From 808653d090658c77f2b4e74dced1c62c1c3ba87e Mon Sep 17 00:00:00 2001 From: "meet.ranoliya" Date: Wed, 19 Mar 2025 17:35:23 +0530 Subject: [PATCH 1/8] Add: tensorflow training --- .gitignore | 7 +- tfvspt/assets/config.yaml | 10 + tfvspt/config/config.py | 18 ++ tfvspt/tf/data.py | 44 +++ tfvspt/tf/model.py | 29 ++ tfvspt/tf/notebooks/eval.ipynb | 207 +++++++++++++ tfvspt/tf/notebooks/train.ipynb | 531 ++++++++++++++++++++++++++++++++ tfvspt/tf/utils.py | 60 ++++ 8 files changed, 905 insertions(+), 1 deletion(-) create mode 100644 tfvspt/assets/config.yaml create mode 100644 tfvspt/config/config.py create mode 100644 tfvspt/tf/data.py create mode 100644 tfvspt/tf/model.py create mode 100644 tfvspt/tf/notebooks/eval.ipynb create mode 100644 tfvspt/tf/notebooks/train.ipynb create mode 100644 tfvspt/tf/utils.py diff --git a/.gitignore b/.gitignore index 1800114..b1e6652 100644 --- a/.gitignore +++ b/.gitignore @@ -171,4 +171,9 @@ cython_debug/ .ruff_cache/ # PyPI configuration file -.pypirc \ No newline at end of file +.pypirc + +# Models +**/*.keras +**/*.pt +**/*.csv \ No newline at end of file diff --git a/tfvspt/assets/config.yaml b/tfvspt/assets/config.yaml new file mode 100644 index 0000000..bc17473 --- /dev/null +++ b/tfvspt/assets/config.yaml @@ -0,0 +1,10 @@ +seed: 25 +n_classes: 100 +bs: 32 +imgsz: + - 32 + - 32 +lr: 0.001 +epochs: 20 +num_workers: 2 +output: "./output" \ No newline at end of file diff --git a/tfvspt/config/config.py b/tfvspt/config/config.py new file mode 100644 index 0000000..08cfec8 --- /dev/null +++ b/tfvspt/config/config.py @@ -0,0 +1,18 @@ +from pydantic import BaseModel +import yaml + + +class Config(BaseModel): + seed: int + n_classes: int + bs: int + imgsz: tuple + lr: float + epochs: int + num_workers: int + output: str + + +def get_config(path: str) -> Config: + config = yaml.safe_load(open(path)) + return Config(**config) diff --git a/tfvspt/tf/data.py b/tfvspt/tf/data.py new file mode 100644 index 0000000..92547a9 --- /dev/null +++ b/tfvspt/tf/data.py @@ -0,0 +1,44 @@ +"""Tensorflow Data Loader.""" + +import numpy as np +import tensorflow as tf +from tensorflow.data import AUTOTUNE + +from tfvspt.config.config import Config + + +class Dataset: + + def __init__(self, config: Config) -> None: + self.config = config + + @staticmethod + def get_data(): + return tf.keras.datasets.cifar100.load_data() + + def preprocess_data(self, image, label): + image = tf.cast(image, tf.float32) + image = tf.cast(image / 255.0, tf.float32) + label = tf.squeeze(tf.one_hot(label, self.config.n_classes), axis=0) + return image, label + + def load_dataset( + self, + images: np.ndarray, + labels: np.ndarray, + shuffle: bool = False, + repeat: bool = False, + ): + # Create tf dataset + dataset = tf.data.Dataset.from_tensor_slices((images, labels)) + dataset = (dataset.map(self.preprocess_data, + num_parallel_calls=AUTOTUNE).cache()) + # Shuffle + if shuffle: + dataset = dataset.shuffle(images.shape[0]) + # Batch + dataset = (dataset.batch(self.config.bs).prefetch(AUTOTUNE)) + # Repeat + if repeat: + dataset = dataset.repeat() + return dataset diff --git a/tfvspt/tf/model.py b/tfvspt/tf/model.py new file mode 100644 index 0000000..404b711 --- /dev/null +++ b/tfvspt/tf/model.py @@ -0,0 +1,29 @@ +"""Tensorflow Model.""" + +from tensorflow.keras import layers +from tensorflow.keras import models + + +def get_model( + input_shape: tuple[int, int, int], + n_classes: int, +): + model = models.Sequential([ + layers.InputLayer(input_shape), + layers.Conv2D(32, (3, 3), activation=None, padding='same'), + layers.BatchNormalization(), + layers.ReLU(), + layers.MaxPooling2D((2, 2)), + layers.Conv2D(64, (3, 3), activation=None, padding='same'), + layers.BatchNormalization(), + layers.ReLU(), + layers.MaxPooling2D((2, 2)), + layers.Conv2D(128, (3, 3), activation=None, padding='same'), + layers.BatchNormalization(), + layers.ReLU(), + layers.MaxPooling2D((2, 2)), + layers.GlobalAveragePooling2D(), + layers.Dense(128, activation='relu'), + layers.Dense(n_classes, activation=None) + ]) + return model diff --git a/tfvspt/tf/notebooks/eval.ipynb b/tfvspt/tf/notebooks/eval.ipynb new file mode 100644 index 0000000..de48e4c --- /dev/null +++ b/tfvspt/tf/notebooks/eval.ipynb @@ -0,0 +1,207 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e24f858d-a269-4008-ab34-22d978d58fe4", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30837142-3340-4691-b3f1-6bbc90684a5a", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import time\n", + "\n", + "import tensorflow as tf\n", + "\n", + "from tfvspt.config.config import get_config\n", + "from tfvspt.tf.data import Dataset\n", + "from tfvspt.tf.utils import plot_images" + ] + }, + { + "cell_type": "markdown", + "id": "2623d158-d3d3-456f-9a82-1d0efc6f4305", + "metadata": {}, + "source": [ + "## Config" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01a9dd40-2ddb-488e-a94c-d54b39155453", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Config(seed=25, n_classes=100, bs=32, imgsz=(32, 32), lr=0.001, epochs=20, output='./output')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config = get_config(\"../../assets/config.yaml\")\n", + "config" + ] + }, + { + "cell_type": "markdown", + "id": "4c9902ce-980e-42a5-adfc-5898661dfb6b", + "metadata": {}, + "source": [ + "## Test Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11a230a1-4e6c-4a1d-a0a8-32880de45517", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = Dataset(config=config)\n", + "(x_train, y_train), (x_test, y_test) = Dataset.get_data()\n", + "print(f\"Testdata size: {len(x_test)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5f7875ac-bf8a-48b0-9859-da19e823a406", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[TF] Dataset loading time: 0.19809412956237793\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-03-18 23:19:46.948010: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n" + ] + } + ], + "source": [ + "st = time.time()\n", + "test_ds = dataset.load_dataset(x_test, y_test, shuffle=False, repeat=False)\n", + "print(f\"[TF] Dataset loading time: {time.time() - st}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "134784fb-bf0c-4116-b532-c0be96860e1e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-03-18 23:20:08.815568: W tensorflow/core/kernels/data/cache_dataset_ops.cc:916] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_images(test_ds)" + ] + }, + { + "cell_type": "markdown", + "id": "9ef44a6a-45f8-4584-8a9c-af117212ef1b", + "metadata": {}, + "source": [ + "## Load Model" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d4e172c6-ea49-476b-9eae-eaf55a0f8599", + "metadata": {}, + "outputs": [], + "source": [ + "st = time.time()\n", + "model_path = Path(config.output) / \"tf/cifar100.keras\"\n", + "model = tf.keras.models.load_model(str(model_path))\n", + "print(f\"[TF] Model Loading time: {time.time() - st}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a0ea6349-334c-459a-80c0-59bed6eccb43", + "metadata": {}, + "source": [ + "## Eval" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "c611662e-bf5c-4a32-8ceb-ce79eea9991c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.4103 - loss: 2.3972\n", + "[TF] Evaluation Results: {'accuracy': 0.4090000092983246, 'loss': 2.411944627761841}\n", + "[TF] Evaluation time: 2.571380615234375\n" + ] + } + ], + "source": [ + "st = time.time()\n", + "results = model.evaluate(test_ds, return_dict=True)\n", + "print(f\"[TF] Evaluation Results: {results}\")\n", + "print(f\"[TF] Evaluation time: {time.time() - st}\")" + ] + } + ], + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tfvspt/tf/notebooks/train.ipynb b/tfvspt/tf/notebooks/train.ipynb new file mode 100644 index 0000000..a28302e --- /dev/null +++ b/tfvspt/tf/notebooks/train.ipynb @@ -0,0 +1,531 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-03-19 11:36:51.248021: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2025-03-19 11:36:51.263609: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "E0000 00:00:1742364411.281834 10272 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "E0000 00:00:1742364411.287084 10272 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "W0000 00:00:1742364411.301335 10272 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742364411.301368 10272 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742364411.301370 10272 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742364411.301371 10272 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "2025-03-19 11:36:51.306791: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], + "source": [ + "import math\n", + "from pathlib import Path\n", + "import time\n", + "\n", + "import tensorflow as tf\n", + "\n", + "from tfvspt.config.config import get_config\n", + "from tfvspt.tf.data import Dataset\n", + "from tfvspt.tf.model import get_model\n", + "from tfvspt.tf.utils import plot_history\n", + "from tfvspt.tf.utils import plot_images\n", + "from tfvspt.tf.utils import set_seed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read Config" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Config(seed=25, n_classes=100, bs=32, imgsz=(32, 32), lr=0.001, epochs=20, output='./output')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config = get_config(\"../../assets/config.yaml\")\n", + "config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set Seed" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "set_seed(config.seed)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset\n", + "\n", + "### Name: CIFAR100" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Traindata size: 50000\n" + ] + } + ], + "source": [ + "dataset = Dataset(config=config)\n", + "(x_train, y_train), (x_test, y_test) = Dataset.get_data()\n", + "print(f\"Traindata size: {len(x_train)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[TF] Dataset loading time: 0.5624265670776367\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-03-19 11:36:55.104323: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n" + ] + } + ], + "source": [ + "st = time.time()\n", + "train_ds = dataset.load_dataset(x_train, y_train, shuffle=True, repeat=True)\n", + "print(f\"[TF] Dataset loading time: {time.time() - st}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_images(train_ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[TF] Model loading time: 0.10998845100402832\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv2d (Conv2D)                 │ (None, 32, 32, 32)     │           896 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization             │ (None, 32, 32, 32)     │           128 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ re_lu (ReLU)                    │ (None, 32, 32, 32)     │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d (MaxPooling2D)    │ (None, 16, 16, 32)     │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_1 (Conv2D)               │ (None, 16, 16, 64)     │        18,496 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_1           │ (None, 16, 16, 64)     │           256 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ re_lu_1 (ReLU)                  │ (None, 16, 16, 64)     │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_1 (MaxPooling2D)  │ (None, 8, 8, 64)       │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_2 (Conv2D)               │ (None, 8, 8, 128)      │        73,856 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_2           │ (None, 8, 8, 128)      │           512 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ re_lu_2 (ReLU)                  │ (None, 8, 8, 128)      │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_2 (MaxPooling2D)  │ (None, 4, 4, 128)      │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_average_pooling2d        │ (None, 128)            │             0 │\n",
+       "│ (GlobalAveragePooling2D)        │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (Dense)                   │ (None, 128)            │        16,512 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (Dense)                 │ (None, 100)            │        12,900 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ re_lu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ re_lu_1 (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ re_lu_2 (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ global_average_pooling2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m16,512\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m) │ \u001b[38;5;34m12,900\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 123,556 (482.64 KB)\n",
+       "
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 Trainable params: 123,108 (480.89 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m123,108\u001b[0m (480.89 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 448 (1.75 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m448\u001b[0m (1.75 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "st = time.time()\n", + "model = get_model(input_shape=(*config.imgsz, 3), n_classes=config.n_classes)\n", + "print(f\"[TF] Model loading time: {time.time() - st}\")\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "output_path = Path(config.output) / \"tf\"\n", + "output_path.mkdir(parents=True, exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimizer\n", + "optimizer = tf.keras.optimizers.Adam(learning_rate=config.lr)\n", + "\n", + "# Loss\n", + "loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True)\n", + "\n", + "# Model\n", + "model.compile(\n", + " optimizer=optimizer,\n", + " loss=loss,\n", + " metrics=[\"accuracy\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Training history logger\n", + "csv_logger = tf.keras.callbacks.CSVLogger(\n", + " str(output_path / 'cifar100_training.csv'), \n", + " separator=\",\", \n", + " append=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 17ms/step - accuracy: 0.0956 - loss: 3.9481\n", + "Epoch 2/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 23ms/step - accuracy: 0.2351 - loss: 3.0588\n", + "Epoch 3/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 25ms/step - accuracy: 0.3013 - loss: 2.7306\n", + "Epoch 4/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.3481 - loss: 2.5203\n", + "Epoch 5/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.3740 - loss: 2.3673\n", + "Epoch 6/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 23ms/step - accuracy: 0.4058 - loss: 2.2271\n", + "Epoch 7/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.4288 - loss: 2.1251\n", + "Epoch 8/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.4437 - loss: 2.0398\n", + "Epoch 9/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 25ms/step - accuracy: 0.4622 - loss: 1.9564\n", + "Epoch 10/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 24ms/step - accuracy: 0.4756 - loss: 1.9049\n", + "Epoch 11/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 25ms/step - accuracy: 0.4942 - loss: 1.8354\n", + "Epoch 12/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 24ms/step - accuracy: 0.5080 - loss: 1.7642\n", + "Epoch 13/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 24ms/step - accuracy: 0.5206 - loss: 1.7127\n", + "Epoch 14/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 24ms/step - accuracy: 0.5315 - loss: 1.6764\n", + "Epoch 15/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 24ms/step - accuracy: 0.5423 - loss: 1.6238\n", + "Epoch 16/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 24ms/step - accuracy: 0.5532 - loss: 1.5803\n", + "Epoch 17/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 24ms/step - accuracy: 0.5636 - loss: 1.5357\n", + "Epoch 18/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5789 - loss: 1.4826\n", + "Epoch 19/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5816 - loss: 1.4595\n", + "Epoch 20/20\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5957 - loss: 1.4119\n", + "[TF] Training time: 735.5993504524231\n" + ] + } + ], + "source": [ + "st = time.time()\n", + "history = model.fit(\n", + " train_ds,\n", + " epochs=config.epochs,\n", + " shuffle=False,\n", + " steps_per_epoch=math.ceil(len(x_train)/config.bs),\n", + " callbacks=[csv_logger],\n", + ")\n", + "print(f\"[TF] Training time: {time.time() - st}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize Training History" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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QrAEAAACQso+bAdrhjebt6BapIKf65xn1ACO+AACNBsEaAAAA0Ng4C6TUH0tHox3eaK6Hdi5bhNR+oNR+kBR7kbT8XikrVZ7XWbOYu4PGD6vt6gEACBgEawAAAEBDl5VafjRaYe45nSxSVA8pbpDUfrAZprW+wH0K5xVPFe0KapF7uFa0Ptr4J1noHwDQqBCsAQAAAA1JYX7RaLTvisK076XMg+X7hUWa4Vn7QWaYFjtACouo/NwJSdKkRebuoGU3MrDHmKFaQlKNXgoAAIGOYA0AAACozxxHzxmNtlVy5p3TySK1SXAfjdaqq28bCiQkST0mmLuEZqdJ4dHm9E9GqgEAGiGCNQAAAKC+KMyTjm0rMxpto+Q4Ur5fk5blR6PZmtdcHdYgNigAAEAEawAAAEDgyjxcOp3z8HfSsR8kZ757H4tViu5ZFKQNluIGSy07SxaLf2oGAKARIVgDAAAAaprLWf2pkgW5ZnBWdm20rKPl+zVtVRSgFY1Ii7lIsoXXznUAAIBKEawBAAAANWlHcgWL+88tXdzfMKTMQ+eMRtsmuQrcz2UJktr2KjMabZDUohOj0QAACBABG6zNnz9fTz/9tFJTU9W3b1+98MILGjx4sMe+r732mqZOnerWZrPZlJt77hbiAAAAQC3akSy9M0WS4d7uOCa9c7PU53opP9sM07JTyx/fLKrMaLTBUkw/KbRZXVQOAAB8EJDB2pIlSzRz5ky99NJLGjJkiJ577jmNGzdOu3btUps2bTweY7fbtWvXrpLnFv4VDwAAAHXJ5TRHqp0bqkmlbdveLm2yBktte5fu0hk3SIqMZzQaAAD1SEAGa88++6zuvPPOklFoL730kj7++GO9+uqrevDBBz0eY7FY1LZt27osEwAAAI3ZmZPSiV1S+i7pxC/SwQ3u0z8rMmCq1Gey1K6vFNq09usEAAC1JuCCtfz8fG3atEmzZs0qabNarUpMTNSGDRsqPC47O1vx8fFyuVy66KKL9MQTT6hnz54e++bl5SkvL6/kucPhqLkLAAAAQMNhGGZYdmKXlP6LlL7TDNHSd0k5J3w7Z8dLpPihNVsnAADwi4AL1k6cOCGn06no6Gi39ujoaO3cudPjMd27d9err76qPn36KDMzU88884yGDRum7du3q3379uX6z5kzR48++mit1A8AAIB6yOWUTh8oCs12miFacZiWn1XxcREdpKgLpKgekizShheqfq/w6Kr7AACAeiHggjVfDB06VEOHlv6r37Bhw3ThhRfq5Zdf1uOPP16u/6xZszRz5syS5w6HQ3FxcXVSKwAAAPyoME86ubd0CmfxNM4TuyVnnudjLEFSy85SVHfz1rq7Gaa1vsB9YwGXU9r+nrlRgcd11izm7qDxw2rjygAAgB8EXLDWunVrBQUFKS0tza09LS3N6zXUQkJC1L9/f+3Zs8fj6zabTTab7bxrBQAAQIDKyy6dslk88uzELunUfslwej4mOExq1a1MgHaBed+yixQcWvV7WoOk8XOLdgW1yD1cK9qQYPyTZj8AANAgBFywFhoaqgEDBmjNmjWaOHGiJMnlcmnNmjWaMWOGV+dwOp368ccfdeWVV9ZipQAAAPCZyymlrJey08ypkfHDfAucck4VTd3cVRqkpe+SHIcrPsZmLwrNehSNPCsK0iI7nH/olZAkTVpk7g5adiMDe4wZqiUknd/5AQBAQAm4YE2SZs6cqVtuuUUDBw7U4MGD9dxzz+nMmTMlu4ROmTJFsbGxmjNnjiTpscce08UXX6yuXbsqIyNDTz/9tFJSUnTHHXf48zIAAADgyY7kCoKnuZ6DJ8OQso6ds/bZrqo3EGgWVRqalYxA6yE1bytZLDV/XcUSkqQeE2omOAQAAAEtIIO1yZMnKz09XQ8//LBSU1PVr18/rVixomRDg4MHD8pqtZb0P336tO68806lpqaqRYsWGjBggNavX6+EhAR/XQIAAAA82ZFcNFXynDXIHMfM9iuekiLjyqx/5s0GAnHnrH1WFKQ1bVmrl1Ipa5DUaYT/3h8AANQJi2EYnlZWbVQcDociIiKUmZkpu93u73IAAAAaJpdTeq6X+0g1b1mCpJadzBFnxWufRXU310Szhdd8rQAAoFHzNisKyBFrAAAAaEDyc6RjP0g/vutdqNaioxQ7oHQEWlQPc1fOYDafAgAAgYVgDQAAADXH5TQ3ETiySTr8vXmftr3inTg9uewhqfd1tVcjAABADSFYAwAAgO8cx6Qj35cGaUe3el4PLTxaatFJOvRN1ecMj67xMgEAAGoDwRoAAAC8k5ctHd1ihmhHvpcOb5KyPEztDGkqxfQ3p3PGDpDaD5TssZLhKlpj7ZjKbV4gSbKYu4PGD6vtKwEAAKgRBGsAAAAoz1kopf9cOp3zyCYpfacZjpVlsUpRF0rti0K02IHmmmhBHn7NtARJ4+cW7QpqkXu4ZjHvxj9p7qgJAABQDxCsAQAANHaGIWUeLjOlc5N0bKtUkFO+r729FHuROQotdoDUrl/1duVMSJImLZJWPOC+kYE9xgzVEpLO92oAAADqDMEaAABAY5ObKR3ZXDoS7fD30pnj5fvZ7KVTOouDtOZtz//9E5KkHhOklPVSdpq5plr8MEaqAQCAeodgDQAAIFC5nOcfPhXmS8e3F03p3GyOSjvxS/l+1mApumfpdM7YAVLrCySrtWaupdz7BUmdRtTOuQEAAOoIwRoAAEAg2pFcwXTJuRVPlzQM6fQB95Fox36QnHnl+0bGl45Cix0otesjhTSplUsBAABoqAjWAAAAAs2O5KIF/s/ZOdNxzGyftMgM13JOlY5CKw7Tck6WP19YpPsOnbEDpGat6+JKAAAAGjSCNQAAgEDicpoj1c4N1aTStg/uklY9LJ3eX75LUKjUtnfpdM72A6WWnSWLpTarBgAAaJQI1gAAAAJJynr36Z+eFJwtDdVadikahVYUpLXtJQXbar9OAAAAEKwBAAD4lbNAOv6zdGyrdHSrtPcz744bfp80/F6pactaLA4AAACVIVgDAACoK4X50vEdpSHasa1S2nbJmV/9c3VNJFQDAADwM4I1AACA2lCYZ4ZmbiHaDslVUL5vWITUrq/Urp+5Ptqnf5Gy0+V5nTWLuTto/LDarB4AAABeIFgDAAA4XwW5RSHalqIQ7QdzeqfHEC1Siulnhmjt+pqPW3Ry31wgOKxoV1CL3MO1oj7jn5SsQbVzLQAAAPAawRoAAEB1FJyVUn8qMxLtByn9Z8lVWL5vk5blQ7TI+Kp36ExIkiYtMncHLbuRgT3GDNUSkmrscgAAAOA7gjUAAICK5J85J0TbKqXvkgxn+b5NW5cP0SLiqg7RKpKQJPWYYO4Smp0mhUeb0z8ZqQYAABAwCNYAAED95XLWXPCUly2l/ugeop34RTJc5fs2a1M+RLPH+h6iVcQaJHUaUbPnBAAAQI0hWAMAAPXTjuQKpkrOrXqqZF6WdGzbOSHabnncLCC8bfkQrXm7mg/RAAAAUO8QrAEAgPpnR3LR4v7nBGGOY2b7pEWl4VpuZvkQ7eTe8sdKUvOYohCtaIfOmH5S87a1dx0AAACo1wjWAABA/eJymiPVPAVjxW3Lpks/vS+l/iCd2uf5PPb25UO08Da1UjIAAAAaJoI1AABQv6Ssd5/+6UmeQ9rxQenziA5STN+iEK2/eR8eVbt1AgAAoMEjWAMAAPVDfo50eKO08d/e9e95jdT/JnM0WrNWtVoaAAAAGieCNQAAEJhyTkmHvjVHqB3cIB3dIrkKvT9+4G3sqAkAAIBaRbAGAAACQ+YRM0ArDtKO7yjfp3mM1GGotHe1uSmBRxZzd9D4YbVaLgAAAECwBgAA6p5hSCf3lIZoKeuljJTy/Vp1k+KHSh2GmfeR8ZLFUmZXUMl9EwOLeTf+SckaVNtXAQAAgEaOYA0AANQ+l1NK/bEoRPtaOviNdCbdvY/FKrXtLcUPN0eldRha8QYDCUnSpEXm7qBlNzKwx5ihWkJS7V0LAAAAUIRgDQAA1LyCXOnIJungeillg3ToOyk/y71PkE1qP9AM0OKHSu0HS2F2798jIUnqMcEc7ZadJoVHm9M/GakGAACAOkKwBgAAzl9uphmepaw3b0c3S8589z42uxQ3xAzR4odLMf2lYNv5va81iA0KAAAA4DcEawAAoPqy0kpHox1cL6VtlwyXe59mbcwRZPHDzFFp0T0ZTQYAAIAGhWANAABUzjCk0/tLQ7SUDdKpveX7tehUGqLFD5NadjY3GgAAAAAaKII1AAAaMpez+muQuVzS8e3uQVp26jmdLOYItOIQrcNQyd6u1i4DAAAACEQEawAANFQ7kivYNXOu+66ZhfnS0S1lNhr4xlwzrSxriBR7UWmQFjdEahJZJ5cBAAAABCqCNQAAGqIdydI7UyQZ7u2OY2b7qP81p3imbJCOfC8V5rr3C2kmxQ0uXSMtdoAU0qTOygcAAADqA4I1AAAaGpfTHKl2bqgmlbZ9/pR7c9NW7tM62/aRgvg1AQAAAKgMvzEDANDQ7FnjPv2zIp0vlRKuNsO01hew0QAAAABQTQRrAADUd4V50uHvpf2fS/u/kA59691x/W+Sel9Xu7UBAAAADRjBGgAA9Y3LKR37oTRIS9kgFZ6t/nnCo2u+NgAAAKARIVgDACDQGYaUvtMM0fZ/IR34svyunc2ipE4jpU6jpPjh0qKrzI0KPK6zZjF3B40fVhfVAwAAAA0WwRoAAIHodIo5Im1f0ai0M8fdX7fZpY6XmEFap5FSmwvd10gbP7doV1CL3MO1oj7jn5SsQbV8EQAAAEDDRrAGAEAgyEozR6IVh2kZKe6vB4dJHS4uCtJGSe36Vr5rZ0KSNGmRuTto2Y0M7DFmqJaQVDvXAQAAADQiBGsAAPjD2Qwp5evSEWnpP7u/bg2WYgeUjkiLGywF26r3HglJUo8JUsp6KTvNXFMtfhgj1QAAAIAaQrAGAEBdyM+RDn1TGqQd2yoZLvc+bXuXjkiLHyrZmp//+1qDpE4jzv88AAAAAMohWAMAoDY4C6Qjm0qDtMPfSc589z6tupaOSOs4QmrWyj+1AgAAAPAJwRoAADXB5ZLSfiwN0lLWSwVn3PvYY80grfMoM0iLiPVPrQAAAABqBMEaAAAuZ/XXITMM6eQead86M0g78KV09rR7n6atzACtc9H0zpad3XfuBAAAAFCvEawBABq3HckV7Jw5t/zOmZmHS0ek7f9Cyjrq/npouBQ/vChIGym16SlZrbV/DQAAAAD8gmANANB47UiW3pkiyXBvdxwz26+eL4U0KQrSPpdO7XPvF2Qzd+ssHpEW018KCqmz8gEAAAD4F8EaAKBxcjnNkWrnhmpSaduy37k3W6xSzEWlI9LihpjBGwAAAIBGiWANANA4pax3n/5ZkciOUo8rzSAtfpgUFlHrpQEAAACoHwjWAACNS16WueHANy9513/MQ1Lv62q1JAAAAAD1E8EaAKBhK96985eV0u5PzZFqrgLvjw+Prr3aAAAAANRrBGsAgIanIFdK+Ur65VMzTDu93/31lp2lrpdLPy2Vck7J8zprFnN30PhhdVExAAAAgHqIYA0A0DBkHjZDtF8+NXfwLMgpfS0oVIofLnUbK10wTmrVxWzveEnRrqAWuYdrFvNu/JOSNaiOLgAAAABAfUOwBgCon5yF0uHviqZ4rpKOb3d/vXmM1O1yM0zrPFqyhZc/R0KSNGmRuTto2Y0M7DFmqJaQVKuXAAAAAKB+I1gDANQfZ05Ie1abYdreNVJuZulrFqvUfrAZpl0wToruJVksVZ8zIUnqMcFcey07zVxTLX4YI9UAAAAAVMmnYO3bb7/VkCFDaroWAADcuVxS6g/miLRfVkpHNsltymaTFuZaad3GSl3HSE1b+vY+1iCp04gaKRkAAABA4+FTsDZ06FD17t1bd955p2666SZFRkbWcFkAgEYr1yHtW2uul7Z7lTmKrKy2vaVu48xRabEDGFkGAAAAwG8shmF42gqtUlOmTNF7772ns2fPKiwsTNddd53uvPNOjRhRP/+13+FwKCIiQpmZmbLb7f4uBwAaF8OQTuyWdq80R6Ud3CC5CktfDw0310jrNtac5mmP8VupAAAAABoHb7Min4I1ScrMzNTixYu1YMECbd26VRaLRd26ddOdd96pW265Ra1bt/a5+LpGsAYAdazgrHTgq6JdPFdKGSnur7fqao5K63a5ud5ZsM0/dQIAAABolGo9WCtr8+bNeuWVV/T2228rMzNToaGhuvrqq3XnnXcqMTHxfE9f6wjWAKAOZBwyR6XtXiXt+1wqPFv6WlCo1PGS0jCtVRf/1QkAAACg0avTYK3Y2bNn9e677+rPf/6zjh07JkmKj4/Xb3/7W02bNk3NmzevqbeqUQRrAFAFl7P6u2Y6C6RD3xVN8fxUSv/Z/XV7bNH0zrFS51FSaLPaqx8AAAAAqqHOg7XTp09r0aJF+ve//63t27fLYrFo+PDh+vnnn3Xy5Em1bdtWy5Yt06BBg2ri7WoUwRoAVGJHsrTiAclxtLTNHiONnyslJLn3zU6X9qwyp3ju+UzKyyx9zWKV4oaUhmnRPSWLpW6uAQAAAACqoc6CtbVr1+qVV17Rhx9+qNzcXEVFRenWW2/V3Xffrc6dOysvL0+vvvqq/vSnP6lnz5765ptvzuftagXBGgBUYEey9M4USef+qCgKxH79uhQZV7SD56fSkc3ufZu2krommkFal8ukpi3rqHAAAAAA8J23WVGwLydPS0vTwoULtWDBAu3bt0+GYWjUqFH67W9/q2uuuUYhISElfW02m6ZNm6Y9e/Zo/vz5vrwdAMAfXE5zpFq5UE2lbUtvlQyX+0vt+hatlTZWir2o6imjAAAAAFBP+RSstW/fXi6XSy1atNB9992nu+66S927d6/0mKioKOXn5/tUJADAD1LWu0//9MRwScFNpG6JZpjWNVGyt6ub+gAAAADAz3yaCnrJJZfot7/9rX7961/LZrPVRl11iqmgAODBtnel9++out/El6V+19d+PQAAAABQR7zNiqy+nPyrr77STTfdVKuh2vz589WxY0eFhYVpyJAh+u6777w67u2335bFYtHEiRNrrTYAaNCyUqUvn5VWPeRd/4jY2q0HAAAAAAKUT8Ha4cOHlZycrIyMDI+vnz59WsnJyTpy5IhPRS1ZskQzZ87U7NmztXnzZvXt21fjxo3T8ePHKz3uwIEDuv/++zVixAif3hcAGi1nobRrhfSf30jPJkhrHpWyjqlkkwKPLJI9VoofVldVAgAAAEBA8Wkq6G9/+1u9++67Onr0qMdRa3l5eYqNjdX111+vefPmVbuoIUOGaNCgQSXHulwuxcXF6Z577tGDDz7o8Rin06mRI0fqtttu05dffqmMjAx9+OGHXr0fU0EBNFqn9klbFktb3yoK0orEXSxddLMUFCq9f1dRY9kfF0WB26RFUkJSXVULAAAAAHWiVncF/eyzzzR27NgKp4LabDaNHTtWq1evrva58/PztWnTJs2aNaukzWq1KjExURs2bKjwuMcee0xt2rTR7bffri+//LLS98jLy1NeXl7Jc4fDUe06AaDeKsiVdn4kbX5d2v9FaXvTVlLfG6SLpkhRZTakCQ4zdwctu5GBPUYa/yShGgAAAIBGzadg7ciRI7r22msr7RMfH6/ly5dX+9wnTpyQ0+lUdHS0W3t0dLR27tzp8ZivvvpKCxYs0NatW716jzlz5ujRRx+tdm0AUK+l/iRtXiRtWyLlZhQ1WqSuY8ww7YIrpODQ8sclJEk9Jpi7hGanSeHR5vRPa1BdVg8AAAAAAcenYC00NLTKUV4Oh0MWS2Vr89SMrKws3XzzzXrllVfUunVrr46ZNWuWZs6cWfLc4XAoLi6utkoEAP/JdUg/LZU2vyEd3VzaHhEn9b9J6nejFOnF9581SOrE+pUAAAAAUJZPwVrv3r21fPlyPfvssx6ng+bm5io5OVm9e/eu9rlbt26toKAgpaWlubWnpaWpbdu25frv3btXBw4c0FVXXVXS5nK5JEnBwcHatWuXunTp4naMzWar1R1NAcCvDEM69K05Om37B1JBjtluDTFHnl10s9T5UkacAQAAAMB58mlX0KlTp+rw4cNKSkrSvn373F7bu3evrr76ah09elR33HFHtc8dGhqqAQMGaM2aNSVtLpdLa9as0dChQ8v179Gjh3788Udt3bq15JaUlKRLL71UW7duZSQagMYjO136+nlp3iDp1XHS1jfNUK11d2ns/0l/3ClNel3qmkioBgAAAAA1wKcRa1OnTtUnn3yi9957Tz169FCnTp0UGxurI0eOaP/+/SosLNTkyZM1depUn4qaOXOmbrnlFg0cOFCDBw/Wc889pzNnzpScb8qUKYqNjdWcOXMUFhamXr16uR0fGRkpSeXaAaDBcTmlvZ+Zo9N2fSK5Cs32kKZSr2uki26R2g+S6mBqPgAAAAA0Nj4Fa5L0zjvvaP78+frnP/+pnTt3avfu3ZKkhIQETZ8+XdOmTfO5qMmTJys9PV0PP/ywUlNT1a9fP61YsaJkQ4ODBw/KavVpsB0ANAynU8wRaVsWS44jpe2xA82pnj2vkcIq3hIaAAAAAHD+LIZhGOd7kjNnzigzM1MRERFq1qxZTdRVpxwOhyIiIpSZmSm7nb+IAghQhXnSzo/N0Wn71kkq+vpu0kLqc70ZqEX39GeFAAAAANAgeJsV+TxiraxmzZrVy0ANAOqF4z+bu3r+8B/p7KnS9s6jpf43Sz1+JYWE+a08AAAAAGisaiRYAwDUsLxsafv75ui0wxtL25vHSP1vlPrdKLXs5L/6AAAAAAC+B2uHDh3S3/72N61evVpHjx5Vfn5+uT4Wi0WFhYXnVSAANBqGIR3+Xtr8urT9Ayk/22y3BksXjDc3Iug6hh09AQAAACBA+BSs7du3T0OGDNHp06fVs2dP5eXlKT4+XmFhYdq3b58KCgrUt2/fkt05AQCVOHNS2rbEHJ2W/nNpe6uu5lTPvjdIzaP9Vx8AAAAAwCOfgrVHH31UmZmZWrNmjUaNGiWr1aqpU6fq4Ycf1rFjxzRt2jTt2LFDq1evrul6AaBhcLmk/evMMG3nx5KzaNRvcBOp50TpoilSh6GSxeLPKgEAAAAAlfApWFu9erWuvPJKjRo1qqSteHPRdu3aacmSJerdu7f+/Oc/6+WXX66ZSgEg0LmcUsp6KTtNCo+W4oeVn7aZeVja8qa0ZbGUebC0vV0/c1fPXtdJTSLrsmoAAAAAgI98CtZOnDihHj16lJ4kOFg5OTklz202my6//HJ9+OGH510gANQLO5KlFQ9IjqOlbfYYafxcc320X1aYo9P2rJZk/kOEbBFSn0lmoNaur1/KBgAAAAD4zqdgrXXr1jpz5ozb8wMHDrifODhYGRkZ51MbANQPO5Kld6aoJDAr5jgqvXOzZLNLeY7S9o4jzLXTEpKkkCZ1WioAAAAAoOb4FKx169ZNe/fuLXk+ePBgrVy5Uvv27VPnzp2Vnp6upUuXqkuXLjVWKAAEJJfTHKl2bqhWVp5DatZG6n+jGai14rsRAAAAABoCqy8HXXHFFVq7dm3JiLT77rtPWVlZ6tOnjwYNGqQLLrhAqampuueee2qyVgAIPCnr3ad/VuSaf0mJjxCqAQAAAEAD4lOwNm3aNK1bt05BQeai3KNHj9bbb7+t+Ph4/fTTT4qOjtbzzz+vO++8s0aLBYCAkn1c2vy6d31zTtZuLQAAAACAOmcxirfzbMQcDociIiKUmZkpu93u73IABDJnobRnlbmr5y8rJFehd8fd8pHUaUTt1gYAAAAAqBHeZkU+rbF22WWXafjw4Xr88cd9LhAA6pX0X6Sti6Uf3pay00rbYy6STu2Vch3yvM6axdwdNH5YXVUKAAAAAKgjPgVr3377rS6++OKargUAAktelrT9A3N02qFvS9ubtpb6Xi/1u1GKTiizK6hF7uGaxbwb/6RkDarDwgEAAAAAdcGnYK1Hjx5KSUmp6VoAwP8MQzq4wQzTtn8oFZwx2y1WqdtYqf9NUrdxUnBo6TEJSdKkRebuoGU3MrDHmKFaQlKdXgIAAAAAoG74FKzdc889mjFjhnbs2KGEhISargkA6p7jmPTDW9KWN82pncVadTXDtD7XS/Z2FR+fkCT1mGDuEpqdJoVHm9M/GakGAAAAAA2WT8Fa586dNXr0aF188cW6++67NWjQIEVHR8tisZTrO3LkyPMuEgBqRWG+uQHBlsXmhgSGy2wPaSb1+h+p/81S3BDJw3ebR9YgNigAAAAAgEbEp11BrVarLBaLig/1FKgVczqdvldXR9gVFGhk0naYYdq2t6Wck6XtHYaao9MSJkq2cL+VBwAAAADwr1rdFfThhx+uNEwDgIBzNkP66T0zUDu6ubQ9vK3U7wZzI4LW3fxWHgAAAACg/vFpxFpDw4g1oIFyuaQDX5ph2s/JUmGu2W4Nli4YL100ReoyRgry6d8YAAAAAAANVK2OWAOAgJZxSNr6lrR1sZRxsLQ96sKijQgmS+FR/qsPAAAAANAgEKwBaBgKcqWdH5mj0/atk1Q0GNdml3pda25EEHuR9xsRAAAAAABQBZ+CteLNC6pisVhUWFjoy1sAgHeO/VC0EcE7Um5GaXvHEWaYduFVUmhTv5UHAAAAAGi4fArWRo4c6TFYy8zM1O7du3XmzBn17dtXkZGR51sfAJSXc0r68V1pyxtS6o+l7fb2Ur/fmLeWnfxXHwAAAACgUfApWFu3bl2Fr+Xk5OjBBx/UihUrtGrVKl/rAgB3Lqe0b605Om3nx5Iz32wPCpV6/MpcO63zaMka5NcyAQAAAACNR63tCjpo0CD16tVLCxcurI3T1yh2BQUC2Kl9RRsRvCU5jpS2t+1jTvXsfZ3UtKX/6gMAAAAANDh+3xV0xIgRWrx4cW2dHkB95HJKKeul7DQpPFqKH+Z5hFl+jvRzsjk67cCXpe1hkeaOnv1vlNr1rbOyAQAAAADwpNaCtfT0dGVnZ9fW6QHUNzuSpRUPSI6jpW32GGn8XCkhSTIM6cgmc920H9+T8rOKOlmkLpeZUz27XymFhPmlfAAAAAAAzlXjwZrL5dKbb76pJUuWaODAgTV9egD10Y5k6Z0pks6Zee44Zrb3vV46ukVK31n6WmS8OdWz7/VSZFydlgsAAAAAgDd8CtY6d+7ssb2wsFDHjx9XQUGBQkJCNGfOnPMqDkAD4HKaI9XODdWk0rYf/mPeB4dJCVebo9PiL5Gs1rqqEgAAAACAavMpWHO5XLJYLOXaQ0JC1KtXLw0aNEgzZsxQz549z7tAAPVcynr36Z8VuXi6NPoBKSyi9msCAAAAAKAG+BSsHThwoIbLANBgZad51y/2IkI1AAAAAEC9wjwrALXL5fSuX3h07dYBAAAAAEAN8ylYO3z4sJKTk5WRkeHx9dOnTys5OVlHjhw5n9oA1GcFZ6W1c6RlM6roaJHssVL8sDopCwAAAACAmuJTsPa3v/1NU6dOVZMmTTy+3rRpU912221sXgA0Vr98Kv3zYunzJyVXvhTdq+iFc9dmLHo+/knJGlSXFQIAAAAAcN58CtY+++wzjR07VjabzePrNptNY8eO1erVq8+rOAD1TMZB6e0bpbd+LZ0+IDWPkX79mvTbr6RJb0j2du797THSpEVSQpI/qgUAAAAA4Lz4tHnBkSNHdO2111baJz4+XsuXL/epKAD1TGG+tOEF6fOnpcKzkiVIGvo7adQDkq252SchSeoxwdwlNDvNXFMtfhgj1QAAAAAA9ZZPwVpoaKgcDkelfRwOhyyWc6d9AWhw9q2TPr5fOrnbfB4/XLryGSk6oXxfa5DUaUSdlgcAAAAAQG3xaSpo7969tXz5cuXl5Xl8PTc3V8nJyerdu/d5FQcggDmOSu9OlRZdbYZqzaKk/3lZuvVjz6EaAAAAAAANjE/B2tSpU3X48GElJSVp3759bq/t3btXV199tY4ePao77rijRooEEECcBdL6edK8QdL29yWLVRp8lzTje6nv9RIjVQEAAAAAjYTFMAzDlwN//etf67333lNwcLA6deqk2NhYHTlyRPv371dhYaEmT56s//znPzVdb61wOByKiIhQZmam7Ha7v8sBAlfKenPa5/Ht5vPYgdKEv0sx/fxaFgAAAAAANcnbrMjnYM0wDM2fP1///Oc/tXPnzpL2hIQETZ8+XdOmTfPltH5BsAZUITtdWvWw9MNb5vMmLaXER6T+N0tWnwa+AgAAAAAQsGo9WCvrzJkzyszMVEREhJo1a3a+p6tzBGtABVxO6ftXpc8el3IzzbaLbjFDtaYt/VoaAAAAAAC1xdusyKddQc/VrFmzehmoAajE4U3SxzOlY1vN5237SL/6h9R+oF/LAgAAAAAgUPg0h+vrr7/WzJkzlZqa6vH1Y8eOaebMmfrmm2/OqzgAfpBzSlp+r/TvMWaoZouQrnxGumsdoRoAAAAAAGX4NBX02muv1bZt27R79+4K+1xwwQXq37+/lixZcl4F1gWmggKSXC5p62Jp1Wzp7Cmzre8N0uWPSeFt/FsbAAAAAAB1qFangm7cuFFjxoyptM/IkSO1atUqX04PoK4d2yZ9/Efp8Hfm8zYJ5ii1jsP9WxcAAAAAAAHMp2Dt+PHjio2NrbRP27Ztdfz4cZ+KAlBHcjOlz/5P2viKZLik0HBp9CxpyN1SUIi/qwMAAAAAIKD5FKxFRkbq4MGDlfZJSUlReHi4T0UBqGWGIW17R/r0r9KZogC85zXSuP+T7DH+rQ0AAAAAgHrCp2Dt4osv1gcffKBDhw4pLi6u3OsHDx7Uhx9+qMsuu+y8CwRQw47/LH18v5Tylfm8VVdz2meXS/1bFwAAAAAA9YxPu4LOnDlTOTk5Gj58uBYtWqRjx45JMncDff311zV8+HCdPXtWf/zjH2u0WADnIS9b+vQh6aVLzFAtuIl02UPStPWEagAAAAAA+MCnEWsjR47Us88+qz/+8Y+aOnWqJMlisah4g1Gr1ar/9//+n0aOHFlzlQLwjWFIO5ZJK/8sOY6Ybd0nSOPnSC3i/VsbAAAAAAD1mMUoTsN8sG3bNr300kvauHGjMjMzFRkZqcGDB+u3v/2tevXqpby8PNlstpqst1Z4u4UqUO+c3Ct9cr+09zPzeWS8dOXT0gXj/FsXAAAAAAABzNus6LyCtYps3rxZCxYs0Ntvv62TJ0/W9OlrHMEaGpz8HOmrZ6Wv/5/kzJeCbNIl90mX/EEKaeLv6gAAAAAACGjeZkU+TQX1JCMjQ4sXL9aCBQu0bds2GYahJk34CzxQ53b9V/rvn6SMop17uyZKVzwlteri37oAAAAAAGhgzjtYW716tRYsWKBly5YpLy9PhmFo6NChmjp1qiZPnlwTNQLwxukD0n8flH75r/nc3t5cR+3CqySLxa+lAQAAAADQEPkUrB06dEgLFy7UwoULdfDgQRmGodjYWB05ckS33nqrXn311ZquE0BFCvOkr5+XvnxGKsyVrMHS0BnSqD9Joc38XR0AAAAAAA2W18FaQUGBPvzwQy1YsEBr1qyR0+lUs2bNdOONN2rKlCm67LLLFBwcrODgGptdCqAqe9ZIn/yvdGqv+bzjCGnC36Wo7v6tCwAAAACARsDrFCwmJkanTp2SxWLRpZdeqilTpuiaa65Rs2aMiAHqXOYRaeUsaccy83l4tDT2/6Te1zHtEwAAAACAOuJ1sHby5ElZrVb94Q9/0J/+9CdFRUXVZl0APHEWSN/8U1o3Vyo4I1mCpCF3S6NnSWHsaAsAAAAAQF2yetvx1ltvVZMmTfTss8+qffv2SkpK0rvvvqv8/PzarA9oXFxOaf+X0o9LzXuXs/S1A19JL42QVj1shmpxQ6S7Pzc3KCBUAwAAAACgznk9Yu3VV1/V888/r7ffflsLFizQRx99pI8//lh2u12TJk3SzTffXJt1Ag3fjmRpxQOS42hpmz3GHI124Ctp2xKzrWkr6fLHpL6/kaxeZ+MAAAAAAKCGWQzDMHw58Oeff9a///1vLV68WOnp6bIUret0ySWXaNGiRYqPj6/RQmuTw+FQRESEMjMzZbcz8gd+sCNZemeKpMr+d7RIA6dKlz0kNW1ZV5UBAAAAANDoeJsV+RysFSssLCzZLXTVqlVyuVyyWq0aNWqUbr311noxko1gDX7lckrP9XIfqXYua4g09RMpbnDd1QUAAAAAQCNVZ8FaWYcPH9bChQv12muvaf/+/bJYLHI6nVUf6GcEa/Cr/V9Kr/+q6n63fCR1GlH79QAAAAAA0Mh5mxXV6AJN7du310MPPaS9e/dq1apVuv7662vy9EDDlJ1Ws/0AAAAAAECd8HrzguoaM2aMxowZU1unBxqO8Oia7QcAAAAAAOpErQVrALyUkVJFB4u5O2j8sDopBwAAAAAAeKdGp4LWpPnz56tjx44KCwvTkCFD9N1331XY9/3339fAgQMVGRmpZs2aqV+/fnrjjTfqsFrAB84C6ZP/lZZNL9NoOadT0fPxT0rWoLqqDAAAAAAAeCEgg7UlS5Zo5syZmj17tjZv3qy+fftq3LhxOn78uMf+LVu21F/+8hdt2LBB27Zt09SpUzV16lStXLmyjisHvJR9XHo9SfruX+bzUQ9Kv14k2du597PHSJMWSQlJdV8jAAAAAACoVI3uClpThgwZokGDBmnevHmSJJfLpbi4ON1zzz168MEHvTrHRRddpAkTJujxxx+vsi+7gqJOHd4kLblJyjoqhTaXrvmX1ONK8zWXU0pZb25UEB5tTv9kpBoAAAAAAHXK26wo4NZYy8/P16ZNmzRr1qySNqvVqsTERG3YsKHK4w3D0GeffaZdu3Zp7ty5Hvvk5eUpLy+v5LnD4Tj/wgFvbH5D+nim5MyXWl8gTX5Tirqg9HVrkNRphP/qAwAAAAAAXgu4YO3EiRNyOp2KjnbfATE6Olo7d+6s8LjMzEzFxsYqLy9PQUFB+uc//6nLL7/cY985c+b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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_history(history)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save Model" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[TF] Model Saving time: 0.08504319190979004\n" + ] + } + ], + "source": [ + "st = time.time()\n", + "model.save(str(output_path / \"cifar100.keras\"))\n", + "print(f\"[TF] Model Saving time: {time.time() - st}\")" + ] + } + ], + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tfvspt/tf/utils.py b/tfvspt/tf/utils.py new file mode 100644 index 0000000..a5db3ab --- /dev/null +++ b/tfvspt/tf/utils.py @@ -0,0 +1,60 @@ +"""Tensorflow Utils.""" + +import random + +import matplotlib.pyplot as plt +import numpy as np +import tensorflow as tf + + +def plot_images(dataset) -> None: + + images, labels = next(iter(dataset)) + + grid_size = int(len(images)**0.5) + fig, axes = plt.subplots(grid_size, grid_size, figsize=(10, 10)) + + for i, ax in enumerate(axes.flatten()): + if i < len(images): + img = images[i].numpy() + img = (img * 255).astype(np.uint8) + ax.imshow(img) + ax.axis('off') + + plt.show() + + +def plot_history(history) -> None: + + epochs = len(history.history['accuracy']) + + plt.figure(figsize=(15, 5)) + plt.plot(np.arange(epochs), + history.history['accuracy'], + '-o', + label='Train Accuracy', + color='#ff7f0e') + plt.ylabel('Accuracy', size=14) + plt.xlabel('Epoch', size=14) + plt.title("Training Accuracy") + + plt.figure(figsize=(15, 5)) + plt.plot(np.arange(epochs), + history.history['loss'], + '-o', + label='Train Loss', + color='#1f77b4') + plt.ylabel('Loss', size=14) + plt.xlabel('Epoch', size=14) + plt.title("Training Loss") + + plt.show() + + +def set_seed(seed: int) -> None: + # Set random seed for Python + random.seed(seed) + # Set random seed for NumPy + np.random.seed(seed) + # Set random seed for TensorFlow + tf.random.set_seed(seed) From 8642e6c6810a37975036aaa4f6f5e9518ff912bb Mon Sep 17 00:00:00 2001 From: "meet.ranoliya" Date: Wed, 19 Mar 2025 17:42:25 +0530 Subject: [PATCH 2/8] Add: torch training --- tfvspt/pt/data.py | 60 +++ tfvspt/pt/eval.py | 34 ++ tfvspt/pt/model.py | 39 ++ tfvspt/pt/notebooks/eval.ipynb | 231 ++++++++++ tfvspt/pt/notebooks/train.ipynb | 733 ++++++++++++++++++++++++++++++++ tfvspt/pt/train.py | 55 +++ tfvspt/pt/utils.py | 70 +++ 7 files changed, 1222 insertions(+) create mode 100644 tfvspt/pt/data.py create mode 100644 tfvspt/pt/eval.py create mode 100644 tfvspt/pt/model.py create mode 100644 tfvspt/pt/notebooks/eval.ipynb create mode 100644 tfvspt/pt/notebooks/train.ipynb create mode 100644 tfvspt/pt/train.py create mode 100644 tfvspt/pt/utils.py diff --git a/tfvspt/pt/data.py b/tfvspt/pt/data.py new file mode 100644 index 0000000..d147402 --- /dev/null +++ b/tfvspt/pt/data.py @@ -0,0 +1,60 @@ +"""Pytorch Dataset.""" + +import numpy as np +from torch.utils.data import DataLoader +from torch.utils.data import Dataset +from torchvision import transforms + +from tfvspt.config.config import Config + + +class ClassificationDataset(Dataset): + + def __init__( + self, + config: Config, + images: np.ndarray, + labels: np.ndarray, + transforms=None, + ) -> None: + self.config = config + self.images = images + self.labels = labels + self.transforms = transforms + + def __len__(self) -> None: + return len(self.images) + + def __getitem__(self, idx: int): + image = self.images[idx] + label = self.labels[idx] + if self.transforms: + image = self.transforms(image) + return image, label + + +def get_transforms(data: str): + transforms_ = [] + if data == "train": + transforms_ += [] + transforms_ += [transforms.ToTensor()] + return transforms.Compose(transforms_) + + +def get_dataloader(dataset: ClassificationDataset, data: str, config: Config): + dataloader = None + if data == "train": + dataloader = DataLoader( + dataset=dataset, + batch_size=config.bs, + shuffle=True, + num_workers=config.num_workers, + ) + else: + dataloader = DataLoader( + dataset=dataset, + batch_size=config.bs, + shuffle=False, + num_workers=config.num_workers, + ) + return dataloader diff --git a/tfvspt/pt/eval.py b/tfvspt/pt/eval.py new file mode 100644 index 0000000..9686237 --- /dev/null +++ b/tfvspt/pt/eval.py @@ -0,0 +1,34 @@ +"""Torch Evaluation.""" + +import torch +from tqdm.autonotebook import tqdm + + +def eval_model(dataloader, model, criterion, device) -> dict: + + model.to(device) + + model.eval() + val_loss = 0.0 + correct = 0 + total = 0 + + with torch.no_grad(): + for images, targets in tqdm(dataloader): + + images, targets = images.to(device), targets.to(device) + + outputs = model(images) + + loss = criterion(outputs, targets[:, 0]) + + val_loss += loss.item() + + predicted = torch.argmax(outputs, 1) + total += targets.size(0) + correct += (predicted == targets[:, 0]).sum().item() + + val_loss /= len(dataloader) + val_accuracy = round(correct / total, 4) + + return {"loss": val_loss, "accuracy": val_accuracy} diff --git a/tfvspt/pt/model.py b/tfvspt/pt/model.py new file mode 100644 index 0000000..59adcab --- /dev/null +++ b/tfvspt/pt/model.py @@ -0,0 +1,39 @@ +"""Pytorch Model.""" + +from torch import nn + + +class Model(nn.Module): + + def __init__(self, n_classes: int, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + + layers = [] + channels = [3, 32, 64, 128] + for i in range(1, len(channels)): + layers += [ + nn.Conv2d(channels[i - 1], + channels[i], + kernel_size=3, + padding='same'), + nn.BatchNorm2d(channels[i]), + nn.ReLU(), + nn.MaxPool2d((2, 2)), + ] + + self.backbone = nn.Sequential(*layers) + + self.gap = nn.AdaptiveAvgPool2d((1, 1)) + + self.fc = nn.Sequential( + nn.Linear(128, 128), + nn.ReLU(), + nn.Linear(128, n_classes), + ) + + def forward(self, x): + x = self.backbone(x) + x = self.gap(x) + x = x.view(x.size(0), -1) + x = self.fc(x) + return x diff --git a/tfvspt/pt/notebooks/eval.ipynb b/tfvspt/pt/notebooks/eval.ipynb new file mode 100644 index 0000000..f516408 --- /dev/null +++ b/tfvspt/pt/notebooks/eval.ipynb @@ -0,0 +1,231 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e2e6dcdd-7023-4aec-9550-d72361c90198", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "257788cf-7265-4cda-826c-e72fc0519cd9", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import time\n", + "\n", + "import torch\n", + "\n", + "from tfvspt.config.config import get_config\n", + "from tfvspt.pt.data import ClassificationDataset\n", + "from tfvspt.pt.data import get_dataloader\n", + "from tfvspt.pt.data import get_transforms\n", + "from tfvspt.pt.eval import eval_model\n", + "from tfvspt.pt.utils import plot_images\n", + "from tfvspt.tf.data import Dataset as TFDataset" + ] + }, + { + "cell_type": "markdown", + "id": "69527275-8978-4fda-ba4f-d4f94cdafcc4", + "metadata": {}, + "source": [ + "## Config" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d51e2701-5359-4ffe-82ff-d4e214d805de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Config(seed=25, n_classes=100, bs=32, imgsz=(32, 32), lr=0.001, epochs=20, num_workers=2, output='./output')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config = get_config(\"../../assets/config.yaml\")\n", + "config" + ] + }, + { + "cell_type": "markdown", + "id": "2ddc1360-07dd-4cc1-be2e-b8926ff8b2e0", + "metadata": {}, + "source": [ + "## Test Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0b54f8bd-8a47-4576-b640-ef2a290e3522", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testdata size: 10000\n" + ] + } + ], + "source": [ + "(x_train, y_train), (x_test, y_test) = TFDataset.get_data()\n", + "print(f\"Testdata size: {len(x_test)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8303a182-0662-46ca-8722-8f862216c4bd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[PT] Dataset loading time: 0.0003070831298828125\n" + ] + } + ], + "source": [ + "st = time.time()\n", + "test_ds = ClassificationDataset(config=config, images=x_test, labels=y_test, transforms=get_transforms(data=\"test\"))\n", + "test_dataloader = get_dataloader(dataset=test_ds, data=\"test\", config=config)\n", + "print(f\"[PT] Dataset loading time: {time.time() - st}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a3acf55c-9526-433e-983d-0fe44c6da7ab", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_images(test_dataloader)" + ] + }, + { + "cell_type": "markdown", + "id": "4a39046c-5212-44c3-8c4f-51c664426c03", + "metadata": {}, + "source": [ + "## Load Model" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c7d365a4-fb25-47ad-91cb-60e9ddf76746", + "metadata": {}, + "outputs": [], + "source": [ + "model_path = Path(config.output) / \"pt/cifar100.pt\"" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8fb82c3c-be79-4c3d-8c51-4b634febb23a", + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "model = torch.jit.load(str(model_path))" + ] + }, + { + "cell_type": "markdown", + "id": "587ed2a6-739b-4a50-b01e-0d7025ce0c35", + "metadata": {}, + "source": [ + "## Eval" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "81559adb-f7f4-49b0-a9cb-82b09290c6f3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 313/313 [00:02<00:00, 136.92it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[PT] Evaluation Results: {'loss': 2.206257000517921, 'accuracy': 0.437}\n", + "[PT] Evaluation time: 2.29127836227417\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "st = time.time()\n", + "results = eval_model(\n", + " dataloader=test_dataloader,\n", + " model=model,\n", + " criterion=torch.nn.CrossEntropyLoss(),\n", + " device=device,\n", + ")\n", + "print(f\"[PT] Evaluation Results: {results}\")\n", + "print(f\"[PT] Evaluation time: {time.time() - st}\")" + ] + } + ], + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tfvspt/pt/notebooks/train.ipynb b/tfvspt/pt/notebooks/train.ipynb new file mode 100644 index 0000000..f7c8940 --- /dev/null +++ b/tfvspt/pt/notebooks/train.ipynb @@ -0,0 +1,733 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-03-19 15:38:38.284991: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2025-03-19 15:38:38.292815: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "E0000 00:00:1742378918.302161 25774 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "E0000 00:00:1742378918.304973 25774 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "W0000 00:00:1742378918.312426 25774 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742378918.312438 25774 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742378918.312439 25774 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742378918.312440 25774 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "2025-03-19 15:38:38.314974: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "/home/meet.ranoliya/workspace/tf_pt_benchmarking/tfvspt/pt/train.py:4: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from tqdm.autonotebook import tqdm\n" + ] + } + ], + "source": [ + "from pathlib import Path\n", + "import time\n", + "\n", + "import torch\n", + "from torchinfo import summary\n", + "\n", + "from tfvspt.config.config import get_config\n", + "from tfvspt.pt.data import ClassificationDataset\n", + "from tfvspt.pt.data import get_dataloader\n", + "from tfvspt.pt.data import get_transforms\n", + "from tfvspt.pt.model import Model\n", + "from tfvspt.pt.train import train_model\n", + "from tfvspt.pt.utils import plot_history\n", + "from tfvspt.pt.utils import plot_images\n", + "from tfvspt.pt.utils import set_seed\n", + "from tfvspt.tf.data import Dataset as TFDataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read Config" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Config(seed=25, n_classes=100, bs=32, imgsz=(32, 32), lr=0.001, epochs=20, num_workers=2, output='./output')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config = get_config(\"../../assets/config.yaml\")\n", + "config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set Seed" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "set_seed(config.seed)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset\n", + "\n", + "### Name: CIFAR100" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[PT] Traindata size: 50000\n" + ] + } + ], + "source": [ + "(x_train, y_train), (x_test, y_test) = TFDataset.get_data()\n", + "print(f\"[PT] Traindata size: {len(x_train)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[PT] Dataset loading time: 0.00022530555725097656\n" + ] + } + ], + "source": [ + "st = time.time()\n", + "train_ds = ClassificationDataset(config=config, images=x_train, labels=y_train, transforms=get_transforms(data=\"train\"))\n", + "train_dataloader = get_dataloader(dataset=train_ds, data=\"train\", config=config)\n", + "print(f\"[PT] Dataset loading time: {time.time() - st}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_images(train_dataloader)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[PT] Model loading time: 0.004579782485961914\n" + ] + }, + { + "data": { + "text/plain": [ + "==========================================================================================\n", + "Layer (type:depth-idx) Output Shape Param #\n", + "==========================================================================================\n", + "Model [32, 100] --\n", + "├─Sequential: 1-1 [32, 128, 4, 4] --\n", + "│ └─Conv2d: 2-1 [32, 32, 32, 32] 896\n", + "│ └─BatchNorm2d: 2-2 [32, 32, 32, 32] 64\n", + "│ └─ReLU: 2-3 [32, 32, 32, 32] --\n", + "│ └─MaxPool2d: 2-4 [32, 32, 16, 16] --\n", + "│ └─Conv2d: 2-5 [32, 64, 16, 16] 18,496\n", + "│ └─BatchNorm2d: 2-6 [32, 64, 16, 16] 128\n", + "│ └─ReLU: 2-7 [32, 64, 16, 16] --\n", + "│ └─MaxPool2d: 2-8 [32, 64, 8, 8] --\n", + "│ └─Conv2d: 2-9 [32, 128, 8, 8] 73,856\n", + "│ └─BatchNorm2d: 2-10 [32, 128, 8, 8] 256\n", + "│ └─ReLU: 2-11 [32, 128, 8, 8] --\n", + "│ └─MaxPool2d: 2-12 [32, 128, 4, 4] --\n", + "├─AdaptiveAvgPool2d: 1-2 [32, 128, 1, 1] --\n", + "├─Sequential: 1-3 [32, 100] --\n", + "│ └─Linear: 2-13 [32, 128] 16,512\n", + "│ └─ReLU: 2-14 [32, 128] --\n", + "│ └─Linear: 2-15 [32, 100] 12,900\n", + "==========================================================================================\n", + "Total params: 123,108\n", + "Trainable params: 123,108\n", + "Non-trainable params: 0\n", + "Total mult-adds (M): 333.09\n", + "==========================================================================================\n", + "Input size (MB): 0.39\n", + "Forward/backward pass size (MB): 29.42\n", + "Params size (MB): 0.49\n", + "Estimated Total Size (MB): 30.30\n", + "==========================================================================================" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "st = time.time()\n", + "model = Model(n_classes=config.n_classes)\n", + "print(f\"[PT] Model loading time: {time.time() - st}\")\n", + "summary(model, input_size=(config.bs, 3, *config.imgsz))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "output_path = Path(config.output) / \"pt\"\n", + "output_path.mkdir(parents=True, exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "print(f\"Device Selected: {device}\")\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=config.lr)\n", + "criterion = torch.nn.CrossEntropyLoss()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1 / 20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:55<00:00, 28.24it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20, Loss: 3.599246293706766, Accuracy: 0.1406\n", + "\n", + "\n", + "\n", + "Epoch 2 / 20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [01:06<00:00, 23.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/20, Loss: 2.985980004892087, Accuracy: 0.2458\n", + "\n", + "\n", + "\n", + "Epoch 3 / 20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [01:03<00:00, 24.69it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 3/20, Loss: 2.7145111965812787, Accuracy: 0.2991\n", + "\n", + "\n", + "\n", + "Epoch 4 / 20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [01:03<00:00, 24.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 4/20, Loss: 2.526905861331039, Accuracy: 0.3396\n", + "\n", + "\n", + "\n", + "Epoch 5 / 20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + 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0.455\n", + "\n", + "\n", + "\n", + "Epoch 10 / 20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [01:04<00:00, 24.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 10/20, Loss: 1.941395169790174, Accuracy: 0.4662\n", + "\n", + "\n", + "\n", + "Epoch 11 / 20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:58<00:00, 26.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 11/20, Loss: 1.8768491389579065, Accuracy: 0.482\n", + "\n", + "\n", + "\n", + "Epoch 12 / 20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + 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Accuracy: 0.5389\n", + "\n", + "\n", + "\n", + "Epoch 17 / 20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:53<00:00, 29.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 17/20, Loss: 1.5888635097828265, Accuracy: 0.5478\n", + "\n", + "\n", + "\n", + "Epoch 18 / 20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:52<00:00, 29.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 18/20, Loss: 1.5566429326493085, Accuracy: 0.5545\n", + "\n", + "\n", + "\n", + "Epoch 19 / 20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:51<00:00, 30.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 19/20, Loss: 1.5074093909272763, Accuracy: 0.5712\n", + "\n", + "\n", + "\n", + "Epoch 20 / 20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:53<00:00, 29.02it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 20/20, Loss: 1.4741765901741888, Accuracy: 0.5774\n", + "\n", + "\n", + "\n", + "[PT] Training time: 1149.2202188968658\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "st = time.time()\n", + "history = train_model(\n", + " dataloader=train_dataloader,\n", + " model=model,\n", + " criterion=criterion,\n", + " optimizer=optimizer,\n", + " config=config,\n", + " device=device,\n", + ")\n", + "print(f\"[PT] Training time: {time.time() - st}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize Training History" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_history(history)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save Model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[PT] Model Saving time: 0.08316612243652344\n" + ] + } + ], + "source": [ + "st = time.time()\n", + "torch.jit.script(model).save(str(output_path / \"cifar100.pt\"))\n", + "print(f\"[PT] Model Saving time: {time.time() - st}\")" + ] + } + ], + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tfvspt/pt/train.py b/tfvspt/pt/train.py new file mode 100644 index 0000000..1d2cf06 --- /dev/null +++ b/tfvspt/pt/train.py @@ -0,0 +1,55 @@ +"""Torch Training.""" + +import torch +from tqdm.autonotebook import tqdm + +from tfvspt.config.config import Config + + +def train_model(dataloader, model, criterion, optimizer, config: Config, + device: str) -> dict: + + model.to(device) + + training_logs = {"loss": [], "accuracy": []} + for epoch in range(config.epochs): + + model.train() + running_loss = 0.0 + total = 0 + correct = 0 + + print(f"Epoch {epoch + 1} / {config.epochs}") + for images, targets in tqdm(dataloader): + + images, targets = images.to(device), targets.to(device) + optimizer.zero_grad() + + outputs = model(images) + + loss = criterion(outputs, targets[:, 0]) + + loss.backward() + optimizer.step() + + running_loss += loss.item() + + predicted = torch.argmax(outputs, 1) + total += targets.size(0) + correct += (predicted == targets[:, 0]).sum().item() + + # Epoch Stats + epoch_loss = running_loss / len(dataloader) + epoch_accuracy = round(correct / total, 4) + + # Collect logs + training_logs["loss"].append(epoch_loss) + training_logs["accuracy"].append(epoch_accuracy) + + print( + f"Epoch {epoch + 1}/{config.epochs}, Loss: {epoch_loss}, Accuracy: {epoch_accuracy}" + ) + + print("\n") + + return training_logs diff --git a/tfvspt/pt/utils.py b/tfvspt/pt/utils.py new file mode 100644 index 0000000..e9956ae --- /dev/null +++ b/tfvspt/pt/utils.py @@ -0,0 +1,70 @@ +"""Pytorch Utils.""" + +import random + +import matplotlib.pyplot as plt +import numpy as np +import torch + + +def set_seed(seed: int): + """ + Sets the seed for random number generation in PyTorch, NumPy, and Python's random module. + Ensures reproducibility across different devices (CPU and GPU). + + Parameters: + seed (int): The seed value to be set. + """ + # Set the random seed for numpy, random, and torch + np.random.seed(seed) + random.seed(seed) + torch.manual_seed(seed) + + # Set the seed for CUDA (if using GPUs) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + + # Ensure deterministic results for CuDNN (can be slower) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +def plot_images(dataloader) -> None: + + images, labels = next(iter(dataloader)) + + grid_size = int(len(images)**0.5) + fig, axes = plt.subplots(grid_size, grid_size, figsize=(10, 10)) + + for i, ax in enumerate(axes.flatten()): + if i < len(images): + img = images[i].numpy() + img = img.transpose((1, 2, 0)) + img = (img * 255).astype(np.uint8) + ax.imshow(img) + ax.axis('off') + + plt.show() + + +def plot_history(history: dict[str, list]) -> None: + + epochs = range(len(history['loss'])) + + # Plot loss + plt.figure(figsize=(12, 12)) + plt.subplot(2, 1, 1) + plt.plot(epochs, history['loss'], label='Loss') + plt.xlabel('Epoch') + plt.ylabel('Loss') + plt.title('Training Loss') + + # Plot accuracy + plt.subplot(2, 1, 2) + plt.plot(epochs, history['accuracy'], label='Accuracy') + plt.xlabel('Epoch') + plt.ylabel('Accuracy') + plt.title('Training Accuracy') + + plt.tight_layout() + plt.show() From 9014175bc61756ffe6f10ae882060dee10a19063 Mon Sep 17 00:00:00 2001 From: "meet.ranoliya" Date: Wed, 19 Mar 2025 22:25:03 +0530 Subject: [PATCH 3/8] Update: set num_workers to 0 (default) --- tfvspt/assets/config.yaml | 2 +- tfvspt/pt/notebooks/train.ipynb | 106 ++++++++++++++------------------ 2 files changed, 48 insertions(+), 60 deletions(-) diff --git a/tfvspt/assets/config.yaml b/tfvspt/assets/config.yaml index bc17473..02e3dc9 100644 --- a/tfvspt/assets/config.yaml +++ b/tfvspt/assets/config.yaml @@ -6,5 +6,5 @@ imgsz: - 32 lr: 0.001 epochs: 20 -num_workers: 2 +num_workers: 0 output: "./output" \ No newline at end of file diff --git a/tfvspt/pt/notebooks/train.ipynb b/tfvspt/pt/notebooks/train.ipynb index f7c8940..791571e 100644 --- a/tfvspt/pt/notebooks/train.ipynb +++ b/tfvspt/pt/notebooks/train.ipynb @@ -9,26 +9,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-03-19 15:38:38.284991: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2025-03-19 15:38:38.292815: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1742378918.302161 25774 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1742378918.304973 25774 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1742378918.312426 25774 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742378918.312438 25774 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742378918.312439 25774 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742378918.312440 25774 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "2025-03-19 15:38:38.314974: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "/home/meet.ranoliya/workspace/tf_pt_benchmarking/tfvspt/pt/train.py:4: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from tqdm.autonotebook import tqdm\n" + " from tqdm.autonotebook import tqdm\n", + "2025-03-19 22:06:12.614517: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2025-03-19 22:06:12.625091: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "E0000 00:00:1742402172.637652 51117 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "E0000 00:00:1742402172.641287 51117 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "W0000 00:00:1742402172.652044 51117 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742402172.652059 51117 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742402172.652060 51117 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742402172.652061 51117 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "2025-03-19 22:06:12.655835: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] } ], @@ -66,7 +66,7 @@ { "data": { "text/plain": [ - "Config(seed=25, n_classes=100, bs=32, imgsz=(32, 32), lr=0.001, epochs=20, num_workers=2, output='./output')" + "Config(seed=25, n_classes=100, bs=32, imgsz=(32, 32), lr=0.001, epochs=20, num_workers=0, output='./output')" ] }, "execution_count": 2, @@ -131,7 +131,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[PT] Dataset loading time: 0.00022530555725097656\n" + "[PT] Dataset loading time: 0.0004093647003173828\n" ] } ], @@ -180,7 +180,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[PT] Model loading time: 0.004579782485961914\n" + "[PT] Model loading time: 0.005598545074462891\n" ] }, { @@ -254,7 +254,15 @@ "cell_type": "code", "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Device Selected: cpu\n" + ] + } + ], "source": [ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "print(f\"Device Selected: {device}\")\n", @@ -278,7 +286,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:55<00:00, 28.24it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:46<00:00, 33.56it/s]\n" ] }, { @@ -288,7 +296,6 @@ "Epoch 1/20, Loss: 3.599246293706766, Accuracy: 0.1406\n", "\n", "\n", - "\n", "Epoch 2 / 20\n" ] }, @@ -296,7 +303,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [01:06<00:00, 23.50it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:46<00:00, 33.53it/s]\n" ] }, { @@ -306,7 +313,6 @@ "Epoch 2/20, Loss: 2.985980004892087, Accuracy: 0.2458\n", "\n", "\n", - "\n", "Epoch 3 / 20\n" ] }, @@ -314,7 +320,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [01:03<00:00, 24.69it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:47<00:00, 33.22it/s]\n" ] }, { @@ -324,7 +330,6 @@ "Epoch 3/20, Loss: 2.7145111965812787, Accuracy: 0.2991\n", "\n", "\n", - "\n", "Epoch 4 / 20\n" ] }, @@ -332,7 +337,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [01:03<00:00, 24.76it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:45<00:00, 34.42it/s]\n" ] }, { @@ -342,7 +347,6 @@ "Epoch 4/20, Loss: 2.526905861331039, Accuracy: 0.3396\n", "\n", "\n", - "\n", "Epoch 5 / 20\n" ] }, @@ -350,7 +354,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [01:06<00:00, 23.62it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:44<00:00, 35.33it/s]\n" ] }, { @@ -360,7 +364,6 @@ "Epoch 5/20, Loss: 2.386696925623937, Accuracy: 0.365\n", "\n", "\n", - "\n", "Epoch 6 / 20\n" ] }, @@ -368,7 +371,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [01:00<00:00, 25.98it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:48<00:00, 32.44it/s]\n" ] }, { @@ -378,7 +381,6 @@ "Epoch 6/20, Loss: 2.2685544724961693, Accuracy: 0.3922\n", "\n", "\n", - "\n", "Epoch 7 / 20\n" ] }, @@ -386,7 +388,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [01:01<00:00, 25.47it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:45<00:00, 34.49it/s]\n" ] }, { @@ -396,7 +398,6 @@ "Epoch 7/20, Loss: 2.1684138697839592, Accuracy: 0.4175\n", "\n", "\n", - "\n", "Epoch 8 / 20\n" ] }, @@ -404,7 +405,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:55<00:00, 27.99it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:47<00:00, 32.78it/s]\n" ] }, { @@ -414,7 +415,6 @@ "Epoch 8/20, Loss: 2.0855650893404785, Accuracy: 0.4336\n", "\n", "\n", - "\n", "Epoch 9 / 20\n" ] }, @@ -422,7 +422,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:56<00:00, 27.82it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:42<00:00, 36.81it/s]\n" ] }, { @@ -432,7 +432,6 @@ "Epoch 9/20, Loss: 2.0035568024207597, Accuracy: 0.455\n", "\n", "\n", - "\n", "Epoch 10 / 20\n" ] }, @@ -440,7 +439,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [01:04<00:00, 24.39it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:43<00:00, 35.54it/s]\n" ] }, { @@ -450,7 +449,6 @@ "Epoch 10/20, Loss: 1.941395169790174, Accuracy: 0.4662\n", "\n", "\n", - "\n", "Epoch 11 / 20\n" ] }, @@ -458,7 +456,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:58<00:00, 26.60it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:40<00:00, 38.88it/s]\n" ] }, { @@ -468,7 +466,6 @@ "Epoch 11/20, Loss: 1.8768491389579065, Accuracy: 0.482\n", "\n", "\n", - "\n", "Epoch 12 / 20\n" ] }, @@ -476,7 +473,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:53<00:00, 29.19it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:44<00:00, 35.42it/s]\n" ] }, { @@ -486,7 +483,6 @@ "Epoch 12/20, Loss: 1.829061239786203, Accuracy: 0.4941\n", "\n", "\n", - "\n", "Epoch 13 / 20\n" ] }, @@ -494,7 +490,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:52<00:00, 29.63it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:39<00:00, 39.44it/s]\n" ] }, { @@ -504,7 +500,6 @@ "Epoch 13/20, Loss: 1.7745277791624259, Accuracy: 0.5065\n", "\n", "\n", - "\n", "Epoch 14 / 20\n" ] }, @@ -512,7 +507,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:53<00:00, 29.37it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:40<00:00, 38.22it/s]\n" ] }, { @@ -522,7 +517,6 @@ "Epoch 14/20, Loss: 1.7225741607900316, Accuracy: 0.5169\n", "\n", "\n", - "\n", "Epoch 15 / 20\n" ] }, @@ -530,7 +524,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:53<00:00, 29.09it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:40<00:00, 38.90it/s]\n" ] }, { @@ -540,7 +534,6 @@ "Epoch 15/20, Loss: 1.6740848422431824, Accuracy: 0.5282\n", "\n", "\n", - "\n", "Epoch 16 / 20\n" ] }, @@ -548,7 +541,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:54<00:00, 28.84it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:46<00:00, 33.71it/s]\n" ] }, { @@ -558,7 +551,6 @@ "Epoch 16/20, Loss: 1.6345876585727919, Accuracy: 0.5389\n", "\n", "\n", - "\n", "Epoch 17 / 20\n" ] }, @@ -566,7 +558,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:53<00:00, 29.47it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:44<00:00, 35.32it/s]\n" ] }, { @@ -576,7 +568,6 @@ "Epoch 17/20, Loss: 1.5888635097828265, Accuracy: 0.5478\n", "\n", "\n", - "\n", "Epoch 18 / 20\n" ] }, @@ -584,7 +575,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:52<00:00, 29.78it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:44<00:00, 35.46it/s]\n" ] }, { @@ -594,7 +585,6 @@ "Epoch 18/20, Loss: 1.5566429326493085, Accuracy: 0.5545\n", "\n", "\n", - "\n", "Epoch 19 / 20\n" ] }, @@ -602,7 +592,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:51<00:00, 30.40it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:49<00:00, 31.62it/s]\n" ] }, { @@ -612,7 +602,6 @@ "Epoch 19/20, Loss: 1.5074093909272763, Accuracy: 0.5712\n", "\n", "\n", - "\n", "Epoch 20 / 20\n" ] }, @@ -620,7 +609,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:53<00:00, 29.02it/s]" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:44<00:00, 34.94it/s]" ] }, { @@ -630,8 +619,7 @@ "Epoch 20/20, Loss: 1.4741765901741888, Accuracy: 0.5774\n", "\n", "\n", - "\n", - "[PT] Training time: 1149.2202188968658\n" + "[PT] Training time: 891.4652616977692\n" ] }, { @@ -698,7 +686,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[PT] Model Saving time: 0.08316612243652344\n" + "[PT] Model Saving time: 0.0846412181854248\n" ] } ], From f7597967021c8fc2b0933c56e5984c2c9dd34b0b Mon Sep 17 00:00:00 2001 From: "meet.ranoliya" Date: Thu, 20 Mar 2025 10:58:10 +0530 Subject: [PATCH 4/8] Update: history plotting & notebooks --- tfvspt/pt/notebooks/eval.ipynb | 42 ++++++++++++++------ tfvspt/pt/notebooks/train.ipynb | 68 ++++++++++++++++----------------- tfvspt/pt/utils.py | 20 ++++++---- tfvspt/tf/notebooks/eval.ipynb | 68 ++++++++++++++++++++++++--------- tfvspt/tf/notebooks/train.ipynb | 68 ++++++++++++++++----------------- 5 files changed, 162 insertions(+), 104 deletions(-) diff --git a/tfvspt/pt/notebooks/eval.ipynb b/tfvspt/pt/notebooks/eval.ipynb index f516408..5780dbc 100644 --- a/tfvspt/pt/notebooks/eval.ipynb +++ b/tfvspt/pt/notebooks/eval.ipynb @@ -10,10 +10,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "257788cf-7265-4cda-826c-e72fc0519cd9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/meet.ranoliya/workspace/tf_pt_benchmarking/tfvspt/pt/eval.py:4: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from tqdm.autonotebook import tqdm\n", + "2025-03-20 10:57:03.187989: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2025-03-20 10:57:03.195953: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "E0000 00:00:1742448423.205256 13174 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "E0000 00:00:1742448423.208096 13174 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "W0000 00:00:1742448423.215365 13174 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742448423.215374 13174 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742448423.215375 13174 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742448423.215376 13174 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "2025-03-20 10:57:03.217994: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], "source": [ "from pathlib import Path\n", "import time\n", @@ -46,7 +66,7 @@ { "data": { "text/plain": [ - "Config(seed=25, n_classes=100, bs=32, imgsz=(32, 32), lr=0.001, epochs=20, num_workers=2, output='./output')" + "Config(seed=25, n_classes=100, bs=32, imgsz=(32, 32), lr=0.001, epochs=20, num_workers=0, output='./output')" ] }, "execution_count": 2, @@ -88,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "id": "8303a182-0662-46ca-8722-8f862216c4bd", "metadata": {}, "outputs": [ @@ -96,7 +116,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[PT] Dataset loading time: 0.0003070831298828125\n" + "[PT] Dataset loading time: 0.0003762245178222656\n" ] } ], @@ -109,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "id": "a3acf55c-9526-433e-983d-0fe44c6da7ab", "metadata": {}, "outputs": [ @@ -138,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "id": "c7d365a4-fb25-47ad-91cb-60e9ddf76746", "metadata": {}, "outputs": [], @@ -148,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "id": "8fb82c3c-be79-4c3d-8c51-4b634febb23a", "metadata": {}, "outputs": [], @@ -167,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "id": "81559adb-f7f4-49b0-a9cb-82b09290c6f3", "metadata": {}, "outputs": [ @@ -175,7 +195,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 313/313 [00:02<00:00, 136.92it/s]" + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 313/313 [00:02<00:00, 109.12it/s]" ] }, { @@ -183,7 +203,7 @@ "output_type": "stream", "text": [ "[PT] Evaluation Results: {'loss': 2.206257000517921, 'accuracy': 0.437}\n", - "[PT] Evaluation time: 2.29127836227417\n" + "[PT] Evaluation time: 2.8718373775482178\n" ] }, { diff --git a/tfvspt/pt/notebooks/train.ipynb b/tfvspt/pt/notebooks/train.ipynb index 791571e..0073334 100644 --- a/tfvspt/pt/notebooks/train.ipynb +++ b/tfvspt/pt/notebooks/train.ipynb @@ -18,16 +18,16 @@ "text": [ "/home/meet.ranoliya/workspace/tf_pt_benchmarking/tfvspt/pt/train.py:4: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from tqdm.autonotebook import tqdm\n", - "2025-03-19 22:06:12.614517: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2025-03-19 22:06:12.625091: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2025-03-20 10:37:57.061309: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2025-03-20 10:37:57.072912: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1742402172.637652 51117 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1742402172.641287 51117 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1742402172.652044 51117 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742402172.652059 51117 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742402172.652060 51117 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742402172.652061 51117 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "2025-03-19 22:06:12.655835: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "E0000 00:00:1742447277.086335 10794 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "E0000 00:00:1742447277.090246 10794 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "W0000 00:00:1742447277.100544 10794 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742447277.100557 10794 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742447277.100558 10794 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742447277.100559 10794 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "2025-03-20 10:37:57.104764: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] } @@ -131,7 +131,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[PT] Dataset loading time: 0.0004093647003173828\n" + "[PT] Dataset loading time: 0.0002892017364501953\n" ] } ], @@ -180,7 +180,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[PT] Model loading time: 0.005598545074462891\n" + "[PT] Model loading time: 0.006042957305908203\n" ] }, { @@ -286,7 +286,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 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+575,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:44<00:00, 35.46it/s]\n" + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:52<00:00, 30.04it/s]\n" ] }, { @@ -592,7 +592,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:49<00:00, 31.62it/s]\n" + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:44<00:00, 34.82it/s]\n" ] }, { @@ -609,7 +609,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:44<00:00, 34.94it/s]" + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:50<00:00, 30.76it/s]" ] }, { @@ -619,7 +619,7 @@ "Epoch 20/20, Loss: 1.4741765901741888, Accuracy: 0.5774\n", "\n", "\n", - "[PT] Training time: 891.4652616977692\n" + "[PT] Training time: 958.3622155189514\n" ] }, { @@ -657,7 +657,7 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -686,7 +686,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[PT] Model Saving time: 0.0846412181854248\n" + "[PT] Model Saving time: 0.07896971702575684\n" ] } ], diff --git a/tfvspt/pt/utils.py b/tfvspt/pt/utils.py index e9956ae..eb8207f 100644 --- a/tfvspt/pt/utils.py +++ b/tfvspt/pt/utils.py @@ -51,20 +51,24 @@ def plot_history(history: dict[str, list]) -> None: epochs = range(len(history['loss'])) - # Plot loss + # Plot accuracy plt.figure(figsize=(12, 12)) plt.subplot(2, 1, 1) - plt.plot(epochs, history['loss'], label='Loss') + plt.plot(epochs, + history['accuracy'], + '-o', + label='Accuracy', + color='#1f77b4') plt.xlabel('Epoch') - plt.ylabel('Loss') - plt.title('Training Loss') + plt.ylabel('Accuracy') + plt.title('Training Accuracy') - # Plot accuracy + # Plot loss plt.subplot(2, 1, 2) - plt.plot(epochs, history['accuracy'], label='Accuracy') + plt.plot(epochs, history['loss'], '-o', label='Loss', color='#ff7f0e') plt.xlabel('Epoch') - plt.ylabel('Accuracy') - plt.title('Training Accuracy') + plt.ylabel('Loss') + plt.title('Training Loss') plt.tight_layout() plt.show() diff --git a/tfvspt/tf/notebooks/eval.ipynb b/tfvspt/tf/notebooks/eval.ipynb index de48e4c..e48b13a 100644 --- a/tfvspt/tf/notebooks/eval.ipynb +++ b/tfvspt/tf/notebooks/eval.ipynb @@ -10,10 +10,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "30837142-3340-4691-b3f1-6bbc90684a5a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-03-20 10:57:26.176712: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2025-03-20 10:57:26.185102: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "E0000 00:00:1742448446.195011 13280 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "E0000 00:00:1742448446.198024 13280 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "W0000 00:00:1742448446.205860 13280 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742448446.205874 13280 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742448446.205874 13280 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742448446.205875 13280 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "2025-03-20 10:57:26.208887: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], "source": [ "from pathlib import Path\n", "import time\n", @@ -35,17 +53,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "01a9dd40-2ddb-488e-a94c-d54b39155453", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Config(seed=25, n_classes=100, bs=32, imgsz=(32, 32), lr=0.001, epochs=20, output='./output')" + "Config(seed=25, n_classes=100, bs=32, imgsz=(32, 32), lr=0.001, epochs=20, num_workers=0, output='./output')" ] }, - "execution_count": 5, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -65,10 +83,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "11a230a1-4e6c-4a1d-a0a8-32880de45517", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testdata size: 10000\n" + ] + } + ], "source": [ "dataset = Dataset(config=config)\n", "(x_train, y_train), (x_test, y_test) = Dataset.get_data()\n", @@ -77,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "id": "5f7875ac-bf8a-48b0-9859-da19e823a406", "metadata": {}, "outputs": [ @@ -85,14 +111,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[TF] Dataset loading time: 0.19809412956237793\n" + "[TF] Dataset loading time: 0.11871790885925293\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2025-03-18 23:19:46.948010: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n" + "2025-03-20 10:57:28.636985: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n" ] } ], @@ -104,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "id": "134784fb-bf0c-4116-b532-c0be96860e1e", "metadata": {}, "outputs": [ @@ -112,7 +138,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2025-03-18 23:20:08.815568: W tensorflow/core/kernels/data/cache_dataset_ops.cc:916] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.\n" + "2025-03-20 10:57:28.754776: W tensorflow/core/kernels/data/cache_dataset_ops.cc:916] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.\n" ] }, { @@ -140,10 +166,18 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "id": "d4e172c6-ea49-476b-9eae-eaf55a0f8599", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[TF] Model Loading time: 0.1349625587463379\n" + ] + } + ], "source": [ "st = time.time()\n", "model_path = Path(config.output) / \"tf/cifar100.keras\"\n", @@ -161,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "id": "c611662e-bf5c-4a32-8ceb-ce79eea9991c", "metadata": {}, "outputs": [ @@ -169,9 +203,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - accuracy: 0.4103 - loss: 2.3972\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.4103 - loss: 2.3972\n", "[TF] Evaluation Results: {'accuracy': 0.4090000092983246, 'loss': 2.411944627761841}\n", - "[TF] Evaluation time: 2.571380615234375\n" + "[TF] Evaluation time: 1.4030342102050781\n" ] } ], diff --git a/tfvspt/tf/notebooks/train.ipynb b/tfvspt/tf/notebooks/train.ipynb index a28302e..ff23caa 100644 --- a/tfvspt/tf/notebooks/train.ipynb +++ b/tfvspt/tf/notebooks/train.ipynb @@ -9,23 +9,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-03-19 11:36:51.248021: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2025-03-19 11:36:51.263609: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2025-03-20 09:33:35.601420: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2025-03-20 09:33:35.617409: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1742364411.281834 10272 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1742364411.287084 10272 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1742364411.301335 10272 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742364411.301368 10272 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742364411.301370 10272 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742364411.301371 10272 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "2025-03-19 11:36:51.306791: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "E0000 00:00:1742443415.635509 6526 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "E0000 00:00:1742443415.641158 6526 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "W0000 00:00:1742443415.655443 6526 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742443415.655466 6526 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742443415.655468 6526 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742443415.655469 6526 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "2025-03-20 09:33:35.660348: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] } @@ -54,13 +54,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Config(seed=25, n_classes=100, bs=32, imgsz=(32, 32), lr=0.001, epochs=20, output='./output')" + "Config(seed=25, n_classes=100, bs=32, imgsz=(32, 32), lr=0.001, epochs=20, num_workers=0, output='./output')" ] }, "execution_count": 2, @@ -126,14 +126,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[TF] Dataset loading time: 0.5624265670776367\n" + "[TF] Dataset loading time: 0.5471243858337402\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2025-03-19 11:36:55.104323: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n" + "2025-03-20 09:33:39.616167: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n" ] } ], @@ -179,7 +179,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[TF] Model loading time: 0.10998845100402832\n" + "[TF] Model loading time: 0.09880900382995605\n" ] }, { @@ -388,46 +388,46 @@ "output_type": "stream", "text": [ "Epoch 1/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 17ms/step - accuracy: 0.0956 - loss: 3.9481\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 14ms/step - accuracy: 0.0956 - loss: 3.9481\n", "Epoch 2/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 23ms/step - accuracy: 0.2351 - loss: 3.0588\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 22ms/step - accuracy: 0.2351 - loss: 3.0588\n", "Epoch 3/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 25ms/step - accuracy: 0.3013 - loss: 2.7306\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 25ms/step - accuracy: 0.3013 - loss: 2.7306\n", "Epoch 4/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.3481 - loss: 2.5203\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 24ms/step - accuracy: 0.3481 - loss: 2.5203\n", "Epoch 5/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.3740 - loss: 2.3673\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 23ms/step - accuracy: 0.3740 - loss: 2.3673\n", "Epoch 6/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 23ms/step - accuracy: 0.4058 - loss: 2.2271\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 25ms/step - accuracy: 0.4058 - loss: 2.2271\n", "Epoch 7/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.4288 - loss: 2.1251\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 24ms/step - accuracy: 0.4288 - loss: 2.1251\n", "Epoch 8/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.4437 - loss: 2.0398\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 24ms/step - accuracy: 0.4437 - loss: 2.0398\n", "Epoch 9/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 25ms/step - accuracy: 0.4622 - loss: 1.9564\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 24ms/step - accuracy: 0.4622 - loss: 1.9564\n", "Epoch 10/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 24ms/step - accuracy: 0.4756 - loss: 1.9049\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.4756 - loss: 1.9049\n", "Epoch 11/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 25ms/step - accuracy: 0.4942 - loss: 1.8354\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.4942 - loss: 1.8354\n", "Epoch 12/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 24ms/step - accuracy: 0.5080 - loss: 1.7642\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5080 - loss: 1.7642\n", "Epoch 13/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 24ms/step - accuracy: 0.5206 - loss: 1.7127\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5206 - loss: 1.7127\n", "Epoch 14/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 24ms/step - accuracy: 0.5315 - loss: 1.6764\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5315 - loss: 1.6764\n", "Epoch 15/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 24ms/step - accuracy: 0.5423 - loss: 1.6238\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5423 - loss: 1.6238\n", "Epoch 16/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 24ms/step - accuracy: 0.5532 - loss: 1.5803\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5532 - loss: 1.5803\n", "Epoch 17/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 24ms/step - accuracy: 0.5636 - loss: 1.5357\n", + "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5636 - loss: 1.5357\n", "Epoch 18/20\n", "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5789 - loss: 1.4826\n", "Epoch 19/20\n", "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5816 - loss: 1.4595\n", "Epoch 20/20\n", "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5957 - loss: 1.4119\n", - "[TF] Training time: 735.5993504524231\n" + "[TF] Training time: 716.1055512428284\n" ] } ], @@ -496,7 +496,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[TF] Model Saving time: 0.08504319190979004\n" + "[TF] Model Saving time: 0.0896461009979248\n" ] } ], From 8117e3876973a7aa917932136bd405daafc74215 Mon Sep 17 00:00:00 2001 From: "meet.ranoliya" Date: Thu, 20 Mar 2025 11:36:45 +0530 Subject: [PATCH 5/8] Update: dependencies --- pyproject.toml | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 6305ef2..cadca78 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,15 +14,23 @@ requires-python = ">=3.10" # Required dependencies ------------------------------------------------------------------------------------------------ dependencies = [ - + "tqdm >= 4.67.1", + "torch >= 2.6.0", + "PyYAML >= 6.0.2", + "notebook >= 7.3.3", + "pydantic >= 2.10.6", + "torchinfo >= 1.8.0", + "tensorflow >= 2.19.0", + "matplotlib >= 3.10.1", + "torchvision >= 0.21.0", ] # Optional dependencies ------------------------------------------------------------------------------------------------ [project.optional-dependencies] dev = [ + "yapf", "isort", "pre-commit", - "yapf", ] [project.urls] From b68ed76c049634b64707c4aaf5be1af85b5cb892 Mon Sep 17 00:00:00 2001 From: "meet.ranoliya" Date: Thu, 20 Mar 2025 22:46:43 +0530 Subject: [PATCH 6/8] Add: benchmarking scripts - Add: Data downloading script - Add: CLI tool to download data & run benchmarking - Update: README.md --- .gitignore | 5 +- README.md | 38 +- pyproject.toml | 4 + tfvspt/assets/config.yaml | 5 +- tfvspt/base.py | 111 +++++ tfvspt/config/config.py | 31 +- tfvspt/download_data.py | 51 +++ tfvspt/logger.py | 33 ++ tfvspt/main.py | 45 ++ tfvspt/pt/data.py | 46 +- tfvspt/pt/eval.py | 34 -- tfvspt/pt/main.py | 214 ++++++++++ tfvspt/pt/model.py | 4 +- tfvspt/pt/notebooks/eval.ipynb | 251 ----------- tfvspt/pt/notebooks/train.ipynb | 721 -------------------------------- tfvspt/pt/train.py | 55 --- tfvspt/pt/utils.py | 74 ---- tfvspt/tf/data.py | 43 +- tfvspt/tf/main.py | 117 ++++++ tfvspt/tf/model.py | 35 +- tfvspt/tf/notebooks/eval.ipynb | 241 ----------- tfvspt/tf/notebooks/train.ipynb | 531 ----------------------- tfvspt/tf/utils.py | 60 --- tfvspt/utils.py | 14 + 24 files changed, 712 insertions(+), 2051 deletions(-) create mode 100644 tfvspt/base.py create mode 100644 tfvspt/download_data.py create mode 100644 tfvspt/logger.py create mode 100644 tfvspt/main.py delete mode 100644 tfvspt/pt/eval.py create mode 100644 tfvspt/pt/main.py delete mode 100644 tfvspt/pt/notebooks/eval.ipynb delete mode 100644 tfvspt/pt/notebooks/train.ipynb delete mode 100644 tfvspt/pt/train.py delete mode 100644 tfvspt/pt/utils.py create mode 100644 tfvspt/tf/main.py delete mode 100644 tfvspt/tf/notebooks/eval.ipynb delete mode 100644 tfvspt/tf/notebooks/train.ipynb delete mode 100644 tfvspt/tf/utils.py create mode 100644 tfvspt/utils.py diff --git a/.gitignore b/.gitignore index b1e6652..b79f322 100644 --- a/.gitignore +++ b/.gitignore @@ -176,4 +176,7 @@ cython_debug/ # Models **/*.keras **/*.pt -**/*.csv \ No newline at end of file +**/*.csv +**/*.jpg + +benchmarking/ \ No newline at end of file diff --git a/README.md b/README.md index 513c6a0..0dc97b8 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,38 @@ -# tf_pt_benchmarking +# Tensorflow VS Pytorch + Benchmarking of training and inference performance between Tensorflow and Pytorch. + + +## Config + +```yaml +framework: pt # [pt, tf] +seed: 25 # seed for reproducibility +n_classes: 100 # number of classes +bs: 32 # batch size +imgsz: # image size + - 32 + - 32 + - 3 +lr: 0.001 # learning rate +epochs: 2 # number of epochs +num_workers: 0 # number of parallel workers +output: "output/pt" # output directory path +data: "cifar100" # dataset path +``` + +## Installation + +- `pip install git+https://github.com/infocusp/tf_pt_benchmarking.git` + +## How to run ? + +### Download the data + +- `downloadcifar100 --output path/to/data` + +### Run benchmarking + +- Update the `config.yaml` with required details +- `tfvspt --framework tf --config path/to/config.yaml` +- Benchmarking results will be saved in the `output` folder \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index cadca78..4e2690c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,6 +36,10 @@ dev = [ [project.urls] "Source" = "https://github.com/infocusp/tf_pt_benchmarking" +[project.scripts] +tfvspt = "tfvspt.main:entrypoint" +downloadcifar100 = "tfvspt.download_data:entrypoint" + # Tools settings ------------------------------------------------------------------------------------------------------- [tool.setuptools] # configuration specific to the `setuptools` build backend. packages = { find = { where = ["."], include = ["tfvspt", "tfvspt.*"] } } diff --git a/tfvspt/assets/config.yaml b/tfvspt/assets/config.yaml index 02e3dc9..44e5c55 100644 --- a/tfvspt/assets/config.yaml +++ b/tfvspt/assets/config.yaml @@ -1,10 +1,13 @@ +framework: tf seed: 25 n_classes: 100 bs: 32 imgsz: - 32 - 32 + - 3 lr: 0.001 epochs: 20 num_workers: 0 -output: "./output" \ No newline at end of file +output: "./output" +data: "" \ No newline at end of file diff --git a/tfvspt/base.py b/tfvspt/base.py new file mode 100644 index 0000000..36a3c69 --- /dev/null +++ b/tfvspt/base.py @@ -0,0 +1,111 @@ +"""Benchmarking Base.""" + +import logging + +import matplotlib.pyplot as plt +import numpy as np + +from tfvspt.config.config import get_config +from tfvspt.logger import get_logger +from tfvspt.utils import save_yaml + + +class BenchmarkingBase: + + def __init__(self, config_path: str): + self.config = get_config(config_path) + self.logger = get_logger(name=self.config.framework.value, + path=str(self.config.output / "logs.log")) + self.stats = self._init_stats() + self.raw_data = None + self.dataloaders = None + + def _init_stats(self) -> dict: + keys = [ + "data_loading_time", "model_building_time", "training_time", + "model_saving_time", "eval_time" + ] + return {key: None for key in keys} + + def set_seed(self, seed: int) -> None: + raise NotImplementedError + + def _log_stats(self, key: str, value: float) -> None: + self.stats[key] = value + self.logger.info(f"{self.config.framework}, {key}, {value}") + + def _get_raw_data(self) -> dict[str, list[str]]: + train = list(map(str, self.config.data.glob("train/*/*.jpg"))) + test = list(map(str, self.config.data.glob("test/*/*.jpg"))) + self.logger.info( + f"{self.config.framework}, train data, {len(train)}, test data, {len(test)}" + ) + return {"train": train, "test": test} + + def load_dataloaders(self) -> dict: + raise NotImplementedError + + def train(self) -> None: + raise NotImplementedError + + def eval(self) -> dict: + raise NotImplementedError + + def plot_images(self, images: np.ndarray, name: str) -> None: + grid_size = int(len(images)**0.5) + _, axes = plt.subplots(grid_size, grid_size, figsize=(10, 10)) + for i, ax in enumerate(axes.flatten()): + if i < len(images): + img = (images[i] * 255).astype(np.uint8) + ax.imshow(img) + ax.axis('off') + plt.savefig(str(self.config.output / f"{name}.jpg")) + plt.show() + + def plot_history(self, history: dict) -> None: + + epochs = range(len(history['loss'])) + + plt.figure(figsize=(12, 12)) + plt.subplot(2, 1, 1) + plt.plot(epochs, + history['accuracy'], + '-o', + label='Train Accuracy', + color='#ff7f0e') + plt.ylabel('Accuracy', size=14) + plt.xlabel('Epoch', size=14) + plt.title("Training Accuracy") + + plt.subplot(2, 1, 2) + plt.plot(epochs, + history['loss'], + '-o', + label='Train Loss', + color='#1f77b4') + plt.ylabel('Loss', size=14) + plt.xlabel('Epoch', size=14) + plt.title("Training Loss") + + plt.tight_layout() + plt.savefig(str(self.config.output / "history.png")) + plt.show() + + def start(self) -> None: + # set seed + self.set_seed(seed=self.config.seed) + # save the config + save_yaml(self.config.model_dump(), + str(self.config.output / "config.yaml")) + # get raw data + self.raw_data = self._get_raw_data() + # load dataloaders + self.dataloaders = self.load_dataloaders() + # train + self.train() + # eval + results = self.eval() + # save results + save_yaml(results, str(self.config.output / "results.yaml")) + # save stats + save_yaml(self.stats, str(self.config.output / "stats.yaml")) diff --git a/tfvspt/config/config.py b/tfvspt/config/config.py index 08cfec8..b76587b 100644 --- a/tfvspt/config/config.py +++ b/tfvspt/config/config.py @@ -1,8 +1,20 @@ +"""Config.""" + +from enum import Enum +from pathlib import Path + from pydantic import BaseModel -import yaml + +from tfvspt.utils import read_yaml + + +class Framework(str, Enum): + tensorflow = "tf" + pytorch = "pt" class Config(BaseModel): + framework: Framework seed: int n_classes: int bs: int @@ -10,9 +22,20 @@ class Config(BaseModel): lr: float epochs: int num_workers: int - output: str + output: Path + data: Path + + def model_post_init(self, __context): + self.output.mkdir(parents=True, exist_ok=True) + + def model_dump(self, *args, **kwargs): + data = super().model_dump(*args, **kwargs) + data["framework"] = data["framework"].value + # Convert PosixPath to string in the dictionary + for path in ["output", "data"]: + data[path] = str(data[path]) + return data def get_config(path: str) -> Config: - config = yaml.safe_load(open(path)) - return Config(**config) + return Config(**read_yaml(path)) diff --git a/tfvspt/download_data.py b/tfvspt/download_data.py new file mode 100644 index 0000000..61fc713 --- /dev/null +++ b/tfvspt/download_data.py @@ -0,0 +1,51 @@ +"""Download CIFAR100 Data.""" + +import argparse +from pathlib import Path + +import numpy as np +from PIL import Image +import tensorflow as tf +from tqdm.autonotebook import tqdm + + +# Function to save images as JPG +def save_images(x_data: np.ndarray, y_data: np.ndarray, + directory: Path) -> None: + for i, (img, label) in tqdm(enumerate(zip(x_data, y_data))): + # Convert the image to a PIL Image object + img_pil = Image.fromarray(img).resize((64, 64)) + + # Create a label-specific folder if it doesn't exist + label_dir = directory / str(label[0]) + label_dir.mkdir(parents=True, exist_ok=True) + + # Save the image as a .jpg file + img_filename = str(label_dir / f'{i}.jpg') + img_pil.save(img_filename) + + +def entrypoint() -> None: + + # Create the argument parser + parser = argparse.ArgumentParser(description="Download CIFAR100 Dataset") + + # Add the framework argument (string, e.g., 'pytorch', 'tensorflow') + parser.add_argument('--output', + type=Path, + required=True, + help='Specify the output path') + + args = parser.parse_args() + + # Load CIFAR-100 data from TensorFlow + data = tf.keras.datasets.cifar100.load_data() + + output_path = args.output + # Create directories & Save Images + for idx, split in enumerate(["train", "test"]): + path = output_path / split + path.mkdir(parents=True, exist_ok=True) + save_images(*data[idx], path) + + print("CIFAR-100 images have been saved as JPG files!") diff --git a/tfvspt/logger.py b/tfvspt/logger.py new file mode 100644 index 0000000..0024887 --- /dev/null +++ b/tfvspt/logger.py @@ -0,0 +1,33 @@ +"""Logger.""" + +import logging +import sys + + +def get_logger(name: str, path: str) -> logging.Logger: + + # Create a logger + logger = logging.getLogger(name) + logger.setLevel(logging.INFO) + + # Create a file handler to log to the specified log file + file_handler = logging.FileHandler(path) + file_handler.setLevel(logging.INFO) + + # Create a stream handler to log to stdout (console) + console_handler = logging.StreamHandler(sys.stdout) + console_handler.setLevel(logging.DEBUG) + + # Define a formatter for log messages + formatter = logging.Formatter( + '%(asctime)s - %(name)s - %(levelname)s - %(message)s') + + # Set the formatter for both handlers + file_handler.setFormatter(formatter) + console_handler.setFormatter(formatter) + + # Add handlers to the logger + logger.addHandler(file_handler) + logger.addHandler(console_handler) + + return logger diff --git a/tfvspt/main.py b/tfvspt/main.py new file mode 100644 index 0000000..336ce11 --- /dev/null +++ b/tfvspt/main.py @@ -0,0 +1,45 @@ +"""Main Script.""" + +import argparse + +from tfvspt.config.config import Framework +from tfvspt.pt.main import PytorchBenchmarking +from tfvspt.tf.main import TensorflowBenchmarking + +MAPPING = { + Framework.tensorflow.value: TensorflowBenchmarking, + Framework.pytorch.value: PytorchBenchmarking, +} + + +def entrypoint(): + # Create the argument parser + parser = argparse.ArgumentParser( + description="Select framework and provide configuration") + + # Add the framework argument (string, e.g., 'pytorch', 'tensorflow') + parser.add_argument( + '--framework', + type=Framework, + required=True, + help= + 'Specify the machine learning framework to use (e.g., pytorch, tensorflow)' + ) + + # Add the config argument (string, e.g., path to config file) + parser.add_argument('--config', + type=str, + required=True, + help='Path to the configuration file') + + # Parse arguments + args = parser.parse_args() + + # Print the arguments to verify + print(f"Selected framework: {args.framework}") + print(f"Configuration file: {args.config}") + + # Run Benchmarking + Benchmarking = MAPPING[args.framework] + benchmarking = Benchmarking(config_path=args.config) + benchmarking.start() diff --git a/tfvspt/pt/data.py b/tfvspt/pt/data.py index d147402..8829045 100644 --- a/tfvspt/pt/data.py +++ b/tfvspt/pt/data.py @@ -1,9 +1,9 @@ """Pytorch Dataset.""" -import numpy as np -from torch.utils.data import DataLoader +import os + +from PIL import Image from torch.utils.data import Dataset -from torchvision import transforms from tfvspt.config.config import Config @@ -13,48 +13,20 @@ class ClassificationDataset(Dataset): def __init__( self, config: Config, - images: np.ndarray, - labels: np.ndarray, + paths: list[str], transforms=None, ) -> None: self.config = config - self.images = images - self.labels = labels + self.paths = paths self.transforms = transforms def __len__(self) -> None: - return len(self.images) + return len(self.paths) def __getitem__(self, idx: int): - image = self.images[idx] - label = self.labels[idx] + path = self.paths[idx] + image = Image.open(path).convert('RGB') + label = int(path.split(os.path.sep)[-2]) if self.transforms: image = self.transforms(image) return image, label - - -def get_transforms(data: str): - transforms_ = [] - if data == "train": - transforms_ += [] - transforms_ += [transforms.ToTensor()] - return transforms.Compose(transforms_) - - -def get_dataloader(dataset: ClassificationDataset, data: str, config: Config): - dataloader = None - if data == "train": - dataloader = DataLoader( - dataset=dataset, - batch_size=config.bs, - shuffle=True, - num_workers=config.num_workers, - ) - else: - dataloader = DataLoader( - dataset=dataset, - batch_size=config.bs, - shuffle=False, - num_workers=config.num_workers, - ) - return dataloader diff --git a/tfvspt/pt/eval.py b/tfvspt/pt/eval.py deleted file mode 100644 index 9686237..0000000 --- a/tfvspt/pt/eval.py +++ /dev/null @@ -1,34 +0,0 @@ -"""Torch Evaluation.""" - -import torch -from tqdm.autonotebook import tqdm - - -def eval_model(dataloader, model, criterion, device) -> dict: - - model.to(device) - - model.eval() - val_loss = 0.0 - correct = 0 - total = 0 - - with torch.no_grad(): - for images, targets in tqdm(dataloader): - - images, targets = images.to(device), targets.to(device) - - outputs = model(images) - - loss = criterion(outputs, targets[:, 0]) - - val_loss += loss.item() - - predicted = torch.argmax(outputs, 1) - total += targets.size(0) - correct += (predicted == targets[:, 0]).sum().item() - - val_loss /= len(dataloader) - val_accuracy = round(correct / total, 4) - - return {"loss": val_loss, "accuracy": val_accuracy} diff --git a/tfvspt/pt/main.py b/tfvspt/pt/main.py new file mode 100644 index 0000000..01219d5 --- /dev/null +++ b/tfvspt/pt/main.py @@ -0,0 +1,214 @@ +"""Train, Eval & Log Pytorch training.""" + +import random +import time + +import numpy as np +import torch +from torch.utils.data import DataLoader +from torchinfo import summary +from torchvision import transforms +from tqdm.autonotebook import tqdm + +from tfvspt.base import BenchmarkingBase +from tfvspt.pt.data import ClassificationDataset +from tfvspt.pt.model import Model + + +class PytorchBenchmarking(BenchmarkingBase): + + def __init__(self, config_path): + super().__init__(config_path) + self.device = torch.device( + "cuda" if torch.cuda.is_available() else "cpu") + self.logger.info(f"{self.config.framework}, device, {self.device}") + + def set_seed(self, seed): + # Set the random seed for numpy, random, and torch + np.random.seed(seed) + random.seed(seed) + torch.manual_seed(seed) + # Set the seed for CUDA (if using GPUs) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + # Ensure deterministic results for CuDNN (can be slower) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + self.logger.info(f"{self.config.framework}, seed, {self.config.seed}") + + def get_transforms(self, data: str): + transforms_ = [] + if data == "train": + transforms_ += [] + transforms_ += [ + transforms.Resize(self.config.imgsz[:2]), + transforms.ToTensor(), + ] + return transforms.Compose(transforms_) + + def _get_dataset(self, data: str) -> ClassificationDataset: + return ClassificationDataset( + config=self.config, + paths=self.raw_data[data], + transforms=self.get_transforms(data), + ) + + def load_dataloaders(self) -> dict: + st = time.time() + dataloaders = { + "train": + DataLoader( + dataset=self._get_dataset("train"), + batch_size=self.config.bs, + shuffle=True, + num_workers=self.config.num_workers, + ), + "test": + DataLoader( + dataset=self._get_dataset("test"), + batch_size=self.config.bs, + shuffle=False, + num_workers=self.config.num_workers, + ) + } + self._log_stats("data_loading_time", time.time() - st) + return dataloaders + + def plot_images(self, dataloader, name: str) -> None: + images, _ = next(iter(dataloader)) + images = images.numpy().transpose((0, 2, 3, 1)) + return super().plot_images(images, name) + + def _train(self, dataloader: DataLoader, model: torch.nn.Module, + criterion: torch.nn.Module, + optimizer: torch.optim.Optimizer) -> dict: + + model.to(self.device) + + history = {"loss": [], "accuracy": []} + for epoch in range(self.config.epochs): + + model.train() + running_loss = 0.0 + total = 0 + correct = 0 + + self.logger.info(f"Epoch {epoch + 1} / {self.config.epochs}") + for images, targets in tqdm(dataloader): + + images, targets = images.to(self.device), targets.to( + self.device) + + optimizer.zero_grad() + outputs = model(images) + loss = criterion(outputs, targets) + loss.backward() + optimizer.step() + + running_loss += loss.item() + + predicted = torch.argmax(outputs, 1) + total += targets.size(0) + correct += (predicted == targets).sum().item() + + # Epoch Stats + epoch_loss = running_loss / len(dataloader) + epoch_accuracy = round(correct / total, 4) + + # Collect logs + history["loss"].append(epoch_loss) + history["accuracy"].append(epoch_accuracy) + + self.logger.info( + f"Epoch {epoch + 1}/{self.config.epochs}, Loss: {epoch_loss}, Accuracy: {epoch_accuracy}" + ) + + return history + + def _eval(self, dataloader: DataLoader, model: torch.nn.Module, + criterion: torch.nn.Module) -> dict: + + model.to(self.device) + model.eval() + val_loss = 0.0 + correct = 0 + total = 0 + + with torch.no_grad(): + for images, targets in tqdm(dataloader): + + images, targets = images.to(self.device), targets.to( + self.device) + + outputs = model(images) + loss = criterion(outputs, targets) + + val_loss += loss.item() + + predicted = torch.argmax(outputs, 1) + total += targets.size(0) + correct += (predicted == targets).sum().item() + + val_loss /= len(dataloader) + val_accuracy = round(correct / total, 4) + + return {"loss": val_loss, "accuracy": val_accuracy} + + def train(self) -> None: + # plot train images + self.plot_images(dataloader=self.dataloaders["train"], + name="train_data") + + # load model + st = time.time() + model = Model(n_classes=self.config.n_classes) + self._log_stats("model_building_time", time.time() - st) + model_summary = summary(model, + input_size=(self.config.bs, + self.config.imgsz[-1], + *self.config.imgsz[:-1])) + self.logger.info(f"{self.config.framework}, {model_summary}") + + # optimizer + optimizer = torch.optim.Adam(model.parameters(), lr=self.config.lr) + # criterion + criterion = torch.nn.CrossEntropyLoss() + + # train model + st = time.time() + history = self._train( + dataloader=self.dataloaders["train"], + model=model, + criterion=criterion, + optimizer=optimizer, + ) + self._log_stats("training_time", time.time() - st) + + # plot the history + self.plot_history(history=history) + + # save model + st = time.time() + torch.jit.script(model).save(str(self.config.output / "cifar100.pt")) + self._log_stats("model_saving_time", time.time() - st) + + def eval(self) -> dict: + # plot test images + self.plot_images(dataloader=self.dataloaders["test"], name="test_data") + + # load model + st = time.time() + model = torch.jit.load(str(self.config.output / "cifar100.pt")) + self._log_stats("model_loading_time", time.time() - st) + + # evaluate + st = time.time() + results = self._eval( + dataloader=self.dataloaders["test"], + model=model, + criterion=torch.nn.CrossEntropyLoss(), + ) + self.logger.info(f"{self.config.framework}, test results, {results}") + self._log_stats("eval_time", time.time() - st) + + return results diff --git a/tfvspt/pt/model.py b/tfvspt/pt/model.py index 59adcab..bce02db 100644 --- a/tfvspt/pt/model.py +++ b/tfvspt/pt/model.py @@ -9,7 +9,7 @@ def __init__(self, n_classes: int, *args, **kwargs) -> None: super().__init__(*args, **kwargs) layers = [] - channels = [3, 32, 64, 128] + channels = [3, 32, 64, 128, 128] for i in range(1, len(channels)): layers += [ nn.Conv2d(channels[i - 1], @@ -26,7 +26,7 @@ def __init__(self, n_classes: int, *args, **kwargs) -> None: self.gap = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Sequential( - nn.Linear(128, 128), + nn.Linear(channels[-1], 128), nn.ReLU(), nn.Linear(128, n_classes), ) diff --git a/tfvspt/pt/notebooks/eval.ipynb b/tfvspt/pt/notebooks/eval.ipynb deleted file mode 100644 index 5780dbc..0000000 --- a/tfvspt/pt/notebooks/eval.ipynb +++ /dev/null @@ -1,251 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e2e6dcdd-7023-4aec-9550-d72361c90198", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "257788cf-7265-4cda-826c-e72fc0519cd9", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/meet.ranoliya/workspace/tf_pt_benchmarking/tfvspt/pt/eval.py:4: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from tqdm.autonotebook import tqdm\n", - "2025-03-20 10:57:03.187989: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2025-03-20 10:57:03.195953: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1742448423.205256 13174 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1742448423.208096 13174 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1742448423.215365 13174 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742448423.215374 13174 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742448423.215375 13174 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742448423.215376 13174 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "2025-03-20 10:57:03.217994: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" - ] - } - ], - "source": [ - "from pathlib import Path\n", - "import time\n", - "\n", - "import torch\n", - "\n", - "from tfvspt.config.config import get_config\n", - "from tfvspt.pt.data import ClassificationDataset\n", - "from tfvspt.pt.data import get_dataloader\n", - "from tfvspt.pt.data import get_transforms\n", - "from tfvspt.pt.eval import eval_model\n", - "from tfvspt.pt.utils import plot_images\n", - "from tfvspt.tf.data import Dataset as TFDataset" - ] - }, - { - "cell_type": "markdown", - "id": "69527275-8978-4fda-ba4f-d4f94cdafcc4", - "metadata": {}, - "source": [ - "## Config" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d51e2701-5359-4ffe-82ff-d4e214d805de", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Config(seed=25, n_classes=100, bs=32, imgsz=(32, 32), lr=0.001, epochs=20, num_workers=0, output='./output')" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "config = get_config(\"../../assets/config.yaml\")\n", - "config" - ] - }, - { - "cell_type": "markdown", - "id": "2ddc1360-07dd-4cc1-be2e-b8926ff8b2e0", - "metadata": {}, - "source": [ - "## Test Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "0b54f8bd-8a47-4576-b640-ef2a290e3522", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Testdata size: 10000\n" - ] - } - ], - "source": [ - "(x_train, y_train), (x_test, y_test) = TFDataset.get_data()\n", - "print(f\"Testdata size: {len(x_test)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8303a182-0662-46ca-8722-8f862216c4bd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[PT] Dataset loading time: 0.0003762245178222656\n" - ] - } - ], - "source": [ - "st = time.time()\n", - "test_ds = ClassificationDataset(config=config, images=x_test, labels=y_test, transforms=get_transforms(data=\"test\"))\n", - "test_dataloader = get_dataloader(dataset=test_ds, data=\"test\", config=config)\n", - "print(f\"[PT] Dataset loading time: {time.time() - st}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a3acf55c-9526-433e-983d-0fe44c6da7ab", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_images(test_dataloader)" - ] - }, - { - "cell_type": "markdown", - "id": "4a39046c-5212-44c3-8c4f-51c664426c03", - "metadata": {}, - "source": [ - "## Load Model" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c7d365a4-fb25-47ad-91cb-60e9ddf76746", - "metadata": {}, - "outputs": [], - "source": [ - "model_path = Path(config.output) / \"pt/cifar100.pt\"" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "8fb82c3c-be79-4c3d-8c51-4b634febb23a", - "metadata": {}, - "outputs": [], - "source": [ - "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "model = torch.jit.load(str(model_path))" - ] - }, - { - "cell_type": "markdown", - "id": "587ed2a6-739b-4a50-b01e-0d7025ce0c35", - "metadata": {}, - "source": [ - "## Eval" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "81559adb-f7f4-49b0-a9cb-82b09290c6f3", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 313/313 [00:02<00:00, 109.12it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[PT] Evaluation Results: {'loss': 2.206257000517921, 'accuracy': 0.437}\n", - "[PT] Evaluation time: 2.8718373775482178\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "st = time.time()\n", - "results = eval_model(\n", - " dataloader=test_dataloader,\n", - " model=model,\n", - " criterion=torch.nn.CrossEntropyLoss(),\n", - " device=device,\n", - ")\n", - "print(f\"[PT] Evaluation Results: {results}\")\n", - "print(f\"[PT] Evaluation time: {time.time() - st}\")" - ] - } - ], - "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": "3.10.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tfvspt/pt/notebooks/train.ipynb b/tfvspt/pt/notebooks/train.ipynb deleted file mode 100644 index 0073334..0000000 --- a/tfvspt/pt/notebooks/train.ipynb +++ /dev/null @@ -1,721 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/meet.ranoliya/workspace/tf_pt_benchmarking/tfvspt/pt/train.py:4: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from tqdm.autonotebook import tqdm\n", - "2025-03-20 10:37:57.061309: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2025-03-20 10:37:57.072912: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1742447277.086335 10794 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1742447277.090246 10794 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1742447277.100544 10794 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742447277.100557 10794 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742447277.100558 10794 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742447277.100559 10794 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "2025-03-20 10:37:57.104764: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" - ] - } - ], - "source": [ - "from pathlib import Path\n", - "import time\n", - "\n", - "import torch\n", - "from torchinfo import summary\n", - "\n", - "from tfvspt.config.config import get_config\n", - "from tfvspt.pt.data import ClassificationDataset\n", - "from tfvspt.pt.data import get_dataloader\n", - "from tfvspt.pt.data import get_transforms\n", - "from tfvspt.pt.model import Model\n", - "from tfvspt.pt.train import train_model\n", - "from tfvspt.pt.utils import plot_history\n", - "from tfvspt.pt.utils import plot_images\n", - "from tfvspt.pt.utils import set_seed\n", - "from tfvspt.tf.data import Dataset as TFDataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Read Config" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Config(seed=25, n_classes=100, bs=32, imgsz=(32, 32), lr=0.001, epochs=20, num_workers=0, output='./output')" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "config = get_config(\"../../assets/config.yaml\")\n", - "config" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set Seed" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "set_seed(config.seed)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dataset\n", - "\n", - "### Name: CIFAR100" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[PT] Traindata size: 50000\n" - ] - } - ], - "source": [ - "(x_train, y_train), (x_test, y_test) = TFDataset.get_data()\n", - "print(f\"[PT] Traindata size: {len(x_train)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[PT] Dataset loading time: 0.0002892017364501953\n" - ] - } - ], - "source": [ - "st = time.time()\n", - "train_ds = ClassificationDataset(config=config, images=x_train, labels=y_train, transforms=get_transforms(data=\"train\"))\n", - "train_dataloader = get_dataloader(dataset=train_ds, data=\"train\", config=config)\n", - "print(f\"[PT] Dataset loading time: {time.time() - st}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_images(train_dataloader)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[PT] Model loading time: 0.006042957305908203\n" - ] - }, - { - "data": { - "text/plain": [ - "==========================================================================================\n", - "Layer (type:depth-idx) Output Shape Param #\n", - "==========================================================================================\n", - "Model [32, 100] --\n", - "├─Sequential: 1-1 [32, 128, 4, 4] --\n", - "│ └─Conv2d: 2-1 [32, 32, 32, 32] 896\n", - "│ └─BatchNorm2d: 2-2 [32, 32, 32, 32] 64\n", - "│ └─ReLU: 2-3 [32, 32, 32, 32] --\n", - "│ └─MaxPool2d: 2-4 [32, 32, 16, 16] --\n", - "│ └─Conv2d: 2-5 [32, 64, 16, 16] 18,496\n", - "│ └─BatchNorm2d: 2-6 [32, 64, 16, 16] 128\n", - "│ └─ReLU: 2-7 [32, 64, 16, 16] --\n", - "│ └─MaxPool2d: 2-8 [32, 64, 8, 8] --\n", - "│ └─Conv2d: 2-9 [32, 128, 8, 8] 73,856\n", - "│ └─BatchNorm2d: 2-10 [32, 128, 8, 8] 256\n", - "│ └─ReLU: 2-11 [32, 128, 8, 8] --\n", - "│ └─MaxPool2d: 2-12 [32, 128, 4, 4] --\n", - "├─AdaptiveAvgPool2d: 1-2 [32, 128, 1, 1] --\n", - "├─Sequential: 1-3 [32, 100] --\n", - "│ └─Linear: 2-13 [32, 128] 16,512\n", - "│ └─ReLU: 2-14 [32, 128] --\n", - "│ └─Linear: 2-15 [32, 100] 12,900\n", - "==========================================================================================\n", - "Total params: 123,108\n", - "Trainable params: 123,108\n", - "Non-trainable params: 0\n", - "Total mult-adds (M): 333.09\n", - "==========================================================================================\n", - "Input size (MB): 0.39\n", - "Forward/backward pass size (MB): 29.42\n", - "Params size (MB): 0.49\n", - "Estimated Total Size (MB): 30.30\n", - "==========================================================================================" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "st = time.time()\n", - "model = Model(n_classes=config.n_classes)\n", - "print(f\"[PT] Model loading time: {time.time() - st}\")\n", - "summary(model, input_size=(config.bs, 3, *config.imgsz))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "output_path = Path(config.output) / \"pt\"\n", - "output_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Device Selected: cpu\n" - ] - } - ], - "source": [ - "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "print(f\"Device Selected: {device}\")\n", - "optimizer = torch.optim.Adam(model.parameters(), lr=config.lr)\n", - "criterion = torch.nn.CrossEntropyLoss()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1 / 20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:42<00:00, 36.92it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/20, Loss: 3.599246293706766, Accuracy: 0.1406\n", - "\n", - "\n", - "Epoch 2 / 20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:45<00:00, 34.23it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 2/20, Loss: 2.985980004892087, Accuracy: 0.2458\n", - "\n", - "\n", - "Epoch 3 / 20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:49<00:00, 31.28it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 3/20, Loss: 2.7145111965812787, Accuracy: 0.2991\n", - "\n", - "\n", - "Epoch 4 / 20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:46<00:00, 33.63it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 4/20, Loss: 2.526905861331039, Accuracy: 0.3396\n", - "\n", - "\n", - "Epoch 5 / 20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - 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- "output_type": "stream", - "text": [ - "Epoch 7/20, Loss: 2.1684138697839592, Accuracy: 0.4175\n", - "\n", - "\n", - "Epoch 8 / 20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:47<00:00, 32.69it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 8/20, Loss: 2.0855650893404785, Accuracy: 0.4336\n", - "\n", - "\n", - "Epoch 9 / 20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:49<00:00, 31.71it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 9/20, Loss: 2.0035568024207597, Accuracy: 0.455\n", - "\n", - "\n", - "Epoch 10 / 20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - 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"stdout", - "output_type": "stream", - "text": [ - "Epoch 12/20, Loss: 1.829061239786203, Accuracy: 0.4941\n", - "\n", - "\n", - "Epoch 13 / 20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:48<00:00, 32.51it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 13/20, Loss: 1.7745277791624259, Accuracy: 0.5065\n", - "\n", - "\n", - "Epoch 14 / 20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:42<00:00, 36.62it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 14/20, Loss: 1.7225741607900316, Accuracy: 0.5169\n", - "\n", - "\n", - "Epoch 15 / 20\n" - ] - }, - { - "name": "stderr", - "output_type": 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"name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 17/20, Loss: 1.5888635097828265, Accuracy: 0.5478\n", - "\n", - "\n", - "Epoch 18 / 20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:52<00:00, 30.04it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 18/20, Loss: 1.5566429326493085, Accuracy: 0.5545\n", - "\n", - "\n", - "Epoch 19 / 20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:44<00:00, 34.82it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 19/20, Loss: 1.5074093909272763, Accuracy: 0.5712\n", - "\n", - "\n", - "Epoch 20 / 20\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1563/1563 [00:50<00:00, 30.76it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 20/20, Loss: 1.4741765901741888, Accuracy: 0.5774\n", - "\n", - "\n", - "[PT] Training time: 958.3622155189514\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "st = time.time()\n", - "history = train_model(\n", - " dataloader=train_dataloader,\n", - " model=model,\n", - " criterion=criterion,\n", - " optimizer=optimizer,\n", - " config=config,\n", - " device=device,\n", - ")\n", - "print(f\"[PT] Training time: {time.time() - st}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualize Training History" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - 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fYWFhkuqX1i1evFhXXHGFAgIClJiYqHfeecft/E2bNunyyy9XQECAIiIiNGvWLB09etRtzIsvvqhBgwbJz89PMTExmjt3rtvjhw8f1s9+9jMFBgaqX79++vDDD9v2SQMAADQTpRQAAIBJ/vjHP+rnP/+5NmzYoJtuukk33HCDtmzZIkk6duyYJk+erLCwMK1Zs0b//ve/tWzZMrfSafHixZozZ45mzZqlTZs26cMPP1Tfvn3dvsaCBQt03XXXaePGjbryyit10003qaSkxKPPEwAAoDEWwzAMs0MAAAB0NrfccosyMjLk7+/vdvzBBx/Ugw8+KIvFol//+tdavHix67HRo0dr+PDh+sc//qHnn39e9913n/bt26du3bpJkj755BNNmTJFBw8eVFRUlGJjY///9u48PKry7v/4e5JAWEICCISEXVR2EHEDxX0BtYIbarWotYt1eerT+vysfWrd2iK1+6Z2UcS16iNKraJoBRdcWZRNFGUnCbIlhCWEzPz+OCEhJAMhJHOyvF/Xda6ZOec+J9+RcYIf7/t7uOaaa/jZz35WZQ2RSISf/OQn3HPPPUAQdKWlpfHyyy/b20qSJIXOnlKSJEl15NRTT60QOgG0b9++7Pnw4cMrHBs+fDjz5s0DYPHixQwZMqQskAI44YQTiEajLFmyhEgkwtq1azn99NP3WcPgwYPLnrdu3Zr09HTWrVtX07ckSZJUawylJEmS6kjr1q0rLaerLS1btqzWuGbNmlV4HYlEiEajdVGSJEnSAbGnlCRJUkjee++9Sq/79esHQL9+/fj444/ZunVr2fF33nmHpKQk+vTpQ5s2bejZsyevv/56QmuWJEmqLc6UkiRJqiNFRUXk5uZW2JeSkkKHDh0AeOaZZzj66KM58cQTefzxx/nggw/4xz/+AcAVV1zBHXfcwVVXXcWdd97JV199xU033cQ3vvENMjMzAbjzzju57rrr6NSpE6NHj2bLli2888473HTTTYl9o5IkSTVgKCVJklRHpk2bRlZWVoV9ffr04dNPPwWCO+M99dRTXH/99WRlZfHkk0/Sv39/AFq1asUrr7zC97//fY455hhatWrFRRddxG9+85uya1111VXs2LGD3/72t9xyyy106NCBiy++OHFvUJIk6SB49z1JkqQQRCIRpkyZwtixY8MuRZIkKRT2lJIkSZIkSVLCGUpJkiRJkiQp4ewpJUmSFAI7KEiSpKbOmVKSJEmSJElKOEMpSZIkSZIkJZyhlCRJkiRJkhLOUEqSJEmSJEkJZyglSZIkSZKkhDOUkiRJkiRJUsIZSkmSJEmSJCnhDKUkSZIkSZKUcIZSkiRJkiRJSjhDKUmSJEmSJCWcoZQkSZIkSZISzlBKkiRJkiRJCWcoJUmSJEmSpIQzlJIkSaqBq6++mp49e9bo3DvvvJNIJFK7BUmSJDUwhlKSJKlRiUQi1dpmzJgRdqmhuPrqq0lLSwu7DEmSJCKxWCwWdhGSJEm15bHHHqvwevLkyUyfPp1HH320wv4zzzyTzMzMGv+c4uJiotEoqampB3zurl272LVrFy1atKjxz6+pq6++mmeffZbCwsKE/2xJkqQ9pYRdgCRJUm268sorK7x+7733mD59eqX9e9u2bRutWrWq9s9p1qxZjeoDSElJISXFv4ZJkqSmzeV7kiSpyTnllFMYOHAgs2fP5qSTTqJVq1b8+Mc/BuCFF17g3HPPJTs7m9TUVHr37s0999xDSUlJhWvs3VNq+fLlRCIRfvWrX/HXv/6V3r17k5qayjHHHMOHH35Y4dyqekpFIhFuvPFGnn/+eQYOHEhqaioDBgxg2rRpleqfMWMGRx99NC1atKB37948+OCDtd6n6plnnmHYsGG0bNmSDh06cOWVV7JmzZoKY3Jzc7nmmmvo2rUrqampZGVlMWbMGJYvX1425qOPPuLss8+mQ4cOtGzZkl69evHNb36z1uqUJEkNl/+LTpIkNUkbNmxg9OjRXHbZZVx55ZVlS/kmTZpEWloaP/jBD0hLS+M///kPP/3pTykoKOC+++7b73WfeOIJtmzZwne/+10ikQi//OUvufDCC/nyyy/3O7vq7bff5rnnnuP666+nTZs2/OEPf+Ciiy5i5cqVHHLIIQDMnTuXUaNGkZWVxV133UVJSQl33303HTt2PPh/KKUmTZrENddcwzHHHMOECRPIy8vj97//Pe+88w5z586lbdu2AFx00UUsXLiQm266iZ49e7Ju3TqmT5/OypUry16fddZZdOzYkR/96Ee0bduW5cuX89xzz9VarZIkqeEylJIkSU1Sbm4uDzzwAN/97ncr7H/iiSdo2bJl2evrrruO6667jr/85S/87Gc/228PqZUrV/L555/Trl07APr06cOYMWN45ZVXOO+88/Z57uLFi1m0aBG9e/cG4NRTT2XIkCE8+eST3HjjjQDccccdJCcn884775CdnQ3AuHHj6Nev34H9A4ijuLiYW2+9lYEDB/Lmm2+W9b068cQTOe+88/jtb3/LXXfdxebNm5k1axb33Xcft9xyS9n5t912W9nzWbNmsWnTJl599VWOPvrosv0/+9nPaqVWSZLUsLl8T5IkNUmpqalcc801lfbvGUht2bKF9evXM3LkSLZt28ann3663+teeumlZYEUwMiRIwH48ssv93vuGWecURZIAQwePJj09PSyc0tKSnjttdcYO3ZsWSAFcNhhhzF69Oj9Xr86PvroI9atW8f1119foRH7ueeeS9++ffn3v/8NBP+cmjdvzowZM9i0aVOV19o9o+rFF1+kuLi4VuqTJEmNh6GUJElqkrp06ULz5s0r7V+4cCEXXHABGRkZpKen07Fjx7Im6fn5+fu9bvfu3Su83h1QxQtu9nXu7vN3n7tu3Tq2b9/OYYcdVmlcVftqYsWKFUAww2tvffv2LTuemprKxIkTefnll8nMzOSkk07il7/8Jbm5uWXjTz75ZC666CLuuusuOnTowJgxY3j44YcpKiqqlVolSVLDZiglSZKapD1nRO22efNmTj75ZD7++GPuvvtu/vWvfzF9+nQmTpwIQDQa3e91k5OTq9wfi8Xq9Nww3HzzzXz22WdMmDCBFi1acPvtt9OvXz/mzp0LBM3bn332Wd59911uvPFG1qxZwze/+U2GDRtGYWFhyNVLkqSwGUpJkiSVmjFjBhs2bGDSpEl8//vf57zzzuOMM86osBwvTJ06daJFixYsXbq00rGq9tVEjx49AFiyZEmlY0uWLCk7vlvv3r354Q9/yKuvvsqCBQvYuXMnv/71ryuMOf744/n5z3/ORx99xOOPP87ChQt56qmnaqVeSZLUcBlKSZIkldo9U2nPmUk7d+7kL3/5S1glVZCcnMwZZ5zB888/z9q1a8v2L126lJdffrlWfsbRRx9Np06deOCBByoss3v55ZdZvHgx5557LgDbtm1jx44dFc7t3bs3bdq0KTtv06ZNlWZ5HXnkkQAu4ZMkSd59T5IkabcRI0bQrl07rrrqKv7rv/6LSCTCo48+Wq+Wz9155528+uqrnHDCCXzve9+jpKSEP/3pTwwcOJB58+ZV6xrFxcVV3gGvffv2XH/99UycOJFrrrmGk08+mcsvv5y8vDx+//vf07NnT/77v/8bgM8++4zTTz+dcePG0b9/f1JSUpgyZQp5eXlcdtllADzyyCP85S9/4YILLqB3795s2bKFv/3tb6Snp3POOefU2j8TSZLUMBlKSZIklTrkkEN48cUX+eEPf8hPfvIT2rVrx5VXXsnpp5/O2WefHXZ5AAwbNoyXX36ZW265hdtvv51u3bpx9913s3jx4mrdHRCC2V+33357pf29e/fm+uuv5+qrr6ZVq1bce++93HrrrbRu3ZoLLriAiRMnlt1Rr1u3blx++eW8/vrrPProo6SkpNC3b1+efvppLrroIiBodP7BBx/w1FNPkZeXR0ZGBsceeyyPP/44vXr1qrV/JpIkqWGKxOrT//qTJElSjYwdO5aFCxfy+eefh12KJElStdhTSpIkqYHZvn17hdeff/45L730Eqeccko4BUmSJNWAM6UkSZIamKysLK6++moOPfRQVqxYwf33309RURFz587l8MMPD7s8SZKkarGnlCRJUgMzatQonnzySXJzc0lNTWX48OH84he/MJCSJEkNijOlJEmSJEmSlHD2lJIkSZIkSVLCGUpJkiRJkiQp4ZpcT6loNMratWtp06YNkUgk7HIkSZIkSZIalVgsxpYtW8jOziYpKf58qCYXSq1du5Zu3bqFXYYkSZIkSVKjtmrVKrp27Rr3eJMLpdq0aQME/2DS09NDrkaSJEmSJKlxKSgooFu3bmUZTDxNLpTavWQvPT3dUEqSJEmSJKmO7K9tko3OJUmSJEmSlHCGUpIkSZIkSUo4QylJkiRJkiQlnKGUJEmSJEmSEs5QSpIkSZIkSQlnKCVJkiRJkqSEM5SSJEmSJElSwhlKSZIkSZIkKeEMpSRJkiRJkpRwhlKSJEmSJElKOEMpSZIkSZIkJZyhlCRJkiRJkhLOUEqSJEmSJEkJZyglSZIkSZKkhDOUkiRJkiRJUsKlhF2AaihaAitmQWEepGVCjxGQlBx2VZIkSZIkSdViKNUQLZoK026FgrXl+9KzYdRE6H9+eHVJkiRJkiRVk8v3GppFU+Hp8RUDKYCCnGD/oqnh1CVJkiRJknQADKUakmhJMEOKWBUHS/dN+1EwTpIkSZIkqR4zlGpIVsyqPEOqghgUrAnGSZIkSZIk1WOGUg1JYV7tjpMkSZIkSQqJoVRDkpZZu+MkSZIkSZJCYijVkPQYEdxlj0icARFI7xKMkyRJkiRJqscMpRqSpGQYNbH0RZxgatS9wThJkiRJkqR6zFCqoel/PoybDOlZex2IwAUPBsclSZIkSZLquZSwC1AN9D8f+p4b3GVvSy68dicUrIbirWFXJkmSJEmSVC3OlGqokpKh10gYfAkMvz7YN3tSqCVJkiRJkiRVl6FUYzDkckhuDjkfw9q5YVcjSZIkSZK0X4ZSjUGr9tB/TPDc2VKSJEmSJKkBMJRqLIZdHTzOfxaKtoRaiiRJkiRJ0v4YSjUWPU6AQw6DnYWw4P/CrkaSJEmSJGmfDKUai0ikfLaUS/gkSZIkSVI9ZyjVmAz5etDwfO1cWDsv7GokSZIkSZLiMpRqTFofAv2+Fjyf80i4tUiSJEmSJO2DoVRjs3sJ3yfPQFFhqKVIkiRJkiTFYyjV2PQcCe0PhZ1bYOFzYVcjSZIkSZJUJUOpxsaG55IkSZIkqQEwlGqMhnwdkprBmtmQ80nY1UiSJEmSJFUSaih1//33M3jwYNLT00lPT2f48OG8/PLLccdPmjSJSCRSYWvRokUCK24g0jpCv/OC5zY8lyRJkiRJ9VCooVTXrl259957mT17Nh999BGnnXYaY8aMYeHChXHPSU9PJycnp2xbsWJFAituQMoanj8NO7eGWookSZIkSdLeUsL84V/72tcqvP75z3/O/fffz3vvvceAAQOqPCcSidC5c+dElNew9TwJ2vWCTctg4RQYemXYFUmSJEmSJJWpNz2lSkpKeOqpp9i6dSvDhw+PO66wsJAePXrQrVu3/c6qAigqKqKgoKDC1iQkJcGwq4LnNjyXJEmSJEn1TOih1Pz580lLSyM1NZXrrruOKVOm0L9//yrH9unTh4ceeogXXniBxx57jGg0yogRI1i9enXc60+YMIGMjIyyrVu3bnX1VuqfI6+ApBRY/SHkLgi7GkmSJEmSpDKRWCwWC7OAnTt3snLlSvLz83n22Wf5+9//zsyZM+MGU3sqLi6mX79+XH755dxzzz1VjikqKqKoqKjsdUFBAd26dSM/P5/09PRaex/11tPjYdELcOx34Jz7wq5GkiRJkiQ1cgUFBWRkZOw3ewl9plTz5s057LDDGDZsGBMmTGDIkCH8/ve/r9a5zZo1Y+jQoSxdujTumNTU1LK7++3empTdDc8//ifs3BZqKZIkSZIkSbuFHkrtLRqNVpjZtC8lJSXMnz+frKysOq6qAet1CrTtAUX5sOj5kIuRJEmSJEkKhBpK3Xbbbbz55pssX76c+fPnc9tttzFjxgyuuOIKAMaPH89tt91WNv7uu+/m1Vdf5csvv2TOnDlceeWVrFixgm9961thvYX6z4bnkiRJkiSpHkoJ84evW7eO8ePHk5OTQ0ZGBoMHD+aVV17hzDPPBGDlypUkJZXnZps2beLb3/42ubm5tGvXjmHDhjFr1qxq9Z9q0o68Et74Bax6H/IWQab/vCRJkiRJUrhCb3SeaNVtttXo/PNKWPwvOO46GD0x7GokSZIkSVIj1WAanStByhqePwnF20MtRZIkSZIkyVCqqTj0NGjbHXbkw8Lnw65GkiRJkiQ1cYZSTUVSEhxlw3NJkiRJklQ/GEo1JUOvhEgyrHoP1i0OuxpJkiRJktSEGUo1JW06Q5/RwfPZj4RbiyRJkiRJatIMpZqaYdcEjzY8lyRJkiRJITKUamp6nwoZ3WHHZlg0NexqJEmSJElSE2Uo1dQkJcNR44PnNjyXJEmSJEkhMZRqioZeETQ8XzkLvloSdjWSJEmSJKkJMpRqitKz4YhRwXMbnkuSJEmSpBAYSjVVw64OHj9+Aop3hFqKJEmSJElqegylmqrDTof0rrB9Eyz+V9jVSJIkSZKkJsZQqqmy4bkkSZIkSQqRoVRTNvRKiCTBirdh/edhVyNJkiRJkpoQQ6mmLKMLHH528NzZUpIkSZIkKYEMpZq63Q3P5z0Bu4pCLUWSJEmSJDUdhlJN3WFnQHoX2L7RhueSJEmSJClhDKWauuQUGPqN4LlL+CRJkiRJUoIYSqm84fnyt2D90rCrkSRJkiRJTYChlKBtNzjszOD5nEfCrUWSJEmSJDUJhlIKlDU8f9yG55IkSZIkqc4ZSilw+FnQJgu2bYBP/x12NZIkSZIkqZEzlFLAhueSJEmSJCmBDKVU7qhvABFYNhM2fBF2NZIkSZIkqREzlFK5tt3hsDOC53Mmh1uLJEmSJElq1AylVFGFhuc7Qy1FkiRJkiQ1XoZSquiIsyGtM2z9Cpa8FHY1kiRJkiSpkTKUUkXJzWDolcFzG55LkiRJkqQ6YiilynY3PP/yDdj4ZdjVSJIkSZKkRshQSpW16wm9Twue2/BckiRJkiTVAUMpVW13w/O5j9nwXJIkSZIk1TpDKVWtz2ho3SloeP7Zy2FXI0mSJEmSGhlDKVXNhueSJEmSJKkOGUopvqPGB49f/Ac2LQ+1FEmSJEmS1LgYSim+9r1seC5JkiRJkuqEoZT2bc+G5yXFoZYiSZIkSZIaD0Mp7Vufc4KG54V58Nm0sKuRJEmSJEmNhKGU9i25GQy9Inhuw3NJkiRJklRLDKW0f7sbni99HTatCLcWSZIkSZLUKBhKaf/aHwqHngLEYO6jYVcjSZIkSZIaAUMpVc/uhudzHoWSXaGWIkmSJEmSGj5DKVVPn3OhVQcozIXPXwm7GkmSJEmS1MAZSql6Uprb8FySJEmSJNUaQylV31FXBY+fT4fNq8KtRZIkSZIkNWiGUqq+Q3pDr5Ow4bkkSZIkSTpYhlI6MDY8lyRJkiRJtcBQSgem73nQ6hDYshaWTg+7GkmSJEmS1EAZSunApKTCkV8PntvwXJIkSZIk1ZChlA7cUVcHj5+/CvmrQy1FkiRJkiQ1TIZSOnAdDoOeIyEWhbmPhV2NJEmSJElqgAylVDNlDc8nQ7Qk1FIkSZIkSVLDYyilmul7HrRsDwVrYOlrYVcjSZIkSZIaGEMp1UyzFjY8lyRJkiRJNWYopZo76qrg8bNpULA23FokSZIkSVKDYiilmut4BPQ4wYbnkiRJkiTpgBlK6eDY8FySJEmSJNWAoZQOTr/zoUVbyF8FS18PuxpJkiRJktRAGErp4NjwXJIkSZIk1YChlA6eDc8lSZIkSdIBMpTSwevUF7oPh1gJzH087GokSZIkSVIDYCil2mHDc0mSJEmSdAAMpVQ7+o+BFhmQvxK+eCPsaiRJkiRJUj1nKKXa0awlDLk8eD774XBrkSRJkiRJ9Z6hlGrP7obnS16GLbnh1iJJkiRJkuo1QynVnsz+0O240obnj4VdjSRJkiRJqscMpVS7hl0TPM55BKLRcGuRJEmSJEn1lqGUateAsUHD880r4UsbnkuSJEmSpKoZSql2NWsJgy8Lns+eFGopkiRJkiSp/jKUUu0btrvh+UuwJS/cWiRJkiRJUr1kKKXalzkAuh4L0V0w7/Gwq5EkSZIkSfWQoZTqxrCrg0cbnkuSJEmSpCoYSqluDLgAUjNg03JYNjPsaiRJkiRJUj1jKKW60bwVDB4XPLfhuSRJkiRJ2ouhlOrO7obnn74IhevCrUWSJEmSJNUrhlKqO50HQZejSxuePxF2NZIkSZIkqR4xlFLdsuG5JEmSJEmqgqGU6tbAC6F5G9j4JSx/K+xqJEmSJElSPWEopbrVvLUNzyVJkiRJUiWGUqp7u5fwLf4XbF0faimSJEmSJKl+MJRS3csaDNlHQbTYhueSJEmSJAkwlFKi7J4tNXsSxGJhViJJkiRJkuoBQyklxsCLoHkabPwClr8ddjWSJEmSJClkhlJKjNQ0GHRJ8NyG55IkSZIkNXmGUkqcsobnU2HrhlBLkSRJkiRJ4TKUUuJkHwlZR0LJTvjYhueSJEmSJDVlhlJKLBueS5IkSZIkDKWUaIMuhmatYcNSWPFO2NVIkiRJkqSQGEopsVLbBMEU2PBckiRJkqQmzFBKibd7Cd+iF2DbxlBLkSRJkiRJ4TCUUuJlD4XOg0sbnj8ZdjWSJEmSJCkEhlJKvEjEhueSJEmSJDVxhlIKx6BLoFkrWP8ZrHw37GokSZIkSVKCGUopHC3SYeBFwXMbnkuSJEmS1OQYSik8w64JHhc+b8NzSZIkSZKaGEMphafLUZA5CEqK4JN/hl2NJEmSJElKIEMphScSgWFXBc9teC5JkiRJUpNiKKVwDR4XNDz/6lNY9X7Y1UiSJEmSpAQxlFK4WmTAwAuD5zY8lyRJkiSpyTCUUvjKGp5Pge2bwq1FkiRJkiQlRKih1P3338/gwYNJT08nPT2d4cOH8/LLL+/znGeeeYa+ffvSokULBg0axEsvvZSgalVnugyDzIGwawd88nTY1UiSJEmSpAQINZTq2rUr9957L7Nnz+ajjz7itNNOY8yYMSxcuLDK8bNmzeLyyy/n2muvZe7cuYwdO5axY8eyYMGCBFeuWhWJwLCrg+cfPQzL3oT5z8KytyBaEmppkiRJkiSpbkRisfp1y7P27dtz3333ce2111Y6dumll7J161ZefPHFsn3HH388Rx55JA888ECV1ysqKqKoqKjsdUFBAd26dSM/P5/09PTafwOqme2b4b7DIbqz4v70bBg1EfqfH0pZkiRJkiTpwBQUFJCRkbHf7KXe9JQqKSnhqaeeYuvWrQwfPrzKMe+++y5nnHFGhX1nn3027777btzrTpgwgYyMjLKtW7dutVq3asmyNysHUgAFOfD0eFg0NfE1SZIkSZKkOhN6KDV//nzS0tJITU3luuuuY8qUKfTv37/Ksbm5uWRmZlbYl5mZSW5ubtzr33bbbeTn55dtq1atqtX6VQuiJTDt1jgHSyfyTfuRS/kkSZIkSWpEUsIuoE+fPsybN4/8/HyeffZZrrrqKmbOnBk3mDpQqamppKam1sq1VEdWzIKCtfsYEIOCNcG4XiMTVpYkSZIkSao7oYdSzZs357DDDgNg2LBhfPjhh/z+97/nwQcfrDS2c+fO5OXlVdiXl5dH586dE1Kr6khh3v7HHMg4SZIkSZJU74W+fG9v0Wi0QmPyPQ0fPpzXX3+9wr7p06fH7UGlBiItc/9jDmScJEmSJEmq90KdKXXbbbcxevRounfvzpYtW3jiiSeYMWMGr7zyCgDjx4+nS5cuTJgwAYDvf//7nHzyyfz617/m3HPP5amnnuKjjz7ir3/9a5hvQwerx4jgLnsFOZT1kKogEhzvMSLRlUmSJEmSpDoS6kypdevWMX78ePr06cPpp5/Ohx9+yCuvvMKZZ54JwMqVK8nJySkbP2LECJ544gn++te/MmTIEJ599lmef/55Bg4cGNZbUG1ISoZRE0tfRKoYEIMz7gjGSZIkSZKkRiESi8WqmprSaBUUFJCRkUF+fj7p6elhl6M9LZoa3IVvz6bnkSSIReHQU+Drz0BK89DKkyRJkiRJ+1fd7MVQSvVLtCS4y15hXtBDKqUFTB4DxVth4MVw4d8gqd61QpMkSZIkSaWqm72Efvc9qYKkZOg1suK+Sx+FJ8bBgmeDoOrsn0OkqmV+kiRJkiSpoXDKieq/w06HMX8Jnr/3Z5j1x3DrkSRJkiRJB81QSg3DkEvhzHuC59Nvh4+fCrceSZIkSZJ0UAyl1HCc8F8w/Mbg+Qs3wNLXwq1HkiRJkiTVmKGUGpYz74FBl0B0F/xzPKyZHXZFkiRJkiSpBgyl1LAkJQX9pQ49Nbgj3+PjYMMXYVclSZIkSZIOkKGUGp6U5sEd+bKGwLb18OgFsCUv7KokSZIkSdIBMJRSw5TaBq54Ftr1gs0r4PGLYUdB2FVJkiRJkqRqMpRSw5XWCb7xHLTuCLmfwD+vhF1FYVclSZIkSZKqwVBKDVv7Q+GKZ6B5GiybCc9/D6LRsKuSJEmSJEn7YSilhi97aNBjKikFFvwfvPq/EIuFXZUkSZIkSdoHQyk1Dr1Pg7H3B8/f+wvM+kO49UiSJEmSpH0ylFLjMXgcnPXz4Pn0n8LHT4VbjyRJkiRJistQSo3LiBth+I3B8xdugM9fC7ceSZIkSZJUJUMpNT5n3gODxkF0Fzz9DVg9O+yKJEmSJEnSXgyl1PgkJcGYPwd9poq3wROXwPqlYVclSZIkSZL2YCilximlOYybDFlHwrYN8NgFsCUv7KokSZIkSVIpQyk1Xqlt4IpnoV0v2LwSHr8IdhSEXZUkSZIkScJQSo1dWkf4xnPQuiPkzod/XgG7isKuSpIkSZKkJs9QSo1f+0ODGVPN02DZmzDluxCNhl2VJEmSJElNmqGUmobsI+HSRyGpGSycAq/cBrFY2FVJkiRJktRkGUqp6eh9GlzwQPD8/Qfgnd+FWo4kSZIkSU2ZoZSalkEXw9m/CJ6/difMeyLUciRJkiRJaqoMpdT0DL8BRtwUPH/hRvh8erj1SJIkSZLUBBlKqWk6424YfCnESuDp8bB6dtgVSZIkSZLUpBhKqWlKSoLz/xT0mSreBk9cAuuXhl2VJEmSJElNhqGUmq6U5jDuUcgeCts2wGMXwJbcsKuSJEmSJKlJMJRS05aaBl9/BtofCptXwmMXw478sKuSJEmSJKnRM5SS0jrClc9B646QNx+eugJ2FYVdlSRJkiRJjZqhlATQvhdc8Sw0T4Plb8GU70I0GnZVkiRJkiQ1WoZS0m7ZR8Klj0FSM1g4Bab9CGKxsKuSJEmSJKlRMpSS9tT7VLjggeD5Bw/C278Ntx5JkiRJkhopQylpb4MuhrMnBM9fvwvmPh5uPZIkSZIkNUKGUlJVhl8PI/4reD71Jvjs1XDrkSRJkiSpkTGUkuI54y4YfBnESuCZq2D1R2FXJEmSJElSo2EoJcWTlARj/gS9T4fibfD4JbD+87CrkiRJkiSpUTCUkvYluRmMmwzZQ2H7Rnj0QijICbsqSZIkSZIaPEMpaX9S0+Drz0D7QyF/ZTBjakd+2FVJkiRJktSgGUpJ1ZHWEa58Dlp3grz58NQVsKso7KokSZIkSWqwDKWk6mrfC658Fpq3geVvwXPfgWhJ2FVJkiRJktQgGUpJByJrCFz2OCQ1g0XPw7QfQSwWdlWSJEmSJDU4hlLSgTr0ZLjwweD5B3+Ft38Tbj2SJEmSJDVAhlJSTQy8CEbdGzx//W6Y+3i49UiSJEmS1MAYSkk1dfz34ISbg+dTb4LPXgm1HEmSJEmSGhJDKelgnHEnDLkcYiXw9FWw+qOwK5IkSZIkqUEwlJIORiQC5/8RDjsDdm2Hxy+B9Z+HXZUkSZIkSfWeoZR0sJKbwSWPQPZRsH0jPHohFOSEXZUkSZIkSfWaoZRUG1LT4IpnoH1vyF8Jj18MO/LDrkqSJEmSpHrLUEqqLa07wDeeg7RMyFsAT34dineEXZUkSZIkSfWSoZRUm9r1hCueheZtYMXbMOU7sGsnLHsL5j8bPEZLwq5SkiRJkqTQRWKxWCzsIhKpoKCAjIwM8vPzSU9PD7scNVZfzgyW8JXshGatoXhr+bH0bBg1EfqfH159kiRJkiTVkepmL86UkurCoSfDsd8Onu8ZSEHQBP3p8bBoauLrkiRJkiSpnjCUkupCtAQWTolzsHRy4rQfuZRPkiRJktRkGUpJdWHFLChYu48BMShYE4yTJEmSJKkJMpSS6kJhXu2OkyRJkiSpkTGUkupCWmbtjpMkSZIkqZExlJLqQo8RwV32iOx73Ial0LRugClJkiRJEmAoJdWNpGQYNbH0xd7B1B6vX7wZnr8edm5LUGGSJEmSJNUPhlJSXel/PoybDOlZFfenZ8Mlk+H0OyCSBB8/AX8/A9YvDadOSZIkSZJCEInFmtbaoYKCAjIyMsjPzyc9PT3sctQUREuCu+wV5gU9pHqMCGZSASx7C579JmxdB83bwJg/wYCxoZYrSZIkSdLBqG72YiglhW1LbhBMrXgneH38DXDmXZDcLNy6JEmSJEmqgepmLy7fk8LWpjOMnwoj/it4/d6fYdK5ULA23LokSZIkSapDhlJSfZCcAmfdA5c+DqkZsOp9eGAkfPFG2JVJkiRJklQnDKWk+qTfefDdGdB5EGxbD49eADPvg2g07MokSZIkSapVhlJSfdP+ULh2Ohw1HojBGz+DJ8bBto1hVyZJkiRJUq0xlJLqo2Yt4fw/wpi/QEoLWDodHjwJVs8OuzJJkiRJkmqFoZRUnw29Ar71WjB7Kn8VPHQ2fPA3aFo3zZQkSZIkNUKGUlJ913kQfGcG9PsaRIvhpVvguW9DUWHYlUmSJEmSVGOGUlJD0CIDxj0KZ/0cIskw/xn422nw1ZKwK5MkSZIkqUYMpaSGIhKBETfC1f+GNlmwfgn89VSY/2zYlUmSJEmSdMAMpaSGpsdw+O5b0OskKN4K/3ct/PsW2FUUdmWSJEmSJFWboZTUEKV1hG88Dyf9T/D6w7/Bw6Nh88pQy5IkSZIkqboMpaSGKikZTvsJfP0ZaNEW1syGB0+Cz18LuzJJkiRJkvbLUEpq6I44C777JmQPhe2b4PGL4T8/h2hJ2JVJkiRJkhSXoZTUGLTrAd98BY6+FojBm7+Exy6ErevDrkySJEmSpCoZSkmNRUoqnPcbuPBv0KwVfDkDHhgJK98PuzJJkiRJkioxlJIam8Hj4Nv/gUMOhy1rYdI58O5fIBYLuzJJkiRJksoYSkmNUad+8J03YMCFEN0Fr9wGz1wFOwrCrkySJEmSJMBQSmq8UtvAxQ/B6PsgqRksegH+dirkLQy7MkmSJEmSDKWkRi0SgeO+A9+cBuldYcNS+NvpMO/JsCuTJEmSJDVxhlJSU9D1aPjum9D7dNi1HZ6/Dqb+FxTvCLsySZIkSVITZSglNRWtD4ErnoVTfgxEYM4j8I8zYeOysCuTJEmSJDVBNQqlVq1axerVq8tef/DBB9x888389a9/rbXCJNWBpCQ45Vb4xnPQ6hDI/QT+ejIseTnsyiRJkiRJTUyNQqmvf/3rvPHGGwDk5uZy5pln8sEHH/C///u/3H333bVaoKQ60Pu0YDlf12NgRz48eRlMvwNKdoVdmSRJkiSpiahRKLVgwQKOPfZYAJ5++mkGDhzIrFmzePzxx5k0aVJt1ieprmR0hatfguO+F7x+53cweQxsyQu1LEmSJElS01CjUKq4uJjU1FQAXnvtNc4//3wA+vbtS05OTu1VJ6lupTSH0ffCJZOgeRqseBseHAnL3wm7MkmSJElSI1ejUGrAgAE88MADvPXWW0yfPp1Ro0YBsHbtWg455JBaLVBSAgy4AL4zAzr2g8I8eORr8PbvIBYLuzJJkiRJUiNVo1Bq4sSJPPjgg5xyyilcfvnlDBkyBICpU6eWLeuT1MB0OBy+/ToMvgxiJfDaHfDUFbB9c9iVSZIkSZIaoUgsVrOpECUlJRQUFNCuXbuyfcuXL6dVq1Z06tSp1gqsbQUFBWRkZJCfn096enrY5Uj1TywGsyfBy/8PSnZCu54wbjJkDQm7MkmSJElSA1Dd7KVGM6W2b99OUVFRWSC1YsUKfve737FkyZJ6HUhJqoZIBI6+Bq59Fdp2h03L4e9nwuxHXM4nSZIkSao1NQqlxowZw+TJkwHYvHkzxx13HL/+9a8ZO3Ys999/f60WKCkk2UPhu2/CEaOgpAj+9V/wwg2wc1twPFoCy96C+c8Gj9GScOuVJEmSJDUoNQql5syZw8iRIwF49tlnyczMZMWKFUyePJk//OEPtVqgpBC1bAeXPQmn3wGRJJj3OPzjTHj/r/C7gfDIefB/1waPvxsIi6aGXbEkSZIkqYGoUSi1bds22rRpA8Crr77KhRdeSFJSEscffzwrVqyo1QIlhSwpCUb+AMa/AK07Qt4CePl/oGBtxXEFOfD0eIMpSZIkSVK11CiUOuyww3j++edZtWoVr7zyCmeddRYA69ats3m41Fj1Ogm+PQOSm8cZUNpvatqPXMonSZIkSdqvGoVSP/3pT7nlllvo2bMnxx57LMOHDweCWVNDhw6t1QIl1SOblgV35IsrBgVrYMWshJUkSZIkSWqYUmpy0sUXX8yJJ55ITk4OQ4aU3yb+9NNP54ILLqi14iTVM4V5tTtOkiRJktRk1SiUAujcuTOdO3dm9erVAHTt2pVjjz221gqTVA+lZVZvXPM2dVuHJEmSJKnBq9HyvWg0yt13301GRgY9evSgR48etG3blnvuuYdoNFrt60yYMIFjjjmGNm3a0KlTJ8aOHcuSJUv2ec6kSZOIRCIVthYtWtTkbUg6UD1GQHo2ENn3uBdugDmT7S0lSZIkSYqrRqHU//7v//KnP/2Je++9l7lz5zJ37lx+8Ytf8Mc//pHbb7+92teZOXMmN9xwA++99x7Tp0+nuLiYs846i61bt+7zvPT0dHJycso27/gnJUhSMoyaWPpi72Cq9HVaJmz7CqbeBH89xf5SkiRJkqQqRWKxWOxAT8rOzuaBBx7g/PPPr7D/hRde4Prrr2fNmjU1Kuarr76iU6dOzJw5k5NOOqnKMZMmTeLmm29m8+bNNfoZBQUFZGRkkJ+f750CpZpaNBWm3QoFa8v3pXeBUffCEaPggwdh5i+hqCA41n8snHk3tOsRSrmSJEmSpMSpbvZSo55SGzdupG/fvpX29+3bl40bN9bkkgDk5+cD0L59+32OKywspEePHkSjUY466ih+8YtfMGDAgCrHFhUVUVRUVPa6oKCgxvVJKtX/fOh7bjALqjAvmB3VY0QwkwpgxE0w+DJ44+cw5xFY9DwseTnYf+J/Q2paqOVLkiRJksJXo+V7Q4YM4U9/+lOl/X/6058YPHhwjQqJRqPcfPPNnHDCCQwcODDuuD59+vDQQw/xwgsv8NhjjxGNRhkxYkRZw/W9TZgwgYyMjLKtW7duNapP0l6SkqHXSBh0cfC4O5DaLa0jfO138N23oOdIKCmCt34FfxwG856AA+g/J0mSJElqfGq0fG/mzJmce+65dO/eneHDhwPw7rvvsmrVKl566SVGjhx5wIV873vf4+WXX+btt9+ma9eu1T6vuLiYfv36cfnll3PPPfdUOl7VTKlu3bq5fE9KpFgMPn0RXv0JbFoe7Ms+Klju1/24UEuTJEmSJNWu6i7fq9FMqZNPPpnPPvuMCy64gM2bN7N582YuvPBCFi5cyKOPPnrA17vxxht58cUXeeONNw4okAJo1qwZQ4cOZenSpVUeT01NJT09vcImKcEiEej3NbjhAzjjTmieBmvnwENnwbPXQn7VMx0lSZIkSY1XjWZKxfPxxx9z1FFHUVJSvdvAx2IxbrrpJqZMmcKMGTM4/PDDD/hnlpSUMGDAAM455xx+85vf7He8jc6lemBLHvznHpj7GBCDlJZwwveDrXmrsKuTJEmSJB2EOp0pVVtuuOEGHnvsMZ544gnatGlDbm4uubm5bN++vWzM+PHjue2228pe33333bz66qt8+eWXzJkzhyuvvJIVK1bwrW99K4y3IKkm2mTCmD/Bd2ZA9xGwazvMvBf+dDR88nSw3E+SJEmS1KiFGkrdf//95Ofnc8opp5CVlVW2/fOf/ywbs3LlSnJycspeb9q0iW9/+9v069ePc845h4KCAmbNmkX//v3DeAuSDkb2kXDNS3DJJMjoDgVr4Llvwz/OhNWzw65OkiRJklSHQl2+FwaX70n1VPF2ePfP8NZvoHhrsG/wZXDGHZCeHW5tkiRJkqRqq272knIgF73wwgv3eXzz5s0HcjlJKtesJZx0Cxx5Bbx+N3z8BHzyFCyeCif+N4y4KRgjSZIkSWoUDmim1DXXXFOtcQ8//HCNC6przpSSGog1s2HabbDq/eB1Rjc48y4YcGFwNz9JkiRJUr1U3eylVpfvNQSGUlIDEovBgv+D6T8N+k0BdB8OoyZA9tBwa5MkSZIkValB3H1PkvYpEoFBF8ONH8EpP4aUlrDyXfjrqfD8DbAlL+wKJUmSJEk1ZCglqf5r3gpOuRVumg2DxgExmPcY/PEoeOvXULwj7AolSZIkSQfIUEpSw5HRBS76G1z7GnQZBjsLg6bofz4WFr0QLPeTJEmSJDUIhlKSGp5uxwTB1AUPQpss2LwCnh4Pk86DnE/Crk6SJEmSVA2GUpIapqQkGHJZsKTvpP8HKS1gxdvw4Ekw9b+gcF3YFUqSJEmS9sFQSlLD1rw1nPa/cOOHMOBCIAZzHoE/HAXv/B52FYVdoSRJkiSpCoZSkhqHtt3hkofhmmmQdSTs3ALTfwp/Pg4+/bf9piRJkiSpnjGUktS49BgO334DxvwZ0jJh0zJ46usweQzkLQy7OkmSJElSKUMpSY1PUhIMvTLoN3XiDyA5FZbNhAdOhBd/AFvXh12hJEmSJDV5hlKSGq/UNnDGHXDjB9DvfIhF4aN/BP2m3v0z7NoZdoWSJEmS1GQZSklq/Nr1hEsfhatehMxBUJQPr/wY7h8On71S3m8qWgLL3oL5zwaP0ZJQy5YkSZKkxiwSizWt7r8FBQVkZGSQn59Penp62OVISrRoCcx9DP5zD2z9KtjX+3Q47Ex49w9QsLZ8bHo2jJoI/c8Pp1ZJkiRJaoCqm70YSklqmnbkw5u/gvfuh2hxnEGR4GHcZIMpSZIkSaqm6mYvLt+T1DS1yICz7oHvzYKUFnEGlWb2037kUj5JkiRJqmWGUpKatsI82LVjHwNiULAGVsxKWEmSJEmS1BQYSklq2grzqjdu07K6rUOSJEmSmhhDKUlNW1pm9ca9/CN44xewbWPd1iNJkiRJTYShlKSmrceI4C57u5uaVyUpBYq3wsyJ8LtB8OrtsKWaM6wkSZIkSVUylJLUtCUlw6iJpS/2DqYiwXbRP+CSRyBzEOwshFl/gN8Phpf+H+SvTnDBkiRJktQ4GEpJUv/zYdxkSM+quD89O9g/YGywXfcWXP5P6HJ00Bz9gwfh90fC1Jtg45chFC5JkiRJDVckFovFwi4ikQoKCsjIyCA/P5/09PSwy5FUn0RLgrvsFeYFvaZ6jAhmUu0tFoNlM+HNX8Hyt4J9kSQYdAmc+APo1DexdUuSJElSPVLd7MVQSpIOxsr3gnBq6fTSHRHo9zU46RbIGhJqaZIkSZIUhupmLy7fk6SD0f14uPJZ+M4M6HseEIPFU+HBk+DxcbDqg7ArlCRJkqR6yZlSklSb8hbB27+BBf8HsWiwr9dJcNL/QM+RENnHXf4kSZIkqRFw+V4chlKSEmLDF0E49fFTEN0V7Ot6bBBOHX6m4ZQkSZKkRstQKg5DKUkJtXklvPMHmDMZSoqCfZ0HB+FU3/MgyVXUkiRJkhoXQ6k4DKUkhWJLLsz6I3z0MBRvDfZ17AsjfwgDLoTklHDrkyRJkqRaYigVh6GUpFBt3QDv3w/vPwhFBcG+dr1g5A9g8GWQ0jzc+iRJkiTpIBlKxWEoJale2L4ZPvwbvPsX2L4x2JfeFU74Phz1DWjWMtTyJEmSJKmmDKXiMJSSVK8UFcLsSTDrD1CYF+xr3QlG3AhHfxNS24RaniRJkiQdKEOpOAylJNVLxTtg7qPwzu8hf1Wwr2U7OP56OPY70LJtqOVJkiRJUnUZSsVhKCWpXtu1E+Y/DW/9GjZ+GexLTYdjvgXDb4DWHcKtT5IkSZL2w1AqDkMpSQ1CtAQWToE3fwVfLQ72NWsFw66BETdBela49UmSJElSHIZScRhKSWpQolFY8hK8eR/kzAv2JTeHoVfCCTdDux5hVidJkiRJlRhKxWEoJalBisVg6etBOLXqvWBfUgoMvhRO/AF0OCzc+iRJkiSplKFUHIZSkhq0WAxWvBOEU1/OKN0ZgQEXwMgfQueBlc+JlsCKWcHd/dIyoccISEpOZNWSJEmSmhBDqTgMpSQ1Gqs/CnpOffZy+b4+58JJP4Quw4LXi6bCtFuhYG35mPRsGDUR+p+f2HolSZIkNQmGUnEYSklqdHI+Ce7Wt+gFoPQrvfdp0H0EvPHz8n1lIsHDuMkGU5IkSZJqnaFUHIZSkhqtrz6Dt38DnzwNsZL9DI4EM6Zunu9SPkmSJEm1qrrZS1ICa5Ik1aWOR8AFD8BNs+GIs/czOAYFa4JeU5IkSZIUAkMpSWps2veCQeOqN7Ywr25rkSRJkqQ4DKUkqTFKy6zeuPzVEI3WbS2SJEmSVAVDKUlqjHqMCHpG7W5qHs9rd8Afh8Lbv4Ot6xNRmSRJkiQBhlKS1DglJcOoiaUv9g6mSl8ffhakZsCm5UE49Zt+8H/fhhXvQtO6B4YkSZKkEHj3PUlqzBZNhWm3QsHa8n3pXWDUvdD/fNi5FRb8H3z4D8iZVz6m0wA4+hoYfCm08LtSkiRJUvVVN3sxlJKkxi5aEtxlrzAv6DXVY0Qwk2pva2bDhw8FIdWu7cG+5mkw6BI45lroPCixdUuSJElqkAyl4jCUkqT92L4JPn4qmD214fPy/V2PDcKp/mOhWYvQypMkSZJUvxlKxWEoJUnVFIvB8reCcOrTFyG6K9jfsj0MvQKGXQOH9A63RkmSJEn1jqFUHIZSklQDW3JhzqMwexIUrC7ff+ipweypI0ZDckpo5UmSJEmqPwyl4jCUkqSDEC2Bz18NZk8tfQ0o/RXSJhuGXQVHjYf07FBLlCRJkhQuQ6k4DKUkqZZsXBbMnJr7KGzbEOyLJEPfc+Doa6HXyZCUFGqJkiRJkhLPUCoOQylJqmW7imDRVPjoH7Dy3fL97XvD0d+EI78OrdqHV58kSZKkhDKUisNQSpLqUN5C+Ogh+PifsHNLsC85FQZeGMye6no0RCLh1ihJkiSpThlKxWEoJUkJUFQI858JZk/lzi/f33lQEE4NugRS08KrT5IkSVKdMZSKw1BKkhIoFoPVHwXh1ILnoKQo2N+8DQy5LFjel9k/3BolSZIk1SpDqTgMpSQpJNs2wrzHg+V9G78s3999eDB7qv/5kJIaXn2SJEmSaoWhVByGUpIUsmgUls0MZk99+hLESoL9rTrA0Cvh6GugXc9QS5QkSZJUc4ZScRhKSVI9UrAW5kyG2Y/AlrWlOyNw2BlwzLVw+FmQlFz5vGgJrJgFhXmQlgk9RlQ9TpIkSVLCGUrFYSglSfVQyS747OVgad8X/ynfn9ENhl0FQ8dDm8xg36KpMO3WINDaLT0bRk0MlgBKkiRJCpWhVByGUpJUz234AmY/DHMfg+2bgn1JKdD3POjUD2bcC+z9qysSPIybbDAlSZIkhcxQKg5DKUlqIIp3wKLn4cN/wOoPqnFCJJgxdfN8l/JJkiRJIapu9pKUwJokSaq+Zi1gyGXwrelw3dtwxOj9nBCDgjVBrylJkiRJ9Z6hlCSp/us8CAZdXL2xW3LqthZJkiRJtcJQSpLUMKRlVm/cS/8v2Fa+B9Fo3dYkSZIkqcZSwi5AkqRq6TEi6BlVkEPlRue7RWDHJvjgwWBL7wIDLgi2LsMgEklkxZIkSZL2wUbnkqSGY9FUeHp86Ys9f32Vhk0XPwTNW8OC5+DTf8POLeVD2nYvDaguhKwhBlSSJElSHfHue3EYSklSA7doKky7FQrWlu9L7wKj7oX+55fvK94BS1+DhVNgyctQvLX8WPtDywOqzAEGVJIkSVItMpSKw1BKkhqBaElwl73CvKDXVI8RkJQcf/zObfD5q7DwOfjsVdi1vfxYhyPKA6pOfeu+dkmSJKmRM5SKw1BKkpq4okL4bFowg+rz6VBSVH6sU/8gnBpwAXQ4LLwaJUmSpAbMUCoOQylJUpkdBcHSvoXPwdLXIVpcfqzzoPKAqn2v8GqUJEmSGhhDqTgMpSRJVdq+KWiOvuA5+HIGxErKj2UPLQ+o2nYLrURJkiSpITCUisNQSpK0X1s3wKf/CgKq5W9BLFp+rOuxpT2oxkJ6dmglSpIkSfWVoVQchlKSpANSuA4WT4UFU2DFO8DuX5sR6D4cBl4I/c6HNplhVilJkiTVG4ZScRhKSZJqrCAHFr0Q9KBa9X75/kgS9DihPKBq3SG8GiVJkqSQGUrFYSglSaoV+ath4fNBQLVmdvn+SDL0OikIqPqeB63ah1aiJEmSFAZDqTgMpSRJtW7TClg4JQiocj4u35+UAr1PC3pQ9T0XWmSEV6MkSZKUIIZScRhKSZLq1IYvgnBq4fOQt6B8f3JzOOyM4C5+fUZBapvK50ZLYMUsKMyDtEzoMQKSkhNWuiRJklQbDKXiMJSSJCXMV0uCGVQLnoP1S8r3p7SAw88MAqojzobmrWHRVJh2KxSsLR+Xng2jJkL/8xNfuyRJklRDhlJxGEpJkhIuFoN1i4MZVAueg41flB9r1go6D6rYOL1MJHgYN9lgSpIkSQ2GoVQchlKSpFDFYpD7SRBOLZwCm1fs54RIMGPq5vku5ZMkSVKDUN3sJSmBNUmSpEgEsobAmXfB9z+Gc3+znxNiULAm6DUlSZIkNSKGUpIkhSUSqf4d+d74BXz2KuzaWbc1SZIkSQmSEnYBkiQ1aWmZ1Ru3chY8MSsIsfp+DQZcAIeeDMnN6rY+SZIkqY4YSkmSFKYeI4KeUQU5QFVtHiPQugP0HwOL/wWFeTDvsWBr0Rb6nRcEVL0MqCRJktSw2OhckqSwLZoKT48vfbHnr+W97r4XLYGV7wYN0he9AFu/Kh/ash30K51B1fMkSPb/O0mSJCkc3n0vDkMpSVK9tGgqTLsVCtaW70vvAqPuDQKpvUVLYMU7pQHVVNi2vvxYq0PKA6oeJxpQSZIkKaEMpeIwlJIk1VvRkuAue4V5Qa+pHiMgKXn/55XsKg+oFk+FbRvKj7XqEIRaAy6AHidU73qSJEnSQTCUisNQSpLUqJXsguVvlQZU/4LtG8uPte4I/XYHVNUMvCRJkqQDZCgVh6GUJKnJKCmGZW8GAdWnL8L2TeXHWncKmqcPuAC6H29AJUmSpFpjKBWHoZQkqUkqKYZlM0tnUL0IOzaXH0vrXB5QdTsOkpJCK1OSJEkNn6FUHIZSkqQmb9fOigFVUX75sTZZ0H8sDBgLXY81oJIkSdIBM5SKw1BKkqQ97NoJX75RusTvpb0CquwgnBpwAXQ52oBKkiRJ1WIoFYehlCRJcewqgi92B1T/hp1byo+ld90joBoGkUhoZUqSJKl+M5SKw1BKkqRqKN4BX/wnCKiWvAQ7C8uPZXQLAqr+F0CXowyoJEmSVIGhVByGUpIkHaDi7bD09dKA6mUo3lp+rG330h5UF0D20MoBVbQEVsyCwjxIy4QeI7zTnyRJUiNnKBWHoZQkSQeheDssfa00oJq2V0DVIwinBlwAWUNg8b9g2q1QsLZ8THo2jJoI/c9PfO2SJElKiOpmL6F2LJ0wYQLHHHMMbdq0oVOnTowdO5YlS5bs97xnnnmGvn370qJFCwYNGsRLL72UgGolSRLNWkK/r8HFD8H/LIVxk4MQqlkr2LwC3vkd/PVk+NUR8PQ3KgZSAAU58PR4WDQ1lPIlSZJUf4QaSs2cOZMbbriB9957j+nTp1NcXMxZZ53F1q1b454za9YsLr/8cq699lrmzp3L2LFjGTt2LAsWLEhg5ZIkieatoP8YuGRSEFBdMil4ndwCtq6Lc1LpBO1pPwqW9kmSJKnJqlfL97766is6derEzJkzOemkk6occ+mll7J161ZefPHFsn3HH388Rx55JA888ECl8UVFRRQVFZW9LigooFu3bi7fkySprnz2Kjxxyf7HjZoIR38TUprXfU2SJElKmAaxfG9v+fn5ALRv3z7umHfffZczzjijwr6zzz6bd999t8rxEyZMICMjo2zr1q1b7RUsSZIqKyqo3rhpt8LEHjB5LLz1a1j1IZTsqtPSJEmSVH+khF3AbtFolJtvvpkTTjiBgQMHxh2Xm5tLZmZmhX2ZmZnk5uZWOf62227jBz/4Qdnr3TOlJElSHUnL3P8YgNT0IMD68o1gA2ieBt2HQ6+R0HNk0DDdu/VJkiQ1SvUmlLrhhhtYsGABb7/9dq1eNzU1ldTU1Fq9piRJ2oceI4K77BXkUNZDqoJIcPz7n8D6z2D5W7DsTVjxDmzfBEunBxtAakZwvV4joddJ0GkAJNWrid6SJEmqoXoRSt144428+OKLvPnmm3Tt2nWfYzt37kxeXl6FfXl5eXTu3LkuS5QkSdWVlBz0i3p6PBChYjAVCR5G3QvJKZDZP9iO+y5Eo5C3oDSkeisIqYry4bOXgw2gZXvoeQL0PCkIqjr2hUgkwW9QkiRJtSHURuexWIybbrqJKVOmMGPGDA4//PD9nnPppZeybds2/vWvf5XtGzFiBIMHD66y0fneqttsS5IkHaRFU4O+UQVry/eldwkCqf7n7//8aAnkfLxHSDULive6Q2/rjtDzxGCpX6+T4JDDDKkkSZJCVt3sJdRQ6vrrr+eJJ57ghRdeoE+fPmX7MzIyaNmyJQDjx4+nS5cuTJgwAYBZs2Zx8sknc++993Luuefy1FNP8Ytf/II5c+bssxfVboZSkiQlULQkCJMK84JeUz1G1LxHVEkxrJ0bLPVb/hasfB92ba84pk3WHiHVSGjXy5BKkiQpwRpEKBWJ85fEhx9+mKuvvhqAU045hZ49ezJp0qSy48888ww/+clPWL58OYcffji//OUvOeecc6r1Mw2lJElqJHYVwZrZwSyq5W/Bqg+gpKjimIxu5QFVz5HQ1pudSJIk1bUGEUqFwVBKkqRGqng7rP6wPKRa/RFEiyuOadezfKlfz5GQnhVKqZIkSY2ZoVQchlKSJDURO7fCyvfKe1KtnQuxkopjDjmsPKDqORLSOlbv2rW5LFGSJKmRMZSKw1BKkqQmakdBaUj1ZhBS5XxMxTsDAh37lS/163kitGpf+TpVNnDPDu44WJ0G7pIkSY2coVQchlKSJAmA7ZuC2U67l/vlLdhrQAQyB5aHVD1GBE3Wnx5PpTCL0j6Z4yYbTEmSpCbPUCoOQylJklSlrRtgxdvlIdVXn+41IAJJKZX7VO15PD0bbp7vUj5JktSkVTd7SUlgTZIkSfVX60Og/5hgAyhcV96PavlbsGHpPgIpgBgUrAlmX/UamZCSJUmSGjJDKUmSpKqkdYKBFwUbwAd/g5du2f95sx+BFhnB0r+kpLqtUZIkqQEzlJIkSaqOjn2rN27BM8HW6pDgzn69ToZDT4H2veq0PEmSpIbGUEqSJKk6eowIekYV5FC50XmpFhnQ9ThYOQu2bYCFU4INoG0POLQ0oOp1MrTukKjKJUmS6iUbnUuSJFXXoqmld9+DisHUXnffKymGNbPhyxnw5UxY/QFEd1W8Vuag8pCq+3BITav7+iVJkhLAu+/FYSglSZIOyqKpMO1WKFhbvi+9C4y6NwikqlJUGDRAXzYzCKryFlQ8ntQMuh4TBFSHngJdjoLkZnX0BiRJkuqWoVQchlKSJOmgRUuCkKkwD9Iyg6V9ScnVP7/wq/KA6suZkL+y4vHmadDzxPKlfp36QSRSm+9AkiSpzhhKxWEoJUmS6pVYDDYtC8KpL2cEYdX2TRXHpGUGTdN3h1Rtu4VRqSRJUrUYSsVhKCVJkuq1aBTy5pfPoloxC3Ztrzimfe/SpX4nQ8+R0Kp9GJVKkiRVyVAqDkMpSZLUoOwqglUflC/3WzMbYtE9BkQg+8hgBtWhp0D346FZy3BqlSRJwlAqLkMpSZLUoO3Ih+XvlC/1++rTiseTU6H7caVL/U4JAqvq9Ls62D5ZkiRJpQyl4jCUkiRJjUpBTuksqtKZVFvWVjzeIiNY4rf7zn6HHFa5aXqVdxTMhlET499RUJIkKQ5DqTgMpSRJUqMVi8H6z8uX+i17C4ryK45pk10eUB16crA08OnxwN5/JSwNrsZNNpiSJEkHxFAqDkMpSZLUZJTsgpyPYdmMIKRa+R6U7Kw4JikForviXCASzJi6eb5L+SRJUrVVN3tJSWBNkiRJSqTkFOg6LNhG/hCKtwfB1Jczgi1n3j4CKYAYFKwJek31GpmYmiVJUpPhTClJkqSm6qNJ8OL39z+ubU/ofSpkDQm2Tv2hWYu6rk6SJDVQzpSSJEnSvh3Su3rjNi+H2Q+Xv05KgY79ykOqrCHQeSA0b10nZUqSpMbJUEqSJKmp6jEi6BlVkEPlRucAEUjrBKMmQM4nQX+qnI9h+0bImx9s8x4rHZoEhxwO2UfuEVQNCu7+J0mSVAWX70mSJDVli6aW3n0PKgZTce6+F4tB/urygGr3Vphb9fXbH7rXjKoh0PqQungnkiSpnvDue3EYSkmSJO1l0VSYdisUrC3fl94FRt1bMZDaly25e8ymmhc8z19Z9diMbhWDqqwh0KbzQb8NSZJUPxhKxWEoJUmSVIVoSXCXvcI8SMsMlvYlJR/cNbdtrDyjauMXVY9Ny6wcVGV0g0jk4GqQJEkJZygVh6GUJElSiHbkQ+78ikHV+s8gFq08tmW70oDqyPKgql0vSEqq/s+ri7BNkiTtk6FUHIZSkiRJ9czOrZC3cI+lfx/DusUQ3VV5bGo6dB5ccUZVh8OrDpqqXJaYDaMmVn9ZoiRJOmCGUnEYSkmSJDUAu4pg3aKKM6pyF0BJUeWxzVpB5sCKQdWGpfDsN6l8V8E4DdwlSVKtMZSKw1BKkiSpgSopDpb6VehT9QkUbz3AC0WCGVM3z3cpnyRJdaC62UtKAmuSJEmSai65GWQOCLYjvx7si5bAxi+DgGrt3OBxzZz9BFUxKFgDn74E/b+WkNIlSVJlzpSSJElS4/LJM/Dct6o3tl1P6HosdDsWuh4TLANM9v/bSpJ0MJwpJUmSpKapTefqj920PNjmPx28btYKugwLAqrdQVXrDnVRpSRJTZ6hlCRJkhqXHiOCnlEFOVRudA5lPaW++1Zwt79VH8DqD2D1bCjKh+VvBdtu7Q8tnU11TPDYqb+zqSRJqgUu35MkSVLjs2gqPD2+9MWef93dx933olFYv6Q8pFr1YfB6b81aQ5ejoNtx5bOpWrWvi3chSVKD5N334jCUkiRJaiIWTYVpt0LB2vJ96V1g1L2VA6l4tm8KZlCter98NtXOLZXHHXLYXrOp+nlnP0lSk2UoFYehlCRJUhMSLYEVs6AwD9Iyg6V9BxMWRUvgq09LZ1N9GDxu+LzyuOZtSmdTHRvMqOp6NLRsV/OfK0lSA2IoFYehlCRJkmrVto2w+qPSJX/vw5o5sLOw8rgOR1ScTdWxLyQlJb5eSZLqmKFUHIZSkiRJqlPREli3qOJsqo1fVB6Xmh7c6a/bscHW5Who2bZ616/N2V+SJNUyQ6k4DKUkSZKUcFs3BAHV6g+CkGrNHCjeWnlcx75B4/RuxwazqTocUXE2VZV9srJh1MTq98mSJKmOGUrFYSglSZKk0JXsgnULK86m2rSs8rgWGcEMqm7HQqwEZt5HxbsJwj7vKChJUggMpeIwlJIkSVK9VPjVHrOpPoS1c6B4WzVPjgQzpm6e71I+SVLoqpu9pCSwJkmSJEnxpHWEvucEG0BJMeQtCAKqT1+EZTP3cXIMCtbAW7+God+A9KyElCxJ0sEwlJIkSZLqo+RmkD002Fq1308oVeqNnwdbehfoclTQSL3L0ZB9JKS2qfOSJUk6EIZSkiRJUn2Xllm9ce16wuaVwaypgjWw+F+lByLQqV/FoKpTf0j2PwckSeHxt5AkSZJU3/UYEfSMKsihcqNzKOspddMcKN4OOR/Dmo9gzWxYPRsKVsO6RcE297HglJSWwQyqLsNKw6qjoW13iEQS+MYkSU2ZoZQkSZJU3yUlw6iJ8PR4grvt7RlMlYZIo+4NxqWmQc8Tgm23LbmwZk55ULVmDhQVwMp3g2231h1LQ6ph5WFVy3YJeIOSpKbIu+9JkiRJDcWiqTDtVihYW74vvUsQSPU/v/rXiUZhw9I9QqrZkDsforsqj23fG7oeXb7sr/NASEk9+PciSWq0qpu9GEpJkiRJDUm0BFbMgsK8oNdUjxHBDKmDVbwjCKbKlv19BJuWVR6X3Bw6D9pjNtXR0P5QSEo6+BokSY2CoVQchlKSJElSNW3bWHHZ3+qPYPvGyuNaZOy17O9oSOt4YD+rrsI2SVLCGUrFYSglSZIk1VAsBpuWly/5WzM7aKq+a0flsRndoeseQVXWkdC8VdXXrXJZYnbQR+tAliVKkuoFQ6k4DKUkSZKkWlRSDHkLS2dTzQlmU63/jEp3CYwkQ6f+FYOqjn3h03+XNnDf+z9LShu4j5tsMCVJDYyhVByGUpIkSVId25EPa+dVDKoKcyuPS2kFsV1QsjPOhSLBjKmb57uUT5IakOpmLykJrEmSJElSU9AiAw49OdggWPZXsLZ0yV9pULVmDhRv3c+FYlCwBla8A71OqvOyJUmJ5UwpSZIkSYkXLYFZf4TX7tj/2NQ20O24oC9V9pHBY0ZXiETquEhJUk04U0qSJElS/ZWUHPSVqo6iLbD0tWDbrdUhkDWkYlDVtrtBlSQ1IIZSkiRJksLRY0TQM6ogh8qNziHoKZUFFz0MefMhZx6s/Ri+WgzbNsAX/wm23Vq2qxxUtetpUCVJ9ZTL9yRJkiSFZ9HU0rvvQcVgah933yveEdzxL2deaVA1D9Ythmhx5eu3yKgiqOoFSUm1+z4kSWW8+14chlKSJElSPbNoKky7NWiGvlt6Fxh1b+VAKp5dRbBuURBQlQVVi6q+s19qBmQNDsKq7KHBY/veBlWSVEsMpeIwlJIkSZLqoWgJrJgFhXmQlhks7UtKPrhr7toZLPXbM6jKWwglRZXHNm9TGlQdWTqjaggcctjB1yBJTZChVByGUpIkSVITVlIMX31aHlTlfAy582HXjspjm7WuHFR1OOLAgqq6CNskqZ4zlIrDUEqSJElSBSW7YP2SykFV8bbKY5u1gs6DgqAqa0gQVnXoA8lV3EOqymWJ2TBqYvWXJUpSA2QoFYehlCRJkqT9ipbA+s9Kg6qPS8OqT6B4a+WxKS2h88CKQdX6pfDsNVS+q+A+GrhLUiNhKBWHoZQkSZKkGomWwIalQUhVNqvqE9i55QAvFAlmTN0836V8khql6mYvVcwxlSRJkiRVkpQMHfsE2+Bxwb5oFDZ+URpUzQ0e18yueulfmRgUrIFXfgx9zwuu17ojRCIJeRuSVF84U0qSJEmSatMnT8Nz3z6wc1q0hY59oeMRwWOHPsHz9K6QlFQnZUpSXXGmlCRJkiSFoU1W9cZ1PQa2fgWbVsCOzbDqvWDbU7PW0OHw8hlaHfoEoVW7nlU3V5ekBsRvMUmSJEmqTT1GBD2jCnKo3OgcynpKffOVYElg8fagV9VXS0q3T4Mm6xu+CBqr58wLtj0lN4f2vfcIq0pnWB1yGDRrUffvUZJqgaGUJEmSJNWmpGQYNRGeHk9wt709g6nSvlGj7i1vct6sJXQeFGx7KimGjctg/ZLywGr9EvjqM9i1Hb5aHGx7iiRB2x7lSwF3z6zqcDi0qKX2JdESWDELCvMgLTMI4WzYLqkG7CklSZIkSXVh0VSYdisUrC3fl94lCKT6n1/z60ajkL8qmE311aelYVXp8x358c9L71I6o2rPpYB9oHWHg3xP2UEIdzDvSVKjUt3sxVBKkiRJkupKImcVxWJQuK58+d+eSwEL8+Kf1+qQ8oBqz6WA6dkV7wi4aGrp7K+9/xOydMy4yQZTkgBDqbgMpSRJkiQ1Ods3Bcv+9l4KuHll/HOatyltst4XOhwGs/4E2zfGGVzaJ+vm+S7lk+Td9yRJkiRJpVq2g+7HBduedm6F9Z9XXAr41RLY+CXs3AJr5wTbfsWgYE0wK6zXyDp5C5IaH0MpSZIkSWqqmreG7CODbU+7dgbB1O7lf59Ph9Uf7P96034Eh59Z2rh9MLQ/1JlTkuIylJIkSZIkVZTSHDr1DTaA7sPhkfP2f17egmDbrVkryBxQfnfBzoOhU39o3qpu6pbUoBhKSZIkSZL2rceIoGdUQQ6VG50DRIK7+J38/yBvIeTOh7xFULwNVn8YbGVDk6B974pBVedB0CYzUe9GUj1hKCVJkiRJ2rekZBg1sfTuexEqBlOld9879zcV774XLYENX0DuJ0FIlTs/eL71K9jwebAtfK58fOtOewRVpWHVIb1d/ic1Yt59T5IkSZJUPYumwrRboWBt+b70LjDq3oqB1L5sySsPqPIWBM/Xf06VM7BSWkJm/8rL/1LTauXtSKob1c1eDKUkSZIkSdUXLQnusleYB2mZwdK+g53NtHMrrFtccVZV3sJg+V8lkWAG1e6gKrP0sU1niERqXkNdvC+piTKUisNQSpIkSZIagGgJbFy21/K/+VCYW/X4Vh0q96k65DBIrkbXmipngGUHSxarOwNMUhlDqTgMpSRJkiSpAStcVzGkylsA6z+DWLTy2JQWwXK/PXtVZQ6A1DblYxZNLe2Vtfd/GpfOuho32WBKOkCGUnEYSkmSJElSI7NzG3y1uGJYlbsAirdWPb79oeUB1fsPwrYNcS4cCWZM3TzfpXzSATCUisNQSpIkSZKagGgUNu1e/regPKzasnb/5+7tqheh18jar1FqpAyl4jCUkiRJkqQmbOv68oDq03/Dqvf2f06zVtDhCGjXE9r1KH3sCW17QEY3SGlex0VLDYuhVByGUpIkSZIkAJa9BY+cd3DXiCRBetfSsGp3YNWrPLRq3eHg7gooNUDVzV6qcRsCSZIkSZIaoR4jgp5RBTlUbnQOEIE2WfD1pyF/FWxaHmybV5Q/37UD8lcG2/K3Kl+iWevymVV7zrJq1xPadodmLevozRHcwXDFLCjMg7TM4P3aG0v1iKGUJEmSJKlpSkqGURNL774XoWIwVTq7afREyBoUbHuLxYLAZ9MeIdWeoVXB2qDZ+rqFwVaVtM5xAqseQSCWlFSz97ZoKky7Nahht/Ts4P16N0HVEy7fkyRJkiQ1bVUGOF1g1L0HF+AU7yidYbUiaLq+Z2i1cTns3LLv85NTg9lU8UKrFnH+m3bR1NKgbe//3C8N2sZNNphSnbKnVByGUpIkSZKkShK91C0Wg+2bSsOqKmZabV4FsZJ9X6Nl+8pLAzO6wfPfC95HlSLBjKmb57uUT3XGnlKSJEmSJFVXUjL0Gpm4nxeJQKv2wdZlWOXjJbugYHVpUFVFaLVtA2zfGGxr5xzAD45BwZoggEvk+5WqYCglSZIkSVJ9k5xSPguqKjsK9mi4vkdolTcftuTu//pv/To4P+tI6Ng3+HlSgrl8T5IkSZKkxmLZW/DIeQd2TkpL6DwQsocGW9aR0OEIgyrVmMv3JEmSJElqanqMCHpGFeRQudE5QARatoMhl0HOJ5DzcdBwffWHwbZbs1bQeVB5SJU9FDocbh8q1SpDKUmSJEmSGoukZBg1sfTuexEqBlOld9/72u/L774XjcLGL2DtXFg7L3jM/QR2FsKq94Ntt2atIWtwxaDqkMMgKSkhb02Nj8v3JEmSJElqbBZNhWm3QsHa8n3pXWDUveWBVDzREtiwtDykypkXzKgq3lZ5bPM0yBpSHlJlD4X2hxpUNXHVzV4MpSRJkiRJaoyiJcFd9grzIC0zWNpX0+V30RJY/3npjKrdQdUnsGt75bGp6aVB1ZDyoKpdL4OqJsRQKg5DKUmSJEmSakHJLlj/WXlItXYu5M6HXTsqj03NgOw9Z1QdGQRVkciB/9zaDNtUJwyl4jCUkiRJkiSpjpTsgq8+LQ+p1s6F3AVQUlR5bIu2e8ymOjJ4bNtj30FVlcsSs4M+WvtblqiEMZSKw1BKkiRJkqQEKimGdYv3CKrmQd4CKNlZeWzLdhVnU2UPhYxuQVC1aGppA/e9Y4zSEGvcZIOpeqJBhFJvvvkm9913H7NnzyYnJ4cpU6YwduzYuONnzJjBqaeeWml/Tk4OnTt3rtbPNJSSJEmSJClku3bCV4sr3vUvbyFEiyuPbdk+CKpWfxDcFbBKkWDG1M3zXcpXD1Q3e0lJYE2VbN26lSFDhvDNb36TCy+8sNrnLVmypMKb6tSpU12UJ0mSJEmS6kJK8/Jm6MNK9+0qgnWLKgZV6xbB9o3w5X/2c8EYFKyBJdOg7zk161WlhAs1lBo9ejSjR48+4PM6depE27Zta78gSZIkSZIUjpTU8rv17Va8A9YthA8fgnmP7f8a//x6cPe/dj2CRurte+3x2BPSu0JyqFGI9tAg/ySOPPJIioqKGDhwIHfeeScnnHBC3LFFRUUUFZU3VCsoKEhEiZIkSZIk6WA1awFdhsHObdULpQCKCoK7AObOr3wsKQXadq8YWLXrWR5aNW9dm9VrPxpUKJWVlcUDDzzA0UcfTVFREX//+9855ZRTeP/99znqqKOqPGfChAncddddCa5UkiRJkiTVmh4jgp5RBTlUbnQOZT2lbvgA8lfDpuWwaRlsXFb+uHlF0Fx945fB9kUVl0nL3Cuo2iO8at2h7pYFRktgxSwozAtq6DGiSfTGqjd334tEIvttdF6Vk08+me7du/Poo49WebyqmVLdunWz0bkkSZIkSQ1J2d33oGIwVc2770WjsGVtxaBq07IgwNq4DHZs3vfPb54WhFV7Bla7n2d0g+RmNX9f026FgrXl+9KzYdTEBns3wQbR6Lw2HHvssbz99ttxj6emppKamprAiiRJkiRJUq3rf34QPFUZ4Ny7/wAnKQkyugZbr5GVj2/ftFdgtbw8sCpYE9z5L29BsO0tkgxtu8WZZdUTUttUXVNZ0LbXfKGCnGD//oK2Bq7Bh1Lz5s0jKysr7DIkSZIkSVJd638+9D23bpa6tWwHXdpBlyraAxXvgM0rq14WuGk5lBSVh1hVadWh8nLAtt3hpf+h6uWIMSAC034UvN9GupQv1FCqsLCQpUuXlr1etmwZ8+bNo3379nTv3p3bbruNNWvWMHnyZAB+97vf0atXLwYMGMCOHTv4+9//zn/+8x9effXVsN6CJEmSJElKpKTkqmc61aVmLaDjEcG2t2gUCnMrLwvcHVht3wjb1gfb6g8P4IfGghlaK2Yl/v0mSKih1EcffcSpp55a9voHP/gBAFdddRWTJk0iJyeHlStXlh3fuXMnP/zhD1mzZg2tWrVi8ODBvPbaaxWuIUmSJEmSlDBJScESwvRs6HlC5eM78qtYFrgMchfC9g37v35hXq2XXF/Um0bniVLdZluSJEmSJEl1Ztlb8Mh5+x931YsNbqZUdbOXpATWJEmSJEmSJAh6YaVnU3b3wEoikN4lGNdIGUpJkiRJkiQlWlIyjJpY+mLvYKr09ah7G22TczCUkiRJkiRJCkf/82HcZEjPqrg/PTvY3//8cOpKkFAbnUuSJEmSJDVp/c+HvucGd9krzIO0zGDJXiOeIbWboZQkSZIkSVKYkpIbXDPz2uDyPUmSJEmSJCWcoZQkSZIkSZISzlBKkiRJkiRJCWcoJUmSJEmSpIQzlJIkSZIkSVLCGUpJkiRJkiQp4QylJEmSJEmSlHCGUpIkSZIkSUo4QylJkiRJkiQlnKGUJEmSJEmSEs5QSpIkSZIkSQlnKCVJkiRJkqSEM5SSJEmSJElSwhlKSZIkSZIkKeEMpSRJkiRJkpRwhlKSJEmSJElKuJSwC0i0WCwGQEFBQciVSJIkSZIkNT67M5fdGUw8TS6U2rJlCwDdunULuRJJkiRJkqTGa8uWLWRkZMQ9HontL7ZqZKLRKGvXrqVNmzZEIpGwyzkoBQUFdOvWjVWrVpGenh52Oaon/FyoKn4uVBU/F9qbnwlVxc+FquLnQlXxc6HdYrEYW7ZsITs7m6Sk+J2jmtxMqaSkJLp27Rp2GbUqPT3df+FViZ8LVcXPhari50J78zOhqvi5UFX8XKgqfi4E7HOG1G42OpckSZIkSVLCGUpJkiRJkiQp4QylGrDU1FTuuOMOUlNTwy5F9YifC1XFz4Wq4udCe/Mzoar4uVBV/FyoKn4udKCaXKNzSZIkSZIkhc+ZUpIkSZIkSUo4QylJkiRJkiQlnKGUJEmSJEmSEs5QSpIkSZIkSQlnKFXP/fnPf6Znz560aNGC4447jg8++GCf45955hn69u1LixYtGDRoEC+99FKCKlUiTJgwgWOOOYY2bdrQqVMnxo4dy5IlS/Z5zqRJk4hEIhW2Fi1aJKhiJcKdd95Z6c+4b9+++zzH74rGr2fPnpU+F5FIhBtuuKHK8X5XNE5vvvkmX/va18jOziYSifD8889XOB6LxfjpT39KVlYWLVu25IwzzuDzzz/f73UP9O8nqj/29ZkoLi7m1ltvZdCgQbRu3Zrs7GzGjx/P2rVr93nNmvweUv2yv++Kq6++utKf8ahRo/Z7Xb8rGrb9fS6q+ntGJBLhvvvui3tNvy+0N0Opeuyf//wnP/jBD7jjjjuYM2cOQ4YM4eyzz2bdunVVjp81axaXX3451157LXPnzmXs2LGMHTuWBQsWJLhy1ZWZM2dyww038N577zF9+nSKi4s566yz2Lp16z7PS09PJycnp2xbsWJFgipWogwYMKDCn/Hbb78dd6zfFU3Dhx9+WOEzMX36dAAuueSSuOf4XdH4bN26lSFDhvDnP/+5yuO//OUv+cMf/sADDzzA+++/T+vWrTn77LPZsWNH3Gse6N9PVL/s6zOxbds25syZw+23386cOXN47rnnWLJkCeeff/5+r3sgv4dU/+zvuwJg1KhRFf6Mn3zyyX1e0++Khm9/n4s9Pw85OTk89NBDRCIRLrroon1e1+8LVRBTvXXsscfGbrjhhrLXJSUlsezs7NiECROqHD9u3LjYueeeW2HfcccdF/vud79bp3UqPOvWrYsBsZkzZ8Yd8/DDD8cyMjISV5QS7o477ogNGTKk2uP9rmiavv/978d69+4di0ajVR73u6LxA2JTpkwpex2NRmOdO3eO3XfffWX7Nm/eHEtNTY09+eSTca9zoH8/Uf2192eiKh988EEMiK1YsSLumAP9PaT6rarPxVVXXRUbM2bMAV3H74rGpTrfF2PGjImddtpp+xzj94X25kypemrnzp3Mnj2bM844o2xfUlISZ5xxBu+++26V57z77rsVxgOcffbZccer4cvPzwegffv2+xxXWFhIjx496NatG2PGjGHhwoWJKE8J9Pnnn5Odnc2hhx7KFVdcwcqVK+OO9bui6dm5cyePPfYY3/zmN4lEInHH+V3RtCxbtozc3NwK3wcZGRkcd9xxcb8PavL3EzVs+fn5RCIR2rZtu89xB/J7SA3TjBkz6NSpE3369OF73/seGzZsiDvW74qmJy8vj3//+99ce+21+x3r94X2ZChVT61fv56SkhIyMzMr7M/MzCQ3N7fKc3Jzcw9ovBq2aDTKzTffzAknnMDAgQPjjuvTpw8PPfQQL7zwAo899hjRaJQRI0awevXqBFarunTccccxadIkpk2bxv3338+yZcsYOXIkW7ZsqXK83xVNz/PPP8/mzZu5+uqr447xu6Lp2f3v/IF8H9Tk7ydquHbs2MGtt97K5ZdfTnp6etxxB/p7SA3PqFGjmDx5Mq+//joTJ05k5syZjB49mpKSkirH+13R9DzyyCO0adOGCy+8cJ/j/L7Q3lLCLkBSzdxwww0sWLBgv2uwhw8fzvDhw8tejxgxgn79+vHggw9yzz331HWZSoDRo0eXPR88eDDHHXccPXr04Omnn67W/61S4/ePf/yD0aNHk52dHXeM3xWS9lRcXMy4ceOIxWLcf//9+xzr76HG77LLLit7PmjQIAYPHkzv3r2ZMWMGp59+eoiVqb546KGHuOKKK/Z7kxS/L7Q3Z0rVUx06dCA5OZm8vLwK+/Py8ujcuXOV53Tu3PmAxqvhuvHGG3nxxRd544036Nq16wGd26xZM4YOHcrSpUvrqDqFrW3bthxxxBFx/4z9rmhaVqxYwWuvvca3vvWtAzrP74rGb/e/8wfyfVCTv5+o4dkdSK1YsYLp06fvc5ZUVfb3e0gN36GHHkqHDh3i/hn7XdG0vPXWWyxZsuSA/64Bfl/IUKreat68OcOGDeP1118v2xeNRnn99dcr/J/sPQ0fPrzCeIDp06fHHa+GJxaLceONNzJlyhT+85//0KtXrwO+RklJCfPnzycrK6sOKlR9UFhYyBdffBH3z9jviqbl4YcfplOnTpx77rkHdJ7fFY1fr1696Ny5c4Xvg4KCAt5///243wc1+fuJGpbdgdTnn3/Oa6+9xiGHHHLA19jf7yE1fKtXr2bDhg1x/4z9rmha/vGPfzBs2DCGDBlywOf6fSHvvlePPfXUU7HU1NTYpEmTYosWLYp95zvfibVt2zaWm5sbi8VisW984xuxH/3oR2Xj33nnnVhKSkrsV7/6VWzx4sWxO+64I9asWbPY/Pnzw3oLqmXf+973YhkZGbEZM2bEcnJyyrZt27aVjdn7c3HXXXfFXnnlldgXX3wRmz17duyyyy6LtWjRIrZw4cIw3oLqwA9/+MPYjBkzYsuWLYu98847sTPOOCPWoUOH2Lp162KxmN8VTVlJSUmse/fusVtvvbXSMb8rmoYtW7bE5s6dG5s7d24MiP3mN7+JzZ07t+xOavfee2+sbdu2sRdeeCH2ySefxMaMGRPr1atXbPv27WXXOO2002J//OMfy17v7+8nqt/29ZnYuXNn7Pzzz4917do1Nm/evAp/1ygqKiq7xt6fif39HlL9t6/PxZYtW2K33HJL7N13340tW7Ys9tprr8WOOuqo2OGHHx7bsWNH2TX8rmh89vc7JBaLxfLz82OtWrWK3X///VVew+8L7Y+hVD33xz/+Mda9e/dY8+bNY8cee2zsvffeKzt28sknx6666qoK459++unYEUccEWvevHlswIABsX//+98Jrlh1Cahye/jhh8vG7P25uPnmm8s+Q5mZmbFzzjknNmfOnMQXrzpz6aWXxrKysmLNmzePdenSJXbppZfGli5dWnbc74qm65VXXokBsSVLllQ65ndF0/DGG29U+Xtj9599NBqN3X777bHMzMxYampq7PTTT6/0eenRo0fsjjvuqLBvX38/Uf22r8/EsmXL4v5d44033ii7xt6fif39HlL9t6/PxbZt22JnnXVWrGPHjrFmzZrFevToEfv2t79dKVzyu6Lx2d/vkFgsFnvwwQdjLVu2jG3evLnKa/h9of2JxGKxWJ1OxZIkSZIkSZL2Yk8pSZIkSZIkJZyhlCRJkiRJkhLOUEqSJEmSJEkJZyglSZIkSZKkhDOUkiRJkiRJUsIZSkmSJEmSJCnhDKUkSZIkSZKUcIZSkiRJkiRJSjhDKUmSpEYqEonw/PPPh12GJElSlQylJEmS6sDVV19NJBKptI0aNSrs0iRJkuqFlLALkCRJaqxGjRrFww8/XGFfampqSNVIkiTVL86UkiRJqiOpqal07ty5wtauXTsgWFp3//33M3r0aFq2bMmhhx7Ks88+W+H8+fPnc9ppp9GyZUsOOeQQvvOd71BYWFhhzEMPPcSAAQNITU0lKyuLG2+8scLx9evXc8EFF9CqVSsOP/xwpk6dWrdvWpIkqZoMpSRJkkJy++23c9FFF/Hxxx9zxRVXcNlll7F48WIAtm7dytlnn027du348MMPeeaZZ3jttdcqhE73338/N9xwA9/5zneYP38+U6dO5bDDDqvwM+666y7GjRvHJ598wjnnnMMVV1zBxo0bE/o+JUmSqhKJxWKxsIuQJElqbK6++moee+wxWrRoUWH/j3/8Y3784x8TiUS47rrruP/++8uOHX/88Rx11FH85S9/4W9/+xu33norq1atonXr1gC89NJLfO1rX2Pt2rVkZmbSpUsXrrnmGn72s59VWUMkEuEnP/kJ99xzDxAEXWlpabz88sv2tpIkSaGzp5QkSVIdOfXUUyuETgDt27cvez58+PAKx4YPH868efMAWLx4MUOGDCkLpABOOOEEotEoS5YsIRKJsHbtWk4//fR91jB48OCy561btyY9PZ1169bV9C1JkiTVGkMpSZKkOtK6detKy+lqS8uWLas1rlmzZhVeRyIRotFoXZQkSZJ0QOwpJUmSFJL33nuv0ut+/foB0K9fPz7++GO2bt1advydd94hKSmJPn360KZNG3r27Mnrr7+e0JolSZJqizOlJEmS6khRURG5ubkV9qWkpNChQwcAnnnmGY4++mhOPPFEHn/8cT744AP+8Y9/AHDFFVdwxx13cNVVV3HnnXfy1VdfcdNNN/GNb3yDzMxMAO68806uu+46OnXqxOjRo9myZQvvvPMON910U2LfqCRJUg0YSkmSJNWRadOmkZWVVWFfnz59+PTTT4HgznhPPfUU119/PVlZWTz55JP0798fgFatWvHKK6/w/e9/n2OOOYZWrVpx0UUX8Zvf/KbsWldddRU7duzgt7/9LbfccgsdOnTg4osvTtwblCRJOgjefU+SJCkEkUiEKVOmMHbs2LBLkSRJCoU9pSRJkiRJkpRwhlKSJEmSJElKOHtKSZIkhcAOCpIkqalzppQkSZIkSZISzlBKkiRJkiRJCWcoJUmSJEmSpIQzlJIkSZIkSVLCGUpJkiRJkiQp4QylJEmSJEmSlHCGUpIkSZIkSUo4QylJkiRJkiQl3P8HLv4C3HdmhqEAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_history(history)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Save Model" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[PT] Model Saving time: 0.07896971702575684\n" - ] - } - ], - "source": [ - "st = time.time()\n", - "torch.jit.script(model).save(str(output_path / \"cifar100.pt\"))\n", - "print(f\"[PT] Model Saving time: {time.time() - st}\")" - ] - } - ], - "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": "3.10.16" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tfvspt/pt/train.py b/tfvspt/pt/train.py deleted file mode 100644 index 1d2cf06..0000000 --- a/tfvspt/pt/train.py +++ /dev/null @@ -1,55 +0,0 @@ -"""Torch Training.""" - -import torch -from tqdm.autonotebook import tqdm - -from tfvspt.config.config import Config - - -def train_model(dataloader, model, criterion, optimizer, config: Config, - device: str) -> dict: - - model.to(device) - - training_logs = {"loss": [], "accuracy": []} - for epoch in range(config.epochs): - - model.train() - running_loss = 0.0 - total = 0 - correct = 0 - - print(f"Epoch {epoch + 1} / {config.epochs}") - for images, targets in tqdm(dataloader): - - images, targets = images.to(device), targets.to(device) - optimizer.zero_grad() - - outputs = model(images) - - loss = criterion(outputs, targets[:, 0]) - - loss.backward() - optimizer.step() - - running_loss += loss.item() - - predicted = torch.argmax(outputs, 1) - total += targets.size(0) - correct += (predicted == targets[:, 0]).sum().item() - - # Epoch Stats - epoch_loss = running_loss / len(dataloader) - epoch_accuracy = round(correct / total, 4) - - # Collect logs - training_logs["loss"].append(epoch_loss) - training_logs["accuracy"].append(epoch_accuracy) - - print( - f"Epoch {epoch + 1}/{config.epochs}, Loss: {epoch_loss}, Accuracy: {epoch_accuracy}" - ) - - print("\n") - - return training_logs diff --git a/tfvspt/pt/utils.py b/tfvspt/pt/utils.py deleted file mode 100644 index eb8207f..0000000 --- a/tfvspt/pt/utils.py +++ /dev/null @@ -1,74 +0,0 @@ -"""Pytorch Utils.""" - -import random - -import matplotlib.pyplot as plt -import numpy as np -import torch - - -def set_seed(seed: int): - """ - Sets the seed for random number generation in PyTorch, NumPy, and Python's random module. - Ensures reproducibility across different devices (CPU and GPU). - - Parameters: - seed (int): The seed value to be set. - """ - # Set the random seed for numpy, random, and torch - np.random.seed(seed) - random.seed(seed) - torch.manual_seed(seed) - - # Set the seed for CUDA (if using GPUs) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(seed) - - # Ensure deterministic results for CuDNN (can be slower) - torch.backends.cudnn.deterministic = True - torch.backends.cudnn.benchmark = False - - -def plot_images(dataloader) -> None: - - images, labels = next(iter(dataloader)) - - grid_size = int(len(images)**0.5) - fig, axes = plt.subplots(grid_size, grid_size, figsize=(10, 10)) - - for i, ax in enumerate(axes.flatten()): - if i < len(images): - img = images[i].numpy() - img = img.transpose((1, 2, 0)) - img = (img * 255).astype(np.uint8) - ax.imshow(img) - ax.axis('off') - - plt.show() - - -def plot_history(history: dict[str, list]) -> None: - - epochs = range(len(history['loss'])) - - # Plot accuracy - plt.figure(figsize=(12, 12)) - plt.subplot(2, 1, 1) - plt.plot(epochs, - history['accuracy'], - '-o', - label='Accuracy', - color='#1f77b4') - plt.xlabel('Epoch') - plt.ylabel('Accuracy') - plt.title('Training Accuracy') - - # Plot loss - plt.subplot(2, 1, 2) - plt.plot(epochs, history['loss'], '-o', label='Loss', color='#ff7f0e') - plt.xlabel('Epoch') - plt.ylabel('Loss') - plt.title('Training Loss') - - plt.tight_layout() - plt.show() diff --git a/tfvspt/tf/data.py b/tfvspt/tf/data.py index 92547a9..6b9175e 100644 --- a/tfvspt/tf/data.py +++ b/tfvspt/tf/data.py @@ -1,44 +1,47 @@ """Tensorflow Data Loader.""" -import numpy as np +import os + import tensorflow as tf from tensorflow.data import AUTOTUNE from tfvspt.config.config import Config -class Dataset: +class ClassificationDataset: def __init__(self, config: Config) -> None: self.config = config - @staticmethod - def get_data(): - return tf.keras.datasets.cifar100.load_data() - - def preprocess_data(self, image, label): - image = tf.cast(image, tf.float32) + def preprocess_data(self, path: str): + # read the image from disk, decode it, convert the data type to + # floating point, and resize it + image = tf.io.read_file(path) + image = tf.image.decode_jpeg(image, channels=3) + image = tf.image.resize(image, self.config.imgsz[:2]) image = tf.cast(image / 255.0, tf.float32) - label = tf.squeeze(tf.one_hot(label, self.config.n_classes), axis=0) + # parse the class label from the file path + label = tf.strings.split(path, os.path.sep)[-2] + label = tf.strings.to_number(label, tf.int32) + label = tf.one_hot(label, self.config.n_classes) return image, label - def load_dataset( + def get_dataloader( self, - images: np.ndarray, - labels: np.ndarray, + paths: list, shuffle: bool = False, repeat: bool = False, ): - # Create tf dataset - dataset = tf.data.Dataset.from_tensor_slices((images, labels)) - dataset = (dataset.map(self.preprocess_data, - num_parallel_calls=AUTOTUNE).cache()) + # Create tf dataloader + dataloader = tf.data.Dataset.from_tensor_slices(paths) + dataloader = (dataloader.map(self.preprocess_data, + num_parallel_calls=AUTOTUNE).cache()) # Shuffle if shuffle: - dataset = dataset.shuffle(images.shape[0]) + dataloader = dataloader.shuffle(len(paths)) # Batch - dataset = (dataset.batch(self.config.bs).prefetch(AUTOTUNE)) + dataloader = (dataloader.batch(self.config.bs).prefetch(AUTOTUNE)) # Repeat if repeat: - dataset = dataset.repeat() - return dataset + dataloader = dataloader.repeat() + return dataloader diff --git a/tfvspt/tf/main.py b/tfvspt/tf/main.py new file mode 100644 index 0000000..60dd187 --- /dev/null +++ b/tfvspt/tf/main.py @@ -0,0 +1,117 @@ +"""Train, Eval & Log Tensorflow training.""" + +import math +import random +import time + +import numpy as np +import tensorflow as tf + +from tfvspt.base import BenchmarkingBase +from tfvspt.tf.data import ClassificationDataset +from tfvspt.tf.model import get_model + + +class TensorflowBenchmarking(BenchmarkingBase): + + def set_seed(self, seed: int) -> None: + # Set random seed for Python + random.seed(seed) + # Set random seed for NumPy + np.random.seed(seed) + # Set random seed for TensorFlow + tf.random.set_seed(seed) + self.logger.info(f"{self.config.framework}, seed, {self.config.seed}") + + def load_dataloaders(self) -> dict: + dataset = ClassificationDataset(config=self.config) + st = time.time() + dataloaders = { + "train": + dataset.get_dataloader(self.raw_data["train"], + shuffle=True, + repeat=True), + "test": + dataset.get_dataloader(self.raw_data["test"], + shuffle=False, + repeat=False), + } + self._log_stats("data_loading_time", time.time() - st) + return dataloaders + + def plot_images(self, dataloader, name: str) -> None: + images, _ = next(iter(dataloader)) + images = images.numpy() + return super().plot_images(images, name) + + def get_callbacks(self) -> list: + # train history logger + csv_logger = tf.keras.callbacks.CSVLogger(str(self.config.output / + 'cifar100_training.csv'), + separator=",", + append=False) + return [csv_logger] + + def get_steps_per_epoch(self) -> None: + return math.ceil(len(self.raw_data["train"]) / self.config.bs) + + def train(self) -> None: + + # plot train images + self.plot_images(dataloader=self.dataloaders["train"], + name="train_data") + + # load model + st = time.time() + model = get_model(input_shape=self.config.imgsz, + n_classes=self.config.n_classes) + self._log_stats("model_building_time", time.time() - st) + self.logger.info(f"{self.config.framework}, {model.summary()}") + + # optimizer + optimizer = tf.keras.optimizers.Adam(learning_rate=self.config.lr) + # loss + loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True) + # compile model + model.compile( + optimizer=optimizer, + loss=loss, + metrics=["accuracy"], + ) + + # train model + st = time.time() + history = model.fit( + self.dataloaders["train"], + epochs=self.config.epochs, + shuffle=False, + steps_per_epoch=self.get_steps_per_epoch(), + callbacks=self.get_callbacks(), + ) + self._log_stats("training_time", time.time() - st) + + # plot the history + self.plot_history(history=history.history) + + # save model + st = time.time() + model.save(str(self.config.output / "cifar100.keras")) + self._log_stats("model_saving_time", time.time() - st) + + def eval(self) -> dict: + # plot test images + self.plot_images(dataloader=self.dataloaders["test"], name="test_data") + + # load model + st = time.time() + model = tf.keras.models.load_model( + str(self.config.output / "cifar100.keras")) + self._log_stats("model_loading_time", time.time() - st) + + # evaluate + st = time.time() + results = model.evaluate(self.dataloaders["test"], return_dict=True) + self.logger.info(f"{self.config.framework}, test results, {results}") + self._log_stats("eval_time", time.time() - st) + + return results diff --git a/tfvspt/tf/model.py b/tfvspt/tf/model.py index 404b711..477ea7b 100644 --- a/tfvspt/tf/model.py +++ b/tfvspt/tf/model.py @@ -8,22 +8,21 @@ def get_model( input_shape: tuple[int, int, int], n_classes: int, ): - model = models.Sequential([ - layers.InputLayer(input_shape), - layers.Conv2D(32, (3, 3), activation=None, padding='same'), - layers.BatchNormalization(), - layers.ReLU(), - layers.MaxPooling2D((2, 2)), - layers.Conv2D(64, (3, 3), activation=None, padding='same'), - layers.BatchNormalization(), - layers.ReLU(), - layers.MaxPooling2D((2, 2)), - layers.Conv2D(128, (3, 3), activation=None, padding='same'), - layers.BatchNormalization(), - layers.ReLU(), - layers.MaxPooling2D((2, 2)), - layers.GlobalAveragePooling2D(), + # backbone + channels = [32, 64, 128, 128] + layers_ = [layers.InputLayer(input_shape)] + for i in range(len(channels)): + layers_ += [ + layers.Conv2D(channels[i], (3, 3), activation=None, padding='same'), + layers.BatchNormalization(), + layers.ReLU(), + layers.MaxPooling2D((2, 2)), + ] + # global average pooling + layers_ += [layers.GlobalAveragePooling2D()] + # fc + layers_ += [ layers.Dense(128, activation='relu'), - layers.Dense(n_classes, activation=None) - ]) - return model + layers.Dense(n_classes, activation=None), + ] + return models.Sequential(layers_) diff --git a/tfvspt/tf/notebooks/eval.ipynb b/tfvspt/tf/notebooks/eval.ipynb deleted file mode 100644 index e48b13a..0000000 --- a/tfvspt/tf/notebooks/eval.ipynb +++ /dev/null @@ -1,241 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e24f858d-a269-4008-ab34-22d978d58fe4", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "30837142-3340-4691-b3f1-6bbc90684a5a", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-03-20 10:57:26.176712: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2025-03-20 10:57:26.185102: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1742448446.195011 13280 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1742448446.198024 13280 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1742448446.205860 13280 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742448446.205874 13280 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742448446.205874 13280 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742448446.205875 13280 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "2025-03-20 10:57:26.208887: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" - ] - } - ], - "source": [ - "from pathlib import Path\n", - "import time\n", - "\n", - "import tensorflow as tf\n", - "\n", - "from tfvspt.config.config import get_config\n", - "from tfvspt.tf.data import Dataset\n", - "from tfvspt.tf.utils import plot_images" - ] - }, - { - "cell_type": "markdown", - "id": "2623d158-d3d3-456f-9a82-1d0efc6f4305", - "metadata": {}, - "source": [ - "## Config" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "01a9dd40-2ddb-488e-a94c-d54b39155453", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Config(seed=25, n_classes=100, bs=32, imgsz=(32, 32), lr=0.001, epochs=20, num_workers=0, output='./output')" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "config = get_config(\"../../assets/config.yaml\")\n", - "config" - ] - }, - { - "cell_type": "markdown", - "id": "4c9902ce-980e-42a5-adfc-5898661dfb6b", - "metadata": {}, - "source": [ - "## Test Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "11a230a1-4e6c-4a1d-a0a8-32880de45517", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Testdata size: 10000\n" - ] - } - ], - "source": [ - "dataset = Dataset(config=config)\n", - "(x_train, y_train), (x_test, y_test) = Dataset.get_data()\n", - "print(f\"Testdata size: {len(x_test)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "5f7875ac-bf8a-48b0-9859-da19e823a406", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[TF] Dataset loading time: 0.11871790885925293\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-03-20 10:57:28.636985: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n" - ] - } - ], - "source": [ - "st = time.time()\n", - "test_ds = dataset.load_dataset(x_test, y_test, shuffle=False, repeat=False)\n", - "print(f\"[TF] Dataset loading time: {time.time() - st}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "134784fb-bf0c-4116-b532-c0be96860e1e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-03-20 10:57:28.754776: W tensorflow/core/kernels/data/cache_dataset_ops.cc:916] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_images(test_ds)" - ] - }, - { - "cell_type": "markdown", - "id": "9ef44a6a-45f8-4584-8a9c-af117212ef1b", - "metadata": {}, - "source": [ - "## Load Model" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d4e172c6-ea49-476b-9eae-eaf55a0f8599", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[TF] Model Loading time: 0.1349625587463379\n" - ] - } - ], - "source": [ - "st = time.time()\n", - "model_path = Path(config.output) / \"tf/cifar100.keras\"\n", - "model = tf.keras.models.load_model(str(model_path))\n", - "print(f\"[TF] Model Loading time: {time.time() - st}\")" - ] - }, - { - "cell_type": "markdown", - "id": "a0ea6349-334c-459a-80c0-59bed6eccb43", - "metadata": {}, - "source": [ - "## Eval" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c611662e-bf5c-4a32-8ceb-ce79eea9991c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.4103 - loss: 2.3972\n", - "[TF] Evaluation Results: {'accuracy': 0.4090000092983246, 'loss': 2.411944627761841}\n", - "[TF] Evaluation time: 1.4030342102050781\n" - ] - } - ], - "source": [ - "st = time.time()\n", - "results = model.evaluate(test_ds, return_dict=True)\n", - "print(f\"[TF] Evaluation Results: {results}\")\n", - "print(f\"[TF] Evaluation time: {time.time() - st}\")" - ] - } - ], - "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": "3.10.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tfvspt/tf/notebooks/train.ipynb b/tfvspt/tf/notebooks/train.ipynb deleted file mode 100644 index ff23caa..0000000 --- a/tfvspt/tf/notebooks/train.ipynb +++ /dev/null @@ -1,531 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-03-20 09:33:35.601420: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2025-03-20 09:33:35.617409: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "E0000 00:00:1742443415.635509 6526 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "E0000 00:00:1742443415.641158 6526 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "W0000 00:00:1742443415.655443 6526 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742443415.655466 6526 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742443415.655468 6526 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "W0000 00:00:1742443415.655469 6526 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", - "2025-03-20 09:33:35.660348: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" - ] - } - ], - "source": [ - "import math\n", - "from pathlib import Path\n", - "import time\n", - "\n", - "import tensorflow as tf\n", - "\n", - "from tfvspt.config.config import get_config\n", - "from tfvspt.tf.data import Dataset\n", - "from tfvspt.tf.model import get_model\n", - "from tfvspt.tf.utils import plot_history\n", - "from tfvspt.tf.utils import plot_images\n", - "from tfvspt.tf.utils import set_seed" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Read Config" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Config(seed=25, n_classes=100, bs=32, imgsz=(32, 32), lr=0.001, epochs=20, num_workers=0, output='./output')" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "config = get_config(\"../../assets/config.yaml\")\n", - "config" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set Seed" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "set_seed(config.seed)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dataset\n", - "\n", - "### Name: CIFAR100" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Traindata size: 50000\n" - ] - } - ], - "source": [ - "dataset = Dataset(config=config)\n", - "(x_train, y_train), (x_test, y_test) = Dataset.get_data()\n", - "print(f\"Traindata size: {len(x_train)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[TF] Dataset loading time: 0.5471243858337402\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-03-20 09:33:39.616167: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n" - ] - } - ], - "source": [ - "st = time.time()\n", - "train_ds = dataset.load_dataset(x_train, y_train, shuffle=True, repeat=True)\n", - "print(f\"[TF] Dataset loading time: {time.time() - st}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_images(train_ds)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[TF] Model loading time: 0.09880900382995605\n" - ] - }, - { - "data": { - "text/html": [ - "
Model: \"sequential\"\n",
-       "
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ conv2d (Conv2D)                 │ (None, 32, 32, 32)     │           896 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ batch_normalization             │ (None, 32, 32, 32)     │           128 │\n",
-       "│ (BatchNormalization)            │                        │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ re_lu (ReLU)                    │ (None, 32, 32, 32)     │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling2d (MaxPooling2D)    │ (None, 16, 16, 32)     │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ conv2d_1 (Conv2D)               │ (None, 16, 16, 64)     │        18,496 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ batch_normalization_1           │ (None, 16, 16, 64)     │           256 │\n",
-       "│ (BatchNormalization)            │                        │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ re_lu_1 (ReLU)                  │ (None, 16, 16, 64)     │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling2d_1 (MaxPooling2D)  │ (None, 8, 8, 64)       │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ conv2d_2 (Conv2D)               │ (None, 8, 8, 128)      │        73,856 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ batch_normalization_2           │ (None, 8, 8, 128)      │           512 │\n",
-       "│ (BatchNormalization)            │                        │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ re_lu_2 (ReLU)                  │ (None, 8, 8, 128)      │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling2d_2 (MaxPooling2D)  │ (None, 4, 4, 128)      │             0 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ global_average_pooling2d        │ (None, 128)            │             0 │\n",
-       "│ (GlobalAveragePooling2D)        │                        │               │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense (Dense)                   │ (None, 128)            │        16,512 │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_1 (Dense)                 │ (None, 100)            │        12,900 │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-       "
\n" - ], - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", - "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n", - "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ re_lu (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n", - "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ re_lu_1 (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n", - "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ re_lu_2 (\u001b[38;5;33mReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ global_average_pooling2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", - "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m16,512\u001b[0m │\n", - "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", - "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m) │ \u001b[38;5;34m12,900\u001b[0m │\n", - "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Total params: 123,556 (482.64 KB)\n",
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 Trainable params: 123,108 (480.89 KB)\n",
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\n" - ], - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m123,108\u001b[0m (480.89 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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\n" - ], - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m448\u001b[0m (1.75 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "st = time.time()\n", - "model = get_model(input_shape=(*config.imgsz, 3), n_classes=config.n_classes)\n", - "print(f\"[TF] Model loading time: {time.time() - st}\")\n", - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "output_path = Path(config.output) / \"tf\"\n", - "output_path.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Optimizer\n", - "optimizer = tf.keras.optimizers.Adam(learning_rate=config.lr)\n", - "\n", - "# Loss\n", - "loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True)\n", - "\n", - "# Model\n", - "model.compile(\n", - " optimizer=optimizer,\n", - " loss=loss,\n", - " metrics=[\"accuracy\"],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Training history logger\n", - "csv_logger = tf.keras.callbacks.CSVLogger(\n", - " str(output_path / 'cifar100_training.csv'), \n", - " separator=\",\", \n", - " append=False\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 14ms/step - accuracy: 0.0956 - loss: 3.9481\n", - "Epoch 2/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 22ms/step - accuracy: 0.2351 - loss: 3.0588\n", - "Epoch 3/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 25ms/step - accuracy: 0.3013 - loss: 2.7306\n", - "Epoch 4/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 24ms/step - accuracy: 0.3481 - loss: 2.5203\n", - "Epoch 5/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 23ms/step - accuracy: 0.3740 - loss: 2.3673\n", - "Epoch 6/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 25ms/step - accuracy: 0.4058 - loss: 2.2271\n", - "Epoch 7/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 24ms/step - accuracy: 0.4288 - loss: 2.1251\n", - "Epoch 8/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 24ms/step - accuracy: 0.4437 - loss: 2.0398\n", - "Epoch 9/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 24ms/step - accuracy: 0.4622 - loss: 1.9564\n", - "Epoch 10/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.4756 - loss: 1.9049\n", - "Epoch 11/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.4942 - loss: 1.8354\n", - "Epoch 12/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5080 - loss: 1.7642\n", - "Epoch 13/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5206 - loss: 1.7127\n", - "Epoch 14/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5315 - loss: 1.6764\n", - "Epoch 15/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5423 - loss: 1.6238\n", - "Epoch 16/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5532 - loss: 1.5803\n", - "Epoch 17/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5636 - loss: 1.5357\n", - "Epoch 18/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5789 - loss: 1.4826\n", - "Epoch 19/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5816 - loss: 1.4595\n", - "Epoch 20/20\n", - "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 23ms/step - accuracy: 0.5957 - loss: 1.4119\n", - "[TF] Training time: 716.1055512428284\n" - ] - } - ], - "source": [ - "st = time.time()\n", - "history = model.fit(\n", - " train_ds,\n", - " epochs=config.epochs,\n", - " shuffle=False,\n", - " steps_per_epoch=math.ceil(len(x_train)/config.bs),\n", - " callbacks=[csv_logger],\n", - ")\n", - "print(f\"[TF] Training time: {time.time() - st}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualize Training History" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_history(history)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Save Model" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[TF] Model Saving time: 0.0896461009979248\n" - ] - } - ], - "source": [ - "st = time.time()\n", - "model.save(str(output_path / \"cifar100.keras\"))\n", - "print(f\"[TF] Model Saving time: {time.time() - st}\")" - ] - } - ], - "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": "3.10.16" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tfvspt/tf/utils.py b/tfvspt/tf/utils.py deleted file mode 100644 index a5db3ab..0000000 --- a/tfvspt/tf/utils.py +++ /dev/null @@ -1,60 +0,0 @@ -"""Tensorflow Utils.""" - -import random - -import matplotlib.pyplot as plt -import numpy as np -import tensorflow as tf - - -def plot_images(dataset) -> None: - - images, labels = next(iter(dataset)) - - grid_size = int(len(images)**0.5) - fig, axes = plt.subplots(grid_size, grid_size, figsize=(10, 10)) - - for i, ax in enumerate(axes.flatten()): - if i < len(images): - img = images[i].numpy() - img = (img * 255).astype(np.uint8) - ax.imshow(img) - ax.axis('off') - - plt.show() - - -def plot_history(history) -> None: - - epochs = len(history.history['accuracy']) - - plt.figure(figsize=(15, 5)) - plt.plot(np.arange(epochs), - history.history['accuracy'], - '-o', - label='Train Accuracy', - color='#ff7f0e') - plt.ylabel('Accuracy', size=14) - plt.xlabel('Epoch', size=14) - plt.title("Training Accuracy") - - plt.figure(figsize=(15, 5)) - plt.plot(np.arange(epochs), - history.history['loss'], - '-o', - label='Train Loss', - color='#1f77b4') - plt.ylabel('Loss', size=14) - plt.xlabel('Epoch', size=14) - plt.title("Training Loss") - - plt.show() - - -def set_seed(seed: int) -> None: - # Set random seed for Python - random.seed(seed) - # Set random seed for NumPy - np.random.seed(seed) - # Set random seed for TensorFlow - tf.random.set_seed(seed) diff --git a/tfvspt/utils.py b/tfvspt/utils.py new file mode 100644 index 0000000..a121cb3 --- /dev/null +++ b/tfvspt/utils.py @@ -0,0 +1,14 @@ +"""Common Utils.""" + +from typing import Any + +import yaml + + +def read_yaml(path: str) -> dict: + return yaml.safe_load(open(path)) + + +def save_yaml(data: Any, path: str) -> None: + with open(path, 'w') as file: + yaml.dump(data, file, default_flow_style=False) From 520412a15fe71568ab4daae5a1df0a826b545d6d Mon Sep 17 00:00:00 2001 From: "meet.ranoliya" Date: Mon, 24 Mar 2025 10:11:54 +0530 Subject: [PATCH 7/8] Add: flip augmentations --- tfvspt/pt/main.py | 5 ++++- tfvspt/tf/data.py | 10 ++++++++++ tfvspt/tf/main.py | 2 ++ 3 files changed, 16 insertions(+), 1 deletion(-) diff --git a/tfvspt/pt/main.py b/tfvspt/pt/main.py index 01219d5..bf51483 100644 --- a/tfvspt/pt/main.py +++ b/tfvspt/pt/main.py @@ -39,7 +39,10 @@ def set_seed(self, seed): def get_transforms(self, data: str): transforms_ = [] if data == "train": - transforms_ += [] + transforms_ += [ + transforms.RandomHorizontalFlip(), + transforms.RandomVerticalFlip(), + ] transforms_ += [ transforms.Resize(self.config.imgsz[:2]), transforms.ToTensor(), diff --git a/tfvspt/tf/data.py b/tfvspt/tf/data.py index 6b9175e..781044b 100644 --- a/tfvspt/tf/data.py +++ b/tfvspt/tf/data.py @@ -26,9 +26,15 @@ def preprocess_data(self, path: str): label = tf.one_hot(label, self.config.n_classes) return image, label + def augment_data(self, image, label): + image = tf.image.random_flip_left_right(image) + image = tf.image.random_flip_up_down(image) + return image, label + def get_dataloader( self, paths: list, + augment: bool = False, shuffle: bool = False, repeat: bool = False, ): @@ -36,6 +42,10 @@ def get_dataloader( dataloader = tf.data.Dataset.from_tensor_slices(paths) dataloader = (dataloader.map(self.preprocess_data, num_parallel_calls=AUTOTUNE).cache()) + # Augment + if augment: + dataloader = dataloader.map(self.augment_data, + num_parallel_calls=AUTOTUNE) # Shuffle if shuffle: dataloader = dataloader.shuffle(len(paths)) diff --git a/tfvspt/tf/main.py b/tfvspt/tf/main.py index 60dd187..9b9e7fb 100644 --- a/tfvspt/tf/main.py +++ b/tfvspt/tf/main.py @@ -29,10 +29,12 @@ def load_dataloaders(self) -> dict: dataloaders = { "train": dataset.get_dataloader(self.raw_data["train"], + augment=True, shuffle=True, repeat=True), "test": dataset.get_dataloader(self.raw_data["test"], + augment=False, shuffle=False, repeat=False), } From 7d6d38ddb260cf01cca1213a9f34cdb0ddbe5e86 Mon Sep 17 00:00:00 2001 From: "meet.ranoliya" Date: Mon, 24 Mar 2025 17:46:06 +0530 Subject: [PATCH 8/8] Add: caching in pytorch dataset --- pyproject.toml | 1 + tfvspt/pt/data.py | 28 +++++++++++++++++++++++++--- tfvspt/pt/main.py | 23 ++++++++++++----------- 3 files changed, 38 insertions(+), 14 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 4e2690c..2844683 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,6 +20,7 @@ dependencies = [ "notebook >= 7.3.3", "pydantic >= 2.10.6", "torchinfo >= 1.8.0", + "stocaching >= 0.2.0", "tensorflow >= 2.19.0", "matplotlib >= 3.10.1", "torchvision >= 0.21.0", diff --git a/tfvspt/pt/data.py b/tfvspt/pt/data.py index 8829045..f547e4e 100644 --- a/tfvspt/pt/data.py +++ b/tfvspt/pt/data.py @@ -3,6 +3,9 @@ import os from PIL import Image +from stocaching import get_shm_size +from stocaching import SharedCache +import torch from torch.utils.data import Dataset from tfvspt.config.config import Config @@ -15,18 +18,37 @@ def __init__( config: Config, paths: list[str], transforms=None, + augmentations=None, + cache: bool = False, ) -> None: self.config = config self.paths = paths self.transforms = transforms + self.augmentations = augmentations + self.cache = None + if cache: + self.cache = SharedCache( + size_limit_gib=get_shm_size(), + dataset_len=len(self.paths), + data_dims=(self.config.imgsz[-1], *self.config.imgsz[:-1]), + dtype=torch.float32, + ) def __len__(self) -> None: return len(self.paths) def __getitem__(self, idx: int): path = self.paths[idx] - image = Image.open(path).convert('RGB') label = int(path.split(os.path.sep)[-2]) - if self.transforms: - image = self.transforms(image) + image = None + if self.cache: + image = self.cache.get_slot(idx) + if image is None: + image = Image.open(path).convert('RGB') + if self.transforms: + image = self.transforms(image) + if self.cache: + self.cache.set_slot(idx, image) + if self.augmentations: + image = self.augmentations(image) return image, label diff --git a/tfvspt/pt/main.py b/tfvspt/pt/main.py index bf51483..d54285a 100644 --- a/tfvspt/pt/main.py +++ b/tfvspt/pt/main.py @@ -36,24 +36,25 @@ def set_seed(self, seed): torch.backends.cudnn.benchmark = False self.logger.info(f"{self.config.framework}, seed, {self.config.seed}") - def get_transforms(self, data: str): - transforms_ = [] - if data == "train": - transforms_ += [ - transforms.RandomHorizontalFlip(), - transforms.RandomVerticalFlip(), - ] - transforms_ += [ + def get_transforms(self): + return transforms.Compose([ transforms.Resize(self.config.imgsz[:2]), transforms.ToTensor(), - ] - return transforms.Compose(transforms_) + ]) + + def get_augmentations(self): + return transforms.Compose([ + transforms.RandomHorizontalFlip(), + transforms.RandomVerticalFlip(), + ]) def _get_dataset(self, data: str) -> ClassificationDataset: return ClassificationDataset( config=self.config, paths=self.raw_data[data], - transforms=self.get_transforms(data), + transforms=self.get_transforms(), + augmentations=self.get_augmentations() if data == "train" else None, + cache=data == "train", ) def load_dataloaders(self) -> dict: