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{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"id": "82d89348-fb09-462d-b78f-f1dab3447c3d",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# !pip install -U unibox omegaconf -q\n",
"import unibox\n",
"import subprocess\n",
"import re\n",
"import os\n",
"import math\n",
"from omegaconf import OmegaConf\n",
"\n",
"# default values for different optimizers\n",
"optimizer_dict = {\n",
" \"prodigy\": {\n",
" \"name\": \"prodigyopt.Prodigy\",\n",
" \"params\": {\n",
" \"lr\": 1,\n",
" \"d_coef\": 2,\n",
" \"d0\": 1e-6,\n",
" \"safeguard_warmup\": True,\n",
" \"use_bias_correction\": True,\n",
" \"weight_decay\": 1e-2,\n",
" \"eps\": 1e-8,\n",
" } \n",
" },\n",
"\"adamw\":{\n",
" \"name\": \"torch.optim.AdamW\",\n",
" \"params\":{\n",
" \"lr\": 3e-5,\n",
" \"weight_decay\": 1e-2,\n",
" },\n",
" \n",
"}\n",
"}\n",
"\n",
"# default scheduler dict\n",
"default_scheduler_dict = {\n",
" \"scheduler\":{\n",
" \"name\": \"transformers.get_cosine_schedule_with_warmup\",\n",
" \"params\": {\n",
" \"num_warmup_steps\": 0,\n",
" \"num_training_steps\": 1000,\n",
" \"last_epoch\": -1,\n",
" }\n",
" }\n",
"}\n",
"\n",
"# assuming training on 1024x1024 resolution\n",
"default_batch_size_dict = {\n",
" \"prodigy\": {\n",
" 80: 8, # For 80 GB VRAM, batch size is 8\n",
" 20: 1, \n",
" },\n",
" \"adamw\": {\n",
" 80: 24\n",
" },\n",
" \"lion\": {\n",
" 78: 48\n",
" },\n",
"}\n",
"\n",
"\n",
"def get_vram_in_gb():\n",
" \"\"\" Returns the total GPU memory in GB. \"\"\"\n",
" try:\n",
" # Running the command 'nvidia-smi' and capturing its output\n",
" output = subprocess.check_output(['nvidia-smi'], text=True)\n",
"\n",
" # Regular expression to find the memory part\n",
" mem_regex = re.compile(r'\\|\\s+\\d+MiB / (\\d+)MiB\\s+\\|')\n",
" match = mem_regex.search(output)\n",
" if match:\n",
" total_memory_mib = int(match.group(1))\n",
" # Converting MiB to GiB (1 GiB = 1024 MiB) and rounding to 2 decimal places\n",
" total_memory_gb = round(total_memory_mib / 1024, 2)\n",
" return total_memory_gb\n",
" else:\n",
" raise ValueError(\"Could not parse total memory from nvidia-smi output.\")\n",
" except Exception as e:\n",
" return f\"An error occurred: {e}\"\n",
"\n",
"\n",
"def get_batch_size(optimizer: str, vram: int) -> int:\n",
" # allocate batch size based on vram, assuming training on 1024x1024 resolution\n",
" _bs_dict = default_batch_size_dict\n",
" \n",
" if optimizer in _bs_dict:\n",
" # Find the closest lower VRAM value that we have a batch size for\n",
" closest_vram = max(vram_key for vram_key in _bs_dict[optimizer] if vram_key <= vram)\n",
" return _bs_dict[optimizer][closest_vram]\n",
" else:\n",
" raise ValueError(f\"Optimizer '{optimizer}' not supported.\")\n",
"\n",
"\n",
"def get_train_image_count(dataset_dir:str) -> int:\n",
" files = unibox.traverses(DATASET_DIR, include_extensions = unibox.constants.IMG_FILES)\n",
" return len(files)\n",
"\n",
"\n",
"def get_scheduler_dict(it_per_epoch:int, epoch_per_cycle:int, warmup_epochs:float):\n",
"\n",
" _warmup_step_count = int(it_per_epoch * warmup_epochs)\n",
" print(f\"_warmup_step_count: {_warmup_step_count}\")\n",
"\n",
" _cycle_step_count = it_per_epoch * epoch_per_cycle\n",
" print(f\"_cycle_step_count: {_cycle_step_count}\")\n",
"\n",
" scheduler_dict = default_scheduler_dict.copy()\n",
" scheduler_dict[\"scheduler\"][\"params\"][\"num_training_steps\"] = _cycle_step_count\n",
" scheduler_dict[\"scheduler\"][\"params\"][\"num_warmup_steps\"] = _warmup_step_count\n",
" return scheduler_dict\n",
"\n",
"\n",
"def evaluate_template_dict(template_dict):\n",
" # generate a filled dictionary from a template\n",
" new_dict = {}\n",
" for key, value in template_dict.items():\n",
" if isinstance(value, dict):\n",
" new_dict[key] = evaluate_template_dict(value)\n",
" elif callable(value):\n",
" new_dict[key] = value()\n",
" else:\n",
" new_dict[key] = value\n",
" return new_dict\n",
"\n",
"\n",
"def write_config_to_yaml(config_dict, yaml_path):\n",
" yaml_config = OmegaConf.to_yaml(config_dict)\n",
"\n",
" # Splitting the YAML string into lines\n",
" lines = yaml_config.split('\\n')\n",
"\n",
" # Iterating through the lines and adding an empty line before each major section\n",
" formatted_lines = []\n",
" for line in lines:\n",
" if line.startswith(' ') or line == '':\n",
" # It's a subline or already an empty line, just add it\n",
" formatted_lines.append(line)\n",
" else:\n",
" # It's a new major section, add an empty line before it (if it's not the first line)\n",
" if formatted_lines:\n",
" formatted_lines.append('')\n",
" formatted_lines.append(line)\n",
"\n",
" # Joining the lines back into a single string\n",
" formatted_yaml_config = '\\n'.join(formatted_lines)\n",
"\n",
" # Write the formatted YAML string to a file\n",
" with open(yaml_path, 'w') as file:\n",
" file.write(formatted_yaml_config)\n",
"\n",
" print()\n",
" print(f\"Configuration saved to [{yaml_path}]\")\n",
"\n",
"\n",
"def get_optimizer_dict(optimizer:str):\n",
"\n",
" return_dict = {\n",
" \"optimizer\": optimizer_dict[optimizer],\n",
" }\n",
"\n",
" return return_dict"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "12a8d495-565e-455b-a8ee-fcaf09b199a5",
"metadata": {},
"outputs": [],
"source": [
"DEFAULT_CONFIG = \"https://huggingface.co/kiriyamaX/nd-configs/resolve/main/nd_config_template_sdxl_80g.yaml\"\n",
"\n",
"# ============= CONFIGS =============\n",
"\n",
"# IMPORTANT\n",
"CONFIG_VERSION = 1\n",
"RUN_NAME = \"qft_twitter_aes_167k-of-798k\"\n",
"DATASET_DIR = \"../datasets/twitter-aes_trained-best-167k-of-798k\"\n",
"# MODEL_PATH = \"../models/playground-v2-1024px-aesthetic.safetensors\"\n",
"MODEL_PATH = \"../models/fd5me9.ckpt\" \n",
"\n",
"# ===================================\n",
"\n",
"# hyperparams\n",
"OFFSET_NOISE_VAL = 0.12\n",
"UCG = 0.1\n",
"\n",
"# optimizer\n",
"TRAIN_OPTIMIZER = \"adamw\"\n",
"WARMUP_EPOCHS = 0.3\n",
"EPOCH_PER_CYCLE = 10\n",
"\n",
"# saving\n",
"SAVE_INTERVAL_EPOCH = 1\n",
"SAVE_INTERVAL_STEPS = -1\n",
"# ==================================="
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "c4089d9b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-12-18 07:51:41,207 [INFO] UniLogger: UniLoader.loads: .yaml LOADED from \"/tmp/tmptm3kzw5a.yaml\" in 0.04s\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"sys_vram: 80 GB \n",
"train_batch_size: 24 \n",
"train_image_count: 166110 \n",
"_it_per_epoch: 6921\n",
"_warmup_step_count: 2076\n",
"_cycle_step_count: 69210\n",
"\n",
"Configuration saved to [./config_nd_qft_twitter_aes_167k-of-798k_v1.yaml]\n"
]
}
],
"source": [
"regulars_dict_template = {\n",
" \"trainer\": {\n",
" \"model_path\": lambda: MODEL_PATH,\n",
" \"checkpoint_dir\": lambda: CHECKPOINT_DIR,\n",
" \"offset_noise\": True,\n",
" \"offset_noise_val\": lambda: OFFSET_NOISE_VAL,\n",
" \"checkpoint_steps\": lambda: SAVE_INTERVAL_STEPS,\n",
" \"checkpoint_freq\": lambda: SAVE_INTERVAL_EPOCH,\n",
" },\n",
" \"dataset\": {\n",
" \"ucg\": lambda: UCG,\n",
" \"img_path\": lambda: [DATASET_DIR],\n",
" },\n",
" \"sampling\": {\n",
" \"every_n_steps\": lambda: SAVE_INTERVAL_STEPS,\n",
" \"every_n_epochs\": lambda: SAVE_INTERVAL_EPOCH,\n",
" },\n",
"}\n",
"\n",
"def get_regulars_dict():\n",
" return evaluate_template_dict(regulars_dict_template)\n",
"\n",
"\n",
"CHECKPOINT_DIR = f\"checkpoint_{RUN_NAME}_v{CONFIG_VERSION}\"\n",
"\n",
"# sys_vram = get_vram_in_gb()\n",
"sys_vram = 80\n",
"train_batch_size = get_batch_size(TRAIN_OPTIMIZER, sys_vram)\n",
"train_image_count = get_train_image_count(DATASET_DIR)\n",
"config = unibox.loads(DEFAULT_CONFIG)\n",
"\n",
"if not config:\n",
" raise FileNotFoundError\n",
"\n",
"_it_per_epoch = math.floor(train_image_count / train_batch_size)\n",
"print(f\"sys_vram: {sys_vram} GB \\ntrain_batch_size: {train_batch_size} \\ntrain_image_count: {train_image_count} \\n_it_per_epoch: {_it_per_epoch}\")\n",
"\n",
"config = OmegaConf.merge(config, get_optimizer_dict(TRAIN_OPTIMIZER))\n",
"config = OmegaConf.merge(config, get_scheduler_dict(_it_per_epoch, EPOCH_PER_CYCLE, WARMUP_EPOCHS))\n",
"config = OmegaConf.merge(config, get_regulars_dict())\n",
"\n",
"\n",
"YAML_FOLDER = \"./\"\n",
"YAML_NAME = f\"config_nd_{RUN_NAME}_v{CONFIG_VERSION}.yaml\"\n",
"_yaml_path = os.path.join(YAML_FOLDER, YAML_NAME)\n",
"write_config_to_yaml(config, _yaml_path)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "fe376cc7",
"metadata": {},
"outputs": [],
"source": [
"# !pip install -U unibox omegaconf -q\n",
"import unibox\n",
"import subprocess\n",
"import re\n",
"import os\n",
"import math\n",
"from omegaconf import OmegaConf\n",
"\n",
"# default values for different optimizers\n",
"optimizer_dict = {\n",
" \"prodigy\": {\n",
" \"name\": \"prodigyopt.Prodigy\",\n",
" \"params\": {\n",
" \"lr\": 1,\n",
" \"d_coef\": 2,\n",
" \"d0\": 1e-6,\n",
" \"safeguard_warmup\": True,\n",
" \"use_bias_correction\": True,\n",
" \"weight_decay\": 1e-2,\n",
" \"eps\": 1e-8,\n",
" } \n",
" },\n",
"\"adamw\":{\n",
" \"name\": \"torch.optim.AdamW\",\n",
" \"params\":{\n",
" \"lr\": 3e-5,\n",
" \"weight_decay\": 1e-2,\n",
" },\n",
" \n",
"}\n",
"}\n",
"\n",
"# default scheduler dict\n",
"default_scheduler_dict = {\n",
" \"scheduler\":{\n",
" \"name\": \"transformers.get_cosine_schedule_with_warmup\",\n",
" \"params\": {\n",
" \"num_warmup_steps\": 0,\n",
" \"num_training_steps\": 1000,\n",
" \"last_epoch\": -1,\n",
" }\n",
" }\n",
"}\n",
"\n",
"# assuming training on 1024x1024 resolution\n",
"default_batch_size_dict = {\n",
" \"prodigy\": {\n",
" 80: 8, # For 80 GB VRAM, batch size is 8\n",
" 20: 1, \n",
" },\n",
" \"adamw\": {\n",
" 80: 24,\n",
" },\n",
" \"lion\": {\n",
" 78: 48\n",
" },\n",
"}\n",
"\n",
"\n",
"def get_vram_in_gb():\n",
" \"\"\" Returns the total GPU memory in GB. \"\"\"\n",
" try:\n",
" # Running the command 'nvidia-smi' and capturing its output\n",
" output = subprocess.check_output(['nvidia-smi'], text=True)\n",
"\n",
" # Regular expression to find the memory part\n",
" mem_regex = re.compile(r'\\|\\s+\\d+MiB / (\\d+)MiB\\s+\\|')\n",
" match = mem_regex.search(output)\n",
" if match:\n",
" total_memory_mib = int(match.group(1))\n",
" # Converting MiB to GiB (1 GiB = 1024 MiB) and rounding to 2 decimal places\n",
" total_memory_gb = round(total_memory_mib / 1024, 2)\n",
" return total_memory_gb\n",
" else:\n",
" raise ValueError(\"Could not parse total memory from nvidia-smi output.\")\n",
" except Exception as e:\n",
" return f\"An error occurred: {e}\"\n",
"\n",
"\n",
"def get_batch_size(optimizer: str, vram: int) -> int:\n",
" # allocate batch size based on vram, assuming training on 1024x1024 resolution\n",
" _bs_dict = default_batch_size_dict\n",
" \n",
" if optimizer in _bs_dict:\n",
" # Find the closest lower VRAM value that we have a batch size for\n",
" closest_vram = max(vram_key for vram_key in _bs_dict[optimizer] if vram_key <= vram)\n",
" return _bs_dict[optimizer][closest_vram]\n",
" else:\n",
" raise ValueError(f\"Optimizer '{optimizer}' not supported.\")\n",
"\n",
"\n",
"def get_train_image_count(dataset_dir:str) -> int:\n",
" files = unibox.traverses(DATASET_DIR, include_extensions = unibox.constants.IMG_FILES)\n",
" return len(files)\n",
"\n",
"\n",
"def get_scheduler_dict(it_per_epoch:int, epoch_per_cycle:int, warmup_epochs:float):\n",
"\n",
" _warmup_step_count = int(it_per_epoch * warmup_epochs)\n",
" print(f\"_warmup_step_count: {_warmup_step_count}\")\n",
"\n",
" _cycle_step_count = it_per_epoch * epoch_per_cycle\n",
" print(f\"_cycle_step_count: {_cycle_step_count}\")\n",
"\n",
" scheduler_dict = default_scheduler_dict.copy()\n",
" scheduler_dict[\"scheduler\"][\"params\"][\"num_training_steps\"] = _cycle_step_count\n",
" scheduler_dict[\"scheduler\"][\"params\"][\"num_warmup_steps\"] = _warmup_step_count\n",
" return scheduler_dict\n",
"\n",
"\n",
"def evaluate_template_dict(template_dict):\n",
" # generate a filled dictionary from a template\n",
" new_dict = {}\n",
" for key, value in template_dict.items():\n",
" if isinstance(value, dict):\n",
" new_dict[key] = evaluate_template_dict(value)\n",
" elif callable(value):\n",
" new_dict[key] = value()\n",
" else:\n",
" new_dict[key] = value\n",
" return new_dict\n",
"\n",
"\n",
"def write_config_to_yaml(config_dict, yaml_path):\n",
" yaml_config = OmegaConf.to_yaml(config_dict)\n",
"\n",
" # Splitting the YAML string into lines\n",
" lines = yaml_config.split('\\n')\n",
"\n",
" # Iterating through the lines and adding an empty line before each major section\n",
" formatted_lines = []\n",
" for line in lines:\n",
" if line.startswith(' ') or line == '':\n",
" # It's a subline or already an empty line, just add it\n",
" formatted_lines.append(line)\n",
" else:\n",
" # It's a new major section, add an empty line before it (if it's not the first line)\n",
" if formatted_lines:\n",
" formatted_lines.append('')\n",
" formatted_lines.append(line)\n",
"\n",
" # Joining the lines back into a single string\n",
" formatted_yaml_config = '\\n'.join(formatted_lines)\n",
"\n",
" # Write the formatted YAML string to a file\n",
" with open(yaml_path, 'w') as file:\n",
" file.write(formatted_yaml_config)\n",
"\n",
" print()\n",
" print(f\"Configuration saved to [{yaml_path}]\")\n",
"\n",
"\n",
"def get_optimizer_dict(optimizer:str):\n",
"\n",
" return_dict = {\n",
" \"optimizer\": optimizer_dict[optimizer],\n",
" }\n",
"\n",
" return return_dict"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "33db0266",
"metadata": {},
"outputs": [],
"source": [
"DEFAULT_CONFIG = \"https://huggingface.co/kiriyamaX/nd-configs/resolve/main/nd_config_template_sdxl_80g.yaml\"\n",
"\n",
"# ============= CONFIGS =============\n",
"\n",
"# IMPORTANT\n",
"CONFIG_VERSION = 1\n",
"RUN_NAME = \"qft_twitter_aes_trained-best-26k-of-798k\"\n",
"DATASET_DIR = \"../datasets/twitter-aes_trained-best-26k-of-798k\"\n",
"# MODEL_PATH = \"../models/playground-v2-1024px-aesthetic.safetensors\"\n",
"MODEL_PATH = \"../models/fd5me9.ckpt\" \n",
"\n",
"# ===================================\n",
"\n",
"# hyperparams\n",
"OFFSET_NOISE_VAL = 0.1\n",
"UCG = 0.1\n",
"\n",
"# optimizer\n",
"TRAIN_OPTIMIZER = \"adamw\"\n",
"WARMUP_EPOCHS = 0.3\n",
"EPOCH_PER_CYCLE = 10\n",
"\n",
"# saving\n",
"SAVE_INTERVAL_EPOCH = 1\n",
"SAVE_INTERVAL_STEPS = -1\n",
"# ==================================="
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "68e09efd",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-12-16 15:43:45,081 [INFO] UniLogger: UniLoader.loads: .yaml LOADED from \"/tmp/tmpsszr87yd.yaml\" in 0.04s\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"sys_vram: 80 GB \n",
"train_batch_size: 24 \n",
"train_image_count: 26655 \n",
"_it_per_epoch: 1110\n",
"_warmup_step_count: 333\n",
"_cycle_step_count: 11100\n",
"\n",
"Configuration saved to [./config_nd_qft_twitter_aes_trained-best-26k-of-798k_v1.yaml]\n"
]
}
],
"source": [
"regulars_dict_template = {\n",
" \"trainer\": {\n",
" \"model_path\": lambda: MODEL_PATH,\n",
" \"checkpoint_dir\": lambda: CHECKPOINT_DIR,\n",
" \"offset_noise\": True,\n",
" \"offset_noise_val\": lambda: OFFSET_NOISE_VAL,\n",
" \"checkpoint_steps\": lambda: SAVE_INTERVAL_STEPS,\n",
" \"checkpoint_freq\": lambda: SAVE_INTERVAL_EPOCH,\n",
" },\n",
" \"dataset\": {\n",
" \"ucg\": lambda: UCG,\n",
" \"img_path\": lambda: [DATASET_DIR],\n",
" },\n",
" \"sampling\": {\n",
" \"every_n_steps\": lambda: SAVE_INTERVAL_STEPS,\n",
" \"every_n_epochs\": lambda: SAVE_INTERVAL_EPOCH,\n",
" },\n",
"}\n",
"\n",
"def get_regulars_dict():\n",
" return evaluate_template_dict(regulars_dict_template)\n",
"\n",
"\n",
"CHECKPOINT_DIR = f\"checkpoint_{RUN_NAME}_v{CONFIG_VERSION}\"\n",
"\n",
"# sys_vram = get_vram_in_gb()\n",
"sys_vram = 80\n",
"train_batch_size = get_batch_size(TRAIN_OPTIMIZER, sys_vram)\n",
"train_image_count = get_train_image_count(DATASET_DIR)\n",
"config = unibox.loads(DEFAULT_CONFIG)\n",
"\n",
"if not config:\n",
" raise FileNotFoundError\n",
"\n",
"_it_per_epoch = math.floor(train_image_count / train_batch_size)\n",
"print(f\"sys_vram: {sys_vram} GB \\ntrain_batch_size: {train_batch_size} \\ntrain_image_count: {train_image_count} \\n_it_per_epoch: {_it_per_epoch}\")\n",
"\n",
"config = OmegaConf.merge(config, get_optimizer_dict(TRAIN_OPTIMIZER))\n",
"config = OmegaConf.merge(config, get_scheduler_dict(_it_per_epoch, EPOCH_PER_CYCLE, WARMUP_EPOCHS))\n",
"config = OmegaConf.merge(config, get_regulars_dict())\n",
"\n",
"\n",
"YAML_FOLDER = \"./\"\n",
"YAML_NAME = f\"config_nd_{RUN_NAME}_v{CONFIG_VERSION}.yaml\"\n",
"_yaml_path = os.path.join(YAML_FOLDER, YAML_NAME)\n",
"write_config_to_yaml(config, _yaml_path)"
]
},
{
"cell_type": "markdown",
"id": "70859336-6ae3-4b55-a88d-3bc21a0e6a09",
"metadata": {},
"source": [
"## docker transformer engine"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "569387df-9e56-44f9-8001-1bd2d61ea8b5",
"metadata": {},
"outputs": [],
"source": [
"# https://github.com/NVIDIA/TransformerEngine?tab=readme-ov-file#installation\n",
"docker run --gpus all -it -v /home/ubuntu/datasets:/datasets -v /home/ubuntu/models:/models -v /home/ubuntu/ndtr:/ndtr --rm nvcr.io/nvidia/pytorch:23.10-py3"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9e4a5dad-0c9d-402b-9cf7-fa3fd08e4f37",
"metadata": {},
"outputs": [],
"source": [
"git config --global --add safe.directory /ndtr\n",
"wandb login 0025f0bc67dba1846edaf9c2425b288b23ae0f99"
]
},
{
"cell_type": "markdown",
"id": "11eae193-980f-449b-ac8f-4976ca235da4",
"metadata": {},
"source": [
"## create txt if not exist"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0d8cf284-cb15-4218-b68e-b99e72ef53cf",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m"
]
}
],
"source": [
"!pip install -q unibox"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "701cae45-da02-4ea7-81f3-9ee1c2f14d47",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"data": {
"text/plain": [
"{'metadata': {'len': 40022, 'item_type': 'str'},\n",
" 'preview': ['1604906847521017857_3.jpg',\n",
" '703970524313956352_1.jpg',\n",
" '1631451367620370434_1.jpg']}"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import unibox as ub\n",
"from tqdm.auto import tqdm\n",
"# /home/ubuntu/datasets/twitter-aes_trained-best-167k-of-798k\"\n",
"TARGET_DIR = \"/notebooks/datasets/twitter-aes_trained-best-167k-of-798k\"\n",
"\n",
"# read\n",
"files_in_dir = ub.traverses(TARGET_DIR, relative_unix=True, \n",
" include_extensions=ub.constants.IMG_FILES)\n",
"ub.peeks(files_in_dir)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "79746897-03cc-4aa2-ae74-ac62ea00e389",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7a3dfcb29c6640b3a7638fecb9b2a1e7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/40022 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[11], line 12\u001b[0m\n\u001b[1;32m 10\u001b[0m os\u001b[38;5;241m.\u001b[39mmakedirs(full_subdir_path, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 11\u001b[0m txt_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(txt_root_dir, txt_file)\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtxt_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mw\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 13\u001b[0m f\u001b[38;5;241m.\u001b[39mwrite(placeholder_txt_content)\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFiles and directories created successfully.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py:310\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m 303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m 304\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 305\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 306\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 307\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 308\u001b[0m )\n\u001b[0;32m--> 310\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/lib/python3.10/codecs.py:186\u001b[0m, in \u001b[0;36mIncrementalEncoder.__init__\u001b[0;34m(self, errors)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mIncrementalEncoder\u001b[39;00m(\u001b[38;5;28mobject\u001b[39m):\n\u001b[1;32m 181\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 182\u001b[0m \u001b[38;5;124;03m An IncrementalEncoder encodes an input in multiple steps. The input can\u001b[39;00m\n\u001b[1;32m 183\u001b[0m \u001b[38;5;124;03m be passed piece by piece to the encode() method. The IncrementalEncoder\u001b[39;00m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;124;03m remembers the state of the encoding process between calls to encode().\u001b[39;00m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 186\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 187\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 188\u001b[0m \u001b[38;5;124;03m Creates an IncrementalEncoder instance.\u001b[39;00m\n\u001b[1;32m 189\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[38;5;124;03m for a list of possible values.\u001b[39;00m\n\u001b[1;32m 193\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 194\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39merrors \u001b[38;5;241m=\u001b[39m errors\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"# create\n",
"txt_root_dir = TARGET_DIR\n",
"placeholder_txt_content = \"\"\n",
"\n",
"txt_files_todo = [os.path.splitext(file)[0] + '.txt' for file in files_in_dir]\n",
"os.makedirs(txt_root_dir, exist_ok=True)\n",
"for txt_file in tqdm(txt_files_todo):\n",
" subdir = os.path.dirname(txt_file)\n",
" full_subdir_path = os.path.join(txt_root_dir, subdir)\n",
" os.makedirs(full_subdir_path, exist_ok=True)\n",
" txt_path = os.path.join(txt_root_dir, txt_file)\n",
" with open(txt_path, 'w') as f:\n",
" f.write(placeholder_txt_content)\n",
"\n",
"print(\"Files and directories created successfully.\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "38e5d854-e66b-4644-8119-02051789bcde",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"data": {
"text/plain": [
"{'metadata': {'len': 40022, 'item_type': 'str'},\n",
" 'preview': ['1615643911099138048_1.txt',\n",
" '1587049940366204928_1.txt',\n",
" '1416561591043166211_2.txt']}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# verify\n",
"files_in_dir = unibox.traverses(TARGET_DIR, relative_unix=True, include_extensions=[\".txt\"])\n",
"ub.peeks(files_in_dir)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|