212 lines
7.4 KiB
Markdown
212 lines
7.4 KiB
Markdown
# Analysis
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<!-- TOC -->
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- [Log Analysis](#log-analysis)
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- [Plot Curves](#plot-curves)
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- [Calculate Training Time](#calculate-training-time)
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- [Result Analysis](#result-analysis)
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- [Evaluate Results](#evaluate-results)
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- [View Typical Results](#view-typical-results)
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- [Model Complexity](#model-complexity)
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- [FAQs](#faqs)
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<!-- TOC -->
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## Log Analysis
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### Plot Curves
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`tools/analysis_tools/analyze_logs.py` plots curves of given keys according to the log files.
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<div align=center><img src="../_static/image/tools/analysis/analyze_log.jpg" style=" width: 75%; height: 30%; "></div>
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```shell
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python tools/analysis_tools/analyze_logs.py plot_curve \
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${JSON_LOGS} \
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[--keys ${KEYS}] \
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[--title ${TITLE}] \
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[--legend ${LEGEND}] \
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[--backend ${BACKEND}] \
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[--style ${STYLE}] \
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[--out ${OUT_FILE}] \
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[--window-size ${WINDOW_SIZE}]
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```
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**Description of all arguments**：
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- `json_logs` : The paths of the log files, separate multiple files by spaces.
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- `--keys` : The fields of the logs to analyze, separate multiple keys by spaces. Defaults to 'loss'.
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- `--title` : The title of the figure. Defaults to use the filename.
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- `--legend` : The names of legend, the number of which must be equal to `len(${JSON_LOGS}) * len(${KEYS})`. Defaults to use `"${JSON_LOG}-${KEYS}"`.
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- `--backend` : The backend of matplotlib. Defaults to auto selected by matplotlib.
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- `--style` : The style of the figure. Default to `whitegrid`.
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- `--out` : The path of the output picture. If not set, the figure won't be saved.
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- `--window-size`: The shape of the display window. The format should be `'W*H'`. Defaults to `'12*7'`.
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```{note}
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The `--style` option depends on `seaborn` package, please install it before setting it.
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```
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Examples:
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- Plot the loss curve in training.
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```shell
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python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys loss --legend loss
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```
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- Plot the top-1 accuracy and top-5 accuracy curves, and save the figure to results.jpg.
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```shell
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python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys accuracy_top-1 accuracy_top-5 --legend top1 top5 --out results.jpg
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```
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- Compare the top-1 accuracy of two log files in the same figure.
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```shell
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python tools/analysis_tools/analyze_logs.py plot_curve log1.json log2.json --keys accuracy_top-1 --legend exp1 exp2
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```
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```{note}
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The tool will automatically select to find keys in training logs or validation logs according to the keys.
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Therefore, if you add a custom evaluation metric, please also add the key to `TEST_METRICS` in this tool.
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```
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### Calculate Training Time
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`tools/analysis_tools/analyze_logs.py` can also calculate the training time according to the log files.
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```shell
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python tools/analysis_tools/analyze_logs.py cal_train_time \
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${JSON_LOGS}
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[--include-outliers]
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```
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**Description of all arguments**:
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- `json_logs` : The paths of the log files, separate multiple files by spaces.
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- `--include-outliers` : If set, include the first iteration in each epoch (Sometimes the time of first iterations is longer).
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Example:
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```shell
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python tools/analysis_tools/analyze_logs.py cal_train_time work_dirs/some_exp/20200422_153324.log.json
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```
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The output is expected to be like the below.
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```text
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-----Analyze train time of work_dirs/some_exp/20200422_153324.log.json-----
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slowest epoch 68, average time is 0.3818
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fastest epoch 1, average time is 0.3694
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time std over epochs is 0.0020
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average iter time: 0.3777 s/iter
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```
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## Result Analysis
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With the `--out` argument in `tools/train.py`, we can save the inference results of all samples as a file.
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And with this result file, we can do further analysis.
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### Evaluate Results
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`tools/analysis_tools/eval_metric.py` can evaluate metrics again.
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```shell
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python tools/analysis_tools/eval_metric.py \
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${CONFIG} \
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${RESULT} \
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[--metrics ${METRICS}] \
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[--cfg-options ${CFG_OPTIONS}] \
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[--metric-options ${METRIC_OPTIONS}]
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```
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Description of all arguments:
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- `config` : The path of the model config file.
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- `result`: The Output result file in json/pickle format from `tools/test.py`.
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- `--metrics` : Evaluation metrics, the acceptable values depend on the dataset.
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- `--cfg-options`: If specified, the key-value pair config will be merged into the config file, for more details please refer to [Tutorial 1: Learn about Configs](../tutorials/config.md)
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- `--metric-options`: If specified, the key-value pair arguments will be passed to the `metric_options` argument of dataset's `evaluate` function.
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```{note}
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In `tools/test.py`, we support using `--out-items` option to select which kind of results will be saved. Please ensure the result file includes "class_scores" to use this tool.
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```
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**Examples**:
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```shell
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python tools/analysis_tools/eval_metric.py configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py your_result.pkl --metrics accuracy --metric-options "topk=(1,5)"
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```
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### View Typical Results
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`tools/analysis_tools/analyze_results.py` can save the images with the highest scores in successful or failed prediction.
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```shell
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python tools/analysis_tools/analyze_results.py \
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${CONFIG} \
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${RESULT} \
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[--out-dir ${OUT_DIR}] \
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[--topk ${TOPK}] \
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[--cfg-options ${CFG_OPTIONS}]
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```
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**Description of all arguments**:
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- `config` : The path of the model config file.
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- `result`: Output result file in json/pickle format from `tools/test.py`.
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- `--out_dir`: Directory to store output files.
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- `--topk`: The number of images in successful or failed prediction with the highest `topk` scores to save. If not specified, it will be set to 20.
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- `--cfg-options`: If specified, the key-value pair config will be merged into the config file, for more details please refer to [Tutorial 1: Learn about Configs](../tutorials/config.md)
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```{note}
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In `tools/test.py`, we support using `--out-items` option to select which kind of results will be saved. Please ensure the result file includes "pred_score", "pred_label" and "pred_class" to use this tool.
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```
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**Examples**:
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```shell
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python tools/analysis_tools/analyze_results.py \
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configs/resnet/resnet50_b32x8_imagenet.py \
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result.pkl \
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--out_dir results \
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--topk 50
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```
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## Model Complexity
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### Get the FLOPs and params (experimental)
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We provide a script adapted from [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch) to compute the FLOPs and params of a given model.
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```shell
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python tools/analysis_tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}]
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```
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Description of all arguments:
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- `config` : The path of the model config file.
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- `--shape`: Input size, support single value or double value parameter, such as `--shape 256` or `--shape 224 256`. If not set, default to be `224 224`.
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You will get a result like this.
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```text
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==============================
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Input shape: (3, 224, 224)
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Flops: 4.12 GFLOPs
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Params: 25.56 M
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==============================
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```
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```{warning}
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This tool is still experimental and we do not guarantee that the number is correct. You may well use the result for simple comparisons, but double-check it before you adopt it in technical reports or papers.
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- FLOPs are related to the input shape while parameters are not. The default input shape is (1, 3, 224, 224).
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- Some operators are not counted into FLOPs like GN and custom operators. Refer to [`mmcv.cnn.get_model_complexity_info()`](https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/utils/flops_counter.py) for details.
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```
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## FAQs
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- None
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