[add] ava.py

.ipynb_checkpoints/AVA-checkpoint.py

@@ -0,0 +1,66 @@
+# coding=utf-8
+import os
+import datasets
+import joblib
+from pathlib import Path
+
+
+_BASE_HF_URL = Path("./data")
+_CITATION = ""
+_HOMEPAGE = ""
+_DESCRIPTION = ""
+_DATA_URL = {
+    "train": [_BASE_HF_URL/"images.tar.gz"]
+}
+DICT_METADATA = joblib.load(_BASE_HF_URL / "metadata.pkl")
+
+
+class AVA(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "rating_counts": datasets.features.Sequence(datasets.Value("int32")),
+                    "text_tag_0": datasets.Value("string"),
+                    "text_tag_1": datasets.Value("string")
+                }
+            ),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        archives = dl_manager.download(_DATA_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "archives": [dl_manager.iter_archive(archive) for archive in archives["train"]],
+                    "split": "train",
+                },
+            )
+            ]
+
+    def _generate_examples(self, archives, split):
+        """Yields examples."""
+        idx = 0
+        for archive in archives:
+            for path, file in archive:
+                if path.endswith(".jpg"):
+                    # image filepath format: <IMAGE_FILE NAME>_<SYNSET_ID>.JPEG
+                    _id = int(os.path.splitext(b[0])[0].split('/')[-1])
+                    _metadata = DICT_METADATA[_id]
+                    ex = {"image": {"path": path, "bytes": file.read()}, 
+                          "rating_counts": _metadata[0],
+                          "text_tag0":_metadata[1],
+                          "text_tag1": _metadata[2]}
+                    yield idx, ex
+                    idx += 1

AVA.py

@@ -0,0 +1,66 @@
+# coding=utf-8
+import os
+import datasets
+import joblib
+from pathlib import Path
+
+
+_BASE_HF_URL = Path("./data")
+_CITATION = ""
+_HOMEPAGE = ""
+_DESCRIPTION = ""
+_DATA_URL = {
+    "train": [_BASE_HF_URL/"images.tar.gz"]
+}
+DICT_METADATA = joblib.load(_BASE_HF_URL / "metadata.pkl")
+
+
+class AVA(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "rating_counts": datasets.features.Sequence(datasets.Value("int32")),
+                    "text_tag_0": datasets.Value("string"),
+                    "text_tag_1": datasets.Value("string")
+                }
+            ),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        archives = dl_manager.download(_DATA_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "archives": [dl_manager.iter_archive(archive) for archive in archives["train"]],
+                    "split": "train",
+                },
+            )
+            ]
+
+    def _generate_examples(self, archives, split):
+        """Yields examples."""
+        idx = 0
+        for archive in archives:
+            for path, file in archive:
+                if path.endswith(".jpg"):
+                    # image filepath format: <IMAGE_FILE NAME>_<SYNSET_ID>.JPEG
+                    _id = int(os.path.splitext(b[0])[0].split('/')[-1])
+                    _metadata = DICT_METADATA[_id]
+                    ex = {"image": {"path": path, "bytes": file.read()}, 
+                          "rating_counts": _metadata[0],
+                          "text_tag0":_metadata[1],
+                          "text_tag1": _metadata[2]}
+                    yield idx, ex
+                    idx += 1

data/metadata.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7f4717e56ab446a1c6b58c6ea44b28c180e05e4eda3f895eeec704569dbe328f
+size 42369204

notebooks/.ipynb_checkpoints/EDA-checkpoint.ipynb

@@ -0,0 +1,168 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "0dbc59b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "import glob\n",
+    "from omegaconf import OmegaConf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "1fac2763",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_len = {}\n",
+    "train_len = {}\n",
+    "files = glob.glob(\"../../AVA_src/aesthetics_image_lists/*.jpgl\")\n",
+    "for file in files:\n",
+    "    file = file.split('/')[-1]\n",
+    "    if 'test' in file:\n",
+    "        test_len[file] = len(pd.read_csv(os.path.join(\"../../AVA_src/aesthetics_image_lists\",file), sep=\" \",header = None))\n",
+    "    elif 'train' in file:\n",
+    "        train_len[file] = len(pd.read_csv(os.path.join(\"../../AVA_src/aesthetics_image_lists\",file), sep=\" \",header = None))\n",
+    "    else:\n",
+    "        print(f\"Pass {file}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "0d089d64",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cityscape_test.jpgl: 2500\n",
+      "portrait_test.jpgl: 2500\n",
+      "stilllife_test.jpgl: 2500\n",
+      "floral_test.jpgl: 2500\n",
+      "architecture_test.jpgl: 2500\n",
+      "animal_test.jpgl: 2500\n",
+      "generic_test.jpgl: 20000\n",
+      "landscape_test.jpgl: 2500\n",
+      "fooddrink_test.jpgl: 2500\n",
+      "\n",
+      "generic_ls_train.jpgl: 20000\n",
+      "portrait_train.jpgl: 2500\n",
+      "landscape_train.jpgl: 2500\n",
+      "architecture_train.jpgl: 2500\n",
+      "animal_train.jpgl: 2500\n",
+      "cityscape_train.jpgl: 2500\n",
+      "floral_train.jpgl: 2500\n",
+      "stilllife_train.jpgl: 2500\n",
+      "fooddrink_train.jpgl: 2500\n",
+      "generic_ss_train.jpgl: 2500\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(OmegaConf.to_yaml(test_len))\n",
+    "print(OmegaConf.to_yaml(train_len))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "7e25c9f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_len = {}\n",
+    "train_len = {}\n",
+    "files = glob.glob(\"../../AVA_src/style_image_lists/*.jpgl\")\n",
+    "for file in files:\n",
+    "    file = file.split('/')[-1]\n",
+    "    if 'test' in file:\n",
+    "        test_len[file] = len(pd.read_csv(os.path.join(\"../../AVA_src/style_image_lists\",file), sep=\" \",header = None))\n",
+    "    elif 'train' in file:\n",
+    "        train_len[file] = len(pd.read_csv(os.path.join(\"../../AVA_src/style_image_lists\",file), sep=\" \",header = None))\n",
+    "    else:\n",
+    "        print(f\"Pass {file}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "86ff27df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test.jpgl: 2809\n",
+      "\n",
+      "train.jpgl: 11270\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(OmegaConf.to_yaml(test_len))\n",
+    "print(OmegaConf.to_yaml(train_len))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "huggingface",
+   "language": "python",
+   "name": "huggingface"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.15"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/.ipynb_checkpoints/Test-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/EDA.ipynb

@@ -0,0 +1,168 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "0dbc59b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "import glob\n",
+    "from omegaconf import OmegaConf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "1fac2763",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_len = {}\n",
+    "train_len = {}\n",
+    "files = glob.glob(\"../../AVA_src/aesthetics_image_lists/*.jpgl\")\n",
+    "for file in files:\n",
+    "    file = file.split('/')[-1]\n",
+    "    if 'test' in file:\n",
+    "        test_len[file] = len(pd.read_csv(os.path.join(\"../../AVA_src/aesthetics_image_lists\",file), sep=\" \",header = None))\n",
+    "    elif 'train' in file:\n",
+    "        train_len[file] = len(pd.read_csv(os.path.join(\"../../AVA_src/aesthetics_image_lists\",file), sep=\" \",header = None))\n",
+    "    else:\n",
+    "        print(f\"Pass {file}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "0d089d64",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cityscape_test.jpgl: 2500\n",
+      "portrait_test.jpgl: 2500\n",
+      "stilllife_test.jpgl: 2500\n",
+      "floral_test.jpgl: 2500\n",
+      "architecture_test.jpgl: 2500\n",
+      "animal_test.jpgl: 2500\n",
+      "generic_test.jpgl: 20000\n",
+      "landscape_test.jpgl: 2500\n",
+      "fooddrink_test.jpgl: 2500\n",
+      "\n",
+      "generic_ls_train.jpgl: 20000\n",
+      "portrait_train.jpgl: 2500\n",
+      "landscape_train.jpgl: 2500\n",
+      "architecture_train.jpgl: 2500\n",
+      "animal_train.jpgl: 2500\n",
+      "cityscape_train.jpgl: 2500\n",
+      "floral_train.jpgl: 2500\n",
+      "stilllife_train.jpgl: 2500\n",
+      "fooddrink_train.jpgl: 2500\n",
+      "generic_ss_train.jpgl: 2500\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(OmegaConf.to_yaml(test_len))\n",
+    "print(OmegaConf.to_yaml(train_len))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "7e25c9f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_len = {}\n",
+    "train_len = {}\n",
+    "files = glob.glob(\"../../AVA_src/style_image_lists/*.jpgl\")\n",
+    "for file in files:\n",
+    "    file = file.split('/')[-1]\n",
+    "    if 'test' in file:\n",
+    "        test_len[file] = len(pd.read_csv(os.path.join(\"../../AVA_src/style_image_lists\",file), sep=\" \",header = None))\n",
+    "    elif 'train' in file:\n",
+    "        train_len[file] = len(pd.read_csv(os.path.join(\"../../AVA_src/style_image_lists\",file), sep=\" \",header = None))\n",
+    "    else:\n",
+    "        print(f\"Pass {file}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "86ff27df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test.jpgl: 2809\n",
+      "\n",
+      "train.jpgl: 11270\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(OmegaConf.to_yaml(test_len))\n",
+    "print(OmegaConf.to_yaml(train_len))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "huggingface",
+   "language": "python",
+   "name": "huggingface"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.15"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/Test.ipynb

@@ -0,0 +1,1095 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "aef315bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import PIL\n",
+    "x = os.listdir(\"../../AVA_src/images/images\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c0ed6498",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       " '653812.jpg',\n",
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+       " '904792.jpg',\n",
+       " '897639.jpg',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1611451",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "huggingface",
+   "language": "python",
+   "name": "huggingface"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.15"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

scripts/.ipynb_checkpoints/run-checkpoint.py

@@ -0,0 +1,24 @@
+import pandas as pd
+from tqdm import tqdm
+import joblib
+
+if __name__ == '__main__':
+    df_scores = pd.read_csv('../../AVA_src/AVA.txt',sep = " ",header = None)
+    df_scores.columns = df_scores.columns + 1
+    df_scores = df_scores.drop(columns=df_scores.columns[0], axis=1)
+    df_tags = pd.read_csv('../../AVA_src/tags.txt',sep = "|",header = None)
+    
+    dict_tags = {0:'None'}
+    for row in df_tags.iterrows():
+        row = row[1]
+        dict_tags[row[0]] = row[1]
+        
+    dict_ = {}
+    for idx in tqdm(range(len(df_scores))):
+        row = df_scores.iloc[idx,:]
+        _id = row[2]
+        ratings = row.values[1:11]
+        textual_tag0 = dict_tags[row[13]]
+        textual_tag1 = dict_tags[row[14]]
+        dict_[_id] = (ratings,textual_tag0,textual_tag1)
+    joblib.dump(dict_,"../data/metadata.pkl")

scripts/run.py

@@ -0,0 +1,24 @@
+import pandas as pd
+from tqdm import tqdm
+import joblib
+
+if __name__ == '__main__':
+    df_scores = pd.read_csv('../../AVA_src/AVA.txt',sep = " ",header = None)
+    df_scores.columns = df_scores.columns + 1
+    df_scores = df_scores.drop(columns=df_scores.columns[0], axis=1)
+    df_tags = pd.read_csv('../../AVA_src/tags.txt',sep = "|",header = None)
+    
+    dict_tags = {0:'None'}
+    for row in df_tags.iterrows():
+        row = row[1]
+        dict_tags[row[0]] = row[1]
+        
+    dict_ = {}
+    for idx in tqdm(range(len(df_scores))):
+        row = df_scores.iloc[idx,:]
+        _id = row[2]
+        ratings = row.values[1:11]
+        textual_tag0 = dict_tags[row[13]]
+        textual_tag1 = dict_tags[row[14]]
+        dict_[_id] = (ratings,textual_tag0,textual_tag1)
+    joblib.dump(dict_,"../data/metadata.pkl")