From e7740208a7f3ed2a9edfd435606f6e6b48cff9bb Mon Sep 17 00:00:00 2001 From: jpcbertoldo <24547377+jpcbertoldo@users.noreply.github.com> Date: Sun, 20 Oct 2024 17:10:19 +0200 Subject: [PATCH 1/4] initial draft Signed-off-by: jpcbertoldo <24547377+jpcbertoldo@users.noreply.github.com> --- .../700_metrics/701e_aupimo_advanced_iv.ipynb | 372 +++++++----------- src/anomalib/metrics/pimo/__init__.py | 9 + src/anomalib/metrics/pimo/utils_benchmark.py | 172 ++++++++ 3 files changed, 321 insertions(+), 232 deletions(-) create mode 100644 src/anomalib/metrics/pimo/utils_benchmark.py diff --git a/notebooks/700_metrics/701e_aupimo_advanced_iv.ipynb b/notebooks/700_metrics/701e_aupimo_advanced_iv.ipynb index e117006951..fbd1a68a64 100644 --- a/notebooks/700_metrics/701e_aupimo_advanced_iv.ipynb +++ b/notebooks/700_metrics/701e_aupimo_advanced_iv.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# AUPIMO statistical comparison between two models\n", + "# [TO BE REVIEWED] AUPIMO statistical comparison between two models\n", "\n", "Model A has a higher average AUPIMO than model B. Can you be _sure_ that A is better than B? \n", "\n", @@ -66,9 +66,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing /home/jcasagrandebertoldo/repos/anomalib-dev\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: omegaconf>=2.1.1 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from anomalib==1.2.0.dev0) (2.3.0)\n", + "Requirement already satisfied: rich>=13.5.2 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from anomalib==1.2.0.dev0) (13.7.1)\n", + "Requirement already satisfied: jsonargparse>=4.27.7 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from jsonargparse[signatures]>=4.27.7->anomalib==1.2.0.dev0) (4.32.0)\n", + "Requirement already satisfied: docstring-parser in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from anomalib==1.2.0.dev0) (0.16)\n", + "Requirement already satisfied: rich-argparse in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from anomalib==1.2.0.dev0) (1.5.2)\n", + "Requirement already satisfied: PyYAML>=3.13 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from jsonargparse>=4.27.7->jsonargparse[signatures]>=4.27.7->anomalib==1.2.0.dev0) (6.0.2)\n", + "Requirement already satisfied: typeshed-client>=2.1.0 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from jsonargparse[signatures]>=4.27.7->anomalib==1.2.0.dev0) (2.7.0)\n", + "Requirement already satisfied: antlr4-python3-runtime==4.9.* in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from omegaconf>=2.1.1->anomalib==1.2.0.dev0) (4.9.3)\n", + "Requirement already satisfied: markdown-it-py>=2.2.0 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from rich>=13.5.2->anomalib==1.2.0.dev0) (3.0.0)\n", + "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from rich>=13.5.2->anomalib==1.2.0.dev0) (2.18.0)\n", + "Requirement already satisfied: mdurl~=0.1 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich>=13.5.2->anomalib==1.2.0.dev0) (0.1.2)\n", + "Requirement already satisfied: importlib-resources>=1.4.0 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from typeshed-client>=2.1.0->jsonargparse[signatures]>=4.27.7->anomalib==1.2.0.dev0) (6.4.4)\n", + "Requirement already satisfied: typing-extensions>=4.5.0 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from typeshed-client>=2.1.0->jsonargparse[signatures]>=4.27.7->anomalib==1.2.0.dev0) (4.11.0)\n", + "Building wheels for collected packages: anomalib\n", + " Building wheel for anomalib (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for anomalib: filename=anomalib-1.2.0.dev0-py3-none-any.whl size=491631 sha256=3f0069a35f2e1e2a41e3394bd582457bdb759bb154275799f12f8189b5f5954b\n", + " Stored in directory: /home/jcasagrandebertoldo/.cache/pip/wheels/bd/3b/91/961b3d37cb837fd176783f27cbecacee412bc3c5a35cd76b36\n", + "Successfully built anomalib\n", + "Installing collected packages: anomalib\n", + " Attempting uninstall: anomalib\n", + " Found existing installation: anomalib 1.2.0.dev0\n", + " Uninstalling anomalib-1.2.0.dev0:\n", + " Successfully uninstalled anomalib-1.2.0.dev0\n", + "Successfully installed anomalib-1.2.0.dev0\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ "# TODO(jpcbertoldo): replace by `pip install anomalib` when AUPIMO is released # noqa: TD003\n", "%pip install ../.." @@ -83,12 +119,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages/kornia/feature/lightglue.py:44: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n", + " @torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)\n" + ] + } + ], "source": [ "import json\n", - "import urllib.request\n", "from pathlib import Path\n", "\n", "import numpy as np\n", @@ -98,7 +142,11 @@ "from matplotlib.ticker import FixedLocator, IndexLocator, MaxNLocator, PercentFormatter\n", "from scipy import stats\n", "\n", - "from anomalib.metrics.pimo import AUPIMOResult" + "from anomalib.metrics.pimo import (\n", + " get_benchmark_aupimo_scores,\n", + " load_aupimo_result_from_json_dict,\n", + " save_aupimo_result_to_json_dict,\n", + ")" ] }, { @@ -134,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -153,121 +201,6 @@ } ], "source": [ - "def get_benchmark_scores_url(model: str, dataset: str) -> str:\n", - " \"\"\"Generate the URL for the JSON file of a specific model and dataset.\"\"\"\n", - " root_url = \"https://raw.githubusercontent.com/jpcbertoldo/aupimo/refs/heads/main/data/experiments/benchmark\"\n", - " models = {\n", - " \"efficientad_wr101_m_ext\",\n", - " \"efficientad_wr101_s_ext\",\n", - " \"fastflow_cait_m48_448\",\n", - " \"fastflow_wr50\",\n", - " \"padim_r18\",\n", - " \"padim_wr50\",\n", - " \"patchcore_wr101\",\n", - " \"patchcore_wr50\",\n", - " \"pyramidflow_fnf_ext\",\n", - " \"pyramidflow_r18_ext\",\n", - " \"rd++_wr50_ext\",\n", - " \"simplenet_wr50_ext\",\n", - " \"uflow_ext\",\n", - " }\n", - " if model not in models:\n", - " msg = f\"Model '{model}' not available. Choose one of {sorted(models)}.\"\n", - " raise ValueError(msg)\n", - " datasets = {\n", - " \"mvtec/bottle\",\n", - " \"mvtec/cable\",\n", - " \"mvtec/capsule\",\n", - " \"mvtec/carpet\",\n", - " \"mvtec/grid\",\n", - " \"mvtec/hazelnut\",\n", - " \"mvtec/leather\",\n", - " \"mvtec/metal_nut\",\n", - " \"mvtec/pill\",\n", - " \"mvtec/screw\",\n", - " \"mvtec/tile\",\n", - " \"mvtec/toothbrush\",\n", - " \"mvtec/transistor\",\n", - " \"mvtec/wood\",\n", - " \"mvtec/zipper\",\n", - " \"visa/candle\",\n", - " \"visa/capsules\",\n", - " \"visa/cashew\",\n", - " \"visa/chewinggum\",\n", - " \"visa/fryum\",\n", - " \"visa/macaroni1\",\n", - " \"visa/macaroni2\",\n", - " \"visa/pcb1\",\n", - " \"visa/pcb2\",\n", - " \"visa/pcb3\",\n", - " \"visa/pcb4\",\n", - " \"visa/pipe_fryum\",\n", - " }\n", - " if dataset not in datasets:\n", - " msg = f\"Dataset '{dataset}' not available. Choose one of {sorted(datasets)}.\"\n", - " raise ValueError(msg)\n", - " return f\"{root_url}/{model}/{dataset}/aupimo/aupimos.json\"\n", - "\n", - "\n", - "def download_json(url_str: str) -> dict[str, str | float | int | list[str]]:\n", - " \"\"\"Download the JSON content from an URL.\"\"\"\n", - " with urllib.request.urlopen(url_str) as url: # noqa: S310\n", - " return json.load(url)\n", - "\n", - "\n", - "def load_aupimo_result_from_json_dict(payload: dict[str, str | float | int | list[str]]) -> AUPIMOResult:\n", - " \"\"\"Convert the JSON payload to an AUPIMOResult dataclass.\"\"\"\n", - " if not isinstance(payload, dict):\n", - " msg = f\"Invalid payload. Must be a dictionary. Got {type(payload)}.\"\n", - " raise TypeError(msg)\n", - " try:\n", - " return AUPIMOResult(\n", - " fpr_lower_bound=payload[\"fpr_lower_bound\"],\n", - " fpr_upper_bound=payload[\"fpr_upper_bound\"],\n", - " # `num_threshs` vs `num_thresholds` is an inconsistency with an older version of the JSON file\n", - " num_thresholds=payload[\"num_threshs\"] if \"num_threshs\" in payload else payload[\"num_thresholds\"],\n", - " thresh_lower_bound=payload[\"thresh_lower_bound\"],\n", - " thresh_upper_bound=payload[\"thresh_upper_bound\"],\n", - " aupimos=torch.tensor(payload[\"aupimos\"], dtype=torch.float64),\n", - " )\n", - "\n", - " except KeyError as ex:\n", - " msg = f\"Invalid payload. Missing key {ex}.\"\n", - " raise ValueError(msg) from ex\n", - "\n", - " except (TypeError, ValueError) as ex:\n", - " msg = f\"Invalid payload. Cause: {ex}.\"\n", - " raise ValueError(msg) from ex\n", - "\n", - "\n", - "def get_benchmark_aupimo_scores(model: str, dataset: str, verbose: bool = True) -> AUPIMOResult:\n", - " \"\"\"Get the benchmark AUPIMO scores for a specific model and dataset.\n", - "\n", - " Args:\n", - " model: The model name. See `_get_json_url` for the available models.\n", - " dataset: The \"collection/dataset\", where 'collection' is either 'mvtec' or 'visa', and 'dataset' is\n", - " the name of the dataset within the collection. See `_get_json_url` for the available datasets.\n", - " verbose: Whether to print the progress.\n", - "\n", - " Returns:\n", - " A `AUPIMOResult` dataclass with the AUPIMO scores from the benchmark results.\n", - "\n", - " More details in our paper: https://arxiv.org/abs/2401.01984\n", - " \"\"\"\n", - " if verbose:\n", - " print(f\"Loading benchmark results for model '{model}' and dataset '{dataset}'\")\n", - " url = get_benchmark_scores_url(model, dataset)\n", - " if verbose:\n", - " print(f\"Dowloading JSON file from {url}\")\n", - " payload = download_json(url)\n", - " if verbose:\n", - " print(\"Converting payload to dataclass\")\n", - " aupimo_result = load_aupimo_result_from_json_dict(payload)\n", - " if verbose:\n", - " print(\"Done!\")\n", - " return payload, aupimo_result\n", - "\n", - "\n", "json_model_a, aupimo_result_model_a = get_benchmark_aupimo_scores(\"patchcore_wr101\", \"mvtec/capsule\")\n", "_, aupimo_result_model_b = get_benchmark_aupimo_scores(\"patchcore_wr50\", \"mvtec/capsule\")" ] @@ -841,38 +774,13 @@ } ], "source": [ - "def save_aupimo_result_to_json_dict(\n", - " aupimo_result: AUPIMOResult,\n", - " paths: list[str | Path] | None = None,\n", - ") -> dict[str, str | float | int | list[str]]:\n", - " \"\"\"Convert the AUPIMOResult dataclass to a JSON payload.\"\"\"\n", - " payload = {\n", - " \"fpr_lower_bound\": aupimo_result.fpr_lower_bound,\n", - " \"fpr_upper_bound\": aupimo_result.fpr_upper_bound,\n", - " \"num_thresholds\": aupimo_result.num_thresholds,\n", - " \"thresh_lower_bound\": aupimo_result.thresh_lower_bound,\n", - " \"thresh_upper_bound\": aupimo_result.thresh_upper_bound,\n", - " \"aupimos\": aupimo_result.aupimos.tolist(),\n", - " }\n", - " if paths is not None:\n", - " if len(paths) != aupimo_result.aupimos.shape[0]:\n", - " msg = (\n", - " \"Invalid paths. It must have the same length as the AUPIMO scores. \"\n", - " f\"Got {len(paths)} paths and {aupimo_result.aupimos.shape[0]} scores.\"\n", - " )\n", - " raise ValueError(msg)\n", - " # make sure the paths are strings, not pathlib.Path objects\n", - " payload[\"paths\"] = [str(p) for p in paths]\n", - " return payload\n", - "\n", - "\n", "payload = save_aupimo_result_to_json_dict(aupimo_result_model_a)\n", "print(f\"{payload.keys()=}\")" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -890,7 +798,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -910,14 +818,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "8,0K\t/tmp/tmpsuauy_de/aupimo_result.json\n" + "8,0K\t/tmp/tmpwhpnd7x_/aupimo_result.json\n" ] } ], @@ -954,7 +862,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1108,7 +1016,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1157,7 +1065,7 @@ "0 A B 0.995 0.005 2.872" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1169,7 +1077,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1218,7 +1126,7 @@ "0 A B 0.998 0.002 1965.500" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1230,7 +1138,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1279,7 +1187,7 @@ "0 A B 1.000 0.000 1823.000" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1302,126 +1210,126 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", - "\n", + "
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 modelamodelbconfidencepvaluestatisticmodelamodelbconfidencepvaluestatistic
0efficientad_wr101_s_extpatchcore_wr1010.9994020.0005981580.0000000efficientad_wr101_s_extpatchcore_wr1010.9994020.0005981580.000000
1efficientad_wr101_s_extrd++_wr50_ext0.7736590.2263412193.5000001efficientad_wr101_s_extrd++_wr50_ext0.7736590.2263412193.500000
2efficientad_wr101_s_extsimplenet_wr50_ext1.0000000.000000690.5000002efficientad_wr101_s_extsimplenet_wr50_ext1.0000000.000000690.500000
3efficientad_wr101_s_extuflow_ext0.9994470.0005531550.5000003efficientad_wr101_s_extuflow_ext0.9994470.0005531550.500000
4patchcore_wr101rd++_wr50_ext0.9999800.0000201333.0000004patchcore_wr101rd++_wr50_ext0.9999800.0000201333.000000
5patchcore_wr101simplenet_wr50_ext1.0000000.000000351.5000005patchcore_wr101simplenet_wr50_ext1.0000000.000000351.500000
6patchcore_wr101uflow_ext0.7318750.2681252213.0000006patchcore_wr101uflow_ext0.7318750.2681252213.000000
7rd++_wr50_extsimplenet_wr50_ext1.0000000.000000967.0000007rd++_wr50_extsimplenet_wr50_ext1.0000000.000000967.000000
8rd++_wr50_extuflow_ext0.9999450.0000551383.0000008rd++_wr50_extuflow_ext0.9999450.0000551383.000000
9simplenet_wr50_extuflow_ext1.0000000.000000318.5000009simplenet_wr50_extuflow_ext1.0000000.000000318.500000
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1452,7 +1360,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ diff --git a/src/anomalib/metrics/pimo/__init__.py b/src/anomalib/metrics/pimo/__init__.py index 174f546e4d..1ddf8ca9ba 100644 --- a/src/anomalib/metrics/pimo/__init__.py +++ b/src/anomalib/metrics/pimo/__init__.py @@ -9,6 +9,11 @@ from .binary_classification_curve import ThresholdMethod from .pimo import AUPIMO, PIMO, AUPIMOResult, PIMOResult +from .utils_benchmark import ( + get_benchmark_aupimo_scores, + load_aupimo_result_from_json_dict, + save_aupimo_result_to_json_dict, +) __all__ = [ # constants @@ -20,4 +25,8 @@ "PIMO", "AUPIMO", "StatsOutliersPolicy", + # utils_benchmark + "get_benchmark_aupimo_scores", + "load_aupimo_result_from_json_dict", + "save_aupimo_result_to_json_dict", ] diff --git a/src/anomalib/metrics/pimo/utils_benchmark.py b/src/anomalib/metrics/pimo/utils_benchmark.py new file mode 100644 index 0000000000..09fd210af0 --- /dev/null +++ b/src/anomalib/metrics/pimo/utils_benchmark.py @@ -0,0 +1,172 @@ +"""Utility functions to compare AUPIMO scores with the benchmark results from AUPIMO's official repository.""" + +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +import json +import logging +import urllib.request +from pathlib import Path + +import torch + +from .dataclasses import AUPIMOResult + +logger = logging.getLogger(__name__) + + +def _get_benchmark_scores_url(model: str, dataset: str) -> str: + """Generate the URL for the JSON file of a specific model and dataset. + + Args: + model: The model name. See `_get_json_url` for the available models. + Available models: https://github.com/jpcbertoldo/aupimo/tree/main/data/experiments/benchmark + dataset: The "collection/dataset", where 'collection' is either 'mvtec' or 'visa', and 'dataset' is + the name of the dataset within the collection (lowercase, words split by '_'). + Available datasets: + https://github.com/jpcbertoldo/aupimo/tree/main/data/experiments/benchmark/efficientad_wr101_m_ext + Returns: + The URL for the JSON file of the model and dataset in the benchmark from AUPIMO's official repository. + Reference: https://github.com/jpcbertoldo/aupimo + """ + root_url = "https://raw.githubusercontent.com/jpcbertoldo/aupimo/refs/heads/main/data/experiments/benchmark" + models = { + "efficientad_wr101_m_ext", + "efficientad_wr101_s_ext", + "fastflow_cait_m48_448", + "fastflow_wr50", + "padim_r18", + "padim_wr50", + "patchcore_wr101", + "patchcore_wr50", + "pyramidflow_fnf_ext", + "pyramidflow_r18_ext", + "rd++_wr50_ext", + "simplenet_wr50_ext", + "uflow_ext", + } + if model not in models: + msg = f"Model '{model}' not available. Choose one of {sorted(models)}." + raise ValueError(msg) + datasets = { + "mvtec/bottle", + "mvtec/cable", + "mvtec/capsule", + "mvtec/carpet", + "mvtec/grid", + "mvtec/hazelnut", + "mvtec/leather", + "mvtec/metal_nut", + "mvtec/pill", + "mvtec/screw", + "mvtec/tile", + "mvtec/toothbrush", + "mvtec/transistor", + "mvtec/wood", + "mvtec/zipper", + "visa/candle", + "visa/capsules", + "visa/cashew", + "visa/chewinggum", + "visa/fryum", + "visa/macaroni1", + "visa/macaroni2", + "visa/pcb1", + "visa/pcb2", + "visa/pcb3", + "visa/pcb4", + "visa/pipe_fryum", + } + if dataset not in datasets: + msg = f"Dataset '{dataset}' not available. Choose one of {sorted(datasets)}." + raise ValueError(msg) + return f"{root_url}/{model}/{dataset}/aupimo/aupimos.json" + + +def _download_json(url_str: str) -> dict[str, str | float | int | list[str]]: + """Download the JSON content from an URL.""" + with urllib.request.urlopen(url_str) as url: # noqa: S310 + return json.load(url) + + +def load_aupimo_result_from_json_dict(payload: dict) -> AUPIMOResult: + """Convert the JSON payload to an AUPIMOResult dataclass.""" + if not isinstance(payload, dict): + msg = f"Invalid payload. Must be a dictionary. Got {type(payload)}." + raise TypeError(msg) + try: + # `num_threshs` vs `num_thresholds` is an inconsistency with an older version of the JSON file + num_thresholds: int | None = payload["num_threshs"] if "num_threshs" in payload else payload["num_thresholds"] + return AUPIMOResult( + fpr_lower_bound=float(payload["fpr_lower_bound"]), + fpr_upper_bound=float(payload["fpr_upper_bound"]), + num_thresholds=num_thresholds if num_thresholds is None else int(num_thresholds), + thresh_lower_bound=float(payload["thresh_lower_bound"]), + thresh_upper_bound=float(payload["thresh_upper_bound"]), + aupimos=torch.tensor(payload["aupimos"], dtype=torch.float64), + ) + + except KeyError as ex: + msg = f"Invalid payload. Missing key {ex}." + raise ValueError(msg) from ex + + except (TypeError, ValueError) as ex: + msg = f"Invalid payload. Cause: {ex}." + raise ValueError(msg) from ex + + +def get_benchmark_aupimo_scores( + model: str, + dataset: str, + verbose: bool = True, +) -> tuple[dict[str, str | float | int | list[str]], AUPIMOResult]: + """Get the benchmark AUPIMO scores for a specific model and dataset. + + Args: + model: The model name. See `_get_json_url` for the available models. + dataset: The "collection/dataset", where 'collection' is either 'mvtec' or 'visa', and 'dataset' is + the name of the dataset within the collection. See `_get_json_url` for the available datasets. + verbose: Whether to logger.debug the progress. + + Returns: + A `AUPIMOResult` dataclass with the AUPIMO scores from the benchmark results. + + More details in our paper: https://arxiv.org/abs/2401.01984 + """ + if verbose: + logger.debug(f"Loading benchmark results for model '{model}' and dataset '{dataset}'") + url = _get_benchmark_scores_url(model, dataset) + if verbose: + logger.debug(f"Dowloading JSON file from {url}") + payload = _download_json(url) + if verbose: + logger.debug("Converting payload to dataclass") + aupimo_result = load_aupimo_result_from_json_dict(payload) + if verbose: + logger.debug("Done!") + return payload, aupimo_result + + +def save_aupimo_result_to_json_dict( + aupimo_result: AUPIMOResult, + paths: list[str | Path] | None = None, +) -> dict[str, str | float | int | list[str]]: + """Convert the AUPIMOResult dataclass to a JSON payload.""" + payload = { + "fpr_lower_bound": aupimo_result.fpr_lower_bound, + "fpr_upper_bound": aupimo_result.fpr_upper_bound, + "num_thresholds": aupimo_result.num_thresholds, + "thresh_lower_bound": aupimo_result.thresh_lower_bound, + "thresh_upper_bound": aupimo_result.thresh_upper_bound, + "aupimos": aupimo_result.aupimos.tolist(), + } + if paths is not None: + if len(paths) != aupimo_result.aupimos.shape[0]: + msg = ( + "Invalid paths. It must have the same length as the AUPIMO scores. " + f"Got {len(paths)} paths and {aupimo_result.aupimos.shape[0]} scores." + ) + raise ValueError(msg) + # make sure the paths are strings, not pathlib.Path objects + payload["paths"] = [str(p) for p in paths] + return payload From 0c4fecdd9fec80a7149e24b7d674e23cce8dc80e Mon Sep 17 00:00:00 2001 From: jpcbertoldo <24547377+jpcbertoldo@users.noreply.github.com> Date: Mon, 21 Oct 2024 15:09:43 +0200 Subject: [PATCH 2/4] refactor utils benchmark Signed-off-by: jpcbertoldo <24547377+jpcbertoldo@users.noreply.github.com> --- src/anomalib/metrics/pimo/__init__.py | 18 +- src/anomalib/metrics/pimo/utils_benchmark.py | 360 ++++++++++++++----- 2 files changed, 275 insertions(+), 103 deletions(-) diff --git a/src/anomalib/metrics/pimo/__init__.py b/src/anomalib/metrics/pimo/__init__.py index 1ddf8ca9ba..f8c63a335c 100644 --- a/src/anomalib/metrics/pimo/__init__.py +++ b/src/anomalib/metrics/pimo/__init__.py @@ -10,9 +10,12 @@ from .binary_classification_curve import ThresholdMethod from .pimo import AUPIMO, PIMO, AUPIMOResult, PIMOResult from .utils_benchmark import ( - get_benchmark_aupimo_scores, - load_aupimo_result_from_json_dict, - save_aupimo_result_to_json_dict, + AUPIMO_BENCHMARK_DATASETS, + AUPIMO_BENCHMARK_MODELS, + aupimo_result_from_json_dict, + aupimo_result_to_json_dict, + download_aupimo_benchmark_scores, + get_aupimo_benchmark, ) __all__ = [ @@ -26,7 +29,10 @@ "AUPIMO", "StatsOutliersPolicy", # utils_benchmark - "get_benchmark_aupimo_scores", - "load_aupimo_result_from_json_dict", - "save_aupimo_result_to_json_dict", + "AUPIMO_BENCHMARK_DATASETS", + "AUPIMO_BENCHMARK_MODELS", + "aupimo_result_from_json_dict", + "aupimo_result_to_json_dict", + "download_aupimo_benchmark_scores", + "get_aupimo_benchmark", ] diff --git a/src/anomalib/metrics/pimo/utils_benchmark.py b/src/anomalib/metrics/pimo/utils_benchmark.py index 09fd210af0..aabdf3c4f4 100644 --- a/src/anomalib/metrics/pimo/utils_benchmark.py +++ b/src/anomalib/metrics/pimo/utils_benchmark.py @@ -1,96 +1,125 @@ -"""Utility functions to compare AUPIMO scores with the benchmark results from AUPIMO's official repository.""" +"""Utility functions to compare AUPIMO scores with the benchmark results from AUPIMO's official repository. + +Official repository: https://github.com/jpcbertoldo/aupimo +Benchmark data: https://github.com/jpcbertoldo/aupimo/tree/main/data/experiments/benchmark +""" # Copyright (C) 2024 Intel Corporation # SPDX-License-Identifier: Apache-2.0 import json import logging -import urllib.request +from concurrent.futures import ThreadPoolExecutor +from itertools import product from pathlib import Path +import pandas as pd +import requests import torch +from pandas import DataFrame from .dataclasses import AUPIMOResult logger = logging.getLogger(__name__) +AUPIMO_BENCHMARK_MODELS = { + "efficientad_wr101_m_ext", + "efficientad_wr101_s_ext", + "fastflow_cait_m48_448", + "fastflow_wr50", + "padim_r18", + "padim_wr50", + "patchcore_wr101", + "patchcore_wr50", + "pyramidflow_fnf_ext", + "pyramidflow_r18_ext", + "rd++_wr50_ext", + "simplenet_wr50_ext", + "uflow_ext", +} + +AUPIMO_BENCHMARK_DATASETS = { + "mvtec/bottle", + "mvtec/cable", + "mvtec/capsule", + "mvtec/carpet", + "mvtec/grid", + "mvtec/hazelnut", + "mvtec/leather", + "mvtec/metal_nut", + "mvtec/pill", + "mvtec/screw", + "mvtec/tile", + "mvtec/toothbrush", + "mvtec/transistor", + "mvtec/wood", + "mvtec/zipper", + "visa/candle", + "visa/capsules", + "visa/cashew", + "visa/chewinggum", + "visa/fryum", + "visa/macaroni1", + "visa/macaroni2", + "visa/pcb1", + "visa/pcb2", + "visa/pcb3", + "visa/pcb4", + "visa/pipe_fryum", +} + + +def _validate_benchmark_model(model: str) -> None: + if model not in AUPIMO_BENCHMARK_MODELS: + msg = f"Model '{model}' not available. Choose one of {sorted(AUPIMO_BENCHMARK_MODELS)}." + raise ValueError(msg) + + +def _validate_benchmark_dataset(dataset: str) -> None: + if dataset not in AUPIMO_BENCHMARK_DATASETS: + msg = f"Dataset '{dataset}' not available. Choose one of {sorted(AUPIMO_BENCHMARK_DATASETS)}." + raise ValueError(msg) + -def _get_benchmark_scores_url(model: str, dataset: str) -> str: +def _get_benchmark_json_url(model: str, dataset: str) -> str: """Generate the URL for the JSON file of a specific model and dataset. Args: - model: The model name. See `_get_json_url` for the available models. - Available models: https://github.com/jpcbertoldo/aupimo/tree/main/data/experiments/benchmark - dataset: The "collection/dataset", where 'collection' is either 'mvtec' or 'visa', and 'dataset' is - the name of the dataset within the collection (lowercase, words split by '_'). - Available datasets: - https://github.com/jpcbertoldo/aupimo/tree/main/data/experiments/benchmark/efficientad_wr101_m_ext + model: see `anomalib.metrics.pimo.AUPIMO_BENCHMARK_MODELS` + dataset: "collection/category", see `anomalib.metrics.pimo.AUPIMO_BENCHMARK_DATASETS` + Returns: The URL for the JSON file of the model and dataset in the benchmark from AUPIMO's official repository. - Reference: https://github.com/jpcbertoldo/aupimo """ root_url = "https://raw.githubusercontent.com/jpcbertoldo/aupimo/refs/heads/main/data/experiments/benchmark" - models = { - "efficientad_wr101_m_ext", - "efficientad_wr101_s_ext", - "fastflow_cait_m48_448", - "fastflow_wr50", - "padim_r18", - "padim_wr50", - "patchcore_wr101", - "patchcore_wr50", - "pyramidflow_fnf_ext", - "pyramidflow_r18_ext", - "rd++_wr50_ext", - "simplenet_wr50_ext", - "uflow_ext", - } - if model not in models: - msg = f"Model '{model}' not available. Choose one of {sorted(models)}." - raise ValueError(msg) - datasets = { - "mvtec/bottle", - "mvtec/cable", - "mvtec/capsule", - "mvtec/carpet", - "mvtec/grid", - "mvtec/hazelnut", - "mvtec/leather", - "mvtec/metal_nut", - "mvtec/pill", - "mvtec/screw", - "mvtec/tile", - "mvtec/toothbrush", - "mvtec/transistor", - "mvtec/wood", - "mvtec/zipper", - "visa/candle", - "visa/capsules", - "visa/cashew", - "visa/chewinggum", - "visa/fryum", - "visa/macaroni1", - "visa/macaroni2", - "visa/pcb1", - "visa/pcb2", - "visa/pcb3", - "visa/pcb4", - "visa/pipe_fryum", - } - if dataset not in datasets: - msg = f"Dataset '{dataset}' not available. Choose one of {sorted(datasets)}." - raise ValueError(msg) + _validate_benchmark_model(model) + _validate_benchmark_dataset(dataset) return f"{root_url}/{model}/{dataset}/aupimo/aupimos.json" -def _download_json(url_str: str) -> dict[str, str | float | int | list[str]]: +def _download_benchmark_json(url: str) -> dict: """Download the JSON content from an URL.""" - with urllib.request.urlopen(url_str) as url: # noqa: S310 - return json.load(url) + request = requests.get(url, timeout=10) + return json.loads(request.text) + +def aupimo_result_from_json_dict(payload: dict) -> AUPIMOResult: + """Convert the dictionary from a JSON payload to an AUPIMOResult dataclass. + + Args: + payload: The JSON from the benchmark results: + { + "fpr_lower_bound": float, + "fpr_upper_bound": float, + "num_thresholds": int | None, # or "num_threshs" + "thresh_lower_bound": float, + "thresh_upper_bound": float, + "aupimos": list[float], + } -def load_aupimo_result_from_json_dict(payload: dict) -> AUPIMOResult: - """Convert the JSON payload to an AUPIMOResult dataclass.""" + Returns: + An `anomalib.metrics.pimo.AUPIMOResult` dataclass. + """ if not isinstance(payload, dict): msg = f"Invalid payload. Must be a dictionary. Got {type(payload)}." raise TypeError(msg) @@ -115,43 +144,19 @@ def load_aupimo_result_from_json_dict(payload: dict) -> AUPIMOResult: raise ValueError(msg) from ex -def get_benchmark_aupimo_scores( - model: str, - dataset: str, - verbose: bool = True, -) -> tuple[dict[str, str | float | int | list[str]], AUPIMOResult]: - """Get the benchmark AUPIMO scores for a specific model and dataset. +def aupimo_result_to_json_dict( + aupimo_result: AUPIMOResult, + paths: list[str | Path] | None = None, +) -> dict: + """Convert the AUPIMOResult dataclass to a dictionary for JSON serialization. Args: - model: The model name. See `_get_json_url` for the available models. - dataset: The "collection/dataset", where 'collection' is either 'mvtec' or 'visa', and 'dataset' is - the name of the dataset within the collection. See `_get_json_url` for the available datasets. - verbose: Whether to logger.debug the progress. + aupimo_result: The AUPIMO scores from the benchmark results. + paths: The paths of the images used to compute the AUPIMO scores. Optional. Returns: - A `AUPIMOResult` dataclass with the AUPIMO scores from the benchmark results. - - More details in our paper: https://arxiv.org/abs/2401.01984 + A dictionary with the AUPIMO scores and the paths. """ - if verbose: - logger.debug(f"Loading benchmark results for model '{model}' and dataset '{dataset}'") - url = _get_benchmark_scores_url(model, dataset) - if verbose: - logger.debug(f"Dowloading JSON file from {url}") - payload = _download_json(url) - if verbose: - logger.debug("Converting payload to dataclass") - aupimo_result = load_aupimo_result_from_json_dict(payload) - if verbose: - logger.debug("Done!") - return payload, aupimo_result - - -def save_aupimo_result_to_json_dict( - aupimo_result: AUPIMOResult, - paths: list[str | Path] | None = None, -) -> dict[str, str | float | int | list[str]]: - """Convert the AUPIMOResult dataclass to a JSON payload.""" payload = { "fpr_lower_bound": aupimo_result.fpr_lower_bound, "fpr_upper_bound": aupimo_result.fpr_upper_bound, @@ -170,3 +175,164 @@ def save_aupimo_result_to_json_dict( # make sure the paths are strings, not pathlib.Path objects payload["paths"] = [str(p) for p in paths] return payload + + +def _download_aupimo_benchmark_scores(model: str, dataset: str) -> tuple[dict, AUPIMOResult]: + """Get the benchmark AUPIMO scores for a specific model and dataset from AUPIMO's official repository. + + Args: + model: see `anomalib.metrics.pimo.AUPIMO_BENCHMARK_MODELS` + dataset: "collection/category", see `anomalib.metrics.pimo.AUPIMO_BENCHMARK_DATASETS` + + Returns: + (dict, AUPIMOResult): A tuple with the JSON payload and the AUPIMO scores. + dict: The unserialized JSON from the benchmark results. See `aupimo_result_from_json_dict`. + AUPIMOResult: The AUPIMO scores in dataclass format. See `anomalib.metrics.pimo.AUPIMOResult`. + """ + logger.debug(f"Loading benchmark results for {model=} {dataset=}") + url = _get_benchmark_json_url(model, dataset) + logger.debug(f"Dowloading JSON from {url=}") + payload = _download_benchmark_json(url) + logger.debug("Converting json payload to dataclass") + aupimo_result = aupimo_result_from_json_dict(payload) + logger.debug(f"Done loading benchmark results for {model=} {dataset=}") + return payload, aupimo_result + + +def _download_aupimo_benchmark_scores_multithreaded( + model: str, + dataset: str, +) -> tuple[tuple[str, str], tuple[dict, AUPIMOResult]]: + """Do the same as `_download_aupimo_benchmark_scores` but return the job's arguments as well.""" + return (model, dataset), _download_aupimo_benchmark_scores(model, dataset) + + +def download_aupimo_benchmark_scores( + model: str | None, + dataset: str | None, + avoid_multithread_download: bool = False, +) -> dict[tuple[str, str], tuple[dict, AUPIMOResult]]: + """Dowload AUPIMO scores AUPIMO's paper benchmark (stored in the official repository). + + If `model` is None, all models are considered. + If `dataset` is None, all datasets are considered. + If both `model` and `dataset` are None, all combinations of models and datasets are considered. + + Official repository: https://github.com/jpcbertoldo/aupimo + Benchmark data: https://github.com/jpcbertoldo/aupimo/tree/main/data/experiments/benchmark + + Args: + model: The model name. Available models: `anomalib.metrics.pimo.AUPIMO_BENCHMARK_MODELS`. If None, all models. + dataset: The "collection/category", where 'collection' is either 'mvtec' or 'visa', and 'category' is + the name of the dataset within the collection. Lowercase, words split by '_' (e.g. 'metal_nut'). + Available datasets: `anomalib.metrics.pimo.AUPIMO_BENCHMARK_DATASETS`. If None, all datasets. + avoid_multithread_download: Multi-threaded download is used by default when downloading multiple files. + Set this to `True` to force single-threaded download. + + Returns: + dict[(model, dataset), (dict, AUPIMOResult)]: dictionary of results. + key: (model, dataset) pair, e.g. ('efficientad_wr101_m_ext', 'mvtec/bottle') + value: tuple with the JSON payload dictionary and the AUPIMO scores (dataclass). + dict: The unserialized JSON from the benchmark results. See `aupimo_result_from_json_dict`. + AUPIMOResult: The AUPIMO scores in dataclass format. See `anomalib.metrics.pimo.AUPIMOResult`. + """ + if model is None: + models = sorted(AUPIMO_BENCHMARK_MODELS) + else: + _validate_benchmark_model(model) + models = [model] + + if dataset is None: + datasets = sorted(AUPIMO_BENCHMARK_DATASETS) + else: + _validate_benchmark_dataset(dataset) + datasets = [dataset] + + args = list(product(models, datasets)) + logger.debug(f"Downloading benchmark results for {len(args)} (model, dataset) pairs") + + if len(args) == 1: + logger.debug("Using single-threaded download.") + return {args[0]: _download_aupimo_benchmark_scores(models[0], datasets[0])} + + if avoid_multithread_download: + logger.debug(f"Using single-threaded download due to {avoid_multithread_download=}") + results = {} + for model_, dataset_ in args: + results[model_, dataset_] = _download_aupimo_benchmark_scores(model_, dataset_) + return results + + logger.debug("Using multi-threaded download.") + models, datasets = list(zip(*args, strict=True)) # type: ignore # noqa: PGH003 + with ThreadPoolExecutor(thread_name_prefix="download_from_aupimo_benchmark_") as executor: + results = executor.map(_download_aupimo_benchmark_scores_multithreaded, models, datasets) # type: ignore # noqa: PGH003 + return dict(results) + + +def get_aupimo_benchmark( + model: str | None, + dataset: str | None, + avoid_multithread_download: bool = False, +) -> tuple[DataFrame, DataFrame]: + """Dowload results from AUPIMO's paper benchmark (stored in the official repository) and format in DataFrames. + + If `model` is None, all models are considered. + If `dataset` is None, all datasets are considered. + If both `model` and `dataset` are None, all combinations of models and datasets are considered. + + Official repository: https://github.com/jpcbertoldo/aupimo + Benchmark data: https://github.com/jpcbertoldo/aupimo/tree/main/data/experiments/benchmark + + Args: + model: The model name. Available models: `anomalib.metrics.pimo.AUPIMO_BENCHMARK_MODELS`. If None, all models. + dataset: The "collection/category", where 'collection' is either 'mvtec' or 'visa', and 'category' is + the name of the dataset within the collection. Lowercase, words split by '_' (e.g. 'metal_nut'). + Available datasets: `anomalib.metrics.pimo.AUPIMO_BENCHMARK_DATASETS`. If None, all datasets. + avoid_multithread_download: Multi-threaded download is used by default when downloading multiple files. + Set this to `True` to force single-threaded download. + + Returns: + (data_per_set, data_per_image): A tuple with two DataFrames. + data_per_set: for example, at which anomaly scores of are the integration FPRn bounds met? + data_per_image: AUPIMO scores of each image and the path to the input image. + """ + # don't validate model and dataset here, it's done in `download_aupimo_benchmark_scores` + results = download_aupimo_benchmark_scores( + model=model, + dataset=dataset, + avoid_multithread_download=avoid_multithread_download, + ) + + data = pd.DataFrame.from_records([ + { + "model": model, + "dataset": dataset, + # per-set data + "fpr_lower_bound": aupimo_result.fpr_lower_bound, + "fpr_upper_bound": aupimo_result.fpr_upper_bound, + "num_thresholds": aupimo_result.num_thresholds, + "thresh_lower_bound": aupimo_result.thresh_lower_bound, + "thresh_upper_bound": aupimo_result.thresh_upper_bound, + # per-image data + "sample_index": list(range(num_samples := len(aupimo_result.aupimos))), + "aupimo": aupimo_result.aupimos.tolist(), + "path": paths if (paths := json_dict["paths"]) is not None else [None] * num_samples, + } + for (model, dataset), (json_dict, aupimo_result) in results.items() + ]) + data["model"] = data["model"].astype("category") + data["dataset"] = data["dataset"].astype("category") + + data_per_set = ( + data.drop(columns=["sample_index", "aupimo", "path"]).sort_values(["model", "dataset"]).reset_index(drop=True) + ) + + data_per_image = ( + data[["model", "dataset", "sample_index", "aupimo", "path"]] + .explode(["sample_index", "aupimo", "path"]) + .sort_values(["model", "dataset", "sample_index"]) + .reset_index(drop=True) + .astype({"sample_index": int, "aupimo": float, "path": "string"}) + ) + + return data_per_set, data_per_image From e05a20bf47d089452de66057de0a1ba235fb4c93 Mon Sep 17 00:00:00 2001 From: jpcbertoldo <24547377+jpcbertoldo@users.noreply.github.com> Date: Mon, 21 Oct 2024 15:14:15 +0200 Subject: [PATCH 3/4] split the statistical comparisons in a single notebook Signed-off-by: jpcbertoldo <24547377+jpcbertoldo@users.noreply.github.com> --- .../700_metrics/701f_aupimo_advanced_v.ipynb | 1413 +++++++++++++++++ 1 file changed, 1413 insertions(+) create mode 100644 notebooks/700_metrics/701f_aupimo_advanced_v.ipynb diff --git a/notebooks/700_metrics/701f_aupimo_advanced_v.ipynb b/notebooks/700_metrics/701f_aupimo_advanced_v.ipynb new file mode 100644 index 0000000000..2160de7c36 --- /dev/null +++ b/notebooks/700_metrics/701f_aupimo_advanced_v.ipynb @@ -0,0 +1,1413 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AUPIMO statistical comparison between two models\n", + "\n", + "Say, model A has a higher average AUPIMO than model B. \n", + "\n", + "Can you be _sure_ that A is better than B? \n", + "\n", + "We'll use statistical tests here to make informed decisions about this.\n", + "\n", + "> For basic usage, please check the notebook [701a_aupimo.ipynb](./701a_aupimo.ipynb).\n", + ">\n", + "> For fetching AUPIMO results from the paper's benchmark, please check the notebook [701e_aupimo_advanced_iv.ipynb](./701e_aupimo_advanced_iv.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# What is AUPIMO?\n", + "\n", + "The `Area Under the Per-Image Overlap [curve]` (AUPIMO) is a metric of recall (higher is better) designed for visual anomaly detection.\n", + "\n", + "Inspired by the [ROC](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) and [PRO](https://link.springer.com/article/10.1007/s11263-020-01400-4) curves, \n", + "\n", + "> AUPIMO is the area under a curve of True Positive Rate (TPR or _recall_) as a function of False Positive Rate (FPR) restricted to a fixed range. \n", + "\n", + "But:\n", + "- the TPR (Y-axis) is *per-image* (1 image = 1 curve/score);\n", + "- the FPR (X-axis) considers the (average of) **normal** images only; \n", + "- the FPR (X-axis) is in log scale and its range is [1e-5, 1e-4]\\* (harder detection task!).\n", + "\n", + "\\* The score (the area under the curve) is normalized to be in [0, 1].\n", + "\n", + "AUPIMO can be interpreted as\n", + "\n", + "> average segmentation recall in an image given that the model (nearly) does not yield false positives in normal images.\n", + "\n", + "References in the last cell.\n", + "\n", + "![AUROC vs. AUPRO vs. AUPIMO](./roc_pro_pimo.svg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Install `anomalib` using `pip`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO(jpcbertoldo): replace by `pip install anomalib` when AUPIMO is released # noqa: TD003\n", + "# %pip install ../.." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages/kornia/feature/lightglue.py:44: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n", + " @torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "from matplotlib.ticker import FixedLocator, IndexLocator, MaxNLocator, PercentFormatter\n", + "from scipy import stats\n", + "\n", + "from anomalib.metrics.pimo import get_aupimo_benchmark" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "pd.options.display.float_format = \"{:.3f}\".format" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fetch results from AUPIMO's benchmark\n", + "\n", + "Unlike previous notebook, we will not train and evaluate the models here.\n", + "\n", + "We'll load the AUPIMO scores from the benchmark presented in our paper (check the reference in the last cell).\n", + "\n", + "These scores can be found in AUPIMO's official repository in [`jpcbertoldo:aupimo/data/experiments/benchmark`](https://github.com/jpcbertoldo/aupimo/tree/main/data/experiments/benchmark).\n", + "\n", + "> For details, see the notebook [701e_aupimo_advanced_iv.ipynb](./701e_aupimo_advanced_iv.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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modeldatasetsample_indexaupimopath
792patchcore_wr101mvtec/capsule00.250MVTec/capsule/test/crack/000.png
793patchcore_wr101mvtec/capsule10.996MVTec/capsule/test/crack/001.png
794patchcore_wr101mvtec/capsule20.996MVTec/capsule/test/crack/002.png
795patchcore_wr101mvtec/capsule30.984MVTec/capsule/test/crack/003.png
796patchcore_wr101mvtec/capsule40.996MVTec/capsule/test/crack/004.png
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1051patchcore_wr50mvtec/capsule1270.933MVTec/capsule/test/squeeze/015.png
1052patchcore_wr50mvtec/capsule1280.984MVTec/capsule/test/squeeze/016.png
1053patchcore_wr50mvtec/capsule1290.933MVTec/capsule/test/squeeze/017.png
1054patchcore_wr50mvtec/capsule1300.964MVTec/capsule/test/squeeze/018.png
1055patchcore_wr50mvtec/capsule1310.846MVTec/capsule/test/squeeze/019.png
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264 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " model dataset sample_index aupimo \\\n", + "792 patchcore_wr101 mvtec/capsule 0 0.250 \n", + "793 patchcore_wr101 mvtec/capsule 1 0.996 \n", + "794 patchcore_wr101 mvtec/capsule 2 0.996 \n", + "795 patchcore_wr101 mvtec/capsule 3 0.984 \n", + "796 patchcore_wr101 mvtec/capsule 4 0.996 \n", + "... ... ... ... ... \n", + "1051 patchcore_wr50 mvtec/capsule 127 0.933 \n", + "1052 patchcore_wr50 mvtec/capsule 128 0.984 \n", + "1053 patchcore_wr50 mvtec/capsule 129 0.933 \n", + "1054 patchcore_wr50 mvtec/capsule 130 0.964 \n", + "1055 patchcore_wr50 mvtec/capsule 131 0.846 \n", + "\n", + " path \n", + "792 MVTec/capsule/test/crack/000.png \n", + "793 MVTec/capsule/test/crack/001.png \n", + "794 MVTec/capsule/test/crack/002.png \n", + "795 MVTec/capsule/test/crack/003.png \n", + "796 MVTec/capsule/test/crack/004.png \n", + "... ... \n", + "1051 MVTec/capsule/test/squeeze/015.png \n", + "1052 MVTec/capsule/test/squeeze/016.png \n", + "1053 MVTec/capsule/test/squeeze/017.png \n", + "1054 MVTec/capsule/test/squeeze/018.png \n", + "1055 MVTec/capsule/test/squeeze/019.png \n", + "\n", + "[264 rows x 5 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# this will download the aupimo scores for all models and a single dataset\n", + "_, data_per_image = get_aupimo_benchmark(model=None, dataset=\"mvtec/capsule\")\n", + "# we'll only use two models\n", + "data_per_image = data_per_image.query(\"model in ['patchcore_wr101', 'patchcore_wr50']\")\n", + "data_per_image # noqa: B018, RUF100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll remove the `nan` values from the normal images." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "modela.shape=(109,) modelb.shape=(109,) labels.shape=(109,)\n" + ] + } + ], + "source": [ + "# extract the labels (i.e. anomaly type or 'good')\n", + "# from the paths where the AUPIMO scores were computed from\n", + "data_per_image[\"label\"] = data_per_image[\"path\"].map(lambda path: path.split(\"/\")[-2])\n", + "\n", + "# let's extract only the AUPIMO scores from anomalies\n", + "modela = data_per_image.query(\"model == 'patchcore_wr50' and label != 'good'\")[\"aupimo\"].to_numpy()\n", + "modelb = data_per_image.query(\"model == 'patchcore_wr101' and label != 'good'\")[\"aupimo\"].to_numpy()\n", + "labels = data_per_image.query(\"model == 'patchcore_wr50' and label != 'good'\")[\"label\"].to_numpy()\n", + "print(f\"{modela.shape=} {modelb.shape=} {labels.shape=}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(6, 3))\n", + "ax.boxplot(\n", + " [modela, modelb],\n", + " tick_labels=[f\"A mean: {modela.mean():.0%}\", f\"B mean: {modelb.mean():.0%}\"],\n", + " vert=False,\n", + " showmeans=True,\n", + " meanline=True,\n", + " widths=0.5,\n", + ")\n", + "ax.invert_yaxis()\n", + "ax.set_title(\"AUPIMO scores distributions from two models\")\n", + "fig # noqa: B018, RUF100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Is this difference significant?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Image by image comparison\n", + "\n", + "Since we have the scores of each model for each image, we can compare them image by image." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(5, 5))\n", + "modela_is_better = modela > modelb\n", + "ax.scatter(modela[modela_is_better], modelb[modela_is_better], alpha=0.3, s=10, color=\"red\", marker=\"o\")\n", + "ax.scatter(modela[~modela_is_better], modelb[~modela_is_better], alpha=0.3, s=10, color=\"blue\", marker=\"o\")\n", + "ax.plot([0, 1], [0, 1], color=\"black\", linestyle=\"--\")\n", + "ax.set_xlabel(\"Model A\")\n", + "ax.set_ylabel(\"Model B\")\n", + "ax.set_title(\"AUPIMO scores direct comparison\")\n", + "ax.grid()\n", + "fig # noqa: B018, RUF100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dashed line is where both models have the same AUPIMO score.\n", + "\n", + "Notice that there are images where one performs better than the other and vice-versa." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parametric Comparison\n", + "\n", + "Before using the statistical test, let's first visualize the data seen by the test.\n", + "\n", + "We'll use a _paired_ t-test, which means we'll compare the AUPIMO scores of the same image one by one." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_samples = modela.shape[0]\n", + "indexes = np.arange(num_samples)\n", + "\n", + "fig, ax = plt.subplots(figsize=(18, 4))\n", + "\n", + "# plot sample index vs score and their mean\n", + "ax.scatter(indexes, modela, s=30, color=\"tab:blue\", marker=\"o\", label=\"Model A\", zorder=3, alpha=0.6)\n", + "ax.axhline(modela.mean(), color=\"tab:blue\", linestyle=\"--\", label=\"Mean\", zorder=3)\n", + "ax.scatter(indexes, modelb, s=30, color=\"tab:red\", marker=\"o\", label=\"Model B\", zorder=3, alpha=0.6)\n", + "ax.axhline(modelb.mean(), color=\"tab:red\", linestyle=\"--\", label=\"Mean\", zorder=3)\n", + "\n", + "# configure the x-axis\n", + "ax.set_xlabel(\"Sample index\")\n", + "ax.set_xlim(0 - (eps := 0.01 * num_samples), num_samples + eps)\n", + "ax.xaxis.set_major_locator(IndexLocator(5, 0))\n", + "ax.xaxis.set_minor_locator(IndexLocator(1, 0))\n", + "\n", + "# configure the y-axis\n", + "ax.set_ylabel(\"AUPIMO [%]\")\n", + "ax.set_ylim(0 - 0.05, 1 + 0.05)\n", + "ax.yaxis.set_major_locator(MaxNLocator(6))\n", + "ax.yaxis.set_major_formatter(PercentFormatter(1))\n", + "\n", + "# configure the grid, legend, etc\n", + "ax.grid(axis=\"both\", which=\"major\", linestyle=\"-\")\n", + "ax.grid(axis=\"x\", which=\"minor\", linestyle=\"--\", alpha=0.5)\n", + "ax.legend(ncol=4, loc=\"upper left\", bbox_to_anchor=(0, -0.08))\n", + "ax.set_title(\"AUPIMO scores direct comparison\")\n", + "\n", + "fig # noqa: B018, RUF100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that several images actually have the same AUPIMO score for both models (e.g. from 10 to 15).\n", + "\n", + "Others like 21 show a big difference -- model B didn't detect the anomaly at all, but model A did a good job (60% AUPIMO).\n", + "\n", + "Let's simplify this and only show the differences." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "differences = modela - modelb\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 3))\n", + "ax.hist(differences, bins=np.linspace(-1, 1, 61), edgecolor=\"black\")\n", + "ax.axvline(differences.mean(), color=\"black\", linestyle=\"--\", label=\"Mean\")\n", + "\n", + "# configure the x-axis\n", + "ax.set_xlabel(\"AUPIMO [%]\")\n", + "ax.set_xlim(-1, 1)\n", + "ax.xaxis.set_major_locator(MaxNLocator(9))\n", + "ax.xaxis.set_minor_locator(MaxNLocator(41))\n", + "ax.xaxis.set_major_formatter(PercentFormatter(1))\n", + "\n", + "# configure the y-axis\n", + "ax.set_ylabel(\"Count\")\n", + "\n", + "# configure the grid, legend, etc\n", + "ax.grid(axis=\"both\", which=\"major\", linestyle=\"-\", alpha=1, linewidth=1.0)\n", + "ax.grid(axis=\"x\", which=\"minor\", linestyle=\"-\", alpha=0.3)\n", + "ax.legend(loc=\"upper right\")\n", + "ax.set_title(\"AUPIMO scores differences distribution (Model A - Model B)\")\n", + "\n", + "fig # noqa: B018, RUF100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks like there is a bias to the right indeed (so model A > model B). \n", + "\n", + "Is that statistically significant or just random?\n", + "\n", + "> **Dependent t-test for paired samples**\n", + "> \n", + "> - null hypothesis: `average(A) == average(B)` \n", + "> - alternative hypothesis: `average(A) != average(B)`\n", + "> \n", + "> See [`scipy.stats.ttest_rel`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_rel.html) and [\" Wikipedia's page on \"Student's t-test\"](https://en.wikipedia.org/wiki/Student's_t-test#Dependent_t-test_for_paired_samples).\n", + ">\n", + "> **Confidence Level**\n", + "> \n", + "> Instead of reporting the p-value, we'll report the \"confidence level\" [that the null hypothesis is false], which is `1 - pvalue`.\n", + "> \n", + "> *Higher* confidence level *more confident* that `average(A) > average(B)`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test_result=TtestResult(statistic=-2.8715471705520033, pvalue=0.004917091449731462, df=108)\n", + "confidence=99.5%\n" + ] + } + ], + "source": [ + "test_result = stats.ttest_rel(modela, modelb)\n", + "confidence = 1.0 - float(test_result.pvalue)\n", + "print(f\"{test_result=}\")\n", + "print(f\"{confidence=:.1%}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, we're very confident that model A has a higher AUPIMO score than model B.\n", + "\n", + "Maybe is that due to some big differences in a few images?\n", + "\n", + "What if we don't count much for these big differences?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Non-parametric (rank comparison)\n", + "\n", + "In non-parametric comparison, bigger differences don't matter more than smaller differences. \n", + "\n", + "It's all about their relative position.\n", + "\n", + "Let's look at the analogous plots for this type of comparison." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the `-` sign is to sort in descending order because higher AUPIMO is better\n", + "# the rank values are 1 or 2 because there are only two models\n", + "# where 1 is the best and 2 is the worst\n", + "# when the scores are the same, 1.5 is assigned to both models\n", + "ranks = stats.rankdata(-np.stack([modela, modelb], axis=1), method=\"average\", axis=1)\n", + "ranksa, ranksb = ranks[:, 0], ranks[:, 1]\n", + "\n", + "num_samples = ranks.shape[0]\n", + "indexes = np.arange(num_samples)\n", + "\n", + "fig, ax = plt.subplots(figsize=(18, 2.5))\n", + "\n", + "# plot sample index vs score and their mean\n", + "ax.scatter(indexes, ranksa, s=30, color=\"tab:blue\", marker=\"o\", label=\"Model A\", zorder=3, alpha=0.6)\n", + "ax.axhline(ranksa.mean(), color=\"tab:blue\", linestyle=\"--\", label=\"Mean\", zorder=3)\n", + "ax.scatter(indexes, ranksb, s=30, color=\"tab:red\", marker=\"o\", label=\"Model B\", zorder=3, alpha=0.6)\n", + "ax.axhline(ranksb.mean(), color=\"tab:red\", linestyle=\"--\", label=\"Mean\", zorder=3)\n", + "\n", + "# configure the x-axis\n", + "ax.set_xlabel(\"Sample index\")\n", + "ax.set_xlim(0 - (eps := 0.01 * num_samples), num_samples + eps)\n", + "ax.xaxis.set_major_locator(IndexLocator(5, 0))\n", + "ax.xaxis.set_minor_locator(IndexLocator(1, 0))\n", + "\n", + "# configure the y-axis\n", + "ax.set_ylabel(\"AUPIMO Rank\")\n", + "ax.set_ylim(1 - 0.1, 2 + 0.1)\n", + "ax.yaxis.set_major_locator(FixedLocator([1, 1.5, 2]))\n", + "ax.invert_yaxis()\n", + "\n", + "# configure the grid, legend, etc\n", + "ax.grid(axis=\"both\", which=\"major\", linestyle=\"-\")\n", + "ax.grid(axis=\"x\", which=\"minor\", linestyle=\"--\", alpha=0.5)\n", + "ax.legend(ncol=4, loc=\"upper left\", bbox_to_anchor=(0, -0.15))\n", + "ax.set_title(\"AUPIMO scores ranks\")\n", + "\n", + "fig.text(\n", + " 0.9,\n", + " -0.1,\n", + " \"Ranks: 1 is the best, 2 is the worst, 1.5 when the scores are the same.\",\n", + " ha=\"right\",\n", + " va=\"top\",\n", + " fontsize=\"small\",\n", + ")\n", + "\n", + "fig # noqa: B018, RUF100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, blue seems to have a slight advantage, but -- again -- is it significant enough to be sure that model A is better than model B?\n", + "\n", + "Remember that AUPIMO is a recall metric, so it is basically a ratio of the area of anomalies. \n", + "\n", + "Is it relevant if model A has 1% more recall than model B in a given image?\n", + "\n", + "> You can check that out in [`701b_aupimo_advanced_i.ipybn`](./701b_aupimo_advanced_i.ipynb).\n", + "\n", + "We'll --arbitrarily -- assume that only differences above 5% are relevant." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "MIN_ABS_DIFF = 0.05\n", + "scores = np.stack([modela, modelb], axis=1)\n", + "ranks = stats.rankdata(-scores, method=\"average\", axis=1)\n", + "abs_diff = np.abs(np.diff(scores, axis=1)).flatten()\n", + "ranks[abs_diff < MIN_ABS_DIFF, :] = 1.5\n", + "ranksa, ranksb = ranks[:, 0], ranks[:, 1]\n", + "\n", + "num_samples = ranks.shape[0]\n", + "indexes = np.arange(num_samples)\n", + "\n", + "fig, ax = plt.subplots(figsize=(18, 2.5))\n", + "\n", + "# plot sample index vs score and their mean\n", + "ax.scatter(indexes, ranksa, s=30, color=\"tab:blue\", marker=\"o\", label=\"Model A\", zorder=3, alpha=0.6)\n", + "ax.axhline(ranksa.mean(), color=\"tab:blue\", linestyle=\"--\", label=\"Mean\", zorder=3)\n", + "ax.scatter(indexes, ranksb, s=30, color=\"tab:red\", marker=\"o\", label=\"Model B\", zorder=3, alpha=0.6)\n", + "ax.axhline(ranksb.mean(), color=\"tab:red\", linestyle=\"--\", label=\"Mean\", zorder=3)\n", + "\n", + "# configure the x-axis\n", + "ax.set_xlabel(\"Sample index\")\n", + "ax.set_xlim(0 - (eps := 0.01 * num_samples), num_samples + eps)\n", + "ax.xaxis.set_major_locator(IndexLocator(5, 0))\n", + "ax.xaxis.set_minor_locator(IndexLocator(1, 0))\n", + "\n", + "# configure the y-axis\n", + "ax.set_ylabel(\"AUPIMO Rank\")\n", + "ax.set_ylim(1 - 0.1, 2 + 0.1)\n", + "ax.yaxis.set_major_locator(FixedLocator([1, 1.5, 2]))\n", + "ax.invert_yaxis()\n", + "\n", + "# configure the grid, legend, etc\n", + "ax.grid(axis=\"both\", which=\"major\", linestyle=\"-\")\n", + "ax.grid(axis=\"x\", which=\"minor\", linestyle=\"--\", alpha=0.5)\n", + "ax.legend(ncol=4, loc=\"upper left\", bbox_to_anchor=(0, -0.15))\n", + "ax.set_title(\"AUPIMO scores ranks\")\n", + "\n", + "fig.text(\n", + " 0.9,\n", + " -0.1,\n", + " \"Ranks: 1 is the best, 2 is the worst, 1.5 when the scores are the same.\",\n", + " ha=\"right\",\n", + " va=\"top\",\n", + " fontsize=\"small\",\n", + ")\n", + "\n", + "fig # noqa: B018, RUF100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The advantage of A over B is clearer now.\n", + "\n", + "Most of cases where B was better were within the difference margin of 5%.\n", + "\n", + "The average ranks also got more distant.\n", + "\n", + "Could it be by chance or can we be confident that model A is better than model B?\n", + "\n", + "> **Wilcoxon signed rank test**\n", + "> \n", + "> - null hypothesis: `average(rankA) == average(rankB)` \n", + "> - alternative hypothesis: `average(rankA) != average(rankB)`\n", + "> \n", + "> See [`scipy.stats.wilcoxon`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wilcoxon.html#scipy.stats.wilcoxon) and [\"Wilcoxon signed-rank test\" in Wikipedia](https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test).\n", + ">\n", + "> Confidence Level (reminder): *higher* confidence level *more confident* that `average(rankA) > average(rankB)`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "WilcoxonResult(statistic=1965.5, pvalue=0.001788856917447151)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.wilcoxon(differences, zero_method=\"zsplit\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test_result=WilcoxonResult(statistic=1823.0, pvalue=0.0002876893285960681)\n", + "confidence=100.0%\n" + ] + } + ], + "source": [ + "MIN_ABS_DIFF = 0.05\n", + "differences = modela - modelb\n", + "differences[abs_diff < MIN_ABS_DIFF] = 0.0\n", + "test_result = stats.wilcoxon(differences, zero_method=\"zsplit\")\n", + "confidence = 1.0 - float(test_result.pvalue)\n", + "print(f\"{test_result=}\")\n", + "print(f\"{confidence=:.1%}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We got such a high confidence that we can say for sure that these differences are not due to chance.\n", + "\n", + "So we can say that model A is _consistently_ better than model B -- even though some counter examples exist as we saw in the image by image comparison." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cite Us\n", + "\n", + "AUPIMO was developed during [Google Summer of Code 2023 (GSoC 2023)](https://summerofcode.withgoogle.com/archive/2023/projects/SPMopugd) with the `anomalib` team from Intel's OpenVINO Toolkit.\n", + "\n", + "arXiv: [arxiv.org/abs/2401.01984](https://arxiv.org/abs/2401.01984) (accepted to BMVC 2024)\n", + "\n", + "Official repository: [github.com/jpcbertoldo/aupimo](https://github.com/jpcbertoldo/aupimo) (numpy-only API and numba-accelerated versions available)\n", + "\n", + "```bibtex\n", + "@misc{bertoldo2024aupimo,\n", + " author={Joao P. C. Bertoldo and Dick Ameln and Ashwin Vaidya and Samet Akçay},\n", + " title={{AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance}}, \n", + " year={2024},\n", + " url={https://arxiv.org/abs/2401.01984}, \n", + "}\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Utils: pairwise statistical tests with multiple models\n", + "\n", + "What if you have multiple models to compare?\n", + "\n", + "Here we define a functions that will return all the pairwise comparisons between the models." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "import itertools\n", + "from typing import Any, Literal\n", + "\n", + "import numpy as np\n", + "from numpy import ndarray\n", + "from scipy import stats\n", + "from torch import Tensor\n", + "\n", + "\n", + "def _validate_models(models: dict[str, Tensor | ndarray]) -> dict[str, ndarray]:\n", + " \"\"\"Make sure the input `models` is valid and convert all the dict's values to `ndarray`.\n", + "\n", + " Args:\n", + " models (dict[str, Tensor | ndarray]): {\"model name\": sequence of shape (num_images,)}.\n", + " Validations:\n", + " - keys are strings (model names)\n", + " - there are at least two models\n", + " - values are sequences of floats in [0, 1] or `nan`\n", + " - all sequences have the same shape\n", + " - all `nan` values are at the positions\n", + " Returns:\n", + " dict[str, ndarray]: {\"model name\": array (num_images,)}.\n", + " \"\"\"\n", + " if not isinstance(models, dict):\n", + " msg = f\"Expected argument `models` to be a dict, but got {type(models)}.\"\n", + " raise TypeError(msg)\n", + "\n", + " if len(models) < 2:\n", + " msg = \"Expected argument `models` to have at least one key, but got none.\"\n", + " raise ValueError(msg)\n", + "\n", + " ref_num_samples = None\n", + " ref_nans = None\n", + " for key in models:\n", + " if not isinstance(key, str):\n", + " msg = f\"Expected argument `models` to have all keys of type str. Found {type(key)}.\"\n", + " raise TypeError(msg)\n", + "\n", + " value = models[key]\n", + "\n", + " if not isinstance(value, Tensor | ndarray):\n", + " msg = (\n", + " \"Expected argument `models` to have all values of type Tensor or ndarray. \"\n", + " f\"Found {type(value)} on {key=}.\"\n", + " )\n", + " raise TypeError(msg)\n", + "\n", + " if isinstance(value, Tensor):\n", + " models[key] = value = value.numpy()\n", + "\n", + " if not np.issubdtype(value.dtype, np.floating):\n", + " msg = f\"Expected argument `models` to have all values of floating type. Found {value.dtype} on {key=}.\"\n", + " raise ValueError(msg)\n", + "\n", + " if value.ndim != 1:\n", + " msg = f\"Expected argument `models` to have all values of 1D arrays. Found {value.ndim} on {key=}.\"\n", + " raise ValueError(msg)\n", + "\n", + " if ref_num_samples is None:\n", + " ref_num_samples = num_samples = value.shape[0]\n", + " ref_nans = nans = np.isnan(value)\n", + "\n", + " if num_samples != ref_num_samples:\n", + " msg = \"Argument `models` has inconsistent number of samples.\"\n", + " raise ValueError(msg)\n", + "\n", + " if (nans != ref_nans).any():\n", + " msg = \"Argument `models` has inconsistent `nan` values (in different positions).\"\n", + " raise ValueError(msg)\n", + "\n", + " if (value[~nans] < 0).any() or (value[~nans] > 1).any():\n", + " msg = (\n", + " \"Expected argument `models` to have all sequences of floats \\\\in [0, 1]. \"\n", + " f\"Key {key} has values outside this range.\"\n", + " )\n", + " raise ValueError(msg)\n", + "\n", + " return models\n", + "\n", + "\n", + "def test_pairwise(\n", + " models: dict[str, Tensor | ndarray],\n", + " *,\n", + " test: Literal[\"ttest_rel\", \"wilcoxon\"],\n", + " min_abs_diff: float | None = None,\n", + ") -> list[dict[str, Any]]:\n", + " \"\"\"Compare all pairs of models using statistical tests.\n", + "\n", + " Scores are assumed to be *higher is better*.\n", + "\n", + " General hypothesis in the tests:\n", + " - Null hypothesis: two models are equivalent on average.\n", + " - Alternative hypothesis: one model is better than the other (two-sided test).\n", + "\n", + " Args:\n", + " models (dict[str, Tensor | ndarray]): {\"model name\": sequence of shape (num_images,)}.\n", + " test (Literal[\"ttest_rel\", \"wilcoxon\"]): The statistical test to use.\n", + " - \"ttest_rel\": Paired Student's t-test (parametric).\n", + " - \"wilcoxon\": Wilcoxon signed-rank test (non-parametric).\n", + " min_abs_diff (float | None): Minimum absolute difference to consider in the Wilcoxon test. If `None`, all\n", + " differences are considered. Default is `None`. Ignored in the t-test.\n", + " \"\"\"\n", + " models = _validate_models(models)\n", + " if test not in {\"ttest_rel\", \"wilcoxon\"}:\n", + " msg = f\"Expected argument `test` to be 'ttest_rel' or 'wilcoxon', but got '{test}'.\"\n", + " raise ValueError(msg)\n", + " # remove nan values\n", + " models = {k: v[~np.isnan(v)] for k, v in models.items()}\n", + " models_names = sorted(models.keys())\n", + " num_models = len(models)\n", + " comparisons = list(itertools.combinations(range(num_models), 2))\n", + "\n", + " # for each comparison, compute the test and confidence (1 - p-value)\n", + " test_results = []\n", + " for modela_idx, modelb_idx in comparisons: # indices of the sorted model names\n", + " modela = models_names[modela_idx]\n", + " modelb = models_names[modelb_idx]\n", + " modela_scores = models[modela]\n", + " modelb_scores = models[modelb]\n", + " if test == \"ttest_rel\":\n", + " test_result = stats.ttest_rel(modela_scores, modelb_scores, alternative=\"two-sided\")\n", + " else: # test == \"wilcoxon\"\n", + " differences = modela_scores - modelb_scores\n", + " if min_abs_diff is not None:\n", + " differences[np.abs(differences) < min_abs_diff] = 0.0\n", + " # extreme case\n", + " if (differences == 0).all():\n", + " test_result = stats._morestats.WilcoxonResult(np.nan, 1.0) # noqa: SLF001\n", + " else:\n", + " test_result = stats.wilcoxon(differences, zero_method=\"zsplit\", alternative=\"two-sided\")\n", + " test_results.append({\n", + " \"modela\": modela,\n", + " \"modelb\": modelb,\n", + " \"confidence\": 1 - test_result.pvalue,\n", + " \"pvalue\": test_result.pvalue,\n", + " \"statistic\": test_result.statistic,\n", + " })\n", + "\n", + " return test_results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's first test it with the same two models we used before." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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 modelamodelbconfidencepvaluestatistic
0efficientad_wr101_s_extpatchcore_wr1010.9994020.0005981580.000000
1efficientad_wr101_s_extrd++_wr50_ext0.7736590.2263412193.500000
2efficientad_wr101_s_extsimplenet_wr50_ext1.0000000.000000690.500000
3efficientad_wr101_s_extuflow_ext0.9994470.0005531550.500000
4patchcore_wr101rd++_wr50_ext0.9999800.0000201333.000000
5patchcore_wr101simplenet_wr50_ext1.0000000.000000351.500000
6patchcore_wr101uflow_ext0.7318750.2681252213.000000
7rd++_wr50_extsimplenet_wr50_ext1.0000000.000000967.000000
8rd++_wr50_extuflow_ext0.9999450.0000551383.000000
9simplenet_wr50_extuflow_ext1.0000000.000000318.500000
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# fetch scores from the benchmark\n", + "_, data = get_aupimo_benchmark(model=None, dataset=\"visa/cashew\")\n", + "\n", + "# only select a few models\n", + "model_selection = (\"efficientad_wr101_s_ext\", \"patchcore_wr101\", \"rd++_wr50_ext\", \"simplenet_wr50_ext\", \"uflow_ext\")\n", + "data = data.query(\"model in @model_selection\").reset_index(drop=True)\n", + "\n", + "# this is the format expected by the util function\n", + "models = {model: df.to_numpy() for model, df in data.groupby(\"model\", observed=True)[\"aupimo\"]}\n", + "\n", + "pairwise_test_results = test_pairwise(models, test=\"wilcoxon\", min_abs_diff=0.1)\n", + "pd.DataFrame.from_records(pairwise_test_results).style.background_gradient(\n", + " cmap=\"jet\",\n", + " vmin=0,\n", + " vmax=1,\n", + " subset=[\"confidence\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cite Us\n", + "\n", + "AUPIMO was developed during [Google Summer of Code 2023 (GSoC 2023)](https://summerofcode.withgoogle.com/archive/2023/projects/SPMopugd) with the `anomalib` team from Intel's OpenVINO Toolkit.\n", + "\n", + "arXiv: [arxiv.org/abs/2401.01984](https://arxiv.org/abs/2401.01984) (accepted to BMVC 2024)\n", + "\n", + "Official repository: [github.com/jpcbertoldo/aupimo](https://github.com/jpcbertoldo/aupimo) (numpy-only API and numba-accelerated versions available)\n", + "\n", + "```bibtex\n", + "@misc{bertoldo2024aupimo,\n", + " author={Joao P. C. Bertoldo and Dick Ameln and Ashwin Vaidya and Samet Akçay},\n", + " title={{AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance}}, \n", + " year={2024},\n", + " url={https://arxiv.org/abs/2401.01984}, \n", + "}\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "anomalib-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 8fcdde0f2efbf54d157b48e1a62f19c651fd2047 Mon Sep 17 00:00:00 2001 From: jpcbertoldo <24547377+jpcbertoldo@users.noreply.github.com> Date: Mon, 21 Oct 2024 17:38:02 +0200 Subject: [PATCH 4/4] refactor tutorial 701e Signed-off-by: jpcbertoldo <24547377+jpcbertoldo@users.noreply.github.com> --- .../700_metrics/701e_aupimo_advanced_iv.ipynb | 1534 +++++++---------- src/anomalib/metrics/pimo/utils_benchmark.py | 11 +- 2 files changed, 593 insertions(+), 952 deletions(-) diff --git a/notebooks/700_metrics/701e_aupimo_advanced_iv.ipynb b/notebooks/700_metrics/701e_aupimo_advanced_iv.ipynb index fbd1a68a64..d9d4973f2f 100644 --- a/notebooks/700_metrics/701e_aupimo_advanced_iv.ipynb +++ b/notebooks/700_metrics/701e_aupimo_advanced_iv.ipynb @@ -4,21 +4,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# [TO BE REVIEWED] AUPIMO statistical comparison between two models\n", + "# Compare your results with AUPIMO's benchmark\n", "\n", - "Model A has a higher average AUPIMO than model B. Can you be _sure_ that A is better than B? \n", + "Unlike previous notebook, we will not train and evaluate the models here.\n", "\n", - "We'll use statistical tests here to make informed decisions about this.\n", + "We'll load the AUPIMO scores from the benchmark presented in our paper (check the reference in the last cell).\n", "\n", "This notebook covers:\n", + "- fetching results from AUPIMO's paper's benchmark;\n", + " - single model-dataset pair;\n", + " - multiple model-dataset pairs;\n", "- load/save functions to import/export AUPIMO scores;\n", - "- statistical tests between two models, in particular:\n", - " - parametrical test with Student's t-test;\n", - " - non-parametrical test with Wilcoxon signed-rank test;\n", "\n", - "> AUPIMO is pronounced \"a-u-pee-mo\".\n", - "\n", - "> For basic usage, please check the notebook [701a_aupimo.ipynb](./701a_aupimo.ipynb)." + "> For basic usage, please check the notebook [701a_aupimo.ipynb](./701a_aupimo.ipynb).\n", + "> \n", + "> Scores can be found in AUPIMO's official repository in [`jpcbertoldo:aupimo/data/experiments/benchmark`](https://github.com/jpcbertoldo/aupimo/tree/main/data/experiments/benchmark). " ] }, { @@ -68,46 +68,10 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing /home/jcasagrandebertoldo/repos/anomalib-dev\n", - " Installing build dependencies ... \u001b[?25ldone\n", - "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", - "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", - "\u001b[?25hRequirement already satisfied: omegaconf>=2.1.1 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from anomalib==1.2.0.dev0) (2.3.0)\n", - "Requirement already satisfied: rich>=13.5.2 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from anomalib==1.2.0.dev0) (13.7.1)\n", - "Requirement already satisfied: jsonargparse>=4.27.7 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from jsonargparse[signatures]>=4.27.7->anomalib==1.2.0.dev0) (4.32.0)\n", - "Requirement already satisfied: docstring-parser in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from anomalib==1.2.0.dev0) (0.16)\n", - "Requirement already satisfied: rich-argparse in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from anomalib==1.2.0.dev0) (1.5.2)\n", - "Requirement already satisfied: PyYAML>=3.13 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from jsonargparse>=4.27.7->jsonargparse[signatures]>=4.27.7->anomalib==1.2.0.dev0) (6.0.2)\n", - "Requirement already satisfied: typeshed-client>=2.1.0 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from jsonargparse[signatures]>=4.27.7->anomalib==1.2.0.dev0) (2.7.0)\n", - "Requirement already satisfied: antlr4-python3-runtime==4.9.* in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from omegaconf>=2.1.1->anomalib==1.2.0.dev0) (4.9.3)\n", - "Requirement already satisfied: markdown-it-py>=2.2.0 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from rich>=13.5.2->anomalib==1.2.0.dev0) (3.0.0)\n", - "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from rich>=13.5.2->anomalib==1.2.0.dev0) (2.18.0)\n", - "Requirement already satisfied: mdurl~=0.1 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich>=13.5.2->anomalib==1.2.0.dev0) (0.1.2)\n", - "Requirement already satisfied: importlib-resources>=1.4.0 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from typeshed-client>=2.1.0->jsonargparse[signatures]>=4.27.7->anomalib==1.2.0.dev0) (6.4.4)\n", - "Requirement already satisfied: typing-extensions>=4.5.0 in /home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages (from typeshed-client>=2.1.0->jsonargparse[signatures]>=4.27.7->anomalib==1.2.0.dev0) (4.11.0)\n", - "Building wheels for collected packages: anomalib\n", - " Building wheel for anomalib (pyproject.toml) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for anomalib: filename=anomalib-1.2.0.dev0-py3-none-any.whl size=491631 sha256=3f0069a35f2e1e2a41e3394bd582457bdb759bb154275799f12f8189b5f5954b\n", - " Stored in directory: /home/jcasagrandebertoldo/.cache/pip/wheels/bd/3b/91/961b3d37cb837fd176783f27cbecacee412bc3c5a35cd76b36\n", - "Successfully built anomalib\n", - "Installing collected packages: anomalib\n", - " Attempting uninstall: anomalib\n", - " Found existing installation: anomalib 1.2.0.dev0\n", - " Uninstalling anomalib-1.2.0.dev0:\n", - " Successfully uninstalled anomalib-1.2.0.dev0\n", - "Successfully installed anomalib-1.2.0.dev0\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ "# TODO(jpcbertoldo): replace by `pip install anomalib` when AUPIMO is released # noqa: TD003\n", - "%pip install ../.." + "# %pip install ../.." ] }, { @@ -127,11 +91,16 @@ "output_type": "stream", "text": [ "/home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages/kornia/feature/lightglue.py:44: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n", - " @torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)\n" + " @torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)\n", + "/home/jcasagrandebertoldo/miniconda3/envs/anomalib-dev/lib/python3.10/site-packages/torch/cuda/__init__.py:654: UserWarning: Can't initialize NVML\n", + " warnings.warn(\"Can't initialize NVML\")\n" ] } ], "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", "import json\n", "from pathlib import Path\n", "\n", @@ -139,14 +108,21 @@ "import pandas as pd\n", "import torch\n", "from matplotlib import pyplot as plt\n", - "from matplotlib.ticker import FixedLocator, IndexLocator, MaxNLocator, PercentFormatter\n", - "from scipy import stats\n", + "from matplotlib.ticker import MaxNLocator, PercentFormatter\n", "\n", + "from anomalib import TaskType\n", + "from anomalib.data import MVTec\n", + "from anomalib.engine import Engine\n", + "from anomalib.metrics import AUPIMO\n", "from anomalib.metrics.pimo import (\n", - " get_benchmark_aupimo_scores,\n", - " load_aupimo_result_from_json_dict,\n", - " save_aupimo_result_to_json_dict,\n", - ")" + " AUPIMO_BENCHMARK_DATASETS,\n", + " AUPIMO_BENCHMARK_MODELS,\n", + " aupimo_result_from_json_dict,\n", + " aupimo_result_to_json_dict,\n", + " download_aupimo_benchmark_scores,\n", + " get_aupimo_benchmark,\n", + ")\n", + "from anomalib.models import Padim" ] }, { @@ -155,7 +131,10 @@ "metadata": {}, "outputs": [], "source": [ - "pd.options.display.float_format = \"{:.3f}\".format" + "# NOTE: Provide the path to the dataset root directory.\n", + "# If the datasets is not downloaded, it will be downloaded\n", + "# to this directory.\n", + "dataset_root = Path.cwd().parent.parent / \"datasets\" / \"MVTec\"" ] }, { @@ -163,6 +142,15 @@ "execution_count": 4, "metadata": {}, "outputs": [], + "source": [ + "pd.options.display.float_format = \"{:.5f}\".format" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], "source": [ "%matplotlib inline" ] @@ -171,121 +159,71 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Load AUPIMO scores\n", - "\n", - "Unlike previous notebook, we will not train and evaluate the models here.\n", - "\n", - "We'll load the AUPIMO scores from the benchmark presented in our paper (check the reference in the last cell).\n", - "\n", - "These scores can be found in AUPIMO's official repository in [`jpcbertoldo:aupimo/data/experiments/benchmark`](https://github.com/jpcbertoldo/aupimo/tree/main/data/experiments/benchmark). " + "# Single model-dataset pair" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Loading benchmark results for model 'patchcore_wr101' and dataset 'mvtec/capsule'\n", - "Dowloading JSON file from https://raw.githubusercontent.com/jpcbertoldo/aupimo/refs/heads/main/data/experiments/benchmark/patchcore_wr101/mvtec/capsule/aupimo/aupimos.json\n", - "Converting payload to dataclass\n", - "Done!\n", - "Loading benchmark results for model 'patchcore_wr50' and dataset 'mvtec/capsule'\n", - "Dowloading JSON file from https://raw.githubusercontent.com/jpcbertoldo/aupimo/refs/heads/main/data/experiments/benchmark/patchcore_wr50/mvtec/capsule/aupimo/aupimos.json\n", - "Converting payload to dataclass\n", - "Done!\n" + "Available models: ['efficientad_wr101_m_ext', 'efficientad_wr101_s_ext', 'fastflow_cait_m48_448', 'fastflow_wr50', 'padim_r18', 'padim_wr50', 'patchcore_wr101', 'patchcore_wr50', 'pyramidflow_fnf_ext', 'pyramidflow_r18_ext', 'rd++_wr50_ext', 'simplenet_wr50_ext', 'uflow_ext']\n", + "Available datasets: ['mvtec/bottle', 'mvtec/cable', 'mvtec/capsule', 'mvtec/carpet', 'mvtec/grid', 'mvtec/hazelnut', 'mvtec/leather', 'mvtec/metal_nut', 'mvtec/pill', 'mvtec/screw', 'mvtec/tile', 'mvtec/toothbrush', 'mvtec/transistor', 'mvtec/wood', 'mvtec/zipper', 'visa/candle', 'visa/capsules', 'visa/cashew', 'visa/chewinggum', 'visa/fryum', 'visa/macaroni1', 'visa/macaroni2', 'visa/pcb1', 'visa/pcb2', 'visa/pcb3', 'visa/pcb4', 'visa/pipe_fryum']\n" ] } ], "source": [ - "json_model_a, aupimo_result_model_a = get_benchmark_aupimo_scores(\"patchcore_wr101\", \"mvtec/capsule\")\n", - "_, aupimo_result_model_b = get_benchmark_aupimo_scores(\"patchcore_wr50\", \"mvtec/capsule\")" + "print(f\"Available models: {sorted(AUPIMO_BENCHMARK_MODELS)}\")\n", + "print(f\"Available datasets: {sorted(AUPIMO_BENCHMARK_DATASETS)}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's remove the `nan` values from the normal images." + "Download the results" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "modela.shape=(109,) modelb.shape=(109,) labels.shape=(109,)\n" + "data_per_set\n", + "model uflow_ext\n", + "dataset mvtec/bottle\n", + "fpr_lower_bound 0.00001\n", + "fpr_upper_bound 0.00010\n", + "num_thresholds None\n", + "thresh_lower_bound 0.64445\n", + "thresh_upper_bound 0.66532\n", + "aupimo_mean 0.67385\n", + "Name: 0, dtype: object\n", + "data_per_image.columns=Index(['model', 'dataset', 'sample_index', 'aupimo', 'path'], dtype='object')\n" ] } ], "source": [ - "# corresponding paths to the images\n", - "# where the AUPIMO scores were computed from\n", - "paths = json_model_a[\"paths\"]\n", - "\n", - "# extract the labels (i.e. anomaly type or 'good')\n", - "labels = np.array([p.split(\"/\")[-2] for p in paths])\n", + "# download and format results\n", + "data_per_set, data_per_image = get_aupimo_benchmark(model=\"uflow_ext\", dataset=\"mvtec/bottle\")\n", "\n", - "# let's extract only the AUPIMO scores from anomalies\n", - "modela = aupimo_result_model_a.aupimos[labels != \"good\"].numpy()\n", - "modelb = aupimo_result_model_b.aupimos[labels != \"good\"].numpy()\n", - "labels = labels[labels != \"good\"]\n", - "print(f\"{modela.shape=} {modelb.shape=} {labels.shape=}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(6, 3))\n", - "ax.boxplot(\n", - " [modela, modelb],\n", - " tick_labels=[f\"A mean: {modela.mean():.0%}\", f\"B mean: {modelb.mean():.0%}\"],\n", - " vert=False,\n", - " showmeans=True,\n", - " meanline=True,\n", - " widths=0.5,\n", - ")\n", - "ax.invert_yaxis()\n", - "ax.set_title(\"AUPIMO scores distributions from two models\")\n", - "fig # noqa: B018, RUF100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Is this difference significant?" + "print(\"data_per_set\", data_per_set.iloc[0], sep=\"\\n\")\n", + "print(f\"{data_per_image.columns=}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Image by image comparison\n", - "\n", - "Since we have the scores of each model for each image, we can compare them image by image." + "Plot the scores" ] }, { @@ -295,9 +233,9 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, "execution_count": 8, @@ -306,15 +244,17 @@ } ], "source": [ - "fig, ax = plt.subplots(figsize=(5, 5))\n", - "modela_is_better = modela > modelb\n", - "ax.scatter(modela[modela_is_better], modelb[modela_is_better], alpha=0.3, s=10, color=\"red\", marker=\"o\")\n", - "ax.scatter(modela[~modela_is_better], modelb[~modela_is_better], alpha=0.3, s=10, color=\"blue\", marker=\"o\")\n", - "ax.plot([0, 1], [0, 1], color=\"black\", linestyle=\"--\")\n", - "ax.set_xlabel(\"Model A\")\n", - "ax.set_ylabel(\"Model B\")\n", - "ax.set_title(\"AUPIMO scores direct comparison\")\n", + "fig, ax = plt.subplots()\n", + "ax.hist(data_per_image[\"aupimo\"], bins=np.linspace(0, 1, 11), edgecolor=\"black\", label=\"Histogram\")\n", + "ax.axvline(data_per_set.iloc[0][\"aupimo_mean\"], color=\"black\", label=\"Average AUPIMO\", linestyle=\"--\", linewidth=2)\n", + "ax.set_ylabel(\"Count (number of images)\")\n", + "ax.yaxis.set_major_locator(MaxNLocator(5, integer=True))\n", + "ax.set_xlim(0, 1)\n", + "ax.set_xlabel(\"AUPIMO [%]\")\n", + "ax.xaxis.set_major_formatter(PercentFormatter(1))\n", "ax.grid()\n", + "ax.set_title(f\"Model: {data_per_set.iloc[0]['model']} | Dataset: {data_per_set.iloc[0]['dataset']}\")\n", + "ax.legend(loc=\"upper left\")\n", "fig # noqa: B018, RUF100" ] }, @@ -322,20 +262,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The dashed line is where both models have the same AUPIMO score.\n", - "\n", - "Notice that there are images where one performs better than the other and vice-versa." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parametric Comparison\n", - "\n", - "Before using the statistical test, let's first visualize the data seen by the test.\n", - "\n", - "We'll use a _paired_ t-test, which means we'll compare the AUPIMO scores of the same image one by one." + "Let's see the score distributions _per anomaly type_." ] }, { @@ -345,9 +272,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
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" ] }, "execution_count": 9, @@ -356,35 +283,24 @@ } ], "source": [ - "num_samples = modela.shape[0]\n", - "indexes = np.arange(num_samples)\n", - "\n", - "fig, ax = plt.subplots(figsize=(18, 4))\n", - "\n", - "# plot sample index vs score and their mean\n", - "ax.scatter(indexes, modela, s=30, color=\"tab:blue\", marker=\"o\", label=\"Model A\", zorder=3, alpha=0.6)\n", - "ax.axhline(modela.mean(), color=\"tab:blue\", linestyle=\"--\", label=\"Mean\", zorder=3)\n", - "ax.scatter(indexes, modelb, s=30, color=\"tab:red\", marker=\"o\", label=\"Model B\", zorder=3, alpha=0.6)\n", - "ax.axhline(modelb.mean(), color=\"tab:red\", linestyle=\"--\", label=\"Mean\", zorder=3)\n", - "\n", - "# configure the x-axis\n", - "ax.set_xlabel(\"Sample index\")\n", - "ax.set_xlim(0 - (eps := 0.01 * num_samples), num_samples + eps)\n", - "ax.xaxis.set_major_locator(IndexLocator(5, 0))\n", - "ax.xaxis.set_minor_locator(IndexLocator(1, 0))\n", - "\n", - "# configure the y-axis\n", - "ax.set_ylabel(\"AUPIMO [%]\")\n", - "ax.set_ylim(0 - 0.05, 1 + 0.05)\n", - "ax.yaxis.set_major_locator(MaxNLocator(6))\n", - "ax.yaxis.set_major_formatter(PercentFormatter(1))\n", - "\n", - "# configure the grid, legend, etc\n", - "ax.grid(axis=\"both\", which=\"major\", linestyle=\"-\")\n", - "ax.grid(axis=\"x\", which=\"minor\", linestyle=\"--\", alpha=0.5)\n", - "ax.legend(ncol=4, loc=\"upper left\", bbox_to_anchor=(0, -0.08))\n", - "ax.set_title(\"AUPIMO scores direct comparison\")\n", - "\n", + "data_per_image[\"label\"] = data_per_image[\"path\"].map(lambda path: path.split(\"/\")[-2])\n", + "\n", + "fig, ax = plt.subplots()\n", + "data_per_image.query(\"label != 'good'\").groupby(\"label\", observed=True)[\"aupimo\"].plot.hist(\n", + " ax=ax,\n", + " bins=np.linspace(0, 1, 11),\n", + " edgecolor=\"black\",\n", + " alpha=0.4,\n", + " width=0.07,\n", + ")\n", + "ax.set_ylabel(\"Count (number of images)\")\n", + "ax.yaxis.set_major_locator(MaxNLocator(5, integer=True))\n", + "ax.set_xlim(0, 1)\n", + "ax.set_xlabel(\"AUPIMO [%]\")\n", + "ax.xaxis.set_major_formatter(PercentFormatter(1))\n", + "ax.grid()\n", + "ax.set_title(f\"Model: {data_per_set.iloc[0]['model']} | Dataset: {data_per_set.iloc[0]['dataset']}\")\n", + "ax.legend(loc=\"upper left\", title=\"Anomaly type\")\n", "fig # noqa: B018, RUF100" ] }, @@ -392,76 +308,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Notice that several images actually have the same AUPIMO score for both models (e.g. from 10 to 15).\n", - "\n", - "Others like 21 show a big difference -- model B didn't detect the anomaly at all, but model A did a good job (60% AUPIMO).\n", - "\n", - "Let's simplify this and only show the differences." + "# Single model, all datasets" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "differences = modela - modelb\n", - "\n", - "fig, ax = plt.subplots(figsize=(9, 3))\n", - "ax.hist(differences, bins=np.linspace(-1, 1, 61), edgecolor=\"black\")\n", - "ax.axvline(differences.mean(), color=\"black\", linestyle=\"--\", label=\"Mean\")\n", - "\n", - "# configure the x-axis\n", - "ax.set_xlabel(\"AUPIMO [%]\")\n", - "ax.set_xlim(-1, 1)\n", - "ax.xaxis.set_major_locator(MaxNLocator(9))\n", - "ax.xaxis.set_minor_locator(MaxNLocator(41))\n", - "ax.xaxis.set_major_formatter(PercentFormatter(1))\n", - "\n", - "# configure the y-axis\n", - "ax.set_ylabel(\"Count\")\n", - "\n", - "# configure the grid, legend, etc\n", - "ax.grid(axis=\"both\", which=\"major\", linestyle=\"-\", alpha=1, linewidth=1.0)\n", - "ax.grid(axis=\"x\", which=\"minor\", linestyle=\"-\", alpha=0.3)\n", - "ax.legend(loc=\"upper right\")\n", - "ax.set_title(\"AUPIMO scores differences distribution (Model A - Model B)\")\n", - "\n", - "fig # noqa: B018, RUF100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, + "outputs": [], "source": [ - "It looks like there is a bias to the right indeed (so model A > model B). \n", - "\n", - "Is that statistically significant or just random?\n", - "\n", - "> **Dependent t-test for paired samples**\n", - "> \n", - "> - null hypothesis: `average(A) == average(B)` \n", - "> - alternative hypothesis: `average(A) != average(B)`\n", - "> \n", - "> See [`scipy.stats.ttest_rel`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_rel.html) and [\" Wikipedia's page on \"Student's t-test\"](https://en.wikipedia.org/wiki/Student's_t-test#Dependent_t-test_for_paired_samples).\n", - ">\n", - "> **Confidence Level**\n", - "> \n", - "> Instead of reporting the p-value, we'll report the \"confidence level\" [that the null hypothesis is false], which is `1 - pvalue`.\n", - "> \n", - "> *Higher* confidence level *more confident* that `average(A) > average(B)`." + "# this will download the aupimo scores for all datasets given a single model\n", + "data_per_set, data_per_image = get_aupimo_benchmark(model=\"uflow_ext\", dataset=None)" ] }, { @@ -470,43 +327,94 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "test_result=TtestResult(statistic=2.8715471705520033, pvalue=0.004917091449731462, df=108)\n", - "confidence=99.5%\n" - ] + "data": { + "text/html": [ + "
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modeldatasetfpr_lower_boundfpr_upper_boundnum_thresholdsthresh_lower_boundthresh_upper_boundaupimo_mean
0uflow_extmvtec/bottle0.000010.00010None0.644450.665320.67385
1uflow_extmvtec/cable0.000010.00010None0.670870.725760.45526
2uflow_extmvtec/capsule0.000010.00010None0.696520.726300.83383
\n", + "
" + ], + "text/plain": [ + " model dataset fpr_lower_bound fpr_upper_bound num_thresholds \\\n", + "0 uflow_ext mvtec/bottle 0.00001 0.00010 None \n", + "1 uflow_ext mvtec/cable 0.00001 0.00010 None \n", + "2 uflow_ext mvtec/capsule 0.00001 0.00010 None \n", + "\n", + " thresh_lower_bound thresh_upper_bound aupimo_mean \n", + "0 0.64445 0.66532 0.67385 \n", + "1 0.67087 0.72576 0.45526 \n", + "2 0.69652 0.72630 0.83383 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "test_result = stats.ttest_rel(modela, modelb)\n", - "confidence = 1.0 - float(test_result.pvalue)\n", - "print(f\"{test_result=}\")\n", - "print(f\"{confidence=:.1%}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So, we're very confident that model A has a higher AUPIMO score than model B.\n", - "\n", - "Maybe is that due to some big differences in a few images?\n", - "\n", - "What if we don't count much for these big differences?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Non-parametric (rank comparison)\n", - "\n", - "In non-parametric comparison, bigger differences don't matter more than smaller differences. \n", - "\n", - "It's all about their relative position.\n", - "\n", - "Let's look at the analogous plots for this type of comparison." + "# per dataset information\n", + "data_per_set.head(3)" ] }, { @@ -516,9 +424,71 @@ "outputs": [ { "data": { - "image/png": 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modeldatasetsample_indexaupimopath
0uflow_extmvtec/bottle00.95297MVTec/bottle/test/broken_large/000.png
1uflow_extmvtec/bottle10.91126MVTec/bottle/test/broken_large/001.png
2uflow_extmvtec/bottle20.92038MVTec/bottle/test/broken_large/002.png
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" + " model dataset sample_index aupimo \\\n", + "0 uflow_ext mvtec/bottle 0 0.95297 \n", + "1 uflow_ext mvtec/bottle 1 0.91126 \n", + "2 uflow_ext mvtec/bottle 2 0.92038 \n", + "\n", + " path \n", + "0 MVTec/bottle/test/broken_large/000.png \n", + "1 MVTec/bottle/test/broken_large/001.png \n", + "2 MVTec/bottle/test/broken_large/002.png " ] }, "execution_count": 12, @@ -527,67 +497,8 @@ } ], "source": [ - "# the `-` sign is to sort in descending order because higher AUPIMO is better\n", - "# the rank values are 1 or 2 because there are only two models\n", - "# where 1 is the best and 2 is the worst\n", - "# when the scores are the same, 1.5 is assigned to both models\n", - "ranks = stats.rankdata(-np.stack([modela, modelb], axis=1), method=\"average\", axis=1)\n", - "ranksa, ranksb = ranks[:, 0], ranks[:, 1]\n", - "\n", - "num_samples = ranks.shape[0]\n", - "indexes = np.arange(num_samples)\n", - "\n", - "fig, ax = plt.subplots(figsize=(18, 2.5))\n", - "\n", - "# plot sample index vs score and their mean\n", - "ax.scatter(indexes, ranksa, s=30, color=\"tab:blue\", marker=\"o\", label=\"Model A\", zorder=3, alpha=0.6)\n", - "ax.axhline(ranksa.mean(), color=\"tab:blue\", linestyle=\"--\", label=\"Mean\", zorder=3)\n", - "ax.scatter(indexes, ranksb, s=30, color=\"tab:red\", marker=\"o\", label=\"Model B\", zorder=3, alpha=0.6)\n", - "ax.axhline(ranksb.mean(), color=\"tab:red\", linestyle=\"--\", label=\"Mean\", zorder=3)\n", - "\n", - "# configure the x-axis\n", - "ax.set_xlabel(\"Sample index\")\n", - "ax.set_xlim(0 - (eps := 0.01 * num_samples), num_samples + eps)\n", - "ax.xaxis.set_major_locator(IndexLocator(5, 0))\n", - "ax.xaxis.set_minor_locator(IndexLocator(1, 0))\n", - "\n", - "# configure the y-axis\n", - "ax.set_ylabel(\"AUPIMO Rank\")\n", - "ax.set_ylim(1 - 0.1, 2 + 0.1)\n", - "ax.yaxis.set_major_locator(FixedLocator([1, 1.5, 2]))\n", - "ax.invert_yaxis()\n", - "\n", - "# configure the grid, legend, etc\n", - "ax.grid(axis=\"both\", which=\"major\", linestyle=\"-\")\n", - "ax.grid(axis=\"x\", which=\"minor\", linestyle=\"--\", alpha=0.5)\n", - "ax.legend(ncol=4, loc=\"upper left\", bbox_to_anchor=(0, -0.15))\n", - "ax.set_title(\"AUPIMO scores ranks\")\n", - "\n", - "fig.text(\n", - " 0.9,\n", - " -0.1,\n", - " \"Ranks: 1 is the best, 2 is the worst, 1.5 when the scores are the same.\",\n", - " ha=\"right\",\n", - " va=\"top\",\n", - " fontsize=\"small\",\n", - ")\n", - "\n", - "fig # noqa: B018, RUF100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, blue seems to have a slight advantage, but -- again -- is it significant enough to be sure that model A is better than model B?\n", - "\n", - "Remember that AUPIMO is a recall metric, so it is basically a ratio of the area of anomalies. \n", - "\n", - "Is it relevant if model A has 1% more recall than model B in a given image?\n", - "\n", - "> You can check that out in [`701b_aupimo_advanced_i.ipybn`](./701b_aupimo_advanced_i.ipynb).\n", - "\n", - "We'll --arbitrarily -- assume that only differences above 5% are relevant." + "# per image information\n", + "data_per_image.head(3)" ] }, { @@ -597,9 +508,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
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" ] }, "execution_count": 13, @@ -608,51 +519,19 @@ } ], "source": [ - "MIN_ABS_DIFF = 0.05\n", - "scores = np.stack([modela, modelb], axis=1)\n", - "ranks = stats.rankdata(-scores, method=\"average\", axis=1)\n", - "abs_diff = np.abs(np.diff(scores, axis=1)).flatten()\n", - "ranks[abs_diff < MIN_ABS_DIFF, :] = 1.5\n", - "ranksa, ranksb = ranks[:, 0], ranks[:, 1]\n", - "\n", - "num_samples = ranks.shape[0]\n", - "indexes = np.arange(num_samples)\n", - "\n", - "fig, ax = plt.subplots(figsize=(18, 2.5))\n", - "\n", - "# plot sample index vs score and their mean\n", - "ax.scatter(indexes, ranksa, s=30, color=\"tab:blue\", marker=\"o\", label=\"Model A\", zorder=3, alpha=0.6)\n", - "ax.axhline(ranksa.mean(), color=\"tab:blue\", linestyle=\"--\", label=\"Mean\", zorder=3)\n", - "ax.scatter(indexes, ranksb, s=30, color=\"tab:red\", marker=\"o\", label=\"Model B\", zorder=3, alpha=0.6)\n", - "ax.axhline(ranksb.mean(), color=\"tab:red\", linestyle=\"--\", label=\"Mean\", zorder=3)\n", - "\n", - "# configure the x-axis\n", - "ax.set_xlabel(\"Sample index\")\n", - "ax.set_xlim(0 - (eps := 0.01 * num_samples), num_samples + eps)\n", - "ax.xaxis.set_major_locator(IndexLocator(5, 0))\n", - "ax.xaxis.set_minor_locator(IndexLocator(1, 0))\n", - "\n", - "# configure the y-axis\n", - "ax.set_ylabel(\"AUPIMO Rank\")\n", - "ax.set_ylim(1 - 0.1, 2 + 0.1)\n", - "ax.yaxis.set_major_locator(FixedLocator([1, 1.5, 2]))\n", - "ax.invert_yaxis()\n", - "\n", - "# configure the grid, legend, etc\n", - "ax.grid(axis=\"both\", which=\"major\", linestyle=\"-\")\n", - "ax.grid(axis=\"x\", which=\"minor\", linestyle=\"--\", alpha=0.5)\n", - "ax.legend(ncol=4, loc=\"upper left\", bbox_to_anchor=(0, -0.15))\n", - "ax.set_title(\"AUPIMO scores ranks\")\n", - "\n", - "fig.text(\n", - " 0.9,\n", - " -0.1,\n", - " \"Ranks: 1 is the best, 2 is the worst, 1.5 when the scores are the same.\",\n", - " ha=\"right\",\n", - " va=\"top\",\n", - " fontsize=\"small\",\n", + "fig, ax = plt.subplots(figsize=(5, 9))\n", + "table = data_per_image.dropna(axis=0).pivot_table(\n", + " index=\"sample_index\",\n", + " columns=\"dataset\",\n", + " values=\"aupimo\",\n", + " observed=True,\n", ")\n", - "\n", + "table.boxplot(vert=False, rot=15, grid=True, ax=ax)\n", + "ax.invert_yaxis()\n", + "ax.set_xlabel(\"AUPIMO [%]\")\n", + "ax.xaxis.set_major_formatter(PercentFormatter(1))\n", + "ax.set_title(f\"Model: {data_per_set.iloc[0]['model']}\")\n", + "ax.grid(axis=\"x\", linestyle=\"--\", alpha=0.5)\n", "fig # noqa: B018, RUF100" ] }, @@ -660,363 +539,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The advantage of A over B is clearer now.\n", - "\n", - "Most of cases where B was better were within the difference margin of 5%.\n", - "\n", - "The average ranks also got more distant.\n", - "\n", - "Could it be by chance or can we be confident that model A is better than model B?\n", - "\n", - "> **Wilcoxon signed rank test**\n", - "> \n", - "> - null hypothesis: `average(rankA) == average(rankB)` \n", - "> - alternative hypothesis: `average(rankA) != average(rankB)`\n", - "> \n", - "> See [`scipy.stats.wilcoxon`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wilcoxon.html#scipy.stats.wilcoxon) and [\"Wilcoxon signed-rank test\" in Wikipedia](https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test).\n", - ">\n", - "> Confidence Level (reminder): *higher* confidence level *more confident* that `average(rankA) > average(rankB)`.\n" + "# All models on a single dataset" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test_result=WilcoxonResult(statistic=1823.0, pvalue=0.0002876893285960681)\n", - "confidence=100.0%\n" - ] - } - ], - "source": [ - "MIN_ABS_DIFF = 0.05\n", - "differences = modela - modelb\n", - "differences[abs_diff < MIN_ABS_DIFF] = 0.0\n", - "test_result = stats.wilcoxon(differences, zero_method=\"zsplit\")\n", - "confidence = 1.0 - float(test_result.pvalue)\n", - "print(f\"{test_result=}\")\n", - "print(f\"{confidence=:.1%}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We got such a high confidence that we can say for sure that these differences are not due to chance.\n", - "\n", - "So we can say that model A is _consistently_ better than model B -- even though some counter examples exist as we saw in the image by image comparison." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cite Us\n", - "\n", - "AUPIMO was developed during [Google Summer of Code 2023 (GSoC 2023)](https://summerofcode.withgoogle.com/archive/2023/projects/SPMopugd) with the `anomalib` team from Intel's OpenVINO Toolkit.\n", - "\n", - "arXiv: [arxiv.org/abs/2401.01984](https://arxiv.org/abs/2401.01984) (accepted to BMVC 2024)\n", - "\n", - "Official repository: [github.com/jpcbertoldo/aupimo](https://github.com/jpcbertoldo/aupimo) (numpy-only API and numba-accelerated versions available)\n", - "\n", - "```bibtex\n", - "@misc{bertoldo2024aupimo,\n", - " author={Joao P. C. Bertoldo and Dick Ameln and Ashwin Vaidya and Samet Akçay},\n", - " title={{AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance}}, \n", - " year={2024},\n", - " url={https://arxiv.org/abs/2401.01984}, \n", - "}\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Utils\n", - "\n", - "Some utility functions to expand what this notebook shows." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Save AUPIMO scores\n", - "\n", - "At the begin of the notebook we defined a function `load_aupimo_result_from_json_dict()` that deserializes `AUPIMOResult` objects.\n", - "\n", - "Let's define the opposite operator so you can save and publish your AUPIMO scores." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "payload.keys()=dict_keys(['fpr_lower_bound', 'fpr_upper_bound', 'num_thresholds', 'thresh_lower_bound', 'thresh_upper_bound', 'aupimos'])\n" - ] - } - ], - "source": [ - "payload = save_aupimo_result_to_json_dict(aupimo_result_model_a)\n", - "print(f\"{payload.keys()=}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "payload.keys()=dict_keys(['fpr_lower_bound', 'fpr_upper_bound', 'num_thresholds', 'thresh_lower_bound', 'thresh_upper_bound', 'aupimos'])\n" - ] - } - ], - "source": [ - "payload = save_aupimo_result_to_json_dict(aupimo_result_model_a)\n", - "print(f\"{payload.keys()=}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "payload.keys()=dict_keys(['fpr_lower_bound', 'fpr_upper_bound', 'num_thresholds', 'thresh_lower_bound', 'thresh_upper_bound', 'aupimos', 'paths'])\n" - ] - } - ], - "source": [ - "# you can optionally save the paths to the images\n", - "# where the AUPIMO scores were computed from\n", - "payload = save_aupimo_result_to_json_dict(aupimo_result_model_a, paths)\n", - "print(f\"{payload.keys()=}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "8,0K\t/tmp/tmpwhpnd7x_/aupimo_result.json\n" - ] - } - ], - "source": [ - "# let's check that it can be saved to a file and loaded back\n", - "\n", - "from tempfile import TemporaryDirectory\n", - "\n", - "with TemporaryDirectory() as tmpdir:\n", - " cache_dir = Path(tmpdir)\n", - "\n", - " with (cache_dir / \"aupimo_result.json\").open(\"w\") as file:\n", - " json.dump(payload, file)\n", - "\n", - " !du -sh {cache_dir / \"aupimo_result.json\"}\n", - "\n", - " with (cache_dir / \"aupimo_result.json\").open(\"r\") as file:\n", - " payload_reloaded = json.load(file)\n", - "\n", - "aupimo_result_reloaded = load_aupimo_result_from_json_dict(payload_reloaded)\n", - "assert torch.allclose(aupimo_result_model_a.aupimos, aupimo_result_reloaded.aupimos, equal_nan=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pairwise statistical tests (multiple models)\n", - "\n", - "What if you have multiple models to compare?\n", - "\n", - "Here we define a functions that will return all the pairwise comparisons between the models." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, "outputs": [], "source": [ - "import itertools\n", - "from typing import Any, Literal\n", - "\n", - "import numpy as np\n", - "from numpy import ndarray\n", - "from scipy import stats\n", - "from torch import Tensor\n", - "\n", - "\n", - "def _validate_models(models: dict[str, Tensor | ndarray]) -> dict[str, ndarray]:\n", - " \"\"\"Make sure the input `models` is valid and convert all the dict's values to `ndarray`.\n", - "\n", - " Args:\n", - " models (dict[str, Tensor | ndarray]): {\"model name\": sequence of shape (num_images,)}.\n", - " Validations:\n", - " - keys are strings (model names)\n", - " - there are at least two models\n", - " - values are sequences of floats in [0, 1] or `nan`\n", - " - all sequences have the same shape\n", - " - all `nan` values are at the positions\n", - " Returns:\n", - " dict[str, ndarray]: {\"model name\": array (num_images,)}.\n", - " \"\"\"\n", - " if not isinstance(models, dict):\n", - " msg = f\"Expected argument `models` to be a dict, but got {type(models)}.\"\n", - " raise TypeError(msg)\n", - "\n", - " if len(models) < 2:\n", - " msg = \"Expected argument `models` to have at least one key, but got none.\"\n", - " raise ValueError(msg)\n", - "\n", - " ref_num_samples = None\n", - " ref_nans = None\n", - " for key in models:\n", - " if not isinstance(key, str):\n", - " msg = f\"Expected argument `models` to have all keys of type str. Found {type(key)}.\"\n", - " raise TypeError(msg)\n", - "\n", - " value = models[key]\n", - "\n", - " if not isinstance(value, Tensor | ndarray):\n", - " msg = (\n", - " \"Expected argument `models` to have all values of type Tensor or ndarray. \"\n", - " f\"Found {type(value)} on {key=}.\"\n", - " )\n", - " raise TypeError(msg)\n", - "\n", - " if isinstance(value, Tensor):\n", - " models[key] = value = value.numpy()\n", - "\n", - " if not np.issubdtype(value.dtype, np.floating):\n", - " msg = f\"Expected argument `models` to have all values of floating type. Found {value.dtype} on {key=}.\"\n", - " raise ValueError(msg)\n", - "\n", - " if value.ndim != 1:\n", - " msg = f\"Expected argument `models` to have all values of 1D arrays. Found {value.ndim} on {key=}.\"\n", - " raise ValueError(msg)\n", - "\n", - " if ref_num_samples is None:\n", - " ref_num_samples = num_samples = value.shape[0]\n", - " ref_nans = nans = np.isnan(value)\n", - "\n", - " if num_samples != ref_num_samples:\n", - " msg = \"Argument `models` has inconsistent number of samples.\"\n", - " raise ValueError(msg)\n", - "\n", - " if (nans != ref_nans).any():\n", - " msg = \"Argument `models` has inconsistent `nan` values (in different positions).\"\n", - " raise ValueError(msg)\n", - "\n", - " if (value[~nans] < 0).any() or (value[~nans] > 1).any():\n", - " msg = (\n", - " \"Expected argument `models` to have all sequences of floats \\\\in [0, 1]. \"\n", - " f\"Key {key} has values outside this range.\"\n", - " )\n", - " raise ValueError(msg)\n", - "\n", - " return models\n", - "\n", - "\n", - "def test_pairwise(\n", - " models: dict[str, Tensor | ndarray],\n", - " *,\n", - " test: Literal[\"ttest_rel\", \"wilcoxon\"],\n", - " min_abs_diff: float | None = None,\n", - ") -> list[dict[str, Any]]:\n", - " \"\"\"Compare all pairs of models using statistical tests.\n", - "\n", - " Scores are assumed to be *higher is better*.\n", - "\n", - " General hypothesis in the tests:\n", - " - Null hypothesis: two models are equivalent on average.\n", - " - Alternative hypothesis: one model is better than the other (two-sided test).\n", - "\n", - " Args:\n", - " models (dict[str, Tensor | ndarray]): {\"model name\": sequence of shape (num_images,)}.\n", - " test (Literal[\"ttest_rel\", \"wilcoxon\"]): The statistical test to use.\n", - " - \"ttest_rel\": Paired Student's t-test (parametric).\n", - " - \"wilcoxon\": Wilcoxon signed-rank test (non-parametric).\n", - " min_abs_diff (float | None): Minimum absolute difference to consider in the Wilcoxon test. If `None`, all\n", - " differences are considered. Default is `None`. Ignored in the t-test.\n", - " \"\"\"\n", - " models = _validate_models(models)\n", - " if test not in {\"ttest_rel\", \"wilcoxon\"}:\n", - " msg = f\"Expected argument `test` to be 'ttest_rel' or 'wilcoxon', but got '{test}'.\"\n", - " raise ValueError(msg)\n", - " # remove nan values\n", - " models = {k: v[~np.isnan(v)] for k, v in models.items()}\n", - " models_names = sorted(models.keys())\n", - " num_models = len(models)\n", - " comparisons = list(itertools.combinations(range(num_models), 2))\n", - "\n", - " # for each comparison, compute the test and confidence (1 - p-value)\n", - " test_results = []\n", - " for modela_idx, modelb_idx in comparisons: # indices of the sorted model names\n", - " modela = models_names[modela_idx]\n", - " modelb = models_names[modelb_idx]\n", - " modela_scores = models[modela]\n", - " modelb_scores = models[modelb]\n", - " if test == \"ttest_rel\":\n", - " test_result = stats.ttest_rel(modela_scores, modelb_scores, alternative=\"two-sided\")\n", - " else: # test == \"wilcoxon\"\n", - " differences = modela_scores - modelb_scores\n", - " if min_abs_diff is not None:\n", - " differences[np.abs(differences) < min_abs_diff] = 0.0\n", - " # extreme case\n", - " if (differences == 0).all():\n", - " test_result = stats._morestats.WilcoxonResult(np.nan, 1.0) # noqa: SLF001\n", - " else:\n", - " test_result = stats.wilcoxon(differences, zero_method=\"zsplit\", alternative=\"two-sided\")\n", - " test_results.append({\n", - " \"modela\": modela,\n", - " \"modelb\": modelb,\n", - " \"confidence\": 1 - test_result.pvalue,\n", - " \"pvalue\": test_result.pvalue,\n", - " \"statistic\": test_result.statistic,\n", - " })\n", - "\n", - " return test_results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's first test it with the same two models we used before." + "# this will download the aupimo scores for all models given a single dataset\n", + "data_per_set, data_per_image = get_aupimo_benchmark(model=None, dataset=\"mvtec/zipper\")" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1040,44 +578,79 @@ " \n", " \n", " \n", - " modela\n", - " modelb\n", - " confidence\n", - " pvalue\n", - " statistic\n", + " model\n", + " dataset\n", + " fpr_lower_bound\n", + " fpr_upper_bound\n", + " num_thresholds\n", + " thresh_lower_bound\n", + " thresh_upper_bound\n", + " aupimo_mean\n", " \n", " \n", " \n", " \n", - " 0\n", - " A\n", - " B\n", - " 0.995\n", - " 0.005\n", - " 2.872\n", + " 10\n", + " rd++_wr50_ext\n", + " mvtec/zipper\n", + " 0.00001\n", + " 0.00010\n", + " None\n", + " 0.50659\n", + " 0.56788\n", + " 0.31626\n", + " \n", + " \n", + " 11\n", + " simplenet_wr50_ext\n", + " mvtec/zipper\n", + " 0.00001\n", + " 0.00010\n", + " None\n", + " 1.06594\n", + " 1.22361\n", + " 0.75264\n", + " \n", + " \n", + " 12\n", + " uflow_ext\n", + " mvtec/zipper\n", + " 0.00001\n", + " 0.00010\n", + " None\n", + " 0.72529\n", + " 0.80578\n", + " 0.38509\n", " \n", " \n", "\n", "" ], "text/plain": [ - " modela modelb confidence pvalue statistic\n", - "0 A B 0.995 0.005 2.872" + " model dataset fpr_lower_bound fpr_upper_bound \\\n", + "10 rd++_wr50_ext mvtec/zipper 0.00001 0.00010 \n", + "11 simplenet_wr50_ext mvtec/zipper 0.00001 0.00010 \n", + "12 uflow_ext mvtec/zipper 0.00001 0.00010 \n", + "\n", + " num_thresholds thresh_lower_bound thresh_upper_bound aupimo_mean \n", + "10 None 0.50659 0.56788 0.31626 \n", + "11 None 1.06594 1.22361 0.75264 \n", + "12 None 0.72529 0.80578 0.38509 " ] }, - "execution_count": 20, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# parametric test\n", - "pd.DataFrame.from_records(test_pairwise({\"A\": modela, \"B\": modelb}, test=\"ttest_rel\"))" + "# per model information\n", + "data_per_set.tail(3)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1101,270 +674,329 @@ " \n", " \n", " \n", - " modela\n", - " modelb\n", - " confidence\n", - " pvalue\n", - " statistic\n", + " model\n", + " dataset\n", + " sample_index\n", + " aupimo\n", + " path\n", " \n", " \n", " \n", " \n", " 0\n", - " A\n", - " B\n", - " 0.998\n", - " 0.002\n", - " 1965.500\n", + " efficientad_wr101_m_ext\n", + " mvtec/zipper\n", + " 0\n", + " 0.20912\n", + " MVTec/zipper/test/broken_teeth/000.png\n", + " \n", + " \n", + " 1\n", + " efficientad_wr101_m_ext\n", + " mvtec/zipper\n", + " 1\n", + " 0.32493\n", + " MVTec/zipper/test/broken_teeth/001.png\n", + " \n", + " \n", + " 2\n", + " efficientad_wr101_m_ext\n", + " mvtec/zipper\n", + " 2\n", + " 0.30946\n", + " MVTec/zipper/test/broken_teeth/002.png\n", " \n", " \n", "\n", "" ], "text/plain": [ - " modela modelb confidence pvalue statistic\n", - "0 A B 0.998 0.002 1965.500" + " model dataset sample_index aupimo \\\n", + "0 efficientad_wr101_m_ext mvtec/zipper 0 0.20912 \n", + "1 efficientad_wr101_m_ext mvtec/zipper 1 0.32493 \n", + "2 efficientad_wr101_m_ext mvtec/zipper 2 0.30946 \n", + "\n", + " path \n", + "0 MVTec/zipper/test/broken_teeth/000.png \n", + "1 MVTec/zipper/test/broken_teeth/001.png \n", + "2 MVTec/zipper/test/broken_teeth/002.png " ] }, - "execution_count": 21, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# non-parametric test\n", - "pd.DataFrame.from_records(test_pairwise({\"A\": modela, \"B\": modelb}, test=\"wilcoxon\"))" + "# per image information\n", + "data_per_image.head(3)" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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", "text/plain": [ - " modela modelb confidence pvalue statistic\n", - "0 A B 1.000 0.000 1823.000" + "
" ] }, - "execution_count": 22, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# non-parametric test with a minimum absolute difference\n", - "pd.DataFrame.from_records(test_pairwise({\"A\": modela, \"B\": modelb}, test=\"wilcoxon\", min_abs_diff=0.05))" + "fig, ax = plt.subplots(figsize=(5, 6))\n", + "table = data_per_image.dropna(axis=0).pivot_table(index=\"sample_index\", columns=\"model\", values=\"aupimo\", observed=True)\n", + "table.boxplot(vert=False, rot=15, grid=True, ax=ax)\n", + "ax.invert_yaxis()\n", + "ax.set_xlabel(\"AUPIMO [%]\")\n", + "ax.xaxis.set_major_formatter(PercentFormatter(1))\n", + "ax.set_title(f\"Dataset: {data_per_set.iloc[0]['dataset']}\")\n", + "ax.grid(axis=\"x\", linestyle=\"--\", alpha=0.5)\n", + "fig # noqa: B018, RUF100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now let's get the best models from the benchmark in our paper and compare them two by two.\n", + "# Save and load (JSON format)\n", "\n", - "We'll look at the dataset `cashew` from `VisA`.\n", + "How can save and publish your AUPIMO scores?\n", "\n", - "> More details in the paper (see the last cell)." + "Under the hood, `get_aupimo_benchmark()` is downloading a JSON file and deserializing it.\n", + "\n", + "Let's see operations to convert to/from this JSON format. " ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 modelamodelbconfidencepvaluestatistic
0efficientad_wr101_s_extpatchcore_wr1010.9994020.0005981580.000000
1efficientad_wr101_s_extrd++_wr50_ext0.7736590.2263412193.500000
2efficientad_wr101_s_extsimplenet_wr50_ext1.0000000.000000690.500000
3efficientad_wr101_s_extuflow_ext0.9994470.0005531550.500000
4patchcore_wr101rd++_wr50_ext0.9999800.0000201333.000000
5patchcore_wr101simplenet_wr50_ext1.0000000.000000351.500000
6patchcore_wr101uflow_ext0.7318750.2681252213.000000
7rd++_wr50_extsimplenet_wr50_ext1.0000000.000000967.000000
8rd++_wr50_extuflow_ext0.9999450.0000551383.000000
9simplenet_wr50_extuflow_ext1.0000000.000000318.500000
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "dic.keys()=dict_keys([('uflow_ext', 'mvtec/cable')])\n", + "json_dict.keys()=dict_keys(['shared_fpr_metric', 'fpr_lower_bound', 'fpr_upper_bound', 'num_threshs', 'thresh_lower_bound', 'thresh_upper_bound', 'aupimos', 'paths'])\n", + "aupimo_result=AUPIMOResult(fpr_lower_bound=1e-05, fpr_upper_bound=0.0001, num_thresholds=None)\n", + "paths[:2]=['MVTec/cable/test/bent_wire/000.png', 'MVTec/cable/test/bent_wire/001.png']\n" + ] } ], "source": [ - "models = {\n", - " model_name: get_benchmark_aupimo_scores(model_name, \"visa/cashew\", verbose=False)[1].aupimos.numpy()\n", - " for model_name in [\n", - " \"efficientad_wr101_s_ext\",\n", - " \"patchcore_wr101\",\n", - " \"rd++_wr50_ext\",\n", - " \"simplenet_wr50_ext\",\n", - " \"uflow_ext\",\n", - " ]\n", - "}\n", - "models = test_pairwise(models, test=\"wilcoxon\", min_abs_diff=0.1)\n", - "pd.DataFrame.from_records(models).style.background_gradient(cmap=\"jet\", vmin=0, vmax=1, subset=[\"confidence\"])" + "# download the results from the benchmark\n", + "dic = download_aupimo_benchmark_scores(model=\"uflow_ext\", dataset=\"mvtec/cable\")\n", + "print(f\"{dic.keys()=}\")\n", + "\n", + "# it returns the result in two formats\n", + "json_dict, aupimo_result = dic[\"uflow_ext\", \"mvtec/cable\"]\n", + "\n", + "# the raw deserialized JSON dictionary\n", + "print(f\"{json_dict.keys()=}\")\n", + "\n", + "# and the parsed `AUPIMOResult` object\n", + "print(f\"{aupimo_result=}\")\n", + "\n", + "# this is not present in the dataclass `AUPIMOResult`\n", + "paths = json_dict[\"paths\"]\n", + "print(f\"{paths[:2]=}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Compare to the benchmark (coming up)\n", + "Saving from the runtime returned value to a file (`AUPIMOResult` -> `dict`)\n", "\n", - "Compare your freshly trained models to the benchmark datasets in our paper." + "> Recall that the functional and torchmetrics API return `AUPIMOResult` objects\n", + ">\n", + "> See the notebook [701a_aupimo.ipynb](./701a_aupimo.ipynb)." ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "payload.keys()=dict_keys(['fpr_lower_bound', 'fpr_upper_bound', 'num_thresholds', 'thresh_lower_bound', 'thresh_upper_bound', 'aupimos'])\n", + "payload.keys()=dict_keys(['fpr_lower_bound', 'fpr_upper_bound', 'num_thresholds', 'thresh_lower_bound', 'thresh_upper_bound', 'aupimos', 'paths'])\n" + ] + } + ], + "source": [ + "payload = aupimo_result_to_json_dict(aupimo_result)\n", + "print(f\"{payload.keys()=}\")\n", + "\n", + "# you can optionally save the paths to the images\n", + "# where the AUPIMO scores were computed from\n", + "payload = aupimo_result_to_json_dict(aupimo_result, paths=paths)\n", + "print(f\"{payload.keys()=}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check that it can be saved to a file and loaded back." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12K\t/tmp/tmp5ji7o55e/aupimo_result.json\n", + "{\n", + " \"fpr_lower_bound\": 1e-05,\n", + " \"fpr_upper_bound\": 0.0001,\n", + " \"num_thresholds\": null,\n", + " \"thresh_lower_bound\": 0.6708730459213257,\n", + " \"thresh_upper_bound\": 0.7257592082023621,\n", + " \"aupimos\": [\n", + " 0.7839027033058256,\n", + " 0.9115826454761932,\n", + " 0.5865399118429107,\n" + ] + } + ], + "source": [ + "from tempfile import TemporaryDirectory\n", + "\n", + "with TemporaryDirectory() as tmpdir:\n", + " cache_dir = Path(tmpdir)\n", + "\n", + " # SAVE\n", + " payload = aupimo_result_to_json_dict(aupimo_result, paths=paths)\n", + " with (cache_dir / \"aupimo_result.json\").open(\"w\") as file_out:\n", + " json.dump(payload, file_out, indent=4)\n", + "\n", + " !du -sh {cache_dir / \"aupimo_result.json\"}\n", + " !head -n 10 {cache_dir / \"aupimo_result.json\"}\n", + "\n", + " # LOAD\n", + " with (cache_dir / \"aupimo_result.json\").open(\"r\") as file_in:\n", + " payload_reloaded = json.load(file_in)\n", + "\n", + "# compare the original and reloaded objects\n", + "aupimo_result_reloaded = aupimo_result_from_json_dict(payload_reloaded)\n", + "assert torch.allclose(aupimo_result.aupimos, aupimo_result_reloaded.aupimos, equal_nan=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Compare your own scores\n", + "\n", + "Let's make a quick traing and evaluation of a model and compare it with the benchmark." + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# TODO(jpcbertoldo): implement utility function to load and compare to the results from the benchmark # noqa: TD003" + "# train the model\n", + "task = TaskType.SEGMENTATION\n", + "datamodule = MVTec(\n", + " root=dataset_root,\n", + " category=\"zipper\",\n", + " task=task,\n", + " # warning, this will make an unfair comparison with the benchmark\n", + " # because the test will be in lower resolution\n", + " # we're overseeing this issue here for the sake of simplicity and speed\n", + " image_size=256,\n", + ")\n", + "model = Padim(layers=[\"layer1\"], n_features=64, backbone=\"resnet18\", pre_trained=True)\n", + "engine = Engine(pixel_metrics=\"AUPIMO\", logger=False)\n", + "engine.fit(datamodule=datamodule, model=model)\n", + "predictions = engine.predict(dataloaders=datamodule.test_dataloader(), model=model, return_predictions=True)\n", + "\n", + "# with `False` all the values are returned in a dataclass\n", + "aupimo = AUPIMO(return_average=False)\n", + "\n", + "labels = []\n", + "for batch in predictions:\n", + " labels.append(batch[\"label\"])\n", + " aupimo.update(anomaly_maps=batch[\"anomaly_maps\"].squeeze(dim=1), masks=batch[\"mask\"])\n", + "labels = torch.cat(labels, dim=0)\n", + "\n", + "_, aupimo_result = aupimo.compute()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Combine the results from the benchmark with the new results and plot them together." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# re-fetch the benchmark results\n", + "_, data_per_image = get_aupimo_benchmark(model=None, dataset=\"mvtec/zipper\")\n", + "data_benchmark = data_per_image[[\"model\", \"aupimo\"]].copy()\n", + "data_benchmark[\"from\"] = \"benchmark\"\n", + "\n", + "# create a DataFrame with the results of our model\n", + "data_ours = pd.DataFrame({\"aupimo\": aupimo_result.aupimos})\n", + "data_ours[\"model\"] = \"my_cool_model\"\n", + "data_ours[\"from\"] = \"ours\"\n", + "\n", + "# concatenate the two DataFrames\n", + "data_compare = pd.concat([data_benchmark, data_ours], axis=0)\n", + "\n", + "# plot the results\n", + "fig, ax = plt.subplots(figsize=(5, 6))\n", + "grouped = data_compare.groupby([\"from\", \"model\"])\n", + "grouped.boxplot(vert=False, rot=10, grid=True, ax=ax, subplots=False)\n", + "ax.invert_yaxis()\n", + "ax.set_xlabel(\"AUPIMO [%]\")\n", + "ax.xaxis.set_major_formatter(PercentFormatter(1))\n", + "\n", + "\n", + "def fmt_yticklabel(string: str) -> str:\n", + " \"\"\"Better format for the y-tick labels.\"\"\"\n", + " parts = string[1:-1].split(\", \")\n", + " return f\"({parts[0]}) {parts[1]}\"\n", + "\n", + "\n", + "ax.set_yticklabels([fmt_yticklabel(tick.get_text()) for tick in ax.get_yticklabels()])\n", + "ax.set_title(f\"Dataset: {data_per_set.iloc[0]['dataset']}\")\n", + "ax.grid(axis=\"x\", linestyle=\"--\", alpha=0.5)\n", + "fig # noqa: B018, RUF100" ] }, { @@ -1406,7 +1038,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.15" }, "orig_nbformat": 4 }, diff --git a/src/anomalib/metrics/pimo/utils_benchmark.py b/src/anomalib/metrics/pimo/utils_benchmark.py index aabdf3c4f4..aafac07d79 100644 --- a/src/anomalib/metrics/pimo/utils_benchmark.py +++ b/src/anomalib/metrics/pimo/utils_benchmark.py @@ -13,10 +13,11 @@ from itertools import product from pathlib import Path +import numpy as np import pandas as pd import requests import torch -from pandas import DataFrame +from pandas import DataFrame, Series from .dataclasses import AUPIMOResult @@ -335,4 +336,12 @@ def get_aupimo_benchmark( .astype({"sample_index": int, "aupimo": float, "path": "string"}) ) + def get_average_aupimo(row: Series) -> float: + model = row["model"] # noqa: F841 + dataset = row["dataset"] # noqa: F841 + aupimos = data_per_image.query("model == @model and dataset == @dataset")["aupimo"] + return np.nanmean(aupimos) + + data_per_set["aupimo_mean"] = data_per_set.apply(get_average_aupimo, axis=1) + return data_per_set, data_per_image