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Merged
zhulinchng merged 8 commits into from
Jan 14, 2026
Merged

formatting and simplification #2

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4a4a07c
formatting and simplification
zhulinchng Jan 14, 2026
d7bc93c
Initial plan
Copilot Jan 14, 2026
19cda36
Remove hardcoded class assertions to support multiple datasets
Copilot Jan 14, 2026
2c4f215
Update trainClassifier.py
zhulinchng Jan 14, 2026
bb4a888
Merge pull request #3 from zhulinchng/copilot/sub-pr-2
zhulinchng Jan 14, 2026
b45c4e1
Update tools/synth.py
zhulinchng Jan 14, 2026
ff4bfc5
Initial plan
Copilot Jan 14, 2026
cf4beb7
Merge pull request #4 from zhulinchng/copilot/sub-pr-2-again
zhulinchng Jan 14, 2026
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36 changes: 24 additions & 12 deletions analysis_CLIP.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -110,7 +110,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -128,7 +128,7 @@
"genInput_dir = f\"{dataset_type}/train2\"\n",
"val_path = f\"{dataset_type}/val2\"\n",
"synth_path = f\"{dataset_type}/synthetic2\"\n",
"metadata_path = f\"{dataset_type}/metadata/analysis\" # create the metadata folder if it does not exist"
"metadata_path = f\"{dataset_type}/metadata/analysis\" # create the metadata folder if it does not exist"
]
},
{
Expand Down Expand Up @@ -240,7 +240,9 @@
"metadata": {},
"outputs": [],
"source": [
"clip_df.to_csv(f\"{metadata_path}/clip_analyze_{synth_path.replace('/','_')}.csv\", index=False)"
"clip_df.to_csv(\n",
" f\"{metadata_path}/clip_analyze_{synth_path.replace('/','_')}.csv\", index=False\n",
")"
]
},
{
Expand Down Expand Up @@ -321,7 +323,7 @@
],
"source": [
"# get base model class_to_idx\n",
"val2_path = f\"{dataset_type}/val\" # path to the full validation dataset that the classifier was tested on\n",
"val2_path = f\"{dataset_type}/val\" # path to the full validation dataset that the classifier was tested on\n",
"data.cacheValData(val2_path, \"imagenet2_val\", save_path=cache_path)\n",
"val2_dataset = data.loadData(\"imagenet2_val\", cache_path=cache_path)\n",
"base_class_to_idx = val2_dataset.class_to_idx\n",
Expand Down Expand Up @@ -967,11 +969,15 @@
},
{
"cell_type": "code",
"execution_count": 76,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"analysis.analyze_clf_stats(df, class_pairs=class_pairs, save_csv=f\"{metadata_path}/stats_clf_{synth_path.replace('/','_')}.csv\")"
"analysis.analyze_clf_stats(\n",
" df,\n",
" class_pairs=class_pairs,\n",
" save_csv=f\"{metadata_path}/stats_clf_{synth_path.replace('/','_')}.csv\",\n",
")"
]
},
{
Expand Down Expand Up @@ -1489,7 +1495,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -1506,10 +1512,14 @@
" genInput_dir = f\"{combinations[ds]['dataset_type']}/train\"\n",
" val_path = f\"{combinations[ds]['dataset_type']}/val\"\n",
" synth_path = f\"{combinations[ds]['dataset_type']}/synthetic2{combinations[ds]['c']}\"\n",
" metadata_path = f\"{combinations[ds]['dataset_type']}/metadata/analysis{combinations[ds]['c']}\"\n",
" metadata_path = (\n",
" f\"{combinations[ds]['dataset_type']}/metadata/analysis{combinations[ds]['c']}\"\n",
" )\n",
" prompt_format = combinations[ds][\"prompt_format\"]\n",
"\n",
" df = pd.read_csv(f\"{metadata_path}/clip_clf_{synth_path.replace('/','_')}.csv\").copy()\n",
" df = pd.read_csv(\n",
" f\"{metadata_path}/clip_clf_{synth_path.replace('/','_')}.csv\"\n",
" ).copy()\n",
" classpairs = sorted(list(df[\"actual_class\"].unique()))\n",
" classnames = dict()\n",
" prompts = dict()\n",
Expand Down Expand Up @@ -1553,7 +1563,7 @@
" # main[\"Class\"].append(classnames[row[\"actual_class\"]])\n",
" # main[\"Prompt\"].append(prompts[row[\"actual_class\"]])\n",
" # main[\"CLIP Score to Prompt 1\"].append(row[\"sim_0\"])\n",
" # main[\"CLIP Score to Prompt 2\"].append(row[\"sim_1\"]) "
" # main[\"CLIP Score to Prompt 2\"].append(row[\"sim_1\"])"
]
},
{
Expand Down Expand Up @@ -1624,7 +1634,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand All @@ -1650,7 +1660,9 @@
],
"source": [
"g = sns.FacetGrid(df_main, col=\"Type\", row=\"Dataset\", hue=\"Class\", sharex=False)\n",
"ax = g.map_dataframe(sns.kdeplot, x=\"CLIP Score to Prompt 1\", y=\"CLIP Score to Prompt 2\")\n",
"ax = g.map_dataframe(\n",
" sns.kdeplot, x=\"CLIP Score to Prompt 1\", y=\"CLIP Score to Prompt 2\"\n",
")\n",
"ax.set_titles(\"CLIP Score for {row_name}: {col_name}\")\n",
"ax.figure.legend(\n",
" loc=\"upper center\",\n",
Expand Down
24 changes: 12 additions & 12 deletions analysis_Classifier.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -40,12 +40,12 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"wandb_results_dir = 'results/wandb'\n",
"output_dir = 'results'"
"wandb_results_dir = \"results/wandb\"\n",
"output_dir = \"results\""
]
},
{
Expand All @@ -69,16 +69,16 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dfs = dict()\n",
"for t in type:\n",
" fs = [f for f in files if t in f]\n",
" df = pd.concat([pd.read_csv(f'{wandb_results_dir}/' + f) for f in fs], axis=1)\n",
" df = pd.concat([pd.read_csv(f\"{wandb_results_dir}/\" + f) for f in fs], axis=1)\n",
" # drop columns if contain __MIN, __MAX, _step\n",
" df = df.drop([c for c in df.columns if '__MIN' in c], axis=1)\n",
" df = df.drop([c for c in df.columns if \"__MIN\" in c], axis=1)\n",
" df = df.drop([c for c in df.columns if \"__MAX\" in c], axis=1)\n",
" df = df.drop([c for c in df.columns if \"_step\" in c], axis=1)\n",
" # rename columns by replacing model names\n",
Expand All @@ -103,15 +103,15 @@
" \"epoch\": [],\n",
" }\n",
" for p in params:\n",
" pivot_dict[p.replace('epoch/','')] = []\n",
" pivot_dict[p.replace(\"epoch/\", \"\")] = []\n",
" for i, row in df.iterrows():\n",
" ep = row[\"epoch/epoch\"]\n",
" for m in models:\n",
" pivot_dict[\"type\"].append(t)\n",
" pivot_dict[\"model\"].append(m)\n",
" pivot_dict[\"epoch\"].append(ep)\n",
" for p in params:\n",
" param = p.replace('epoch/','')\n",
" param = p.replace(\"epoch/\", \"\")\n",
" pivot_dict[param].append(row[m + \" - \" + p])\n",
" pivot_df = pd.DataFrame(pivot_dict)\n",
" dfs[t] = pivot_df"
Expand Down Expand Up @@ -299,7 +299,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -376,14 +376,14 @@
}
],
"source": [
"df_plot['data'] = df_plot['model'].str.split('_').str[1]\n",
"df_plot[\"data\"] = df_plot[\"model\"].str.split(\"_\").str[1]\n",
"df_plot[\"model\"] = df_plot[\"model\"].str.split(\"_\").str[0]\n",
"df_plot.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand All @@ -407,7 +407,7 @@
"g.add_legend()\n",
"g.set_titles(\"{row_name}: {col_name}\")\n",
"g.set_axis_labels(\"Epoch\", \"Accuracy@1\")\n",
"g.savefig(f'{output_dir}/merged_results.svg')"
"g.savefig(f\"{output_dir}/merged_results.svg\")"
]
},
{
Expand Down
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