{ "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.nn.functional as F\n", "import torch\n", "from torch import nn\n", "from torchsummary import summary\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": 5, "metadata": {}, "outputs": [], "source": [ "class Hej:\n", " a = 2\n", " \n", "class Hejjj:\n", " b = 1" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "l = [Hej(), Hejjj()]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<__main__.Hej at 0x7efefc77f370>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "next(o for o in l if isinstance(o, Hej))" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "' tes\".t '" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\" tes\".t \"\"\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from text_recognizer.networks import CNN, TDS2d" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "tds2d = TDS2d(**{\n", " \"depth\" : 4,\n", " \"tds_groups\" : [\n", " { \"channels\" : 4, \"num_blocks\" : 3, \"stride\" : [2, 2] },\n", " { \"channels\" : 32, \"num_blocks\" : 3, \"stride\" : [2, 2] },\n", " { \"channels\" : 64, \"num_blocks\" : 3, \"stride\" : [2, 2] },\n", " { \"channels\" : 128, \"num_blocks\" : 3, \"stride\" : [2, 1] },\n", " ],\n", " \"kernel_size\" : [5, 7],\n", " \"dropout_rate\" : 0.1\n", " }, input_dim=32, output_dim=128)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "TDS2d(\n", " (tds): Sequential(\n", " (0): Conv2d(1, 16, kernel_size=[5, 7], stride=[2, 2], padding=(2, 3))\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " (3): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " (4): TDSBlock2d(\n", " (conv): Sequential(\n", " (0): Conv3d(4, 4, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " )\n", " (mlp): Sequential(\n", " (0): Linear(in_features=16, out_features=16, bias=True)\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " (3): Linear(in_features=16, out_features=16, bias=True)\n", " (4): Dropout(p=0.1, inplace=False)\n", " )\n", " (instance_norm): ModuleList(\n", " (0): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " (1): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " )\n", " )\n", " (5): TDSBlock2d(\n", " (conv): Sequential(\n", " (0): Conv3d(4, 4, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " )\n", " (mlp): Sequential(\n", " (0): Linear(in_features=16, out_features=16, bias=True)\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " (3): Linear(in_features=16, out_features=16, bias=True)\n", " (4): Dropout(p=0.1, inplace=False)\n", " )\n", " (instance_norm): ModuleList(\n", " (0): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " (1): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " )\n", " )\n", " (6): TDSBlock2d(\n", " (conv): Sequential(\n", " (0): Conv3d(4, 4, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " )\n", " (mlp): Sequential(\n", " (0): Linear(in_features=16, out_features=16, bias=True)\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " (3): Linear(in_features=16, out_features=16, bias=True)\n", " (4): Dropout(p=0.1, inplace=False)\n", " )\n", " (instance_norm): ModuleList(\n", " (0): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " (1): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " )\n", " )\n", " (7): Conv2d(16, 128, kernel_size=[5, 7], stride=[2, 2], padding=(2, 3))\n", " (8): ReLU(inplace=True)\n", " (9): Dropout(p=0.1, inplace=False)\n", " (10): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " (11): TDSBlock2d(\n", " (conv): Sequential(\n", " (0): Conv3d(32, 32, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " )\n", " (mlp): Sequential(\n", " (0): Linear(in_features=128, out_features=128, bias=True)\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " (3): Linear(in_features=128, out_features=128, bias=True)\n", " (4): Dropout(p=0.1, inplace=False)\n", " )\n", " (instance_norm): ModuleList(\n", " (0): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " (1): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " )\n", " )\n", " (12): TDSBlock2d(\n", " (conv): Sequential(\n", " (0): Conv3d(32, 32, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " )\n", " (mlp): Sequential(\n", " (0): Linear(in_features=128, out_features=128, bias=True)\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " (3): Linear(in_features=128, out_features=128, bias=True)\n", " (4): Dropout(p=0.1, inplace=False)\n", " )\n", " (instance_norm): ModuleList(\n", " (0): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " (1): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " )\n", " )\n", " (13): TDSBlock2d(\n", " (conv): Sequential(\n", " (0): Conv3d(32, 32, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " )\n", " (mlp): Sequential(\n", " (0): Linear(in_features=128, out_features=128, bias=True)\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " (3): Linear(in_features=128, out_features=128, bias=True)\n", " (4): Dropout(p=0.1, inplace=False)\n", " )\n", " (instance_norm): ModuleList(\n", " (0): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " (1): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " )\n", " )\n", " (14): Conv2d(128, 256, kernel_size=[5, 7], stride=[2, 2], padding=(2, 3))\n", " (15): ReLU(inplace=True)\n", " (16): Dropout(p=0.1, inplace=False)\n", " (17): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " (18): TDSBlock2d(\n", " (conv): Sequential(\n", " (0): Conv3d(64, 64, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " )\n", " (mlp): Sequential(\n", " (0): Linear(in_features=256, out_features=256, bias=True)\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " (3): Linear(in_features=256, out_features=256, bias=True)\n", " (4): Dropout(p=0.1, inplace=False)\n", " )\n", " (instance_norm): ModuleList(\n", " (0): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " (1): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " )\n", " )\n", " (19): TDSBlock2d(\n", " (conv): Sequential(\n", " (0): Conv3d(64, 64, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " )\n", " (mlp): Sequential(\n", " (0): Linear(in_features=256, out_features=256, bias=True)\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " (3): Linear(in_features=256, out_features=256, bias=True)\n", " (4): Dropout(p=0.1, inplace=False)\n", " )\n", " (instance_norm): ModuleList(\n", " (0): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " (1): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " )\n", " )\n", " (20): TDSBlock2d(\n", " (conv): Sequential(\n", " (0): Conv3d(64, 64, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " )\n", " (mlp): Sequential(\n", " (0): Linear(in_features=256, out_features=256, bias=True)\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " (3): Linear(in_features=256, out_features=256, bias=True)\n", " (4): Dropout(p=0.1, inplace=False)\n", " )\n", " (instance_norm): ModuleList(\n", " (0): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " (1): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " )\n", " )\n", " (21): Conv2d(256, 512, kernel_size=[5, 7], stride=[2, 1], padding=(2, 3))\n", " (22): ReLU(inplace=True)\n", " (23): Dropout(p=0.1, inplace=False)\n", " (24): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " (25): TDSBlock2d(\n", " (conv): Sequential(\n", " (0): Conv3d(128, 128, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " )\n", " (mlp): Sequential(\n", " (0): Linear(in_features=512, out_features=512, bias=True)\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " (3): Linear(in_features=512, out_features=512, bias=True)\n", " (4): Dropout(p=0.1, inplace=False)\n", " )\n", " (instance_norm): ModuleList(\n", " (0): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " )\n", " )\n", " (26): TDSBlock2d(\n", " (conv): Sequential(\n", " (0): Conv3d(128, 128, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " )\n", " (mlp): Sequential(\n", " (0): Linear(in_features=512, out_features=512, bias=True)\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " (3): Linear(in_features=512, out_features=512, bias=True)\n", " (4): Dropout(p=0.1, inplace=False)\n", " )\n", " (instance_norm): ModuleList(\n", " (0): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " )\n", " )\n", " (27): TDSBlock2d(\n", " (conv): Sequential(\n", " (0): Conv3d(128, 128, kernel_size=(1, 5, 7), stride=(1, 1, 1), padding=(0, 2, 3))\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " )\n", " (mlp): Sequential(\n", " (0): Linear(in_features=512, out_features=512, bias=True)\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.1, inplace=False)\n", " (3): Linear(in_features=512, out_features=512, bias=True)\n", " (4): Dropout(p=0.1, inplace=False)\n", " )\n", " (instance_norm): ModuleList(\n", " (0): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n", " )\n", " )\n", " )\n", " (fc): Linear(in_features=1024, out_features=128, bias=True)\n", ")" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tds2d" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "===============================================================================================\n", "Layer (type:depth-idx) Output Shape Param #\n", "===============================================================================================\n", "├─Sequential: 1-1 [-1, 512, 2, 119] --\n", "| └─Conv2d: 2-1 [-1, 16, 14, 476] 576\n", "| └─ReLU: 2-2 [-1, 16, 14, 476] --\n", "| └─Dropout: 2-3 [-1, 16, 14, 476] --\n", "| └─InstanceNorm2d: 2-4 [-1, 16, 14, 476] 32\n", "| └─TDSBlock2d: 2-5 [-1, 16, 14, 476] --\n", "| | └─Sequential: 3-1 [-1, 4, 4, 14, 476] 564\n", "| | └─Sequential: 3-2 [-1, 476, 14, 16] 544\n", "| └─TDSBlock2d: 2-6 [-1, 16, 14, 476] --\n", "| | └─Sequential: 3-3 [-1, 4, 4, 14, 476] 564\n", "| | └─Sequential: 3-4 [-1, 476, 14, 16] 544\n", "| └─TDSBlock2d: 2-7 [-1, 16, 14, 476] --\n", "| | └─Sequential: 3-5 [-1, 4, 4, 14, 476] 564\n", "| | └─Sequential: 3-6 [-1, 476, 14, 16] 544\n", "| └─Conv2d: 2-8 [-1, 128, 7, 238] 71,808\n", "| └─ReLU: 2-9 [-1, 128, 7, 238] --\n", "| └─Dropout: 2-10 [-1, 128, 7, 238] --\n", "| └─InstanceNorm2d: 2-11 [-1, 128, 7, 238] 256\n", "| └─TDSBlock2d: 2-12 [-1, 128, 7, 238] --\n", "| | └─Sequential: 3-7 [-1, 32, 4, 7, 238] 35,872\n", "| | └─Sequential: 3-8 [-1, 238, 7, 128] 33,024\n", "| └─TDSBlock2d: 2-13 [-1, 128, 7, 238] --\n", "| | └─Sequential: 3-9 [-1, 32, 4, 7, 238] 35,872\n", "| | └─Sequential: 3-10 [-1, 238, 7, 128] 33,024\n", "| └─TDSBlock2d: 2-14 [-1, 128, 7, 238] --\n", "| | └─Sequential: 3-11 [-1, 32, 4, 7, 238] 35,872\n", "| | └─Sequential: 3-12 [-1, 238, 7, 128] 33,024\n", "| └─Conv2d: 2-15 [-1, 256, 4, 119] 1,147,136\n", "| └─ReLU: 2-16 [-1, 256, 4, 119] --\n", "| └─Dropout: 2-17 [-1, 256, 4, 119] --\n", "| └─InstanceNorm2d: 2-18 [-1, 256, 4, 119] 512\n", "| └─TDSBlock2d: 2-19 [-1, 256, 4, 119] --\n", "| | └─Sequential: 3-13 [-1, 64, 4, 4, 119] 143,424\n", "| | └─Sequential: 3-14 [-1, 119, 4, 256] 131,584\n", "| └─TDSBlock2d: 2-20 [-1, 256, 4, 119] --\n", "| | └─Sequential: 3-15 [-1, 64, 4, 4, 119] 143,424\n", "| | └─Sequential: 3-16 [-1, 119, 4, 256] 131,584\n", "| └─TDSBlock2d: 2-21 [-1, 256, 4, 119] --\n", "| | └─Sequential: 3-17 [-1, 64, 4, 4, 119] 143,424\n", "| | └─Sequential: 3-18 [-1, 119, 4, 256] 131,584\n", "| └─Conv2d: 2-22 [-1, 512, 2, 119] 4,588,032\n", "| └─ReLU: 2-23 [-1, 512, 2, 119] --\n", "| └─Dropout: 2-24 [-1, 512, 2, 119] --\n", "| └─InstanceNorm2d: 2-25 [-1, 512, 2, 119] 1,024\n", "| └─TDSBlock2d: 2-26 [-1, 512, 2, 119] --\n", "| | └─Sequential: 3-19 [-1, 128, 4, 2, 119] 573,568\n", "| | └─Sequential: 3-20 [-1, 119, 2, 512] 525,312\n", "| └─TDSBlock2d: 2-27 [-1, 512, 2, 119] --\n", "| | └─Sequential: 3-21 [-1, 128, 4, 2, 119] 573,568\n", "| | └─Sequential: 3-22 [-1, 119, 2, 512] 525,312\n", "| └─TDSBlock2d: 2-28 [-1, 512, 2, 119] --\n", "| | └─Sequential: 3-23 [-1, 128, 4, 2, 119] 573,568\n", "| | └─Sequential: 3-24 [-1, 119, 2, 512] 525,312\n", "├─Linear: 1-2 [-1, 119, 128] 131,200\n", "===============================================================================================\n", "Total params: 10,272,252\n", "Trainable params: 10,272,252\n", "Non-trainable params: 0\n", "Total mult-adds (G): 5.00\n", "===============================================================================================\n", "Input size (MB): 0.10\n", "Forward/backward pass size (MB): 73.21\n", "Params size (MB): 39.19\n", "Estimated Total Size (MB): 112.50\n", "===============================================================================================\n" ] }, { "data": { "text/plain": [ "===============================================================================================\n", "Layer (type:depth-idx) Output Shape Param #\n", "===============================================================================================\n", "├─Sequential: 1-1 [-1, 512, 2, 119] --\n", "| └─Conv2d: 2-1 [-1, 16, 14, 476] 576\n", "| └─ReLU: 2-2 [-1, 16, 14, 476] --\n", "| └─Dropout: 2-3 [-1, 16, 14, 476] --\n", "| └─InstanceNorm2d: 2-4 [-1, 16, 14, 476] 32\n", "| └─TDSBlock2d: 2-5 [-1, 16, 14, 476] --\n", "| | └─Sequential: 3-1 [-1, 4, 4, 14, 476] 564\n", "| | └─Sequential: 3-2 [-1, 476, 14, 16] 544\n", "| └─TDSBlock2d: 2-6 [-1, 16, 14, 476] --\n", "| | └─Sequential: 3-3 [-1, 4, 4, 14, 476] 564\n", "| | └─Sequential: 3-4 [-1, 476, 14, 16] 544\n", "| └─TDSBlock2d: 2-7 [-1, 16, 14, 476] --\n", "| | └─Sequential: 3-5 [-1, 4, 4, 14, 476] 564\n", "| | └─Sequential: 3-6 [-1, 476, 14, 16] 544\n", "| └─Conv2d: 2-8 [-1, 128, 7, 238] 71,808\n", "| └─ReLU: 2-9 [-1, 128, 7, 238] --\n", "| └─Dropout: 2-10 [-1, 128, 7, 238] --\n", "| └─InstanceNorm2d: 2-11 [-1, 128, 7, 238] 256\n", "| └─TDSBlock2d: 2-12 [-1, 128, 7, 238] --\n", "| | └─Sequential: 3-7 [-1, 32, 4, 7, 238] 35,872\n", "| | └─Sequential: 3-8 [-1, 238, 7, 128] 33,024\n", "| └─TDSBlock2d: 2-13 [-1, 128, 7, 238] --\n", "| | └─Sequential: 3-9 [-1, 32, 4, 7, 238] 35,872\n", "| | └─Sequential: 3-10 [-1, 238, 7, 128] 33,024\n", "| └─TDSBlock2d: 2-14 [-1, 128, 7, 238] --\n", "| | └─Sequential: 3-11 [-1, 32, 4, 7, 238] 35,872\n", "| | └─Sequential: 3-12 [-1, 238, 7, 128] 33,024\n", "| └─Conv2d: 2-15 [-1, 256, 4, 119] 1,147,136\n", "| └─ReLU: 2-16 [-1, 256, 4, 119] --\n", "| └─Dropout: 2-17 [-1, 256, 4, 119] --\n", "| └─InstanceNorm2d: 2-18 [-1, 256, 4, 119] 512\n", "| └─TDSBlock2d: 2-19 [-1, 256, 4, 119] --\n", "| | └─Sequential: 3-13 [-1, 64, 4, 4, 119] 143,424\n", "| | └─Sequential: 3-14 [-1, 119, 4, 256] 131,584\n", "| └─TDSBlock2d: 2-20 [-1, 256, 4, 119] --\n", "| | └─Sequential: 3-15 [-1, 64, 4, 4, 119] 143,424\n", "| | └─Sequential: 3-16 [-1, 119, 4, 256] 131,584\n", "| └─TDSBlock2d: 2-21 [-1, 256, 4, 119] --\n", "| | └─Sequential: 3-17 [-1, 64, 4, 4, 119] 143,424\n", "| | └─Sequential: 3-18 [-1, 119, 4, 256] 131,584\n", "| └─Conv2d: 2-22 [-1, 512, 2, 119] 4,588,032\n", "| └─ReLU: 2-23 [-1, 512, 2, 119] --\n", "| └─Dropout: 2-24 [-1, 512, 2, 119] --\n", "| └─InstanceNorm2d: 2-25 [-1, 512, 2, 119] 1,024\n", "| └─TDSBlock2d: 2-26 [-1, 512, 2, 119] --\n", "| | └─Sequential: 3-19 [-1, 128, 4, 2, 119] 573,568\n", "| | └─Sequential: 3-20 [-1, 119, 2, 512] 525,312\n", "| └─TDSBlock2d: 2-27 [-1, 512, 2, 119] --\n", "| | └─Sequential: 3-21 [-1, 128, 4, 2, 119] 573,568\n", "| | └─Sequential: 3-22 [-1, 119, 2, 512] 525,312\n", "| └─TDSBlock2d: 2-28 [-1, 512, 2, 119] --\n", "| | └─Sequential: 3-23 [-1, 128, 4, 2, 119] 573,568\n", "| | └─Sequential: 3-24 [-1, 119, 2, 512] 525,312\n", "├─Linear: 1-2 [-1, 119, 128] 131,200\n", "===============================================================================================\n", "Total params: 10,272,252\n", "Trainable params: 10,272,252\n", "Non-trainable params: 0\n", "Total mult-adds (G): 5.00\n", "===============================================================================================\n", "Input size (MB): 0.10\n", "Forward/backward pass size (MB): 73.21\n", "Params size (MB): 39.19\n", "Estimated Total Size (MB): 112.50\n", "===============================================================================================" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "summary(tds2d, (1, 28, 952), device=\"cpu\", depth=3)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "t = torch.randn(2,1, 28, 952)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "torch.Size([2, 119, 128])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tds2d(t).shape" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "cnn = CNN().cuda()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "i = nn.Sequential(nn.Conv2d(1,1,1,1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nn.Sequential(i,i)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cnn(t).shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from text_recognizer.networks.vqvae import Encoder, Decoder, VQVAE" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "vqvae = VQVAE(1, [32, 128, 128, 256], [4, 4, 4, 4], [2, 2, [1, 2], [1, 2]], 2, 32, 256, [[6, 119], [7, 238]])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t = torch.randn(2, 1, 28, 952)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x, l = vqvae(t)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "5 * 59 / 10" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "summary(vqvae, (1, 28, 952), device=\"cpu\", depth=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "up = nn.Upsample([4, 59])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "up(tt).shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tt.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class GEGLU(nn.Module):\n", " def __init__(self, dim_in, dim_out):\n", " super().__init__()\n", " self.proj = nn.Linear(dim_in, dim_out * 2)\n", "\n", " def forward(self, x):\n", " x, gate = self.proj(x).chunk(2, dim = -1)\n", " return x * F.gelu(gate)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "e = GEGLU(256, 2048)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "e(t).shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "emb = nn.Embedding(56, 256)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with torch.no_grad():\n", " e = emb(torch.Tensor([55]).long())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from einops import repeat" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ee = repeat(e, \"() n -> b n\", b=16)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "emb.device" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ee" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ee.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t = torch.randn(16, 10, 256)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t = torch.cat((ee.unsqueeze(1), t, ee.unsqueeze(1)), dim=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "e.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from text_recognizer.networks.residual_network import IdentityBlock, ResidualBlock, BasicBlock, BottleNeckBlock, ResidualLayer, ResidualNetwork, ResidualNetworkEncoder" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from text_recognizer.networks import WideResidualNetwork" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "wr = WideResidualNetwork(\n", " in_channels= 1,\n", " num_classes= 80,\n", " in_planes=64,\n", " depth=10,\n", " num_layers=4,\n", " width_factor=2,\n", " num_stages=[64, 128, 256, 256],\n", " dropout_rate= 0.1,\n", " activation= \"SELU\",\n", " use_decoder= False,\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from torchsummary import summary" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "backbone = ResidualNetworkEncoder(1, [64, 65, 66, 67, 68], [2, 2, 2, 2, 2])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "summary(backbone, (1, 28, 952), device=\"cpu\", depth=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ " backbone = nn.Sequential(\n", " *list(wr.children())[:][:]\n", " )\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "backbone" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "summary(wr, (1, 28, 952), device=\"cpu\", depth=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = torch.rand(1, 1, 28, 952)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "b = wr(a)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from einops import rearrange" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "b = rearrange(b, \"b c h w -> b w c h\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "c = nn.AdaptiveAvgPool2d((None, 1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "d = c(b)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "d.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "d.squeeze(3).shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "b.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from torch import nn" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "32 + 64" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "3 * 112" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "col_embed = nn.Parameter(torch.rand(1000, 256 // 2))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "W, H = 196, 4" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "col_embed[:W].unsqueeze(0).repeat(H, 1, 1).shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "col_embed[:H].unsqueeze(1).repeat(1, W, 1).shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ " torch.cat(\n", " [\n", " col_embed[:W].unsqueeze(0).repeat(H, 1, 1),\n", " col_embed[:H].unsqueeze(1).repeat(1, W, 1),\n", " ],\n", " dim=-1,\n", " ).unsqueeze(0).shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "4 * 196" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "target = torch.tensor([1,1,12,1,1,1,1,1,9,9,9,9,9,9])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "torch.nonzero(target == 9, as_tuple=False)[0].item()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "target[:9]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.inf" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from text_recognizer.networks.transformer.positional_encoding import PositionalEncoding" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(15, 5))\n", "pe = PositionalEncoding(20, 0)\n", "y = pe.forward(torch.zeros(1, 100, 20))\n", "plt.plot(np.arange(100), y[0, :, 4:8].data.numpy())\n", "plt.legend([\"dim %d\"%p for p in [4,5,6,7]])\n", "None" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from text_recognizer.networks.densenet import DenseNet,_DenseLayer,_DenseBlock" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dnet = DenseNet(12, (6, 12, 10), 1, 24, 80, 4, 0, True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "216 / 8" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "summary(dnet, (1, 28, 952), device=\"cpu\", depth=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ " backbone = nn.Sequential(\n", " *list(dnet.children())[:][:-4]\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "backbone" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from text_recognizer.networks import WideResidualNetwork" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "w = WideResidualNetwork(\n", " in_channels = 1,\n", " in_planes = 32,\n", " num_classes = 80,\n", " depth = 10,\n", " width_factor = 1,\n", " dropout_rate = 0.0,\n", " num_layers = 5,\n", " activation = \"relu\",\n", " use_decoder = False,)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "summary(w, (1, 28, 952), device=\"cpu\", depth=2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sz= 5" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mask = torch.triu(torch.ones(sz, sz), 1)\n", "mask = mask.masked_fill(mask==1, float('-inf'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "h = torch.rand(1, 256, 10, 10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h.flatten(2).permute(2, 0, 1).shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h.flatten(2).permute(2, 0, 1).shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mask\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pred = torch.Tensor([1,21,2,45,31, 81, 1, 79, 79, 79, 2,1,1,1,1, 81, 1, 79, 79, 79, 1,1,1,1,1, 81, 79, 79, 79, 79]).long()\n", "target = torch.Tensor([1,1,1,1,1, 81, 79, 79, 79, 79, 1,1,1,1,1, 81, 79, 79, 79, 79, 1,1,1,1,1, 81, 79, 79, 79, 79]).long()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mask = (target != 79)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mask" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pred * mask" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "target * mask" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from text_recognizer.models.metrics import accuracy" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pad_indcies = torch.nonzero(target == 79, as_tuple=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t1 = torch.nonzero(target == 81, as_tuple=False).squeeze(1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "target.shape[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t2 = torch.arange(10, target.shape[0] + 1, 10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for start, stop in zip(t1, t2):\n", " pred[start+1:stop] = 79" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pred" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[pred[start+1:stop] = 79 for start, stop in zip(t1, t2)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "pad_indcies" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pred[pad_indcies:pad_indcies] = 79" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pred.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "target.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "accuracy(pred, target)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "acc = (pred == target).sum().float() / target.shape[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "acc" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 4 }