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Merged
daavoo merged 2 commits into from
Jan 30, 2023
Merged

Add eval metrics notebook #162

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f4fc8cd
add results/evaluate metrics
alex000kim Jan 26, 2023
659ac47
clear notebook
alex000kim Jan 26, 2023
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50 changes: 45 additions & 5 deletions example-get-started-cv/code/notebooks/TrainSegModel.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -39,7 +39,8 @@
"from fastai.metrics import DiceMulti\n",
"from fastai.vision.all import (Resize, SegmentationDataLoaders, aug_transforms,\n",
" imagenet_stats, models, unet_learner)\n",
"from ruamel.yaml import YAML"
"from ruamel.yaml import YAML\n",
"from PIL import Image"
]
},
{
Expand Down Expand Up @@ -100,7 +101,7 @@
"source": [
"bs = 8\n",
"valid_pct = 0.20\n",
"img_size = 512\n",
"img_size = 256\n",
"\n",
"data_loader = SegmentationDataLoaders.from_label_func(\n",
" path=train_data_dir,\n",
Expand Down Expand Up @@ -142,6 +143,23 @@
"### Train multiple models with different learning rates using `DVCLiveCallback`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def dice(mask_pred, mask_true, classes=[0, 1], eps=1e-6):\n",
" dice_list = []\n",
" for c in classes:\n",
" y_true = mask_true == c\n",
" y_pred = mask_pred == c\n",
" intersection = 2.0 * np.sum(y_true * y_pred)\n",
" dice = intersection / (np.sum(y_true) + np.sum(y_pred) + eps)\n",
" dice_list.append(dice)\n",
" return np.mean(dice_list)"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand All @@ -154,7 +172,8 @@
" live = Live(dir=os.path.join('results', 'train'), \n",
" report=\"md\", \n",
" save_dvc_exp=True)\n",
" live.log_param(\"base_lr\", base_lr)\n",
" live.summary[\"base_lr\"] =base_lr\n",
" live.make_summary()\n",
Comment on lines +175 to +176
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@daavoo daavoo Jan 30, 2023

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@alex000kim any reason to not make this a parameter?

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@alex000kim alex000kim Jan 30, 2023

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Do you mean via .log_param()?
I can update that, but for this one, I simply followed the docs examples for fastai:
https://dvc.org/doc/dvclive/api-reference/ml-frameworks/fastai#examples
image

" learn = unet_learner(data_loader, \n",
" arch=getattr(models, train_arch), \n",
" metrics=DiceMulti)\n",
Expand All @@ -164,7 +183,25 @@
" }\n",
" learn.fine_tune(\n",
" **fine_tune_args,\n",
" cbs=[DVCLiveCallback(live=live)])"
" cbs=[DVCLiveCallback(live=live)])\n",
" \n",
" test_img_fpaths = get_files(Path(\"data\") / \"test_data\", extensions=\".jpg\")\n",
" test_dl = learn.dls.test_dl(test_img_fpaths)\n",
" preds, _ = learn.get_preds(dl=test_dl)\n",
" masks_pred = np.array(preds[:, 1, :] > 0.5, dtype=int)\n",
" test_mask_fpaths = [\n",
" get_mask_path(fpath, Path(\"data\") / \"test_data\") for fpath in test_img_fpaths\n",
" ]\n",
" masks_true = [Image.open(mask_path) for mask_path in test_mask_fpaths]\n",
" masks_true = [\n",
" np.array(img.resize((img_size, img_size)), dtype=int) for img in masks_true\n",
" ]\n",
" with Live(\"results/evaluate\", report=\"md\") as live:\n",
" dice_multi = 0.0\n",
" for ii in range(len(masks_true)):\n",
" mask_pred, mask_true = masks_pred[ii], masks_true[ii]\n",
" dice_multi += dice(mask_true, mask_pred) / len(masks_true)\n",
" live.summary[\"dice_multi\"] = dice_multi"
]
},
{
Expand All @@ -183,8 +220,11 @@
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"# Apply best performing experiment to the workspace\n",
"!EXP=$(dvc exp show --csv --sort-by dice_multi | tail -n 1 | cut -d , -f 1) && dvc exp apply $EXP"
"BEST_EXP_NAME=$(dvc exp show --csv --sort-by dice_multi | tail -n 1 | cut -d , -f 1)\n",
"echo \"Applying $BEST_EXP_NAME\"\n",
"dvc exp apply \"$BEST_EXP_NAME\""
]
},
{
Expand Down