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| -rw-r--r-- | notebooks/04-quantizer.ipynb | 295 |
1 files changed, 0 insertions, 295 deletions
diff --git a/notebooks/04-quantizer.ipynb b/notebooks/04-quantizer.ipynb deleted file mode 100644 index 66c9c26..0000000 --- a/notebooks/04-quantizer.ipynb +++ /dev/null @@ -1,295 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "7c02ae76-b540-4b16-9492-e9210b3b9249", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.environ['CUDA_VISIBLE_DEVICE'] = ''\n", - "import random\n", - "\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import numpy as np\n", - "from omegaconf import OmegaConf\n", - "\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "from importlib.util import find_spec\n", - "if find_spec(\"text_recognizer\") is None:\n", - " import sys\n", - " sys.path.append('..')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ccdb6dde-47e5-429a-88f2-0764fb7e259a", - "metadata": {}, - "outputs": [], - "source": [ - "from hydra import compose, initialize\n", - "from omegaconf import OmegaConf\n", - "from hydra.utils import instantiate" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3cf50475-39f2-4642-a7d1-5bcbc0a036f7", - "metadata": {}, - "outputs": [], - "source": [ - "path = \"../training/conf/network/quantizer.yaml\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e52ecb01-c975-4e55-925d-1182c7aea473", - "metadata": {}, - "outputs": [], - "source": [ - "with open(path, \"rb\") as f:\n", - " cfg = OmegaConf.load(f)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f939aa37-7b1d-45cc-885c-323c4540bda1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'_target_': 'text_recognizer.networks.quantizer.quantizer.VectorQuantizer', 'input_dim': 192, 'codebook': {'_target_': 'text_recognizer.networks.quantizer.codebook.CosineSimilarityCodebook', 'dim': 16, 'codebook_size': 2048, 'kmeans_init': True, 'kmeans_iters': 10, 'decay': 0.8, 'eps': 1e-05, 'threshold_dead': 2}, 'commitment': 1.0}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cfg" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "aaeab329-aeb0-4a1b-aa35-5a2aab81b1d0", - "metadata": {}, - "outputs": [], - "source": [ - "vq = instantiate(cfg)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "7a0c9f4f-3d95-4722-9212-915a4b9ed096", - "metadata": {}, - "outputs": [], - "source": [ - "import torch" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "284ec8aa-43a0-4e59-a86f-91bab6c97dca", - "metadata": {}, - "outputs": [], - "source": [ - "t = torch.randn(2, 192, 18, 20)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "d3f6dad0-33f9-4f80-b514-dd0f71c8b93a", - "metadata": {}, - "outputs": [], - "source": [ - "(tt, ii, l) = vq(t)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "80fd228a-d9a2-4334-ab26-c283d215a456", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([2, 192, 18, 20])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tt.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "22d04553-fd12-4f6c-8a43-9105083b0b82", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([2, 192, 360])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tt.flatten(start_dim=2).shape" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "b79649ce-3623-4dd2-9a38-bef7bc5c9ac1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[ 484, 223, 752, 735, 199, 1428, 238, 65, 1357, 950, 1792, 87,\n", - " 1006, 264, 1425, 357, 375, 131, 958, 807, 1903, 577, 552, 104,\n", - " 278, 495, 578, 1415, 1737, 593, 1442, 269, 62, 1274, 346, 667,\n", - " 1296, 421, 455, 1151, 475, 880, 1112, 279, 44, 171, 101, 638,\n", - " 719, 1502, 185, 103, 157, 802, 123, 30, 877, 691, 92, 151,\n", - 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{ - "cell_type": "code", - "execution_count": null, - "id": "25759b7b-8deb-4163-b75d-a1357c9fe88f", - "metadata": {}, - "outputs": [], - "source": [ - "summary(net, (2, 1, 224, 224), device=\"cpu\", depth=4)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6aa04f07-12d4-4e06-b921-d54367c50a9a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} |