set seed

kaggle/kernel-metadata.json

@@ -1,6 +1,5 @@
 {
   "id": "anujpanthri/train-image-colorization",
-  "id_no":345,
   "title": "training-image-colorization-model",
   "code_file": "train-image-colorization.ipynb",
   "language": "python",

kaggle/train-image-colorization.ipynb

@@ -1,55 +1 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git clone https://github.com/AnujPanthri/Image-Colorization.git\n",
-    "%cd Image-Colorization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install -r requirements.txt --quiet\n",
-    "!pip install -e ."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!python3 src/scripts/train.py configs/experiment1.yaml"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!python3 src/scripts/visualize_results.py configs/experiment1.yaml"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.12.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":30698,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!git clone https://github.com/AnujPanthri/Image-Colorization.git\n%cd Image-Colorization","metadata":{"execution":{"iopub.status.busy":"2024-05-07T05:57:35.422571Z","iopub.execute_input":"2024-05-07T05:57:35.423893Z","iopub.status.idle":"2024-05-07T05:57:37.744423Z","shell.execute_reply.started":"2024-05-07T05:57:35.423838Z","shell.execute_reply":"2024-05-07T05:57:37.743082Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# %cd ..\n# !rm -r Image-Colorization","metadata":{"execution":{"iopub.status.busy":"2024-05-07T05:57:38.982736Z","iopub.execute_input":"2024-05-07T05:57:38.983147Z","iopub.status.idle":"2024-05-07T05:57:38.989575Z","shell.execute_reply.started":"2024-05-07T05:57:38.983115Z","shell.execute_reply":"2024-05-07T05:57:38.987925Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install -r requirements.txt --quiet\n!pip install -e .","metadata":{"execution":{"iopub.status.busy":"2024-05-07T05:57:39.454444Z","iopub.execute_input":"2024-05-07T05:57:39.454828Z","iopub.status.idle":"2024-05-07T05:58:13.154377Z","shell.execute_reply.started":"2024-05-07T05:57:39.4548Z","shell.execute_reply":"2024-05-07T05:58:13.152964Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from kaggle_secrets import UserSecretsClient\nuser_secrets = UserSecretsClient()\nCOMET_API_KEY = user_secrets.get_secret(\"comet_api_key\")\n\n!export COMET_API_KEY={COMET_API_KEY}","metadata":{"execution":{"iopub.status.busy":"2024-05-07T05:58:13.157513Z","iopub.execute_input":"2024-05-07T05:58:13.15798Z","iopub.status.idle":"2024-05-07T05:58:14.401676Z","shell.execute_reply.started":"2024-05-07T05:58:13.157935Z","shell.execute_reply":"2024-05-07T05:58:14.400055Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\n\nos.environ[\"COMET_API_KEY\"]=COMET_API_KEY\nconfig_file = \"configs/experiment1.yaml\"","metadata":{"execution":{"iopub.status.busy":"2024-05-07T05:58:14.412023Z","iopub.execute_input":"2024-05-07T05:58:14.412388Z","iopub.status.idle":"2024-05-07T05:58:14.422002Z","shell.execute_reply.started":"2024-05-07T05:58:14.41236Z","shell.execute_reply":"2024-05-07T05:58:14.420946Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!python3 src/scripts/prepare_dataset.py {config_file}","metadata":{"execution":{"iopub.status.busy":"2024-05-07T05:58:14.423359Z","iopub.execute_input":"2024-05-07T05:58:14.423858Z","iopub.status.idle":"2024-05-07T05:58:20.695681Z","shell.execute_reply.started":"2024-05-07T05:58:14.423828Z","shell.execute_reply":"2024-05-07T05:58:20.694312Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# !cat src/utils/data_utils.py","metadata":{"execution":{"iopub.status.busy":"2024-05-07T05:58:20.697592Z","iopub.execute_input":"2024-05-07T05:58:20.697948Z","iopub.status.idle":"2024-05-07T05:58:20.703728Z","shell.execute_reply.started":"2024-05-07T05:58:20.697915Z","shell.execute_reply":"2024-05-07T05:58:20.702527Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# !python3 src/scripts/visualize_dataset.py {config_file}","metadata":{"execution":{"iopub.status.busy":"2024-05-07T05:58:24.072343Z","iopub.execute_input":"2024-05-07T05:58:24.072758Z","iopub.status.idle":"2024-05-07T05:58:27.518713Z","shell.execute_reply.started":"2024-05-07T05:58:24.072725Z","shell.execute_reply":"2024-05-07T05:58:27.517194Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!python3 src/scripts/train.py {config_file}","metadata":{"execution":{"iopub.status.busy":"2024-05-07T05:58:41.523307Z","iopub.execute_input":"2024-05-07T05:58:41.523718Z","iopub.status.idle":"2024-05-07T06:03:03.088039Z","shell.execute_reply.started":"2024-05-07T05:58:41.523683Z","shell.execute_reply":"2024-05-07T06:03:03.086519Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# !python3 src/scripts/visualize_results.py {config_file}","metadata":{"execution":{"iopub.status.busy":"2024-05-07T06:03:34.594725Z","iopub.execute_input":"2024-05-07T06:03:34.595185Z","iopub.status.idle":"2024-05-07T06:03:46.842693Z","shell.execute_reply.started":"2024-05-07T06:03:34.595147Z","shell.execute_reply":"2024-05-07T06:03:46.841521Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}

src/scripts/train.py

@@ -3,6 +3,7 @@ import argparse
 from comet_ml import Experiment
 from src.utils.config_loader import Config
 from src.utils import config_loader
+from src.utils.data_utils import print_title
 from src.utils.script_utils import validate_config
 import importlib
 from pathlib import Path
@@ -47,6 +48,8 @@ def train(args):
     if "LOCAL_SYSTEM" not in os.environ:
         experiment.log_model(f"{config.task}_{config.dataset}_{config.model}",model_save_path)
 
+    # evaluate model
+    print_title("\nEvaluating Model")
     metrics = model.evaluate()
     print("Model Evaluation Metrics:",metrics)
     

src/simple_regression_colorization/model/dataloaders.py

@@ -19,7 +19,7 @@ def get_datasets():
 
     train_paths,val_paths = sklearn.model_selection.train_test_split(trainval_paths,
                                                                     train_size=0.8,
-                                                                    random_state=324)
+                                                                    random_state=config.seed)
     
     print("train|val|test:",len(train_paths),"|",len(val_paths),"|",len(test_paths))
     
@@ -53,7 +53,7 @@ def load_img(img_path):
     
 def get_tf_ds(image_paths,bs=8,shuffle=False,is_val=False):
     ds = tf.data.Dataset.from_tensor_slices(image_paths)
-    if shuffle:   ds = ds.shuffle(len(image_paths))
+    if shuffle:   ds = ds.shuffle(len(image_paths),seed=config.seed)
     ds = ds.map(load_img,num_parallel_calls=tf.data.AUTOTUNE)
     ds = ds.batch(bs,num_parallel_calls=tf.data.AUTOTUNE,drop_remainder=not is_val)