From f4688482b4898c0b342d6ae59839dc27fbf856c6 Mon Sep 17 00:00:00 2001 From: Gustaf Rydholm Date: Thu, 13 May 2021 23:02:42 +0200 Subject: Remove bloat packages --- notebooks/00-scratch-pad.ipynb | 268 +++++++++++++++++++++++++++++++++++------ 1 file changed, 231 insertions(+), 37 deletions(-) (limited to 'notebooks/00-scratch-pad.ipynb') diff --git a/notebooks/00-scratch-pad.ipynb b/notebooks/00-scratch-pad.ipynb index 0a68168..3c44f2b 100644 --- a/notebooks/00-scratch-pad.ipynb +++ b/notebooks/00-scratch-pad.ipynb @@ -2,9 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -25,7 +34,216 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.cuda.is_available()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from text_recognizer.networks.transformer.layers import Decoder" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "decoder = Decoder(dim=256, depth=4, num_heads=8, ff_kwargs={}, attn_kwargs={}, cross_attend=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Decoder(\n", + " (layers): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (1): Attention(\n", + " (qkv_fn): Sequential(\n", + " (0): Linear(in_features=256, out_features=49152, bias=False)\n", + " (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n", + " )\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (fc): Linear(in_features=16384, out_features=256, bias=True)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (1): ModuleList(\n", + " (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (1): Attention(\n", + " (qkv_fn): Sequential(\n", + " (0): Linear(in_features=256, out_features=49152, bias=False)\n", + " (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n", + " )\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (fc): Linear(in_features=16384, out_features=256, bias=True)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (2): ModuleList(\n", + " (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (1): FeedForward(\n", + " (mlp): Sequential(\n", + " (0): GEGLU(\n", + " (fc): Linear(in_features=256, out_features=2048, bias=True)\n", + " )\n", + " (1): Dropout(p=0.0, inplace=False)\n", + " (2): Linear(in_features=1024, out_features=256, bias=True)\n", + " )\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (3): ModuleList(\n", + " (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (1): Attention(\n", + " (qkv_fn): Sequential(\n", + " (0): Linear(in_features=256, out_features=49152, bias=False)\n", + " (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n", + " )\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (fc): Linear(in_features=16384, out_features=256, bias=True)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (4): ModuleList(\n", + " (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (1): Attention(\n", + " (qkv_fn): Sequential(\n", + " (0): Linear(in_features=256, out_features=49152, bias=False)\n", + " (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n", + " )\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (fc): Linear(in_features=16384, out_features=256, bias=True)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (5): ModuleList(\n", + " (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (1): FeedForward(\n", + " (mlp): Sequential(\n", + " (0): GEGLU(\n", + " (fc): Linear(in_features=256, out_features=2048, bias=True)\n", + " )\n", + " (1): Dropout(p=0.0, inplace=False)\n", + " (2): Linear(in_features=1024, out_features=256, bias=True)\n", + " )\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (6): ModuleList(\n", + " (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (1): Attention(\n", + " (qkv_fn): Sequential(\n", + " (0): Linear(in_features=256, out_features=49152, bias=False)\n", + " (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n", + " )\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (fc): Linear(in_features=16384, out_features=256, bias=True)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (7): ModuleList(\n", + " (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (1): Attention(\n", + " (qkv_fn): Sequential(\n", + " (0): Linear(in_features=256, out_features=49152, bias=False)\n", + " (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n", + " )\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (fc): Linear(in_features=16384, out_features=256, bias=True)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (8): ModuleList(\n", + " (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (1): FeedForward(\n", + " (mlp): Sequential(\n", + " (0): GEGLU(\n", + " (fc): Linear(in_features=256, out_features=2048, bias=True)\n", + " )\n", + " (1): Dropout(p=0.0, inplace=False)\n", + " (2): Linear(in_features=1024, out_features=256, bias=True)\n", + " )\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (9): ModuleList(\n", + " (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (1): Attention(\n", + " (qkv_fn): Sequential(\n", + " (0): Linear(in_features=256, out_features=49152, bias=False)\n", + " (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n", + " )\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (fc): Linear(in_features=16384, out_features=256, bias=True)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (10): ModuleList(\n", + " (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (1): Attention(\n", + " (qkv_fn): Sequential(\n", + " (0): Linear(in_features=256, out_features=49152, bias=False)\n", + " (1): Rearrange('b n (qkv h d) -> qkv b h n d', qkv=3, h=8)\n", + " )\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (fc): Linear(in_features=16384, out_features=256, bias=True)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (11): ModuleList(\n", + " (0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (1): FeedForward(\n", + " (mlp): Sequential(\n", + " (0): GEGLU(\n", + " (fc): Linear(in_features=256, out_features=2048, bias=True)\n", + " )\n", + " (1): Dropout(p=0.0, inplace=False)\n", + " (2): Linear(in_features=1024, out_features=256, bias=True)\n", + " )\n", + " )\n", + " (2): Residual()\n", + " )\n", + " )\n", + ")" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "decoder.cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -43,21 +261,21 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "efficient_transformer = Nystromer(\n", " dim = 128,\n", - " depth = 8,\n", - " num_heads = 6,\n", + " depth = 4,\n", + " num_heads = 8,\n", " num_landmarks = 128\n", ")" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -66,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -80,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -89,31 +307,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "CUDA out of memory. Tried to allocate 12.00 MiB (GPU 0; 7.79 GiB total capacity; 6.44 GiB already allocated; 10.31 MiB free; 6.50 GiB reserved in total by PyTorch)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 887\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 888\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 890\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 891\u001b[0m \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/projects/text-recognizer/text_recognizer/networks/transformer/vit.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos_embedding\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransformer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 46\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 12.00 MiB (GPU 0; 7.79 GiB total capacity; 6.44 GiB already allocated; 10.31 MiB free; 6.50 GiB reserved in total by PyTorch)" - ] - } - ], + "outputs": [], "source": [ "v(t).shape" ] @@ -337,9 +533,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "en(datum).shape" -- cgit v1.2.3-70-g09d2