diff options
| author | aktersnurra <gustaf.rydholm@gmail.com> | 2020-09-08 23:14:23 +0200 |
|---|---|---|
| committer | aktersnurra <gustaf.rydholm@gmail.com> | 2020-09-08 23:14:23 +0200 |
| commit | e1b504bca41a9793ed7e88ef14f2e2cbd85724f2 (patch) | |
| tree | 70b482f890c9ad2be104f0bff8f2172e8411a2be | |
| parent | fe23001b6588e6e6e9e2c5a99b72f3445cf5206f (diff) | |
IAM datasets implemented.
50 files changed, 3538 insertions, 2600 deletions
@@ -1,6 +1,6 @@ [flake8] select = ANN,B,B9,BLK,C,D,DAR,E,F,I,S,W -ignore = E203,E501,W503,ANN101,ANN002,ANN003,F401,D202,S404,D107 +ignore = E203,E501,W503,ANN101,ANN002,ANN003,F401,D202,S404,D107,S607,S603,S310 max-line-length = 120 max-complexity = 10 application-import-names = text_recognizer,tests @@ -17,7 +17,7 @@ TBC - [x] Fix basic test to load model - [x] Fix loading previous experiments - [x] Able to set verbosity level on the logger to terminal output -- [ ] Implement Callbacks for training +- [x] Implement Callbacks for training - [x] Implement early stopping - [x] Implement wandb - [x] Implement lr scheduler as a callback @@ -25,9 +25,9 @@ TBC - [x] Implement TQDM progress bar (Low priority) - [ ] Check that dataset exists, otherwise download it form the web. Do this in run_experiment.py. - [x] Create repr func for data loaders -- [ ] Be able to restart with lr scheduler (May skip this BS) +- [ ] Be able to restart with lr scheduler (May skip this) - [ ] Implement population based training -- [ ] Implement Bayesian hyperparameter search (with W&B maybe) +- [x] Implement Bayesian hyperparameter search (with W&B maybe) - [x] Try to fix shell cmd security issues S404, S602 - [x] Change prepare_experiment.py to print statements st it can be run with tasks/prepare_sample_experiments.sh | parallel -j1 - [x] Fix caption in WandbImageLogger @@ -38,10 +38,50 @@ TBC - [x] Finish Emnist line dataset - [x] SentenceGenerator - [x] Write a Emnist line data loader -- [ ] Implement ctc line model - - [ ] Implement CNN encoder (ResNet style) - - [ ] Implement the RNN + output layer - - [ ] Construct/implement the CTC loss -- [ ] Sweep base config yaml file -- [ ] sweep.py -- [ ] sweep.yaml +- [x] Implement ctc line model + - [x] Implement CNN encoder (ResNet style) + - [x] Implement the RNN + output layer + - [x] Construct/implement the CTC loss +- [x] Sweep base config yaml file +- [x] sweep.py +- [x] sweep.yaml +- [x] Fix dataset splits. +- [x] Implement predict on image +- [x] CTC decoder +- [x] IAM dataset +- [x] IAM Lines dataset +- [x] IAM paragraphs dataset +- [ ] Visual attention: + - [ ] Enriched Deep Recurrent Visual Attention Model for Multiple Object Recognition + - [ ] DRAM (maybe) + - [ ] Dynamic Capacity Network +- [ ] CNN + Transformer +- [ ] fix nosec problem + +## Run Sweeps + Run the following commands to execute hyperparameter search with W&B: + +``` +wandb sweep training/sweep_emnist_resnet.yml +export SWEEP_ID=... +wandb agent $SWEEP_ID + +``` + +## PyTorch Performance Guide +Tips and tricks from ["PyTorch Performance Tuning Guide - Szymon Migacz, NVIDIA"](https://www.youtube.com/watch?v=9mS1fIYj1So&t=125s): + +* Always better to use `num_workers > 0`, allows asynchronous data processing +* Use `pin_memory=True` to allow data loading and computations to happen on the GPU in parallel. +* Have to tune `num_workers` to use based on the problem, too many and data loading becomes slower. +* For CNNs use `torch.backends.cudnn.benchmark=True`, allows cuDNN to select the best algorithm for convolutional computations (autotuner). +* Increase batch size to max out GPU memory. +* Use optimizer for large batch training, e.g. LARS, LAMB etc. +* Set `bias=False` for convolutions directly followed by BatchNorm. +* Use `for p in model.parameters(): p.grad = None` instead of `model.zero_grad()`. +* Careful with disable debug APIs in prod (detect_anomaly, profiler, gradcheck). +* Use `DistributedDataParallel` not `DataParallel`, uses 1 CPU core for each GPU. +* Important to load balance compute on all GPUs, if variably-sized inputs or GPUs will idle. +* Use an apex fused optimizer +* Use checkpointing to recompute memory-intensive compute-efficient ops in backward pass (e.g. activations, upsampling), `torch.utils.checkpoint`. +* Use `@torch.jit.script`, especially to fuse long sequences of pointwise operations like GELU. diff --git a/poetry.lock b/poetry.lock index 89851d9..7176cdb 100644 --- a/poetry.lock +++ b/poetry.lock @@ -257,7 +257,7 @@ python-versions = ">=2.6, !=3.0.*, !=3.1.*" version = "4.4.2" [[package]] -category = "dev" +category = "main" description = "XML bomb protection for Python stdlib modules" name = "defusedxml" optional = false @@ -1331,6 +1331,17 @@ version = "2.8.1" six = ">=1.5" [[package]] +category = "main" +description = "Python extension for computing string edit distances and similarities." +name = "python-levenshtein" +optional = false +python-versions = "*" +version = "0.12.0" + +[package.dependencies] +setuptools = "*" + +[[package]] category = "dev" description = "Python type inferencer" marker = "python_version == \"3.7\"" @@ -1747,11 +1758,11 @@ numpy = "*" [[package]] category = "main" -description = "Model summary in PyTorch similar to `model.summary()` in Keras" -name = "torchsummary" +description = "Model summary in PyTorch, based off of the original torchsummary." +name = "torch-summary" optional = false -python-versions = "*" -version = "1.5.1" +python-versions = ">=3.5" +version = "1.4.2" [[package]] category = "main" @@ -1863,7 +1874,7 @@ description = "A CLI and library for interacting with the Weights and Biases API name = "wandb" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" -version = "0.9.5" +version = "0.9.6" [package.dependencies] Click = ">=7.0" @@ -1970,7 +1981,7 @@ docs = ["sphinx", "jaraco.packaging (>=3.2)", "rst.linker (>=1.9)"] testing = ["jaraco.itertools", "func-timeout"] [metadata] -content-hash = "8aea4adc15683cb4bdd650d199725418e7778687d311bcb6a02db94fa05719f7" +content-hash = "d2554d5ea2a2de69322438b76e2dc21566d82b0aeb0c5e8a0ebcd69c0a057dba" lock-version = "1.0" python-versions = "^3.7" @@ -2653,6 +2664,9 @@ python-dateutil = [ {file = "python-dateutil-2.8.1.tar.gz", hash = "sha256:73ebfe9dbf22e832286dafa60473e4cd239f8592f699aa5adaf10050e6e1823c"}, {file = "python_dateutil-2.8.1-py2.py3-none-any.whl", hash = "sha256:75bb3f31ea686f1197762692a9ee6a7550b59fc6ca3a1f4b5d7e32fb98e2da2a"}, ] +python-levenshtein = [ + {file = "python-Levenshtein-0.12.0.tar.gz", hash = "sha256:033a11de5e3d19ea25c9302d11224e1a1898fe5abd23c61c7c360c25195e3eb1"}, +] pytype = [ {file = "pytype-2020.8.10-cp35-cp35m-macosx_10_14_x86_64.whl", hash = "sha256:da1977a1aa74fbd237e889c1d29421d490e0be9a91a22efd96fbca2570ef9165"}, {file = "pytype-2020.8.10-cp35-cp35m-manylinux2014_x86_64.whl", hash = "sha256:e0909b99aff8eff0ece91fd64e00b935f0e4fecb51359d83d742b27db160dd00"}, @@ -2869,9 +2883,9 @@ torch = [ {file = "torch-1.6.0-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:5357873e243bcfa804c32dc341f564e9a4c12addfc9baae4ee857fcc09a0a216"}, {file = "torch-1.6.0-cp38-none-macosx_10_9_x86_64.whl", hash = "sha256:4f9a4ad7947cef566afb0a323d99009fe8524f0b0f2ca1fb7ad5de0400381a5b"}, ] -torchsummary = [ - {file = "torchsummary-1.5.1-py3-none-any.whl", hash = "sha256:10f41d1743fb918f83293f13183f532ab1bb8f6639a1b89e5f8592ec1919a976"}, - {file = "torchsummary-1.5.1.tar.gz", hash = "sha256:981bf689e22e0cf7f95c746002f20a24ad26aa6b9d861134a14bc6ce92230590"}, +torch-summary = [ + {file = "torch-summary-1.4.2.tar.gz", hash = "sha256:ff26a3b08fe6c763c4dcc7c2a44b110f572f026ea2ed877ea619533cd3c0fd9f"}, + {file = "torch_summary-1.4.2-py3-none-any.whl", hash = "sha256:4123284856c10248632ee533fa13063ed965e6af77540326fb02d7b81d491763"}, ] torchvision = [ {file = "torchvision-0.7.0-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:a70d80bb8749c1e4a46fa56dc2fc857e98d14600841e02cc2fed766daf96c245"}, @@ -2942,8 +2956,8 @@ urllib3 = [ {file = "urllib3-1.25.10.tar.gz", hash = "sha256:91056c15fa70756691db97756772bb1eb9678fa585d9184f24534b100dc60f4a"}, ] wandb = [ - {file = "wandb-0.9.5-py2.py3-none-any.whl", hash = "sha256:f8aa54c75736aafdea600b2d3b803d4d3f999d1ac2568c253552dd557b767d9a"}, - {file = "wandb-0.9.5.tar.gz", hash = "sha256:251ac7bcf9acc1b4ed08b1f3ea9172ed02d1e55a1a9033785e544f009208958e"}, + {file = "wandb-0.9.6-py2.py3-none-any.whl", hash = "sha256:0da3f73992cbb181bbb3080a233c387e28d92c7f6f0be9c67401a941d15402cb"}, + {file = "wandb-0.9.6.tar.gz", hash = "sha256:6d712c3744e4af0d91f3872be0fc96b338706136a6980a653565c381ac3f4550"}, ] watchdog = [ {file = "watchdog-0.10.3.tar.gz", hash = "sha256:4214e1379d128b0588021880ccaf40317ee156d4603ac388b9adcf29165e0c04"}, diff --git a/pyproject.toml b/pyproject.toml index 49bb049..43943e7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,7 +24,6 @@ h5py = "^2.10.0" toml = "^0.10.1" torch = "^1.6.0" torchvision = "^0.7.0" -torchsummary = "^1.5.1" loguru = "^0.5.0" matplotlib = "^3.2.1" tqdm = "^4.46.1" @@ -32,7 +31,10 @@ pytest = "^5.4.3" opencv-python = "^4.3.0" nltk = "^3.5" einops = "^0.2.0" -wandb = "^0.9.5" +wandb = "^0.9.6" +torch-summary = "^1.4.2" +python-Levenshtein = "^0.12.0" +defusedxml = "^0.6.0" [tool.poetry.dev-dependencies] pytest = "^5.4.2" diff --git a/src/notebooks/00-testing-stuff-out.ipynb b/src/notebooks/00-testing-stuff-out.ipynb index 3f008c3..ff9fb20 100644 --- a/src/notebooks/00-testing-stuff-out.ipynb +++ b/src/notebooks/00-testing-stuff-out.ipynb @@ -22,307 +22,11 @@ }, { "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": [ - "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" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.optim.lr_scheduler.StepLR" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "getattr(torch.optim.lr_scheduler, \"StepLR\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = getattr(torch.nn, \"ReLU\")()" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ - "a" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "loss = getattr(torch.nn, \"L1Loss\")()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "input = torch.randn(3, 5, requires_grad=True)\n", - "target = torch.randn(3, 5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "b = torch.randn(2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "b" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a(b)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "output = loss(input, target)\n", - "output.backward()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor(1.1283)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "torch.tensor(output.item())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s = 1.\n", - "if s is not None:\n", - " assert 0.0 < s < 1.0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class A:\n", - " @property\n", - " def __name__(self):\n", - " return \"adafa\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = A()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a.__name__" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from training.gpu_manager import GPUManager" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "gpu_manager = GPUManager(True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2020-07-21 14:10:13.170 | DEBUG | training.gpu_manager:_get_free_gpu:57 - pid 11721 picking gpu 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gpu_manager.get_free_gpu()" + "from text_recognizer.networks.residual_network import IdentityBlock, ResidualBlock, BasicBlock, BottleNeckBlock, ResidualLayer, Encoder, ResidualNetwork" ] }, { @@ -331,7 +35,7 @@ "metadata": {}, "outputs": [], "source": [ - "from pathlib import Path" + "IdentityBlock(32, 64)" ] }, { @@ -340,7 +44,7 @@ "metadata": {}, "outputs": [], "source": [ - "p = Path(\"/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training\")" + "ResidualBlock(32, 64)" ] }, { @@ -349,342 +53,11 @@ "metadata": {}, "outputs": [], "source": [ + "dummy = torch.ones((1, 32, 224, 224))\n", "\n", - "str(p).split(\"/\")[0] + \"/\" + str(p).split(\"/\")[1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p.parents[0].resolve()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p.exists()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "d = 'Experiment JSON, e.g. \\'{\"dataset\": \"EmnistDataset\", \"model\": \"CharacterModel\", \"network\": \"mlp\"}\\''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(d)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "import yaml" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "path = \"/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/sample_experiment.yml\"" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "with open(path) as f:\n", - " d = yaml.safe_load(f)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "experiment_config = d[\"experiments\"][0]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'dataloader': 'EmnistDataLoader',\n", - " 'data_loader_args': {'splits': ['train', 'val'],\n", - " 'sample_to_balance': True,\n", - " 'subsample_fraction': None,\n", - " 'transform': None,\n", - " 'target_transform': None,\n", - " 'batch_size': 256,\n", - " 'shuffle': True,\n", - " 'num_workers': 0,\n", - " 'cuda': True,\n", - " 'seed': 4711},\n", - " 'model': 'CharacterModel',\n", - " 'metrics': ['accuracy'],\n", - " 'network': 'MLP',\n", - " 'network_args': {'input_size': 784, 'num_layers': 2},\n", - " 'train_args': {'batch_size': 256, 'epochs': 16},\n", - " 'criterion': 'CrossEntropyLoss',\n", - " 'criterion_args': {'weight': None, 'ignore_index': -100, 'reduction': 'mean'},\n", - " 'optimizer': 'AdamW',\n", - " 'optimizer_args': {'lr': 0.0003,\n", - " 'betas': [0.9, 0.999],\n", - " 'eps': 1e-08,\n", - " 'weight_decay': 0,\n", - " 'amsgrad': False},\n", - " 'lr_scheduler': 'OneCycleLR',\n", - " 'lr_scheduler_args': {'max_lr': 3e-05, 'epochs': 16}}" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "experiment_config" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "import importlib" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "network_module = importlib.import_module(\"text_recognizer.networks\")\n", - "network_fn_ = getattr(network_module, experiment_config[\"network\"])\n", - "network_args = experiment_config.get(\"network_args\", {})" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1, 784)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(1,) + (network_args[\"input_size\"],)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer_ = getattr(torch.optim, experiment_config[\"optimizer\"])\n", - "optimizer_args = experiment_config.get(\"optimizer_args\", {})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer_" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer_args" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "network_args" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "network_fn_" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "net = network_fn_(**network_args)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer_(net.parameters() , **optimizer_args)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "criterion_ = getattr(torch.nn, experiment_config[\"criterion\"])\n", - "criterion_args = experiment_config.get(\"criterion_args\", {})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "criterion_(**criterion_args)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "models_module = importlib.import_module(\"text_recognizer.models\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "metrics = {metric: getattr(models_module, metric) for metric in experiment_config[\"metrics\"]}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "torch.randn(3, 10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "torch.randn(3, 1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "metrics['accuracy'](torch.randn(3, 10), torch.randn(3, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "metric_fn_ = getattr(models_module, experiment_config[\"metric\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "metric_fn_" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "2.e-3" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lr_scheduler_ = getattr(\n", - " torch.optim.lr_scheduler, experiment_config[\"lr_scheduler\"]\n", - ")\n", - "lr_scheduler_args = experiment_config.get(\"lr_scheduler_args\", {})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"OneCycleLR\" in str(lr_scheduler_)" + "block = BasicBlock(32, 64)\n", + "block(dummy).shape\n", + "print(block)" ] }, { @@ -693,9 +66,11 @@ "metadata": {}, "outputs": [], "source": [ - "datasets_module = importlib.import_module(\"text_recognizer.datasets\")\n", - "data_loader_ = getattr(datasets_module, experiment_config[\"dataloader\"])\n", - "data_loader_args = experiment_config.get(\"data_loader_args\", {})" + "dummy = torch.ones((1, 32, 10, 10))\n", + "\n", + "block = BottleNeckBlock(32, 64)\n", + "block(dummy).shape\n", + "print(block)" ] }, { @@ -704,422 +79,10 @@ "metadata": {}, "outputs": [], "source": [ - "data_loader_(**data_loader_args)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "cuda = \"cuda:0\"" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import re\n", - "cleanString = re.sub('[^A-Za-z]+','', cuda )" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "cleanString = re.sub('[^0-9]+','', cuda )" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'0'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cleanString" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "([28, 28], 1)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "([28, 28], ) + (1,)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(range(3-1))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1,)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tuple([1])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from glob import glob" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/weights/CharacterModel_Emnist_MLP_weights.pt']" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "glob(\"/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/weights/CharacterModel_*MLP_weights.pt\")" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "def test(a, b, c, d):\n", - " print(a,b,c,d)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "f = {\"a\": 2, \"b\": 3, \"c\": 4}" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dict_items([('a', 2), ('b', 3), ('c', 4)])\n" - ] - } - ], - "source": [ - "print(f.items())" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2 3 4 1\n" - ] - } - ], - "source": [ - "test(**f, d=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "path = \"/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/*\"" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "l = glob(path)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "l.sort()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "'/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_124928' in l" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_124928',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_141139',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_141213',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_141433',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_141702',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_145028',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_150212',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_150301',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_150317',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_151135',\n", - " 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'/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_180741',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_181933',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_183347',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_190044',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_190633',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_190738',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_191111',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_191310',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_191412',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_191504',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0721_191826',\n", - " '/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/training/experiments/CharacterModel_Emnist_MLP/0722_191559']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "l" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "class ModeKeys:\n", - " \"\"\"Mode keys for CallbackList.\"\"\"\n", + "dummy = torch.ones((1, 64, 48, 48))\n", "\n", - " TRAIN = \"train\"\n", - " VALIDATION = \"validation\"" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m = ModeKeys()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'train'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m.TRAIN" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.00000000e-05, 1.26485522e-05, 1.59985872e-05, 2.02358965e-05,\n", - " 2.55954792e-05, 3.23745754e-05, 4.09491506e-05, 5.17947468e-05,\n", - " 6.55128557e-05, 8.28642773e-05, 1.04811313e-04, 1.32571137e-04,\n", - " 1.67683294e-04, 2.12095089e-04, 2.68269580e-04, 3.39322177e-04,\n", - " 4.29193426e-04, 5.42867544e-04, 6.86648845e-04, 8.68511374e-04,\n", - " 1.09854114e-03, 1.38949549e-03, 1.75751062e-03, 2.22299648e-03,\n", - " 2.81176870e-03, 3.55648031e-03, 4.49843267e-03, 5.68986603e-03,\n", - " 7.19685673e-03, 9.10298178e-03, 1.15139540e-02, 1.45634848e-02,\n", - " 1.84206997e-02, 2.32995181e-02, 2.94705170e-02, 3.72759372e-02,\n", - " 4.71486636e-02, 5.96362332e-02, 7.54312006e-02, 9.54095476e-02,\n", - " 1.20679264e-01, 1.52641797e-01, 1.93069773e-01, 2.44205309e-01,\n", - " 3.08884360e-01, 3.90693994e-01, 4.94171336e-01, 6.25055193e-01,\n", - " 7.90604321e-01, 1.00000000e+00])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.logspace(-5, 0, base=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.018420699693267165" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.random.choice(np.logspace(-5, 0, base=10))" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "import tqdm" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tqdm.notebook.tqdm_notebook" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tqdm.auto.tqdm" + "layer = ResidualLayer(64, 128, block=BasicBlock, num_blocks=3)\n", + "layer(dummy).shape" ] }, { @@ -1128,380 +91,107 @@ "metadata": {}, "outputs": [], "source": [ - "tqdm.auto.tqdm" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "def test():\n", - " for i in tqdm.auto.tqdm(range(9)):\n", - " pass\n", - " print(i)\n", - " " - ] - }, - { - "cell_type": "code", - "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" - ] - } - ], - "source": [ - "test()" - ] - }, - { - "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()" + "blocks_sizes=[64, 128, 256, 512]\n", + "list(zip(blocks_sizes, blocks_sizes[1:]))" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 69, "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)" + "e = Encoder(depths=[2, 1], block_sizes= [96, 128])" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 75, "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", + "Encoder(\n", + " (gate): Sequential(\n", + " (0): Conv2d(1, 96, kernel_size=(3, 3), stride=(2, 2), padding=(3, 3), bias=False)\n", + " (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU(inplace=True)\n", + " (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", + " )\n", + " (blocks): Sequential(\n", + " (0): ResidualLayer(\n", + " (blocks): Sequential(\n", + " (0): BasicBlock(\n", + " (blocks): Sequential(\n", + " (0): Sequential(\n", + " (0): Conv2dAuto(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " (1): ReLU(inplace=True)\n", + " (2): Sequential(\n", + " (0): Conv2dAuto(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (activation_fn): ReLU(inplace=True)\n", + " (shortcut): None\n", + " )\n", + " (1): BasicBlock(\n", + " (blocks): Sequential(\n", + " (0): Sequential(\n", + " (0): Conv2dAuto(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " (1): ReLU(inplace=True)\n", + " (2): Sequential(\n", + " (0): Conv2dAuto(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (activation_fn): ReLU(inplace=True)\n", + " (shortcut): None\n", + " )\n", + " )\n", + " )\n", + " (1): ResidualLayer(\n", + " (blocks): Sequential(\n", + " (0): BasicBlock(\n", + " (blocks): Sequential(\n", + " (0): Sequential(\n", + " (0): Conv2dAuto(96, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " (1): ReLU(inplace=True)\n", + " (2): Sequential(\n", + " (0): Conv2dAuto(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(128, 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(96, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + " (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " )\n", + " )\n", " )\n", ")" ] }, - "execution_count": 12, + "execution_count": 75, "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)" + "Encoder(**{\"depths\": [2, 1], \"block_sizes\": [96, 128]})" ] }, { "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, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -1510,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -1520,41 +210,50 @@ "----------------------------------------------------------------\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", + " Conv2d-1 [-1, 96, 16, 16] 864\n", + " BatchNorm2d-2 [-1, 96, 16, 16] 192\n", + " ReLU-3 [-1, 96, 16, 16] 0\n", + " MaxPool2d-4 [-1, 96, 8, 8] 0\n", + " Conv2dAuto-5 [-1, 96, 8, 8] 82,944\n", + " BatchNorm2d-6 [-1, 96, 8, 8] 192\n", + " ReLU-7 [-1, 96, 8, 8] 0\n", + " ReLU-8 [-1, 96, 8, 8] 0\n", + " Conv2dAuto-9 [-1, 96, 8, 8] 82,944\n", + " BatchNorm2d-10 [-1, 96, 8, 8] 192\n", + " ReLU-11 [-1, 96, 8, 8] 0\n", + " ReLU-12 [-1, 96, 8, 8] 0\n", + " BasicBlock-13 [-1, 96, 8, 8] 0\n", + " Conv2dAuto-14 [-1, 96, 8, 8] 82,944\n", + " BatchNorm2d-15 [-1, 96, 8, 8] 192\n", + " ReLU-16 [-1, 96, 8, 8] 0\n", + " ReLU-17 [-1, 96, 8, 8] 0\n", + " Conv2dAuto-18 [-1, 96, 8, 8] 82,944\n", + " BatchNorm2d-19 [-1, 96, 8, 8] 192\n", + " ReLU-20 [-1, 96, 8, 8] 0\n", + " ReLU-21 [-1, 96, 8, 8] 0\n", + " BasicBlock-22 [-1, 96, 8, 8] 0\n", + " ResidualLayer-23 [-1, 96, 8, 8] 0\n", + " Conv2d-24 [-1, 128, 4, 4] 12,288\n", + " BatchNorm2d-25 [-1, 128, 4, 4] 256\n", + " Conv2dAuto-26 [-1, 128, 4, 4] 110,592\n", + " BatchNorm2d-27 [-1, 128, 4, 4] 256\n", + " ReLU-28 [-1, 128, 4, 4] 0\n", + " ReLU-29 [-1, 128, 4, 4] 0\n", + " Conv2dAuto-30 [-1, 128, 4, 4] 147,456\n", + " BatchNorm2d-31 [-1, 128, 4, 4] 256\n", + " ReLU-32 [-1, 128, 4, 4] 0\n", + " ReLU-33 [-1, 128, 4, 4] 0\n", + " BasicBlock-34 [-1, 128, 4, 4] 0\n", + " ResidualLayer-35 [-1, 128, 4, 4] 0\n", "================================================================\n", - "Total params: 77,152\n", - "Trainable params: 77,152\n", + "Total params: 604,704\n", + "Trainable params: 604,704\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", + "Forward/backward pass size (MB): 1.69\n", + "Params size (MB): 2.31\n", + "Estimated Total Size (MB): 4.00\n", "----------------------------------------------------------------\n" ] } @@ -1562,99 +261,6 @@ "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": [], - "source": [] } ], "metadata": { diff --git a/src/notebooks/01-look-at-emnist.ipynb b/src/notebooks/01-look-at-emnist.ipynb index 8648afb..93083a5 100644 --- a/src/notebooks/01-look-at-emnist.ipynb +++ b/src/notebooks/01-look-at-emnist.ipynb @@ -31,16 +31,104 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = EmnistDataset(train=False, sample_to_balance=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from torch.utils.data import random_split, DataLoader" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "dataset = EmnistDataset()" + "d1, d2 = random_split(dataset, [len(dataset)-10, 10])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "55898" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(d1)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "dl = DataLoader(d1, batch_size=16)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3494" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(dl)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "only one element tensors can be converted to Python scalars", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m<ipython-input-14-69c3b5027f10>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\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[0m", + "\u001b[0;31mValueError\u001b[0m: only one element tensors can be converted to Python scalars" + ] + } + ], + "source": [ + "d1.dataset.data[0]" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -49,7 +137,7 @@ "torch.Tensor" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -60,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -69,8 +157,8 @@ "text": [ "EMNIST Dataset\n", "Num classes: 80\n", - "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F', 16: 'G', 17: 'H', 18: 'I', 19: 'J', 20: 'K', 21: 'L', 22: 'M', 23: 'N', 24: 'O', 25: 'P', 26: 'Q', 27: 'R', 28: 'S', 29: 'T', 30: 'U', 31: 'V', 32: 'W', 33: 'X', 34: 'Y', 35: 'Z', 36: 'a', 37: 'b', 38: 'c', 39: 'd', 40: 'e', 41: 'f', 42: 'g', 43: 'h', 44: 'i', 45: 'j', 46: 'k', 47: 'l', 48: 'm', 49: 'n', 50: 'o', 51: 'p', 52: 'q', 53: 'r', 54: 's', 55: 't', 56: 'u', 57: 'v', 58: 'w', 59: 'x', 60: 'y', 61: 'z', 62: ' ', 63: '!', 64: '\"', 65: '#', 66: '&', 67: \"'\", 68: '(', 69: ')', 70: '*', 71: '+', 72: ',', 73: '-', 74: '.', 75: '/', 76: ':', 77: ';', 78: '?', 79: '_'}\n", "Input shape: [28, 28]\n", + "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F', 16: 'G', 17: 'H', 18: 'I', 19: 'J', 20: 'K', 21: 'L', 22: 'M', 23: 'N', 24: 'O', 25: 'P', 26: 'Q', 27: 'R', 28: 'S', 29: 'T', 30: 'U', 31: 'V', 32: 'W', 33: 'X', 34: 'Y', 35: 'Z', 36: 'a', 37: 'b', 38: 'c', 39: 'd', 40: 'e', 41: 'f', 42: 'g', 43: 'h', 44: 'i', 45: 'j', 46: 'k', 47: 'l', 48: 'm', 49: 'n', 50: 'o', 51: 'p', 52: 'q', 53: 'r', 54: 's', 55: 't', 56: 'u', 57: 'v', 58: 'w', 59: 'x', 60: 'y', 61: 'z', 62: ' ', 63: '!', 64: '\"', 65: '#', 66: '&', 67: \"'\", 68: '(', 69: ')', 70: '*', 71: '+', 72: ',', 73: '-', 74: '.', 75: '/', 76: ':', 77: ';', 78: '?', 79: '_'}\n", "\n" ] } @@ -81,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -94,17 +182,17 @@ " ax.imshow(x, cmap='gray')\n", " ax.set_xticks([])\n", " ax.set_yticks([])\n", - " ax.set_title(dataset.translator(int(y)))" + " ax.set_title(dataset.mapper(int(y)))" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 648x648 with 9 Axes>" ] @@ -119,12 +207,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 648x648 with 9 Axes>" ] @@ -139,43 +227,10 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1, 28, 28])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset[0][0].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.int64" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset[0][1].dtype" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", diff --git a/src/notebooks/02b-emnist-lines-dataset.ipynb b/src/notebooks/02b-emnist-lines-dataset.ipynb index edd3956..a7aabeb 100644 --- a/src/notebooks/02b-emnist-lines-dataset.ipynb +++ b/src/notebooks/02b-emnist-lines-dataset.ipynb @@ -2,18 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -31,11 +22,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "from text_recognizer.datasets import EmnistDataset, EmnistLinesDataLoaders, EmnistLinesDataset, Transpose, construct_image_from_string, get_samples_by_character" + "from text_recognizer.datasets import EmnistDataset, EmnistLinesDataset, Transpose, construct_image_from_string, get_samples_by_character" ] }, { @@ -50,23 +41,31 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2020-08-23 22:01:45.373 | DEBUG | text_recognizer.datasets.emnist_lines_dataset:_load_data:162 - EmnistLinesDataset loading data from HDF5...\n" + ] + } + ], "source": [ - "emnist_lines = EmnistLinesDataset(train=True, emnist=emnist_train)" + "emnist_lines = EmnistLinesDataset(train=False)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2020-08-04 23:29:22.523 | DEBUG | text_recognizer.datasets.emnist_lines_dataset:_load_data:153 - EmnistLinesDataset loading data from HDF5...\n" + "2020-08-23 22:01:46.598 | DEBUG | text_recognizer.datasets.emnist_lines_dataset:_load_data:162 - EmnistLinesDataset loading data from HDF5...\n" ] } ], @@ -76,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -86,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 17, "metadata": { "scrolled": false }, @@ -95,20 +94,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "412 We____________________________\n", - "new_______________________________\n", - "decided___________________________\n", - "indictment the 10000 bond was_____\n", - "of possessions and living plays___\n", - "Lillys____________________________\n", - "life______________________________\n", - "in circles making_________________\n", - "enlist____________________________\n" + "office in Arkansas after the______\n", + "in________________________________\n", + "by a oneshot technique____________\n", + "office Incumbent__________________\n", + "of the revolutionary______________\n", + "they______________________________\n", + "the scene but_____________________\n", + "Knox Ky___________________________\n", + "workers wife refused to have______\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -120,7 +119,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -132,7 +131,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -144,7 +143,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -156,7 +155,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -168,7 +167,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -180,7 +179,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -192,7 +191,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -204,7 +203,7 @@ }, { "data": { - "image/png": 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QWI69ubmZ7du38/TTT7N3797CilxHjx7l5MmTxOPxD4QMs7OzdHd3881vfpNXXnmF3t5eMpnMVcOImZkZXnjhBZLJJF6vl507d1JZWcnWrVvZv3//goKfSCTC4OAg09PTeL1ejDHU1tZSWlqKx+NZ0uDH5XIRDAYJhUJMTU0xMTGxqPPFYjHa2tro7++ns7OT3//932fjxo2Ul5dz//33s3v3biKRCHv37uXIkSOcP3+eoaEhRkdHOX36NIlEgtbWVqLR6FW/5/nvpK6ujurqampqatixYwcAX/va1wp9mhwOB06nk4ceeogHH3yQH//4x/zxH/8xb775pqZ+iYiIiIiIiOStiODH4XAQCoVoaGigsbGxsHrX4OAg7777Lm+//Tb9/f2F0MfpdOL1elmzZg2PP/4427dvZ9OmTXi9XmZnZxkaGuLw4cO0trZ+oEcMQCKRoKenh+7ubsbGxha8mtb8tLCRkRFmZ2cLfXout1T8L7p4ulQsFqOsrAyA6upqNm/eTHt7O5OTk0sSWhhjaG5uZt++fezdu5fDhw/zzDPPLHq5+Gw2Szwe58yZMxw8eJCxsTHuvvvuQmVWOBxm9+7dVFdXc/78ed58803GxsZ45ZVXiMVinD59mqmpqat+Rmstzz77LG+99RYPP/wwn/3sZwuNoedDn/nm2POcTiePPvooL7/8Mu3t7YsOuURERERERERWi2UPfowxlJSUsGPHDj72sY+xYcMGioqK6O/v5/vf/z5vvfUWg4ODGGOoqakhHA5TVVVFc3MzDQ0N7Nu3j7KyMowxdHV10dnZydGjRwtTf1Kp1CUraQFMT08zNDREJBK5rtW0YrEYXV1djI6OkkwmC316ioqKrtmwGOaqhub71qxfv74QeNXX11NeXo4xZsmCn5qaGnbu3Mn9999PeXk5HR0dvP7668zMzCzqGul0mqGhIZ577jleeeUV7r33Xvbs2cOmTZuoqKigrKyMUCjEhg0baGho4P3336ejo4O+vj5GR0cvmaZ3JWNjY7zzzjt0dXXx0ksvUV5eTmlpKZWVlTQ1NbFnzx7uuOMO0uk0R44cYe/evSQSicI0OhERERERERGZs+zBj8PhIBwO09LSUujPAxCNRguBQTAYJBwOs379eurq6gqratXU1LBhwwastfT09NDW1sa7777Lm2++yeDgIJlMBo/HQyAQIBAIFAKg+WqhYDD4gSXfryadThcChmw2Sy6XY3p6esFNhWdnZxkfH+fChQuF8MXtduP3+ykqKrr+L+8a18pkMgQCAbZv387nPvc5zpw5Q39//4IrnC7HWksymaSzs5O+vj6GhoY4d+4cDQ0N1NfXc/fdd7Nt2zaCwSBbt24lGAxSVVVFZWUlPT09XLhwgYmJiauGT/OVRfF4nN7eXrxeL16vl+LiYjZt2oTf72fnzp2k02na2toYHBzk/fff5/jx44WqMBERERERERFZ5uDH4XDg9Xppampi27ZtrF+/vhDQ+Hw+1q9fT1FREQ6Hg8rKShobG6mtrWXNmjXU1NTgdDrp7e0lEonQ2trKiRMnaGtro7OzsxBuOBwO3G43LperEDbMTysrLy+/rt461trCOeZXBZucnFxQtQ9AJpMhHo8zPj5+ybl+cerSYllrGRsbY2BggNnZWdasWcMjjzzCc889d11T264mm80yPT1NZ2cng4ODBINBKioqaG9v5/HHH2fjxo00NTXR3NzM+vXraWpqIpFIcPToUd54440FVx7Nh2vT09OFCq3+/v7CCmmtra20tbXx3nvvMTU1dc1eSyIiIiIiIiK3k2ULfub79NTU1PDoo4/y0EMP0dDQgN/vxxhDU1MTv/mbv0k6ncblcuH1eguVMalUikgkwsDAAC+88AJnzpzh1KlT9Pb2Mj09fUmwkU6niUQijIyMkE6ncTqduFwu/H4/brd7Qf15lpK19qaHE/ONpIeGhpiZmSlMp2tpaaGjo4Pp6eklG4O1lkQiQSKRYGRkhK6uLk6dOsVDDz3E5z//eTZt2kQoFGLTpk382q/9Gi0tLYXKoxuZluV0OgtVWrOzsxw4cIDOzk41dBYRERERERG5jGULftxuN+Xl5bS0tHDXXXdRVVWF1+stNO/1er1UV1cDFEKK+eqP8fFx3nnnHU6dOsX+/fsZHh5mYmLislUk2Wy20FdnZmaGoqKi2yIkmK8omv+s4XCYT37yk5w8eZLJyclFN3q+0jVnZmbo6urC5/OxceNG/H4/DQ0NuFwuqqqq2LJlC6WlpQwPD99Q8LNu3TqqqqoASKVSS1K9JCIiIiIiIrJaLVvwM79CUygUYv369YRCIZxOJ1NTU4U+Oul0mlwux+nTp+nt7WVsbIyRkRHOnz/PiRMniMfjl12162K5XI7BwUE6OzuJxWIUFxcXtq9m2WyWZDJZmMZmjGHz5s1UVVXh8XgWFPwYYwiHw3g8HqampgrNk6+1Ktfo6Civv/463d3dvPbaa+zZs4etW7dy3333UV9fz0c+8hHGxsYYGhpacH+kefMNpCcnJzl06BB9fX23RZAnIiIiIiIiciOWLfhJp9NMTk5y7tw5urq6CIfDOJ3OQkjT399PNBrFWsupU6fo7+8nEokQi8WIx+OMjY1dM4SYl8vlyOVyhQqY+dW5xsbGrmtVrw+TRCLB+fPnOXnyJC0tLTgcDlwu12WXQ78ch8NBbW0tX/rSl6itreXs2bO0t7fT0dHB6dOnr1mtk8lk6O/v56c//Snvvfce99xzD83NzVRUVPDUU09x+vRp4vH4dfflmZiYIBqN4na7qaysXPD7RERERERERG5Hyxb8ZLNZEokEg4ODnD17lubmZgKBAIlEgv7+ft555x2OHz9OJpNhfHycRCJBOp0mm82SzWZxOBwEg0HcbjepVOqaS3lfPO1pZmaGwcHBQmPm5ZTNZpmdnb3uypdryWQyxGKxwpQqj8eDz+ejurqa4uJiJiYmrvp+t9tNY2MjDzzwADU1NdTU1BAOh3G5XIyOjhKJRAorm12OMYZcLkcikSASiTA5OUk2m8XpdFJVVUUgEMDluv4/fqWlpRQXF+N0Ogv9oERERERERETk8pYt+LHWkslkmJqaYnx8vLBEutfrJRAI4HA4GBwcZHp6mng8Ti6Xw+Px4Ha78Xg8eDweysrK8Pv9jI+PMzw8zNTU1IKunUqlCn1ubiRwmQ+fbqRHzS+anp4uhFBLOWUpl8sxOTlJT08PiUSisKz9unXrKC0tvWRJ+ctxuVzU19fT0NBAOBwu/L6UlJRQUlLC6dOnmZqaYnBwkGQyeUkA5Ha7KS4uprS0FL/fz5o1a9ixY0chqLk4hLtegUAAr9dLOp1mcHBw1U/ZExEREREREVmMZV3OfX6Fq6mpKaampkin05SXl7NlyxZSqRSjo6NMTEzQ09MDQFlZGcXFxbjdbvx+PxUVFcTjcYwxTE5OLjj4yeVyheqhGwkgZmdnSSQSAPh8Plwu14JDIGPMJVUq8Xic7u5uRkZGljT4sdYSjUbp6OhgYmKCcDiMz+ejsrKSYDBYCGCuNk63283MzAw+n4+SkhKKi4vZsGEDu3fv5vjx44yPj3PixAkmJiYKTZaNMQSDQdauXUtDQwMlJSWUlpaybt06SkpKyOVy9Pf3MzU1tajgLJFI0N7eruBHRERERERE5CqWNfiBuV4/7e3tHD9+vDC9aNu2bWzcuJG9e/cSjUZ54403sNayadMm1q5dS1FREel0mmg0yl/+5V8SjUavq1ePy+UiEAjg8XhwOBzXFR7MT1+anJwkEAjQ2NhIKBQqBEnXMt/Uel4mk2FmZuamTDlLpVJEo9HCtK75ay9ketTMzAwHDx6kurqazZs3s3nzZiorKwmFQmzZsoXNmzcXpuGlUqkPVPwEAoFCw+75sCuXyzE+Ps4Pf/hDzp49SyKRWFRwc/H3KCIiIiIiIiIfdM3gxxhTB/wtUAVY4FvW2m8aY/4A+A/AaP7Qr1tr91/vANLpNG1tbYXVtnw+H/X19Xg8Hqqrq1mzZg2lpaVYawkGg3g8HmZnZzl//jwdHR2cPHmS/v7+61qePBAIUFtbS2lpKUVFRddVeZLL5XA4HAQCAcrKyqivrycQCFyzZw7MBRVFRUX4fL5C+HIzK1aSySR9fX288cYb3HnnnQD4/f5CldLVwqZsNsvQ0BAvvPACra2tbN++ncbGRhobG7njjjsoKSkpLNF+sfnP9YshUy6XK/RWam1tJRqNLqqvUVlZGY8++ijf+MY3lmTKnYiIiIiIiMhqtJCKnwzwVWvtu8aYEHDMGPNKft+fWmv/aDEDyGaz9Pb2Mj4+zttvv80bb7xBY2MjHo+n0MB327ZtxONx+vr6GB8fZ2xsjKNHjzI4OMjAwMCCpmxZawt9eTKZTGGVr+sd69jYGO+++y4VFRU0NDTQ1dXF1NTUgs4VDAZpbGzknnvuAeaCmRMnTtDX10cymbyusSxELpcjlUoRj8ex1uJyudi+fTvNzc0cP36csbGxq75/dnaWU6dOcebMGQ4fPozf76eyspK77rqLzZs3EwwGufvuuykrK8Ptdhfe53Q68Xq9pFIpMpkMqVSKsbEx/vmf/5mTJ0/S2tp6XUHdlajiR0REREREROTqrhn8WGsHgcH8z3FjTDtQu5SDuHiFryNHjnDq1ClcLhdOp5NAIEBfXx/RaJSenh4ikQiJRIK+vj5mZmYWVO2RTCbp7Ozk+eefp6ysjMnJSdra2uju7r7u5dxTqRRHjx6lp6eHYDBINBpdcPVKJpNhcnKSzs5OioqK6O7u5tlnn6Wjo+OmBD/w8+92dnYWay2Tk5PE4/EFV8nMh0fpdLowxW1gYIDDhw9TVFREbW0tXq/3khW6/H4/4XCYaDRKMpkkmUwyMzPDhQsXiEajhWbdN6KoqKgQMqm/j4iIiIiIiMjVXVePH2NMA3AXcBR4APiKMeZXgXeYqwq69nynK5ifCjQwMMDo6GihL4zb7WZgYIBUKkUsFiusxDUzM7Pgf/inUil6e3s5cOAAPp+PRCLB8PBwYUny62GtZXR0lGg0itPpJJ1OFxobX8vs7Cznzp3ju9/9LtXV1QwNDfHuu+8yMTFxU0IMay0zMzMcP36cF198EZ/Px7Fjxzhz5sx1V9zMV0xls1mSySSRSARjDENDQ4U+PjA3xcvj8RAKhQqBUyaTKQRQN1JpdbF0Ol34s3D69OlFTRcTERERERERWe3MQv8RbowJAoeBP7TW/oMxpgoYY67vz/8Aaqy1/+4y7/si8MX8y3tuaJD5UOFGAwNjDF6vt9BsOJPJkEwmmZ6evuHg4OIGydczLpfLhd/vx+PxkEwmF93g+FqcTidr1qxhz549eDweenp66O7uZnR09NpvXoDLTbdyOp243e5C4DP//SzF57zvvvvYt28fjY2NHDx4kO985zuLPqeIiIiIiIjIh9wxa+29l9uxoODHGOMGfgK8ZK39k8vsbwB+Yq3dfo3zLN165XJdnE4nMBdSfZinSM0vFx8MBkkkEsRiseUekoiIiIiIiMhyu2Lws5BVvQzw10D7xaGPMaYm3/8H4F8BrUsxUrk5VsuUKGst8XiceDy+3EMRERERERERWfGuWfFjjHkQeA14H5gvFfk68MvAncxN9ToP/MeLgqArnWsUSDA3RUxEVq416D4VWel0n4p8OOheFVn5dJ/KarDeWltxuR0L7vGzVIwx71yp/EhEVgbdpyIrn+5TkQ8H3asiK5/uU1ntPtiZV0REREREREREVgUFPyIiIiIiIiIiq9RyBD/fWoZrisj10X0qsvLpPhX5cNC9KrLy6T6VVe2W9/gREREREREREZFbQ1O9RERERERERERWqVsW/BhjPmGM6TDGnDPG/O6tuq6IXMoYU2eMedUYc8oY02aM+e389jJjzCvGmLP5/4bz240x5s/y9+57xpi7l/cTiNxejDFOY8xxY8xP8q8bjTFH8/fk940xRfntnvzrc/n9Dcs6cJHbhDGm1BjzvDHmtDGm3Rhzv56pIiuPMeY/5//u22qM+a4xxqtnqtwubknwY4xxAv8X+BfAVuCXjTFbb8W1ReQDMsBXrbVbgfuAL+fvx98FDlhrNwEH8q9h7r7dlP/1ReCvbv2QRW5rvw20X/T6fwN/aq1tAiaA38hv/w1gIr/9T/PHicjN903gp9bazcAdzN2veqaKrCDGmFrgt4B7rbXbASfwNHqmym3iVlX87AbOWWu7rLUp4HvAZ27RtUXkItbaQWvtu/mf48z9BbWWuXvyb/KH/Q3wL/M/fwb4WzvnLaDUGFNza0ctcnsyxqwDPgU8k39tgEeA5/OH/OK9On8PPw98PH+8iNwkxpgS4KPAXwNYa1PW2kn0TBVZiVyAzxjjAvzAIHqmym3iVgU/tUDvRa/78ttEZBnly1bvAo4CVdbawfyuIaAq/7PuX5Hl83+A/wrk8q/LgUlrbSb/+uL7sXCv5vdH88eLyM3TCIwC/y8/JfMZY0wAPVNFVhRrbT/wR8AF5gKfKHAMPVPlNqHmziK3KWNMEPgh8DvW2tjF++zccn9a8k9kGRljPg2MWGuPLfdYROSKXMDdwF9Za+8CEvx8WhegZ6rISpDvs/UZ5sLatUAA+MSyDkrkFrpVwU8/UHfR63X5bSKyDIwxbuZCn+estf+Q3zw8X26e/+9IfrvuX5Hl8QDwS8aY88xNkX6EuV4ipfkydbj0fizcq/n9JcD4rRywyG2oD+iz1h7Nv36euSBIz1SRleVRoNtaO2qtTQP/wNxzVs9UuS3cquDnbWBTvmt6EXONtH58i64tIhfJz0/+a6DdWvsnF+36MfCF/M9fAH500fZfza9Ech8Qvah8XURuEmvt16y166y1Dcw9Nw9aa/8N8CrwZP6wX7xX5+/hJ/PHq8pA5Cay1g4BvcaYlvymjwOn0DNVZKW5ANxnjPHn/y48f6/qmSq3BXOr/vwaYz7JXK8CJ/CstfYPb8mFReQSxpgHgdeA9/l535CvM9fn5wdAPdADPGWtjeQfjn/BXDnsNPDr1tp3bvnARW5jxpiHgf9irf20MWYDcxVAZcBx4N9aa2eNMV7g75jr2xUBnrbWdi3TkEVuG8aYO5lrwF4EdAG/ztz/XNUzVWQFMcZ8A/g8cyvcHgf+PXO9fPRMlVXvlgU/IiIiIiIiIiJya6m5s4iIiIiIiIjIKqXgR0RERERERERklVLwIyIiIiIiIiKySin4ERERERERERFZpRT8iIiIiIiIiIisUgp+RERERERERERWKQU/IiIiIiIiIiKrlIIfEREREREREZFV6v8D5d0XhtC/r8wAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -231,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 18, "metadata": {}, "outputs": [ { diff --git a/src/notebooks/02c-image-patches.ipynb b/src/notebooks/02c-image-patches.ipynb index e1772a9..947611d 100644 --- a/src/notebooks/02c-image-patches.ipynb +++ b/src/notebooks/02c-image-patches.ipynb @@ -14,6 +14,7 @@ "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", @@ -38,12 +39,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "2020-08-11 19:26:46.938 | DEBUG | text_recognizer.datasets.emnist_lines_dataset:_load_data:159 - EmnistLinesDataset loading data from HDF5...\n" + "2020-08-30 21:31:37.785 | DEBUG | text_recognizer.datasets.emnist_lines_dataset:_load_data:164 - EmnistLinesDataset loading data from HDF5...\n" ] } ], "source": [ - "emnist_lines = EmnistLinesDataset(train=True)" + "emnist_lines = EmnistLinesDataset(train=False)" ] }, { @@ -65,20 +66,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "412 We____________________________\n", - "new_______________________________\n", - "decided___________________________\n", - "indictment the 10000 bond was_____\n", - "of possessions and living plays___\n", - "Lillys____________________________\n", - "life______________________________\n", - "in circles making_________________\n", - "enlist____________________________\n" + "office in Arkansas after the______\n", + "in________________________________\n", + "by a oneshot technique____________\n", + "office Incumbent__________________\n", + "of the revolutionary______________\n", + "they______________________________\n", + "the scene but_____________________\n", + "Knox Ky___________________________\n", + "workers wife refused to have______\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -90,7 +91,7 @@ }, { "data": { - "image/png": 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ll15idHSUbDZLeXk5tbW1xeVZ79wGfiGM2rJlC42NjVRVVRW3dF/csNnv99PQ0MCuXbvYvn07AH19fZw7d46ZmRn19xERERERERGRq1qW4GdhF60DBw5w4MABSktLSSaTTE1NMT4+zszMzA1X+ywOVxaqdBaqYWKxGG+++SYvvPACZkZnZyef//zn6e7upru7m1OnTpFOp68Z+CxU+WzcuJG7776bhx56iJ07d7J161b6+/uZmppidna2uExrcnKy2PfH7/dTVVXFhg0bCAQC1NbW0tHRwRNPPEFjYyOBQIBsNktpaSlVVVV4nkcul2PdunW8733v49FHH6WtrY1IJMLPfvYzent7icViN/cvQERERERERETWhGVt7rwgmUwyPj5e7K1zow2L/X4/nufheV5xaVUikSguq8pkMly4cIEXX3yRUCjE1q1befTRR9m6dStbtmzhueeeo6+vj0wmQzqdxsyKW6l7nkcoFCpWKG3fvp09e/awY8cOmpubKSsrIxaLcejQId566y2mpqbIZDLF3bcymQwAtbW1PPHEE7S3t+Oco7m5mZqaGpqbmwkGgzjnCAQCNDY28thjj3H06FHOnTvHxo0b2bVrFw0NDQCcOnWK4eFh4vG4mjqLiIiIiIiIyHtaluAnl8sxMjLCkSNHiMfjtLe3MzQ0xKlTp3jrrbduqKmzz+ejsrKS1tZW2tvbqaysxDnH6dOnOX36dLFXztzcHJFIhJGRETKZDNXV1QQCAe677z6SySSNjY0kEgmmp6cpKSlhYmKCTCZDbW1tcSlWa2srO3fupKOjg/r6ekpLS0mlUvT29tLT08Pk5ORllUoLW8LncjkCgQCtra3U1tZiZtTW1lJaWlrcwt45R2lpKcFgkJaWFj70oQ/R2tpa7INUXl5OJpNhYGCAWCxGJpPRMi8REREREREReU/LVvEzMTHByZMnGR8fp6GhgYsXLzI8PFwMXK7Xws5adXV11NfXU15ejnOO4eFhhoeHSaVSAMzPzzM+Pk5vby8DAwN0dnbieR7t7e2UlJSwY8cOkskkMzMzxeAnm81SXV3Npk2bKC0tpbq6mvXr11NWVkYkEuHixYtcvHiRI0eO0NPTw+zsLM45zIxcLkcikeCtt94il8tRX1+P53nU1dVdttPX6OgoAwMD+Hw+mpub2b59OxUVFezfv59t27ZRUVFBQ0MDzjkmJiY4ceIEiURC1T4iIiIiIiIick3LFvykUikikQgzMzOEw2GSySTJZJJ0On3Dn+X3+wkEAgQCAcyMdDpNNBpldna22OcnnU4TiUR47bXXeO6553jqqaeorKws7pq10GfHOUcqlSpWCnmeR3l5Oel0ujjWRCLB4OAgJ06c4Pz585w4caK4jTvkewxlMhlmZmZ4/vnnuXDhAjt37mTz5s3Fip+JiQmmpqY4evQo3d3deJ7H/v37aWtrIxQKsWvXrmKvolQqxdTUFAMDAxw9evSmviMRERERERERWXuWLfhZvL357djBa6GKZmF3rr6+Pqanp4vBz0KAMjw8zDe+8Q36+/upra0thkWBQIC77rqL9evXc+bMGaLRaLHyyDlHLBZjaGiIs2fPMjs7SyKRKL7nStU32WyWWCzG4cOHCYfDhMNh7rnnHtra2vD5fPT19REOhzl27Bh9fX0Eg0Fqa2sZHR2lsbGRYDBINpslnU5z6dIlBgYGeOONN5iYmNASLxERERERERG5LtcMfsysBfhroAFwwLecc18zs/8EfAG4VHjrl51zz92pgV5LLpdjfn6eRCLB2NgYAwMDzMzMvGvZmHOOkZERfv7zn1NaWloMjDzPe1fws7iyJpPJEI/HmZmZIZvNFnv3vJf5+XnOnDlDb28vL774IlVVVYRCIXw+HzMzM8Tj8eIOYH6/n0gkQjQa5ZFHHuHBBx/k7NmzDAwM8Prrr9PT08P4+DgTExO3/8sTERERERERkVXpeip+MsAfO+d+bWaVwHEzO1S49lXn3J/dueFdm3OO6elpjhw5QjqdJhgMcv78efr7+6/aJDqdTjM8PHzZzmIA/f39+Hw+5ufnrxjqLCy9upkxzs3NMTk5WaxuyuVyxWokyAdLExMTvPrqq0SjUcLhMP39/YTDYQYHBxkbGyOdTqvaR0RERERERESu2zWDH+fcKDBaeB0zs16g6U4P7HrlcjmSySRnzpwhFosRCoWIRqPFSpqruVKIMzc3d0fHms1mLwt73imZTHL+/HkikQjHjx8nmUySSqWYm5tjfn5eoY+IiIiIiIiI3BC7kTDBzNqAl4FdwL8H/gCYAV4nXxU0dY2fvyPJhZkRCoUIBoMEAgEymQxjY2N34lZLZqEaSWGPiIiIiIiIiFzDcefc/Ve6cN3Bj5lVAIeBrzjnfmxmDcA4+b4//wXY5Jz751f4uWeAZwqH993E4EVERERERERE5OpuLfgxswDwU+B559yfX+F6G/BT59yua3yOyldERERERERERG6vqwY/vmv9pOXXHH0X6F0c+pjZpkVv+zhw6lZHKSIiIiIiIiIit881K37M7ANAN3ASWNjq6svAZ4B7yC/1Og/8UaER9Ht91iUgTn6JmIisXHVonoqsdJqnIr8bNFdFVj7NU1kNWp1z9Ve6cEPNnW8HM3v9auVHIrIyaJ6KrHyapyK/GzRXRVY+zVNZ7a651EtERERERERERH43KfgREREREREREVmlliP4+dYy3FNEbozmqcjKp3kq8rtBc1Vk5dM8lVVtyXv8iIiIiIiIiIjI0tBSLxERERERERGRVWrJgh8zO2hmb5lZv5n96VLdV0QuZ2YtZvYPZnbGzE6b2ZcK59eZ2SEzO1v4Z23hvJnZXxTm7htmtmd5fwORtcXM/GbWY2Y/LRxvMbPXCnPy78ystHDeKxz3F663LevARdYIM6sxsx+Z2Ztm1mtm+/RMFVl5zOzfFf7ue8rM/sbMyvRMlbViSYIfM/MDfwU8CewAPmNmO5bi3iLyLhngj51zO4D3A/+yMB//FPiFc64L+EXhGPLztqvw5xngG0s/ZJE17UtA76Lj/wZ81TnXCUwBf1g4/4fAVOH8VwvvE5E772vAz51z24D3kZ+veqaKrCBm1gT8G+B+59wuwA98Gj1TZY1YqoqfB4F+59w551wa+FvgY0t0bxFZxDk36pz7deF1jPxfUJvIz8nvF972feAfFV5/DPhrl/f/gBoz27S0oxZZm8ysGfgI8J3CsQG/B/yo8JZ3ztWFOfwj4EDh/SJyh5hZNfAI8F0A51zaORdFz1SRlagEKDezEiAIjKJnqqwRSxX8NAFDi46HC+dEZBkVylbvBV4DGpxzo4VLF4GGwmvNX5Hl89+B/wDkCsfrgahzLlM4Xjwfi3O1cH268H4RuXO2AJeA/1FYkvkdMwuhZ6rIiuKcuwD8GRAmH/hMA8fRM1XWCDV3FlmjzKwC+D/Av3XOzSy+5vLb/WnLP5FlZGYfBSLOuePLPRYRuaoSYA/wDefcvUCc3y7rAvRMFVkJCn22PkY+rG0EQsDBZR2UyBJaquDnAtCy6Li5cE5EloGZBciHPv/TOffjwumxhXLzwj8jhfOavyLLYz/wtJmdJ79E+vfI9xKpKZSpw+XzsThXC9ergYmlHLDIGjQMDDvnXisc/4h8EKRnqsjK8hjwtnPuknNuHvgx+eesnqmyJixV8HMM6Cp0TS8l30jrJ0t0bxFZpLA++btAr3Puzxdd+gnw2cLrzwL/d9H5f1bYieT9wPSi8nURuUOcc//ROdfsnGsj/9x8yTn3T4B/AH6/8LZ3ztWFOfz7hferykDkDnLOXQSGzGxr4dQB4Ax6poqsNGHg/WYWLPxdeGGu6pkqa4It1X+/ZvYU+V4FfuB7zrmvLMmNReQyZvYBoBs4yW/7hnyZfJ+f/wVsBgaBTzrnJgsPx78kXw6bAD7nnHt9yQcusoaZ2YeAP3HOfdTM2slXAK0DeoB/6pybM7My4Afk+3ZNAp92zp1bpiGLrBlmdg/5BuylwDngc+T/56qeqSIriJn9Z+BT5He47QE+T76Xj56psuotWfAjIiIiIiIiIiJLS82dRURERERERERWKQU/IiIiIiIiIiKrlIIfEREREREREZFVSsGPiIiIiIiIiMgqpeBHRERERERERGSVUvAjIiIiIiIiIrJKKfgREREREREREVmlFPyIiIiIiIiIiKxS/x/9NWqTonpQoAAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -102,7 +103,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -114,7 +115,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -126,7 +127,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -138,7 +139,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -150,7 +151,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -162,7 +163,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -174,7 +175,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -199,26 +200,6 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1, 28, 952])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.shape" - ] - }, - { - "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], @@ -237,56 +218,26 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1, 28, 952])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([34])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "target.shape" + "patches = sliding_window(images=data.unsqueeze(0), patch_size=(28, 28), stride=(1, 14))" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "patches = sliding_window(images=data.unsqueeze(0), patch_size=(28, 28), stride=(1, 14))" + "from einops.layers.torch import Rearrange\n", + "slide = nn.Sequential(nn.Unfold(kernel_size=(28, 28), stride=(1, 14)), Rearrange(\"b (c h w) t -> b t c h w\", h=28, w=28, c=1))" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -295,18 +246,18 @@ "torch.Size([1, 67, 1, 28, 28])" ] }, - "execution_count": 12, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "patches.shape" + "slide(data.unsqueeze(0)).shape" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -316,14 +267,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 17, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "<Figure size 1440x1440 with 5 Axes>" ] @@ -350,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -359,24 +310,24 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2020-08-11 19:28:39.339 | DEBUG | text_recognizer.datasets.emnist_lines_dataset:_load_data:159 - EmnistLinesDataset loading data from HDF5...\n" + "2020-08-30 21:31:41.007 | DEBUG | text_recognizer.datasets.emnist_lines_dataset:_load_data:164 - EmnistLinesDataset loading data from HDF5...\n" ] } ], "source": [ - "dls = fetch_data_loaders([\"train\"], \"EmnistLinesDataset\", {}, batch_size=16, shuffle=True, cuda=False)" + "dls = fetch_data_loaders([\"train\"], \"EmnistLinesDataset\", {}, batch_size=2, shuffle=True, cuda=False)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -385,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -396,46 +347,6 @@ "cell_type": "code", "execution_count": 25, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([16, 1, 28, 952])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([16, 34])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, "outputs": [], "source": [ "patches = sliding_window(images=d, patch_size=(28, 28), stride=(1, 14))" @@ -443,49 +354,29 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([16, 67, 1, 28, 28])" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "patches.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 32, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "and the washedout final___________\n" + "might as well stand their_________\n" ] }, { "data": { "text/plain": [ - "<matplotlib.image.AxesImage at 0x7febf2ec4400>" + "<matplotlib.image.AxesImage at 0x7f3e806538e0>" ] }, - "execution_count": 32, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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"text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] @@ -501,17 +392,18 @@ "sentence = convert_y_label_to_string(t[0].numpy()) \n", "print(sentence)\n", "plt.title(sentence)\n", - "plt.imshow(d[0, 0], cmap='gray')" + "plt.imshow(d[0, 0], cmap='gray')\n", + "# plt.imshow(d[0, 0], cmap='gray')" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ "<Figure size 1440x1440 with 5 Axes>" ] @@ -528,258 +420,6 @@ " ax = fig.add_subplot(1, 5, i + 1)\n", " ax.imshow(patches[0, i].squeeze(0), cmap='gray')" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reshape patches for passing through a CNN" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "from einops import rearrange" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "x = rearrange(patches, \"b t c h w -> (b t) c h w\")" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1072, 1, 28, 28])" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "from torch import nn" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "cnn = nn.Conv2d(1, 16, 3, 3)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "xx = cnn(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1072, 16, 9, 9])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xx.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "h = rearrange(xx, \"(b t) c h w -> (b t) (c h w)\", b = 16, t=67)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "h = rearrange(xx, \"(b t) c h w -> b t (c h w)\", b = 16, t=67)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1072, 1296])" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "h.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "l = nn.Linear(1296, 128)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "hh = l(h)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1072, 128])" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hh.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "hhh = rearrange(hh, \"(b t) h -> t b h\", b = 16, t=67)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([67, 16, 128])" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hhh.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "rnn = nn.LSTM(128, 64)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "h0 = torch.randn(1, 16, 64)\n", - "c0 = torch.randn(1, 16, 64)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "output, (hn, cn) = rnn(hhh, (h0, c0))" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([67, 16, 64])" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "output.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/src/notebooks/03a-line-prediction.ipynb b/src/notebooks/03a-line-prediction.ipynb new file mode 100644 index 0000000..65c6dd6 --- /dev/null +++ b/src/notebooks/03a-line-prediction.ipynb @@ -0,0 +1,377 @@ +{ + "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.datasets import EmnistDataset, EmnistLinesDataset, Transpose, construct_image_from_string, get_samples_by_character" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from text_recognizer.models import LineCTCModel\n", + "from text_recognizer.networks import LineRecurrentNetwork" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2020-09-01 23:37:29.664 | DEBUG | text_recognizer.datasets.emnist_lines_dataset:_load_data:164 - EmnistLinesDataset loading data from HDF5...\n" + ] + } + ], + "source": [ + "emnist_lines = EmnistLinesDataset(train=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_y_label_to_string(y, emnist_lines=emnist_lines):\n", + " return ''.join([emnist_lines.mapper(int(i)) for i in y])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2020-09-01 23:37:30.291 | DEBUG | text_recognizer.models.base:load_weights:405 - Loading network with pretrained weights.\n" + ] + } + ], + "source": [ + "network_args = {\n", + " \"encoder\": \"ResidualNetworkEncoder\",\n", + " \"encoder_args\": {\n", + " \"in_channels\": 1,\n", + " \"num_classes\": 80,\n", + " \"depths\": 2,\n", + " \"block_sizes\": 128,\n", + " \"activation\": \"leaky_relu\"},\n", + " \"flatten\": True,\n", + " \"input_size\": 128,\n", + " \"hidden_size\": 128,\n", + " \"num_classes\": 80,\n", + " \"patch_size\": [28, 28],\n", + " \"stride\": [1, 14],}\n", + "line_ctc_model = LineCTCModel(LineRecurrentNetwork, EmnistLinesDataset) #, network_args)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "office in Arkansas after the______\n", + "in________________________________\n", + "by a oneshot technique____________\n", + "office Incumbent__________________\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 1440x1440 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "<Figure size 1440x1440 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 1440x1440 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 1440x1440 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "num_samples_to_plot = 4\n", + "\n", + "for i in range(num_samples_to_plot):\n", + " plt.figure(figsize=(20, 20))\n", + " data, target = emnist_lines[i]\n", + " sentence = convert_y_label_to_string(target.numpy()) \n", + " print(sentence)\n", + " plt.title(sentence)\n", + " plt.imshow(data.squeeze(0), cmap='gray')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "data, target = emnist_lines[3]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "office Incumbent__________________\n" + ] + }, + { + "data": { + "text/plain": [ + "<matplotlib.image.AxesImage at 0x7f05bd77af70>" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 1440x1440 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(20, 20))\n", + "sentence = convert_y_label_to_string(target.numpy()) \n", + "print(sentence)\n", + "plt.title(sentence)\n", + "plt.imshow(data.squeeze(0), cmap='gray')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "data = data.to(\"cuda:0\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('offiee ineumbent', 0.19405342638492584)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "line_ctc_model.predict_on_image(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p, _ = line_ctc_model.predict_on_image(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p = line_ctc_model.swa_network(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p, _ = p.max(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "torch.exp(p.sum()).item()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from text_recognizer.models.metrics import cer, wer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "target.unsqueeze(0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cer(p, target.unsqueeze(0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "wer(p, target.unsqueeze(0))" + ] + }, + { + "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": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/notebooks/04-look-at-iam-paragraphs.ipynb b/src/notebooks/04-look-at-iam-paragraphs.ipynb new file mode 100644 index 0000000..da420b0 --- /dev/null +++ b/src/notebooks/04-look-at-iam-paragraphs.ipynb @@ -0,0 +1,249 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import cv2\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from importlib.util import find_spec\n", + "if find_spec(\"text_recognizer\") is None:\n", + " import sys\n", + " sys.path.append('..')\n", + "\n", + "from text_recognizer.datasets import IamDataset\n", + "from text_recognizer.datasets import IamParagraphsDataset\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IAM Dataset\n", + "Number of forms: 1539\n", + "\n" + ] + } + ], + "source": [ + "dataset = IamDataset()\n", + "print(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2020-09-08 23:04:48.663 | INFO | text_recognizer.datasets.iam_paragraphs_dataset:_decide_on_crop_dims:190 - Max crop width and height were found to be 1240x1156.\n", + "2020-09-08 23:04:48.664 | INFO | text_recognizer.datasets.iam_paragraphs_dataset:_decide_on_crop_dims:193 - Setting them to 1240x1240\n", + "2020-09-08 23:04:48.665 | INFO | text_recognizer.datasets.iam_paragraphs_dataset:_process_iam_paragraphs:161 - Cropping paragraphs, generating ground truth, and saving debugging images to /home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/data/interim/iam_paragraphs/debug_crops\n", + "2020-09-08 23:05:10.585 | ERROR | text_recognizer.datasets.iam_paragraphs_dataset:_crop_paragraph_image:240 - Rescued /home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/data/raw/iam/iamdb/forms/e01-086.jpg: could not broadcast input array from shape (687,1236) into shape (687,1240)\n", + "2020-09-08 23:05:14.430 | ERROR | text_recognizer.datasets.iam_paragraphs_dataset:_crop_paragraph_image:240 - Rescued /home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/data/raw/iam/iamdb/forms/e01-081.jpg: could not broadcast input array from shape (587,1236) into shape (587,1240)\n", + "2020-09-08 23:05:29.910 | INFO | text_recognizer.datasets.iam_paragraphs_dataset:_load_iam_paragraphs:278 - Loading IAM paragraph crops and ground truth from image files...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IAM Paragraph Dataset\n", + "Num classes: 3\n", + "Data: (1229, 256, 256)\n", + "Targets: (1229, 256, 256)\n", + "\n" + ] + } + ], + "source": [ + "paragraphs_dataset = IamParagraphsDataset(True)\n", + "paragraphs_dataset.load_or_generate_data()\n", + "print(paragraphs_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x360 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x360 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x360 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x360 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x360 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x360 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x360 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x360 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x360 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x360 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for ind in range(10):\n", + " x, y = paragraphs_dataset[ind]\n", + " fig = plt.figure(figsize=(10,5))\n", + " ax1 = fig.add_subplot(121)\n", + " ax1.matshow(x.squeeze(0), cmap='gray')\n", + " ax2 = fig.add_subplot(122)\n", + " ax2.matshow(y, cmap='gray')" + ] + }, + { + "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": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/notebooks/04a-look-at-iam-lines.ipynb b/src/notebooks/04a-look-at-iam-lines.ipynb new file mode 100644 index 0000000..aa62d19 --- /dev/null +++ b/src/notebooks/04a-look-at-iam-lines.ipynb @@ -0,0 +1,290 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "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", + "\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": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from text_recognizer.datasets import IamLinesDataset" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IAM Lines Dataset\n", + "Number classes: 80\n", + "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F', 16: 'G', 17: 'H', 18: 'I', 19: 'J', 20: 'K', 21: 'L', 22: 'M', 23: 'N', 24: 'O', 25: 'P', 26: 'Q', 27: 'R', 28: 'S', 29: 'T', 30: 'U', 31: 'V', 32: 'W', 33: 'X', 34: 'Y', 35: 'Z', 36: 'a', 37: 'b', 38: 'c', 39: 'd', 40: 'e', 41: 'f', 42: 'g', 43: 'h', 44: 'i', 45: 'j', 46: 'k', 47: 'l', 48: 'm', 49: 'n', 50: 'o', 51: 'p', 52: 'q', 53: 'r', 54: 's', 55: 't', 56: 'u', 57: 'v', 58: 'w', 59: 'x', 60: 'y', 61: 'z', 62: ' ', 63: '!', 64: '\"', 65: '#', 66: '&', 67: \"'\", 68: '(', 69: ')', 70: '*', 71: '+', 72: ',', 73: '-', 74: '.', 75: '/', 76: ':', 77: ';', 78: '?', 79: '_'}\n", + "Data: (7101, 28, 952)\n", + "Targets: (7101, 97)\n", + "\n" + ] + } + ], + "source": [ + "dataset = IamLinesDataset(train=True)\n", + "dataset.load_or_generate_data()\n", + "print(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(97, 80)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.output_shape" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'A MOVE to stop Mr. Gaitskell from________________________________________________________________'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def convert_y_label_to_string(y, dataset=dataset):\n", + " return ''.join([dataset.mapper(int(i)) for i in y])\n", + "\n", + "convert_y_label_to_string(dataset.targets[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Griffiths resolution. Mr. Foot's line will_______________________________________________________\n", + "be that as Labour M Ps opposed the_______________________________________________________________\n", + "Government Bill which brought life peers_________________________________________________________\n", + "into existence, they should not now put__________________________________________________________\n", + "forward nominees. He believes that the___________________________________________________________\n", + "House of Lords should be abolished and___________________________________________________________\n", + "that Labour should not take any steps____________________________________________________________\n", + "which would appear to \"prop up\" an out-__________________________________________________________\n", + "Since 1958, 13 Labour life Peers and_____________________________________________________________\n", + "Peeresses have been created. Most Labour_________________________________________________________\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 1440x1440 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 1440x1440 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 1440x1440 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 1440x1440 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 1440x1440 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 1440x1440 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 1440x1440 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 1440x1440 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 1440x1440 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 1440x1440 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(10, 20):\n", + " plt.figure(figsize=(20, 20))\n", + " data, target = dataset[i]\n", + " sentence = convert_y_label_to_string(target) \n", + " print(sentence)\n", + " plt.title(sentence)\n", + " plt.imshow(data.squeeze(0).numpy(), cmap='gray')" + ] + }, + { + "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": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/text_recognizer/datasets/__init__.py b/src/text_recognizer/datasets/__init__.py index 05f74f6..ede4541 100644 --- a/src/text_recognizer/datasets/__init__.py +++ b/src/text_recognizer/datasets/__init__.py @@ -10,15 +10,23 @@ from .emnist_lines_dataset import ( EmnistLinesDataset, get_samples_by_character, ) -from .util import fetch_data_loaders, Transpose +from .iam_dataset import IamDataset +from .iam_lines_dataset import IamLinesDataset +from .iam_paragraphs_dataset import IamParagraphsDataset +from .util import _download_raw_dataset, compute_sha256, download_url, Transpose __all__ = [ + "_download_raw_dataset", + "compute_sha256", "construct_image_from_string", "DATA_DIRNAME", + "download_url", "EmnistDataset", "EmnistMapper", "EmnistLinesDataset", - "fetch_data_loaders", "get_samples_by_character", + "IamDataset", + "IamLinesDataset", + "IamParagraphsDataset", "Transpose", ] diff --git a/src/text_recognizer/datasets/emnist_dataset.py b/src/text_recognizer/datasets/emnist_dataset.py index 49ebad3..0715aae 100644 --- a/src/text_recognizer/datasets/emnist_dataset.py +++ b/src/text_recognizer/datasets/emnist_dataset.py @@ -152,8 +152,7 @@ class EmnistDataset(Dataset): """Loads the dataset and the mappings. Args: - train (bool): If True, loads the training set, otherwise the validation set is loaded. Defaults to - False. + train (bool): If True, loads the training set, otherwise the validation set is loaded. Defaults to False. sample_to_balance (bool): Resamples the dataset to make it balanced. Defaults to False. subsample_fraction (float): Description of parameter `subsample_fraction`. Defaults to None. transform (Optional[Callable]): Transform(s) for input data. Defaults to None. @@ -181,17 +180,37 @@ class EmnistDataset(Dataset): self.seed = seed self._mapper = EmnistMapper() - self.input_shape = self._mapper.input_shape + self._input_shape = self._mapper.input_shape self.num_classes = self._mapper.num_classes # Load dataset. - self.data, self.targets = self.load_emnist_dataset() + self._data, self._targets = self.load_emnist_dataset() + + @property + def data(self) -> Tensor: + """The input data.""" + return self._data + + @property + def targets(self) -> Tensor: + """The target data.""" + return self._targets + + @property + def input_shape(self) -> Tuple: + """Input shape of the data.""" + return self._input_shape @property def mapper(self) -> EmnistMapper: """Returns the EmnistMapper.""" return self._mapper + @property + def inverse_mapping(self) -> Dict: + """Returns the inverse mapping from character to index.""" + return self.mapper.inverse_mapping + def __len__(self) -> int: """Returns the length of the dataset.""" return len(self.data) @@ -220,11 +239,6 @@ class EmnistDataset(Dataset): return data, targets - @property - def __name__(self) -> str: - """Returns the name of the dataset.""" - return "EmnistDataset" - def __repr__(self) -> str: """Returns information about the dataset.""" return ( diff --git a/src/text_recognizer/datasets/emnist_lines_dataset.py b/src/text_recognizer/datasets/emnist_lines_dataset.py index b0617f5..656131a 100644 --- a/src/text_recognizer/datasets/emnist_lines_dataset.py +++ b/src/text_recognizer/datasets/emnist_lines_dataset.py @@ -9,8 +9,8 @@ from loguru import logger import numpy as np import torch from torch import Tensor -from torch.utils.data import DataLoader, Dataset -from torchvision.transforms import Compose, Normalize, ToTensor +from torch.utils.data import Dataset +from torchvision.transforms import ToTensor from text_recognizer.datasets import ( DATA_DIRNAME, @@ -20,6 +20,7 @@ from text_recognizer.datasets import ( ) from text_recognizer.datasets.sentence_generator import SentenceGenerator from text_recognizer.datasets.util import Transpose +from text_recognizer.networks import sliding_window DATA_DIRNAME = DATA_DIRNAME / "processed" / "emnist_lines" @@ -55,7 +56,7 @@ class EmnistLinesDataset(Dataset): self.transform = transform if self.transform is None: - self.transform = Compose([ToTensor()]) + self.transform = ToTensor() self.target_transform = target_transform if self.target_transform is None: @@ -63,14 +64,14 @@ class EmnistLinesDataset(Dataset): # Extract dataset information. self._mapper = EmnistMapper() - self.input_shape = self._mapper.input_shape + self._input_shape = self._mapper.input_shape self.num_classes = self._mapper.num_classes self.max_length = max_length self.min_overlap = min_overlap self.max_overlap = max_overlap self.num_samples = num_samples - self.input_shape = ( + self._input_shape = ( self.input_shape[0], self.input_shape[1] * self.max_length, ) @@ -84,6 +85,11 @@ class EmnistLinesDataset(Dataset): # Load dataset. self._load_or_generate_data() + @property + def input_shape(self) -> Tuple: + """Input shape of the data.""" + return self._input_shape + def __len__(self) -> int: """Returns the length of the dataset.""" return len(self.data) @@ -112,11 +118,6 @@ class EmnistLinesDataset(Dataset): return data, targets - @property - def __name__(self) -> str: - """Returns the name of the dataset.""" - return "EmnistLinesDataset" - def __repr__(self) -> str: """Returns information about the dataset.""" return ( @@ -136,13 +137,18 @@ class EmnistLinesDataset(Dataset): return self._mapper @property + def mapping(self) -> Dict: + """Return EMNIST mapping from index to character.""" + return self._mapper.mapping + + @property def data_filename(self) -> Path: """Path to the h5 file.""" filename = f"ml_{self.max_length}_o{self.min_overlap}_{self.max_overlap}_n{self.num_samples}.pt" if self.train: filename = "train_" + filename else: - filename = "val_" + filename + filename = "test_" + filename return DATA_DIRNAME / filename def _load_or_generate_data(self) -> None: @@ -184,7 +190,7 @@ class EmnistLinesDataset(Dataset): ) targets = convert_strings_to_categorical_labels( - targets, self.emnist.inverse_mapping + targets, emnist.inverse_mapping ) f.create_dataset("data", data=data, dtype="u1", compression="lzf") @@ -322,13 +328,13 @@ def create_datasets( min_overlap: float = 0, max_overlap: float = 0.33, num_train: int = 10000, - num_val: int = 1000, + num_test: int = 1000, ) -> None: """Creates a training an validation dataset of Emnist lines.""" emnist_train = EmnistDataset(train=True, sample_to_balance=True) - emnist_val = EmnistDataset(train=False, sample_to_balance=True) - datasets = [emnist_train, emnist_val] - num_samples = [num_train, num_val] + emnist_test = EmnistDataset(train=False, sample_to_balance=True) + datasets = [emnist_train, emnist_test] + num_samples = [num_train, num_test] for num, train, dataset in zip(num_samples, [True, False], datasets): emnist_lines = EmnistLinesDataset( train=train, diff --git a/src/text_recognizer/datasets/iam_dataset.py b/src/text_recognizer/datasets/iam_dataset.py new file mode 100644 index 0000000..5e47350 --- /dev/null +++ b/src/text_recognizer/datasets/iam_dataset.py @@ -0,0 +1,134 @@ +"""Class for loading the IAM dataset, which encompasses both paragraphs and lines, with associated utilities.""" +import os +from typing import Any, Dict, List +import zipfile + +from boltons.cacheutils import cachedproperty +import defusedxml.ElementTree as ET +from loguru import logger +import toml +from torch.utils.data import Dataset + +from text_recognizer.datasets import DATA_DIRNAME +from text_recognizer.datasets.util import _download_raw_dataset + +RAW_DATA_DIRNAME = DATA_DIRNAME / "raw" / "iam" +METADATA_FILENAME = RAW_DATA_DIRNAME / "metadata.toml" +EXTRACTED_DATASET_DIRNAME = RAW_DATA_DIRNAME / "iamdb" + +DOWNSAMPLE_FACTOR = 2 # If images were downsampled, the regions must also be. +LINE_REGION_PADDING = 0 # Add this many pixels around the exact coordinates. + + +class IamDataset(Dataset): + """IAM dataset. + + "The IAM Lines dataset, first published at the ICDAR 1999, contains forms of unconstrained handwritten text, + which were scanned at a resolution of 300dpi and saved as PNG images with 256 gray levels." + From http://www.fki.inf.unibe.ch/databases/iam-handwriting-database + + The data split we will use is + IAM lines Large Writer Independent Text Line Recognition Task (lwitlrt): 9,862 text lines. + The validation set has been merged into the train set. + The train set has 7,101 lines from 326 writers. + The test set has 1,861 lines from 128 writers. + The text lines of all data sets are mutually exclusive, thus each writer has contributed to one set only. + + """ + + def __init__(self) -> None: + self.metadata = toml.load(METADATA_FILENAME) + + def load_or_generate_data(self) -> None: + """Downloads IAM dataset if xml files does not exist.""" + if not self.xml_filenames: + self._download_iam() + + @property + def xml_filenames(self) -> List: + """List of xml filenames.""" + return list((EXTRACTED_DATASET_DIRNAME / "xml").glob("*.xml")) + + @property + def form_filenames(self) -> List: + """List of forms filenames.""" + return list((EXTRACTED_DATASET_DIRNAME / "forms").glob("*.jpg")) + + def _download_iam(self) -> None: + curdir = os.getcwd() + os.chdir(RAW_DATA_DIRNAME) + _download_raw_dataset(self.metadata) + _extract_raw_dataset(self.metadata) + os.chdir(curdir) + + @property + def form_filenames_by_id(self) -> Dict: + """Creates a dictionary with filenames as keys and forms as values.""" + return {filename.stem: filename for filename in self.form_filenames} + + @cachedproperty + def line_strings_by_id(self) -> Dict: + """Return a dict from name of IAM form to a list of line texts in it.""" + return { + filename.stem: _get_line_strings_from_xml_file(filename) + for filename in self.xml_filenames + } + + @cachedproperty + def line_regions_by_id(self) -> Dict: + """Return a dict from name of IAM form to a list of (x1, x2, y1, y2) coordinates of all lines in it.""" + return { + filename.stem: _get_line_regions_from_xml_file(filename) + for filename in self.xml_filenames + } + + def __repr__(self) -> str: + """Print info about dataset.""" + return "IAM Dataset\n" f"Number of forms: {len(self.xml_filenames)}\n" + + +def _extract_raw_dataset(metadata: Dict) -> None: + logger.info("Extracting IAM data.") + with zipfile.ZipFile(metadata["filename"], "r") as zip_file: + zip_file.extractall() + + +def _get_line_strings_from_xml_file(filename: str) -> List[str]: + """Get the text content of each line. Note that we replace " with ".""" + xml_root_element = ET.parse(filename).getroot() # nosec + xml_line_elements = xml_root_element.findall("handwritten-part/line") + return [el.attrib["text"].replace(""", '"') for el in xml_line_elements] + + +def _get_line_regions_from_xml_file(filename: str) -> List[Dict[str, int]]: + """Get the line region dict for each line.""" + xml_root_element = ET.parse(filename).getroot() # nosec + xml_line_elements = xml_root_element.findall("handwritten-part/line") + return [_get_line_region_from_xml_element(el) for el in xml_line_elements] + + +def _get_line_region_from_xml_element(xml_line: Any) -> Dict[str, int]: + """Extracts coordinates for each line of text.""" + # TODO: fix input! + word_elements = xml_line.findall("word/cmp") + x1s = [int(el.attrib["x"]) for el in word_elements] + y1s = [int(el.attrib["y"]) for el in word_elements] + x2s = [int(el.attrib["x"]) + int(el.attrib["width"]) for el in word_elements] + y2s = [int(el.attrib["y"]) + int(el.attrib["height"]) for el in word_elements] + return { + "x1": min(x1s) // DOWNSAMPLE_FACTOR - LINE_REGION_PADDING, + "y1": min(y1s) // DOWNSAMPLE_FACTOR - LINE_REGION_PADDING, + "x2": max(x2s) // DOWNSAMPLE_FACTOR + LINE_REGION_PADDING, + "y2": max(y2s) // DOWNSAMPLE_FACTOR + LINE_REGION_PADDING, + } + + +def main() -> None: + """Initializes the dataset and print info about the dataset.""" + dataset = IamDataset() + dataset.load_or_generate_data() + print(dataset) + + +if __name__ == "__main__": + main() diff --git a/src/text_recognizer/datasets/iam_lines_dataset.py b/src/text_recognizer/datasets/iam_lines_dataset.py new file mode 100644 index 0000000..477f500 --- /dev/null +++ b/src/text_recognizer/datasets/iam_lines_dataset.py @@ -0,0 +1,126 @@ +"""IamLinesDataset class.""" +from typing import Callable, Dict, List, Optional, Tuple, Union + +import h5py +from loguru import logger +import torch +from torch import Tensor +from torch.utils.data import Dataset +from torchvision.transforms import ToTensor + +from text_recognizer.datasets.emnist_dataset import DATA_DIRNAME, EmnistMapper +from text_recognizer.datasets.util import compute_sha256, download_url + + +PROCESSED_DATA_DIRNAME = DATA_DIRNAME / "processed" / "iam_lines" +PROCESSED_DATA_FILENAME = PROCESSED_DATA_DIRNAME / "iam_lines.h5" +PROCESSED_DATA_URL = ( + "https://s3-us-west-2.amazonaws.com/fsdl-public-assets/iam_lines.h5" +) + + +class IamLinesDataset(Dataset): + """IAM lines datasets for handwritten text lines.""" + + def __init__( + self, + train: bool = False, + subsample_fraction: float = None, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + ) -> None: + self.train = train + self.split = "train" if self.train else "test" + self._mapper = EmnistMapper() + self.num_classes = self.mapper.num_classes + + # Set transforms. + self.transform = transform + if self.transform is None: + self.transform = ToTensor() + + self.target_transform = target_transform + if self.target_transform is None: + self.target_transform = torch.tensor + + self.subsample_fraction = subsample_fraction + self.data = None + self.targets = None + + @property + def mapper(self) -> EmnistMapper: + """Returns the EmnistMapper.""" + return self._mapper + + @property + def mapping(self) -> Dict: + """Return EMNIST mapping from index to character.""" + return self._mapper.mapping + + @property + def input_shape(self) -> Tuple: + """Input shape of the data.""" + return self.data.shape[1:] + + @property + def output_shape(self) -> Tuple: + """Output shape of the data.""" + return self.targets.shape[1:] + (self.num_classes,) + + def __len__(self) -> int: + """Returns the length of the dataset.""" + return len(self.data) + + def load_or_generate_data(self) -> None: + """Load or generate dataset data.""" + if not PROCESSED_DATA_FILENAME.exists(): + PROCESSED_DATA_DIRNAME.mkdir(parents=True, exist_ok=True) + logger.info("Downloading IAM lines...") + download_url(PROCESSED_DATA_URL, PROCESSED_DATA_FILENAME) + with h5py.File(PROCESSED_DATA_FILENAME, "r") as f: + self.data = f[f"x_{self.split}"][:] + self.targets = f[f"y_{self.split}"][:] + self._subsample() + + def _subsample(self) -> None: + """Only a fraction of the data will be loaded.""" + if self.subsample_fraction is None: + return + + num_samples = int(self.data.shape[0] * self.subsample_fraction) + self.data = self.data[:num_samples] + self.targets = self.targets[:num_samples] + + def __repr__(self) -> str: + """Print info about the dataset.""" + return ( + "IAM Lines Dataset\n" # pylint: disable=no-member + f"Number classes: {self.num_classes}\n" + f"Mapping: {self.mapper.mapping}\n" + f"Data: {self.data.shape}\n" + f"Targets: {self.targets.shape}\n" + ) + + def __getitem__(self, index: Union[Tensor, int]) -> Tuple[Tensor, Tensor]: + """Fetches data, target pair of the dataset for a given and index or indices. + + Args: + index (Union[int, Tensor]): Either a list or int of indices/index. + + Returns: + Tuple[Tensor, Tensor]: Data target pair. + + """ + if torch.is_tensor(index): + index = index.tolist() + + data = self.data[index] + targets = self.targets[index] + + if self.transform: + data = self.transform(data) + + if self.target_transform: + targets = self.target_transform(targets) + + return data, targets diff --git a/src/text_recognizer/datasets/iam_paragraphs_dataset.py b/src/text_recognizer/datasets/iam_paragraphs_dataset.py new file mode 100644 index 0000000..d65b346 --- /dev/null +++ b/src/text_recognizer/datasets/iam_paragraphs_dataset.py @@ -0,0 +1,322 @@ +"""IamParagraphsDataset class and functions for data processing.""" +from typing import Callable, Dict, List, Optional, Tuple, Union + +import click +import cv2 +import h5py +from loguru import logger +import numpy as np +import torch +from torch import Tensor +from torch.utils.data import Dataset +from torchvision.transforms import ToTensor + +from text_recognizer import util +from text_recognizer.datasets.emnist_dataset import DATA_DIRNAME, EmnistMapper +from text_recognizer.datasets.iam_dataset import IamDataset +from text_recognizer.datasets.util import compute_sha256, download_url + +INTERIM_DATA_DIRNAME = DATA_DIRNAME / "interim" / "iam_paragraphs" +DEBUG_CROPS_DIRNAME = INTERIM_DATA_DIRNAME / "debug_crops" +PROCESSED_DATA_DIRNAME = DATA_DIRNAME / "processed" / "iam_paragraphs" +CROPS_DIRNAME = PROCESSED_DATA_DIRNAME / "crops" +GT_DIRNAME = PROCESSED_DATA_DIRNAME / "gt" + +PARAGRAPH_BUFFER = 50 # Pixels in the IAM form images to leave around the lines. +TEST_FRACTION = 0.2 +SEED = 4711 + + +class IamParagraphsDataset(Dataset): + """IAM Paragraphs dataset for paragraphs of handwritten text. + + TODO: __getitem__, __len__, get_data_target_from_id + + """ + + def __init__( + self, + train: bool = False, + subsample_fraction: float = None, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + ) -> None: + + # Load Iam dataset. + self.iam_dataset = IamDataset() + + self.train = train + self.split = "train" if self.train else "test" + self.num_classes = 3 + self._input_shape = (256, 256) + self._output_shape = self._input_shape + (self.num_classes,) + self.subsample_fraction = subsample_fraction + + # Set transforms. + self.transform = transform + if self.transform is None: + self.transform = ToTensor() + + self.target_transform = target_transform + if self.target_transform is None: + self.target_transform = torch.tensor + + self._data = None + self._targets = None + self._ids = None + + def __len__(self) -> int: + """Returns the length of the dataset.""" + return len(self.data) + + def __getitem__(self, index: Union[Tensor, int]) -> Tuple[Tensor, Tensor]: + """Fetches data, target pair of the dataset for a given and index or indices. + + Args: + index (Union[int, Tensor]): Either a list or int of indices/index. + + Returns: + Tuple[Tensor, Tensor]: Data target pair. + + """ + if torch.is_tensor(index): + index = index.tolist() + + data = self.data[index] + targets = self.targets[index] + + if self.transform: + data = self.transform(data) + + if self.target_transform: + targets = self.target_transform(targets) + + return data, targets + + @property + def input_shape(self) -> Tuple: + """Input shape of the data.""" + return self._input_shape + + @property + def output_shape(self) -> Tuple: + """Output shape of the data.""" + return self._output_shape + + @property + def data(self) -> Tensor: + """The input data.""" + return self._data + + @property + def targets(self) -> Tensor: + """The target data.""" + return self._targets + + @property + def ids(self) -> Tensor: + """Ids of the dataset.""" + return self._ids + + def get_data_and_target_from_id(self, id_: str) -> Tuple[Tensor, Tensor]: + """Get data target pair from id.""" + ind = self.ids.index(id_) + return self.data[ind], self.targets[ind] + + def load_or_generate_data(self) -> None: + """Load or generate dataset data.""" + num_actual = len(list(CROPS_DIRNAME.glob("*.jpg"))) + num_targets = len(self.iam_dataset.line_regions_by_id) + + if num_actual < num_targets - 2: + self._process_iam_paragraphs() + + self._data, self._targets, self._ids = _load_iam_paragraphs() + self._get_random_split() + self._subsample() + + def _get_random_split(self) -> None: + np.random.seed(SEED) + num_train = int((1 - TEST_FRACTION) * self.data.shape[0]) + indices = np.random.permutation(self.data.shape[0]) + train_indices, test_indices = indices[:num_train], indices[num_train:] + if self.train: + self._data = self.data[train_indices] + self._targets = self.targets[train_indices] + else: + self._data = self.data[test_indices] + self._targets = self.targets[test_indices] + + def _process_iam_paragraphs(self) -> None: + """Crop the part with the text. + + For each page, crop out the part of it that correspond to the paragraph of text, and make sure all crops are + self.input_shape. The ground truth data is the same size, with a one-hot vector at each pixel + corresponding to labels 0=background, 1=odd-numbered line, 2=even-numbered line + """ + crop_dims = self._decide_on_crop_dims() + CROPS_DIRNAME.mkdir(parents=True, exist_ok=True) + DEBUG_CROPS_DIRNAME.mkdir(parents=True, exist_ok=True) + GT_DIRNAME.mkdir(parents=True, exist_ok=True) + logger.info( + f"Cropping paragraphs, generating ground truth, and saving debugging images to {DEBUG_CROPS_DIRNAME}" + ) + for filename in self.iam_dataset.form_filenames: + id_ = filename.stem + line_region = self.iam_dataset.line_regions_by_id[id_] + _crop_paragraph_image(filename, line_region, crop_dims, self.input_shape) + + def _decide_on_crop_dims(self) -> Tuple[int, int]: + """Decide on the dimensions to crop out of the form image. + + Since image width is larger than a comfortable crop around the longest paragraph, + we will make the crop a square form factor. + And since the found dimensions 610x610 are pretty close to 512x512, + we might as well resize crops and make it exactly that, which lets us + do all kinds of power-of-2 pooling and upsampling should we choose to. + + Returns: + Tuple[int, int]: A tuple of crop dimensions. + + Raises: + RuntimeError: When max crop height is larger than max crop width. + + """ + + sample_form_filename = self.iam_dataset.form_filenames[0] + sample_image = util.read_image(sample_form_filename, grayscale=True) + max_crop_width = sample_image.shape[1] + max_crop_height = _get_max_paragraph_crop_height( + self.iam_dataset.line_regions_by_id + ) + if not max_crop_height <= max_crop_width: + raise RuntimeError( + f"Max crop height is larger then max crop width: {max_crop_height} >= {max_crop_width}" + ) + + crop_dims = (max_crop_width, max_crop_width) + logger.info( + f"Max crop width and height were found to be {max_crop_width}x{max_crop_height}." + ) + logger.info(f"Setting them to {max_crop_width}x{max_crop_width}") + return crop_dims + + def _subsample(self) -> None: + """Only this fraction of the data will be loaded.""" + if self.subsample_fraction is None: + return + num_subsample = int(self.data.shape[0] * self.subsample_fraction) + self.data = self.data[:num_subsample] + self.targets = self.targets[:num_subsample] + + def __repr__(self) -> str: + """Return info about the dataset.""" + return ( + "IAM Paragraph Dataset\n" # pylint: disable=no-member + f"Num classes: {self.num_classes}\n" + f"Data: {self.data.shape}\n" + f"Targets: {self.targets.shape}\n" + ) + + +def _get_max_paragraph_crop_height(line_regions_by_id: Dict) -> int: + heights = [] + for regions in line_regions_by_id.values(): + min_y1 = min(region["y1"] for region in regions) - PARAGRAPH_BUFFER + max_y2 = max(region["y2"] for region in regions) + PARAGRAPH_BUFFER + height = max_y2 - min_y1 + heights.append(height) + return max(heights) + + +def _crop_paragraph_image( + filename: str, line_regions: Dict, crop_dims: Tuple[int, int], final_dims: Tuple +) -> None: + image = util.read_image(filename, grayscale=True) + + min_y1 = min(region["y1"] for region in line_regions) - PARAGRAPH_BUFFER + max_y2 = max(region["y2"] for region in line_regions) + PARAGRAPH_BUFFER + height = max_y2 - min_y1 + crop_height = crop_dims[0] + buffer = (crop_height - height) // 2 + + # Generate image crop. + image_crop = 255 * np.ones(crop_dims, dtype=np.uint8) + try: + image_crop[buffer : buffer + height] = image[min_y1:max_y2] + except Exception as e: # pylint: disable=broad-except + logger.error(f"Rescued {filename}: {e}") + return + + # Generate ground truth. + gt_image = np.zeros_like(image_crop, dtype=np.uint8) + for index, region in enumerate(line_regions): + gt_image[ + (region["y1"] - min_y1 + buffer) : (region["y2"] - min_y1 + buffer), + region["x1"] : region["x2"], + ] = (index % 2 + 1) + + # Generate image for debugging. + import matplotlib.pyplot as plt + + cmap = plt.get_cmap("Set1") + image_crop_for_debug = np.dstack([image_crop, image_crop, image_crop]) + for index, region in enumerate(line_regions): + color = [255 * _ for _ in cmap(index)[:-1]] + cv2.rectangle( + image_crop_for_debug, + (region["x1"], region["y1"] - min_y1 + buffer), + (region["x2"], region["y2"] - min_y1 + buffer), + color, + 3, + ) + image_crop_for_debug = cv2.resize( + image_crop_for_debug, final_dims, interpolation=cv2.INTER_AREA + ) + util.write_image(image_crop_for_debug, DEBUG_CROPS_DIRNAME / f"{filename.stem}.jpg") + + image_crop = cv2.resize(image_crop, final_dims, interpolation=cv2.INTER_AREA) + util.write_image(image_crop, CROPS_DIRNAME / f"{filename.stem}.jpg") + + gt_image = cv2.resize(gt_image, final_dims, interpolation=cv2.INTER_NEAREST) + util.write_image(gt_image, GT_DIRNAME / f"{filename.stem}.png") + + +def _load_iam_paragraphs() -> None: + logger.info("Loading IAM paragraph crops and ground truth from image files...") + images = [] + gt_images = [] + ids = [] + for filename in CROPS_DIRNAME.glob("*.jpg"): + id_ = filename.stem + image = util.read_image(filename, grayscale=True) + image = 1.0 - image / 255 + + gt_filename = GT_DIRNAME / f"{id_}.png" + gt_image = util.read_image(gt_filename, grayscale=True) + + images.append(image) + gt_images.append(gt_image) + ids.append(id_) + images = np.array(images).astype(np.float32) + gt_images = np.array(gt_images).astype(np.uint8) + ids = np.array(ids) + return images, gt_images, ids + + +@click.command() +@click.option( + "--subsample_fraction", + type=float, + default=0.0, + help="The subsampling factor of the dataset.", +) +def main(subsample_fraction: float) -> None: + """Load dataset and print info.""" + dataset = IamParagraphsDataset(subsample_fraction=subsample_fraction) + dataset.load_or_generate_data() + print(dataset) + + +if __name__ == "__main__": + main() diff --git a/src/text_recognizer/datasets/util.py b/src/text_recognizer/datasets/util.py index 76bd85f..dd16bed 100644 --- a/src/text_recognizer/datasets/util.py +++ b/src/text_recognizer/datasets/util.py @@ -1,10 +1,17 @@ """Util functions for datasets.""" +import hashlib import importlib -from typing import Callable, Dict, List, Type +import os +from pathlib import Path +from typing import Callable, Dict, List, Optional, Type, Union +from urllib.request import urlopen, urlretrieve +import cv2 +from loguru import logger import numpy as np from PIL import Image from torch.utils.data import DataLoader, Dataset +from tqdm import tqdm class Transpose: @@ -15,58 +22,48 @@ class Transpose: return np.array(image).swapaxes(0, 1) -def fetch_data_loaders( - splits: List[str], - dataset: str, - dataset_args: Dict, - batch_size: int = 128, - shuffle: bool = False, - num_workers: int = 0, - cuda: bool = True, -) -> Dict[str, DataLoader]: - """Fetches DataLoaders for given split(s) as a dictionary. - - Loads the dataset class given, and loads it with the dataset arguments, for the number of splits specified. Then - calls the DataLoader. Added to a dictionary with the split as key and DataLoader as value. - - Args: - splits (List[str]): One or both of the dataset splits "train" and "val". - dataset (str): The name of the dataset. - dataset_args (Dict): The dataset arguments. - batch_size (int): How many samples per batch to load. Defaults to 128. - shuffle (bool): Set to True to have the data reshuffled at every epoch. Defaults to False. - num_workers (int): How many subprocesses to use for data loading. 0 means that the data will be - loaded in the main process. Defaults to 0. - cuda (bool): If True, the data loader will copy Tensors into CUDA pinned memory before returning - them. Defaults to True. - - Returns: - Dict[str, DataLoader]: Dictionary with split as key and PyTorch DataLoader as value. +def compute_sha256(filename: Union[Path, str]) -> str: + """Returns the SHA256 checksum of a file.""" + with open(filename, "rb") as f: + return hashlib.sha256(f.read()).hexdigest() - """ - - def check_dataset_args(args: Dict, split: str) -> Dict: - """Adds train flag to the dataset args.""" - args["train"] = True if split == "train" else False - return args - - # Import dataset module. - datasets_module = importlib.import_module("text_recognizer.datasets") - dataset_ = getattr(datasets_module, dataset) - data_loaders = {} +class TqdmUpTo(tqdm): + """TQDM progress bar when downloading files. - for split in ["train", "val"]: - if split in splits: + From https://github.com/tqdm/tqdm/blob/master/examples/tqdm_wget.py - data_loader = DataLoader( - dataset=dataset_(**check_dataset_args(dataset_args, split)), - batch_size=batch_size, - shuffle=shuffle, - num_workers=num_workers, - pin_memory=cuda, - ) - - data_loaders[split] = data_loader + """ - return data_loaders + def update_to( + self, blocks: int = 1, block_size: int = 1, total_size: Optional[int] = None + ) -> None: + """Updates the progress bar. + + Args: + blocks (int): Number of blocks transferred so far. Defaults to 1. + block_size (int): Size of each block, in tqdm units. Defaults to 1. + total_size (Optional[int]): Total size in tqdm units. Defaults to None. + """ + if total_size is not None: + self.total = total_size # pylint: disable=attribute-defined-outside-init + self.update(blocks * block_size - self.n) + + +def download_url(url: str, filename: str) -> None: + """Downloads a file from url to filename, with a progress bar.""" + with TqdmUpTo(unit="B", unit_scale=True, unit_divisor=1024, miniters=1) as t: + urlretrieve(url, filename, reporthook=t.update_to, data=None) # nosec + + +def _download_raw_dataset(metadata: Dict) -> None: + if os.path.exists(metadata["filename"]): + return + logger.info(f"Downloading raw dataset from {metadata['url']}...") + download_url(metadata["url"], metadata["filename"]) + logger.info("Computing SHA-256...") + sha256 = compute_sha256(metadata["filename"]) + if sha256 != metadata["sha256"]: + raise ValueError( + "Downloaded data file SHA-256 does not match that listed in metadata document." + ) diff --git a/src/text_recognizer/models/__init__.py b/src/text_recognizer/models/__init__.py index ff10a07..a3cfc15 100644 --- a/src/text_recognizer/models/__init__.py +++ b/src/text_recognizer/models/__init__.py @@ -1,6 +1,7 @@ """Model modules.""" from .base import Model from .character_model import CharacterModel -from .metrics import accuracy +from .line_ctc_model import LineCTCModel +from .metrics import accuracy, cer, wer -__all__ = ["Model", "CharacterModel", "accuracy"] +__all__ = ["Model", "cer", "CharacterModel", "LineCTCModel", "accuracy", "wer"] diff --git a/src/text_recognizer/models/base.py b/src/text_recognizer/models/base.py index 3a84a11..153e19a 100644 --- a/src/text_recognizer/models/base.py +++ b/src/text_recognizer/models/base.py @@ -2,6 +2,7 @@ from abc import ABC, abstractmethod from glob import glob +import importlib from pathlib import Path import re import shutil @@ -10,9 +11,12 @@ from typing import Callable, Dict, Optional, Tuple, Type from loguru import logger import torch from torch import nn +from torch import Tensor +from torch.optim.swa_utils import AveragedModel, SWALR +from torch.utils.data import DataLoader, Dataset, random_split from torchsummary import summary -from text_recognizer.datasets import EmnistMapper, fetch_data_loaders +from text_recognizer.datasets import EmnistMapper WEIGHT_DIRNAME = Path(__file__).parents[1].resolve() / "weights" @@ -23,8 +27,9 @@ class Model(ABC): def __init__( self, network_fn: Type[nn.Module], + dataset: Type[Dataset], network_args: Optional[Dict] = None, - data_loader_args: Optional[Dict] = None, + dataset_args: Optional[Dict] = None, metrics: Optional[Dict] = None, criterion: Optional[Callable] = None, criterion_args: Optional[Dict] = None, @@ -32,14 +37,16 @@ class Model(ABC): optimizer_args: Optional[Dict] = None, lr_scheduler: Optional[Callable] = None, lr_scheduler_args: Optional[Dict] = None, + swa_args: Optional[Dict] = None, device: Optional[str] = None, ) -> None: """Base class, to be inherited by model for specific type of data. Args: network_fn (Type[nn.Module]): The PyTorch network. + dataset (Type[Dataset]): A dataset class. network_args (Optional[Dict]): Arguments for the network. Defaults to None. - data_loader_args (Optional[Dict]): Arguments for the DataLoader. + dataset_args (Optional[Dict]): Arguments for the dataset. metrics (Optional[Dict]): Metrics to evaluate the performance with. Defaults to None. criterion (Optional[Callable]): The criterion to evaulate the preformance of the network. Defaults to None. @@ -49,107 +56,181 @@ class Model(ABC): lr_scheduler (Optional[Callable]): A PyTorch learning rate scheduler. Defaults to None. lr_scheduler_args (Optional[Dict]): Dict of arguments for learning rate scheduler. Defaults to None. + swa_args (Optional[Dict]): Dict of arguments for stochastic weight averaging. Defaults to + None. device (Optional[str]): Name of the device to train on. Defaults to None. """ + # Has to be set in subclass. + self._mapper = None - # Configure data loaders and dataset info. - dataset_name, self._data_loaders, self._mapper = self._configure_data_loader( - data_loader_args - ) - self._input_shape = self._mapper.input_shape + # Placeholder. + self._input_shape = None + + self.dataset = dataset + self.dataset_args = dataset_args + + # Placeholders for datasets. + self.train_dataset = None + self.val_dataset = None + self.test_dataset = None - self._name = f"{self.__class__.__name__}_{dataset_name}_{network_fn.__name__}" + # Stochastic Weight Averaging placeholders. + self.swa_args = swa_args + self._swa_start = None + self._swa_scheduler = None + self._swa_network = None - if metrics is not None: - self._metrics = metrics + # Experiment directory. + self.model_dir = None + + # Flag for configured model. + self.is_configured = False + self.data_prepared = False + + # Flag for stopping training. + self.stop_training = False + + self._name = ( + f"{self.__class__.__name__}_{dataset.__name__}_{network_fn.__name__}" + ) + + self._metrics = metrics if metrics is not None else None # Set the device. - if device is None: - self._device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - else: - self._device = device + self._device = ( + torch.device("cuda" if torch.cuda.is_available() else "cpu") + if device is None + else device + ) # Configure network. - self._network, self._network_args = self._configure_network( - network_fn, network_args - ) + self._network = None + self._network_args = network_args + self._configure_network(network_fn) - # To device. - self._network.to(self._device) + # Place network on device (GPU). + self.to_device() + + # Loss and Optimizer placeholders for before loading. + self._criterion = criterion + self.criterion_args = criterion_args + + self._optimizer = optimizer + self.optimizer_args = optimizer_args + + self._lr_scheduler = lr_scheduler + self.lr_scheduler_args = lr_scheduler_args + + def configure_model(self) -> None: + """Configures criterion and optimizers.""" + if not self.is_configured: + self._configure_criterion() + self._configure_optimizers() + + # Prints a summary of the network in terminal. + self.summary() + + # Set this flag to true to prevent the model from configuring again. + self.is_configured = True + + def prepare_data(self) -> None: + """Prepare data for training.""" + # TODO add downloading. + if not self.data_prepared: + # Load train dataset. + train_dataset = self.dataset(train=True, **self.dataset_args["args"]) + + # Set input shape. + self._input_shape = train_dataset.input_shape + + # Split train dataset into a training and validation partition. + dataset_len = len(train_dataset) + train_len = int( + self.dataset_args["train_args"]["train_fraction"] * dataset_len + ) + val_len = dataset_len - train_len + self.train_dataset, self.val_dataset = random_split( + train_dataset, lengths=[train_len, val_len] + ) + + # Load test dataset. + self.test_dataset = self.dataset(train=False, **self.dataset_args["args"]) + + # Set the flag to true to disable ability to load data agian. + self.data_prepared = True - # Configure training objects. - self._criterion = self._configure_criterion(criterion, criterion_args) - self._optimizer, self._lr_scheduler = self._configure_optimizers( - optimizer, optimizer_args, lr_scheduler, lr_scheduler_args + def train_dataloader(self) -> DataLoader: + """Returns data loader for training set.""" + return DataLoader( + self.train_dataset, + batch_size=self.dataset_args["train_args"]["batch_size"], + num_workers=self.dataset_args["train_args"]["num_workers"], + shuffle=True, + pin_memory=True, ) - # Experiment directory. - self.model_dir = None + def val_dataloader(self) -> DataLoader: + """Returns data loader for validation set.""" + return DataLoader( + self.val_dataset, + batch_size=self.dataset_args["train_args"]["batch_size"], + num_workers=self.dataset_args["train_args"]["num_workers"], + shuffle=True, + pin_memory=True, + ) - # Flag for stopping training. - self.stop_training = False + def test_dataloader(self) -> DataLoader: + """Returns data loader for test set.""" + return DataLoader( + self.test_dataset, + batch_size=self.dataset_args["train_args"]["batch_size"], + num_workers=self.dataset_args["train_args"]["num_workers"], + shuffle=False, + pin_memory=True, + ) - def _configure_data_loader( - self, data_loader_args: Optional[Dict] - ) -> Tuple[str, Dict, EmnistMapper]: - """Loads data loader, dataset name, and dataset mapper.""" - if data_loader_args is not None: - data_loaders = fetch_data_loaders(**data_loader_args) - dataset = list(data_loaders.values())[0].dataset - dataset_name = dataset.__name__ - mapper = dataset.mapper - else: - self._mapper = EmnistMapper() - dataset_name = "*" - data_loaders = None - return dataset_name, data_loaders, mapper - - def _configure_network( - self, network_fn: Type[nn.Module], network_args: Optional[Dict] - ) -> Tuple[Type[nn.Module], Dict]: + def _configure_network(self, network_fn: Type[nn.Module]) -> None: """Loads the network.""" # If no network arguemnts are given, load pretrained weights if they exist. - if network_args is None: - network, network_args = self.load_weights(network_fn) + if self._network_args is None: + self.load_weights(network_fn) else: - network = network_fn(**network_args) - return network, network_args + self._network = network_fn(**self._network_args) - def _configure_criterion( - self, criterion: Optional[Callable], criterion_args: Optional[Dict] - ) -> Optional[Callable]: + def _configure_criterion(self) -> None: """Loads the criterion.""" - if criterion is not None: - _criterion = criterion(**criterion_args) - else: - _criterion = None - return _criterion + self._criterion = ( + self._criterion(**self.criterion_args) + if self._criterion is not None + else None + ) - def _configure_optimizers( - self, - optimizer: Optional[Callable], - optimizer_args: Optional[Dict], - lr_scheduler: Optional[Callable], - lr_scheduler_args: Optional[Dict], - ) -> Tuple[Optional[Callable], Optional[Callable]]: + def _configure_optimizers(self,) -> None: """Loads the optimizers.""" - if optimizer is not None: - _optimizer = optimizer(self._network.parameters(), **optimizer_args) + if self._optimizer is not None: + self._optimizer = self._optimizer( + self._network.parameters(), **self.optimizer_args + ) else: - _optimizer = None + self._optimizer = None - if _optimizer and lr_scheduler is not None: - if "OneCycleLR" in str(lr_scheduler): - lr_scheduler_args["steps_per_epoch"] = len(self._data_loaders["train"]) - _lr_scheduler = lr_scheduler(_optimizer, **lr_scheduler_args) + if self._optimizer and self._lr_scheduler is not None: + if "OneCycleLR" in str(self._lr_scheduler): + self.lr_scheduler_args["steps_per_epoch"] = len(self.train_dataloader()) + self._lr_scheduler = self._lr_scheduler( + self._optimizer, **self.lr_scheduler_args + ) else: - _lr_scheduler = None + self._lr_scheduler = None - return _optimizer, _lr_scheduler + if self.swa_args is not None: + self._swa_start = self.swa_args["start"] + self._swa_scheduler = SWALR(self._optimizer, swa_lr=self.swa_args["lr"]) + self._swa_network = AveragedModel(self._network).to(self.device) @property - def __name__(self) -> str: + def name(self) -> str: """Returns the name of the model.""" return self._name @@ -159,7 +240,7 @@ class Model(ABC): return self._input_shape @property - def mapper(self) -> Dict: + def mapper(self) -> EmnistMapper: """Returns the mapper that maps between ints and chars.""" return self._mapper @@ -202,13 +283,24 @@ class Model(ABC): return self._lr_scheduler @property - def data_loaders(self) -> Optional[Dict]: - """Dataloaders.""" - return self._data_loaders + def swa_scheduler(self) -> Optional[Callable]: + """Returns the stochastic weight averaging scheduler.""" + return self._swa_scheduler + + @property + def swa_start(self) -> Optional[Callable]: + """Returns the start epoch of stochastic weight averaging.""" + return self._swa_start @property - def network(self) -> nn.Module: + def swa_network(self) -> Optional[Callable]: + """Returns the stochastic weight averaging network.""" + return self._swa_network + + @property + def network(self) -> Type[nn.Module]: """Neural network.""" + # Returns the SWA network if available. return self._network @property @@ -217,15 +309,27 @@ class Model(ABC): WEIGHT_DIRNAME.mkdir(parents=True, exist_ok=True) return str(WEIGHT_DIRNAME / f"{self._name}_weights.pt") - def summary(self) -> None: + def loss_fn(self, output: Tensor, targets: Tensor) -> Tensor: + """Compute the loss.""" + return self.criterion(output, targets) + + def summary( + self, input_shape: Optional[Tuple[int, int, int]] = None, depth: int = 5 + ) -> None: """Prints a summary of the network architecture.""" - device = re.sub("[^A-Za-z]+", "", self.device) - if self._input_shape is not None: + + if input_shape is not None: + summary(self._network, input_shape, depth=depth, device=self.device) + elif self._input_shape is not None: input_shape = (1,) + tuple(self._input_shape) - summary(self._network, input_shape, device=device) + summary(self._network, input_shape, depth=depth, device=self.device) else: logger.warning("Could not print summary as input shape is not set.") + def to_device(self) -> None: + """Places the network on the device (GPU).""" + self._network.to(self._device) + def _get_state_dict(self) -> Dict: """Get the state dict of the model.""" state = {"model_state": self._network.state_dict()} @@ -236,69 +340,67 @@ class Model(ABC): if self._lr_scheduler is not None: state["scheduler_state"] = self._lr_scheduler.state_dict() + if self._swa_network is not None: + state["swa_network"] = self._swa_network.state_dict() + return state - def load_checkpoint(self, path: Path) -> int: + def load_from_checkpoint(self, checkpoint_path: Path) -> None: """Load a previously saved checkpoint. Args: - path (Path): Path to the experiment with the checkpoint. - - Returns: - epoch (int): The last epoch when the checkpoint was created. + checkpoint_path (Path): Path to the experiment with the checkpoint. """ logger.debug("Loading checkpoint...") - if not path.exists(): - logger.debug("File does not exist {str(path)}") + if not checkpoint_path.exists(): + logger.debug("File does not exist {str(checkpoint_path)}") - checkpoint = torch.load(str(path)) + checkpoint = torch.load(str(checkpoint_path)) self._network.load_state_dict(checkpoint["model_state"]) if self._optimizer is not None: self._optimizer.load_state_dict(checkpoint["optimizer_state"]) - # Does not work when loadning from previous checkpoint and trying to train beyond the last max epochs. - # if self._lr_scheduler is not None: - # self._lr_scheduler.load_state_dict(checkpoint["scheduler_state"]) - - epoch = checkpoint["epoch"] + if self._lr_scheduler is not None: + # Does not work when loadning from previous checkpoint and trying to train beyond the last max epochs + # with OneCycleLR. + if self._lr_scheduler.__class__.__name__ != "OneCycleLR": + self._lr_scheduler.load_state_dict(checkpoint["scheduler_state"]) - return epoch + if self._swa_network is not None: + self._swa_network.load_state_dict(checkpoint["swa_network"]) - def save_checkpoint(self, is_best: bool, epoch: int, val_metric: str) -> None: + def save_checkpoint( + self, checkpoint_path: Path, is_best: bool, epoch: int, val_metric: str + ) -> None: """Saves a checkpoint of the model. Args: + checkpoint_path (Path): Path to the experiment with the checkpoint. is_best (bool): If it is the currently best model. epoch (int): The epoch of the checkpoint. val_metric (str): Validation metric. - Raises: - ValueError: If the self.model_dir is not set. - """ state = self._get_state_dict() state["is_best"] = is_best state["epoch"] = epoch state["network_args"] = self._network_args - if self.model_dir is None: - raise ValueError("Experiment directory is not set.") - - self.model_dir.mkdir(parents=True, exist_ok=True) + checkpoint_path.mkdir(parents=True, exist_ok=True) logger.debug("Saving checkpoint...") - filepath = str(self.model_dir / "last.pt") + filepath = str(checkpoint_path / "last.pt") torch.save(state, filepath) if is_best: logger.debug( f"Found a new best {val_metric}. Saving best checkpoint and weights." ) - shutil.copyfile(filepath, str(self.model_dir / "best.pt")) + shutil.copyfile(filepath, str(checkpoint_path / "best.pt")) - def load_weights(self, network_fn: Type[nn.Module]) -> Tuple[Type[nn.Module], Dict]: + def load_weights(self, network_fn: Type[nn.Module]) -> None: """Load the network weights.""" logger.debug("Loading network with pretrained weights.") filename = glob(self.weights_filename)[0] @@ -308,13 +410,16 @@ class Model(ABC): ) # Loading state directory. state_dict = torch.load(filename, map_location=torch.device(self._device)) - network_args = state_dict["network_args"] + self._network_args = state_dict["network_args"] weights = state_dict["model_state"] # Initializes the network with trained weights. - network = network_fn(**self._network_args) - network.load_state_dict(weights) - return network, network_args + self._network = network_fn(**self._network_args) + self._network.load_state_dict(weights) + + if "swa_network" in state_dict: + self._swa_network = AveragedModel(self._network).to(self.device) + self._swa_network.load_state_dict(state_dict["swa_network"]) def save_weights(self, path: Path) -> None: """Save the network weights.""" diff --git a/src/text_recognizer/models/character_model.py b/src/text_recognizer/models/character_model.py index 0fd7afd..64ba693 100644 --- a/src/text_recognizer/models/character_model.py +++ b/src/text_recognizer/models/character_model.py @@ -4,8 +4,10 @@ from typing import Callable, Dict, Optional, Tuple, Type, Union import numpy as np import torch from torch import nn +from torch.utils.data import Dataset from torchvision.transforms import ToTensor +from text_recognizer.datasets import EmnistMapper from text_recognizer.models.base import Model @@ -15,8 +17,9 @@ class CharacterModel(Model): def __init__( self, network_fn: Type[nn.Module], + dataset: Type[Dataset], network_args: Optional[Dict] = None, - data_loader_args: Optional[Dict] = None, + dataset_args: Optional[Dict] = None, metrics: Optional[Dict] = None, criterion: Optional[Callable] = None, criterion_args: Optional[Dict] = None, @@ -24,14 +27,16 @@ class CharacterModel(Model): optimizer_args: Optional[Dict] = None, lr_scheduler: Optional[Callable] = None, lr_scheduler_args: Optional[Dict] = None, + swa_args: Optional[Dict] = None, device: Optional[str] = None, ) -> None: """Initializes the CharacterModel.""" super().__init__( network_fn, + dataset, network_args, - data_loader_args, + dataset_args, metrics, criterion, criterion_args, @@ -39,8 +44,11 @@ class CharacterModel(Model): optimizer_args, lr_scheduler, lr_scheduler_args, + swa_args, device, ) + if self._mapper is None: + self._mapper = EmnistMapper() self.tensor_transform = ToTensor() self.softmax = nn.Softmax(dim=0) @@ -67,9 +75,13 @@ class CharacterModel(Model): # Put the image tensor on the device the model weights are on. image = image.to(self.device) - logits = self.network(image) + logits = ( + self.swa_network(image) + if self.swa_network is not None + else self.network(image) + ) - prediction = self.softmax(logits.data.squeeze()) + prediction = self.softmax(logits.squeeze(0)) index = int(torch.argmax(prediction, dim=0)) confidence_of_prediction = prediction[index] diff --git a/src/text_recognizer/models/line_ctc_model.py b/src/text_recognizer/models/line_ctc_model.py new file mode 100644 index 0000000..97308a7 --- /dev/null +++ b/src/text_recognizer/models/line_ctc_model.py @@ -0,0 +1,105 @@ +"""Defines the LineCTCModel class.""" +from typing import Callable, Dict, Optional, Tuple, Type, Union + +import numpy as np +import torch +from torch import nn +from torch import Tensor +from torch.utils.data import Dataset +from torchvision.transforms import ToTensor + +from text_recognizer.datasets import EmnistMapper +from text_recognizer.models.base import Model +from text_recognizer.networks import greedy_decoder + + +class LineCTCModel(Model): + """Model for predicting a sequence of characters from an image of a text line.""" + + def __init__( + self, + network_fn: Type[nn.Module], + dataset: Type[Dataset], + network_args: Optional[Dict] = None, + dataset_args: Optional[Dict] = None, + metrics: Optional[Dict] = None, + criterion: Optional[Callable] = None, + criterion_args: Optional[Dict] = None, + optimizer: Optional[Callable] = None, + optimizer_args: Optional[Dict] = None, + lr_scheduler: Optional[Callable] = None, + lr_scheduler_args: Optional[Dict] = None, + swa_args: Optional[Dict] = None, + device: Optional[str] = None, + ) -> None: + super().__init__( + network_fn, + dataset, + network_args, + dataset_args, + metrics, + criterion, + criterion_args, + optimizer, + optimizer_args, + lr_scheduler, + lr_scheduler_args, + swa_args, + device, + ) + if self._mapper is None: + self._mapper = EmnistMapper() + self.tensor_transform = ToTensor() + + def loss_fn(self, output: Tensor, targets: Tensor) -> Tensor: + """Computes the CTC loss. + + Args: + output (Tensor): Model predictions. + targets (Tensor): Correct output sequence. + + Returns: + Tensor: The CTC loss. + + """ + input_lengths = torch.full( + size=(output.shape[1],), fill_value=output.shape[0], dtype=torch.long, + ) + target_lengths = torch.full( + size=(output.shape[1],), fill_value=targets.shape[1], dtype=torch.long, + ) + return self.criterion(output, targets, input_lengths, target_lengths) + + @torch.no_grad() + def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tuple[str, float]: + """Predict on a single input.""" + if image.dtype == np.uint8: + # Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1]. + image = self.tensor_transform(image) + + # Rescale image between 0 and 1. + if image.dtype == torch.uint8: + # If the image is an unscaled tensor. + image = image.type("torch.FloatTensor") / 255 + + # Put the image tensor on the device the model weights are on. + image = image.to(self.device) + log_probs = ( + self.swa_network(image) + if self.swa_network is not None + else self.network(image) + ) + + raw_pred, _ = greedy_decoder( + predictions=log_probs, + character_mapper=self.mapper, + blank_label=79, + collapse_repeated=True, + ) + + log_probs, _ = log_probs.max(dim=2) + + predicted_characters = "".join(raw_pred[0]) + confidence_of_prediction = torch.exp(log_probs.sum()).item() + + return predicted_characters, confidence_of_prediction diff --git a/src/text_recognizer/models/metrics.py b/src/text_recognizer/models/metrics.py index ac8d68e..6a26216 100644 --- a/src/text_recognizer/models/metrics.py +++ b/src/text_recognizer/models/metrics.py @@ -1,19 +1,89 @@ """Utility functions for models.""" - +import Levenshtein as Lev import torch +from torch import Tensor + +from text_recognizer.networks import greedy_decoder -def accuracy(outputs: torch.Tensor, labels: torch.Tensor) -> float: +def accuracy(outputs: Tensor, labels: Tensor) -> float: """Computes the accuracy. Args: - outputs (torch.Tensor): The output from the network. - labels (torch.Tensor): Ground truth labels. + outputs (Tensor): The output from the network. + labels (Tensor): Ground truth labels. Returns: float: The accuracy for the batch. """ _, predicted = torch.max(outputs.data, dim=1) - acc = (predicted == labels).sum().item() / labels.shape[0] + acc = (predicted == labels).sum().float() / labels.shape[0] + acc = acc.item() return acc + + +def cer(outputs: Tensor, targets: Tensor) -> float: + """Computes the character error rate. + + Args: + outputs (Tensor): The output from the network. + targets (Tensor): Ground truth labels. + + Returns: + float: The cer for the batch. + + """ + target_lengths = torch.full( + size=(outputs.shape[1],), fill_value=targets.shape[1], dtype=torch.long, + ) + decoded_predictions, decoded_targets = greedy_decoder( + outputs, targets, target_lengths + ) + + lev_dist = 0 + + for prediction, target in zip(decoded_predictions, decoded_targets): + prediction = "".join(prediction) + target = "".join(target) + prediction, target = ( + prediction.replace(" ", ""), + target.replace(" ", ""), + ) + lev_dist += Lev.distance(prediction, target) + return lev_dist / len(decoded_predictions) + + +def wer(outputs: Tensor, targets: Tensor) -> float: + """Computes the Word error rate. + + Args: + outputs (Tensor): The output from the network. + targets (Tensor): Ground truth labels. + + Returns: + float: The wer for the batch. + + """ + target_lengths = torch.full( + size=(outputs.shape[1],), fill_value=targets.shape[1], dtype=torch.long, + ) + decoded_predictions, decoded_targets = greedy_decoder( + outputs, targets, target_lengths + ) + + lev_dist = 0 + + for prediction, target in zip(decoded_predictions, decoded_targets): + prediction = "".join(prediction) + target = "".join(target) + + b = set(prediction.split() + target.split()) + word2char = dict(zip(b, range(len(b)))) + + w1 = [chr(word2char[w]) for w in prediction.split()] + w2 = [chr(word2char[w]) for w in target.split()] + + lev_dist += Lev.distance("".join(w1), "".join(w2)) + + return lev_dist / len(decoded_predictions) diff --git a/src/text_recognizer/networks/__init__.py b/src/text_recognizer/networks/__init__.py index a83ca35..d20c86a 100644 --- a/src/text_recognizer/networks/__init__.py +++ b/src/text_recognizer/networks/__init__.py @@ -1,6 +1,19 @@ """Network modules.""" +from .ctc import greedy_decoder from .lenet import LeNet +from .line_lstm_ctc import LineRecurrentNetwork +from .misc import sliding_window from .mlp import MLP -from .residual_network import ResidualNetwork +from .residual_network import ResidualNetwork, ResidualNetworkEncoder +from .wide_resnet import WideResidualNetwork -__all__ = ["MLP", "LeNet", "ResidualNetwork"] +__all__ = [ + "greedy_decoder", + "MLP", + "LeNet", + "LineRecurrentNetwork", + "ResidualNetwork", + "ResidualNetworkEncoder", + "sliding_window", + "WideResidualNetwork", +] diff --git a/src/text_recognizer/networks/ctc.py b/src/text_recognizer/networks/ctc.py index 00ad47e..fc0d21d 100644 --- a/src/text_recognizer/networks/ctc.py +++ b/src/text_recognizer/networks/ctc.py @@ -1,10 +1,58 @@ """Decodes the CTC output.""" -# -# from typing import Tuple -# import torch -# -# -# def greedy_decoder( -# output, labels, label_length, blank_label, collapse_repeated=True -# ) -> Tuple[torch.Tensor, torch.Tensor]: -# pass +from typing import Callable, List, Optional, Tuple + +from einops import rearrange +import torch +from torch import Tensor + +from text_recognizer.datasets import EmnistMapper + + +def greedy_decoder( + predictions: Tensor, + targets: Optional[Tensor] = None, + target_lengths: Optional[Tensor] = None, + character_mapper: Optional[Callable] = None, + blank_label: int = 79, + collapse_repeated: bool = True, +) -> Tuple[List[str], List[str]]: + """Greedy CTC decoder. + + Args: + predictions (Tensor): Tenor of network predictions, shape [time, batch, classes]. + targets (Optional[Tensor]): Target tensor, shape is [batch, targets]. Defaults to None. + target_lengths (Optional[Tensor]): Length of each target tensor. Defaults to None. + character_mapper (Optional[Callable]): A emnist/character mapper for mapping integers to characters. Defaults + to None. + blank_label (int): The blank character to be ignored. Defaults to 79. + collapse_repeated (bool): Collapase consecutive predictions of the same character. Defaults to True. + + Returns: + Tuple[List[str], List[str]]: Tuple of decoded predictions and decoded targets. + + """ + + if character_mapper is None: + character_mapper = EmnistMapper() + + predictions = rearrange(torch.argmax(predictions, dim=2), "t b -> b t") + decoded_predictions = [] + decoded_targets = [] + for i, prediction in enumerate(predictions): + decoded_prediction = [] + decoded_target = [] + if targets is not None and target_lengths is not None: + for target_index in targets[i][: target_lengths[i]]: + if target_index == blank_label: + continue + decoded_target.append(character_mapper(int(target_index))) + decoded_targets.append(decoded_target) + for j, index in enumerate(prediction): + if index != blank_label: + if collapse_repeated and j != 0 and index == prediction[j - 1]: + continue + decoded_prediction.append(index.item()) + decoded_predictions.append( + [character_mapper(int(pred_index)) for pred_index in decoded_prediction] + ) + return decoded_predictions, decoded_targets diff --git a/src/text_recognizer/networks/lenet.py b/src/text_recognizer/networks/lenet.py index 91d3f2c..53c575e 100644 --- a/src/text_recognizer/networks/lenet.py +++ b/src/text_recognizer/networks/lenet.py @@ -1,4 +1,4 @@ -"""Defines the LeNet network.""" +"""Implementation of the LeNet network.""" from typing import Callable, Dict, Optional, Tuple from einops.layers.torch import Rearrange @@ -9,7 +9,7 @@ from text_recognizer.networks.misc import activation_function class LeNet(nn.Module): - """LeNet network.""" + """LeNet network for character prediction.""" def __init__( self, @@ -17,10 +17,10 @@ class LeNet(nn.Module): kernel_sizes: Tuple[int, ...] = (3, 3, 2), hidden_size: Tuple[int, ...] = (9216, 128), dropout_rate: float = 0.2, - output_size: int = 10, + num_classes: int = 10, activation_fn: Optional[str] = "relu", ) -> None: - """The LeNet network. + """Initialization of the LeNet network. Args: channels (Tuple[int, ...]): Channels in the convolutional layers. Defaults to (1, 32, 64). @@ -28,7 +28,7 @@ class LeNet(nn.Module): hidden_size (Tuple[int, ...]): Size of the flattend output form the convolutional layers. Defaults to (9216, 128). dropout_rate (float): The dropout rate. Defaults to 0.2. - output_size (int): Number of classes. Defaults to 10. + num_classes (int): Number of classes. Defaults to 10. activation_fn (Optional[str]): The name of non-linear activation function. Defaults to relu. """ @@ -55,7 +55,7 @@ class LeNet(nn.Module): nn.Linear(in_features=hidden_size[0], out_features=hidden_size[1]), activation_fn, nn.Dropout(p=dropout_rate), - nn.Linear(in_features=hidden_size[1], out_features=output_size), + nn.Linear(in_features=hidden_size[1], out_features=num_classes), ] self.layers = nn.Sequential(*self.layers) diff --git a/src/text_recognizer/networks/line_lstm_ctc.py b/src/text_recognizer/networks/line_lstm_ctc.py index 2e2c3a5..988b615 100644 --- a/src/text_recognizer/networks/line_lstm_ctc.py +++ b/src/text_recognizer/networks/line_lstm_ctc.py @@ -1,5 +1,81 @@ """LSTM with CTC for handwritten text recognition within a line.""" +import importlib +from typing import Callable, Dict, List, Optional, Tuple, Type, Union +from einops import rearrange, reduce +from einops.layers.torch import Rearrange, Reduce import torch from torch import nn from torch import Tensor + + +class LineRecurrentNetwork(nn.Module): + """Network that takes a image of a text line and predicts tokens that are in the image.""" + + def __init__( + self, + encoder: str, + encoder_args: Dict = None, + flatten: bool = True, + input_size: int = 128, + hidden_size: int = 128, + num_layers: int = 1, + num_classes: int = 80, + patch_size: Tuple[int, int] = (28, 28), + stride: Tuple[int, int] = (1, 14), + ) -> None: + super().__init__() + self.encoder_args = encoder_args or {} + self.patch_size = patch_size + self.stride = stride + self.sliding_window = self._configure_sliding_window() + self.input_size = input_size + self.hidden_size = hidden_size + self.encoder = self._configure_encoder(encoder) + self.flatten = flatten + self.rnn = nn.LSTM( + input_size=self.input_size, + hidden_size=self.hidden_size, + num_layers=num_layers, + ) + self.decoder = nn.Sequential( + nn.Linear(in_features=self.hidden_size, out_features=num_classes), + nn.LogSoftmax(dim=2), + ) + + def _configure_encoder(self, encoder: str) -> Type[nn.Module]: + network_module = importlib.import_module("text_recognizer.networks") + encoder_ = getattr(network_module, encoder) + return encoder_(**self.encoder_args) + + def _configure_sliding_window(self) -> nn.Sequential: + return nn.Sequential( + nn.Unfold(kernel_size=self.patch_size, stride=self.stride), + Rearrange( + "b (c h w) t -> b t c h w", + h=self.patch_size[0], + w=self.patch_size[1], + c=1, + ), + ) + + def forward(self, x: Tensor) -> Tensor: + """Converts images to sequence of patches, feeds them to a CNN, then predictions are made with an LSTM.""" + if len(x.shape) == 3: + x = x.unsqueeze(0) + x = self.sliding_window(x) + + # Rearrange from a sequence of patches for feedforward network. + b, t = x.shape[:2] + x = rearrange(x, "b t c h w -> (b t) c h w", b=b, t=t) + x = self.encoder(x) + + # Avgerage pooling. + x = reduce(x, "(b t) c h w -> t b c", "mean", b=b, t=t) if self.flatten else x + + # Sequence predictions. + x, _ = self.rnn(x) + + # Sequence to classifcation layer. + x = self.decoder(x) + return x diff --git a/src/text_recognizer/networks/misc.py b/src/text_recognizer/networks/misc.py index 6f61b5d..cac9e78 100644 --- a/src/text_recognizer/networks/misc.py +++ b/src/text_recognizer/networks/misc.py @@ -22,9 +22,10 @@ def sliding_window( """ unfold = nn.Unfold(kernel_size=patch_size, stride=stride) # Preform the slidning window, unsqueeze as the channel dimesion is lost. - patches = unfold(images).unsqueeze(1) + c = images.shape[1] + patches = unfold(images) patches = rearrange( - patches, "b c (h w) t -> b t c h w", h=patch_size[0], w=patch_size[1] + patches, "b (c h w) t -> b t c h w", c=c, h=patch_size[0], w=patch_size[1] ) return patches diff --git a/src/text_recognizer/networks/mlp.py b/src/text_recognizer/networks/mlp.py index acebdaa..d66af28 100644 --- a/src/text_recognizer/networks/mlp.py +++ b/src/text_recognizer/networks/mlp.py @@ -14,7 +14,7 @@ class MLP(nn.Module): def __init__( self, input_size: int = 784, - output_size: int = 10, + num_classes: int = 10, hidden_size: Union[int, List] = 128, num_layers: int = 3, dropout_rate: float = 0.2, @@ -24,7 +24,7 @@ class MLP(nn.Module): Args: input_size (int): The input shape of the network. Defaults to 784. - output_size (int): Number of classes in the dataset. Defaults to 10. + num_classes (int): Number of classes in the dataset. Defaults to 10. hidden_size (Union[int, List]): The number of `neurons` in each hidden layer. Defaults to 128. num_layers (int): The number of hidden layers. Defaults to 3. dropout_rate (float): The dropout rate at each layer. Defaults to 0.2. @@ -55,7 +55,7 @@ class MLP(nn.Module): self.layers.append(nn.Dropout(p=dropout_rate)) self.layers.append( - nn.Linear(in_features=hidden_size[-1], out_features=output_size) + nn.Linear(in_features=hidden_size[-1], out_features=num_classes) ) self.layers = nn.Sequential(*self.layers) diff --git a/src/text_recognizer/networks/residual_network.py b/src/text_recognizer/networks/residual_network.py index 47e351a..1b5d6b3 100644 --- a/src/text_recognizer/networks/residual_network.py +++ b/src/text_recognizer/networks/residual_network.py @@ -8,6 +8,7 @@ from torch import nn from torch import Tensor from text_recognizer.networks.misc import activation_function +from text_recognizer.networks.stn import SpatialTransformerNetwork class Conv2dAuto(nn.Conv2d): @@ -197,25 +198,28 @@ class ResidualLayer(nn.Module): return x -class Encoder(nn.Module): +class ResidualNetworkEncoder(nn.Module): """Encoder network.""" def __init__( self, in_channels: int = 1, - block_sizes: List[int] = (32, 64), - depths: List[int] = (2, 2), + block_sizes: Union[int, List[int]] = (32, 64), + depths: Union[int, List[int]] = (2, 2), activation: str = "relu", block: Type[nn.Module] = BasicBlock, + levels: int = 1, + stn: bool = False, *args, **kwargs ) -> None: super().__init__() - - self.block_sizes = block_sizes - self.depths = depths + self.stn = SpatialTransformerNetwork() if stn else None + self.block_sizes = ( + block_sizes if isinstance(block_sizes, list) else [block_sizes] * levels + ) + self.depths = depths if isinstance(depths, list) else [depths] * levels self.activation = activation - self.gate = nn.Sequential( nn.Conv2d( in_channels=in_channels, @@ -227,7 +231,7 @@ class Encoder(nn.Module): ), nn.BatchNorm2d(self.block_sizes[0]), activation_function(self.activation), - nn.MaxPool2d(kernel_size=3, stride=2, padding=1), + nn.MaxPool2d(kernel_size=2, stride=2, padding=1), ) self.blocks = self._configure_blocks(block) @@ -271,11 +275,13 @@ class Encoder(nn.Module): # If batch dimenstion is missing, it needs to be added. if len(x.shape) == 3: x = x.unsqueeze(0) + if self.stn is not None: + x = self.stn(x) x = self.gate(x) return self.blocks(x) -class Decoder(nn.Module): +class ResidualNetworkDecoder(nn.Module): """Classification head.""" def __init__(self, in_features: int, num_classes: int = 80) -> None: @@ -295,19 +301,12 @@ class ResidualNetwork(nn.Module): def __init__(self, in_channels: int, num_classes: int, *args, **kwargs) -> None: super().__init__() - self.encoder = Encoder(in_channels, *args, **kwargs) - self.decoder = Decoder( + self.encoder = ResidualNetworkEncoder(in_channels, *args, **kwargs) + self.decoder = ResidualNetworkDecoder( in_features=self.encoder.blocks[-1].blocks[-1].expanded_channels, num_classes=num_classes, ) - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") - elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - def forward(self, x: Tensor) -> Tensor: """Forward pass.""" x = self.encoder(x) diff --git a/src/text_recognizer/networks/stn.py b/src/text_recognizer/networks/stn.py new file mode 100644 index 0000000..b031128 --- /dev/null +++ b/src/text_recognizer/networks/stn.py @@ -0,0 +1,44 @@ +"""Spatial Transformer Network.""" + +from einops.layers.torch import Rearrange +import torch +from torch import nn +from torch import Tensor +import torch.nn.functional as F + + +class SpatialTransformerNetwork(nn.Module): + """A network with differentiable attention. + + Network that learns how to perform spatial transformations on the input image in order to enhance the + geometric invariance of the model. + + # TODO: add arguements to make it more general. + + """ + + def __init__(self) -> None: + super().__init__() + # Initialize the identity transformation and its weights and biases. + linear = nn.Linear(32, 3 * 2) + linear.weight.data.zero_() + linear.bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float)) + + self.theta = nn.Sequential( + nn.Conv2d(in_channels=1, out_channels=8, kernel_size=7), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.ReLU(inplace=True), + nn.Conv2d(in_channels=8, out_channels=10, kernel_size=5), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.ReLU(inplace=True), + Rearrange("b c h w -> b (c h w)", h=3, w=3), + nn.Linear(in_features=10 * 3 * 3, out_features=32), + nn.ReLU(inplace=True), + linear, + Rearrange("b (row col) -> b row col", row=2, col=3), + ) + + def forward(self, x: Tensor) -> Tensor: + """The spatial transformation.""" + grid = F.affine_grid(self.theta(x), x.shape) + return F.grid_sample(x, grid, align_corners=False) diff --git a/src/text_recognizer/networks/wide_resnet.py b/src/text_recognizer/networks/wide_resnet.py new file mode 100644 index 0000000..d1c8f9a --- /dev/null +++ b/src/text_recognizer/networks/wide_resnet.py @@ -0,0 +1,214 @@ +"""Wide Residual CNN.""" +from functools import partial +from typing import Callable, Dict, List, Optional, Type, Union + +from einops.layers.torch import Rearrange, Reduce +import numpy as np +import torch +from torch import nn +from torch import Tensor + +from text_recognizer.networks.misc import activation_function + + +def conv3x3(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d: + """Helper function for a 3x3 2d convolution.""" + return nn.Conv2d( + in_channels=in_planes, + out_channels=out_planes, + kernel_size=3, + stride=stride, + padding=1, + bias=False, + ) + + +def conv_init(module: Type[nn.Module]) -> None: + """Initializes the weights for convolution and batchnorms.""" + classname = module.__class__.__name__ + if classname.find("Conv") != -1: + nn.init.xavier_uniform_(module.weight, gain=np.sqrt(2)) + nn.init.constant(module.bias, 0) + elif classname.find("BatchNorm") != -1: + nn.init.constant(module.weight, 1) + nn.init.constant(module.bias, 0) + + +class WideBlock(nn.Module): + """Block used in WideResNet.""" + + def __init__( + self, + in_planes: int, + out_planes: int, + dropout_rate: float, + stride: int = 1, + activation: str = "relu", + ) -> None: + super().__init__() + self.in_planes = in_planes + self.out_planes = out_planes + self.dropout_rate = dropout_rate + self.stride = stride + self.activation = activation_function(activation) + + # Build blocks. + self.blocks = nn.Sequential( + nn.BatchNorm2d(self.in_planes), + self.activation, + conv3x3(in_planes=self.in_planes, out_planes=self.out_planes), + nn.Dropout(p=self.dropout_rate), + nn.BatchNorm2d(self.out_planes), + self.activation, + conv3x3( + in_planes=self.out_planes, + out_planes=self.out_planes, + stride=self.stride, + ), + ) + + self.shortcut = ( + nn.Sequential( + nn.Conv2d( + in_channels=self.in_planes, + out_channels=self.out_planes, + kernel_size=1, + stride=self.stride, + bias=False, + ), + ) + if self._apply_shortcut + else None + ) + + @property + def _apply_shortcut(self) -> bool: + """If shortcut should be applied or not.""" + return self.stride != 1 or self.in_planes != self.out_planes + + def forward(self, x: Tensor) -> Tensor: + """Forward pass.""" + residual = x + if self._apply_shortcut: + residual = self.shortcut(x) + x = self.blocks(x) + x += residual + return x + + +class WideResidualNetwork(nn.Module): + """WideResNet for character predictions. + + Can be used for classification or encoding of images to a latent vector. + + """ + + def __init__( + self, + in_channels: int = 1, + in_planes: int = 16, + num_classes: int = 80, + depth: int = 16, + width_factor: int = 10, + dropout_rate: float = 0.0, + num_layers: int = 3, + block: Type[nn.Module] = WideBlock, + activation: str = "relu", + use_decoder: bool = True, + ) -> None: + """The initialization of the WideResNet. + + Args: + in_channels (int): Number of input channels. Defaults to 1. + in_planes (int): Number of channels to use in the first output kernel. Defaults to 16. + num_classes (int): Number of classes. Defaults to 80. + depth (int): Set the number of blocks to use. Defaults to 16. + width_factor (int): Factor for scaling the number of channels in the network. Defaults to 10. + dropout_rate (float): The dropout rate. Defaults to 0.0. + num_layers (int): Number of layers of blocks. Defaults to 3. + block (Type[nn.Module]): The default block is WideBlock. Defaults to WideBlock. + activation (str): Name of the activation to use. Defaults to "relu". + use_decoder (bool): If True, the network output character predictions, if False, the network outputs a + latent vector. Defaults to True. + + Raises: + RuntimeError: If the depth is not of the size `6n+4`. + + """ + + super().__init__() + if (depth - 4) % 6 != 0: + raise RuntimeError("Wide-resnet depth should be 6n+4") + self.in_channels = in_channels + self.in_planes = in_planes + self.num_classes = num_classes + self.num_blocks = (depth - 4) // 6 + self.width_factor = width_factor + self.num_layers = num_layers + self.block = block + self.dropout_rate = dropout_rate + self.activation = activation_function(activation) + + self.num_stages = [self.in_planes] + [ + self.in_planes * 2 ** n * self.width_factor for n in range(self.num_layers) + ] + self.num_stages = list(zip(self.num_stages, self.num_stages[1:])) + self.strides = [1] + [2] * (self.num_layers - 1) + + self.encoder = nn.Sequential( + conv3x3(in_planes=self.in_channels, out_planes=self.in_planes), + *[ + self._configure_wide_layer( + in_planes=in_planes, + out_planes=out_planes, + stride=stride, + activation=activation, + ) + for (in_planes, out_planes), stride in zip( + self.num_stages, self.strides + ) + ], + ) + + self.decoder = ( + nn.Sequential( + nn.BatchNorm2d(self.num_stages[-1][-1], momentum=0.8), + self.activation, + Reduce("b c h w -> b c", "mean"), + nn.Linear( + in_features=self.num_stages[-1][-1], out_features=self.num_classes + ), + ) + if use_decoder + else None + ) + + self.apply(conv_init) + + def _configure_wide_layer( + self, in_planes: int, out_planes: int, stride: int, activation: str + ) -> List: + strides = [stride] + [1] * (self.num_blocks - 1) + planes = [out_planes] * len(strides) + planes = [(in_planes, out_planes)] + list(zip(planes, planes[1:])) + return nn.Sequential( + *[ + self.block( + in_planes=in_planes, + out_planes=out_planes, + dropout_rate=self.dropout_rate, + stride=stride, + activation=activation, + ) + for (in_planes, out_planes), stride in zip(planes, strides) + ] + ) + + def forward(self, x: Tensor) -> Tensor: + """Feedforward pass.""" + if len(x.shape) == 3: + x = x.unsqueeze(0) + x = self.encoder(x) + if self.decoder is not None: + x = self.decoder(x) + return x diff --git a/src/text_recognizer/weights/CharacterModel_EmnistDataset_MLP_weights.pt b/src/text_recognizer/weights/CharacterModel_EmnistDataset_MLP_weights.pt Binary files differindex 86cf103..32c83cc 100644 --- a/src/text_recognizer/weights/CharacterModel_EmnistDataset_MLP_weights.pt +++ b/src/text_recognizer/weights/CharacterModel_EmnistDataset_MLP_weights.pt diff --git a/src/text_recognizer/weights/CharacterModel_EmnistDataset_ResidualNetwork_weights.pt b/src/text_recognizer/weights/CharacterModel_EmnistDataset_ResidualNetwork_weights.pt Binary files differindex a5c6aaf..a25bcd1 100644 --- a/src/text_recognizer/weights/CharacterModel_EmnistDataset_ResidualNetwork_weights.pt +++ b/src/text_recognizer/weights/CharacterModel_EmnistDataset_ResidualNetwork_weights.pt diff --git a/src/text_recognizer/weights/CharacterModel_EmnistDataset_SpinalVGG_weights.pt b/src/text_recognizer/weights/CharacterModel_EmnistDataset_SpinalVGG_weights.pt Binary files differnew file mode 100644 index 0000000..e720299 --- /dev/null +++ b/src/text_recognizer/weights/CharacterModel_EmnistDataset_SpinalVGG_weights.pt diff --git a/src/text_recognizer/weights/LineCTCModel_EmnistLinesDataset_LineRecurrentNetwork_weights.pt b/src/text_recognizer/weights/LineCTCModel_EmnistLinesDataset_LineRecurrentNetwork_weights.pt Binary files differnew file mode 100644 index 0000000..9aec6ae --- /dev/null +++ b/src/text_recognizer/weights/LineCTCModel_EmnistLinesDataset_LineRecurrentNetwork_weights.pt diff --git a/src/training/experiments/sample_experiment.yml b/src/training/experiments/sample_experiment.yml index b00bd5a..17e220e 100644 --- a/src/training/experiments/sample_experiment.yml +++ b/src/training/experiments/sample_experiment.yml @@ -1,17 +1,20 @@ experiment_group: Sample Experiments experiments: - - dataset: EmnistDataset - dataset_args: - sample_to_balance: true - subsample_fraction: null - transform: null - target_transform: null - seed: 4711 - data_loader_args: - splits: [train, val] - shuffle: true - num_workers: 8 - cuda: true + - train_args: + batch_size: 256 + max_epochs: 32 + dataset: + type: EmnistDataset + args: + sample_to_balance: true + subsample_fraction: null + transform: null + target_transform: null + seed: 4711 + train_args: + num_workers: 6 + train_fraction: 0.8 + model: CharacterModel metrics: [accuracy] # network: MLP @@ -19,65 +22,81 @@ experiments: # input_size: 784 # hidden_size: 512 # output_size: 80 - # num_layers: 3 - # dropout_rate: 0 + # num_layers: 5 + # dropout_rate: 0.2 # activation_fn: SELU - network: ResidualNetwork - network_args: - in_channels: 1 - num_classes: 80 - depths: [2, 1] - block_sizes: [96, 32] + network: + type: ResidualNetwork + args: + in_channels: 1 + num_classes: 80 + depths: [2, 2] + block_sizes: [64, 64] + activation: leaky_relu + stn: true + # network: + # type: WideResidualNetwork + # args: + # in_channels: 1 + # num_classes: 80 + # depth: 10 + # num_layers: 3 + # width_factor: 4 + # dropout_rate: 0.2 + # activation: SELU # network: LeNet # network_args: # output_size: 62 # activation_fn: GELU - train_args: - batch_size: 256 - epochs: 32 - criterion: CrossEntropyLoss - criterion_args: - weight: null - ignore_index: -100 - reduction: mean - # optimizer: RMSprop - # optimizer_args: - # lr: 1.e-3 - # alpha: 0.9 - # eps: 1.e-7 - # momentum: 0 - # weight_decay: 0 - # centered: false - optimizer: AdamW - optimizer_args: - lr: 1.e-03 - betas: [0.9, 0.999] - eps: 1.e-08 - # weight_decay: 5.e-4 - amsgrad: false - # lr_scheduler: null - lr_scheduler: OneCycleLR - lr_scheduler_args: - max_lr: 1.e-03 - epochs: 32 - anneal_strategy: linear - callbacks: [Checkpoint, ProgressBar, EarlyStopping, WandbCallback, WandbImageLogger, OneCycleLR] + criterion: + type: CrossEntropyLoss + args: + weight: null + ignore_index: -100 + reduction: mean + optimizer: + type: AdamW + args: + lr: 1.e-02 + betas: [0.9, 0.999] + eps: 1.e-08 + # weight_decay: 5.e-4 + amsgrad: false + # lr_scheduler: + # type: OneCycleLR + # args: + # max_lr: 1.e-03 + # epochs: null + # anneal_strategy: linear + lr_scheduler: + type: CosineAnnealingLR + args: + T_max: null + swa_args: + start: 2 + lr: 5.e-2 + callbacks: [Checkpoint, ProgressBar, WandbCallback, WandbImageLogger, EarlyStopping, SWA] # OneCycleLR] callback_args: Checkpoint: monitor: val_accuracy ProgressBar: - epochs: 32 + epochs: null log_batch_frequency: 100 EarlyStopping: monitor: val_loss min_delta: 0.0 - patience: 3 + patience: 5 mode: min WandbCallback: log_batch_frequency: 10 WandbImageLogger: num_examples: 4 - OneCycleLR: + use_transpose: true + # OneCycleLR: + # null + SWA: null - verbosity: 1 # 0, 1, 2 + verbosity: 0 # 0, 1, 2 resume_experiment: null + test: true + test_metric: test_accuracy diff --git a/src/training/prepare_experiments.py b/src/training/prepare_experiments.py index 4c3f9ba..e00540c 100644 --- a/src/training/prepare_experiments.py +++ b/src/training/prepare_experiments.py @@ -9,14 +9,14 @@ import yaml def run_experiments(experiments_filename: str) -> None: """Run experiment from file.""" - with open(experiments_filename) as f: + with open(experiments_filename, "r") as f: experiments_config = yaml.safe_load(f) num_experiments = len(experiments_config["experiments"]) for index in range(num_experiments): experiment_config = experiments_config["experiments"][index] experiment_config["experiment_group"] = experiments_config["experiment_group"] - cmd = f"python training/run_experiment.py --gpu=-1 --save --experiment_config='{json.dumps(experiment_config)}'" + cmd = f"python training/run_experiment.py --gpu=-1 --save '{json.dumps(experiment_config)}'" print(cmd) diff --git a/src/training/run_experiment.py b/src/training/run_experiment.py index 8c063ff..4317d66 100644 --- a/src/training/run_experiment.py +++ b/src/training/run_experiment.py @@ -6,18 +6,19 @@ import json import os from pathlib import Path import re -from typing import Callable, Dict, Tuple, Type +from typing import Callable, Dict, List, Tuple, Type import click from loguru import logger import torch from tqdm import tqdm from training.gpu_manager import GPUManager -from training.trainer.callbacks import CallbackList +from training.trainer.callbacks import Callback, CallbackList from training.trainer.train import Trainer import wandb import yaml + from text_recognizer.models import Model @@ -37,10 +38,14 @@ def get_level(experiment_config: Dict) -> int: return 10 -def create_experiment_dir(model: Type[Model], experiment_config: Dict) -> Path: +def create_experiment_dir(experiment_config: Dict) -> Path: """Create new experiment.""" EXPERIMENTS_DIRNAME.mkdir(parents=True, exist_ok=True) - experiment_dir = EXPERIMENTS_DIRNAME / model.__name__ + experiment_dir = EXPERIMENTS_DIRNAME / ( + f"{experiment_config['model']}_" + + f"{experiment_config['dataset']['type']}_" + + f"{experiment_config['network']['type']}" + ) if experiment_config["resume_experiment"] is None: experiment = datetime.now().strftime("%m%d_%H%M%S") logger.debug(f"Creating a new experiment called {experiment}") @@ -54,70 +59,89 @@ def create_experiment_dir(model: Type[Model], experiment_config: Dict) -> Path: experiment = experiment_config["resume_experiment"] if not str(experiment_dir / experiment) in available_experiments: raise FileNotFoundError("Experiment does not exist.") - logger.debug(f"Resuming the experiment {experiment}") experiment_dir = experiment_dir / experiment - return experiment_dir + # Create log and model directories. + log_dir = experiment_dir / "log" + model_dir = experiment_dir / "model" + + return experiment_dir, log_dir, model_dir -def check_args(args: Dict) -> Dict: + +def check_args(args: Dict, train_args: Dict) -> Dict: """Checks that the arguments are not None.""" + args = args or {} + + # I just want to set total epochs in train args, instead of changing all parameter. + if "epochs" in args and args["epochs"] is None: + args["epochs"] = train_args["max_epochs"] + + # For CosineAnnealingLR. + if "T_max" in args and args["T_max"] is None: + args["T_max"] = train_args["max_epochs"] + return args or {} def load_modules_and_arguments(experiment_config: Dict) -> Tuple[Callable, Dict]: """Loads all modules and arguments.""" # Import the data loader arguments. - data_loader_args = experiment_config.get("data_loader_args", {}) train_args = experiment_config.get("train_args", {}) - data_loader_args["batch_size"] = train_args["batch_size"] - data_loader_args["dataset"] = experiment_config["dataset"] - data_loader_args["dataset_args"] = experiment_config.get("dataset_args", {}) + + # Load the dataset module. + dataset_args = experiment_config.get("dataset", {}) + dataset_args["train_args"]["batch_size"] = train_args["batch_size"] + datasets_module = importlib.import_module("text_recognizer.datasets") + dataset_ = getattr(datasets_module, dataset_args["type"]) # Import the model module and model arguments. models_module = importlib.import_module("text_recognizer.models") model_class_ = getattr(models_module, experiment_config["model"]) # Import metrics. - metric_fns_ = { - metric: getattr(models_module, metric) - for metric in experiment_config["metrics"] - } + metric_fns_ = ( + { + metric: getattr(models_module, metric) + for metric in experiment_config["metrics"] + } + if experiment_config["metrics"] is not None + else None + ) # Import network module and arguments. network_module = importlib.import_module("text_recognizer.networks") - network_fn_ = getattr(network_module, experiment_config["network"]) - network_args = experiment_config.get("network_args", {}) + network_fn_ = getattr(network_module, experiment_config["network"]["type"]) + network_args = experiment_config["network"].get("args", {}) # Criterion - criterion_ = getattr(torch.nn, experiment_config["criterion"]) - criterion_args = experiment_config.get("criterion_args", {}) + criterion_ = getattr(torch.nn, experiment_config["criterion"]["type"]) + criterion_args = experiment_config["criterion"].get("args", {}) - # Optimizer - optimizer_ = getattr(torch.optim, experiment_config["optimizer"]) - optimizer_args = experiment_config.get("optimizer_args", {}) - - # Callbacks - callback_modules = importlib.import_module("training.trainer.callbacks") - callbacks = [ - getattr(callback_modules, callback)( - **check_args(experiment_config["callback_args"][callback]) - ) - for callback in experiment_config["callbacks"] - ] + # Optimizers + optimizer_ = getattr(torch.optim, experiment_config["optimizer"]["type"]) + optimizer_args = experiment_config["optimizer"].get("args", {}) # Learning rate scheduler + lr_scheduler_ = None + lr_scheduler_args = None if experiment_config["lr_scheduler"] is not None: lr_scheduler_ = getattr( - torch.optim.lr_scheduler, experiment_config["lr_scheduler"] + torch.optim.lr_scheduler, experiment_config["lr_scheduler"]["type"] + ) + lr_scheduler_args = check_args( + experiment_config["lr_scheduler"].get("args", {}), train_args ) - lr_scheduler_args = experiment_config.get("lr_scheduler_args", {}) + + # SWA scheduler. + if "swa_args" in experiment_config: + swa_args = check_args(experiment_config.get("swa_args", {}), train_args) else: - lr_scheduler_ = None - lr_scheduler_args = None + swa_args = None model_args = { - "data_loader_args": data_loader_args, + "dataset": dataset_, + "dataset_args": dataset_args, "metrics": metric_fns_, "network_fn": network_fn_, "network_args": network_args, @@ -127,43 +151,33 @@ def load_modules_and_arguments(experiment_config: Dict) -> Tuple[Callable, Dict] "optimizer_args": optimizer_args, "lr_scheduler": lr_scheduler_, "lr_scheduler_args": lr_scheduler_args, + "swa_args": swa_args, } - return model_class_, model_args, callbacks - + return model_class_, model_args -def run_experiment( - experiment_config: Dict, save_weights: bool, device: str, use_wandb: bool = False -) -> None: - """Runs an experiment.""" - - # Load the modules and model arguments. - model_class_, model_args, callbacks = load_modules_and_arguments(experiment_config) - - # Initializes the model with experiment config. - model = model_class_(**model_args, device=device) - # Instantiate a CallbackList. - callbacks = CallbackList(model, callbacks) - - # Create new experiment. - experiment_dir = create_experiment_dir(model, experiment_config) +def configure_callbacks(experiment_config: Dict, model_dir: Dict) -> CallbackList: + """Configure a callback list for trainer.""" + train_args = experiment_config.get("train_args", {}) - # Create log and model directories. - log_dir = experiment_dir / "log" - model_dir = experiment_dir / "model" + if "Checkpoint" in experiment_config["callback_args"]: + experiment_config["callback_args"]["Checkpoint"]["checkpoint_path"] = model_dir - # Set the model dir to be able to save checkpoints. - model.model_dir = model_dir + # Callbacks + callback_modules = importlib.import_module("training.trainer.callbacks") + callbacks = [ + getattr(callback_modules, callback)( + **check_args(experiment_config["callback_args"][callback], train_args) + ) + for callback in experiment_config["callbacks"] + ] - # Get checkpoint path. - checkpoint_path = model_dir / "last.pt" - if not checkpoint_path.exists(): - checkpoint_path = None + return callbacks - # Make sure the log directory exists. - log_dir.mkdir(parents=True, exist_ok=True) +def configure_logger(experiment_config: Dict, log_dir: Path) -> None: + """Configure the loguru logger for output to terminal and disk.""" # Have to remove default logger to get tqdm to work properly. logger.remove() @@ -176,13 +190,50 @@ def run_experiment( format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}", ) - if "cuda" in device: - gpu_index = re.sub("[^0-9]+", "", device) - logger.info( - f"Running experiment with config {experiment_config} on GPU {gpu_index}" - ) - else: - logger.info(f"Running experiment with config {experiment_config} on CPU") + +def save_config(experiment_dir: Path, experiment_config: Dict) -> None: + """Copy config to experiment directory.""" + config_path = experiment_dir / "config.yml" + with open(str(config_path), "w") as f: + yaml.dump(experiment_config, f) + + +def load_from_checkpoint(model: Type[Model], log_dir: Path, model_dir: Path) -> None: + """If checkpoint exists, load model weights and optimizers from checkpoint.""" + # Get checkpoint path. + checkpoint_path = model_dir / "last.pt" + if checkpoint_path.exists(): + logger.info("Loading and resuming training from last checkpoint.") + model.load_checkpoint(checkpoint_path) + + +def run_experiment( + experiment_config: Dict, save_weights: bool, device: str, use_wandb: bool = False +) -> None: + """Runs an experiment.""" + logger.info(f"Experiment config: {json.dumps(experiment_config)}") + + # Create new experiment. + experiment_dir, log_dir, model_dir = create_experiment_dir(experiment_config) + + # Make sure the log/model directory exists. + log_dir.mkdir(parents=True, exist_ok=True) + model_dir.mkdir(parents=True, exist_ok=True) + + # Load the modules and model arguments. + model_class_, model_args = load_modules_and_arguments(experiment_config) + + # Initializes the model with experiment config. + model = model_class_(**model_args, device=device) + + callbacks = configure_callbacks(experiment_config, model_dir) + + # Setup logger. + configure_logger(experiment_config, log_dir) + + # Load from checkpoint if resuming an experiment. + if experiment_config["resume_experiment"] is not None: + load_from_checkpoint(model, log_dir, model_dir) logger.info(f"The class mapping is {model.mapping}") @@ -193,9 +244,6 @@ def run_experiment( # Lets W&B save the model and track the gradients and optional parameters. wandb.watch(model.network) - # Pŕints a summary of the network in terminal. - model.summary() - experiment_config["train_args"] = { **DEFAULT_TRAIN_ARGS, **experiment_config.get("train_args", {}), @@ -208,41 +256,41 @@ def run_experiment( experiment_config["device"] = device # Save the config used in the experiment folder. - config_path = experiment_dir / "config.yml" - with open(str(config_path), "w") as f: - yaml.dump(experiment_config, f) + save_config(experiment_dir, experiment_config) - # Train the model. + # Load trainer. trainer = Trainer( - model=model, - model_dir=model_dir, - train_args=experiment_config["train_args"], - callbacks=callbacks, - checkpoint_path=checkpoint_path, + max_epochs=experiment_config["train_args"]["max_epochs"], callbacks=callbacks, ) - trainer.fit() + # Train the model. + trainer.fit(model) - logger.info("Loading checkpoint with the best weights.") - model.load_checkpoint(model_dir / "best.pt") + # Run inference over test set. + if experiment_config["test"]: + logger.info("Loading checkpoint with the best weights.") + model.load_from_checkpoint(model_dir / "best.pt") - score = trainer.validate() + logger.info("Running inference on test set.") + score = trainer.test(model) - logger.info(f"Validation set evaluation: {score}") + logger.info(f"Test set evaluation: {score}") - if use_wandb: - wandb.log({"validation_metric": score["val_accuracy"]}) + if use_wandb: + wandb.log( + { + experiment_config["test_metric"]: score[ + experiment_config["test_metric"] + ] + } + ) if save_weights: model.save_weights(model_dir) @click.command() -@click.option( - "--experiment_config", - type=str, - help='Experiment JSON, e.g. \'{"dataloader": "EmnistDataLoader", "model": "CharacterModel", "network": "mlp"}\'', -) +@click.argument("experiment_config",) @click.option("--gpu", type=int, default=0, help="Provide the index of the GPU to use.") @click.option( "--save", diff --git a/src/training/run_sweep.py b/src/training/run_sweep.py index 5c5322a..a578592 100644 --- a/src/training/run_sweep.py +++ b/src/training/run_sweep.py @@ -2,7 +2,91 @@ from ast import literal_eval import json import os +from pathlib import Path import signal import subprocess # nosec import sys -from typing import Tuple +from typing import Dict, List, Tuple + +import click +import yaml + +EXPERIMENTS_DIRNAME = Path(__file__).parents[0].resolve() / "experiments" + + +def load_config() -> Dict: + """Load base hyperparameter config.""" + with open(str(EXPERIMENTS_DIRNAME / "default_config_emnist.yml"), "r") as f: + default_config = yaml.safe_load(f) + return default_config + + +def args_to_json( + default_config: dict, preserve_args: tuple = ("gpu", "save") +) -> Tuple[dict, list]: + """Convert command line arguments to nested config values. + + i.e. run_sweep.py --dataset_args.foo=1.7 + { + "dataset_args": { + "foo": 1.7 + } + } + + Args: + default_config (dict): The base config used for every experiment. + preserve_args (tuple): Arguments preserved for all runs. Defaults to ("gpu", "save"). + + Returns: + Tuple[dict, list]: Tuple of config dictionary and list of arguments. + + """ + + args = [] + config = default_config.copy() + key, val = None, None + for arg in sys.argv[1:]: + if "=" in arg: + key, val = arg.split("=") + elif key: + val = arg + else: + key = arg + if key and val: + parsed_key = key.lstrip("-").split(".") + if parsed_key[0] in preserve_args: + args.append("--{}={}".format(parsed_key[0], val)) + else: + nested = config + for level in parsed_key[:-1]: + nested[level] = config.get(level, {}) + nested = nested[level] + try: + # Convert numerics to floats / ints + val = literal_eval(val) + except ValueError: + pass + nested[parsed_key[-1]] = val + key, val = None, None + return config, args + + +def main() -> None: + """Runs a W&B sweep.""" + default_config = load_config() + config, args = args_to_json(default_config) + env = { + k: v for k, v in os.environ.items() if k not in ("WANDB_PROGRAM", "WANDB_ARGS") + } + # pylint: disable=subprocess-popen-preexec-fn + run = subprocess.Popen( + ["python", "training/run_experiment.py", *args, json.dumps(config)], + env=env, + preexec_fn=os.setsid, + ) # nosec + signal.signal(signal.SIGTERM, lambda *args: run.terminate()) + run.wait() + + +if __name__ == "__main__": + main() diff --git a/src/training/sweep_emnist.yml b/src/training/sweep_emnist.yml new file mode 100644 index 0000000..48d7261 --- /dev/null +++ b/src/training/sweep_emnist.yml @@ -0,0 +1,26 @@ +program: training/run_sweep.py +method: bayes +metric: + name: val_loss + goal: minimize +parameters: + dataset: + value: EmnistDataset + model: + value: CharacterModel + network: + value: MLP + network_args.hidden_size: + values: [128, 256] + network_args.dropout_rate: + values: [0.2, 0.4] + network_args.num_layers: + values: [3, 6] + optimizer_args.lr: + values: [1.e-1, 1.e-5] + lr_scheduler_args.max_lr: + values: [1.0e-1, 1.0e-5] + train_args.batch_size: + values: [64, 128] + train_args.epochs: + value: 5 diff --git a/src/training/sweep_emnist_resnet.yml b/src/training/sweep_emnist_resnet.yml new file mode 100644 index 0000000..19a3040 --- /dev/null +++ b/src/training/sweep_emnist_resnet.yml @@ -0,0 +1,50 @@ +program: training/run_sweep.py +method: bayes +metric: + name: val_accuracy + goal: maximize +parameters: + dataset: + value: EmnistDataset + model: + value: CharacterModel + network: + value: ResidualNetwork + network_args.block_sizes: + distribution: q_uniform + min: 16 + max: 256 + q: 8 + network_args.depths: + distribution: int_uniform + min: 1 + max: 3 + network_args.levels: + distribution: int_uniform + min: 1 + max: 2 + network_args.activation: + distribution: categorical + values: + - gelu + - leaky_relu + - relu + - selu + optimizer_args.lr: + distribution: uniform + min: 1.e-5 + max: 1.e-1 + lr_scheduler_args.max_lr: + distribution: uniform + min: 1.e-5 + max: 1.e-1 + train_args.batch_size: + distribution: q_uniform + min: 32 + max: 256 + q: 8 + train_args.epochs: + value: 5 +early_terminate: + type: hyperband + min_iter: 2 diff --git a/src/training/trainer/callbacks/__init__.py b/src/training/trainer/callbacks/__init__.py index 5942276..c81e4bf 100644 --- a/src/training/trainer/callbacks/__init__.py +++ b/src/training/trainer/callbacks/__init__.py @@ -1,7 +1,16 @@ """The callback modules used in the training script.""" -from .base import Callback, CallbackList, Checkpoint +from .base import Callback, CallbackList +from .checkpoint import Checkpoint from .early_stopping import EarlyStopping -from .lr_schedulers import CyclicLR, MultiStepLR, OneCycleLR, ReduceLROnPlateau, StepLR +from .lr_schedulers import ( + CosineAnnealingLR, + CyclicLR, + MultiStepLR, + OneCycleLR, + ReduceLROnPlateau, + StepLR, + SWA, +) from .progress_bar import ProgressBar from .wandb_callbacks import WandbCallback, WandbImageLogger @@ -9,6 +18,7 @@ __all__ = [ "Callback", "CallbackList", "Checkpoint", + "CosineAnnealingLR", "EarlyStopping", "WandbCallback", "WandbImageLogger", @@ -18,4 +28,5 @@ __all__ = [ "ProgressBar", "ReduceLROnPlateau", "StepLR", + "SWA", ] diff --git a/src/training/trainer/callbacks/base.py b/src/training/trainer/callbacks/base.py index 8df94f3..8c7b085 100644 --- a/src/training/trainer/callbacks/base.py +++ b/src/training/trainer/callbacks/base.py @@ -168,81 +168,3 @@ class CallbackList: def __iter__(self) -> iter: """Iter function for callback list.""" return iter(self._callbacks) - - -class Checkpoint(Callback): - """Saving model parameters at the end of each epoch.""" - - mode_dict = { - "min": torch.lt, - "max": torch.gt, - } - - def __init__( - self, monitor: str = "accuracy", mode: str = "auto", min_delta: float = 0.0 - ) -> None: - """Monitors a quantity that will allow us to determine the best model weights. - - Args: - monitor (str): Name of the quantity to monitor. Defaults to "accuracy". - mode (str): Description of parameter `mode`. Defaults to "auto". - min_delta (float): Description of parameter `min_delta`. Defaults to 0.0. - - """ - super().__init__() - self.monitor = monitor - self.mode = mode - self.min_delta = torch.tensor(min_delta) - - if mode not in ["auto", "min", "max"]: - logger.warning(f"Checkpoint mode {mode} is unkown, fallback to auto mode.") - - self.mode = "auto" - - if self.mode == "auto": - if "accuracy" in self.monitor: - self.mode = "max" - else: - self.mode = "min" - logger.debug( - f"Checkpoint mode set to {self.mode} for monitoring {self.monitor}." - ) - - torch_inf = torch.tensor(np.inf) - self.min_delta *= 1 if self.monitor_op == torch.gt else -1 - self.best_score = torch_inf if self.monitor_op == torch.lt else -torch_inf - - @property - def monitor_op(self) -> float: - """Returns the comparison method.""" - return self.mode_dict[self.mode] - - def on_epoch_end(self, epoch: int, logs: Dict) -> None: - """Saves a checkpoint for the network parameters. - - Args: - epoch (int): The current epoch. - logs (Dict): The log containing the monitored metrics. - - """ - current = self.get_monitor_value(logs) - if current is None: - return - if self.monitor_op(current - self.min_delta, self.best_score): - self.best_score = current - is_best = True - else: - is_best = False - - self.model.save_checkpoint(is_best, epoch, self.monitor) - - def get_monitor_value(self, logs: Dict) -> Union[float, None]: - """Extracts the monitored value.""" - monitor_value = logs.get(self.monitor) - if monitor_value is None: - logger.warning( - f"Checkpoint is conditioned on metric {self.monitor} which is not available. Available" - + f"metrics are: {','.join(list(logs.keys()))}" - ) - return None - return monitor_value diff --git a/src/training/trainer/callbacks/checkpoint.py b/src/training/trainer/callbacks/checkpoint.py new file mode 100644 index 0000000..6fe06d3 --- /dev/null +++ b/src/training/trainer/callbacks/checkpoint.py @@ -0,0 +1,95 @@ +"""Callback checkpoint for training models.""" +from enum import Enum +from pathlib import Path +from typing import Callable, Dict, List, Optional, Type, Union + +from loguru import logger +import numpy as np +import torch +from training.trainer.callbacks import Callback + +from text_recognizer.models import Model + + +class Checkpoint(Callback): + """Saving model parameters at the end of each epoch.""" + + mode_dict = { + "min": torch.lt, + "max": torch.gt, + } + + def __init__( + self, + checkpoint_path: Path, + monitor: str = "accuracy", + mode: str = "auto", + min_delta: float = 0.0, + ) -> None: + """Monitors a quantity that will allow us to determine the best model weights. + + Args: + checkpoint_path (Path): Path to the experiment with the checkpoint. + monitor (str): Name of the quantity to monitor. Defaults to "accuracy". + mode (str): Description of parameter `mode`. Defaults to "auto". + min_delta (float): Description of parameter `min_delta`. Defaults to 0.0. + + """ + super().__init__() + self.checkpoint_path = checkpoint_path + self.monitor = monitor + self.mode = mode + self.min_delta = torch.tensor(min_delta) + + if mode not in ["auto", "min", "max"]: + logger.warning(f"Checkpoint mode {mode} is unkown, fallback to auto mode.") + + self.mode = "auto" + + if self.mode == "auto": + if "accuracy" in self.monitor: + self.mode = "max" + else: + self.mode = "min" + logger.debug( + f"Checkpoint mode set to {self.mode} for monitoring {self.monitor}." + ) + + torch_inf = torch.tensor(np.inf) + self.min_delta *= 1 if self.monitor_op == torch.gt else -1 + self.best_score = torch_inf if self.monitor_op == torch.lt else -torch_inf + + @property + def monitor_op(self) -> float: + """Returns the comparison method.""" + return self.mode_dict[self.mode] + + def on_epoch_end(self, epoch: int, logs: Dict) -> None: + """Saves a checkpoint for the network parameters. + + Args: + epoch (int): The current epoch. + logs (Dict): The log containing the monitored metrics. + + """ + current = self.get_monitor_value(logs) + if current is None: + return + if self.monitor_op(current - self.min_delta, self.best_score): + self.best_score = current + is_best = True + else: + is_best = False + + self.model.save_checkpoint(self.checkpoint_path, is_best, epoch, self.monitor) + + def get_monitor_value(self, logs: Dict) -> Union[float, None]: + """Extracts the monitored value.""" + monitor_value = logs.get(self.monitor) + if monitor_value is None: + logger.warning( + f"Checkpoint is conditioned on metric {self.monitor} which is not available. Available" + + f" metrics are: {','.join(list(logs.keys()))}" + ) + return None + return monitor_value diff --git a/src/training/trainer/callbacks/lr_schedulers.py b/src/training/trainer/callbacks/lr_schedulers.py index ba2226a..bb41d2d 100644 --- a/src/training/trainer/callbacks/lr_schedulers.py +++ b/src/training/trainer/callbacks/lr_schedulers.py @@ -1,6 +1,7 @@ """Callbacks for learning rate schedulers.""" from typing import Callable, Dict, List, Optional, Type +from torch.optim.swa_utils import update_bn from training.trainer.callbacks import Callback from text_recognizer.models import Model @@ -95,3 +96,54 @@ class OneCycleLR(Callback): def on_train_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None: """Takes a step at the end of every training batch.""" self.lr_scheduler.step() + + +class CosineAnnealingLR(Callback): + """Callback for Cosine Annealing.""" + + def __init__(self) -> None: + """Initializes the callback.""" + super().__init__() + self.lr_scheduler = None + + def set_model(self, model: Type[Model]) -> None: + """Sets the model and lr scheduler.""" + self.model = model + self.lr_scheduler = self.model.lr_scheduler + + def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None: + """Takes a step at the end of every epoch.""" + self.lr_scheduler.step() + + +class SWA(Callback): + """Stochastic Weight Averaging callback.""" + + def __init__(self) -> None: + """Initializes the callback.""" + super().__init__() + self.swa_scheduler = None + + def set_model(self, model: Type[Model]) -> None: + """Sets the model and lr scheduler.""" + self.model = model + self.swa_start = self.model.swa_start + self.swa_scheduler = self.model.lr_scheduler + self.lr_scheduler = self.model.lr_scheduler + + def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None: + """Takes a step at the end of every training batch.""" + if epoch > self.swa_start: + self.model.swa_network.update_parameters(self.model.network) + self.swa_scheduler.step() + else: + self.lr_scheduler.step() + + def on_fit_end(self) -> None: + """Update batch norm statistics for the swa model at the end of training.""" + if self.model.swa_network: + update_bn( + self.model.val_dataloader(), + self.model.swa_network, + device=self.model.device, + ) diff --git a/src/training/trainer/callbacks/progress_bar.py b/src/training/trainer/callbacks/progress_bar.py index 1970747..7829fa0 100644 --- a/src/training/trainer/callbacks/progress_bar.py +++ b/src/training/trainer/callbacks/progress_bar.py @@ -18,11 +18,11 @@ class ProgressBar(Callback): def _configure_progress_bar(self) -> None: """Configures the tqdm progress bar with custom bar format.""" self.progress_bar = tqdm( - total=len(self.model.data_loaders["train"]), - leave=True, - unit="step", + total=len(self.model.train_dataloader()), + leave=False, + unit="steps", mininterval=self.log_batch_frequency, - bar_format="{desc} |{bar:30}| {n_fmt}/{total_fmt} ETA: {remaining} {rate_fmt}{postfix}", + bar_format="{desc} |{bar:32}| {n_fmt}/{total_fmt} ETA: {remaining} {rate_fmt}{postfix}", ) def _key_abbreviations(self, logs: Dict) -> Dict: @@ -34,13 +34,16 @@ class ProgressBar(Callback): return {rename(key): value for key, value in logs.items()} - def on_fit_begin(self) -> None: - """Creates a tqdm progress bar.""" - self._configure_progress_bar() + # def on_fit_begin(self) -> None: + # """Creates a tqdm progress bar.""" + # self._configure_progress_bar() def on_epoch_begin(self, epoch: int, logs: Optional[Dict]) -> None: """Updates the description with the current epoch.""" - self.progress_bar.reset() + if epoch == 1: + self._configure_progress_bar() + else: + self.progress_bar.reset() self.progress_bar.set_description(f"Epoch {epoch}/{self.epochs}") def on_epoch_end(self, epoch: int, logs: Dict) -> None: diff --git a/src/training/trainer/callbacks/wandb_callbacks.py b/src/training/trainer/callbacks/wandb_callbacks.py index e44c745..6643a44 100644 --- a/src/training/trainer/callbacks/wandb_callbacks.py +++ b/src/training/trainer/callbacks/wandb_callbacks.py @@ -2,7 +2,8 @@ from typing import Callable, Dict, List, Optional, Type import numpy as np -from torchvision.transforms import Compose, ToTensor +import torch +from torchvision.transforms import ToTensor from training.trainer.callbacks import Callback import wandb @@ -50,43 +51,48 @@ class WandbImageLogger(Callback): self, example_indices: Optional[List] = None, num_examples: int = 4, - transfroms: Optional[Callable] = None, + use_transpose: Optional[bool] = False, ) -> None: """Initializes the WandbImageLogger with the model to train. Args: example_indices (Optional[List]): Indices for validation images. Defaults to None. num_examples (int): Number of random samples to take if example_indices are not specified. Defaults to 4. - transfroms (Optional[Callable]): Transforms to use on the validation images, e.g. transpose. Defaults to - None. + use_transpose (Optional[bool]): Use transpose on image or not. Defaults to False. """ super().__init__() self.example_indices = example_indices self.num_examples = num_examples - self.transfroms = transfroms - if self.transfroms is None: - self.transforms = Compose([Transpose()]) + self.transpose = Transpose() if use_transpose else None def set_model(self, model: Type[Model]) -> None: """Sets the model and extracts validation images from the dataset.""" self.model = model - data_loader = self.model.data_loaders["val"] if self.example_indices is None: self.example_indices = np.random.randint( - 0, len(data_loader.dataset.data), self.num_examples + 0, len(self.model.val_dataset), self.num_examples ) - self.val_images = data_loader.dataset.data[self.example_indices] - self.val_targets = data_loader.dataset.targets[self.example_indices].numpy() + self.val_images = self.model.val_dataset.dataset.data[self.example_indices] + self.val_targets = self.model.val_dataset.dataset.targets[self.example_indices] + self.val_targets = self.val_targets.tolist() def on_epoch_end(self, epoch: int, logs: Dict) -> None: """Get network predictions on validation images.""" images = [] for i, image in enumerate(self.val_images): - image = self.transforms(image) + image = self.transpose(image) if self.transpose is not None else image pred, conf = self.model.predict_on_image(image) - ground_truth = self.model.mapper(int(self.val_targets[i])) + if isinstance(self.val_targets[i], list): + ground_truth = "".join( + [ + self.model.mapper(int(target_index)) + for target_index in self.val_targets[i] + ] + ).rstrip("_") + else: + ground_truth = self.val_targets[i] caption = f"Prediction: {pred} Confidence: {conf:.3f} Ground Truth: {ground_truth}" images.append(wandb.Image(image, caption=caption)) diff --git a/src/training/trainer/train.py b/src/training/trainer/train.py index a75ae8f..b240157 100644 --- a/src/training/trainer/train.py +++ b/src/training/trainer/train.py @@ -8,8 +8,9 @@ from loguru import logger import numpy as np import torch from torch import Tensor +from torch.optim.swa_utils import update_bn from training.trainer.callbacks import Callback, CallbackList -from training.trainer.util import RunningAverage +from training.trainer.util import log_val_metric, RunningAverage import wandb from text_recognizer.models import Model @@ -24,37 +25,55 @@ torch.cuda.manual_seed(4711) class Trainer: """Trainer for training PyTorch models.""" - def __init__( - self, - model: Type[Model], - model_dir: Path, - train_args: Dict, - callbacks: CallbackList, - checkpoint_path: Optional[Path] = None, - ) -> None: + # TODO: proper add teardown? + + def __init__(self, max_epochs: int, callbacks: List[Type[Callback]],) -> None: """Initialization of the Trainer. Args: - model (Type[Model]): A model object. - model_dir (Path): Path to the model directory. - train_args (Dict): The training arguments. + max_epochs (int): The maximum number of epochs in the training loop. callbacks (CallbackList): List of callbacks to be called. - checkpoint_path (Optional[Path]): The path to a previously trained model. Defaults to None. """ - self.model = model - self.model_dir = model_dir - self.checkpoint_path = checkpoint_path + # Training arguments. self.start_epoch = 1 - self.epochs = train_args["epochs"] + self.max_epochs = max_epochs self.callbacks = callbacks - if self.checkpoint_path is not None: - self.start_epoch = self.model.load_checkpoint(self.checkpoint_path) + # Flag for setting callbacks. + self.callbacks_configured = False + + # Model placeholders + self.model = None + + def _configure_callbacks(self) -> None: + if not self.callbacks_configured: + # Instantiate a CallbackList. + self.callbacks = CallbackList(self.model, self.callbacks) + + def compute_metrics( + self, + output: Tensor, + targets: Tensor, + loss: Tensor, + loss_avg: Type[RunningAverage], + ) -> Dict: + """Computes metrics for output and target pairs.""" + # Compute metrics. + loss = loss.detach().float().item() + loss_avg.update(loss) + output = output.detach() + targets = targets.detach() + if self.model.metrics is not None: + metrics = { + metric: self.model.metrics[metric](output, targets) + for metric in self.model.metrics + } + else: + metrics = {} + metrics["loss"] = loss - # Parse the name of the experiment. - experiment_dir = str(self.model_dir.parents[1]).split("/") - self.experiment_name = experiment_dir[-2] + "/" + experiment_dir[-1] + return metrics def training_step( self, @@ -75,11 +94,12 @@ class Trainer: output = self.model.network(data) # Compute the loss. - loss = self.model.criterion(output, targets) + loss = self.model.loss_fn(output, targets) # Backward pass. # Clear the previous gradients. - self.model.optimizer.zero_grad() + for p in self.model.network.parameters(): + p.grad = None # Compute the gradients. loss.backward() @@ -87,15 +107,8 @@ class Trainer: # Perform updates using calculated gradients. self.model.optimizer.step() - # Compute metrics. - loss_avg.update(loss.item()) - output = output.data.cpu() - targets = targets.data.cpu() - metrics = { - metric: self.model.metrics[metric](output, targets) - for metric in self.model.metrics - } - metrics["loss"] = loss_avg() + metrics = self.compute_metrics(output, targets, loss, loss_avg) + return metrics def train(self) -> None: @@ -106,9 +119,7 @@ class Trainer: # Running average for the loss. loss_avg = RunningAverage() - data_loader = self.model.data_loaders["train"] - - for batch, samples in enumerate(data_loader): + for batch, samples in enumerate(self.model.train_dataloader()): self.callbacks.on_train_batch_begin(batch) metrics = self.training_step(batch, samples, loss_avg) self.callbacks.on_train_batch_end(batch, logs=metrics) @@ -119,6 +130,7 @@ class Trainer: batch: int, samples: Tuple[Tensor, Tensor], loss_avg: Type[RunningAverage], + use_swa: bool = False, ) -> Dict: """Performs the validation step.""" # Pass the tensor to the device for computation. @@ -130,44 +142,32 @@ class Trainer: # Forward pass. # Get the network prediction. - output = self.model.network(data) + # Use SWA if available and using test dataset. + if use_swa and self.model.swa_network is None: + output = self.model.swa_network(data) + else: + output = self.model.network(data) # Compute the loss. - loss = self.model.criterion(output, targets) + loss = self.model.loss_fn(output, targets) # Compute metrics. - loss_avg.update(loss.item()) - output = output.data.cpu() - targets = targets.data.cpu() - metrics = { - metric: self.model.metrics[metric](output, targets) - for metric in self.model.metrics - } - metrics["loss"] = loss.item() + metrics = self.compute_metrics(output, targets, loss, loss_avg) return metrics - def _log_val_metric(self, metrics_mean: Dict, epoch: Optional[int] = None) -> None: - log_str = "Validation metrics " + (f"at epoch {epoch} - " if epoch else " - ") - logger.debug( - log_str + " - ".join(f"{k}: {v:.4f}" for k, v in metrics_mean.items()) - ) - - def validate(self, epoch: Optional[int] = None) -> Dict: + def validate(self) -> Dict: """Runs the validation loop for one epoch.""" # Set model to eval mode. self.model.eval() # Running average for the loss. - data_loader = self.model.data_loaders["val"] - - # Running average for the loss. loss_avg = RunningAverage() # Summary for the current eval loop. summary = [] - for batch, samples in enumerate(data_loader): + for batch, samples in enumerate(self.model.val_dataloader()): self.callbacks.on_validation_batch_begin(batch) metrics = self.validation_step(batch, samples, loss_avg) self.callbacks.on_validation_batch_end(batch, logs=metrics) @@ -178,14 +178,19 @@ class Trainer: "val_" + metric: np.mean([x[metric] for x in summary]) for metric in summary[0] } - self._log_val_metric(metrics_mean, epoch) return metrics_mean - def fit(self) -> None: + def fit(self, model: Type[Model]) -> None: """Runs the training and evaluation loop.""" - logger.debug(f"Running an experiment called {self.experiment_name}.") + # Sets model, loads the data, criterion, and optimizers. + self.model = model + self.model.prepare_data() + self.model.configure_model() + + # Configure callbacks. + self._configure_callbacks() # Set start time. t_start = time.time() @@ -193,14 +198,15 @@ class Trainer: self.callbacks.on_fit_begin() # Run the training loop. - for epoch in range(self.start_epoch, self.epochs + 1): + for epoch in range(self.start_epoch, self.max_epochs + 1): self.callbacks.on_epoch_begin(epoch) # Perform one training pass over the training set. self.train() # Evaluate the model on the validation set. - val_metrics = self.validate(epoch) + val_metrics = self.validate() + log_val_metric(val_metrics, epoch) self.callbacks.on_epoch_end(epoch, logs=val_metrics) @@ -214,3 +220,43 @@ class Trainer: self.callbacks.on_fit_end() logger.info(f"Training took {t_training:.2f} s.") + + # "Teardown". + self.model = None + + def test(self, model: Type[Model]) -> Dict: + """Run inference on test data.""" + + # Sets model, loads the data, criterion, and optimizers. + self.model = model + self.model.prepare_data() + self.model.configure_model() + + # Configure callbacks. + self._configure_callbacks() + + self.model.eval() + + # Check if SWA network is available. + use_swa = True if self.model.swa_network is not None else False + + # Running average for the loss. + loss_avg = RunningAverage() + + # Summary for the current test loop. + summary = [] + + for batch, samples in enumerate(self.model.test_dataloader()): + metrics = self.validation_step(batch, samples, loss_avg, use_swa) + summary.append(metrics) + + # Compute mean of all test metrics. + metrics_mean = { + "test_" + metric: np.mean([x[metric] for x in summary]) + for metric in summary[0] + } + + # "Teardown". + self.model = None + + return metrics_mean diff --git a/src/training/trainer/util.py b/src/training/trainer/util.py index 132b2dc..7cf1b45 100644 --- a/src/training/trainer/util.py +++ b/src/training/trainer/util.py @@ -1,4 +1,13 @@ """Utility functions for training neural networks.""" +from typing import Dict, Optional + +from loguru import logger + + +def log_val_metric(metrics_mean: Dict, epoch: Optional[int] = None) -> None: + """Logging of val metrics to file/terminal.""" + log_str = "Validation metrics " + (f"at epoch {epoch} - " if epoch else " - ") + logger.debug(log_str + " - ".join(f"{k}: {v:.4f}" for k, v in metrics_mean.items())) class RunningAverage: |