - # measure accuracy and record loss - loss = criterion(output, target) - - loss = reduce_tensor(loss) - - loss_meter.update(loss.item(), target.size(0)) - - # measure elapsed time - batch_time.update(time.time() - end) - - end = time.time() - - if idx % config.PRINT_FREQ == 0: - - memory_used = torch.cuda.max_memory_allocated() / (1024.0 * 1024.0) - - logger.info( - f'Test: [{idx}/{len(dataloader)}]\t' - f'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' - f'Loss {loss_meter.val:.4f} ({loss_meter.avg:.4f})\t' - f'Mem {memory_used:.0f}MB') - - return loss_meter.avg - - -def main(config): - """ - Performs the main function of building model, loader, etc. and starts - training. - """ - - dataloader_train, dataloader_val = build_finetune_dataloaders( - config, logger) - - model = build_finetune_model(config, logger) - - optimizer = build_optimizer(config, - model, - is_pretrain=False, - logger=logger) - - model, model_wo_ddp = make_ddp(model) - - n_iter_per_epoch = len(dataloader_train) - - lr_scheduler = build_scheduler(config, optimizer, n_iter_per_epoch) - - scaler = amp.GradScaler() - - criterion = build_loss(config) - - train(config, - dataloader_train, - dataloader_val, - model, - model_wo_ddp, - optimizer, - lr_scheduler, - scaler, - criterion) - - -def build_finetune_model(config, logger): - - logger.info(f"Creating model:{config.MODEL.TYPE}/{config.MODEL.NAME}") - - # You can replace this section by simply calling your model class - # For example: model = UNet(parameters) - model = build_model(config, - pretrain=False, - pretrain_method='mim', - logger=logger) - - model.cuda() - - logger.info(str(model)) - - return model - - -def make_ddp(model): - - model = torch.nn.parallel.DistributedDataParallel( - model, - device_ids=[int(os.environ["RANK"])], - broadcast_buffers=False, - find_unused_parameters=True) - - model_without_ddp = model.module - - return model, model_without_ddp - - -def setup_rank_worldsize(): - if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: - rank = int(os.environ["RANK"]) - world_size = int(os.environ['WORLD_SIZE']) - print(f"RANK and WORLD_SIZE in environ: {rank}/{world_size}") - else: - rank = -1 - world_size = -1 - return rank, world_size - - -def setup_distributed_processing(rank, world_size): - torch.cuda.set_device(int(os.environ["RANK"])) - torch.distributed.init_process_group( - backend='nccl', init_method='env://', world_size=world_size, rank=rank) - torch.distributed.barrier() - - -def setup_seeding(config): - seed = config.SEED + dist.get_rank() - torch.manual_seed(seed) - np.random.seed(seed) - - -if __name__ == '__main__': - _, config = parse_args() - - rank, world_size = setup_rank_worldsize() - - setup_distributed_processing(rank, world_size) - - setup_seeding(config) - - cudnn.benchmark = True - - os.makedirs(config.OUTPUT, exist_ok=True) - logger = create_logger(output_dir=config.OUTPUT, - dist_rank=dist.get_rank(), - name=f"{config.MODEL.NAME}") - - if dist.get_rank() == 0: - path = os.path.join(config.OUTPUT, "config.json") - with open(path, "w") as f: - f.write(config.dump()) - logger.info(f"Full config saved to {path}") - logger.info(config.dump()) - config_file_name = f'{config.TAG}.config.sav' - config_file_path = os.path.join(config.OUTPUT, config_file_name) - joblib.dump(config, config_file_path) - - main(config) From 9066446b0c81d72535ccc70f8cb3a28d2fb69ae9 Mon Sep 17 00:00:00 2001 From: cssprad1 Date: Fri, 12 Jul 2024 08:11:45 -0400 Subject: [PATCH 06/12] lc 9 should be 7 channel --- pytorch_caney/data/datasets/modis_lc_nine_dataset.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/pytorch_caney/data/datasets/modis_lc_nine_dataset.py b/pytorch_caney/data/datasets/modis_lc_nine_dataset.py index 9db25f6..ebbe8d5 100644 --- a/pytorch_caney/data/datasets/modis_lc_nine_dataset.py +++ b/pytorch_caney/data/datasets/modis_lc_nine_dataset.py @@ -53,8 +53,7 @@ def __len__(self): def __getitem__(self, idx, transpose=True): # load image - img = np.random.rand(224, 224, 14) - # img = np.load(self.img_list[idx]) + img = np.load(self.img_list[idx]) # load mask mask = np.load(self.mask_list[idx]) From 16d96b907e12984666223b6ad8b33b2012a7b51e Mon Sep 17 00:00:00 2001 From: Caleb Spradlin <72508718+cssprad1@users.noreply.github.com> Date: Thu, 29 Aug 2024 11:07:36 -0400 Subject: [PATCH 07/12] Update dockerhub.yml --- .github/workflows/dockerhub.yml | 20 ++++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/.github/workflows/dockerhub.yml b/.github/workflows/dockerhub.yml index b5e95d3..45abb1f 100644 --- a/.github/workflows/dockerhub.yml +++ b/.github/workflows/dockerhub.yml @@ -28,10 +28,22 @@ jobs: - name: Lower github-runner storage run: | - sudo rm -rf /usr/share/dotnet - sudo rm -rf /opt/ghc - sudo rm -rf "/usr/local/share/boost" - sudo rm -rf "$AGENT_TOOLSDIRECTORY" + # Remove software and language runtimes we're not using + sudo rm -rf \ + "$AGENT_TOOLSDIRECTORY" \ + /opt/google/chrome \ + /opt/microsoft/msedge \ + /opt/microsoft/powershell \ + /opt/pipx \ + /usr/lib/mono \ + /usr/local/julia* \ + /usr/local/lib/android \ + /usr/local/lib/node_modules \ + /usr/local/share/chromium \ + /usr/local/share/powershell \ + /usr/share/dotnet \ + /usr/share/swift + df -h / - name: Build and push From bae384526533c2f11e9f3efd9956f461990e3373 Mon Sep 17 00:00:00 2001 From: Caleb Spradlin <72508718+cssprad1@users.noreply.github.com> Date: Fri, 30 Aug 2024 09:58:05 -0400 Subject: [PATCH 08/12] Update Dockerfile --- requirements/Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/Dockerfile b/requirements/Dockerfile index 46f2e76..5f26ebb 100644 --- a/requirements/Dockerfile +++ b/requirements/Dockerfile @@ -1,5 +1,5 @@ # Arguments to pass to the image -ARG VERSION_DATE=23.01 +ARG VERSION_DATE=24.01 ARG FROM_IMAGE=nvcr.io/nvidia/pytorch # Import RAPIDS container as the BASE Image (cuda base image) From 70fc0e9428c142b1fd14797a58b64d497b6ab275 Mon Sep 17 00:00:00 2001 From: cssprad1 Date: Fri, 20 Sep 2024 14:42:53 -0400 Subject: [PATCH 09/12] added notebook updates --- notebooks/satvision-toa-reconstruction.ipynb | 1658 +++++++++++++++++- pytorch_caney/network/attention.py | 8 +- 2 files changed, 1635 insertions(+), 31 deletions(-) diff --git a/notebooks/satvision-toa-reconstruction.ipynb b/notebooks/satvision-toa-reconstruction.ipynb index 1e06719..d89aa2a 100644 --- a/notebooks/satvision-toa-reconstruction.ipynb +++ b/notebooks/satvision-toa-reconstruction.ipynb @@ -14,17 +14,70 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "6e88ea70-7dbf-4b67-a12d-db36e2bc9914", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Defaulting to user installation because normal site-packages is not writeable\n", + "Requirement already satisfied: yacs in /home/cssprad1/.local/lib/python3.12/site-packages (0.1.8)\n", + "Requirement already satisfied: timm in /home/cssprad1/.local/lib/python3.12/site-packages (0.9.2)\n", + "Requirement already satisfied: segmentation-models-pytorch in /home/cssprad1/.local/lib/python3.12/site-packages (0.3.3)\n", + "Requirement already satisfied: termcolor in /home/cssprad1/.local/lib/python3.12/site-packages (2.4.0)\n", + "Requirement already satisfied: webdataset==0.2.86 in /home/cssprad1/.local/lib/python3.12/site-packages (0.2.86)\n", + "Requirement already satisfied: braceexpand in /home/cssprad1/.local/lib/python3.12/site-packages (from webdataset==0.2.86) (0.1.7)\n", + "Requirement already satisfied: numpy in /panfs/ccds02/app/modules/jupyter/ilab/lab-env-hub-5/lib/python3.12/site-packages (from webdataset==0.2.86) (2.0.0)\n", + "Requirement already satisfied: pyyaml in /panfs/ccds02/app/modules/jupyter/ilab/lab-env-hub-5/lib/python3.12/site-packages (from webdataset==0.2.86) (6.0.1)\n", + "Requirement already satisfied: torch>=1.7 in /home/cssprad1/.local/lib/python3.12/site-packages (from timm) (2.4.0)\n", + "Requirement already satisfied: torchvision in /home/cssprad1/.local/lib/python3.12/site-packages (from timm) (0.19.0)\n", + "Requirement already satisfied: huggingface-hub in /home/cssprad1/.local/lib/python3.12/site-packages (from timm) (0.24.2)\n", + "Requirement already satisfied: safetensors in /home/cssprad1/.local/lib/python3.12/site-packages (from timm) (0.4.3)\n", + "Requirement already satisfied: pretrainedmodels==0.7.4 in /home/cssprad1/.local/lib/python3.12/site-packages (from segmentation-models-pytorch) (0.7.4)\n", + "Requirement already satisfied: efficientnet-pytorch==0.7.1 in /home/cssprad1/.local/lib/python3.12/site-packages (from segmentation-models-pytorch) (0.7.1)\n", + "Requirement already satisfied: tqdm in /panfs/ccds02/app/modules/jupyter/ilab/lab-env-hub-5/lib/python3.12/site-packages (from segmentation-models-pytorch) (4.66.4)\n", + "Requirement already satisfied: pillow in /panfs/ccds02/app/modules/jupyter/ilab/lab-env-hub-5/lib/python3.12/site-packages (from segmentation-models-pytorch) (10.4.0)\n", + "Requirement already satisfied: munch in /home/cssprad1/.local/lib/python3.12/site-packages (from pretrainedmodels==0.7.4->segmentation-models-pytorch) (4.0.0)\n", + "Requirement already satisfied: filelock in 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/panfs/ccds02/app/modules/jupyter/ilab/lab-env-hub-5/lib/python3.12/site-packages (from requests->huggingface-hub->timm) (3.7)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /panfs/ccds02/app/modules/jupyter/ilab/lab-env-hub-5/lib/python3.12/site-packages (from requests->huggingface-hub->timm) (2.2.2)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /panfs/ccds02/app/modules/jupyter/ilab/lab-env-hub-5/lib/python3.12/site-packages (from requests->huggingface-hub->timm) (2024.7.4)\n", + "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /home/cssprad1/.local/lib/python3.12/site-packages (from sympy->torch>=1.7->timm) (1.3.0)\n" + ] + } + ], "source": [ "!pip install yacs timm segmentation-models-pytorch termcolor webdataset==0.2.86" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "d046c3e5-c458-4e03-9c96-e9eb95a04963", "metadata": {}, "outputs": [], @@ -51,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "7c7db1bc-09ee-47e3-9015-e6b148d497e7", "metadata": {}, "outputs": [], @@ -120,13 +173,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "af699ba3-2d98-4daf-9437-c322d7b59a98", "metadata": {}, "outputs": [], "source": [ - "MODEL_PATH: str = '../../satvision-toa-huge-patch8-window8-128/mp_rank_00_model_states.pt'\n", - "CONFIG_PATH: str = '../../satvision-toa-huge-patch8-window8-128/mim_pretrain_swinv2_satvision_huge_128_window8_patch8_100ep.yaml'\n", + "MODEL_PATH: str = '/explore/nobackup/people/cssprad1/projects/satvision-toa/models/satvision-toa-giant-patch8-window8-128/mp_rank_00_model_states.pt'\n", + "CONFIG_PATH: str = '/explore/nobackup/people/cssprad1/projects/satvision-toa/models/satvision-toa-giant-patch8-window8-128/mim_pretrain_swinv2_satvision_giant_128_window08_50ep.yaml'\n", "\n", "OUTPUT: str = '.'\n", "TAG: str = 'satvision-huge-toa-reconstruction'\n", @@ -136,10 +189,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "c4593e8c-6e94-4d01-b86e-5b78b621fc59", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=> merge config from /explore/nobackup/people/cssprad1/projects/satvision-toa/models/satvision-toa-giant-patch8-window8-128/mim_pretrain_swinv2_satvision_giant_128_window08_50ep.yaml\n" + ] + } + ], "source": [ "# Update config given configurations\n", "\n", @@ -156,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "202a4474-88e4-44d5-b899-7aaf6cbed6f4", "metadata": {}, "outputs": [], @@ -187,17 +248,1335 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "68abf348-c6bf-43a3-b00a-cc5f8d80545f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-09-20 14:37:24,629 [INFO] number of params: 2653783008\n" + ] + }, + { + "data": { + 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GELU(approximate='none')\n", + " (fc2): Linear(in_features=16384, out_features=4096, bias=True)\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (norm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", + " (avgpool): AdaptiveAvgPool1d(output_size=1)\n", + " (head): Identity()\n", + " )\n", + " (decoder): Sequential(\n", + " (0): Conv2d(4096, 14336, kernel_size=(1, 1), stride=(1, 1))\n", + " (1): PixelShuffle(upscale_factor=32)\n", + " )\n", + ")" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "checkpoint = torch.load(MODEL_PATH)\n", "model = build_model(config, pretrain=True)\n", "model.load_state_dict(checkpoint['module']) # If 'module' not working, try 'model'\n", "n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", "logger.info(f\"number of params: {n_parameters}\")\n", - "model.cuda()\n", + "# model.cuda()\n", "model.eval()" ] }, @@ -211,7 +1590,91 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, + "id": "e23a0876-4a14-4525-9d6d-8338e16db4f2", + "metadata": {}, + "outputs": [], + "source": [ + "import torchvision.transforms as T\n", + "from pytorch_caney.data.utils import RandomResizedCropNP, SimmimMaskGenerator\n", + "\n", + "class MinMaxEmissiveScaleReflectance(object):\n", + " \"\"\"\n", + " Performs scaling of MODIS TOA data\n", + " - Scales reflectance percentages to reflectance units (% -> (0,1))\n", + " - Performs per-channel minmax scaling for emissive bands (k -> (0,1))\n", + " \"\"\"\n", + "\n", + " def __init__(self):\n", + " \n", + " self.reflectance_indices = [0, 1, 2, 3, 4, 6]\n", + " self.emissive_indices = [5, 7, 8, 9, 10, 11, 12, 13]\n", + "\n", + " self.emissive_mins = np.array(\n", + " [223.1222, 178.9174, 204.3739, 204.7677,\n", + " 194.8686, 202.1759, 201.3823, 203.3537],\n", + " dtype=np.float32)\n", + "\n", + " self.emissive_maxs = np.array(\n", + " [352.7182, 261.2920, 282.5529, 319.0373,\n", + " 295.0209, 324.0677, 321.5254, 285.9848],\n", + " dtype=np.float32)\n", + "\n", + " def __call__(self, img):\n", + " \n", + " # Reflectance % to reflectance units\n", + " img[:, :, self.reflectance_indices] = \\\n", + " img[:, :, self.reflectance_indices] * 0.01\n", + " \n", + " # Brightness temp scaled to (0,1) range\n", + " img[:, :, self.emissive_indices] = \\\n", + " (img[:, :, self.emissive_indices] - self.emissive_mins) / \\\n", + " (self.emissive_maxs - self.emissive_mins)\n", + " \n", + " return img\n", + "\n", + "\n", + "class SimmimTransform:\n", + " \"\"\"\n", + " torchvision transform which transforms the input imagery into\n", + " addition to generating a MiM mask\n", + " \"\"\"\n", + "\n", + " def __init__(self, config):\n", + "\n", + " self.transform_img = \\\n", + " T.Compose([\n", + " MinMaxEmissiveScaleReflectance(), # New transform for MinMax\n", + " T.ToTensor(),\n", + " T.Resize((config.DATA.IMG_SIZE, config.DATA.IMG_SIZE)),\n", + " ])\n", + "\n", + " if config.MODEL.TYPE in ['swin', 'swinv2']:\n", + "\n", + " model_patch_size = config.MODEL.SWINV2.PATCH_SIZE\n", + "\n", + " else:\n", + "\n", + " raise NotImplementedError\n", + "\n", + " self.mask_generator = SimmimMaskGenerator(\n", + " input_size=config.DATA.IMG_SIZE,\n", + " mask_patch_size=config.DATA.MASK_PATCH_SIZE,\n", + " model_patch_size=model_patch_size,\n", + " mask_ratio=config.DATA.MASK_RATIO,\n", + " )\n", + "\n", + " def __call__(self, img):\n", + "\n", + " img = self.transform_img(img)\n", + " mask = self.mask_generator()\n", + "\n", + " return img, mask" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "id": "73a8d307-de9b-4617-abdd-dae1e7c2521a", "metadata": {}, "outputs": [], @@ -235,6 +1698,75 @@ " imgMask in imgMasks])" ] }, + { + "cell_type": "code", + "execution_count": 10, + "id": "151eeee5-7967-451d-a77f-64d65739e9ae", + "metadata": {}, + "outputs": [], + "source": [ + "idx_to_band = {\n", + " 0: 1,\n", + " 1: 2,\n", + " 2: 3,\n", + " 3: 6,\n", + " 4: 7,\n", + " 5: 21,\n", + " 6: 26,\n", + " 7: 27,\n", + " 8: 28,\n", + " 9: 29,\n", + " 10: 30,\n", + " 11: 31,\n", + " 12: 32,\n", + " 13: 33\n", + "}\n", + "\n", + "\n", + "def get_batch_info(img):\n", + " \n", + " channels = img.shape[1]\n", + " \n", + " for channelIdx in range(channels):\n", + " channel = idx_to_band[channelIdx]\n", + " img_band_array = img[:, channelIdx, :, :]\n", + " min_ = img_band_array.min()\n", + " mean_ = img_band_array.mean()\n", + " max_ = img_band_array.max()\n", + " print(f'Channel {channel}, min {min_}, mean {mean_}, max {max_}') " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8cee7aa5-13b6-4644-98b7-6944c1bbfe99", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Channel 1, min 0.02055743709206581, mean 0.23059538006782532, max 1.0075911283493042\n", + "Channel 2, min 0.010388242080807686, mean 0.2926776707172394, max 0.9048148393630981\n", + "Channel 3, min 0.051274485886096954, mean 0.2630060315132141, max 1.048071026802063\n", + "Channel 6, min 0.002602405147626996, mean 0.2257007360458374, max 0.6810641884803772\n", + "Channel 7, min 0.0013291973154991865, mean 0.15059055387973785, max 0.6122391223907471\n", + "Channel 21, min -1.1774119457186316e-07, mean 0.5202993154525757, max 0.9999997615814209\n", + "Channel 26, min -0.0045103696174919605, mean 0.030210332944989204, max 0.5251043438911438\n", + "Channel 27, min 1.85236601168981e-07, mean 0.7143893837928772, max 0.9999996423721313\n", + "Channel 28, min 0.0, mean 0.5836189985275269, max 1.0000008344650269\n", + "Channel 29, min 1.3353324845866155e-07, mean 0.6119444370269775, max 0.9999997615814209\n", + "Channel 30, min 0.0, mean 0.6182008385658264, max 0.9999997019767761\n", + "Channel 31, min 2.5036615625140257e-07, mean 0.6037113666534424, max 1.000000238418579\n", + "Channel 32, min -2.540102457260218e-07, mean 0.6058735251426697, max 0.9999997615814209\n", + "Channel 33, min 1.8466157314378506e-07, mean 0.6282674670219421, max 1.0000003576278687\n" + ] + } + ], + "source": [ + "get_batch_info(img)" + ] + }, { "cell_type": "markdown", "id": "55acf5e9-eb2a-496c-baa6-3b74503a2978", @@ -245,7 +1777,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "595336f8-71b4-418b-b153-2461583ed613", "metadata": {}, "outputs": [], @@ -303,6 +1835,17 @@ " mask_img = np.stack([mask_img, mask_img, mask_img], axis=-1)\n", " return mask_img\n", "\n", + "def reverse_transform(image):\n", + " minMaxTransform = MinMaxEmissiveScaleReflectance()\n", + " image = image.transpose((1,2,0))\n", + " \n", + " image[:, :, minMaxTransform.reflectance_indices] = image[:, :, minMaxTransform.reflectance_indices] * 100\n", + " image[:, :, minMaxTransform.emissive_indices] = (\n", + " image[:, :, minMaxTransform.emissive_indices] * \\\n", + " (minMaxTransform.emissive_maxs - minMaxTransform.emissive_mins)) + minMaxTransform.emissive_mins\n", + "\n", + " image = image.transpose((2,0,1))\n", + " return image\n", "\n", "def process_prediction(image, img_recon, mask, rgb_index):\n", "\n", @@ -312,14 +1855,16 @@ " blue_idx = rgb_index[1]\n", " green_idx = rgb_index[2]\n", "\n", - " image = image.numpy()\n", + " image = reverse_transform(image.numpy())\n", + " \n", + " img_recon = reverse_transform(img_recon.numpy())\n", + "\n", " rgb_image = np.stack((image[red_idx, :, :],\n", " image[blue_idx, :, :],\n", " image[green_idx, :, :]),\n", " axis=-1)\n", " rgb_image = minmax_norm(rgb_image)\n", "\n", - " img_recon = img_recon.numpy()\n", " rgb_image_recon = np.stack((img_recon[red_idx, :, :],\n", " img_recon[blue_idx, :, :],\n", " img_recon[green_idx, :, :]),\n", @@ -374,10 +1919,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "4e695cc3-b869-4fc2-b360-b45f3b81affd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:08<00:00, 1.74s/it]\n" + ] + } + ], "source": [ "inputs = []\n", "outputs = []\n", @@ -387,11 +1940,11 @@ "# We could do this in a single batch however we\n", "# want to report the loss per-image, in place of\n", "# loss per-batch.\n", - "for i in tqdm(range(img.shape[0])):\n", + "for i in tqdm(range(5)):\n", " single_img = img[i].unsqueeze(0)\n", " single_mask = mask[i].unsqueeze(0)\n", - " single_img = single_img.cuda(non_blocking=True)\n", - " single_mask = single_mask.cuda(non_blocking=True)\n", + " # single_img = single_img.cuda(non_blocking=True)\n", + " # single_mask = single_mask.cuda(non_blocking=True)\n", "\n", " with torch.no_grad():\n", " z = model.encoder(single_img, single_mask)\n", @@ -416,12 +1969,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "5ebdcd1d-09db-4ccf-8cc1-58d6f47e3a55", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "pdf_path = '../../satvision-toa-reconstruction-pdf-huge-patch-8-06.21.pdf'\n", + "pdf_path = '../../satvision-toa-reconstruction-pdf-giant-patch-8-09.20.pdf'\n", "rgb_index = [0, 2, 1] # Indices of [Red band, Blue band, Green band]\n", "\n", "plot_export_pdf(pdf_path, inputs, outputs, masks, rgb_index)" @@ -438,9 +2042,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:ilab-pytorch]", + "display_name": "ILAB Kernel (Pytorch)", "language": "python", - "name": "conda-env-ilab-pytorch-py" + "name": "pytorch-kernel" }, "language_info": { "codemirror_mode": { diff --git a/pytorch_caney/network/attention.py b/pytorch_caney/network/attention.py index 1df9976..99f216f 100644 --- a/pytorch_caney/network/attention.py +++ b/pytorch_caney/network/attention.py @@ -152,10 +152,10 @@ def forward(self, x, mask=None): # cosine attention attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) - # logit_scale = torch.clamp( - # self.logit_scale, max=torch.log(torch.tensor(1. / 0.01))).exp() - logit_scale = torch.clamp(self.logit_scale, max=torch.log( - torch.tensor(1. / 0.01)).to(self.logit_scale.get_device())).exp() + logit_scale = torch.clamp( + self.logit_scale, max=torch.log(torch.tensor(1. / 0.01))).exp() + # logit_scale = torch.clamp(self.logit_scale, max=torch.log( + # torch.tensor(1. / 0.01)).to(self.logit_scale.get_device())).exp() attn = attn * logit_scale relative_position_bias_table = self.cpb_mlp( From c9513dada72c7b9b0ef5575017865b70caedb587 Mon Sep 17 00:00:00 2001 From: cssprad1 Date: Fri, 20 Sep 2024 17:02:13 -0400 Subject: [PATCH 10/12] two notebooks, huge and giant --- notebooks/.panfs.2ac640a.1726866101518203341 | 5 + notebooks/satvision-toa-reconstruction.ipynb | 2064 ----------------- .../satvision-toa-reconstruction_giant.ipynb | 2064 +++++++++++++++++ .../satvision-toa-reconstruction_huge.ipynb | 1267 ++++++++++ 4 files changed, 3336 insertions(+), 2064 deletions(-) create mode 100644 notebooks/.panfs.2ac640a.1726866101518203341 delete mode 100644 notebooks/satvision-toa-reconstruction.ipynb create mode 100644 notebooks/satvision-toa-reconstruction_giant.ipynb create mode 100644 notebooks/satvision-toa-reconstruction_huge.ipynb diff --git a/notebooks/.panfs.2ac640a.1726866101518203341 b/notebooks/.panfs.2ac640a.1726866101518203341 new file mode 100644 index 0000000..262b72a --- /dev/null +++ b/notebooks/.panfs.2ac640a.1726866101518203341 @@ -0,0 +1,5 @@ +2024-09-20 14:33:21 [INFO] number of params: 2653783008 +2024-09-20 14:37:24 [INFO] number of params: 2653783008 +2024-09-20 14:46:41 [INFO] number of params: 2653783008 +2024-09-20 16:57:54 [INFO] number of params: 695328632 +2024-09-20 16:58:47 [INFO] number of params: 695328632 diff --git a/notebooks/satvision-toa-reconstruction.ipynb b/notebooks/satvision-toa-reconstruction.ipynb deleted file mode 100644 index d89aa2a..0000000 --- a/notebooks/satvision-toa-reconstruction.ipynb +++ /dev/null @@ -1,2064 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c8ab2075-c488-46b9-8cd2-0cdaf399acfc", - "metadata": {}, - "source": [ - "# Satvision-TOA Reconstruction Notebook\n", - "\n", - "Version: 04.30.24\n", - "\n", - "Env: `Python [conda env:ilab-pytorch]`" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "6e88ea70-7dbf-4b67-a12d-db36e2bc9914", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Defaulting to user installation because normal site-packages is not writeable\n", - "Requirement already satisfied: yacs in /home/cssprad1/.local/lib/python3.12/site-packages (0.1.8)\n", - "Requirement already satisfied: timm in /home/cssprad1/.local/lib/python3.12/site-packages (0.9.2)\n", - "Requirement already satisfied: segmentation-models-pytorch in /home/cssprad1/.local/lib/python3.12/site-packages (0.3.3)\n", - "Requirement already satisfied: termcolor in /home/cssprad1/.local/lib/python3.12/site-packages (2.4.0)\n", - "Requirement already satisfied: webdataset==0.2.86 in /home/cssprad1/.local/lib/python3.12/site-packages (0.2.86)\n", - "Requirement already satisfied: braceexpand in /home/cssprad1/.local/lib/python3.12/site-packages (from webdataset==0.2.86) (0.1.7)\n", - "Requirement already satisfied: numpy in /panfs/ccds02/app/modules/jupyter/ilab/lab-env-hub-5/lib/python3.12/site-packages (from webdataset==0.2.86) (2.0.0)\n", - "Requirement already satisfied: pyyaml in /panfs/ccds02/app/modules/jupyter/ilab/lab-env-hub-5/lib/python3.12/site-packages (from webdataset==0.2.86) (6.0.1)\n", - "Requirement already satisfied: torch>=1.7 in /home/cssprad1/.local/lib/python3.12/site-packages (from timm) (2.4.0)\n", - "Requirement already satisfied: torchvision in /home/cssprad1/.local/lib/python3.12/site-packages (from timm) (0.19.0)\n", - "Requirement already satisfied: huggingface-hub in /home/cssprad1/.local/lib/python3.12/site-packages (from timm) (0.24.2)\n", - "Requirement already satisfied: safetensors in /home/cssprad1/.local/lib/python3.12/site-packages (from timm) (0.4.3)\n", - "Requirement already satisfied: pretrainedmodels==0.7.4 in /home/cssprad1/.local/lib/python3.12/site-packages (from segmentation-models-pytorch) (0.7.4)\n", - 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"Requirement already satisfied: charset-normalizer<4,>=2 in /panfs/ccds02/app/modules/jupyter/ilab/lab-env-hub-5/lib/python3.12/site-packages (from requests->huggingface-hub->timm) (3.3.2)\n", - "Requirement already satisfied: idna<4,>=2.5 in /panfs/ccds02/app/modules/jupyter/ilab/lab-env-hub-5/lib/python3.12/site-packages (from requests->huggingface-hub->timm) (3.7)\n", - "Requirement already satisfied: urllib3<3,>=1.21.1 in /panfs/ccds02/app/modules/jupyter/ilab/lab-env-hub-5/lib/python3.12/site-packages (from requests->huggingface-hub->timm) (2.2.2)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /panfs/ccds02/app/modules/jupyter/ilab/lab-env-hub-5/lib/python3.12/site-packages (from requests->huggingface-hub->timm) (2024.7.4)\n", - "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /home/cssprad1/.local/lib/python3.12/site-packages (from sympy->torch>=1.7->timm) (1.3.0)\n" - ] - } - ], - "source": [ - "!pip install yacs timm segmentation-models-pytorch termcolor webdataset==0.2.86" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d046c3e5-c458-4e03-9c96-e9eb95a04963", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import time\n", - "import random\n", - "import datetime\n", - "from tqdm import tqdm\n", - "import numpy as np\n", - "import logging\n", - "\n", - "import torch\n", - "import torch.cuda.amp as amp\n", - "\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.backends.backend_pdf import PdfPages\n", - "\n", - "import warnings\n", - "\n", - "warnings.filterwarnings('ignore') " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "7c7db1bc-09ee-47e3-9015-e6b148d497e7", - "metadata": {}, - "outputs": [], - "source": [ - "sys.path.append('../../pytorch-caney')\n", - "\n", - "from pytorch_caney.config import get_config\n", - "\n", - "from pytorch_caney.models.build import build_model\n", - "\n", - "from pytorch_caney.ptc_logging import create_logger\n", - "\n", - "from pytorch_caney.data.datasets.mim_modis_22m_dataset import MODIS22MDataset\n", - "\n", - "from pytorch_caney.data.transforms import SimmimTransform, SimmimMaskGenerator\n", - "\n", - "from pytorch_caney.config import _C, _update_config_from_file" - ] - }, - { - "cell_type": "markdown", - "id": "d841e464-f880-4e53-bf31-f9f225713918", - "metadata": {}, - "source": [ - "## 1. Configuration" - ] - }, - { - "cell_type": "markdown", - "id": "6274e323-bc04-41d4-bc49-baed65d027e6", - "metadata": {}, - "source": [ - "### Clone model ckpt from huggingface\n", - "\n", - "### Model repo: https://huggingface.co/nasa-cisto-data-science-group/satvision-toa-huge-patch8-window8-128\n", - "\n", - "```bash\n", - "# On prism/explore\n", - "module load git-lfs\n", - "\n", - "git lfs install\n", - "\n", - "git clone git clone git@hf.co:nasa-cisto-data-science-group/satvision-toa-huge-patch8-window8-128\n", - "```\n", - "\n", - "Note: If using git w/ ssh, make sure you have ssh keys enabled to clone using ssh auth.\n", - "https://huggingface.co/docs/hub/security-git-ssh\n", - "\n", - "```bash\n", - "eval $(ssh-agent)\n", - "\n", - "# If this outputs as anon, follow the next steps.\n", - "ssh -T git@hf.co\n", - "\n", - "# Check if ssh-agent is using the proper key\n", - "ssh-add -l\n", - "\n", - "# If not\n", - "ssh-add ~/.ssh/your-key\n", - "\n", - "# Or if you want to use the default id_* key, just do\n", - "ssh-add\n", - "\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "af699ba3-2d98-4daf-9437-c322d7b59a98", - "metadata": {}, - "outputs": [], - "source": [ - "MODEL_PATH: str = '/explore/nobackup/people/cssprad1/projects/satvision-toa/models/satvision-toa-giant-patch8-window8-128/mp_rank_00_model_states.pt'\n", - "CONFIG_PATH: str = '/explore/nobackup/people/cssprad1/projects/satvision-toa/models/satvision-toa-giant-patch8-window8-128/mim_pretrain_swinv2_satvision_giant_128_window08_50ep.yaml'\n", - "\n", - "OUTPUT: str = '.'\n", - "TAG: str = 'satvision-huge-toa-reconstruction'\n", - "DATA_PATH: str = '/explore/nobackup/projects/ilab/projects/3DClouds/data/validation/sv_toa_128_chip_validation_04_24.npy'\n", - "DATA_PATHS: list = [DATA_PATH]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c4593e8c-6e94-4d01-b86e-5b78b621fc59", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=> merge config from /explore/nobackup/people/cssprad1/projects/satvision-toa/models/satvision-toa-giant-patch8-window8-128/mim_pretrain_swinv2_satvision_giant_128_window08_50ep.yaml\n" - ] - } - ], - "source": [ - "# Update config given configurations\n", - "\n", - "config = _C.clone()\n", - "_update_config_from_file(config, CONFIG_PATH)\n", - "\n", - "config.defrost()\n", - "config.MODEL.RESUME = MODEL_PATH\n", - "config.DATA.DATA_PATHS = DATA_PATHS\n", - "config.OUTPUT = OUTPUT\n", - "config.TAG = TAG\n", - "config.freeze()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "202a4474-88e4-44d5-b899-7aaf6cbed6f4", - "metadata": {}, - "outputs": [], - "source": [ - "# Configure logging\n", - "logging.basicConfig(\n", - " filename='app.log', # Specify the log file name\n", - " level=logging.INFO, # Set logging level to DEBUG\n", - " format='%(asctime)s [%(levelname)s] %(message)s', # Specify log message format\n", - " datefmt='%Y-%m-%d %H:%M:%S' # Specify date format\n", - ")\n", - "\n", - "# Add logging to standard output\n", - "console = logging.StreamHandler() # Create a handler for standard output\n", - "console.setLevel(logging.INFO) # Set logging level for standard output\n", - "console.setFormatter(logging.Formatter('%(asctime)s [%(levelname)s] %(message)s')) # Set log message format for standard output\n", - "logger = logging.getLogger('')\n", - "logger.addHandler(console)" - ] - }, - { - "cell_type": "markdown", - "id": "11ebd497-7741-41a7-af9d-0ee49a6313a4", - "metadata": {}, - "source": [ - "## 2. Load model weights from checkpoint" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "68abf348-c6bf-43a3-b00a-cc5f8d80545f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-09-20 14:37:24,629 [INFO] number of params: 2653783008\n" - ] - }, - { - "data": { - "text/plain": [ - "MiMModel(\n", - " (encoder): SwinTransformerV2ForSimMIM(\n", - " (patch_embed): PatchEmbed(\n", - " (proj): Conv2d(14, 512, kernel_size=(4, 4), stride=(4, 4))\n", - " (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", - " )\n", - " (pos_drop): Dropout(p=0.0, inplace=False)\n", - " (layers): ModuleList(\n", - " (0): BasicLayer(\n", - " dim=512, input_resolution=(32, 32), depth=2\n", - " (blocks): ModuleList(\n", - " (0): SwinTransformerBlock(\n", - " dim=512, input_resolution=(32, 32),num_heads=16, window_size=8, shift_size=0, mlp_ratio=4.0\n", - " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", - " (attn): WindowAttention(\n", - " dim=512, window_size=(8, 8), pretrained_window_size=(0, 0), num_heads=16\n", - " (cpb_mlp): Sequential(\n", - " (0): Linear(in_features=2, out_features=512, bias=True)\n", - " (1): ReLU(inplace=True)\n", - " (2): Linear(in_features=512, out_features=16, bias=False)\n", - " )\n", - " (qkv): Linear(in_features=512, out_features=1536, bias=False)\n", - " (attn_drop): Dropout(p=0.0, inplace=False)\n", - " (proj): Linear(in_features=512, out_features=512, bias=True)\n", - " (proj_drop): Dropout(p=0.0, inplace=False)\n", - " (softmax): Softmax(dim=-1)\n", - " )\n", - " (drop_path): Identity()\n", - " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", - " (norm3): Identity()\n", - " (mlp): Mlp(\n", - " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", - " (drop): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " (1): SwinTransformerBlock(\n", - 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" (softmax): Softmax(dim=-1)\n", - " )\n", - " (drop_path): DropPath(drop_prob=0.083)\n", - " (norm2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", - " (norm3): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", - " (mlp): Mlp(\n", - " (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n", - " (drop): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " (36): SwinTransformerBlock(\n", - " dim=2048, input_resolution=(8, 8),num_heads=64, window_size=8, shift_size=0, mlp_ratio=4.0\n", - " (norm1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", - " (attn): WindowAttention(\n", - " dim=2048, window_size=(8, 8), pretrained_window_size=(0, 0), num_heads=64\n", - " (cpb_mlp): Sequential(\n", - " (0): Linear(in_features=2, out_features=512, bias=True)\n", - " (1): ReLU(inplace=True)\n", - " (2): Linear(in_features=512, out_features=64, bias=False)\n", - " )\n", - " (qkv): Linear(in_features=2048, out_features=6144, bias=False)\n", - " (attn_drop): Dropout(p=0.0, inplace=False)\n", - " (proj): Linear(in_features=2048, out_features=2048, bias=True)\n", - " (proj_drop): Dropout(p=0.0, inplace=False)\n", - " (softmax): Softmax(dim=-1)\n", - " )\n", - " (drop_path): DropPath(drop_prob=0.085)\n", - " (norm2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", - " (norm3): Identity()\n", - " (mlp): Mlp(\n", - " (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n", - " (drop): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " (37): SwinTransformerBlock(\n", - " dim=2048, input_resolution=(8, 8),num_heads=64, window_size=8, shift_size=0, mlp_ratio=4.0\n", - " (norm1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", - " (attn): WindowAttention(\n", - " dim=2048, window_size=(8, 8), pretrained_window_size=(0, 0), num_heads=64\n", - " (cpb_mlp): Sequential(\n", - " (0): Linear(in_features=2, out_features=512, bias=True)\n", - " (1): ReLU(inplace=True)\n", - " (2): Linear(in_features=512, out_features=64, bias=False)\n", - " )\n", - " (qkv): Linear(in_features=2048, out_features=6144, bias=False)\n", - " (attn_drop): Dropout(p=0.0, inplace=False)\n", - " (proj): Linear(in_features=2048, out_features=2048, bias=True)\n", - " (proj_drop): Dropout(p=0.0, inplace=False)\n", - " (softmax): Softmax(dim=-1)\n", - " )\n", - " (drop_path): DropPath(drop_prob=0.087)\n", - " (norm2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", - " (norm3): Identity()\n", - " (mlp): Mlp(\n", - " (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n", - " (drop): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " (38): SwinTransformerBlock(\n", - " dim=2048, input_resolution=(8, 8),num_heads=64, window_size=8, shift_size=0, mlp_ratio=4.0\n", - " (norm1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", - " (attn): WindowAttention(\n", - " dim=2048, window_size=(8, 8), pretrained_window_size=(0, 0), num_heads=64\n", - " (cpb_mlp): Sequential(\n", - " (0): Linear(in_features=2, out_features=512, bias=True)\n", - " (1): ReLU(inplace=True)\n", - " (2): Linear(in_features=512, out_features=64, bias=False)\n", - " )\n", - " (qkv): Linear(in_features=2048, out_features=6144, bias=False)\n", - " (attn_drop): Dropout(p=0.0, inplace=False)\n", - " (proj): Linear(in_features=2048, out_features=2048, bias=True)\n", - " (proj_drop): Dropout(p=0.0, inplace=False)\n", - " (softmax): Softmax(dim=-1)\n", - " )\n", - " (drop_path): DropPath(drop_prob=0.089)\n", - " (norm2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", - " (norm3): Identity()\n", - " (mlp): Mlp(\n", - " (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n", - " (drop): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " (39): SwinTransformerBlock(\n", - " dim=2048, input_resolution=(8, 8),num_heads=64, window_size=8, shift_size=0, mlp_ratio=4.0\n", - " (norm1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", - " (attn): WindowAttention(\n", - " dim=2048, window_size=(8, 8), pretrained_window_size=(0, 0), num_heads=64\n", - " (cpb_mlp): Sequential(\n", - " (0): Linear(in_features=2, out_features=512, bias=True)\n", - " (1): ReLU(inplace=True)\n", - " (2): Linear(in_features=512, out_features=64, bias=False)\n", - " )\n", - " (qkv): Linear(in_features=2048, out_features=6144, bias=False)\n", - " (attn_drop): Dropout(p=0.0, inplace=False)\n", - " (proj): Linear(in_features=2048, out_features=2048, bias=True)\n", - " (proj_drop): Dropout(p=0.0, inplace=False)\n", - " (softmax): Softmax(dim=-1)\n", - " )\n", - " (drop_path): DropPath(drop_prob=0.091)\n", - " (norm2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", - " (norm3): Identity()\n", - " (mlp): Mlp(\n", - " (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n", - " (drop): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " (40): SwinTransformerBlock(\n", - " dim=2048, input_resolution=(8, 8),num_heads=64, window_size=8, shift_size=0, mlp_ratio=4.0\n", - " (norm1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", - " (attn): WindowAttention(\n", - " dim=2048, window_size=(8, 8), pretrained_window_size=(0, 0), num_heads=64\n", - " (cpb_mlp): Sequential(\n", - " (0): Linear(in_features=2, out_features=512, bias=True)\n", - " (1): ReLU(inplace=True)\n", - " (2): Linear(in_features=512, out_features=64, bias=False)\n", - " )\n", - " (qkv): Linear(in_features=2048, out_features=6144, bias=False)\n", - " (attn_drop): Dropout(p=0.0, inplace=False)\n", - " (proj): Linear(in_features=2048, out_features=2048, bias=True)\n", - " (proj_drop): Dropout(p=0.0, inplace=False)\n", - " (softmax): Softmax(dim=-1)\n", - " )\n", - " (drop_path): DropPath(drop_prob=0.094)\n", - " (norm2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", - " (norm3): Identity()\n", - " (mlp): Mlp(\n", - " (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n", - " (drop): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " (41): SwinTransformerBlock(\n", - " dim=2048, input_resolution=(8, 8),num_heads=64, window_size=8, shift_size=0, mlp_ratio=4.0\n", - " (norm1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", - " (attn): WindowAttention(\n", - " dim=2048, window_size=(8, 8), pretrained_window_size=(0, 0), num_heads=64\n", - " (cpb_mlp): Sequential(\n", - " (0): Linear(in_features=2, out_features=512, bias=True)\n", - " (1): ReLU(inplace=True)\n", - " (2): Linear(in_features=512, out_features=64, bias=False)\n", - " )\n", - " (qkv): Linear(in_features=2048, out_features=6144, bias=False)\n", - " (attn_drop): Dropout(p=0.0, inplace=False)\n", - " (proj): Linear(in_features=2048, out_features=2048, bias=True)\n", - " (proj_drop): Dropout(p=0.0, inplace=False)\n", - " (softmax): Softmax(dim=-1)\n", - " )\n", - " (drop_path): DropPath(drop_prob=0.096)\n", - " (norm2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", - " (norm3): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", - " (mlp): Mlp(\n", - " (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n", - " (drop): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " )\n", - " (downsample): PatchMerging(\n", - " input_resolution=(8, 8), dim=2048\n", - " (reduction): Linear(in_features=8192, out_features=4096, bias=False)\n", - " (norm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", - " )\n", - " )\n", - " (3): BasicLayer(\n", - " dim=4096, input_resolution=(4, 4), depth=2\n", - " (blocks): ModuleList(\n", - " (0): SwinTransformerBlock(\n", - " dim=4096, input_resolution=(4, 4),num_heads=128, window_size=4, shift_size=0, mlp_ratio=4.0\n", - " (norm1): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", - " (attn): WindowAttention(\n", - " dim=4096, window_size=(4, 4), pretrained_window_size=(0, 0), num_heads=128\n", - " (cpb_mlp): Sequential(\n", - " (0): Linear(in_features=2, out_features=512, bias=True)\n", - " (1): ReLU(inplace=True)\n", - " (2): Linear(in_features=512, out_features=128, bias=False)\n", - " )\n", - " (qkv): Linear(in_features=4096, out_features=12288, bias=False)\n", - " (attn_drop): Dropout(p=0.0, inplace=False)\n", - " (proj): Linear(in_features=4096, out_features=4096, bias=True)\n", - " (proj_drop): Dropout(p=0.0, inplace=False)\n", - " (softmax): Softmax(dim=-1)\n", - " )\n", - " (drop_path): DropPath(drop_prob=0.098)\n", - " (norm2): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", - " (norm3): Identity()\n", - " (mlp): Mlp(\n", - " (fc1): Linear(in_features=4096, out_features=16384, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (fc2): Linear(in_features=16384, out_features=4096, bias=True)\n", - " (drop): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " (1): SwinTransformerBlock(\n", - " dim=4096, input_resolution=(4, 4),num_heads=128, window_size=4, shift_size=0, mlp_ratio=4.0\n", - " (norm1): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", - " (attn): WindowAttention(\n", - " dim=4096, window_size=(4, 4), pretrained_window_size=(0, 0), num_heads=128\n", - " (cpb_mlp): Sequential(\n", - " (0): Linear(in_features=2, out_features=512, bias=True)\n", - " (1): ReLU(inplace=True)\n", - " (2): Linear(in_features=512, out_features=128, bias=False)\n", - " )\n", - " (qkv): Linear(in_features=4096, out_features=12288, bias=False)\n", - " (attn_drop): Dropout(p=0.0, inplace=False)\n", - " (proj): Linear(in_features=4096, out_features=4096, bias=True)\n", - " (proj_drop): Dropout(p=0.0, inplace=False)\n", - " (softmax): Softmax(dim=-1)\n", - " )\n", - " (drop_path): DropPath(drop_prob=0.100)\n", - " (norm2): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", - " (norm3): Identity()\n", - " (mlp): Mlp(\n", - " (fc1): Linear(in_features=4096, out_features=16384, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (fc2): Linear(in_features=16384, out_features=4096, bias=True)\n", - " (drop): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " )\n", - " )\n", - " )\n", - " (norm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", - " (avgpool): AdaptiveAvgPool1d(output_size=1)\n", - " (head): Identity()\n", - " )\n", - " (decoder): Sequential(\n", - " (0): Conv2d(4096, 14336, kernel_size=(1, 1), stride=(1, 1))\n", - " (1): PixelShuffle(upscale_factor=32)\n", - " )\n", - ")" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "checkpoint = torch.load(MODEL_PATH)\n", - "model = build_model(config, pretrain=True)\n", - "model.load_state_dict(checkpoint['module']) # If 'module' not working, try 'model'\n", - "n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", - "logger.info(f\"number of params: {n_parameters}\")\n", - "# model.cuda()\n", - "model.eval()" - ] - }, - { - "cell_type": "markdown", - "id": "b500d13b-89d7-4cd8-a36a-ab6f10f6a397", - "metadata": {}, - "source": [ - "## 3. Load evaluation set (from numpy file)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "e23a0876-4a14-4525-9d6d-8338e16db4f2", - "metadata": {}, - "outputs": [], - "source": [ - "import torchvision.transforms as T\n", - "from pytorch_caney.data.utils import RandomResizedCropNP, SimmimMaskGenerator\n", - "\n", - "class MinMaxEmissiveScaleReflectance(object):\n", - " \"\"\"\n", - " Performs scaling of MODIS TOA data\n", - " - Scales reflectance percentages to reflectance units (% -> (0,1))\n", - " - Performs per-channel minmax scaling for emissive bands (k -> (0,1))\n", - " \"\"\"\n", - "\n", - " def __init__(self):\n", - " \n", - " self.reflectance_indices = [0, 1, 2, 3, 4, 6]\n", - " self.emissive_indices = [5, 7, 8, 9, 10, 11, 12, 13]\n", - "\n", - " self.emissive_mins = np.array(\n", - " [223.1222, 178.9174, 204.3739, 204.7677,\n", - " 194.8686, 202.1759, 201.3823, 203.3537],\n", - " dtype=np.float32)\n", - "\n", - " self.emissive_maxs = np.array(\n", - " [352.7182, 261.2920, 282.5529, 319.0373,\n", - " 295.0209, 324.0677, 321.5254, 285.9848],\n", - " dtype=np.float32)\n", - "\n", - " def __call__(self, img):\n", - " \n", - " # Reflectance % to reflectance units\n", - " img[:, :, self.reflectance_indices] = \\\n", - " img[:, :, self.reflectance_indices] * 0.01\n", - " \n", - " # Brightness temp scaled to (0,1) range\n", - " img[:, :, self.emissive_indices] = \\\n", - " (img[:, :, self.emissive_indices] - self.emissive_mins) / \\\n", - " (self.emissive_maxs - self.emissive_mins)\n", - " \n", - " return img\n", - "\n", - "\n", - "class SimmimTransform:\n", - " \"\"\"\n", - " torchvision transform which transforms the input imagery into\n", - " addition to generating a MiM mask\n", - " \"\"\"\n", - "\n", - " def __init__(self, config):\n", - "\n", - " self.transform_img = \\\n", - " T.Compose([\n", - " MinMaxEmissiveScaleReflectance(), # New transform for MinMax\n", - " T.ToTensor(),\n", - " T.Resize((config.DATA.IMG_SIZE, config.DATA.IMG_SIZE)),\n", - " ])\n", - "\n", - " if config.MODEL.TYPE in ['swin', 'swinv2']:\n", - "\n", - " model_patch_size = config.MODEL.SWINV2.PATCH_SIZE\n", - "\n", - " else:\n", - "\n", - " raise NotImplementedError\n", - "\n", - " self.mask_generator = SimmimMaskGenerator(\n", - " input_size=config.DATA.IMG_SIZE,\n", - " mask_patch_size=config.DATA.MASK_PATCH_SIZE,\n", - " model_patch_size=model_patch_size,\n", - " mask_ratio=config.DATA.MASK_RATIO,\n", - " )\n", - "\n", - " def __call__(self, img):\n", - "\n", - " img = self.transform_img(img)\n", - " mask = self.mask_generator()\n", - "\n", - " return img, mask" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "73a8d307-de9b-4617-abdd-dae1e7c2521a", - "metadata": {}, - "outputs": [], - "source": [ - "# Use the Masked-Image-Modeling transform\n", - "transform = SimmimTransform(config)\n", - "\n", - "# The reconstruction evaluation set is a single numpy file\n", - "validation_dataset_path = config.DATA.DATA_PATHS[0]\n", - "validation_dataset = np.load(validation_dataset_path)\n", - "len_batch = range(validation_dataset.shape[0])\n", - "\n", - "# Apply transform to each image in the batch\n", - "# A mask is auto-generated in the transform\n", - "imgMasks = [transform(validation_dataset[idx]) for idx \\\n", - " in len_batch]\n", - "\n", - "# Seperate img and masks, cast masks to torch tensor\n", - "img = torch.stack([imgMask[0] for imgMask in imgMasks])\n", - "mask = torch.stack([torch.from_numpy(imgMask[1]) for \\\n", - " imgMask in imgMasks])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "151eeee5-7967-451d-a77f-64d65739e9ae", - "metadata": {}, - "outputs": [], - "source": [ - "idx_to_band = {\n", - " 0: 1,\n", - " 1: 2,\n", - " 2: 3,\n", - " 3: 6,\n", - " 4: 7,\n", - " 5: 21,\n", - " 6: 26,\n", - " 7: 27,\n", - " 8: 28,\n", - " 9: 29,\n", - " 10: 30,\n", - " 11: 31,\n", - " 12: 32,\n", - " 13: 33\n", - "}\n", - "\n", - "\n", - "def get_batch_info(img):\n", - " \n", - " channels = img.shape[1]\n", - " \n", - " for channelIdx in range(channels):\n", - " channel = idx_to_band[channelIdx]\n", - " img_band_array = img[:, channelIdx, :, :]\n", - " min_ = img_band_array.min()\n", - " mean_ = img_band_array.mean()\n", - " max_ = img_band_array.max()\n", - " print(f'Channel {channel}, min {min_}, mean {mean_}, max {max_}') " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "8cee7aa5-13b6-4644-98b7-6944c1bbfe99", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Channel 1, min 0.02055743709206581, mean 0.23059538006782532, max 1.0075911283493042\n", - "Channel 2, min 0.010388242080807686, mean 0.2926776707172394, max 0.9048148393630981\n", - "Channel 3, min 0.051274485886096954, mean 0.2630060315132141, max 1.048071026802063\n", - "Channel 6, min 0.002602405147626996, mean 0.2257007360458374, max 0.6810641884803772\n", - "Channel 7, min 0.0013291973154991865, mean 0.15059055387973785, max 0.6122391223907471\n", - "Channel 21, min -1.1774119457186316e-07, mean 0.5202993154525757, max 0.9999997615814209\n", - "Channel 26, min -0.0045103696174919605, mean 0.030210332944989204, max 0.5251043438911438\n", - "Channel 27, min 1.85236601168981e-07, mean 0.7143893837928772, max 0.9999996423721313\n", - "Channel 28, min 0.0, mean 0.5836189985275269, max 1.0000008344650269\n", - "Channel 29, min 1.3353324845866155e-07, mean 0.6119444370269775, max 0.9999997615814209\n", - "Channel 30, min 0.0, mean 0.6182008385658264, max 0.9999997019767761\n", - "Channel 31, min 2.5036615625140257e-07, mean 0.6037113666534424, max 1.000000238418579\n", - "Channel 32, min -2.540102457260218e-07, mean 0.6058735251426697, max 0.9999997615814209\n", - "Channel 33, min 1.8466157314378506e-07, mean 0.6282674670219421, max 1.0000003576278687\n" - ] - } - ], - "source": [ - "get_batch_info(img)" - ] - }, - { - "cell_type": "markdown", - "id": "55acf5e9-eb2a-496c-baa6-3b74503a2978", - "metadata": {}, - "source": [ - "## 4. Prediction helper functions" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "595336f8-71b4-418b-b153-2461583ed613", - "metadata": {}, - "outputs": [], - "source": [ - "def predict(model, dataloader, num_batches=5):\n", - "\n", - " inputs = []\n", - " outputs = []\n", - " masks = []\n", - " losses = []\n", - " with tqdm(total=num_batches) as pbar:\n", - "\n", - " for idx, img_mask in enumerate(dataloader):\n", - " \n", - " pbar.update(1)\n", - "\n", - " if idx > num_batches:\n", - " return inputs, outputs, masks, losses\n", - "\n", - " img_mask = img_mask[0]\n", - "\n", - " img = torch.stack([pair[0] for pair in img_mask])\n", - " mask = torch.stack([pair[1] for pair in img_mask])\n", - "\n", - " img = img.cuda(non_blocking=True)\n", - " mask = mask.cuda(non_blocking=True)\n", - "\n", - " with torch.no_grad():\n", - " with amp.autocast(enabled=config.ENABLE_AMP):\n", - " z = model.encoder(img, mask)\n", - " img_recon = model.decoder(z)\n", - " loss = model(img, mask)\n", - "\n", - " inputs.extend(img.cpu())\n", - " masks.extend(mask.cpu())\n", - " outputs.extend(img_recon.cpu())\n", - " losses.append(loss.cpu())\n", - " \n", - " return inputs, outputs, masks, losses\n", - "\n", - "\n", - "def minmax_norm(img_arr):\n", - " arr_min = img_arr.min()\n", - " arr_max = img_arr.max()\n", - " img_arr_scaled = (img_arr - arr_min) / (arr_max - arr_min)\n", - " img_arr_scaled = img_arr_scaled * 255\n", - " img_arr_scaled = img_arr_scaled.astype(np.uint8)\n", - " return img_arr_scaled\n", - "\n", - "\n", - "def process_mask(mask):\n", - " mask_img = mask.unsqueeze(0)\n", - " mask_img = mask_img.repeat_interleave(4, 1).repeat_interleave(4, 2).unsqueeze(1).contiguous()\n", - " mask_img = mask_img[0, 0, :, :]\n", - " mask_img = np.stack([mask_img, mask_img, mask_img], axis=-1)\n", - " return mask_img\n", - "\n", - "def reverse_transform(image):\n", - " minMaxTransform = MinMaxEmissiveScaleReflectance()\n", - " image = image.transpose((1,2,0))\n", - " \n", - " image[:, :, minMaxTransform.reflectance_indices] = image[:, :, minMaxTransform.reflectance_indices] * 100\n", - " image[:, :, minMaxTransform.emissive_indices] = (\n", - " image[:, :, minMaxTransform.emissive_indices] * \\\n", - " (minMaxTransform.emissive_maxs - minMaxTransform.emissive_mins)) + minMaxTransform.emissive_mins\n", - "\n", - " image = image.transpose((2,0,1))\n", - " return image\n", - "\n", - "def process_prediction(image, img_recon, mask, rgb_index):\n", - "\n", - " mask = process_mask(mask)\n", - " \n", - " red_idx = rgb_index[0]\n", - " blue_idx = rgb_index[1]\n", - " green_idx = rgb_index[2]\n", - "\n", - " image = reverse_transform(image.numpy())\n", - " \n", - " img_recon = reverse_transform(img_recon.numpy())\n", - "\n", - " rgb_image = np.stack((image[red_idx, :, :],\n", - " image[blue_idx, :, :],\n", - " image[green_idx, :, :]),\n", - " axis=-1)\n", - " rgb_image = minmax_norm(rgb_image)\n", - "\n", - " rgb_image_recon = np.stack((img_recon[red_idx, :, :],\n", - " img_recon[blue_idx, :, :],\n", - " img_recon[green_idx, :, :]),\n", - " axis=-1)\n", - " rgb_image_recon = minmax_norm(rgb_image_recon)\n", - "\n", - " rgb_masked = np.where(mask == 0, rgb_image, rgb_image_recon)\n", - " rgb_image_masked = np.where(mask == 1, 0, rgb_image)\n", - " rgb_recon_masked = rgb_masked\n", - " \n", - " return rgb_image, rgb_image_masked, rgb_recon_masked, mask\n", - "\n", - "\n", - "def plot_export_pdf(path, inputs, outputs, masks, rgb_index):\n", - " pdf_plot_obj = PdfPages(path)\n", - "\n", - " for idx in range(len(inputs)):\n", - " # prediction processing\n", - " image = inputs[idx]\n", - " img_recon = outputs[idx]\n", - " mask = masks[idx]\n", - " rgb_image, rgb_image_masked, rgb_recon_masked, mask = \\\n", - " process_prediction(image, img_recon, mask, rgb_index)\n", - "\n", - " # matplotlib code\n", - " fig, (ax01, ax23) = plt.subplots(2, 2, figsize=(40, 30))\n", - " ax0, ax1 = ax01\n", - " ax2, ax3 = ax23\n", - " ax2.imshow(rgb_image)\n", - " ax2.set_title(f\"Idx: {idx} MOD021KM v6.1 Bands: {rgb_index}\")\n", - "\n", - " ax0.imshow(rgb_recon_masked)\n", - " ax0.set_title(f\"Idx: {idx} Model reconstruction\")\n", - "\n", - " ax1.imshow(rgb_image_masked)\n", - " ax1.set_title(f\"Idx: {idx} MOD021KM Bands: {rgb_index}, masked\")\n", - " \n", - " ax3.matshow(mask[:, :, 0])\n", - " ax3.set_title(f\"Idx: {idx} Reconstruction Mask\")\n", - " pdf_plot_obj.savefig()\n", - "\n", - " pdf_plot_obj.close()" - ] - }, - { - "cell_type": "markdown", - "id": "551c44b5-6d88-45c4-b397-c38de8064544", - "metadata": {}, - "source": [ - "## 5. Predict" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "4e695cc3-b869-4fc2-b360-b45f3b81affd", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 5/5 [00:08<00:00, 1.74s/it]\n" - ] - } - ], - "source": [ - "inputs = []\n", - "outputs = []\n", - "masks = []\n", - "losses = []\n", - "\n", - "# We could do this in a single batch however we\n", - "# want to report the loss per-image, in place of\n", - "# loss per-batch.\n", - "for i in tqdm(range(5)):\n", - " single_img = img[i].unsqueeze(0)\n", - " single_mask = mask[i].unsqueeze(0)\n", - " # single_img = single_img.cuda(non_blocking=True)\n", - " # single_mask = single_mask.cuda(non_blocking=True)\n", - "\n", - " with torch.no_grad():\n", - " z = model.encoder(single_img, single_mask)\n", - " img_recon = model.decoder(z)\n", - " loss = model(single_img, single_mask)\n", - "\n", - " inputs.extend(single_img.cpu())\n", - " masks.extend(single_mask.cpu())\n", - " outputs.extend(img_recon.cpu())\n", - " losses.append(loss.cpu()) " - ] - }, - { - "cell_type": "markdown", - "id": "dc3f102c-94df-4d9e-8040-52197a7e71db", - "metadata": {}, - "source": [ - "## 6. Plot and write to PDF\n", - "\n", - "Writes out all of the predictions to a PDF file" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "5ebdcd1d-09db-4ccf-8cc1-58d6f47e3a55", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pdf_path = '../../satvision-toa-reconstruction-pdf-giant-patch-8-09.20.pdf'\n", - "rgb_index = [0, 2, 1] # Indices of [Red band, Blue band, Green band]\n", - "\n", - "plot_export_pdf(pdf_path, inputs, outputs, masks, rgb_index)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e7a57f4d-5df0-47a3-bfb6-d7f29a95e276", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ILAB Kernel (Pytorch)", - "language": "python", - "name": "pytorch-kernel" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/satvision-toa-reconstruction_giant.ipynb b/notebooks/satvision-toa-reconstruction_giant.ipynb new file mode 100644 index 0000000..85e0758 --- /dev/null +++ b/notebooks/satvision-toa-reconstruction_giant.ipynb @@ -0,0 +1,2064 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c8ab2075-c488-46b9-8cd2-0cdaf399acfc", + "metadata": {}, + "source": [ + "# Satvision-TOA Reconstruction Notebook\n", + "\n", + "Version: 04.30.24\n", + "\n", + "Env: `Python [conda env:ilab-pytorch]`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6e88ea70-7dbf-4b67-a12d-db36e2bc9914", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Defaulting to user installation because normal site-packages is not writeable\n", + "Requirement already satisfied: yacs in /home/cssprad1/.local/lib/python3.12/site-packages (0.1.8)\n", + "Requirement already satisfied: timm in /home/cssprad1/.local/lib/python3.12/site-packages (0.9.2)\n", + "Requirement already satisfied: 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"execution_count": 2, + "id": "d046c3e5-c458-4e03-9c96-e9eb95a04963", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import time\n", + "import random\n", + "import datetime\n", + "from tqdm import tqdm\n", + "import numpy as np\n", + "import logging\n", + "\n", + "import torch\n", + "import torch.cuda.amp as amp\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.backends.backend_pdf import PdfPages\n", + "\n", + "import warnings\n", + "\n", + "warnings.filterwarnings('ignore') " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7c7db1bc-09ee-47e3-9015-e6b148d497e7", + "metadata": {}, + "outputs": [], + "source": [ + "sys.path.append('../../pytorch-caney')\n", + "\n", + "from pytorch_caney.config import get_config\n", + "\n", + "from pytorch_caney.models.build import build_model\n", + "\n", + "from pytorch_caney.ptc_logging import create_logger\n", + "\n", + "from pytorch_caney.data.datasets.mim_modis_22m_dataset import MODIS22MDataset\n", + "\n", + "from pytorch_caney.data.transforms import SimmimTransform, SimmimMaskGenerator\n", + "\n", + "from pytorch_caney.config import _C, _update_config_from_file" + ] + }, + { + "cell_type": "markdown", + "id": "d841e464-f880-4e53-bf31-f9f225713918", + "metadata": {}, + "source": [ + "## 1. Configuration" + ] + }, + { + "cell_type": "markdown", + "id": "6274e323-bc04-41d4-bc49-baed65d027e6", + "metadata": {}, + "source": [ + "### Clone model ckpt from huggingface\n", + "\n", + "### Model repo: https://huggingface.co/nasa-cisto-data-science-group/satvision-toa-huge-patch8-window8-128\n", + "\n", + "```bash\n", + "# On prism/explore\n", + "module load git-lfs\n", + "\n", + "git lfs install\n", + "\n", + "git clone git clone git@hf.co:nasa-cisto-data-science-group/satvision-toa-huge-patch8-window8-128\n", + "```\n", + "\n", + "Note: If using git w/ ssh, make sure you have ssh keys enabled to clone using ssh auth.\n", + "https://huggingface.co/docs/hub/security-git-ssh\n", + "\n", + "```bash\n", + "eval $(ssh-agent)\n", + "\n", + "# If this outputs as anon, follow the next steps.\n", + "ssh -T git@hf.co\n", + "\n", + "# Check if ssh-agent is using the proper key\n", + "ssh-add -l\n", + "\n", + "# If not\n", + "ssh-add ~/.ssh/your-key\n", + "\n", + "# Or if you want to use the default id_* key, just do\n", + "ssh-add\n", + "\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "af699ba3-2d98-4daf-9437-c322d7b59a98", + "metadata": {}, + "outputs": [], + "source": [ + "MODEL_PATH: str = '/explore/nobackup/people/cssprad1/projects/satvision-toa/models/satvision-toa-giant-patch8-window8-128/mp_rank_00_model_states.pt'\n", + "CONFIG_PATH: str = '/explore/nobackup/people/cssprad1/projects/satvision-toa/models/satvision-toa-giant-patch8-window8-128/mim_pretrain_swinv2_satvision_giant_128_window08_50ep.yaml'\n", + "\n", + "OUTPUT: str = '.'\n", + "TAG: str = 'satvision-huge-toa-reconstruction'\n", + "DATA_PATH: str = '/explore/nobackup/projects/ilab/projects/3DClouds/data/validation/sv_toa_128_chip_validation_04_24.npy'\n", + "DATA_PATHS: list = [DATA_PATH]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c4593e8c-6e94-4d01-b86e-5b78b621fc59", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=> merge config from /explore/nobackup/people/cssprad1/projects/satvision-toa/models/satvision-toa-giant-patch8-window8-128/mim_pretrain_swinv2_satvision_giant_128_window08_50ep.yaml\n" + ] + } + ], + "source": [ + "# Update config given configurations\n", + "\n", + "config = _C.clone()\n", + "_update_config_from_file(config, CONFIG_PATH)\n", + "\n", + "config.defrost()\n", + "config.MODEL.RESUME = MODEL_PATH\n", + "config.DATA.DATA_PATHS = DATA_PATHS\n", + "config.OUTPUT = OUTPUT\n", + "config.TAG = TAG\n", + "config.freeze()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "202a4474-88e4-44d5-b899-7aaf6cbed6f4", + "metadata": {}, + "outputs": [], + "source": [ + "# Configure logging\n", + "logging.basicConfig(\n", + " filename='app.log', # Specify the log file name\n", + " level=logging.INFO, # Set logging level to DEBUG\n", + " format='%(asctime)s [%(levelname)s] %(message)s', # Specify log message format\n", + " datefmt='%Y-%m-%d %H:%M:%S' # Specify date format\n", + ")\n", + "\n", + "# Add logging to standard output\n", + "console = logging.StreamHandler() # Create a handler for standard output\n", + "console.setLevel(logging.INFO) # Set logging level for standard output\n", + "console.setFormatter(logging.Formatter('%(asctime)s [%(levelname)s] %(message)s')) # Set log message format for standard output\n", + "logger = logging.getLogger('')\n", + "logger.addHandler(console)" + ] + }, + { + "cell_type": "markdown", + "id": "11ebd497-7741-41a7-af9d-0ee49a6313a4", + "metadata": {}, + "source": [ + "## 2. Load model weights from checkpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "68abf348-c6bf-43a3-b00a-cc5f8d80545f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-09-20 14:46:41,359 [INFO] number of params: 2653783008\n" + ] + }, + { + "data": { + "text/plain": [ + "MiMModel(\n", + " (encoder): SwinTransformerV2ForSimMIM(\n", + " (patch_embed): PatchEmbed(\n", + " (proj): Conv2d(14, 512, kernel_size=(4, 4), stride=(4, 4))\n", + " (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (pos_drop): Dropout(p=0.0, inplace=False)\n", + " (layers): ModuleList(\n", + " (0): BasicLayer(\n", + " dim=512, input_resolution=(32, 32), depth=2\n", + " (blocks): ModuleList(\n", + " (0): SwinTransformerBlock(\n", + " dim=512, input_resolution=(32, 32),num_heads=16, window_size=8, shift_size=0, mlp_ratio=4.0\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (attn): WindowAttention(\n", + " dim=512, window_size=(8, 8), pretrained_window_size=(0, 0), num_heads=16\n", + " (cpb_mlp): Sequential(\n", + " (0): Linear(in_features=2, out_features=512, bias=True)\n", + " (1): ReLU(inplace=True)\n", + " (2): Linear(in_features=512, out_features=16, bias=False)\n", + " )\n", + " (qkv): Linear(in_features=512, out_features=1536, bias=False)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj): Linear(in_features=512, out_features=512, bias=True)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (softmax): Softmax(dim=-1)\n", + " )\n", + " (drop_path): Identity()\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm3): Identity()\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=512, out_features=2048, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (fc2): Linear(in_features=2048, out_features=512, bias=True)\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " (1): 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inplace=False)\n", + " (proj): Linear(in_features=2048, out_features=2048, bias=True)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (softmax): Softmax(dim=-1)\n", + " )\n", + " (drop_path): DropPath(drop_prob=0.087)\n", + " (norm2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", + " (norm3): Identity()\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " (38): SwinTransformerBlock(\n", + " dim=2048, input_resolution=(8, 8),num_heads=64, window_size=8, shift_size=0, mlp_ratio=4.0\n", + " (norm1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", + " (attn): WindowAttention(\n", + " dim=2048, window_size=(8, 8), pretrained_window_size=(0, 0), num_heads=64\n", + " (cpb_mlp): Sequential(\n", + " (0): Linear(in_features=2, out_features=512, bias=True)\n", + " (1): ReLU(inplace=True)\n", + " (2): Linear(in_features=512, out_features=64, bias=False)\n", + " )\n", + " (qkv): Linear(in_features=2048, out_features=6144, bias=False)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj): Linear(in_features=2048, out_features=2048, bias=True)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (softmax): Softmax(dim=-1)\n", + " )\n", + " (drop_path): DropPath(drop_prob=0.089)\n", + " (norm2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", + " (norm3): Identity()\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " (39): SwinTransformerBlock(\n", + " dim=2048, input_resolution=(8, 8),num_heads=64, window_size=8, shift_size=0, mlp_ratio=4.0\n", + " (norm1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", + " (attn): WindowAttention(\n", + " dim=2048, window_size=(8, 8), pretrained_window_size=(0, 0), num_heads=64\n", + " (cpb_mlp): Sequential(\n", + " (0): Linear(in_features=2, out_features=512, bias=True)\n", + " (1): ReLU(inplace=True)\n", + " (2): Linear(in_features=512, out_features=64, bias=False)\n", + " )\n", + " (qkv): Linear(in_features=2048, out_features=6144, bias=False)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj): Linear(in_features=2048, out_features=2048, bias=True)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (softmax): Softmax(dim=-1)\n", + " )\n", + " (drop_path): DropPath(drop_prob=0.091)\n", + " (norm2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", + " (norm3): Identity()\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " (40): SwinTransformerBlock(\n", + " dim=2048, input_resolution=(8, 8),num_heads=64, window_size=8, shift_size=0, mlp_ratio=4.0\n", + " (norm1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", + " (attn): WindowAttention(\n", + " dim=2048, window_size=(8, 8), pretrained_window_size=(0, 0), num_heads=64\n", + " (cpb_mlp): Sequential(\n", + " (0): Linear(in_features=2, out_features=512, bias=True)\n", + " (1): ReLU(inplace=True)\n", + " (2): Linear(in_features=512, out_features=64, bias=False)\n", + " )\n", + " (qkv): Linear(in_features=2048, out_features=6144, bias=False)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj): Linear(in_features=2048, out_features=2048, bias=True)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (softmax): Softmax(dim=-1)\n", + " )\n", + " (drop_path): DropPath(drop_prob=0.094)\n", + " (norm2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", + " (norm3): Identity()\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " (41): SwinTransformerBlock(\n", + " dim=2048, input_resolution=(8, 8),num_heads=64, window_size=8, shift_size=0, mlp_ratio=4.0\n", + " (norm1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", + " (attn): WindowAttention(\n", + " dim=2048, window_size=(8, 8), pretrained_window_size=(0, 0), num_heads=64\n", + " (cpb_mlp): Sequential(\n", + " (0): Linear(in_features=2, out_features=512, bias=True)\n", + " (1): ReLU(inplace=True)\n", + " (2): Linear(in_features=512, out_features=64, bias=False)\n", + " )\n", + " (qkv): Linear(in_features=2048, out_features=6144, bias=False)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj): Linear(in_features=2048, out_features=2048, bias=True)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (softmax): Softmax(dim=-1)\n", + " )\n", + " (drop_path): DropPath(drop_prob=0.096)\n", + " (norm2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", + " (norm3): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (downsample): PatchMerging(\n", + " input_resolution=(8, 8), dim=2048\n", + " (reduction): Linear(in_features=8192, out_features=4096, bias=False)\n", + " (norm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " )\n", + " (3): BasicLayer(\n", + " dim=4096, input_resolution=(4, 4), depth=2\n", + " (blocks): ModuleList(\n", + " (0): SwinTransformerBlock(\n", + " dim=4096, input_resolution=(4, 4),num_heads=128, window_size=4, shift_size=0, mlp_ratio=4.0\n", + " (norm1): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", + " (attn): WindowAttention(\n", + " dim=4096, window_size=(4, 4), pretrained_window_size=(0, 0), num_heads=128\n", + " (cpb_mlp): Sequential(\n", + " (0): Linear(in_features=2, out_features=512, bias=True)\n", + " (1): ReLU(inplace=True)\n", + " (2): Linear(in_features=512, out_features=128, bias=False)\n", + " )\n", + " (qkv): Linear(in_features=4096, out_features=12288, bias=False)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj): Linear(in_features=4096, out_features=4096, bias=True)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (softmax): Softmax(dim=-1)\n", + " )\n", + " (drop_path): DropPath(drop_prob=0.098)\n", + " (norm2): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", + " (norm3): Identity()\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=4096, out_features=16384, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (fc2): Linear(in_features=16384, out_features=4096, bias=True)\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " (1): SwinTransformerBlock(\n", + " dim=4096, input_resolution=(4, 4),num_heads=128, window_size=4, shift_size=0, mlp_ratio=4.0\n", + " (norm1): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", + " (attn): WindowAttention(\n", + " dim=4096, window_size=(4, 4), pretrained_window_size=(0, 0), num_heads=128\n", + " (cpb_mlp): Sequential(\n", + " (0): Linear(in_features=2, out_features=512, bias=True)\n", + " (1): ReLU(inplace=True)\n", + " (2): Linear(in_features=512, out_features=128, bias=False)\n", + " )\n", + " (qkv): Linear(in_features=4096, out_features=12288, bias=False)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj): Linear(in_features=4096, out_features=4096, bias=True)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (softmax): Softmax(dim=-1)\n", + " )\n", + " (drop_path): DropPath(drop_prob=0.100)\n", + " (norm2): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", + " (norm3): Identity()\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=4096, out_features=16384, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (fc2): Linear(in_features=16384, out_features=4096, bias=True)\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (norm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", + " (avgpool): AdaptiveAvgPool1d(output_size=1)\n", + " (head): Identity()\n", + " )\n", + " (decoder): Sequential(\n", + " (0): Conv2d(4096, 14336, kernel_size=(1, 1), stride=(1, 1))\n", + " (1): PixelShuffle(upscale_factor=32)\n", + " )\n", + ")" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "checkpoint = torch.load(MODEL_PATH)\n", + "model = build_model(config, pretrain=True)\n", + "model.load_state_dict(checkpoint['module']) # If 'module' not working, try 'model'\n", + "n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", + "logger.info(f\"number of params: {n_parameters}\")\n", + "# model.cuda()\n", + "model.eval()" + ] + }, + { + "cell_type": "markdown", + "id": "b500d13b-89d7-4cd8-a36a-ab6f10f6a397", + "metadata": {}, + "source": [ + "## 3. Load evaluation set (from numpy file)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e23a0876-4a14-4525-9d6d-8338e16db4f2", + "metadata": {}, + "outputs": [], + "source": [ + "import torchvision.transforms as T\n", + "from pytorch_caney.data.utils import RandomResizedCropNP, SimmimMaskGenerator\n", + "\n", + "class MinMaxEmissiveScaleReflectance(object):\n", + " \"\"\"\n", + " Performs scaling of MODIS TOA data\n", + " - Scales reflectance percentages to reflectance units (% -> (0,1))\n", + " - Performs per-channel minmax scaling for emissive bands (k -> (0,1))\n", + " \"\"\"\n", + "\n", + " def __init__(self):\n", + " \n", + " self.reflectance_indices = [0, 1, 2, 3, 4, 6]\n", + " self.emissive_indices = [5, 7, 8, 9, 10, 11, 12, 13]\n", + "\n", + " self.emissive_mins = np.array(\n", + " [223.1222, 178.9174, 204.3739, 204.7677,\n", + " 194.8686, 202.1759, 201.3823, 203.3537],\n", + " dtype=np.float32)\n", + "\n", + " self.emissive_maxs = np.array(\n", + " [352.7182, 261.2920, 282.5529, 319.0373,\n", + " 295.0209, 324.0677, 321.5254, 285.9848],\n", + " dtype=np.float32)\n", + "\n", + " def __call__(self, img):\n", + " \n", + " # Reflectance % to reflectance units\n", + " img[:, :, self.reflectance_indices] = \\\n", + " img[:, :, self.reflectance_indices] * 0.01\n", + " \n", + " # Brightness temp scaled to (0,1) range\n", + " img[:, :, self.emissive_indices] = \\\n", + " (img[:, :, self.emissive_indices] - self.emissive_mins) / \\\n", + " (self.emissive_maxs - self.emissive_mins)\n", + " \n", + " return img\n", + "\n", + "\n", + "class SimmimTransform:\n", + " \"\"\"\n", + " torchvision transform which transforms the input imagery into\n", + " addition to generating a MiM mask\n", + " \"\"\"\n", + "\n", + " def __init__(self, config):\n", + "\n", + " self.transform_img = \\\n", + " T.Compose([\n", + " MinMaxEmissiveScaleReflectance(), # New transform for MinMax\n", + " T.ToTensor(),\n", + " T.Resize((config.DATA.IMG_SIZE, config.DATA.IMG_SIZE)),\n", + " ])\n", + "\n", + " if config.MODEL.TYPE in ['swin', 'swinv2']:\n", + "\n", + " model_patch_size = config.MODEL.SWINV2.PATCH_SIZE\n", + "\n", + " else:\n", + "\n", + " raise NotImplementedError\n", + "\n", + " self.mask_generator = SimmimMaskGenerator(\n", + " input_size=config.DATA.IMG_SIZE,\n", + " mask_patch_size=config.DATA.MASK_PATCH_SIZE,\n", + " model_patch_size=model_patch_size,\n", + " mask_ratio=config.DATA.MASK_RATIO,\n", + " )\n", + "\n", + " def __call__(self, img):\n", + "\n", + " img = self.transform_img(img)\n", + " mask = self.mask_generator()\n", + "\n", + " return img, mask" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "73a8d307-de9b-4617-abdd-dae1e7c2521a", + "metadata": {}, + "outputs": [], + "source": [ + "# Use the Masked-Image-Modeling transform\n", + "transform = SimmimTransform(config)\n", + "\n", + "# The reconstruction evaluation set is a single numpy file\n", + "validation_dataset_path = config.DATA.DATA_PATHS[0]\n", + "validation_dataset = np.load(validation_dataset_path)\n", + "len_batch = range(validation_dataset.shape[0])\n", + "\n", + "# Apply transform to each image in the batch\n", + "# A mask is auto-generated in the transform\n", + "imgMasks = [transform(validation_dataset[idx]) for idx \\\n", + " in len_batch]\n", + "\n", + "# Seperate img and masks, cast masks to torch tensor\n", + "img = torch.stack([imgMask[0] for imgMask in imgMasks])\n", + "mask = torch.stack([torch.from_numpy(imgMask[1]) for \\\n", + " imgMask in imgMasks])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "151eeee5-7967-451d-a77f-64d65739e9ae", + "metadata": {}, + "outputs": [], + "source": [ + "idx_to_band = {\n", + " 0: 1,\n", + " 1: 2,\n", + " 2: 3,\n", + " 3: 6,\n", + " 4: 7,\n", + " 5: 21,\n", + " 6: 26,\n", + " 7: 27,\n", + " 8: 28,\n", + " 9: 29,\n", + " 10: 30,\n", + " 11: 31,\n", + " 12: 32,\n", + " 13: 33\n", + "}\n", + "\n", + "\n", + "def get_batch_info(img):\n", + " \n", + " channels = img.shape[1]\n", + " \n", + " for channelIdx in range(channels):\n", + " channel = idx_to_band[channelIdx]\n", + " img_band_array = img[:, channelIdx, :, :]\n", + " min_ = img_band_array.min()\n", + " mean_ = img_band_array.mean()\n", + " max_ = img_band_array.max()\n", + " print(f'Channel {channel}, min {min_}, mean {mean_}, max {max_}') " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "8cee7aa5-13b6-4644-98b7-6944c1bbfe99", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Channel 1, min 0.02055743709206581, mean 0.23059538006782532, max 1.0075911283493042\n", + "Channel 2, min 0.010388242080807686, mean 0.2926776707172394, max 0.9048148393630981\n", + "Channel 3, min 0.051274485886096954, mean 0.2630060315132141, max 1.048071026802063\n", + "Channel 6, min 0.002602405147626996, mean 0.2257007360458374, max 0.6810641884803772\n", + "Channel 7, min 0.0013291973154991865, mean 0.15059055387973785, max 0.6122391223907471\n", + "Channel 21, min -1.1774119457186316e-07, mean 0.5202993154525757, max 0.9999997615814209\n", + "Channel 26, min -0.0045103696174919605, mean 0.030210332944989204, max 0.5251043438911438\n", + "Channel 27, min 1.85236601168981e-07, mean 0.7143893837928772, max 0.9999996423721313\n", + "Channel 28, min 0.0, mean 0.5836189985275269, max 1.0000008344650269\n", + "Channel 29, min 1.3353324845866155e-07, mean 0.6119444370269775, max 0.9999997615814209\n", + "Channel 30, min 0.0, mean 0.6182008385658264, max 0.9999997019767761\n", + "Channel 31, min 2.5036615625140257e-07, mean 0.6037113666534424, max 1.000000238418579\n", + "Channel 32, min -2.540102457260218e-07, mean 0.6058735251426697, max 0.9999997615814209\n", + "Channel 33, min 1.8466157314378506e-07, mean 0.6282674670219421, max 1.0000003576278687\n" + ] + } + ], + "source": [ + "get_batch_info(img)" + ] + }, + { + "cell_type": "markdown", + "id": "55acf5e9-eb2a-496c-baa6-3b74503a2978", + "metadata": {}, + "source": [ + "## 4. Prediction helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "595336f8-71b4-418b-b153-2461583ed613", + "metadata": {}, + "outputs": [], + "source": [ + "def predict(model, dataloader, num_batches=5):\n", + "\n", + " inputs = []\n", + " outputs = []\n", + " masks = []\n", + " losses = []\n", + " with tqdm(total=num_batches) as pbar:\n", + "\n", + " for idx, img_mask in enumerate(dataloader):\n", + " \n", + " pbar.update(1)\n", + "\n", + " if idx > num_batches:\n", + " return inputs, outputs, masks, losses\n", + "\n", + " img_mask = img_mask[0]\n", + "\n", + " img = torch.stack([pair[0] for pair in img_mask])\n", + " mask = torch.stack([pair[1] for pair in img_mask])\n", + "\n", + " img = img.cuda(non_blocking=True)\n", + " mask = mask.cuda(non_blocking=True)\n", + "\n", + " with torch.no_grad():\n", + " with amp.autocast(enabled=config.ENABLE_AMP):\n", + " z = model.encoder(img, mask)\n", + " img_recon = model.decoder(z)\n", + " loss = model(img, mask)\n", + "\n", + " inputs.extend(img.cpu())\n", + " masks.extend(mask.cpu())\n", + " outputs.extend(img_recon.cpu())\n", + " losses.append(loss.cpu())\n", + " \n", + " return inputs, outputs, masks, losses\n", + "\n", + "\n", + "def minmax_norm(img_arr):\n", + " arr_min = img_arr.min()\n", + " arr_max = img_arr.max()\n", + " img_arr_scaled = (img_arr - arr_min) / (arr_max - arr_min)\n", + " img_arr_scaled = img_arr_scaled * 255\n", + " img_arr_scaled = img_arr_scaled.astype(np.uint8)\n", + " return img_arr_scaled\n", + "\n", + "\n", + "def process_mask(mask):\n", + " mask_img = mask.unsqueeze(0)\n", + " mask_img = mask_img.repeat_interleave(4, 1).repeat_interleave(4, 2).unsqueeze(1).contiguous()\n", + " mask_img = mask_img[0, 0, :, :]\n", + " mask_img = np.stack([mask_img, mask_img, mask_img], axis=-1)\n", + " return mask_img\n", + "\n", + "def reverse_transform(image):\n", + " minMaxTransform = MinMaxEmissiveScaleReflectance()\n", + " image = image.transpose((1,2,0))\n", + " \n", + " image[:, :, minMaxTransform.reflectance_indices] = image[:, :, minMaxTransform.reflectance_indices] * 100\n", + " image[:, :, minMaxTransform.emissive_indices] = (\n", + " image[:, :, minMaxTransform.emissive_indices] * \\\n", + " (minMaxTransform.emissive_maxs - minMaxTransform.emissive_mins)) + minMaxTransform.emissive_mins\n", + "\n", + " image = image.transpose((2,0,1))\n", + " return image\n", + "\n", + "def process_prediction(image, img_recon, mask, rgb_index):\n", + "\n", + " mask = process_mask(mask)\n", + " \n", + " red_idx = rgb_index[0]\n", + " blue_idx = rgb_index[1]\n", + " green_idx = rgb_index[2]\n", + "\n", + " image = reverse_transform(image.numpy())\n", + " \n", + " img_recon = reverse_transform(img_recon.numpy())\n", + "\n", + " rgb_image = np.stack((image[red_idx, :, :],\n", + " image[blue_idx, :, :],\n", + " image[green_idx, :, :]),\n", + " axis=-1)\n", + " rgb_image = minmax_norm(rgb_image)\n", + "\n", + " rgb_image_recon = np.stack((img_recon[red_idx, :, :],\n", + " img_recon[blue_idx, :, :],\n", + " img_recon[green_idx, :, :]),\n", + " axis=-1)\n", + " rgb_image_recon = minmax_norm(rgb_image_recon)\n", + "\n", + " rgb_masked = np.where(mask == 0, rgb_image, rgb_image_recon)\n", + " rgb_image_masked = np.where(mask == 1, 0, rgb_image)\n", + " rgb_recon_masked = rgb_masked\n", + " \n", + " return rgb_image, rgb_image_masked, rgb_recon_masked, mask\n", + "\n", + "\n", + "def plot_export_pdf(path, inputs, outputs, masks, rgb_index):\n", + " pdf_plot_obj = PdfPages(path)\n", + "\n", + " for idx in range(len(inputs)):\n", + " # prediction processing\n", + " image = inputs[idx]\n", + " img_recon = outputs[idx]\n", + " mask = masks[idx]\n", + " rgb_image, rgb_image_masked, rgb_recon_masked, mask = \\\n", + " process_prediction(image, img_recon, mask, rgb_index)\n", + "\n", + " # matplotlib code\n", + " fig, (ax01, ax23) = plt.subplots(2, 2, figsize=(40, 30))\n", + " ax0, ax1 = ax01\n", + " ax2, ax3 = ax23\n", + " ax2.imshow(rgb_image)\n", + " ax2.set_title(f\"Idx: {idx} MOD021KM v6.1 Bands: {rgb_index}\")\n", + "\n", + " ax0.imshow(rgb_recon_masked)\n", + " ax0.set_title(f\"Idx: {idx} Model reconstruction\")\n", + "\n", + " ax1.imshow(rgb_image_masked)\n", + " ax1.set_title(f\"Idx: {idx} MOD021KM Bands: {rgb_index}, masked\")\n", + " \n", + " ax3.matshow(mask[:, :, 0])\n", + " ax3.set_title(f\"Idx: {idx} Reconstruction Mask\")\n", + " pdf_plot_obj.savefig()\n", + "\n", + " pdf_plot_obj.close()" + ] + }, + { + "cell_type": "markdown", + "id": "551c44b5-6d88-45c4-b397-c38de8064544", + "metadata": {}, + "source": [ + "## 5. Predict" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4e695cc3-b869-4fc2-b360-b45f3b81affd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:08<00:00, 1.74s/it]\n" + ] + } + ], + "source": [ + "inputs = []\n", + "outputs = []\n", + "masks = []\n", + "losses = []\n", + "\n", + "# We could do this in a single batch however we\n", + "# want to report the loss per-image, in place of\n", + "# loss per-batch.\n", + "for i in tqdm(range(img.shape[0])):\n", + " single_img = img[i].unsqueeze(0)\n", + " single_mask = mask[i].unsqueeze(0)\n", + " # single_img = single_img.cuda(non_blocking=True)\n", + " # single_mask = single_mask.cuda(non_blocking=True)\n", + "\n", + " with torch.no_grad():\n", + " z = model.encoder(single_img, single_mask)\n", + " img_recon = model.decoder(z)\n", + " loss = model(single_img, single_mask)\n", + "\n", + " inputs.extend(single_img.cpu())\n", + " masks.extend(single_mask.cpu())\n", + " outputs.extend(img_recon.cpu())\n", + " losses.append(loss.cpu()) " + ] + }, + { + "cell_type": "markdown", + "id": "dc3f102c-94df-4d9e-8040-52197a7e71db", + "metadata": {}, + "source": [ + "## 6. Plot and write to PDF\n", + "\n", + "Writes out all of the predictions to a PDF file" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "5ebdcd1d-09db-4ccf-8cc1-58d6f47e3a55", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pdf_path = '../../satvision-toa-reconstruction-pdf-giant-patch-8-09.20.pdf'\n", + "rgb_index = [0, 2, 1] # Indices of [Red band, Blue band, Green band]\n", + "\n", + "plot_export_pdf(pdf_path, inputs, outputs, masks, rgb_index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7a57f4d-5df0-47a3-bfb6-d7f29a95e276", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ILAB Kernel (Pytorch)", + "language": "python", + "name": "pytorch-kernel" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/satvision-toa-reconstruction_huge.ipynb b/notebooks/satvision-toa-reconstruction_huge.ipynb new file mode 100644 index 0000000..b4ce315 --- /dev/null +++ b/notebooks/satvision-toa-reconstruction_huge.ipynb @@ -0,0 +1,1267 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c8ab2075-c488-46b9-8cd2-0cdaf399acfc", + "metadata": {}, + "source": [ + "# Satvision-TOA Reconstruction Notebook\n", + "\n", + "Version: 04.30.24\n", + "\n", + "Env: `Python [conda env:ilab-pytorch]`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6e88ea70-7dbf-4b67-a12d-db36e2bc9914", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Defaulting to user installation because normal site-packages is not writeable\n", + "Requirement already satisfied: yacs in /panfs/ccds02/nobackup/people/cssprad1/.local/lib/python3.9/site-packages (0.1.8)\n", + "Requirement already satisfied: timm in /panfs/ccds02/nobackup/people/cssprad1/.local/lib/python3.9/site-packages (0.9.2)\n", + "Requirement already satisfied: segmentation-models-pytorch in /panfs/ccds02/nobackup/people/cssprad1/.local/lib/python3.9/site-packages (0.3.3)\n", + "Requirement already satisfied: termcolor in /panfs/ccds02/nobackup/people/cssprad1/.local/lib/python3.9/site-packages (2.4.0)\n", + "Requirement already satisfied: webdataset==0.2.86 in /panfs/ccds02/nobackup/people/cssprad1/.local/lib/python3.9/site-packages (0.2.86)\n", + "Requirement already satisfied: braceexpand in /panfs/ccds02/nobackup/people/cssprad1/.local/lib/python3.9/site-packages (from webdataset==0.2.86) (0.1.7)\n", + "Requirement already satisfied: numpy in /panfs/ccds02/app/modules/jupyter/ilab/pytorch-kernel-bak/lib/python3.9/site-packages (from webdataset==0.2.86) (1.23.5)\n", + "Requirement already satisfied: pyyaml in /panfs/ccds02/app/modules/jupyter/ilab/pytorch-kernel-bak/lib/python3.9/site-packages (from webdataset==0.2.86) (6.0)\n", + "Requirement already satisfied: torch>=1.7 in /panfs/ccds02/app/modules/jupyter/ilab/pytorch-kernel-bak/lib/python3.9/site-packages (from timm) (1.13.0)\n", + "Requirement already satisfied: torchvision in /panfs/ccds02/app/modules/jupyter/ilab/pytorch-kernel-bak/lib/python3.9/site-packages (from timm) (0.14.0)\n", + "Requirement already satisfied: huggingface-hub in /panfs/ccds02/nobackup/people/cssprad1/.local/lib/python3.9/site-packages (from timm) (0.20.3)\n", + "Requirement already satisfied: safetensors in /panfs/ccds02/nobackup/people/cssprad1/.local/lib/python3.9/site-packages (from timm) (0.4.2)\n", + "Requirement already satisfied: tqdm in /panfs/ccds02/app/modules/jupyter/ilab/pytorch-kernel-bak/lib/python3.9/site-packages (from segmentation-models-pytorch) (4.64.1)\n", + "Requirement already satisfied: pretrainedmodels==0.7.4 in /panfs/ccds02/nobackup/people/cssprad1/.local/lib/python3.9/site-packages (from segmentation-models-pytorch) (0.7.4)\n", + "Requirement already satisfied: efficientnet-pytorch==0.7.1 in /panfs/ccds02/nobackup/people/cssprad1/.local/lib/python3.9/site-packages (from segmentation-models-pytorch) (0.7.1)\n", + "Requirement already satisfied: pillow in /panfs/ccds02/app/modules/jupyter/ilab/pytorch-kernel-bak/lib/python3.9/site-packages (from segmentation-models-pytorch) (9.2.0)\n", + "Requirement already satisfied: munch in /panfs/ccds02/app/modules/jupyter/ilab/pytorch-kernel-bak/lib/python3.9/site-packages (from pretrainedmodels==0.7.4->segmentation-models-pytorch) (2.5.0)\n", + "Requirement already satisfied: typing_extensions in /panfs/ccds02/app/modules/jupyter/ilab/pytorch-kernel-bak/lib/python3.9/site-packages (from torch>=1.7->timm) (4.4.0)\n", + "Requirement already satisfied: requests in /panfs/ccds02/app/modules/jupyter/ilab/pytorch-kernel-bak/lib/python3.9/site-packages (from torchvision->timm) (2.28.1)\n", + "Requirement already satisfied: filelock in /panfs/ccds02/app/modules/jupyter/ilab/pytorch-kernel-bak/lib/python3.9/site-packages (from huggingface-hub->timm) (3.8.1)\n", + "Requirement already satisfied: packaging>=20.9 in /panfs/ccds02/app/modules/jupyter/ilab/pytorch-kernel-bak/lib/python3.9/site-packages (from huggingface-hub->timm) (21.3)\n", + "Requirement already satisfied: fsspec>=2023.5.0 in /panfs/ccds02/nobackup/people/cssprad1/.local/lib/python3.9/site-packages (from huggingface-hub->timm) (2023.12.2)\n", + "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /panfs/ccds02/app/modules/jupyter/ilab/pytorch-kernel-bak/lib/python3.9/site-packages (from packaging>=20.9->huggingface-hub->timm) (3.0.9)\n", + "Requirement already satisfied: six in /panfs/ccds02/app/modules/jupyter/ilab/pytorch-kernel-bak/lib/python3.9/site-packages (from munch->pretrainedmodels==0.7.4->segmentation-models-pytorch) (1.16.0)\n", + "Requirement already satisfied: idna<4,>=2.5 in /panfs/ccds02/app/modules/jupyter/ilab/pytorch-kernel-bak/lib/python3.9/site-packages (from requests->torchvision->timm) (3.4)\n", + "Requirement already satisfied: charset-normalizer<3,>=2 in /panfs/ccds02/app/modules/jupyter/ilab/pytorch-kernel-bak/lib/python3.9/site-packages (from requests->torchvision->timm) (2.1.1)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /panfs/ccds02/app/modules/jupyter/ilab/pytorch-kernel-bak/lib/python3.9/site-packages (from requests->torchvision->timm) (1.26.11)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /panfs/ccds02/app/modules/jupyter/ilab/pytorch-kernel-bak/lib/python3.9/site-packages (from requests->torchvision->timm) (2022.9.24)\n" + ] + } + ], + "source": [ + "!pip install yacs timm segmentation-models-pytorch termcolor webdataset==0.2.86" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d046c3e5-c458-4e03-9c96-e9eb95a04963", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import time\n", + "import random\n", + "import datetime\n", + "from tqdm import tqdm\n", + "import numpy as np\n", + "import logging\n", + "\n", + "import torch\n", + "import torch.cuda.amp as amp\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.backends.backend_pdf import PdfPages\n", + "\n", + "import warnings\n", + "\n", + "warnings.filterwarnings('ignore') " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7c7db1bc-09ee-47e3-9015-e6b148d497e7", + "metadata": {}, + "outputs": [], + "source": [ + "sys.path.append('../../pytorch-caney')\n", + "\n", + "from pytorch_caney.config import get_config\n", + "\n", + "from pytorch_caney.models.build import build_model\n", + "\n", + "from pytorch_caney.ptc_logging import create_logger\n", + "\n", + "from pytorch_caney.data.datasets.mim_modis_22m_dataset import MODIS22MDataset\n", + "\n", + "from pytorch_caney.data.transforms import SimmimTransform, SimmimMaskGenerator\n", + "\n", + "from pytorch_caney.config import _C, _update_config_from_file" + ] + }, + { + "cell_type": "markdown", + "id": "d841e464-f880-4e53-bf31-f9f225713918", + "metadata": {}, + "source": [ + "## 1. Configuration" + ] + }, + { + "cell_type": "markdown", + "id": "6274e323-bc04-41d4-bc49-baed65d027e6", + "metadata": {}, + "source": [ + "### Clone model ckpt from huggingface\n", + "\n", + "### Model repo: https://huggingface.co/nasa-cisto-data-science-group/satvision-toa-huge-patch8-window8-128\n", + "\n", + "```bash\n", + "# On prism/explore\n", + "module load git-lfs\n", + "\n", + "git lfs install\n", + "\n", + "git clone git clone git@hf.co:nasa-cisto-data-science-group/satvision-toa-huge-patch8-window8-128\n", + "```\n", + "\n", + "Note: If using git w/ ssh, make sure you have ssh keys enabled to clone using ssh auth.\n", + "https://huggingface.co/docs/hub/security-git-ssh\n", + "\n", + "```bash\n", + "eval $(ssh-agent)\n", + "\n", + "# If this outputs as anon, follow the next steps.\n", + "ssh -T git@hf.co\n", + "\n", + "# Check if ssh-agent is using the proper key\n", + "ssh-add -l\n", + "\n", + "# If not\n", + "ssh-add ~/.ssh/your-key\n", + "\n", + "# Or if you want to use the default id_* key, just do\n", + "ssh-add\n", + "\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "af699ba3-2d98-4daf-9437-c322d7b59a98", + "metadata": {}, + "outputs": [], + "source": [ + "MODEL_PATH: str = '/explore/nobackup/people/cssprad1/projects/satvision-toa/models/satvision-toa-huge-patch8-window8-128/mp_rank_00_model_states.pt'\n", + "CONFIG_PATH: str = '/explore/nobackup/people/cssprad1/projects/satvision-toa/models/satvision-toa-huge-patch8-window8-128/mim_pretrain_swinv2_satvision_huge_128_window8_patch8_100ep.yaml'\n", + "\n", + "OUTPUT: str = '.'\n", + "TAG: str = 'satvision-huge-toa-reconstruction'\n", + "DATA_PATH: str = '/explore/nobackup/projects/ilab/projects/3DClouds/data/validation/sv_toa_128_chip_validation_04_24.npy'\n", + "DATA_PATHS: list = [DATA_PATH]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c4593e8c-6e94-4d01-b86e-5b78b621fc59", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=> merge config from /explore/nobackup/people/cssprad1/projects/satvision-toa/models/satvision-toa-huge-patch8-window8-128/mim_pretrain_swinv2_satvision_huge_128_window8_patch8_100ep.yaml\n" + ] + } + ], + "source": [ + "# Update config given configurations\n", + "\n", + "config = _C.clone()\n", + "_update_config_from_file(config, CONFIG_PATH)\n", + "\n", + "config.defrost()\n", + "config.MODEL.RESUME = MODEL_PATH\n", + "config.DATA.DATA_PATHS = DATA_PATHS\n", + "config.OUTPUT = OUTPUT\n", + "config.TAG = TAG\n", + "config.freeze()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "202a4474-88e4-44d5-b899-7aaf6cbed6f4", + "metadata": {}, + "outputs": [], + "source": [ + "# Configure logging\n", + "logging.basicConfig(\n", + " filename='app.log', # Specify the log file name\n", + " level=logging.INFO, # Set logging level to DEBUG\n", + " format='%(asctime)s [%(levelname)s] %(message)s', # Specify log message format\n", + " datefmt='%Y-%m-%d %H:%M:%S' # Specify date format\n", + ")\n", + "\n", + "# Add logging to standard output\n", + "console = logging.StreamHandler() # Create a handler for standard output\n", + "console.setLevel(logging.INFO) # Set logging level for standard output\n", + "console.setFormatter(logging.Formatter('%(asctime)s [%(levelname)s] %(message)s')) # Set log message format for standard output\n", + "logger = logging.getLogger('')\n", + "logger.addHandler(console)" + ] + }, + { + "cell_type": "markdown", + "id": "11ebd497-7741-41a7-af9d-0ee49a6313a4", + "metadata": {}, + "source": [ + "## 2. Load model weights from checkpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "68abf348-c6bf-43a3-b00a-cc5f8d80545f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-09-20 16:58:47,321 [INFO] number of params: 695328632\n" + ] + }, + { + "data": { + "text/plain": [ + "MiMModel(\n", + " (encoder): SwinTransformerV2ForSimMIM(\n", + " (patch_embed): PatchEmbed(\n", + " (proj): Conv2d(14, 352, kernel_size=(4, 4), stride=(4, 4))\n", + " (norm): LayerNorm((352,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (pos_drop): Dropout(p=0.0, inplace=False)\n", + " (layers): ModuleList(\n", + " (0): BasicLayer(\n", + " dim=352, input_resolution=(32, 32), depth=2\n", + " (blocks): ModuleList(\n", + " (0): SwinTransformerBlock(\n", + " dim=352, input_resolution=(32, 32),num_heads=4, window_size=8, shift_size=0, mlp_ratio=4.0\n", + " (norm1): LayerNorm((352,), eps=1e-05, elementwise_affine=True)\n", + " (attn): 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bias=False)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj): Linear(in_features=1408, out_features=1408, bias=True)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (softmax): Softmax(dim=-1)\n", + " )\n", + " (drop_path): DropPath(drop_prob=0.091)\n", + " (norm2): LayerNorm((1408,), eps=1e-05, elementwise_affine=True)\n", + " (norm3): LayerNorm((1408,), eps=1e-05, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=1408, out_features=5632, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (fc2): Linear(in_features=5632, out_features=1408, bias=True)\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (downsample): PatchMerging(\n", + " input_resolution=(8, 8), dim=1408\n", + " (reduction): Linear(in_features=5632, out_features=2816, bias=False)\n", + " (norm): LayerNorm((2816,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " )\n", + " (3): BasicLayer(\n", + " dim=2816, input_resolution=(4, 4), depth=2\n", + " (blocks): ModuleList(\n", + " (0): SwinTransformerBlock(\n", + " dim=2816, input_resolution=(4, 4),num_heads=32, window_size=4, shift_size=0, mlp_ratio=4.0\n", + " (norm1): LayerNorm((2816,), eps=1e-05, elementwise_affine=True)\n", + " (attn): WindowAttention(\n", + " dim=2816, window_size=(4, 4), pretrained_window_size=(0, 0), num_heads=32\n", + " (cpb_mlp): Sequential(\n", + " (0): Linear(in_features=2, out_features=512, bias=True)\n", + " (1): ReLU(inplace=True)\n", + " (2): Linear(in_features=512, out_features=32, bias=False)\n", + " )\n", + " (qkv): Linear(in_features=2816, out_features=8448, bias=False)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj): Linear(in_features=2816, out_features=2816, bias=True)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (softmax): Softmax(dim=-1)\n", + " )\n", + " (drop_path): DropPath(drop_prob=0.096)\n", + " (norm2): LayerNorm((2816,), eps=1e-05, elementwise_affine=True)\n", + " (norm3): Identity()\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=2816, out_features=11264, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (fc2): Linear(in_features=11264, out_features=2816, bias=True)\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " (1): SwinTransformerBlock(\n", + " dim=2816, input_resolution=(4, 4),num_heads=32, window_size=4, shift_size=0, mlp_ratio=4.0\n", + " (norm1): LayerNorm((2816,), eps=1e-05, elementwise_affine=True)\n", + " (attn): WindowAttention(\n", + " dim=2816, window_size=(4, 4), pretrained_window_size=(0, 0), num_heads=32\n", + " (cpb_mlp): Sequential(\n", + " (0): Linear(in_features=2, out_features=512, bias=True)\n", + " (1): ReLU(inplace=True)\n", + " (2): Linear(in_features=512, out_features=32, bias=False)\n", + " )\n", + " (qkv): Linear(in_features=2816, out_features=8448, bias=False)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj): Linear(in_features=2816, out_features=2816, bias=True)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (softmax): Softmax(dim=-1)\n", + " )\n", + " (drop_path): DropPath(drop_prob=0.100)\n", + " (norm2): LayerNorm((2816,), eps=1e-05, elementwise_affine=True)\n", + " (norm3): Identity()\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=2816, out_features=11264, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (fc2): Linear(in_features=11264, out_features=2816, bias=True)\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (norm): LayerNorm((2816,), eps=1e-05, elementwise_affine=True)\n", + " (avgpool): AdaptiveAvgPool1d(output_size=1)\n", + " (head): Identity()\n", + " )\n", + " (decoder): Sequential(\n", + " (0): Conv2d(2816, 14336, kernel_size=(1, 1), stride=(1, 1))\n", + " (1): PixelShuffle(upscale_factor=32)\n", + " )\n", + ")" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "checkpoint = torch.load(MODEL_PATH)\n", + "model = build_model(config, pretrain=True)\n", + "model.load_state_dict(checkpoint['module']) # If 'module' not working, try 'model'\n", + "n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", + "logger.info(f\"number of params: {n_parameters}\")\n", + "# model.cuda()\n", + "model.eval()" + ] + }, + { + "cell_type": "markdown", + "id": "b500d13b-89d7-4cd8-a36a-ab6f10f6a397", + "metadata": {}, + "source": [ + "## 3. Load evaluation set (from numpy file)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "73a8d307-de9b-4617-abdd-dae1e7c2521a", + "metadata": {}, + "outputs": [], + "source": [ + "# Use the Masked-Image-Modeling transform\n", + "transform = SimmimTransform(config)\n", + "\n", + "# The reconstruction evaluation set is a single numpy file\n", + "validation_dataset_path = config.DATA.DATA_PATHS[0]\n", + "validation_dataset = np.load(validation_dataset_path)\n", + "len_batch = range(validation_dataset.shape[0])\n", + "\n", + "# Apply transform to each image in the batch\n", + "# A mask is auto-generated in the transform\n", + "imgMasks = [transform(validation_dataset[idx]) for idx \\\n", + " in len_batch]\n", + "\n", + "# Seperate img and masks, cast masks to torch tensor\n", + "img = torch.stack([imgMask[0] for imgMask in imgMasks])\n", + "mask = torch.stack([torch.from_numpy(imgMask[1]) for \\\n", + " imgMask in imgMasks])" + ] + }, + { + "cell_type": "markdown", + "id": "55acf5e9-eb2a-496c-baa6-3b74503a2978", + "metadata": {}, + "source": [ + "## 4. Prediction helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "595336f8-71b4-418b-b153-2461583ed613", + "metadata": {}, + "outputs": [], + "source": [ + "def predict(model, dataloader, num_batches=5):\n", + "\n", + " inputs = []\n", + " outputs = []\n", + " masks = []\n", + " losses = []\n", + " with tqdm(total=num_batches) as pbar:\n", + "\n", + " for idx, img_mask in enumerate(dataloader):\n", + " \n", + " pbar.update(1)\n", + "\n", + " if idx > num_batches:\n", + " return inputs, outputs, masks, losses\n", + "\n", + " img_mask = img_mask[0]\n", + "\n", + " img = torch.stack([pair[0] for pair in img_mask])\n", + " mask = torch.stack([pair[1] for pair in img_mask])\n", + "\n", + " img = img.cuda(non_blocking=True)\n", + " mask = mask.cuda(non_blocking=True)\n", + "\n", + " with torch.no_grad():\n", + " with amp.autocast(enabled=config.ENABLE_AMP):\n", + " z = model.encoder(img, mask)\n", + " img_recon = model.decoder(z)\n", + " loss = model(img, mask)\n", + "\n", + " inputs.extend(img.cpu())\n", + " masks.extend(mask.cpu())\n", + " outputs.extend(img_recon.cpu())\n", + " losses.append(loss.cpu())\n", + " \n", + " return inputs, outputs, masks, losses\n", + "\n", + "\n", + "def minmax_norm(img_arr):\n", + " arr_min = img_arr.min()\n", + " arr_max = img_arr.max()\n", + " img_arr_scaled = (img_arr - arr_min) / (arr_max - arr_min)\n", + " img_arr_scaled = img_arr_scaled * 255\n", + " img_arr_scaled = img_arr_scaled.astype(np.uint8)\n", + " return img_arr_scaled\n", + "\n", + "\n", + "def process_mask(mask):\n", + " mask_img = mask.unsqueeze(0)\n", + " mask_img = mask_img.repeat_interleave(4, 1).repeat_interleave(4, 2).unsqueeze(1).contiguous()\n", + " mask_img = mask_img[0, 0, :, :]\n", + " mask_img = np.stack([mask_img, mask_img, mask_img], axis=-1)\n", + " return mask_img\n", + "\n", + "\n", + "def process_prediction(image, img_recon, mask, rgb_index):\n", + "\n", + " mask = process_mask(mask)\n", + " \n", + " red_idx = rgb_index[0]\n", + " blue_idx = rgb_index[1]\n", + " green_idx = rgb_index[2]\n", + "\n", + " image = image.numpy()\n", + " rgb_image = np.stack((image[red_idx, :, :],\n", + " image[blue_idx, :, :],\n", + " image[green_idx, :, :]),\n", + " axis=-1)\n", + " rgb_image = minmax_norm(rgb_image)\n", + "\n", + " img_recon = img_recon.numpy()\n", + " rgb_image_recon = np.stack((img_recon[red_idx, :, :],\n", + " img_recon[blue_idx, :, :],\n", + " img_recon[green_idx, :, :]),\n", + " axis=-1)\n", + " rgb_image_recon = minmax_norm(rgb_image_recon)\n", + "\n", + " rgb_masked = np.where(mask == 0, rgb_image, rgb_image_recon)\n", + " rgb_image_masked = np.where(mask == 1, 0, rgb_image)\n", + " rgb_recon_masked = rgb_masked\n", + " \n", + " return rgb_image, rgb_image_masked, rgb_recon_masked, mask\n", + "\n", + "\n", + "def plot_export_pdf(path, inputs, outputs, masks, rgb_index):\n", + " pdf_plot_obj = PdfPages(path)\n", + "\n", + " for idx in range(len(inputs)):\n", + " # prediction processing\n", + " image = inputs[idx]\n", + " img_recon = outputs[idx]\n", + " mask = masks[idx]\n", + " rgb_image, rgb_image_masked, rgb_recon_masked, mask = \\\n", + " process_prediction(image, img_recon, mask, rgb_index)\n", + "\n", + " # matplotlib code\n", + " fig, (ax01, ax23) = plt.subplots(2, 2, figsize=(40, 30))\n", + " ax0, ax1 = ax01\n", + " ax2, ax3 = ax23\n", + " ax2.imshow(rgb_image)\n", + " ax2.set_title(f\"Idx: {idx} MOD021KM v6.1 Bands: {rgb_index}\")\n", + "\n", + " ax0.imshow(rgb_recon_masked)\n", + " ax0.set_title(f\"Idx: {idx} Model reconstruction\")\n", + "\n", + " ax1.imshow(rgb_image_masked)\n", + " ax1.set_title(f\"Idx: {idx} MOD021KM Bands: {rgb_index}, masked\")\n", + " \n", + " ax3.matshow(mask[:, :, 0])\n", + " ax3.set_title(f\"Idx: {idx} Reconstruction Mask\")\n", + " pdf_plot_obj.savefig()\n", + "\n", + " pdf_plot_obj.close()" + ] + }, + { + "cell_type": "markdown", + "id": "551c44b5-6d88-45c4-b397-c38de8064544", + "metadata": {}, + "source": [ + "## 5. Predict" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "4e695cc3-b869-4fc2-b360-b45f3b81affd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 128/128 [00:53<00:00, 2.41it/s]\n" + ] + } + ], + "source": [ + "inputs = []\n", + "outputs = []\n", + "masks = []\n", + "losses = []\n", + "\n", + "# We could do this in a single batch however we\n", + "# want to report the loss per-image, in place of\n", + "# loss per-batch.\n", + "for i in tqdm(range(img.shape[0])):\n", + " single_img = img[i].unsqueeze(0)\n", + " single_mask = mask[i].unsqueeze(0)\n", + " # single_img = single_img.cuda(non_blocking=True)\n", + " # single_mask = single_mask.cuda(non_blocking=True)\n", + "\n", + " with torch.no_grad():\n", + " z = model.encoder(single_img, single_mask)\n", + " img_recon = model.decoder(z)\n", + " loss = model(single_img, single_mask)\n", + "\n", + " inputs.extend(single_img.cpu())\n", + " masks.extend(single_mask.cpu())\n", + " outputs.extend(img_recon.cpu())\n", + " losses.append(loss.cpu()) " + ] + }, + { + "cell_type": "markdown", + "id": "dc3f102c-94df-4d9e-8040-52197a7e71db", + "metadata": {}, + "source": [ + "## 6. Plot and write to PDF\n", + "\n", + "Writes out all of the predictions to a PDF file" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5ebdcd1d-09db-4ccf-8cc1-58d6f47e3a55", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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uu+yyePe73x2XX355HH300TE9Pb2wf6/ztADsYhUAsF0f+9jHqoiovvOd71RVVVWdTqfaZ599qt/93d+tut3uwna/+MUvqlarVR1wwAGL9r/99tur/fbbrzryyCOr73//+9XY2Fj14he/eKvjPOEJT6gajUbP9frUpz5VRcTCfy9/+cur+fn5dL+vf/3rVURU//AP/1Bdc801Va1Wq770pS9VVVVVz33uc6ujjz66qqqqOuGEExady1e+8pUqIqpzzjlnUXmf+cxnqoioPvKRj1RVVVX/9V//VUVE9YY3vGHRdp/4xCeqiKhe9rKXLfzsD/7gD6qJiYnquuuuW7TtueeeW0VE9aMf/WjhZxFRnXHGGcVzu/baa6uIqA455JBqbm5uUXbYYYdVD33oQ7e6RieeeGK19957V51Op6qqqnrFK15RtVqt6sc//vF2j/OmN72piojq//yf/7Po56961auqWq1WXX311Yvq85CHPKRqt9sL2/3bv/1bFRHVpz71qaqqNreRiKjOO++84vk96EEPqh7/+Mdv9fMt9/Rxj3vcVtkZZ5xRbWu4t6VdX3vttVVVVdU111xTNRqN6kUvelGxDr/eLu7s1+/RC17wgmp4eLj65S9/uWi7448/vhobG6vWrl27qP5PfepTF2332c9+toqI6qqrrirWCQAAAACA317mWTb7wz/8wyoiqp/85CfbLXvLHMurXvWqhZ8dcMAB1QknnLDdfT70oQ9VEVF95jOfqaqqqt7xjndUEVHdfPPNW2173HHHVfe///2L5/eiF72oajab1Xe/+93tbvPe97530RzDnW2ZG9ny31e/+tXtlnNXr+X2vOxlL1t07C3/7b333tW//Mu/FPdtt9vV/Px8deyxx1a/93u/t9X5ZHM9O9KuX/va11bLli1Lz+eQQw6pDjnkkHS77c0F/eu//uuiOt7ZO9/5zioiqptuuiktPzM/P18dffTR1dKlS7eaE7qrxsfHF80pbsvLXvayanx8/C6Vv+Wave9971v088MPP7yKiOqSSy5Z+Nn8/Hy1cuXK6lnPetZ2y9vSfk4++eTqoQ996MLPt8x5bpkT25Y7Pwf//u//Xu2zzz7VYx/72Gr16tUL22zpry6++OJF+37nO9+pIqI6//zzq6rasXlaAHYt37QLADvg6quvjptuuil+//d/f9FfbB9wwAFx1FFHbbX97rvvHp/5zGfi+9//fhx11FFxn/vcJz784Q9vtd3Xvva14l9V/ronP/nJ8Z3vfCeuuOKKeMc73hEXX3xxPPvZz45ut9tzGQcddFAcffTRccEFF8Tq1avj85///Hb/OZYtfwV80kknLfr5c5/73BgfH4+vfe1rERHx9a9/PSIiXvSiFy3a7nnPe95W/6TUl770pTjmmGNin332Wfir53a7Hccff3xERFx55ZU9n8udPf3pT1/0Txz97Gc/i5/85CcLdbrzsZ761KfGzTffHFdffXVERHz5y1+OY445Jh7wgAdst/wrrrgiHvjAB8aRRx656OcnnXRSVFW11V9Mn3DCCdFoNBb+f8tfNG/5VoDly5fHIYccEu9973vj/e9/f/z7v//7Dt3HLZ797Gfv8D5bXH755dHpdOI1r3nNXS7j111xxRVx7LHHxv7777/o5yeddFJs2rRpq2/pffrTn77o/3/9OgEAAAAAcPd3b51n6UVVVRERO/yNwduyvTJKZf+v//W/4hOf+ER84AMfiIc97GE912FbnvzkJ8fw8HCceuqpcdttt6Xb76xrOTo6Gt/5znfiO9/5Tvyf//N/4pJLLon73//+8dSnPnWreYkPf/jD8bu/+7sxMjISzWYzWq1WfO1rX4v/+q//2qrcbK5nR9r1kUceGWvXro0XvvCF8fnPfz5uv/32bZ7Lz372s/jZz362w9fg15Xu+Y60tW2pqipOPvnk+OY3vxkXXXTRVnNCv+1OPPHERf//gAc8IGq12sJcZUREs9mM+973vlvNV/3DP/xDPPrRj46JiYmF9vPRj350Uft5+MMfHhGb50k/+9nPxo033rjduvzzP/9zPPaxj43HPe5xcfnll8fy5csXsi996UuxbNmyeNrTnrZonvPwww+Pvfbaa+HboXdknhaAXcuiXQDYAatXr46IiL322murbFs/i4h4xCMeEQ960INiZmYmXvWqV8X4+Hjf9dhtt93iiCOOiGOOOSZOO+20+MhHPhJf+MIX4vOf//wOlXPyySfHF7/4xXj/+98fo6Oj8ZznPGeb261evTqazWasXLly0c9rtVrstddeC9dle9en2WzG7rvvvuhnt9xyS3zxi1+MVqu16L8t/3TO9n5Jkdl77723Ok5ExJ/+6Z9udaxXv/rVi4512223xX777Vcsf/Xq1VsdIyJin332Wcjv7NfPe3h4OCJi4Z+qqdVq8bWvfS2e/OQnxznnnBO/+7u/GytXrozXve51sWHDhp7OOWLr894RW35hlp37jtjZ1wkAAAAAgLu/e+s8y33uc5+IiLj22mu3W9YvfvGLiIgdWvi4ZSHhlt+9b/ld+6//Dj4i4o477li0EPDOzjrrrHj7298e73jHO+K1r31tz8ffnic+8Ylx6aWXxk9/+tM45phj4tZbb0336fValtTr9TjiiCPiiCOOiCOPPDJ+7/d+L/7pn/4pms1mnHrqqQvbvf/9749XvepV8YhHPCIuvvji+Pa3vx3f+c534ilPeco25yWyOYwdadcveclL4oILLojrrrsunv3sZ8cee+wRj3jEI+Lyyy/f4fMtydpCrVaLZcuW3eXyq6qKU045Jf7+7/8+LrzwwnjGM55xl8vaVX79eRgaGoqxsbEYGRnZ6uczMzML/3/JJZfE8573vNh3333j7//+7+Oqq66K73znO/GKV7xi0XaPe9zj4nOf+1y02+146UtfGvvtt188+MEPjk996lNb1eVzn/tcTE9Px6te9aqF9rXFLbfcEmvXro2hoaGt5jpXrVq1MM+5I/O0AOxaFu0CwA7Y8oFm1apVW2Xb+llExBlnnBH/+Z//GQ972MPi9NNPj2uuuWan12vLt77+93//9w7t96xnPSvGxsbi3e9+d7zgBS+I0dHRbW63++67R7vd3uqvoauqilWrVsWKFSsWtovY+lq02+2tfimwYsWKOO644xb+4vnX/zv55JN36Fy2+PW/Ct5Stze/+c3bPdbhhx8eERErV66MG264oVj+7rvvHjfffPNWP7/pppsWHW9HHHDAAfHRj340Vq1aFVdffXW84Q1viPPPPz/+7M/+rOcytvXX0Ft+qTA7O7vo57++IHrLYuzs3HfEIK4TAAAAAAB3b/fWeZYnPelJEbF5Yd72bMm2bJupqiq++MUvxvj4eBxxxBEREfGQhzwkIiL+8z//c9G27XY7fvKTn8SDH/zgrco566yz4swzz4wzzzwzTjvttJ6O3Yvjjz8+Pv/5z8fPf/7zOOaYYxa+ZGV7er2WO2psbCwOOeSQ+I//+I+Fn/393/99HH300fGhD30oTjjhhHjEIx4RRxxxxA59mcqd7Wi7fvnLXx7f+ta3Yt26dXHZZZdFVVVx4okn7tR/ffCQQw6J0dHRrdpCxOb2cd/73nerxam92rJg92Mf+1j87d/+bbz4xS/ut7p3K3//938fBx10UHzmM5+JZz7zmfHIRz4yjjjiiK3m4yIinvGMZ8TXvva1WLduXXzjG9+I/fbbL37/939/q29+/sAHPhDHH398HH/88fHVr351UbZixYrYfffdtzvPef7550fEjs3TArBrWbQLADvg0EMPjb333js+9alPLfonh6677rr41re+tdX2l19+ebzrXe+Kt771rXH55ZfH5ORkPP/5z4+5ubmdWq8t/9zJfe973x3ab3R0NE4//fR42tOeFq961au2u92xxx4bEZs/hN7ZxRdfHFNTUwv50UcfHRERn/jEJxZt99nPfnarf5bqxBNPjB/+8IdxyCGHLPzV853/2/JX4f069NBD4373u1/8x3/8xzaPc8QRR8SSJUsiYvMvkL7+9a/H1Vdfvd3yjj322Pjxj38c3//+9xf9/KKLLoparRbHHHNMX/W9//3vH29961vjIQ95yKJjDA8P7/C3zh544IEREfGDH/xg0c+/+MUvLvr/4447LhqNRnzoQx8qlrcjdTj22GPjiiuuWFiku8VFF10UY2Nj8chHPrKncgAAAAAAuOe4t86zHHHEEXHcccfFRz/60fjXf/3XrfJ/+Zd/iQsuuCCe8pSnxMMe9rCejn3WWWfFj3/84/jjP/7jhcWXj3jEI2LvvfeOCy+8cNG2//iP/xgbN26MZz3rWYt+/ra3vS3OPPPMeOtb3xpnnHFGT8fdEU9+8pPj85//fFxzzTVxzDHHbHdhdkTv13JHbdy4MX72s5/FHnvssfCzWq221beZ/uAHP9hqIWWvdrRdbzE+Ph7HH398vOUtb4m5ubn40Y9+dJeOvy3NZjOe9rSnxSWXXLJoMfIvf/nL+PrXv75VW+hVVVXxyle+Mj72sY/F3/zN38TLX/7ynVXlu41arRZDQ0OLvlBn1apVxW/qHh4ejsc//vHxnve8JyIi/v3f/31RPjIyEpdcckmceOKJ8fSnP31RWSeeeGKsXr06Op3ONuc5Dz300IjYsXlaAHat5q6uAADcndTr9Xjb294Wp5xySvze7/1evPKVr4y1a9fGmWeeudU/NXLzzTfHi1/84nj84x8fZ5xxRtTr9fjMZz4Tj3vc4+LP//zP47zzzlvY9thjj40rr7wy/cD0N3/zN/HNb34zjjvuuNh///1jamoqvvnNb8Zf/uVfxlFHHXWX/umZU089ddE/CbQtT3rSk+LJT35yvPGNb4z169fHox/96PjBD34QZ5xxRjz0oQ+Nl7zkJRER8YAHPCBe/OIXx3nnnRetViue+MQnxg9/+MM499xzY+nSpYvKPPvss+Pyyy+Po446Kl73utfFoYceGjMzM/GLX/wi/umf/ik+/OEPx37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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pdf_path = '../../satvision-toa-reconstruction-pdf-huge-patch-8-09.20.pdf'\n", + "rgb_index = [0, 2, 1] # Indices of [Red band, Blue band, Green band]\n", + "\n", + "plot_export_pdf(pdf_path, inputs, outputs, masks, rgb_index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7a57f4d-5df0-47a3-bfb6-d7f29a95e276", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:ilab-pytorch]", + "language": "python", + "name": "conda-env-ilab-pytorch-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From cf86a76740ca4354f7a05f65df5cc2225caa778d Mon Sep 17 00:00:00 2001 From: cssprad1 Date: Fri, 20 Sep 2024 17:02:35 -0400 Subject: [PATCH 11/12] delete fs artifact --- notebooks/.panfs.2ac640a.1726866101518203341 | 5 ----- 1 file changed, 5 deletions(-) delete mode 100644 notebooks/.panfs.2ac640a.1726866101518203341 diff --git a/notebooks/.panfs.2ac640a.1726866101518203341 b/notebooks/.panfs.2ac640a.1726866101518203341 deleted file mode 100644 index 262b72a..0000000 --- a/notebooks/.panfs.2ac640a.1726866101518203341 +++ /dev/null @@ -1,5 +0,0 @@ -2024-09-20 14:33:21 [INFO] number of params: 2653783008 -2024-09-20 14:37:24 [INFO] number of params: 2653783008 -2024-09-20 14:46:41 [INFO] number of params: 2653783008 -2024-09-20 16:57:54 [INFO] number of params: 695328632 -2024-09-20 16:58:47 [INFO] number of params: 695328632 From 62718cf091aca12147b6f9a37ef24f12ab7f382d Mon Sep 17 00:00:00 2001 From: cssprad1 Date: Fri, 20 Sep 2024 23:12:28 -0400 Subject: [PATCH 12/12] added inference examples --- .../satvision-toa-reconstruction_giant.py | 296 ++++++++++++++++++ .../satvision-toa-reconstruction_huge.py | 207 ++++++++++++ .../svtoa_reconstruction_giant_slurm.sh | 9 + .../svtoa_reconstruction_huge_slurm.sh | 9 + 4 files changed, 521 insertions(+) create mode 100644 examples/inference/satvision-toa-reconstruction_giant.py create mode 100644 examples/inference/satvision-toa-reconstruction_huge.py create mode 100644 examples/inference/svtoa_reconstruction_giant_slurm.sh create mode 100644 examples/inference/svtoa_reconstruction_huge_slurm.sh diff --git a/examples/inference/satvision-toa-reconstruction_giant.py b/examples/inference/satvision-toa-reconstruction_giant.py new file mode 100644 index 0000000..a47755e --- /dev/null +++ b/examples/inference/satvision-toa-reconstruction_giant.py @@ -0,0 +1,296 @@ +import argparse +import glob +import datetime +import os +import logging +import numpy as np +import torch +import torchvision.transforms as T +from pytorch_caney.data.utils import SimmimMaskGenerator +import matplotlib.pyplot as plt +from matplotlib.backends.backend_pdf import PdfPages +from tqdm import tqdm + +import warnings +warnings.filterwarnings('ignore') + +from pytorch_caney.config import _C, _update_config_from_file +from pytorch_caney.models.build import build_model +from pytorch_caney.data.transforms import SimmimMaskGenerator + + +# Dictionary to map indices to band numbers +idx_to_band = { + 0: 1, + 1: 2, + 2: 3, + 3: 6, + 4: 7, + 5: 21, + 6: 26, + 7: 27, + 8: 28, + 9: 29, + 10: 30, + 11: 31, + 12: 32, + 13: 33 +} + + +def parse_args(): + parser = argparse.ArgumentParser(description="Predict and generate PDF using a pre-trained model.") + parser.add_argument('--pretrained_model_dir', type=str, required=True, help="Directory containing pre-trained model files (including .pt and .yaml)") + parser.add_argument("--output_dir", required=True, help="Directory where the output PDF will be saved.") + parser.add_argument("--data_path", default='/explore/nobackup/projects/ilab/projects/3DClouds/data/validation/sv_toa_128_chip_validation_04_24.npy', help="Path to validation data file.") + return parser.parse_args() + + +# Load model and config +def load_config_and_model(pretrained_model_dir, validation_data_path): + # Search for .pt and .yaml files + model_path = os.path.join(pretrained_model_dir, 'mp_rank_00_model_states.pt') + config_path = glob.glob(os.path.join(pretrained_model_dir, '*.yaml')) + if len(config_path) == 0: + raise FileNotFoundError(f"No YAML config found in {pretrained_model_dir}") + config_path = config_path[0] + + # Load config + config = _C.clone() + _update_config_from_file(config, config_path) + config.defrost() + config.MODEL.RESUME = model_path + config.DATA.DATA_PATHS = [validation_data_path] + config.OUTPUT = pretrained_model_dir + config.TAG = 'satvision-huge-toa-reconstruction' + config.freeze() + + # Load model + checkpoint = torch.load(model_path) + model = build_model(config, pretrain=True) + model.load_state_dict(checkpoint['module']) # Use 'model' if 'module' not present + model.eval() + + return model, config + + +def configure_logging(): + logging.basicConfig(filename='app.log', level=logging.INFO, format='%(asctime)s [%(levelname)s] %(message)s', datefmt='%Y-%m-%d %H:%M:%S') + console = logging.StreamHandler() + console.setLevel(logging.INFO) + console.setFormatter(logging.Formatter('%(asctime)s [%(levelname)s] %(message)s')) + logger = logging.getLogger('') + logger.addHandler(console) + return logger + + +class MinMaxEmissiveScaleReflectance(object): + """ + Performs scaling of MODIS TOA data + - Scales reflectance percentages to reflectance units (% -> (0,1)) + - Performs per-channel minmax scaling for emissive bands (k -> (0,1)) + """ + + def __init__(self): + + self.reflectance_indices = [0, 1, 2, 3, 4, 6] + self.emissive_indices = [5, 7, 8, 9, 10, 11, 12, 13] + + self.emissive_mins = np.array( + [223.1222, 178.9174, 204.3739, 204.7677, + 194.8686, 202.1759, 201.3823, 203.3537], + dtype=np.float32) + + self.emissive_maxs = np.array( + [352.7182, 261.2920, 282.5529, 319.0373, + 295.0209, 324.0677, 321.5254, 285.9848], + dtype=np.float32) + + def __call__(self, img): + + # Reflectance % to reflectance units + img[:, :, self.reflectance_indices] = \ + img[:, :, self.reflectance_indices] * 0.01 + + # Brightness temp scaled to (0,1) range + img[:, :, self.emissive_indices] = \ + (img[:, :, self.emissive_indices] - self.emissive_mins) / \ + (self.emissive_maxs - self.emissive_mins) + + return img + + +class SimmimTransform: + """ + torchvision transform which transforms the input imagery into + addition to generating a MiM mask + """ + + def __init__(self, config): + + self.transform_img = \ + T.Compose([ + MinMaxEmissiveScaleReflectance(), # New transform for MinMax + T.ToTensor(), + T.Resize((config.DATA.IMG_SIZE, config.DATA.IMG_SIZE)), + ]) + + if config.MODEL.TYPE in ['swin', 'swinv2']: + + model_patch_size = config.MODEL.SWINV2.PATCH_SIZE + + else: + + raise NotImplementedError + + self.mask_generator = SimmimMaskGenerator( + input_size=config.DATA.IMG_SIZE, + mask_patch_size=config.DATA.MASK_PATCH_SIZE, + model_patch_size=model_patch_size, + mask_ratio=config.DATA.MASK_RATIO, + ) + + def __call__(self, img): + + img = self.transform_img(img) + mask = self.mask_generator() + + return img, mask + + +def get_batch_info(img): + channels = img.shape[1] + + for channelIdx in range(channels): + channel = idx_to_band.get(channelIdx, 'Unknown') # Retrieve band number or mark as 'Unknown' + img_band_array = img[:, channelIdx, :, :] + min_ = img_band_array.min().item() + mean_ = img_band_array.mean().item() + max_ = img_band_array.max().item() + print(f'Channel {channel}, min {min_}, mean {mean_}, max {max_}') + + +def load_validation_data(config): + validation_dataset_path = config.DATA.DATA_PATHS[0] + validation_dataset = np.load(validation_dataset_path) + transform = SimmimTransform(config) + imgMasks = [transform(validation_dataset[idx]) for idx in range(validation_dataset.shape[0])] + img = torch.stack([imgMask[0] for imgMask in imgMasks]) + mask = torch.stack([torch.from_numpy(imgMask[1]) for imgMask in imgMasks]) + return img, mask + + +def predict(model, img, mask): + inputs, outputs, masks, losses = [], [], [], [] + for i in tqdm(range(img.shape[0])): + single_img = img[i].unsqueeze(0) + single_mask = mask[i].unsqueeze(0) + + with torch.no_grad(): + z = model.encoder(single_img, single_mask) + img_recon = model.decoder(z) + loss = model(single_img, single_mask) + + inputs.extend(single_img.cpu()) + masks.extend(single_mask.cpu()) + outputs.extend(img_recon.cpu()) + losses.append(loss.cpu()) + + return inputs, outputs, masks, losses + +def process_mask(mask): + mask_img = mask.unsqueeze(0) + mask_img = mask_img.repeat_interleave(4, 1).repeat_interleave(4, 2).unsqueeze(1).contiguous() + mask_img = mask_img[0, 0, :, :] + mask_img = np.stack([mask_img, mask_img, mask_img], axis=-1) + return mask_img + + +def minmax_norm(img_arr): + arr_min = img_arr.min() + arr_max = img_arr.max() + img_arr_scaled = (img_arr - arr_min) / (arr_max - arr_min) + img_arr_scaled = img_arr_scaled * 255 + img_arr_scaled = img_arr_scaled.astype(np.uint8) + return img_arr_scaled + + +def reverse_transform(image): + minMaxTransform = MinMaxEmissiveScaleReflectance() + image = image.transpose((1,2,0)) + + image[:, :, minMaxTransform.reflectance_indices] = image[:, :, minMaxTransform.reflectance_indices] * 100 + image[:, :, minMaxTransform.emissive_indices] = ( + image[:, :, minMaxTransform.emissive_indices] * \ + (minMaxTransform.emissive_maxs - minMaxTransform.emissive_mins)) + minMaxTransform.emissive_mins + + image = image.transpose((2,0,1)) + return image + + +def process_prediction(image, img_recon, mask, rgb_index): + mask = process_mask(mask) + + red_idx = rgb_index[0] + blue_idx = rgb_index[1] + green_idx = rgb_index[2] + + image = reverse_transform(image.numpy()) + img_recon = reverse_transform(img_recon.numpy()) + + rgb_image = np.stack((image[red_idx, :, :], image[blue_idx, :, :], image[green_idx, :, :]), axis=-1) + rgb_image = minmax_norm(rgb_image) + + rgb_image_recon = np.stack((img_recon[red_idx, :, :], img_recon[blue_idx, :, :], img_recon[green_idx, :, :]), axis=-1) + rgb_image_recon = minmax_norm(rgb_image_recon) + + rgb_masked = np.where(mask == 0, rgb_image, rgb_image_recon) + rgb_image_masked = np.where(mask == 1, 0, rgb_image) + rgb_recon_masked = rgb_masked + + return rgb_image, rgb_image_masked, rgb_recon_masked, mask + + +def plot_export_pdf(path, inputs, outputs, masks, rgb_index): + pdf_plot_obj = PdfPages(path) + for idx in range(len(inputs)): + rgb_image, rgb_image_masked, rgb_recon_masked, mask = process_prediction(inputs[idx], outputs[idx], masks[idx], rgb_index) + + fig, (ax01, ax23) = plt.subplots(2, 2, figsize=(40, 30)) + ax0, ax1 = ax01 + ax2, ax3 = ax23 + + ax2.imshow(rgb_image) + ax2.set_title(f"Idx: {idx} MOD021KM v6.1 Bands: {rgb_index}") + + ax0.imshow(rgb_recon_masked) + ax0.set_title(f"Idx: {idx} Model reconstruction") + + ax1.imshow(rgb_image_masked) + ax1.set_title(f"Idx: {idx} MOD021KM Bands: {rgb_index}, masked") + + ax3.matshow(mask[:, :, 0]) + ax3.set_title(f"Idx: {idx} Reconstruction Mask") + + pdf_plot_obj.savefig() + + pdf_plot_obj.close() + + +if __name__ == "__main__": + args = parse_args() + model, config = load_config_and_model(args.pretrained_model_dir, args.data_path) + logger = configure_logging() + + img, mask = load_validation_data(config) + logger.info("Logging batch information before predictions:") + get_batch_info(img) + imgs = np.asarray(img) + channel_ranges = [abs(imgs[:, channel].max() - imgs[:, channel].min()) for channel in range(0, 14)] + + inputs, outputs, masks, losses = predict(model, img, mask) + + output_pdf_path = os.path.join(args.output_dir, f'satvision-toa-reconstruction-giant-{datetime.datetime.now().strftime("%Y-%m-%d")}.pdf') + rgb_index = [0, 2, 1] # Red, Green, Blue band indices + plot_export_pdf(output_pdf_path, inputs, outputs, masks, rgb_index) + logger.info(f"PDF saved to {output_pdf_path}") diff --git a/examples/inference/satvision-toa-reconstruction_huge.py b/examples/inference/satvision-toa-reconstruction_huge.py new file mode 100644 index 0000000..f006f65 --- /dev/null +++ b/examples/inference/satvision-toa-reconstruction_huge.py @@ -0,0 +1,207 @@ +import argparse +import glob +import datetime +import os +import logging +import numpy as np +import torch +import matplotlib.pyplot as plt +from matplotlib.backends.backend_pdf import PdfPages +from tqdm import tqdm + +import warnings +warnings.filterwarnings('ignore') + +from pytorch_caney.config import _C, _update_config_from_file +from pytorch_caney.models.build import build_model +from pytorch_caney.data.transforms import SimmimTransform + + +# Dictionary to map indices to band numbers +idx_to_band = { + 0: 1, + 1: 2, + 2: 3, + 3: 6, + 4: 7, + 5: 21, + 6: 26, + 7: 27, + 8: 28, + 9: 29, + 10: 30, + 11: 31, + 12: 32, + 13: 33 +} + + +def parse_args(): + parser = argparse.ArgumentParser(description="Predict and generate PDF using a pre-trained model.") + parser.add_argument('--pretrained_model_dir', type=str, required=True, help="Directory containing pre-trained model files (including .pt and .yaml)") + parser.add_argument("--output_dir", required=True, help="Directory where the output PDF will be saved.") + parser.add_argument("--data_path", default='/explore/nobackup/projects/ilab/projects/3DClouds/data/validation/sv_toa_128_chip_validation_04_24.npy', help="Path to validation data file.") + return parser.parse_args() + + +# Load model and config +def load_config_and_model(pretrained_model_dir, validation_data_path): + # Search for .pt and .yaml files + model_path = os.path.join(pretrained_model_dir, 'mp_rank_00_model_states.pt') + config_path = glob.glob(os.path.join(pretrained_model_dir, '*.yaml')) + if len(config_path) == 0: + raise FileNotFoundError(f"No YAML config found in {pretrained_model_dir}") + config_path = config_path[0] + + # Load config + config = _C.clone() + _update_config_from_file(config, config_path) + config.defrost() + config.MODEL.RESUME = model_path + config.DATA.DATA_PATHS = [validation_data_path] + config.OUTPUT = pretrained_model_dir + config.TAG = 'satvision-huge-toa-reconstruction' + config.freeze() + + # Load model + checkpoint = torch.load(model_path) + model = build_model(config, pretrain=True) + model.load_state_dict(checkpoint['module']) # Use 'model' if 'module' not present + model.eval() + + return model, config + + +def configure_logging(): + logging.basicConfig(filename='app.log', level=logging.INFO, format='%(asctime)s [%(levelname)s] %(message)s', datefmt='%Y-%m-%d %H:%M:%S') + console = logging.StreamHandler() + console.setLevel(logging.INFO) + console.setFormatter(logging.Formatter('%(asctime)s [%(levelname)s] %(message)s')) + logger = logging.getLogger('') + logger.addHandler(console) + return logger + + +def load_validation_data(config): + validation_dataset_path = config.DATA.DATA_PATHS[0] + validation_dataset = np.load(validation_dataset_path) + transform = SimmimTransform(config) + imgMasks = [transform(validation_dataset[idx]) for idx in range(validation_dataset.shape[0])] + img = torch.stack([imgMask[0] for imgMask in imgMasks]) + mask = torch.stack([torch.from_numpy(imgMask[1]) for imgMask in imgMasks]) + return img, mask + + +def get_batch_info(img): + channels = img.shape[1] + + for channelIdx in range(channels): + channel = idx_to_band.get(channelIdx, 'Unknown') # Retrieve band number or mark as 'Unknown' + img_band_array = img[:, channelIdx, :, :] + min_ = img_band_array.min().item() + mean_ = img_band_array.mean().item() + max_ = img_band_array.max().item() + print(f'Channel {channel}, min {min_}, mean {mean_}, max {max_}') + + +def predict(model, img, mask): + inputs, outputs, masks, losses = [], [], [], [] + for i in tqdm(range(img.shape[0])): + single_img = img[i].unsqueeze(0) + single_mask = mask[i].unsqueeze(0) + + with torch.no_grad(): + z = model.encoder(single_img, single_mask) + img_recon = model.decoder(z) + loss = model(single_img, single_mask) + + inputs.extend(single_img.cpu()) + masks.extend(single_mask.cpu()) + outputs.extend(img_recon.cpu()) + losses.append(loss.cpu()) + + return inputs, outputs, masks, losses + + +def process_mask(mask): + mask_img = mask.unsqueeze(0) + mask_img = mask_img.repeat_interleave(4, 1).repeat_interleave(4, 2).unsqueeze(1).contiguous() + mask_img = mask_img[0, 0, :, :] + mask_img = np.stack([mask_img, mask_img, mask_img], axis=-1) + return mask_img + + +def minmax_norm(img_arr): + arr_min = img_arr.min() + arr_max = img_arr.max() + img_arr_scaled = (img_arr - arr_min) / (arr_max - arr_min) + img_arr_scaled = img_arr_scaled * 255 + img_arr_scaled = img_arr_scaled.astype(np.uint8) + return img_arr_scaled + + +def process_prediction(image, img_recon, mask, rgb_index): + mask = process_mask(mask) + + red_idx = rgb_index[0] + blue_idx = rgb_index[1] + green_idx = rgb_index[2] + + image = image.numpy() + rgb_image = np.stack((image[red_idx, :, :], image[blue_idx, :, :], image[green_idx, :, :]), axis=-1) + rgb_image = minmax_norm(rgb_image) + + img_recon = img_recon.numpy() + rgb_image_recon = np.stack((img_recon[red_idx, :, :], img_recon[blue_idx, :, :], img_recon[green_idx, :, :]), axis=-1) + rgb_image_recon = minmax_norm(rgb_image_recon) + + rgb_masked = np.where(mask == 0, rgb_image, rgb_image_recon) + rgb_image_masked = np.where(mask == 1, 0, rgb_image) + rgb_recon_masked = rgb_masked + + return rgb_image, rgb_image_masked, rgb_recon_masked, mask + + +def plot_export_pdf(path, inputs, outputs, masks, rgb_index): + pdf_plot_obj = PdfPages(path) + for idx in range(len(inputs)): + rgb_image, rgb_image_masked, rgb_recon_masked, mask = process_prediction(inputs[idx], outputs[idx], masks[idx], rgb_index) + + fig, (ax01, ax23) = plt.subplots(2, 2, figsize=(40, 30)) + ax0, ax1 = ax01 + ax2, ax3 = ax23 + + ax2.imshow(rgb_image) + ax2.set_title(f"Idx: {idx} MOD021KM v6.1 Bands: {rgb_index}") + + ax0.imshow(rgb_recon_masked) + ax0.set_title(f"Idx: {idx} Model reconstruction") + + ax1.imshow(rgb_image_masked) + ax1.set_title(f"Idx: {idx} MOD021KM Bands: {rgb_index}, masked") + + ax3.matshow(mask[:, :, 0]) + ax3.set_title(f"Idx: {idx} Reconstruction Mask") + + pdf_plot_obj.savefig() + + pdf_plot_obj.close() + + +if __name__ == "__main__": + args = parse_args() + model, config = load_config_and_model(args.pretrained_model_dir, args.data_path) + logger = configure_logging() + + img, mask = load_validation_data(config) + get_batch_info(img) + + imgs = np.asarray(img) + channel_ranges = [abs(imgs[:, channel].max() - imgs[:, channel].min()) for channel in range(0, 14)] + + inputs, outputs, masks, losses = predict(model, img, mask) + + output_pdf_path = os.path.join(args.output_dir, f'satvision-toa-reconstruction-huge-{datetime.datetime.now().strftime("%Y-%m-%d")}.pdf') + rgb_index = [0, 2, 1] # Red, Green, Blue band indices + plot_export_pdf(output_pdf_path, inputs, outputs, masks, rgb_index) + logger.info(f"PDF saved to {output_pdf_path}") diff --git a/examples/inference/svtoa_reconstruction_giant_slurm.sh b/examples/inference/svtoa_reconstruction_giant_slurm.sh new file mode 100644 index 0000000..3dc6ea2 --- /dev/null +++ b/examples/inference/svtoa_reconstruction_giant_slurm.sh @@ -0,0 +1,9 @@ +#!/bin/bash +#SBATCH -G 1 +#SBATCH --time 2:00:00 +#SBATCH -N 1 +#SBATCH -J sv-inference-giant + +module load singularity + +srun singularity exec --nv --env PYTHONPATH=$PWD:$PWD/pytorch-caney -B /explore,/panfs /explore/nobackup/projects/ilab/containers/pytorch-caney-2024-08.dev python pytorch-caney/examples/inference/satvision-toa-reconstruction_giant.py --pretrained_model_dir /explore/nobackup/people/cssprad1/projects/satvision-toa/models/satvision-toa-giant-patch8-window8-128 --output_dir . \ No newline at end of file diff --git a/examples/inference/svtoa_reconstruction_huge_slurm.sh b/examples/inference/svtoa_reconstruction_huge_slurm.sh new file mode 100644 index 0000000..2ad686d --- /dev/null +++ b/examples/inference/svtoa_reconstruction_huge_slurm.sh @@ -0,0 +1,9 @@ +#!/bin/bash +#SBATCH -G 1 +#SBATCH --time 2:00:00 +#SBATCH -N 1 +#SBATCH -J sv-inference-huge + +module load singularity + +srun singularity exec --nv --env PYTHONPATH=$PWD:$PWD/pytorch-caney -B /explore,/panfs /explore/nobackup/projects/ilab/containers/pytorch-caney-2024-08.dev python pytorch-caney/examples/inference/satvision-toa-reconstruction_giant.py --pretrained_model_dir /explore/nobackup/people/cssprad1/projects/satvision-toa/models/satvision-toa-giant-patch8-window8-128 --output_dir . \ No newline at end of file