166 lines
4.0 KiB
Plaintext
166 lines
4.0 KiB
Plaintext
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"name": "Numpy_Vs_Torch_object_computation_speed_comparison.ipynb",
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"provenance": [],
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"collapsed_sections": [],
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"include_colab_link": true
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.6"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/PacktPublishing/Hands-On-Computer-Vision-with-PyTorch/blob/master/Chapter02/Numpy_Vs_Torch_object_computation_speed_comparison.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "28AUPOlYcu3r"
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},
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"source": [
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"import torch\n",
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"x = torch.rand(1, 6400)\n",
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"y = torch.rand(6400, 5000)"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Sv_d4T5wcwyd"
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},
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"source": [
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"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
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"assert device == 'cuda', \"This exercise assumes the notebook is on a GPU machine\""
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Xc8oysTVczCG"
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},
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"source": [
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"x, y = x.to(device), y.to(device)"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "jdzaTd7Rc0fc",
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"outputId": "70159e60-eacb-4855-c791-a95de753f494",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 51
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}
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},
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"source": [
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"%timeit z=(x@y)"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"The slowest run took 22.35 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
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"10000 loops, best of 3: 974 µs per loop\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "B7nijUnjc2BC",
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"outputId": "047a5c2f-58c5-4567-c3e9-14734d056bd8",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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}
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},
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"source": [
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"x, y = x.cpu(), y.cpu()\n",
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"%timeit z=(x@y)"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"100 loops, best of 3: 9.4 ms per loop\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "wEGUO99Ec3Z4",
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"outputId": "11cd14b4-5c89-4ea3-d43a-104d7ceb3370",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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}
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},
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"source": [
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"import numpy as np\n",
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"x = np.random.random((1, 6400))\n",
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"y = np.random.random((6400, 5000))\n",
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"%timeit z = np.matmul(x,y)"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"10 loops, best of 3: 19.9 ms per loop\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "nczGQXKqc6H0"
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},
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"source": [
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""
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],
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"execution_count": null,
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"outputs": []
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}
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]
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}
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