From 8c380f60a4f84f69ab4d2030cce663b4136fa0a7 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Mon, 25 Oct 2021 22:30:12 +0200
Subject: Remove vq and unet notebooks
---
notebooks/04-vq-transformer.ipynb | 253 --------------------
notebooks/04-vqvae.ipynb | 233 ------------------
notebooks/05a-UNet.ipynb | 482 --------------------------------------
3 files changed, 968 deletions(-)
delete mode 100644 notebooks/04-vq-transformer.ipynb
delete mode 100644 notebooks/04-vqvae.ipynb
delete mode 100644 notebooks/05a-UNet.ipynb
diff --git a/notebooks/04-vq-transformer.ipynb b/notebooks/04-vq-transformer.ipynb
deleted file mode 100644
index 69d2688..0000000
--- a/notebooks/04-vq-transformer.ipynb
+++ /dev/null
@@ -1,253 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "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": null,
- "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": null,
- "id": "3cf50475-39f2-4642-a7d1-5bcbc0a036f7",
- "metadata": {},
- "outputs": [],
- "source": [
- "path = \"../training/conf/experiment/vqgan_htr_char_iam_lines.yaml\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "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": null,
- "id": "f939aa37-7b1d-45cc-885c-323c4540bda1",
- "metadata": {},
- "outputs": [],
- "source": [
- "cfg"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "aaeab329-aeb0-4a1b-aa35-5a2aab81b1d0",
- "metadata": {},
- "outputs": [],
- "source": [
- "net = instantiate(cfg.network)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a564ac7a-b67f-4bc1-af36-0fe0a58c1bc9",
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "aeddcc5c-e48d-4d90-8efa-963011ef40bc",
- "metadata": {},
- "outputs": [],
- "source": [
- "x = torch.randn((16, 1, 16, 64))\n",
- "y = torch.randint(0, 56, (16, 89))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0f0d78bc-7e0a-4d06-8e38-49b29ad25933",
- "metadata": {},
- "outputs": [],
- "source": [
- "y.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e9f4ee2a-c93f-4461-8d75-40c8c12d9d48",
- "metadata": {},
- "outputs": [],
- "source": [
- "yy = net(x, y)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7a7493a9-0e1d-46ef-8180-27605e18d082",
- "metadata": {},
- "outputs": [],
- "source": [
- "yy[0].shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "75bc9695-2afd-455c-a4fb-2e182456ccbd",
- "metadata": {},
- "outputs": [],
- "source": [
- "z = torch.randn((16, 8, 32))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3df6f9a0-6e66-4f46-a5b7-c0bb71b16b9b",
- "metadata": {},
- "outputs": [],
- "source": [
- "z, _ = net.encode(x)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6d6e9dd1-c56e-4169-8216-bcc84ea980e3",
- "metadata": {},
- "outputs": [],
- "source": [
- "z.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8f1539cb-b9b2-40b7-a843-d7479ddbddd7",
- "metadata": {},
- "outputs": [],
- "source": [
- "yy = net.decode(z, y[:, :2])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5cdba0a9-da7d-4e33-b209-7f360d1a38e5",
- "metadata": {},
- "outputs": [],
- "source": [
- "yy.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6da8065f-f93f-4aec-a60e-408712a28c3b",
- "metadata": {},
- "outputs": [],
- "source": [
- "torch.argmax(yy,dim=-2).shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "beabbda7-6a1f-4294-8f01-f9d866ffe088",
- "metadata": {},
- "outputs": [],
- "source": [
- "yy[0].shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "618b997c-e6a6-4487-b70c-9d260cb556d3",
- "metadata": {},
- "outputs": [],
- "source": [
- "from torchinfo import summary"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "25759b7b-8deb-4163-b75d-a1357c9fe88f",
- "metadata": {},
- "outputs": [],
- "source": [
- "summary(net, (1, 1, 576, 640), device=\"cpu\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "62ca0d97-625c-474b-8d6c-d0caba79e198",
- "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
-}
diff --git a/notebooks/04-vqvae.ipynb b/notebooks/04-vqvae.ipynb
deleted file mode 100644
index 1b31671..0000000
--- a/notebooks/04-vqvae.ipynb
+++ /dev/null
@@ -1,233 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "136a80f5-10e1-40c4-973a-a7eb7939bb1f",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The autoreload extension is already loaded. To reload it, use:\n",
- " %reload_ext autoreload\n"
- ]
- }
- ],
- "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",
- "from hydra import compose, initialize\n",
- "from omegaconf import OmegaConf\n",
- "from hydra.utils import instantiate\n",
- "from torchinfo import summary\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": "1a0fb9ca-1886-4fd4-839f-dc111a450cfd",
- "metadata": {},
- "outputs": [],
- "source": [
- "path = \"../training/conf/network/vqvae.yaml\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "0182a614-5781-44a6-b659-008e7c584fa7",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "encoder:\n",
- " _target_: text_recognizer.networks.vqvae.encoder.Encoder\n",
- " in_channels: 1\n",
- " hidden_dim: 32\n",
- " channels_multipliers:\n",
- " - 1\n",
- " - 2\n",
- " - 4\n",
- " dropout_rate: 0.0\n",
- " activation: mish\n",
- " use_norm: true\n",
- " num_residuals: 4\n",
- " residual_channels: 32\n",
- "decoder:\n",
- " _target_: text_recognizer.networks.vqvae.decoder.Decoder\n",
- " out_channels: 1\n",
- " hidden_dim: 32\n",
- " channels_multipliers:\n",
- " - 4\n",
- " - 2\n",
- " - 1\n",
- " dropout_rate: 0.0\n",
- " activation: mish\n",
- " use_norm: true\n",
- " num_residuals: 4\n",
- " residual_channels: 32\n",
- "_target_: text_recognizer.networks.vqvae.vqvae.VQVAE\n",
- "hidden_dim: 128\n",
- "embedding_dim: 32\n",
- "num_embeddings: 8192\n",
- "decay: 0.99\n",
- "\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/aktersnurra/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'vqvae': Defaults list is missing `_self_`. See https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order for more information\n",
- " warnings.warn(msg, UserWarning)\n"
- ]
- }
- ],
- "source": [
- "with initialize(config_path=\"../training/conf/network/\", job_name=\"test_app\"):\n",
- " cfg = compose(config_name=\"vqvae\")\n",
- " print(OmegaConf.to_yaml(cfg))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "a500f94c-7dae-477e-a3fb-2a2d62ee7b72",
- "metadata": {},
- "outputs": [],
- "source": [
- "net = instantiate(cfg)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "7f3b3559-5e23-485e-bf57-9405568a1fbf",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "====================================================================================================\n",
- "Layer (type:depth-idx) Output Shape Param #\n",
- "====================================================================================================\n",
- "VQVAE -- --\n",
- "├─Encoder: 1-1 [1, 128, 72, 80] --\n",
- "│ └─Sequential: 2-1 [1, 128, 72, 80] --\n",
- "│ │ └─Conv2d: 3-1 [1, 32, 576, 640] 320\n",
- "│ │ └─Normalize: 3-2 [1, 32, 576, 640] 64\n",
- "│ │ └─Mish: 3-3 [1, 32, 576, 640] --\n",
- "│ │ └─Mish: 3-4 [1, 32, 576, 640] --\n",
- "│ │ └─Mish: 3-5 [1, 32, 576, 640] --\n",
- "│ │ └─Conv2d: 3-6 [1, 32, 288, 320] 16,416\n",
- "│ │ └─Normalize: 3-7 [1, 32, 288, 320] 64\n",
- "│ │ └─Mish: 3-8 [1, 32, 288, 320] --\n",
- "│ │ └─Mish: 3-9 [1, 32, 288, 320] --\n",
- "│ │ └─Mish: 3-10 [1, 32, 288, 320] --\n",
- "│ │ └─Conv2d: 3-11 [1, 64, 144, 160] 32,832\n",
- "│ │ └─Normalize: 3-12 [1, 64, 144, 160] 128\n",
- "│ │ └─Mish: 3-13 [1, 64, 144, 160] --\n",
- "│ │ └─Mish: 3-14 [1, 64, 144, 160] --\n",
- "│ │ └─Mish: 3-15 [1, 64, 144, 160] --\n",
- "│ │ └─Conv2d: 3-16 [1, 128, 72, 80] 131,200\n",
- "│ │ └─Residual: 3-17 [1, 128, 72, 80] 41,280\n",
- "│ │ └─Residual: 3-18 [1, 128, 72, 80] 41,280\n",
- "│ │ └─Residual: 3-19 [1, 128, 72, 80] 41,280\n",
- "│ │ └─Residual: 3-20 [1, 128, 72, 80] 41,280\n",
- "├─Conv2d: 1-2 [1, 32, 72, 80] 4,128\n",
- "├─VectorQuantizer: 1-3 [1, 32, 72, 80] --\n",
- "├─Conv2d: 1-4 [1, 128, 72, 80] 4,224\n",
- "├─Decoder: 1-5 [1, 1, 576, 640] --\n",
- "│ └─Sequential: 2-2 [1, 1, 576, 640] --\n",
- "│ │ └─Residual: 3-21 [1, 128, 72, 80] 41,280\n",
- "│ │ └─Residual: 3-22 [1, 128, 72, 80] 41,280\n",
- "│ │ └─Residual: 3-23 [1, 128, 72, 80] 41,280\n",
- "│ │ └─Residual: 3-24 [1, 128, 72, 80] 41,280\n",
- "│ │ └─Normalize: 3-25 [1, 128, 72, 80] 256\n",
- "│ │ └─Mish: 3-26 [1, 128, 72, 80] --\n",
- "│ │ └─Mish: 3-27 [1, 128, 72, 80] --\n",
- "│ │ └─Mish: 3-28 [1, 128, 72, 80] --\n",
- "│ │ └─ConvTranspose2d: 3-29 [1, 64, 144, 160] 131,136\n",
- "│ │ └─Normalize: 3-30 [1, 64, 144, 160] 128\n",
- "│ │ └─Mish: 3-31 [1, 64, 144, 160] --\n",
- "│ │ └─Mish: 3-32 [1, 64, 144, 160] --\n",
- "│ │ └─Mish: 3-33 [1, 64, 144, 160] --\n",
- "│ │ └─ConvTranspose2d: 3-34 [1, 32, 288, 320] 32,800\n",
- "│ │ └─Normalize: 3-35 [1, 32, 288, 320] 64\n",
- "│ │ └─Mish: 3-36 [1, 32, 288, 320] --\n",
- "│ │ └─Mish: 3-37 [1, 32, 288, 320] --\n",
- "│ │ └─Mish: 3-38 [1, 32, 288, 320] --\n",
- "│ │ └─ConvTranspose2d: 3-39 [1, 32, 576, 640] 16,416\n",
- "│ │ └─Normalize: 3-40 [1, 32, 576, 640] 64\n",
- "│ │ └─Conv2d: 3-41 [1, 1, 576, 640] 289\n",
- "====================================================================================================\n",
- "Total params: 700,769\n",
- "Trainable params: 700,769\n",
- "Non-trainable params: 0\n",
- "Total mult-adds (G): 17.28\n",
- "====================================================================================================\n",
- "Input size (MB): 1.47\n",
- "Forward/backward pass size (MB): 659.13\n",
- "Params size (MB): 2.80\n",
- "Estimated Total Size (MB): 663.41\n",
- "===================================================================================================="
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "summary(net, (1, 1, 576, 640), device=\"cpu\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9f880b03-d641-4640-acd3-aa5666ca5184",
- "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
-}
diff --git a/notebooks/05a-UNet.ipynb b/notebooks/05a-UNet.ipynb
deleted file mode 100644
index 3070e2d..0000000
--- a/notebooks/05a-UNet.ipynb
+++ /dev/null
@@ -1,482 +0,0 @@
-{
- "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 torch import nn\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": [],
- "source": [
- "from text_recognizer.networks.unet import UNet"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "net = UNet()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "x = torch.rand(1, 1, 256, 256)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "ModuleList(\n",
- " (0): _DilationBlock(\n",
- " (activation): ELU(alpha=1.0, inplace=True)\n",
- " (conv): Sequential(\n",
- " (0): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3))\n",
- " (1): ELU(alpha=1.0, inplace=True)\n",
- " )\n",
- " (conv1): Sequential(\n",
- " (0): Conv2d(1, 32, kernel_size=(1, 1), stride=(1, 1))\n",
- " (1): ELU(alpha=1.0, inplace=True)\n",
- " )\n",
- " (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (down_sampling): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
- " )\n",
- " (1): _DilationBlock(\n",
- " (activation): ELU(alpha=1.0, inplace=True)\n",
- " (conv): Sequential(\n",
- " (0): Conv2d(64, 64, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3))\n",
- " (1): ELU(alpha=1.0, inplace=True)\n",
- " )\n",
- " (conv1): Sequential(\n",
- " (0): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n",
- " (1): ELU(alpha=1.0, inplace=True)\n",
- " )\n",
- " (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (down_sampling): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
- " )\n",
- " (2): _DilationBlock(\n",
- " (activation): ELU(alpha=1.0, inplace=True)\n",
- " (conv): Sequential(\n",
- " (0): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3))\n",
- " (1): ELU(alpha=1.0, inplace=True)\n",
- " )\n",
- " (conv1): Sequential(\n",
- " (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
- " (1): ELU(alpha=1.0, inplace=True)\n",
- " )\n",
- " (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (down_sampling): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
- " )\n",
- " (3): _DilationBlock(\n",
- " (activation): ELU(alpha=1.0, inplace=True)\n",
- " (conv): Sequential(\n",
- " (0): Conv2d(256, 256, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3))\n",
- " (1): ELU(alpha=1.0, inplace=True)\n",
- " )\n",
- " (conv1): Sequential(\n",
- " (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
- " (1): ELU(alpha=1.0, inplace=True)\n",
- " )\n",
- " (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- ")"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "net.encoder_blocks"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "ModuleList(\n",
- " (0): _UpSamplingBlock(\n",
- " (conv_block): _ConvBlock(\n",
- " (activation): ReLU(inplace=True)\n",
- " (block): Sequential(\n",
- " (0): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
- " (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (2): ReLU(inplace=True)\n",
- " (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
- " (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (5): ReLU(inplace=True)\n",
- " )\n",
- " )\n",
- " (up_sampling): Upsample(scale_factor=2.0, mode=bilinear)\n",
- " )\n",
- " (1): _UpSamplingBlock(\n",
- " (conv_block): _ConvBlock(\n",
- " (activation): ReLU(inplace=True)\n",
- " (block): Sequential(\n",
- " (0): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
- " (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (2): ReLU(inplace=True)\n",
- " (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
- " (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (5): ReLU(inplace=True)\n",
- " )\n",
- " )\n",
- " (up_sampling): Upsample(scale_factor=2.0, mode=bilinear)\n",
- " )\n",
- " (2): _UpSamplingBlock(\n",
- " (conv_block): _ConvBlock(\n",
- " (activation): ReLU(inplace=True)\n",
- " (block): Sequential(\n",
- " (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
- " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (2): ReLU(inplace=True)\n",
- " (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
- " (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (5): ReLU(inplace=True)\n",
- " )\n",
- " )\n",
- " (up_sampling): Upsample(scale_factor=2.0, mode=bilinear)\n",
- " )\n",
- ")"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "net.decoder_blocks"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Conv2d(64, 3, kernel_size=(1, 1), stride=(1, 1))"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "net.head"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "yy = net(x)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "y = (torch.randn(1, 256, 256) > 0).long()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "torch.Size([1, 3, 256, 256])"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "yy.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor([[[1, 0, 1, ..., 0, 1, 0],\n",
- " [1, 0, 1, ..., 0, 1, 0],\n",
- " [1, 1, 0, ..., 1, 1, 0],\n",
- " ...,\n",
- " [1, 0, 0, ..., 0, 1, 1],\n",
- " [0, 0, 1, ..., 1, 1, 0],\n",
- " [0, 0, 1, ..., 0, 0, 0]]])"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "y"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 54,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "loss = nn.CrossEntropyLoss()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 55,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor(1.2502, grad_fn=<NllLoss2DBackward>)"
- ]
- },
- "execution_count": 55,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "loss(yy, y)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor([[[[-0.1692, 0.1223, 0.1750, ..., -0.1869, -0.0585, 0.0462],\n",
- " [-0.1302, -0.0230, 0.3185, ..., -0.3760, 0.0204, -0.0686],\n",
- " [-0.1062, -0.0216, 0.4592, ..., 0.0990, 0.0808, -0.1419],\n",
- " ...,\n",
- " [ 0.1386, -0.2856, 0.3074, ..., -0.3874, -0.0322, 0.0503],\n",
- " [ 0.3562, -0.0960, 0.0815, ..., 0.1893, 0.1438, 0.2804],\n",
- " [-0.2106, -0.1988, 0.0016, ..., -0.0031, -0.2820, 0.0113]],\n",
- "\n",
- " [[-0.1542, -0.1322, -0.3917, ..., -0.2297, -0.2328, 0.0103],\n",
- " [ 0.1040, 0.2189, -0.3661, ..., 0.4818, -0.3737, 0.1117],\n",
- " [ 0.0735, -0.6487, -0.1899, ..., 0.2213, -0.1529, -0.1020],\n",
- " ...,\n",
- " [-0.2046, -0.1477, 0.2941, ..., 0.0652, -0.7276, 0.1676],\n",
- " [ 0.0413, -0.2013, -0.3192, ..., -0.4947, -0.1179, -0.1000],\n",
- " [-0.4108, 0.0199, 0.2238, ..., -0.4482, -0.2370, 0.0119]],\n",
- "\n",
- " [[ 0.0834, 0.1303, 0.0629, ..., 0.4766, -0.0481, 0.2538],\n",
- " [ 0.1218, 0.1324, 0.2464, ..., 0.0081, 0.4444, 0.4583],\n",
- " [ 0.1155, 0.1417, 0.2248, ..., 0.6365, -0.0040, 0.3144],\n",
- " ...,\n",
- " [ 0.0744, -0.0751, -0.5654, ..., -0.2890, -0.0437, 0.2719],\n",
- " [ 0.1057, -0.1093, -0.3803, ..., 0.0229, 0.1403, 0.0944],\n",
- " [-0.0958, -0.3931, -0.0186, ..., 0.2102, -0.0842, 0.1909]]]],\n",
- " grad_fn=<MkldnnConvolutionBackward>)"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "yy"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [],
- "source": [
- "from torchsummary import summary"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "==========================================================================================\n",
- "Layer (type:depth-idx) Output Shape Param #\n",
- "==========================================================================================\n",
- "├─ModuleList: 1 [] --\n",
- "| └─DownSamplingBlock: 2-1 [-1, 64, 128, 128] --\n",
- "| | └─ConvBlock: 3-1 [-1, 64, 256, 256] 37,824\n",
- "| | └─MaxPool2d: 3-2 [-1, 64, 128, 128] --\n",
- "| └─DownSamplingBlock: 2-2 [-1, 128, 64, 64] --\n",
- "| | └─ConvBlock: 3-3 [-1, 128, 128, 128] 221,952\n",
- "| | └─MaxPool2d: 3-4 [-1, 128, 64, 64] --\n",
- "| └─DownSamplingBlock: 2-3 [-1, 256, 32, 32] --\n",
- "| | └─ConvBlock: 3-5 [-1, 256, 64, 64] 886,272\n",
- "| | └─MaxPool2d: 3-6 [-1, 256, 32, 32] --\n",
- "| └─DownSamplingBlock: 2-4 [-1, 512, 32, 32] --\n",
- "| | └─ConvBlock: 3-7 [-1, 512, 32, 32] 3,542,016\n",
- "├─ModuleList: 1 [] --\n",
- "| └─UpSamplingBlock: 2-5 [-1, 256, 64, 64] --\n",
- "| | └─Upsample: 3-8 [-1, 512, 64, 64] --\n",
- "| | └─ConvBlock: 3-9 [-1, 256, 64, 64] 2,360,832\n",
- "| └─UpSamplingBlock: 2-6 [-1, 128, 128, 128] --\n",
- "| | └─Upsample: 3-10 [-1, 256, 128, 128] --\n",
- "| | └─ConvBlock: 3-11 [-1, 128, 128, 128] 590,592\n",
- "| └─UpSamplingBlock: 2-7 [-1, 64, 256, 256] --\n",
- "| | └─Upsample: 3-12 [-1, 128, 256, 256] --\n",
- "| | └─ConvBlock: 3-13 [-1, 64, 256, 256] 147,840\n",
- "├─Conv2d: 1-1 [-1, 3, 256, 256] 195\n",
- "==========================================================================================\n",
- "Total params: 7,787,523\n",
- "Trainable params: 7,787,523\n",
- "Non-trainable params: 0\n",
- "Total mult-adds (M): 35.93\n",
- "==========================================================================================\n",
- "Input size (MB): 0.25\n",
- "Forward/backward pass size (MB): 1.50\n",
- "Params size (MB): 29.71\n",
- "Estimated Total Size (MB): 31.46\n",
- "==========================================================================================\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "==========================================================================================\n",
- "Layer (type:depth-idx) Output Shape Param #\n",
- "==========================================================================================\n",
- "├─ModuleList: 1 [] --\n",
- "| └─DownSamplingBlock: 2-1 [-1, 64, 128, 128] --\n",
- "| | └─ConvBlock: 3-1 [-1, 64, 256, 256] 37,824\n",
- "| | └─MaxPool2d: 3-2 [-1, 64, 128, 128] --\n",
- "| └─DownSamplingBlock: 2-2 [-1, 128, 64, 64] --\n",
- "| | └─ConvBlock: 3-3 [-1, 128, 128, 128] 221,952\n",
- "| | └─MaxPool2d: 3-4 [-1, 128, 64, 64] --\n",
- "| └─DownSamplingBlock: 2-3 [-1, 256, 32, 32] --\n",
- "| | └─ConvBlock: 3-5 [-1, 256, 64, 64] 886,272\n",
- "| | └─MaxPool2d: 3-6 [-1, 256, 32, 32] --\n",
- "| └─DownSamplingBlock: 2-4 [-1, 512, 32, 32] --\n",
- "| | └─ConvBlock: 3-7 [-1, 512, 32, 32] 3,542,016\n",
- "├─ModuleList: 1 [] --\n",
- "| └─UpSamplingBlock: 2-5 [-1, 256, 64, 64] --\n",
- "| | └─Upsample: 3-8 [-1, 512, 64, 64] --\n",
- "| | └─ConvBlock: 3-9 [-1, 256, 64, 64] 2,360,832\n",
- "| └─UpSamplingBlock: 2-6 [-1, 128, 128, 128] --\n",
- "| | └─Upsample: 3-10 [-1, 256, 128, 128] --\n",
- "| | └─ConvBlock: 3-11 [-1, 128, 128, 128] 590,592\n",
- "| └─UpSamplingBlock: 2-7 [-1, 64, 256, 256] --\n",
- "| | └─Upsample: 3-12 [-1, 128, 256, 256] --\n",
- "| | └─ConvBlock: 3-13 [-1, 64, 256, 256] 147,840\n",
- "├─Conv2d: 1-1 [-1, 3, 256, 256] 195\n",
- "==========================================================================================\n",
- "Total params: 7,787,523\n",
- "Trainable params: 7,787,523\n",
- "Non-trainable params: 0\n",
- "Total mult-adds (M): 35.93\n",
- "==========================================================================================\n",
- "Input size (MB): 0.25\n",
- "Forward/backward pass size (MB): 1.50\n",
- "Params size (MB): 29.71\n",
- "Estimated Total Size (MB): 31.46\n",
- "=========================================================================================="
- ]
- },
- "execution_count": 47,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "\n",
- "summary(net, (1, 256, 256), device=\"cpu\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "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": "3.9.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
--
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