From ea641508c929e7705c5006ebc19a99dfaaf3f882 Mon Sep 17 00:00:00 2001 From: "Jeremy A. Prescott" Date: Thu, 12 Dec 2024 16:11:54 +0100 Subject: [PATCH] remove old docker archived docs --- docs/source/_templates/layout.html | 2 +- .../active_learning_worker_config.txt | 21 - .../load_model_from_checkpoint.py | 53 - .../object_level_worker_config.txt | 24 - .../object_level_worker_config_pretagging.txt | 24 - .../python_create_dataset_azure_example.py | 29 - .../python_create_dataset_gcs_example.py | 29 - .../python_create_dataset_s3_example.py | 31 - .../python_create_frame_predictions.py | 76 -- .../python_run_datapool_example.py | 59 -- .../python_run_datapool_example_2.py | 59 -- .../code_examples/python_run_object_level.py | 135 --- .../python_run_object_level_pretagging.py | 120 --- .../code_examples/python_run_pretagging.py | 118 --- .../python_run_sequence_selection.py | 112 --- .../semantic_segmentation_inference.py | 121 --- docs/source/docker/advanced/datapool.rst | 139 --- .../docker/advanced/datasource_metadata.rst | 240 ----- 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docs/source/docker_archive/integration/s3fs-fuse.rst delete mode 100644 docs/source/docker_archive/known_issues_faq.rst diff --git a/docs/source/_templates/layout.html b/docs/source/_templates/layout.html index e39f8042f..18c721f9d 100644 --- a/docs/source/_templates/layout.html +++ b/docs/source/_templates/layout.html @@ -36,7 +36,7 @@ }
- Looking to easily do active learning on millions of samples? See our Lighly Worker docs. + Looking to easily do active learning on millions of samples? See our LighlyOne Worker docs.
{% if theme_style_external_links|tobool %}
diff --git a/docs/source/docker/advanced/code_examples/active_learning_worker_config.txt b/docs/source/docker/advanced/code_examples/active_learning_worker_config.txt deleted file mode 100644 index 1f52ad826..000000000 --- a/docs/source/docker/advanced/code_examples/active_learning_worker_config.txt +++ /dev/null @@ -1,21 +0,0 @@ -{ - enable_corruptness_check: true, - remove_exact_duplicates: true, - enable_training: false, - pretagging: false, - pretagging_debug: false, - method: 'coral', - stopping_condition: { - n_samples: 0.1, - min_distance: -1 - }, - scorer: 'object-frequency', - scorer_config: { - frequency_penalty: 0.25, - min_score: 0.9 - }, - active_learning: { - task_name: 'my-classification-task', - score_name: 'uncertainty_margin' - } -} \ No newline at end of file diff --git a/docs/source/docker/advanced/code_examples/load_model_from_checkpoint.py b/docs/source/docker/advanced/code_examples/load_model_from_checkpoint.py deleted file mode 100644 index 93c907cfc..000000000 --- a/docs/source/docker/advanced/code_examples/load_model_from_checkpoint.py +++ /dev/null @@ -1,53 +0,0 @@ -from collections import OrderedDict - -import torch - -import lightly - - -def load_ckpt(ckpt_path, model_name="resnet-18", model_width=1, map_location="cpu"): - ckpt = torch.load(ckpt_path, map_location=map_location) - - state_dict = OrderedDict() - for key, value in ckpt["state_dict"].items(): - if ("projection_head" in key) or ("backbone.7" in key): - # drop layers used for projection head - continue - state_dict[key.replace("model.backbone.", "")] = value - - resnet = lightly.models.ResNetGenerator(name=model_name, width=model_width) - model = torch.nn.Sequential( - lightly.models.batchnorm.get_norm_layer(3, 0), - *list(resnet.children())[:-1], - torch.nn.AdaptiveAvgPool2d(1), - torch.nn.Flatten(1), - ) - try: - model.load_state_dict(state_dict) - except RuntimeError: - raise RuntimeError( - f"It looks like you tried loading a checkpoint from a model that is not a {model_name} with width={model_width}! " - f"Please set model_name and model_width to the lightly.model.name and lightly.model.width parameters from the " - f"configuration you used to run Lightly. The configuration from a Lightly worker run can be found in output_dir/config/config.yaml" - ) - return model - - -# loading the model -model = load_ckpt("output_dir/lightly_epoch_X.ckpt") - - -# example usage -image_batch = torch.rand(16, 3, 224, 224) -out = model(image_batch) -print(out.shape) # prints: torch.Size([16, 512]) - - -# creating a classifier from the pre-trained model -num_classes = 10 -classifier = torch.nn.Sequential( - model, torch.nn.Linear(512, num_classes) # use 2048 instead of 512 for resnet-50 -) - -out = classifier(image_batch) -print(out.shape) # prints: torch.Size(16, 10) diff --git a/docs/source/docker/advanced/code_examples/object_level_worker_config.txt b/docs/source/docker/advanced/code_examples/object_level_worker_config.txt deleted file mode 100644 index a4740f931..000000000 --- a/docs/source/docker/advanced/code_examples/object_level_worker_config.txt +++ /dev/null @@ -1,24 +0,0 @@ -{ - object_level: { - task_name: 'vehicles_object_detections' - }, - enable_corruptness_check: true, - remove_exact_duplicates: true, - enable_training: false, - pretagging: false, - pretagging_debug: false, - method: 'coreset', - stopping_condition: { - n_samples: 0.1, - min_distance: -1 - }, - scorer: 'object-frequency', - scorer_config: { - frequency_penalty: 0.25, - min_score: 0.9 - }, - active_learning: { - task_name: '', - score_name: 'uncertainty_margin' - } -} \ No newline at end of file diff --git a/docs/source/docker/advanced/code_examples/object_level_worker_config_pretagging.txt b/docs/source/docker/advanced/code_examples/object_level_worker_config_pretagging.txt deleted file mode 100644 index 39ce861cf..000000000 --- a/docs/source/docker/advanced/code_examples/object_level_worker_config_pretagging.txt +++ /dev/null @@ -1,24 +0,0 @@ -{ - object_level: { - task_name: 'lightly_pretagging' - }, - enable_corruptness_check: true, - remove_exact_duplicates: true, - enable_training: false, - pretagging: true, - pretagging_debug: false, - method: 'coreset', - stopping_condition: { - n_samples: 0.1, - min_distance: -1 - }, - scorer: 'object-frequency', - scorer_config: { - frequency_penalty: 0.25, - min_score: 0.9 - }, - active_learning: { - task_name: '', - score_name: 'uncertainty_margin' - } -} \ No newline at end of file diff --git a/docs/source/docker/advanced/code_examples/python_create_dataset_azure_example.py b/docs/source/docker/advanced/code_examples/python_create_dataset_azure_example.py deleted file mode 100644 index 4badd92a2..000000000 --- a/docs/source/docker/advanced/code_examples/python_create_dataset_azure_example.py +++ /dev/null @@ -1,29 +0,0 @@ -import json - -import lightly -from lightly.openapi_generated.swagger_client.models.dataset_type import DatasetType -from lightly.openapi_generated.swagger_client.models.datasource_purpose import ( - DatasourcePurpose, -) - -# Create the Lightly client to connect to the API. -client = lightly.api.ApiWorkflowClient(token="YOUR_TOKEN") - -# Create a new dataset on the Lightly Platform. -client.create_dataset("pedestrian-videos-datapool", dataset_type=DatasetType.VIDEOS) - -# Azure Blob Storage -# Input bucket -client.set_azure_config( - container_name="my-container/input/", - account_name="ACCOUNT-NAME", - sas_token="SAS-TOKEN", - purpose=DatasourcePurpose.INPUT, -) -# Output bucket -client.set_azure_config( - container_name="my-container/output/", - account_name="ACCOUNT-NAME", - sas_token="SAS-TOKEN", - purpose=DatasourcePurpose.LIGHTLY, -) diff --git a/docs/source/docker/advanced/code_examples/python_create_dataset_gcs_example.py b/docs/source/docker/advanced/code_examples/python_create_dataset_gcs_example.py deleted file mode 100644 index f8d86cc56..000000000 --- a/docs/source/docker/advanced/code_examples/python_create_dataset_gcs_example.py +++ /dev/null @@ -1,29 +0,0 @@ -import json - -import lightly -from lightly.openapi_generated.swagger_client.models.dataset_type import DatasetType -from lightly.openapi_generated.swagger_client.models.datasource_purpose import ( - DatasourcePurpose, -) - -# Create the Lightly client to connect to the API. -client = lightly.api.ApiWorkflowClient(token="YOUR_TOKEN") - -# Create a new dataset on the Lightly Platform. -client.create_dataset("pedestrian-videos-datapool", dataset_type=DatasetType.VIDEOS) - -# Google Cloud Storage -# Input bucket -client.set_gcs_config( - resource_path="gs://bucket/input/", - project_id="PROJECT-ID", - credentials=json.dumps(json.load(open("credentials_read.json"))), - purpose=DatasourcePurpose.INPUT, -) -# Output bucket -client.set_gcs_config( - resource_path="gs://bucket/output/", - project_id="PROJECT-ID", - credentials=json.dumps(json.load(open("credentials_write.json"))), - purpose=DatasourcePurpose.LIGHTLY, -) diff --git a/docs/source/docker/advanced/code_examples/python_create_dataset_s3_example.py b/docs/source/docker/advanced/code_examples/python_create_dataset_s3_example.py deleted file mode 100644 index 8dea5a7b0..000000000 --- a/docs/source/docker/advanced/code_examples/python_create_dataset_s3_example.py +++ /dev/null @@ -1,31 +0,0 @@ -import json - -import lightly -from lightly.openapi_generated.swagger_client.models.dataset_type import DatasetType -from lightly.openapi_generated.swagger_client.models.datasource_purpose import ( - DatasourcePurpose, -) - -# Create the Lightly client to connect to the API. -client = lightly.api.ApiWorkflowClient(token="YOUR_TOKEN") - -# Create a new dataset on the Lightly Platform. -client.create_dataset("pedestrian-videos-datapool", dataset_type=DatasetType.VIDEOS) - -# AWS S3 -# Input bucket -client.set_s3_config( - resource_path="s3://bucket/input/", - region="eu-central-1", - access_key="S3-ACCESS-KEY", - secret_access_key="S3-SECRET-ACCESS-KEY", - purpose=DatasourcePurpose.INPUT, -) -# Output bucket -client.set_s3_config( - resource_path="s3://bucket/output/", - region="eu-central-1", - access_key="S3-ACCESS-KEY", - secret_access_key="S3-SECRET-ACCESS-KEY", - purpose=DatasourcePurpose.LIGHTLY, -) diff --git a/docs/source/docker/advanced/code_examples/python_create_frame_predictions.py b/docs/source/docker/advanced/code_examples/python_create_frame_predictions.py deleted file mode 100644 index d71133616..000000000 --- a/docs/source/docker/advanced/code_examples/python_create_frame_predictions.py +++ /dev/null @@ -1,76 +0,0 @@ -import json -from pathlib import Path -from typing import Dict, List - -import av - -dataset_dir = Path("/datasets/my_dataset") -predictions_dir = dataset_dir / ".lightly" / "predictions" / "my_prediction_task" - - -def model_predict(frame) -> List[Dict]: - # This function must be overwritten to generate predictions for a frame using - # a prediction model of your choice. Here we just return an example prediction. - # See https://docs.lightly.ai/docker/advanced/datasource_predictions.html#prediction-format - # for possible prediction formats. - return [{"category_id": 0, "bbox": [0, 10, 100, 30], "score": 0.8}] - - -for video_path in dataset_dir.glob("**/*.mp4"): - # get predictions for frames - predictions = [] - with av.open(str(video_path)) as container: - stream = container.streams.video[0] - for frame in container.decode(stream): - predictions.append(model_predict(frame.to_image())) - - # save predictions - num_frames = len(predictions) - zero_padding = len(str(num_frames)) - for frame_index, frame_predictions in enumerate(predictions): - video_name = video_path.relative_to(dataset_dir).with_suffix("") - frame_name = Path( - f"{video_name}-{frame_index:0{zero_padding}}-{video_path.suffix[1:]}.png" - ) - prediction = { - "file_name": str(frame_name), - "predictions": frame_predictions, - } - out_path = predictions_dir / frame_name.with_suffix(".json") - out_path.parent.mkdir(parents=True, exist_ok=True) - with open(out_path, "w") as file: - json.dump(prediction, file) - - -# example directory structure before -# . -# ├── test -# │ └── video_0.mp4 -# └── train -# ├── video_1.mp4 -# └── video_2.mp4 -# -# example directory structure after -# . -# ├── .lightly -# │ └── predictions -# │ └── my_prediction_task -# │ ├── test -# │ │ ├── video_0-000-mp4.json -# │ │ ├── video_0-001-mp4.json -# │ │ ├── video_0-002-mp4.json -# │ │ └── ... -# │ └── train -# │ ├── video_1-000-mp4.json -# │ ├── video_1-001-mp4.json -# │ ├── video_1-002-mp4.json -# | ├── ... -# | ├── video_2-000-mp4.json -# | ├── video_2-001-mp4.json -# | ├── video_2-002-mp4.json -# │ └── ... -# ├── test -# │ └── video_0.mp4 -# └── train -# ├── video_1.mp4 -# └── video_2.mp4 diff --git a/docs/source/docker/advanced/code_examples/python_run_datapool_example.py b/docs/source/docker/advanced/code_examples/python_run_datapool_example.py deleted file mode 100644 index 669c42ba3..000000000 --- a/docs/source/docker/advanced/code_examples/python_run_datapool_example.py +++ /dev/null @@ -1,59 +0,0 @@ -import lightly - -# Create the Lightly client to connect to the API. -client = lightly.api.ApiWorkflowClient(token="YOUR_TOKEN") - -# Let's fetch the dataset we created above, by name -client.set_dataset_id_by_name("pedestrian-videos-datapool") - -# Schedule the compute run using our custom config. -# We show here the full default config so you can easily edit the -# values according to your needs. -client.schedule_compute_worker_run( - worker_config={ - "enable_corruptness_check": True, - "remove_exact_duplicates": True, - "enable_training": False, - "pretagging": False, - "pretagging_debug": False, - }, - selection_config={ - "n_samples": 100, - "strategies": [ - { - "input": {"type": "EMBEDDINGS"}, - "strategy": { - "type": "DIVERSITY", - "stopping_condition_minimum_distance": 0.1, - }, - } - ], - }, - lightly_config={ - "loader": { - "batch_size": 128, - "shuffle": True, - "num_workers": -1, - "drop_last": True, - }, - "model": {"name": "resnet-18", "out_dim": 128, "num_ftrs": 32, "width": 1}, - "trainer": {"gpus": 1, "max_epochs": 1, "precision": 16}, - "criterion": {"temperature": 0.5}, - "optimizer": {"lr": 1, "weight_decay": 0.00001}, - "collate": { - "input_size": 64, - "cj_prob": 0.8, - "cj_bright": 0.7, - "cj_contrast": 0.7, - "cj_sat": 0.7, - "cj_hue": 0.2, - "min_scale": 0.15, - "random_gray_scale": 0.2, - "gaussian_blur": 0.0, - "kernel_size": 0.1, - "vf_prob": 0, - "hf_prob": 0.5, - "rr_prob": 0, - }, - }, -) diff --git a/docs/source/docker/advanced/code_examples/python_run_datapool_example_2.py b/docs/source/docker/advanced/code_examples/python_run_datapool_example_2.py deleted file mode 100644 index d625a3e15..000000000 --- a/docs/source/docker/advanced/code_examples/python_run_datapool_example_2.py +++ /dev/null @@ -1,59 +0,0 @@ -import lightly - -# Create the Lightly client to connect to the API. -client = lightly.api.ApiWorkflowClient(token="YOUR_TOKEN") - -# Let's fetch the dataset we created above, by name -client.set_dataset_id_by_name("pedestrian-videos-datapool") - -# Schedule the compute run using our custom config. -# We show here the full default config so you can easily edit the -# values according to your needs. -client.schedule_compute_worker_run( - worker_config={ - "enable_corruptness_check": True, - "remove_exact_duplicates": True, - "enable_training": False, - "pretagging": False, - "pretagging_debug": False, - }, - selection_config={ - "n_samples": 100, - "strategies": [ - { - "input": {"type": "EMBEDDINGS"}, - "strategy": { - "type": "DIVERSITY", - "stopping_condition_minimum_distance": 0.2, - }, - } - ], - }, - lightly_config={ - "loader": { - "batch_size": 128, - "shuffle": True, - "num_workers": -1, - "drop_last": True, - }, - "model": {"name": "resnet-18", "out_dim": 128, "num_ftrs": 32, "width": 1}, - "trainer": {"gpus": 1, "max_epochs": 1, "precision": 16}, - "criterion": {"temperature": 0.5}, - "optimizer": {"lr": 1, "weight_decay": 0.00001}, - "collate": { - "input_size": 64, - "cj_prob": 0.8, - "cj_bright": 0.7, - "cj_contrast": 0.7, - "cj_sat": 0.7, - "cj_hue": 0.2, - "min_scale": 0.15, - "random_gray_scale": 0.2, - "gaussian_blur": 0.0, - "kernel_size": 0.1, - "vf_prob": 0, - "hf_prob": 0.5, - "rr_prob": 0, - }, - }, -) diff --git a/docs/source/docker/advanced/code_examples/python_run_object_level.py b/docs/source/docker/advanced/code_examples/python_run_object_level.py deleted file mode 100644 index 85239ae2b..000000000 --- a/docs/source/docker/advanced/code_examples/python_run_object_level.py +++ /dev/null @@ -1,135 +0,0 @@ -import json - -import lightly -from lightly.openapi_generated.swagger_client.models.dataset_type import DatasetType -from lightly.openapi_generated.swagger_client.models.datasource_purpose import ( - DatasourcePurpose, -) - -# Create the Lightly client to connect to the API. -client = lightly.api.ApiWorkflowClient(token="YOUR_TOKEN") - -# Create a new dataset on the Lightly Platform. -client.create_dataset("dataset-name", dataset_type=DatasetType.IMAGES) - -# Pick one of the following three blocks depending on where your data is -# AWS S3 -# Input bucket -client.set_s3_config( - resource_path="s3://bucket/input/", - region="eu-central-1", - access_key="S3-ACCESS-KEY", - secret_access_key="S3-SECRET-ACCESS-KEY", - purpose=DatasourcePurpose.INPUT, -) -# Output bucket -client.set_s3_config( - resource_path="s3://bucket/output/", - region="eu-central-1", - access_key="S3-ACCESS-KEY", - secret_access_key="S3-SECRET-ACCESS-KEY", - purpose=DatasourcePurpose.LIGHTLY, -) - - -# or Google Cloud Storage -# Input bucket -client.set_gcs_config( - resource_path="gs://bucket/input/", - project_id="PROJECT-ID", - credentials=json.dumps(json.load(open("credentials_read.json"))), - purpose=DatasourcePurpose.INPUT, -) -# Output bucket -client.set_gcs_config( - resource_path="gs://bucket/output/", - project_id="PROJECT-ID", - credentials=json.dumps(json.load(open("credentials_write.json"))), - purpose=DatasourcePurpose.LIGHTLY, -) - - -# or Azure Blob Storage -# Input bucket -client.set_azure_config( - container_name="my-container/input/", - account_name="ACCOUNT-NAME", - sas_token="SAS-TOKEN", - purpose=DatasourcePurpose.INPUT, -) -# Output bucket -client.set_azure_config( - container_name="my-container/output/", - account_name="ACCOUNT-NAME", - sas_token="SAS-TOKEN", - purpose=DatasourcePurpose.LIGHTLY, -) - -# Schedule the docker run with the "object_level.task_name" argument set. -# All other settings are default values and we show them so you can easily edit -# the values according to your need. -client.schedule_compute_worker_run( - worker_config={ - "enable_corruptness_check": True, - "remove_exact_duplicates": True, - "enable_training": False, - "pretagging": False, - "pretagging_debug": False, - "object_level": { # used for object level workflow - "task_name": "vehicles_object_detections" - }, - }, - selection_config={ - "n_samples": 100, - "strategies": [ - { - "input": {"type": "EMBEDDINGS"}, - "strategy": { - "type": "DIVERSITY", - }, - }, - # Optionally, you can combine diversity selection with active learning - # to prefer selecting objects the model struggles with. - # If you want that, just include the following code: - """ - { - "input": { - "type": "SCORES", - "task": "vehicles_object_detections", # change to your task - "score": "uncertainty_entropy" # change to your preferred score - }, - "strategy": { - "type": "WEIGHTS" - } - } - """, - ], - }, - lightly_config={ - "loader": { - "batch_size": 16, - "shuffle": True, - "num_workers": -1, - "drop_last": True, - }, - "model": {"name": "resnet-18", "out_dim": 128, "num_ftrs": 32, "width": 1}, - "trainer": {"gpus": 1, "max_epochs": 100, "precision": 32}, - "criterion": {"temperature": 0.5}, - "optimizer": {"lr": 1, "weight_decay": 0.00001}, - "collate": { - "input_size": 64, - "cj_prob": 0.8, - "cj_bright": 0.7, - "cj_contrast": 0.7, - "cj_sat": 0.7, - "cj_hue": 0.2, - "min_scale": 0.15, - "random_gray_scale": 0.2, - "gaussian_blur": 0.5, - "kernel_size": 0.1, - "vf_prob": 0, - "hf_prob": 0.5, - "rr_prob": 0, - }, - }, -) diff --git a/docs/source/docker/advanced/code_examples/python_run_object_level_pretagging.py b/docs/source/docker/advanced/code_examples/python_run_object_level_pretagging.py deleted file mode 100644 index 8dd55de20..000000000 --- a/docs/source/docker/advanced/code_examples/python_run_object_level_pretagging.py +++ /dev/null @@ -1,120 +0,0 @@ -import json - -import lightly -from lightly.openapi_generated.swagger_client.models.dataset_type import DatasetType -from lightly.openapi_generated.swagger_client.models.datasource_purpose import ( - DatasourcePurpose, -) - -# Create the Lightly client to connect to the API. -client = lightly.api.ApiWorkflowClient(token="YOUR_TOKEN") - -# Create a new dataset on the Lightly Platform. -client.create_dataset("dataset-name", dataset_type=DatasetType.IMAGES) - - -# Pick one of the following three blocks depending on where your data is -# AWS S3 -# Input bucket -client.set_s3_config( - resource_path="s3://bucket/input/", - region="eu-central-1", - access_key="S3-ACCESS-KEY", - secret_access_key="S3-SECRET-ACCESS-KEY", - purpose=DatasourcePurpose.INPUT, -) -# Output bucket -client.set_s3_config( - resource_path="s3://bucket/output/", - region="eu-central-1", - access_key="S3-ACCESS-KEY", - secret_access_key="S3-SECRET-ACCESS-KEY", - purpose=DatasourcePurpose.LIGHTLY, -) - - -# or Google Cloud Storage -# Input bucket -client.set_gcs_config( - resource_path="gs://bucket/input/", - project_id="PROJECT-ID", - credentials=json.dumps(json.load(open("credentials_read.json"))), - purpose=DatasourcePurpose.INPUT, -) -# Output bucket -client.set_gcs_config( - resource_path="gs://bucket/output/", - project_id="PROJECT-ID", - credentials=json.dumps(json.load(open("credentials_write.json"))), - purpose=DatasourcePurpose.LIGHTLY, -) - - -# or Azure Blob Storage -# Input bucket -client.set_azure_config( - container_name="my-container/input/", - account_name="ACCOUNT-NAME", - sas_token="SAS-TOKEN", - purpose=DatasourcePurpose.INPUT, -) -# Output bucket -client.set_azure_config( - container_name="my-container/output/", - account_name="ACCOUNT-NAME", - sas_token="SAS-TOKEN", - purpose=DatasourcePurpose.LIGHTLY, -) - -# Schedule the docker run with the "object_level.task_name" argument set to -# "lightly_pretagging" and with "pretagging" set to True. -# All other settings are default values and we show them so you can easily edit -# the values according to your need. -client.schedule_compute_worker_run( - worker_config={ - "enable_corruptness_check": True, - "remove_exact_duplicates": True, - "enable_training": False, - "pretagging": True, - "pretagging_debug": False, - "object_level": {"task_name": "lightly_pretagging"}, - }, - selection_config={ - "n_samples": 100, - "strategies": [ - { - "input": {"type": "EMBEDDINGS"}, - "strategy": { - "type": "DIVERSITY", - }, - } - ], - }, - lightly_config={ - "loader": { - "batch_size": 16, - "shuffle": True, - "num_workers": -1, - "drop_last": True, - }, - "model": {"name": "resnet-18", "out_dim": 128, "num_ftrs": 32, "width": 1}, - "trainer": {"gpus": 1, "max_epochs": 100, "precision": 32}, - "criterion": {"temperature": 0.5}, - "optimizer": {"lr": 1, "weight_decay": 0.00001}, - "collate": { - "input_size": 64, - "cj_prob": 0.8, - "cj_bright": 0.7, - "cj_contrast": 0.7, - "cj_sat": 0.7, - "cj_hue": 0.2, - "min_scale": 0.15, - "random_gray_scale": 0.2, - "gaussian_blur": 0.5, - "kernel_size": 0.1, - "vf_prob": 0, - "hf_prob": 0.5, - "rr_prob": 0, - }, - }, -) diff --git a/docs/source/docker/advanced/code_examples/python_run_pretagging.py b/docs/source/docker/advanced/code_examples/python_run_pretagging.py deleted file mode 100644 index fe7dedd7b..000000000 --- a/docs/source/docker/advanced/code_examples/python_run_pretagging.py +++ /dev/null @@ -1,118 +0,0 @@ -import json - -import lightly -from lightly.openapi_generated.swagger_client.models.dataset_type import DatasetType -from lightly.openapi_generated.swagger_client.models.datasource_purpose import ( - DatasourcePurpose, -) - -# Create the Lightly client to connect to the API. -client = lightly.api.ApiWorkflowClient(token="YOUR_TOKEN") - -# Create a new dataset on the Lightly Platform. In this example we use pretagging -# on images. We can also use videos instead by setting dataset_type=DatasetType.VIDEOS -client.create_dataset("your-dataset-name", dataset_type=DatasetType.IMAGES) - -# Pick one of the following three blocks depending on where your data is -# AWS S3 -# Input bucket -client.set_s3_config( - resource_path="s3://bucket/input/", - region="eu-central-1", - access_key="S3-ACCESS-KEY", - secret_access_key="S3-SECRET-ACCESS-KEY", - purpose=DatasourcePurpose.INPUT, -) -# Output bucket -client.set_s3_config( - resource_path="s3://bucket/output/", - region="eu-central-1", - access_key="S3-ACCESS-KEY", - secret_access_key="S3-SECRET-ACCESS-KEY", - purpose=DatasourcePurpose.LIGHTLY, -) - - -# or Google Cloud Storage -# Input bucket -client.set_gcs_config( - resource_path="gs://bucket/input/", - project_id="PROJECT-ID", - credentials=json.dumps(json.load(open("credentials_read.json"))), - purpose=DatasourcePurpose.INPUT, -) -# Output bucket -client.set_gcs_config( - resource_path="gs://bucket/output/", - project_id="PROJECT-ID", - credentials=json.dumps(json.load(open("credentials_write.json"))), - purpose=DatasourcePurpose.LIGHTLY, -) - - -# or Azure Blob Storage -# Input bucket -client.set_azure_config( - container_name="my-container/input/", - account_name="ACCOUNT-NAME", - sas_token="SAS-TOKEN", - purpose=DatasourcePurpose.INPUT, -) -# Output bucket -client.set_azure_config( - container_name="my-container/output/", - account_name="ACCOUNT-NAME", - sas_token="SAS-TOKEN", - purpose=DatasourcePurpose.LIGHTLY, -) - -# Schedule the compute run using our custom config. -# We show here the full default config so you can easily edit the -# values according to your needs. -client.schedule_compute_worker_run( - worker_config={ - "enable_corruptness_check": True, - "remove_exact_duplicates": True, - "enable_training": False, - "pretagging": True, # to enable pretagging - "pretagging_debug": True, # we also want debugging images in the report - }, - selection_config={ - "n_samples": 100, - "strategies": [ - { - "input": {"type": "EMBEDDINGS"}, - "strategy": { - "type": "DIVERSITY", - }, - } - ], - }, - lightly_config={ - "loader": { - "batch_size": 128, - "shuffle": True, - "num_workers": -1, - "drop_last": True, - }, - "model": {"name": "resnet-18", "out_dim": 128, "num_ftrs": 32, "width": 1}, - "trainer": {"gpus": 1, "max_epochs": 1, "precision": 16}, - "criterion": {"temperature": 0.5}, - "optimizer": {"lr": 1, "weight_decay": 0.00001}, - "collate": { - "input_size": 64, - "cj_prob": 0.8, - "cj_bright": 0.7, - "cj_contrast": 0.7, - "cj_sat": 0.7, - "cj_hue": 0.2, - "min_scale": 0.15, - "random_gray_scale": 0.2, - "gaussian_blur": 0.0, - "kernel_size": 0.1, - "vf_prob": 0, - "hf_prob": 0.5, - "rr_prob": 0, - }, - }, -) diff --git a/docs/source/docker/advanced/code_examples/python_run_sequence_selection.py b/docs/source/docker/advanced/code_examples/python_run_sequence_selection.py deleted file mode 100644 index dfc405793..000000000 --- a/docs/source/docker/advanced/code_examples/python_run_sequence_selection.py +++ /dev/null @@ -1,112 +0,0 @@ -import json - -import lightly -from lightly.openapi_generated.swagger_client.models.dataset_type import DatasetType -from lightly.openapi_generated.swagger_client.models.datasource_purpose import ( - DatasourcePurpose, -) - -# Create the Lightly client to connect to the API. -client = lightly.api.ApiWorkflowClient(token="YOUR_TOKEN") - -# Create a new dataset on the Lightly Platform. -client.create_dataset("pexels", dataset_type=DatasetType.VIDEOS) - -# Pick one of the following three blocks depending on where your data is -# AWS S3 -# Input bucket -client.set_s3_config( - resource_path="s3://bucket/input/", - region="eu-central-1", - access_key="S3-ACCESS-KEY", - secret_access_key="S3-SECRET-ACCESS-KEY", - purpose=DatasourcePurpose.INPUT, -) -# Output bucket -client.set_s3_config( - resource_path="s3://bucket/output/", - region="eu-central-1", - access_key="S3-ACCESS-KEY", - secret_access_key="S3-SECRET-ACCESS-KEY", - purpose=DatasourcePurpose.LIGHTLY, -) - - -# or Google Cloud Storage -# Input bucket -client.set_gcs_config( - resource_path="gs://bucket/input/", - project_id="PROJECT-ID", - credentials=json.dumps(json.load(open("credentials_read.json"))), - purpose=DatasourcePurpose.INPUT, -) -# Output bucket -client.set_gcs_config( - resource_path="gs://bucket/output/", - project_id="PROJECT-ID", - credentials=json.dumps(json.load(open("credentials_write.json"))), - purpose=DatasourcePurpose.LIGHTLY, -) - - -# or Azure Blob Storage -# Input bucket -client.set_azure_config( - container_name="my-container/input/", - account_name="ACCOUNT-NAME", - sas_token="SAS-TOKEN", - purpose=DatasourcePurpose.INPUT, -) -# Output bucket -client.set_azure_config( - container_name="my-container/output/", - account_name="ACCOUNT-NAME", - sas_token="SAS-TOKEN", - purpose=DatasourcePurpose.LIGHTLY, -) - -# Schedule the compute run using our custom config. -# We show here the full default config so you can easily edit the -# values according to your needs. -client.schedule_compute_worker_run( - worker_config={ - "enable_corruptness_check": False, - "remove_exact_duplicates": False, - "enable_training": False, - "pretagging": False, - "pretagging_debug": False, - "method": "coreset", - "stopping_condition": { - "n_samples": 200, # select 200 frames of length 10 frames -> 20 sequences - "min_distance": -1, - }, - "selected_sequence_length": 10, # we want sequences of 10 frames lenght - }, - lightly_config={ - "loader": { - "batch_size": 128, - "shuffle": True, - "num_workers": -1, - "drop_last": True, - }, - "model": {"name": "resnet-18", "out_dim": 128, "num_ftrs": 32, "width": 1}, - "trainer": {"gpus": 1, "max_epochs": 1, "precision": 16}, - "criterion": {"temperature": 0.5}, - "optimizer": {"lr": 1, "weight_decay": 0.00001}, - "collate": { - "input_size": 64, - "cj_prob": 0.8, - "cj_bright": 0.7, - "cj_contrast": 0.7, - "cj_sat": 0.7, - "cj_hue": 0.2, - "min_scale": 0.15, - "random_gray_scale": 0.2, - "gaussian_blur": 0.0, - "kernel_size": 0.1, - "vf_prob": 0, - "hf_prob": 0.5, - "rr_prob": 0, - }, - }, -) diff --git a/docs/source/docker/advanced/code_examples/semantic_segmentation_inference.py b/docs/source/docker/advanced/code_examples/semantic_segmentation_inference.py deleted file mode 100644 index 62a4f379f..000000000 --- a/docs/source/docker/advanced/code_examples/semantic_segmentation_inference.py +++ /dev/null @@ -1,121 +0,0 @@ -import json -import os - -import numpy as np - -TASK_NAME = "lightly_semantic_segmentation" -CATEGORIES = ["background", "car", "person"] - - -def get_dummy_prediction(height: int = 500, width: int = 500): - """Returns a dummy prediction of shape h x w x n_classes. - - Height and width are in pixels. - """ - return np.random.rand(height, width, len(CATEGORIES)) - - -def filename_to_json(filename: str): - """Turns an image filename into the respective json filename.""" - root, _ = os.path.splitext(filename) - return f"{root}.json" - - -def binary_to_rle(binary_mask: np.ndarray) -> np.ndarray: - """Converts a binary segmentation mask to RLE.""" - # Flatten mask and add -1 at beginning and end of array - flat = np.concatenate(([-1], np.ravel(binary_mask), [-1])) - # Find indices where a change to 0 or 1 happens - borders = np.nonzero(np.diff(flat))[0] - # Find counts of subsequent 0s and 1s - rle = np.diff(borders) - if flat[1]: - # The first value in the encoding must always be the count - # of initial 0s. If the mask starts with a 1 we must set - # this count to 0. - rle = np.concatenate(([0], rle)) - return rle - - -def convert_to_lightly_prediction(filename: str, seg_map: np.ndarray): - """Converts a segmentation map of shape W x H x C to Lightly format.""" - seg_map_argmax = np.argmax(seg_map, axis=-1) - - prediction = {"file_name": filename, "predictions": []} - for category_id in np.unique(seg_map_argmax): - rle = binary_to_rle(seg_map_argmax == category_id) - logits = np.mean(seg_map[seg_map_argmax == category_id], axis=0) - assert np.argmax(logits) == category_id - probabilities = np.exp(logits) / np.sum(np.exp(logits)) - assert abs(np.sum(probabilities) - 1.0) < 1e-6 - - prediction["predictions"].append( - { - "category_id": int(category_id), - "segmentation": [int(r) for r in rle], - "score": float(probabilities[category_id]), - "probabilities": [float(p) for p in probabilities], - } - ) - - return prediction - - -# The following code will generate a tasks.json, a schema.json, and a dummy -# prediction file called my_image.json. To use them with the Lightly worker, -# arrange them as follows in a .lightly directory -# -# .lightly/ -# L predictions/ -# L tasks.json -# L lightly_semantic_segmentation/ -# L schema.json -# L // add the real prediction files here -# - - -# add tasks.json -tasks = [TASK_NAME] -with open("tasks.json", "w") as f: - json.dump(tasks, f) - -# add schema.json -schema = { - "task_type": "semantic-segmentation", - "categories": [ - { - "id": i, - "name": name, - } - for i, name in enumerate(CATEGORIES) - ], -} -with open("schema.json", "w") as f: - json.dump(schema, f) - -# generate a dummy prediction -filename = "my_image.png" -prediction = get_dummy_prediction() # this is a h x w x n_classes numpy array -category_ids = np.argmax(prediction, axis=-1) - -lightly_prediction = {"file_name": filename, "predictions": []} -for category_id in np.unique(category_ids): - # get the run-length encoding - rle = binary_to_rle(category_ids == category_id) - # get the logits - logits = np.mean(prediction[category_ids == category_id], axis=0) - assert np.argmax(logits) == category_id - probabilities = np.exp(logits) / np.sum(np.exp(logits)) - assert abs(np.sum(probabilities) - 1.0) < 1e-6 - - lightly_prediction["predictions"].append( - { - "category_id": int(category_id), - "segmentation": [int(r) for r in rle], - "score": float(probabilities[category_id]), - "probabilities": [float(p) for p in probabilities], - } - ) - -with open(filename_to_json(filename), "w") as f: - json.dump(lightly_prediction, f) diff --git a/docs/source/docker/advanced/datapool.rst b/docs/source/docker/advanced/datapool.rst deleted file mode 100644 index 389af3336..000000000 --- a/docs/source/docker/advanced/datapool.rst +++ /dev/null @@ -1,139 +0,0 @@ -.. _datapool: - -Datapool -================= - -Lightly Worker has been designed in a way that you can incrementally build up a -dataset for your project. The software automatically keeps track of the -representations of previously selected samples and uses this information -to pick new samples in order to maximize the quality of the final dataset. -It also allows for combining two different datasets into one. - -For example, let's imagine we have a dataset of street videos. After running -the Lightly Worker once we added 4 more street videos to the bucket. -The new raw data might include samples which should be added to your dataset -in the Lightly Platform, so you want to add a subset of them to your dataset. - -This workflow is supported by the Lightly Platform using a datapool. -It remembers which raw data in your bucket has already been processed -and will ignore it in future Lightly Worker runs. - -Thus you can run the Lightly Worker with the same command again. It will find -your new raw data in the bucket, stream, embed and subsample it and then add it to -your existing dataset. The selection strategy will take the existing data in your dataset -into account when selecting new data to be added to your dataset. - -.. image:: ./images/webapp-embedding-after-2nd-docker.png - -After the Lightly Worker run we can go to the embedding view of the Lightly Platform -to see the newly added samples there in a new tag. We see that the new samples -(in green) fill some gaps left by the images in the first iteration (in grey). -However, there are still some gaps left, which could be filled by adding more videos -to the bucket and running the Lightly Worker again. - -This workflow of iteratively growing your dataset with the Lightly Worker -has the following advantages: - -- You can learn from your findings after each iteration - to know which raw data you need to collect next. -- Only your new data is processed, saving you time and compute cost. -- You don't need to configure anything, just run the same command again. -- Only samples which are different to the existing ones are added to the dataset. - -If you want to search all data in your bucket for new samples -instead of only newly added data, -then set :code:`'datasource.process_all': True` in your worker config. This has the -same effect as creating a new dataset and running the Lightly Worker from scratch -on the full dataset. We process all data instead of only the newly added ones. - - -Example ---------------- - -In this example we will do the following steps: - -#. Schedule a run to process a cloud bucket with 3 videos -#. Add 2 more videos to the same bucket -#. Run the Lightly Worker with the same config again to use the datapool feature - - -Here we show the content of the bucket before running the Lightly Worker for the -first time. - -.. code-block:: console - - videos/ - |-- campus4-c0.avi - |-- passageway1-c1.avi - `-- terrace1-c0.avi - -Let's create a dataset which uses that bucket (choose your tab - S3, GCS or Azure): - -.. tabs:: - .. tab:: AWS S3 Datasource - - .. literalinclude:: ./code_examples/python_create_dataset_s3_example.py - :linenos: - :language: python - - .. tab:: GCS Datasource - - .. literalinclude:: ./code_examples/python_create_dataset_gcs_example.py - :linenos: - :language: python - - .. tab:: Azure Datasource - - .. literalinclude:: ./code_examples/python_create_dataset_azure_example.py - :linenos: - :language: python - -Now we can run the following code to select a subset based on the -:code:`'stopping_condition_minimum_distance': 0.1` stopping condition. In a first, -selection run we only select images with the specific minimum distance between -each other based on the embeddings. - -.. literalinclude:: ./code_examples/python_run_datapool_example.py - :linenos: - :language: python - :emphasize-lines: 8, 30 - -After running the code we have to make sure we have a running Lightly Worker -to process the job. -We can start the Lightly Worker using the following command - -.. code-block:: console - - docker run --shm-size="1024m" --rm --gpus all -it \ - lightly/worker:latest \ - token=YOUR_TOKEN worker.worker_id=YOUR_WORKER_ID - -After we have processed the initial data and created a dataset, -we've collected more data and our bucket now looks like this: - -.. code-block:: console - - videos/ - |-- campus4-c0.avi - |-- campus7-c0.avi - |-- passageway1-c1.avi - |-- terrace1-c0.avi - `-- terrace1-c3.avi - -We can run the same script again (it won't create a new dataset but use the -existing one based on the dataset name). Let's increase the -stopping_condition_minimum_distance to 0.2: - -.. literalinclude:: ./code_examples/python_run_datapool_example_2.py - :linenos: - :language: python - :emphasize-lines: 30 - - -How It Works ---------------- - -The Lightly Datapool keeps track of the selected samples in a csv file called -`datapool_latest.csv`. It contains the filenames of the selected images and their -embeddings. This feature is currently only supported without training of a custom -model. Please make sure :code:`'enable_training': False` is set in your worker config. diff --git a/docs/source/docker/advanced/datasource_metadata.rst b/docs/source/docker/advanced/datasource_metadata.rst deleted file mode 100644 index da7288393..000000000 --- a/docs/source/docker/advanced/datasource_metadata.rst +++ /dev/null @@ -1,240 +0,0 @@ -.. _docker-datasource-metadata: - -Add Metadata to a Datasource -=============================== - -Lightly Worker can make use of metadata collected alongside your images or videos. Provided, -metadata can be used to steer the selection process and to analyze the selected dataset -in the Lightly Platform. - - -Metadata Folder Structure ----------------------------- - -Following, we outline the format in which metadata can be added to a -Lightly datasource. Everything regarding metadata will take place in a subdirectory -of your configured **lightly datasource** called :code:`.lightly/metadata`. The general structure -your input and lightly bucket will look like this: - -.. code-block:: bash - - input/bucket/ - + image_0.png - + subdir/ - + image_1.png - + image_2.png - + ... - + image_N.png - - lightly/bucket/ - + .lightly/metadata - + schema.json - + image_0.json - + subdir/ - + image_1.json - ... - + image_N.json - - -All of the `.json` files are explained in the next sections. - - - -Metadata Schema ---------------- -The schema defines the format of the metadata and helps the Lightly Platform to correctly identify -and display different types of metadata. - -You can provide this information to Lightly Worker by adding a `schema.json` to the -`.lightly/metadata` directory. The `schema.json` file must contain a list of -configuration entries. Each of the entries is a dictionary with the following keys: - - - `name`: Identifier of the metadata in the UI. - - `path`: Concatenation of the keys to access the metadata in a dictionary. - - `defaultValue`: The fallback value if there is no metadata available. - - `valueDataType`: One of - - - `NUMERIC_INT` - - `NUMERIC_FLOAT` - - `CATEGORICAL_INT` - - `CATEGORICAL_STRING` - - `CATEGORICAL_BOOLEAN` - - -For example, let's say we have additional information about the scene and weather for each -of the images we have collected. A possible schema could look like this: - -.. code-block:: javascript - :caption: .lightly/metadata/schema.json - - [ - { - "name": "Scene", - "path": "scene", - "defaultValue": "undefined", - "valueDataType": "CATEGORICAL_STRING" - } - { - "name": "Weather description", - "path": "weather.description", - "defaultValue": "nothing", - "valueDataType": "CATEGORICAL_STRING" - }, - { - "name": "Temperature", - "path": "weather.temperature", - "defaultValue": 0.0, - "valueDataType": "NUMERIC_FLOAT" - }, - { - "name": "Air pressure", - "path": "weather.air_pressure", - "defaultValue": 0, - "valueDataType": "NUMERIC_INT" - }, - { - "name": "Vehicle ID", - "path": "vehicle_id", - "defaultValue": 0, - "valueDataType": "CATEGORICAL_INT" - } - ] - - - - -Metadata Files --------------- -Lightly Worker requires a single metadata file per image or video. If an image or video has no corresponding metadata file, -Lightly Worker assumes the default value from the `schema.json`. If a metadata file is provided for a full video, -Lightly Worker assumes that the metadata is valid for all frames in that video. - -To provide metadata for an image or a video, place a metadata file with the same name -as the image or video in the `.lightly/metadata` directory but change the file extension to -`.json`. The file should contain the metadata in the format defined under :ref:`metadata-format`. - - -.. code-block:: bash - - # filename of the metadata for file input/bucket/FILENAME.EXT - .lightly/metadata/${FILENAME}.json - - # example: input/bucket/subdir/image_1.png - .lightly/metadata/subdir/image_1.json - - # example: input/bucket/image_0.png - .lightly/metadata/image_0.json - - # example: input/bucket/subdir/video_1.mp4 - .lightly/metadata/subdir/video_1.json - - -When working with videos it's also possible to provide metadata on a per-frame basis. -Then, Lightly Worker requires a metadata file per frame. If a frame has no corresponding metadata file, -Lightly Worker assumes the default value from the `schema.json`. Lightly Worker uses a naming convention to -identify frames: The filename of a frame consists of the video filename, the frame number -(padded to the length of the number of frames in the video), the video format separated -by hyphens. For example, for a video with 200 frames, the frame number will be padded -to length three. For a video with 1000 frames, the frame number will be padded to length four (99 becomes 0099). - - -.. code-block:: bash - - # filename of the metadata of the Xth frame of video input/bucket/FILENAME.EXT - # with 200 frames (padding: len(str(200)) = 3) - .lightly/metadata/${FILENAME}-${X:03d}-${EXT}.json - - # example: input/bucket/subdir/video_1.mp4, frame 99/200 - .lightly/metadata/subdir/video_1-099-mp4.json - - # example: input/bucket/video_0.mp4, frame 99/200 - .lightly/metadata/video_0-099-mp4.json - - -.. _metadata-format: - -Metadata Format ---------------- - -The metadata json files for images and videos require the keys `file_name`, `type`, and `metadata` key. -Here, `file_name` serves as a unique identifier to retrieve the original file for which the metadata was collected, -`type` indicates whether the metadata is per "video", "frame", or "image", and `metadata` contains the actual metadata. - -For our example from above, a metadata file corresponding to a image/video/frame should look like this: - - -.. tabs:: - - - .. tab:: Video - - .. code-block:: javascript - :caption: .lightly/metadata/subdir/video_1.json - - { - "file_name": "subdir/video_1.mp4", - "type": "video", - "metadata": { - "scene": "city street", - "weather": { - "description": "sunny", - "temperature": 23.2, - "air_pressure": 1 - }, - "vehicle_id": 321, - } - } - - .. tab:: Frame - - .. code-block:: javascript - :caption: .lightly/metadata/subdir/video_1-099-mp4.json - - { - "file_name": "subdir/video_1-099-mp4.png", - "type": "frame", - "metadata": { - "scene": "city street", - "weather": { - "description": "sunny", - "temperature": 23.2, - "air_pressure": 1 - }, - "vehicle_id": 321, - } - } - - .. tab:: Image - - .. code-block:: javascript - :caption: .lightly/metadata/subdir/image_1.json - - { - "file_name": "subdir/image_1.png", - "type": "image", - "metadata": { - "scene": "highway", - "weather": { - "description": "rainy", - "temperature": 10.5, - "air_pressure": 1 - }, - "vehicle_id": 321, - } - } - - - - -Next Steps ----------- - -If metadata is provided, the Lightly Worker will automatically detect and load it into -the Lightly Platform where it can be visualized and analyzed after running a selection. - -For example, it's possible to visualize the different categories of metadata in the Lightly -Platform scatter plot. In the following example we visualized the categorical metadata "Scene" -from the BDD100k dataset. - - -.. figure:: images/bdd100k_demo_metadata.jpg diff --git a/docs/source/docker/advanced/datasource_predictions.rst b/docs/source/docker/advanced/datasource_predictions.rst deleted file mode 100644 index 584bc4d93..000000000 --- a/docs/source/docker/advanced/datasource_predictions.rst +++ /dev/null @@ -1,559 +0,0 @@ -.. _docker-datasource-predictions: - -Add Predictions to a Datasource -=============================== - -Lightly Worker can not only use images you provided in a datasource, but also predictions of a ML model on your images. -They are used for active learning for selecting images based on the objects in them. -Furthermore, object detection predictions can be used running Lightly Worker on object level. -By providing the predictions in the datasource, -you have full control over them and they scale well to millions of samples. -Furthermore, if you add new samples to your datasource, you can simultaneously -add their predictions to the datasource. -If you already have labels instead of predictions, you can treat them -just as predictions and upload them the same way. - -.. note:: Note that working with predictions requires a minimum - Lightly Worker of version 2.2. You can check your installed version of the - Lightly Worker by running the :ref:`docker-setup-sanity-check`. - -Predictions Folder Structure ----------------------------- - -In the following, we will outline the format of the predictions required by the -Lightly Worker. Everything regarding predictions will take place in a subdirectory -of your configured **lightly datasource** called :code:`.lightly/predictions`. The general structure -of your input and lightly bucket will look like this: - - -.. code-block:: bash - - input/bucket/ - + image_0.png - + subdir/ - + image_1.png - + image_2.png - + ... - + image_N.png - - lightly/bucket/ - + .lightly/predictions/ - + tasks.json - + task_1/ - + schema.json - + image_0.json - + subdir/ - + image_1.json - ... - + image_N.json - + task_2/ - + schema.json - + image_0.json - + subdir/ - + image_1.json - ... - + image_N.json - - - -Where each subdirectory corresponds to one prediction task (e.g. a classification task -and an object detection task). All of the files are explained in the next sections. - - -Prediction Tasks ----------------- -To let the Lightly Worker know what kind of prediction tasks you want to work with, the Lightly Worker -needs to know their names. It's very easy to let the Lightly Worker know which tasks exist: -simply add a `tasks.json` in your lightly bucket stored at the subdirectory `.lightly/predictions/`. - -The `tasks.json` file must include a list of your task names which must match name -of the subdirectory where your prediction schemas will be located. - -.. note:: - - Only the task names listed within `tasks.json` will be considered. - Please ensure that the task name corresponds with the location of your prediction schema. - This allows you to specify which subfolder are considered by the Lightly Worker. - -For example, let's say we are working with the following folder structure: - -.. code-block:: bash - - .lightly/predictions/ - + tasks.json - + classification_weather/ - + schema.json - ... - + classification_scenery/ - + schema.json - ... - + object_detection_people/ - + schema.json - ... - + semantic_segmentation_cars/ - + schema.json - ... - + some_directory_containing_irrelevant_things/ - - -we can specify which subfolders contain relevant predictions in the `tasks.json`: - -.. code-block:: javascript - :caption: .lightly/predictions/tasks.json - - [ - "classification_weather", - "classification_scenery", - "object_detection_people", - "semantic_segmentation_cars", - ] - -.. note:: - - If you list a subfolder which doesn't contain a valid `schema.json` file, - the Lightly Worker will report an error! See below how to create a good `schema.json` file. - - -Prediction Schema ------------------ -It's required to store a prediction schema. The schema defines the -format of the predictions and helps the Lightly Platform to correctly identify -and display classes. It also helps to prevent errors as all predictions which -are loaded are validated against this schema. - -Every schema must include the type of the predictions for this task. -For classification and object detection the prediction schema must also include -all the categories and their corresponding ids. For other tasks, such as keypoint -detection, it can be useful to store additional information like which keypoints -are connected with each other by an edge. - -You can provide all this information to the Lightly Worker by adding a `schema.json` to the -directory of the respective task. The schema.json file must have a key `categories` -with a corresponding list of categories following the COCO annotation format. -It must also have a key `task_type` indicating the type of the predictions. -The `task_type` must be one of: - - - classification - - object-detection - - semantic-segmentation - - -For example, let's say we are working with a classification model predicting the weather on an image. -The three classes are sunny, clouded, and rainy. - - -.. code-block:: javascript - :caption: .lightly/predictions/classification_weather/schema.json - - { - "task_type": "classification", - "categories": [ - { - "id": 0, - "name": "sunny" - }, - { - "id": 1, - "name": "clouded" - }, - { - "id": 2, - "name": "rainy" - } - ] - } - - - -Prediction Files ----------------- -The Lightly Worker requires a **single prediction file per image**. The file should be a .json -following the format defined under :ref:`prediction-format` and stored in the subdirectory -`.lightly/predictions/${TASK_NAME}` in the storage bucket the dataset was configured with. -In order to make sure the Lightly Worker can match the predictions to the correct source image, -it's necessary to follow the naming convention: - -.. code-block:: bash - - # filename of the prediction for image input/bucket/FILENAME.EXT - .lightly/predictions/${TASK_NAME}/${FILENAME}.json - - # example: input/bucket/subdir/image_1.png, classification - .lightly/predictions/my_classification_task/subdir/image_1.json - - # example: input/bucket/image_0.png, classification - .lightly/predictions/my_classification_task/image_0.json - - -.. _prediction-files-for-videos: - -Prediction Files for Videos ---------------------------- -When working with videos, the Lightly Worker requires a prediction file per frame. Lightly -uses a naming convention to identify frames: The filename of a frame consists of -the video filename, the video format, and the frame number (padded to the length -of the number of frames in the video) separated by hyphens. For example, for a -video with 200 frames, the frame number will be padded to length three. For a video -with 1000 frames, the frame number will be padded to length four (99 becomes 0099). - -.. code-block:: bash - - # filename of the predictions of the Xth frame of video input/bucket/FILENAME.EXT - # with 200 frames (padding: len(str(200)) = 3) - .lightly/predictions/${TASK_NAME}/${FILENAME}-${X:03d}-${EXT}.json - - # example: input/bucket/subdir/video_1.mp4, frame 99/200 - .lightly/predictions/my_classification_task/subdir/video_1-099-mp4.json - - # example: input/bucket/video_0.mp4, frame 99/200 - .lightly/predictions/my_classification_task/video_0-099-mp4.json - -See :ref:`creating-prediction-files-for-videos` on how to extract video frames -and create predictions using `ffmpeg `_ or Python. - -.. _prediction-format: - -Prediction Format ------------------ -Predictions for an image must have a `file_name` and `predictions`. -Here, `file_name` serves as a unique identifier to retrieve the image for which -the predictions are made and predictions is a list of `Prediction Singletons` for the corresponding task. - -- :code:`probabilities` are the per class probabilities of the prediction -- :code:`score` is the final prediction score/confidence - -.. note:: Some frameworks only provide the score as the model output. - The score is typically calculated during the Non-Max Suppression (NMS) - by multiplying the objectness probability with the highest class probability. - - But having not only a single score, but also the class probabilities - can be valuable information for - active learning. For example, an object detection model could have a - score of `0.6` and the predicted class is a tree. However, without - class probabilities, we cannot know what the prediction margin or - entropy is. With the class - probabilities we would additionally know whether the model thought - that it's `0.5` tree, `0.4` person and `0.1` car or `0.5` tree, - `0.25` person and `0.25` car. - - - -Example classification: - -.. code-block:: javascript - :caption: .lightly/predictions/classification_weather/subdir/image_1.json - - { - "file_name": "subdir/image_1.png", - "predictions": [ // classes: [sunny, clouded, rainy] - { - "category_id": 0, - "probabilities": [0.8, 0.1, 0.1] - } - ] - } - -Example object detection: - -.. code-block:: javascript - :caption: .lightly/predictions/object_detection/subdir/image_1.json - - { - "file_name": "subdir/image_1.png", - "predictions": [ // classes: [person, car] - { - "category_id": 0, - "bbox": [140, 100, 80, 90], // x, y, w, h coordinates in pixels - "score": 0.8, - "probabilities": [0.2, 0.8] // optional, sum up to 1.0 - }, - { - "category_id": 1, - "bbox": [...], - "score": 0.9, - "probabilities": [0.9, 0.1] // optional, sum up to 1.0 - }, - { - "category_id": 0, - "bbox": [...], - "score": 0.5, - "probabilities": [0.6, 0.4] // optional, sum up to 1.0 - } - ] - } - -Example semantic segmentation: - -.. code-block:: javascript - :caption: .lightly/predictions/semantic_segmentation_cars/subdir/image_1.json - - { - "file_name": "subdir/image_1.png", - "predictions": [ // classes: [background, car, tree] - { - "category_id": 0, - "segmentation": [100, 80, 90, 85, ...], //run length encoded binary segmentation mask - "score": 0.8, - "probabilities": [0.15, 0.8, 0.05] // optional, sum up to 1.0 - }, - { - "category_id": 1, - "segmentation": [...], - "score": 0.9, - "probabilities": [0.02, 0.08, 0.9] // optional, sum up to 1.0 - }, - ] - } - -Note: The filename should always be the relative path from the root directory. - - -Prediction Singletons ---------------------- -The prediction singletons closely follow the `COCO results `_ format while dropping -the `image_id`. Note the the `category_id` must be the same as the one defined -in the schema and that the probabilities (if provided) must follow the order of the category ids. - -**Classification:** - -For classification, please use the following format: - -.. code-block:: javascript - - [{ - "category_id" : int, - "probabilities" : [p0, p1, ..., pN] // optional, sum up to 1.0 - }] - -**Object Detection:** - -For detection with bounding boxes, please use the following format: - -.. code-block:: javascript - - [{ - "category_id" : int, - "bbox" : [x, y, width, height], // coordinates in pixels - "score" : float, - "probabilities" : [p0, p1, ..., pN] // optional, sum up to 1.0 - }] - -The bounding box format follows the `COCO results `_ documentation. - -.. note:: - - Bounding Box coordinates are pixels measured from the top left image corner. - -**Semantic Segmentation:** - -For semantic segmentation, please use the following format: - -.. code-block:: javascript - - [{ - "category_id" : int, - "segmentation" : [int, int, ...], // run length encoded binary segmentation mask - "score" : float, - "probabilities" : [p0, p1, ..., pN] // optional, sum up to 1.0 - }] - -Each segmentation prediction contains the binary mask for one category and a -corresponding score. The score determines the likelihood of the segmentation -belonging to that category. Optionally, a list of probabilities can be provided -containing a probability for each category, indicating the likeliness that the -segment belongs to that category. - -To kickstart using the Lightly Worker with semantic segmentation predictions we created an -example script that takes model predictions and converts them to the correct -format :download:`semantic_segmentation_inference.py ` - -Segmentations are defined with binary masks where each pixel is either set to 0 -or 1 if it belongs to the background or the object, respectively. -The segmentation masks are compressed using run length encoding to reduce file size. -Binary segmentation masks can be converted to the required format using the -following function: - -.. code-block:: python - - import numpy as np - - def encode(binary_mask): - """Encodes a (H, W) binary segmentation mask with run length encoding. - - The run length encoding is an array with counts of subsequent 0s and 1s - in the binary mask. The first value in the array is always the count of - initial 0s. - - Examples: - - >>> binary_mask = [ - >>> [0, 0, 1, 1], - >>> [0, 1, 1, 1], - >>> [0, 0, 0, 1], - >>> ] - >>> encode(binary_mask) - [2, 2, 1, 3, 3, 1] - """ - flat = np.concatenate(([-1], np.ravel(binary_mask), [-1])) - borders = np.nonzero(np.diff(flat))[0] - rle = np.diff(borders) - if flat[1]: - rle = np.concatenate(([0], rle)) - return rle.tolist() - -Segmentation models oftentimes output a probability for each pixel and category. -Storing such probabilities can quickly result in large file sizes if the input -images have a high resolution. To reduce storage requirements, Lightly Worker expects -only a single score or probability per segmentation. If you have scores or -probabilities for each pixel in the image, you have to first aggregate them -into a single score/probability. We recommend to take either the median or mean -score/probability over all pixels within the segmentation mask. The example -below shows how pixelwise segmentation predictions can be converted to the -format required by the Lightly Worker. - -.. code-block:: python - - # Make prediction for a single image. The output is assumed to be a tensor - # with shape (categories, height, width). - segmentation = model(image) - - # Most probable object category per pixel. - category = segmentation.argmax(dim=0) - - # Convert to lightly predictions. - predictions = [] - for category_id in category.unique(): - binary_mask = category == category_id - median_score = segmentation[category_id, binary_mask].median() - predictions.append({ - 'category_id': int(category_id), - 'segmentation': encode(binary_mask), - 'score': float(median_score), - }) - - prediction = { - 'file_name': 'subdir/image_name.png', - 'predictions': predictions, - } - - -.. note:: - - Support for keypoint detection is coming soon! - - - -Creating the predictions folder from COCO ------------------------------------------ - -For creating the predictions folder, we recommend writing a script that takes your predictions and -saves them in the format just outlined. You can either save the predictions first on your local machine -and then upload them to your datasource or save them directly to your datasource. - -As an example, the following script takes an object detection `COCO predictions file `_. -It needs the path to the predictions file and the lightly directory -where the `.lightly` folder should be created as input. -Don't forget to change these 2 parameters at the top of the script. - -.. code-block:: python - - ### CHANGE THESE PARAMETERS - output_filepath = "/path/to/create/.lightly/dir" - annotation_filepath = "/path/to/_annotations.coco.json" - - ### Optionally change these parameters - task_name = "my_object_detection_task" - task_type = "object-detection" - - import json - import os - from pathlib import Path - - # create prediction directory - path_predictions = os.path.join(output_filepath, '.lightly/predictions') - Path(path_predictions).mkdir(exist_ok=True, parents=True) - - # Create task.json - path_task_json = os.path.join(path_predictions, 'tasks.json') - tasks = [task_name] - with open(path_task_json, 'w') as f: - json.dump(tasks, f) - - # read coco annotations - with open(annotation_filepath, 'r') as f: - coco_dict = json.load(f) - - # Create schema.json for task - path_predictions_task = os.path.join(path_predictions, tasks[0]) - Path(path_predictions_task).mkdir(exist_ok=True) - schema = { - "task_type": task_type, - "categories": coco_dict['categories'] - } - path_schema_json = os.path.join(path_predictions_task, 'schema.json') - with open(path_schema_json, 'w') as f: - json.dump(schema, f) - - # Create predictions themselves - image_id_to_prediction = dict() - for image in coco_dict['images']: - prediction = { - 'file_name': image['file_name'], - 'predictions': [], - } - image_id_to_prediction[image['id']] = prediction - for ann in coco_dict['annotations']: - pred = { - 'category_id': ann['category_id'], - 'bbox': ann['bbox'], - 'score': ann.get('score', 0) - } - image_id_to_prediction[ann['image_id']]['predictions'].append(pred) - - for prediction in image_id_to_prediction.values(): - filename_prediction = os.path.splitext(prediction['file_name'])[0] + '.json' - path_to_prediction = os.path.join(path_predictions_task, filename_prediction) - with open(path_to_prediction, 'w') as f: - json.dump(prediction, f) - - -.. _creating-prediction-files-for-videos: - -Creating Prediction Files for Videos -------------------------------------- - -The Lightly Worker expects one prediction file per frame in a video. Predictions can be -created following the Python example code below. Make sure that `PyAV `_ -is installed on your system for it to work correctly. - -.. literalinclude:: code_examples/python_create_frame_predictions.py - -.. warning:: - - It is discouraged to use another library than `PyAV `_ - for loading videos with Python as the order and number of loaded frames - might differ. - - -Extracting Frames with FFMPEG -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Alternatively, frames can also first be extracted as images with `ffmpeg `_ -and then further processed by any prediction model that supports images. -The example command below shows how to extract frames and save them with the -:ref:`filename expected by Lightly `. Make sure that -`ffmpeg `_ is installed on your system before running the -command. - -.. code:: bash - - VIDEO=video.mp4; NUM_FRAMES=$(ffprobe -v error -select_streams v:0 -count_frames -show_entries stream=nb_read_frames -of csv=p=0 ${VIDEO}); ffmpeg -r 1 -i ${VIDEO} -start_number 0 ${VIDEO%.mp4}-%0${#NUM_FRAMES}d-mp4.png - - # results in the following file structure - . - ├── video.mp4 - ├── video-000-mp4.png - ├── video-001-mp4.png - ├── video-002-mp4.png - ├── video-003-mp4.png - └── ... diff --git a/docs/source/docker/advanced/images/bdd100k_demo_metadata.jpg b/docs/source/docker/advanced/images/bdd100k_demo_metadata.jpg deleted file mode 100644 index 552140d0d9a72dd426e474adef58ba5a9c886e0e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 140542 zcmeFZ1z4O*x-Rx1PJcZ5ZnU^?!lq4009C7cWZ91 znYpvp%$a-l%syw!c{bfoK~;7ARrS3k|7TVAbNAl?97S0LSpW_W4lsp%f%{dQQyE`7 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