diff options
Diffstat (limited to 'src/notebooks/00-testing-stuff-out.ipynb')
| -rw-r--r-- | src/notebooks/00-testing-stuff-out.ipynb | 639 |
1 files changed, 617 insertions, 22 deletions
diff --git a/src/notebooks/00-testing-stuff-out.ipynb b/src/notebooks/00-testing-stuff-out.ipynb index 49ca4c4..3f008c3 100644 --- a/src/notebooks/00-testing-stuff-out.ipynb +++ b/src/notebooks/00-testing-stuff-out.ipynb @@ -2,11 +2,120 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from PIL import Image\n", + "import torch\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, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.cuda.is_available()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.nn.modules.activation.SELU" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.nn.SELU" + ] + }, + { + "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "import torch" + "a = \"nNone\"" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "b = a or \"relu\"" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'nnone'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b.lower()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'nNone'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b" ] }, { @@ -986,28 +1095,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 51, "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'tqdm.auto.tqdm'; 'tqdm.auto' is not a package", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m<ipython-input-20-68e3c8bf3e1f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtqdm\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtqdm_auto\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tqdm.auto.tqdm'; 'tqdm.auto' is not a package" - ] - } - ], + "outputs": [], "source": [ - "import tqdm.auto.tqdm as tqdm_auto" + "import tqdm" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -1016,7 +1113,7 @@ "tqdm.notebook.tqdm_notebook" ] }, - "execution_count": 19, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -1027,25 +1124,50 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tqdm.auto.tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "def test():\n", - " for i in range(9):\n", + " for i in tqdm.auto.tqdm(range(9)):\n", " pass\n", - " print(i)" + " print(i)\n", + " " ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 55, "metadata": {}, "outputs": [ { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e1d3b25d4ee141e882e316ec54e79d60", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=9.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { "name": "stdout", "output_type": "stream", "text": [ + "\n", "8\n" ] } @@ -1056,6 +1178,479 @@ }, { "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "from time import sleep" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "41b743273ce14236bcb65782dbcd2e75", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "pbar = tqdm.auto.tqdm([\"a\", \"b\", \"c\", \"d\"], leave=True)\n", + "for char in pbar:\n", + " pbar.set_description(\"Processing %s\" % char)\n", + "# pbar.set_prefix()\n", + " sleep(0.25)\n", + "pbar.set_postfix({\"hej\": 0.32})" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "pbar.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cb5ad8d6109f4b1495b8fc7422bafd01", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "with tqdm.auto.tqdm(total=10, bar_format=\"{postfix[0]} {postfix[1][value]:>8.2g}\",\n", + " postfix=[\"Batch\", dict(value=0)]) as t:\n", + " for i in range(10):\n", + " sleep(0.1)\n", + "# t.postfix[2][\"value\"] = 3 \n", + " t.postfix[1][\"value\"] = i / 2\n", + " t.update()" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0b341d49ad074823881e84a538bcad0c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "with tqdm.auto.tqdm(total=100, leave=True) as pbar:\n", + " for i in range(2):\n", + " for i in range(10):\n", + " sleep(0.1)\n", + " pbar.update(10)\n", + " pbar.set_postfix({\"adaf\": 23})\n", + " pbar.set_postfix({\"hej\": 0.32})\n", + " pbar.reset()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from text_recognizer.networks.residual_network import IdentityBlock, ResidualBlock, BasicBlock, BottleNeckBlock, ResidualLayer, Encoder, ResidualNetwork" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "IdentityBlock(\n", + " (blocks): Identity()\n", + " (activation_fn): ReLU(inplace=True)\n", + " (shortcut): Identity()\n", + ")" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IdentityBlock(32, 64)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ResidualBlock(\n", + " (blocks): Identity()\n", + " (activation_fn): ReLU(inplace=True)\n", + " (shortcut): Sequential(\n", + " (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + ")" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ResidualBlock(32, 64)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BasicBlock(\n", + " (blocks): Sequential(\n", + " (0): Sequential(\n", + " (0): Conv2dAuto(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " (1): ReLU(inplace=True)\n", + " (2): Sequential(\n", + " (0): Conv2dAuto(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (activation_fn): ReLU(inplace=True)\n", + " (shortcut): Sequential(\n", + " (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + ")\n" + ] + } + ], + "source": [ + "dummy = torch.ones((1, 32, 224, 224))\n", + "\n", + "block = BasicBlock(32, 64)\n", + "block(dummy).shape\n", + "print(block)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BottleNeckBlock(\n", + " (blocks): Sequential(\n", + " (0): Sequential(\n", + " (0): Conv2dAuto(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " (1): ReLU(inplace=True)\n", + " (2): Sequential(\n", + " (0): Conv2dAuto(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " (3): ReLU(inplace=True)\n", + " (4): Sequential(\n", + " (0): Conv2dAuto(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (activation_fn): ReLU(inplace=True)\n", + " (shortcut): Sequential(\n", + " (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + ")\n" + ] + } + ], + "source": [ + "dummy = torch.ones((1, 32, 10, 10))\n", + "\n", + "block = BottleNeckBlock(32, 64)\n", + "block(dummy).shape\n", + "print(block)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 128, 24, 24])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dummy = torch.ones((1, 64, 48, 48))\n", + "\n", + "layer = ResidualLayer(64, 128, block=BasicBlock, num_blocks=3)\n", + "layer(dummy).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(64, 128), (128, 256), (256, 512)]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "blocks_sizes=[64, 128, 256, 512]\n", + "list(zip(blocks_sizes, blocks_sizes[1:]))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "e = Encoder(depths=[1, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "from torchsummary import summary" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------\n", + " Layer (type) Output Shape Param #\n", + "================================================================\n", + " Conv2d-1 [-1, 32, 15, 15] 800\n", + " BatchNorm2d-2 [-1, 32, 15, 15] 64\n", + " ReLU-3 [-1, 32, 15, 15] 0\n", + " MaxPool2d-4 [-1, 32, 8, 8] 0\n", + " Conv2dAuto-5 [-1, 32, 8, 8] 9,216\n", + " BatchNorm2d-6 [-1, 32, 8, 8] 64\n", + " ReLU-7 [-1, 32, 8, 8] 0\n", + " ReLU-8 [-1, 32, 8, 8] 0\n", + " Conv2dAuto-9 [-1, 32, 8, 8] 9,216\n", + " BatchNorm2d-10 [-1, 32, 8, 8] 64\n", + " ReLU-11 [-1, 32, 8, 8] 0\n", + " ReLU-12 [-1, 32, 8, 8] 0\n", + " BasicBlock-13 [-1, 32, 8, 8] 0\n", + " ResidualLayer-14 [-1, 32, 8, 8] 0\n", + " Conv2d-15 [-1, 64, 4, 4] 2,048\n", + " BatchNorm2d-16 [-1, 64, 4, 4] 128\n", + " Conv2dAuto-17 [-1, 64, 4, 4] 18,432\n", + " BatchNorm2d-18 [-1, 64, 4, 4] 128\n", + " ReLU-19 [-1, 64, 4, 4] 0\n", + " ReLU-20 [-1, 64, 4, 4] 0\n", + " Conv2dAuto-21 [-1, 64, 4, 4] 36,864\n", + " BatchNorm2d-22 [-1, 64, 4, 4] 128\n", + " ReLU-23 [-1, 64, 4, 4] 0\n", + " ReLU-24 [-1, 64, 4, 4] 0\n", + " BasicBlock-25 [-1, 64, 4, 4] 0\n", + " ResidualLayer-26 [-1, 64, 4, 4] 0\n", + "================================================================\n", + "Total params: 77,152\n", + "Trainable params: 77,152\n", + "Non-trainable params: 0\n", + "----------------------------------------------------------------\n", + "Input size (MB): 0.00\n", + "Forward/backward pass size (MB): 0.43\n", + "Params size (MB): 0.29\n", + "Estimated Total Size (MB): 0.73\n", + "----------------------------------------------------------------\n" + ] + } + ], + "source": [ + "summary(e, (1, 28, 28), device=\"cpu\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "resnet = ResidualNetwork(1, 80, activation=\"selu\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------\n", + " Layer (type) Output Shape Param #\n", + "================================================================\n", + " Conv2d-1 [-1, 32, 15, 15] 800\n", + " BatchNorm2d-2 [-1, 32, 15, 15] 64\n", + " SELU-3 [-1, 32, 15, 15] 0\n", + " MaxPool2d-4 [-1, 32, 8, 8] 0\n", + " Conv2dAuto-5 [-1, 32, 8, 8] 9,216\n", + " BatchNorm2d-6 [-1, 32, 8, 8] 64\n", + " SELU-7 [-1, 32, 8, 8] 0\n", + " SELU-8 [-1, 32, 8, 8] 0\n", + " Conv2dAuto-9 [-1, 32, 8, 8] 9,216\n", + " BatchNorm2d-10 [-1, 32, 8, 8] 64\n", + " SELU-11 [-1, 32, 8, 8] 0\n", + " SELU-12 [-1, 32, 8, 8] 0\n", + " BasicBlock-13 [-1, 32, 8, 8] 0\n", + " Conv2dAuto-14 [-1, 32, 8, 8] 9,216\n", + " BatchNorm2d-15 [-1, 32, 8, 8] 64\n", + " SELU-16 [-1, 32, 8, 8] 0\n", + " SELU-17 [-1, 32, 8, 8] 0\n", + " Conv2dAuto-18 [-1, 32, 8, 8] 9,216\n", + " BatchNorm2d-19 [-1, 32, 8, 8] 64\n", + " SELU-20 [-1, 32, 8, 8] 0\n", + " SELU-21 [-1, 32, 8, 8] 0\n", + " BasicBlock-22 [-1, 32, 8, 8] 0\n", + " ResidualLayer-23 [-1, 32, 8, 8] 0\n", + " Conv2d-24 [-1, 64, 4, 4] 2,048\n", + " BatchNorm2d-25 [-1, 64, 4, 4] 128\n", + " Conv2dAuto-26 [-1, 64, 4, 4] 18,432\n", + " BatchNorm2d-27 [-1, 64, 4, 4] 128\n", + " SELU-28 [-1, 64, 4, 4] 0\n", + " SELU-29 [-1, 64, 4, 4] 0\n", + " Conv2dAuto-30 [-1, 64, 4, 4] 36,864\n", + " BatchNorm2d-31 [-1, 64, 4, 4] 128\n", + " SELU-32 [-1, 64, 4, 4] 0\n", + " SELU-33 [-1, 64, 4, 4] 0\n", + " BasicBlock-34 [-1, 64, 4, 4] 0\n", + " Conv2dAuto-35 [-1, 64, 4, 4] 36,864\n", + " BatchNorm2d-36 [-1, 64, 4, 4] 128\n", + " SELU-37 [-1, 64, 4, 4] 0\n", + " SELU-38 [-1, 64, 4, 4] 0\n", + " Conv2dAuto-39 [-1, 64, 4, 4] 36,864\n", + " BatchNorm2d-40 [-1, 64, 4, 4] 128\n", + " SELU-41 [-1, 64, 4, 4] 0\n", + " SELU-42 [-1, 64, 4, 4] 0\n", + " BasicBlock-43 [-1, 64, 4, 4] 0\n", + " ResidualLayer-44 [-1, 64, 4, 4] 0\n", + " Encoder-45 [-1, 64, 4, 4] 0\n", + " Reduce-46 [-1, 64] 0\n", + " Linear-47 [-1, 80] 5,200\n", + " Decoder-48 [-1, 80] 0\n", + "================================================================\n", + "Total params: 174,896\n", + "Trainable params: 174,896\n", + "Non-trainable params: 0\n", + "----------------------------------------------------------------\n", + "Input size (MB): 0.00\n", + "Forward/backward pass size (MB): 0.65\n", + "Params size (MB): 0.67\n", + "Estimated Total Size (MB): 1.32\n", + "----------------------------------------------------------------\n" + ] + } + ], + "source": [ + "summary(resnet, (1, 28, 28), device=\"cpu\")" + ] + }, + { + "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], |