How to use optional_out method in pandera

Best Python code snippet using pandera_python

validation.py

Source:validation.py

1# -*- coding: utf-8 -*-2import os3import numpy as np4from deep_image_prior import DeepImagePriorReconstructor5from copy import deepcopy6def val_sub_sub_path(i, i_sample):7    sub_sub_path_sample = os.path.join('rep_{:d}'.format(i),8                                       'sample_{:d}'.format(i_sample))9    return sub_sub_path_sample10def reconstruct(noisy_obs, fbp, gt, ray_trafo, save_val_sub_path, cfg, cfg_mdl_val):11    """12    Run a DIP validation reconstruction.13    Parameters14    ----------15    noisy_observation : :class:`torch.Tensor`16        Noisy observation.17    fbp : :class:`torch.Tensor`18        Input reconstruction (e.g. filtered backprojection).19    ground_truth : :class:`torch.Tensor`20        Ground truth image.21    ray_trafo : dict22        Dictionary with the following entries:23            `'ray_trafo_module'` : :class:`torch.nn.Module`24                Ray transform module.25            `'reco_space'` : :class:`odl.DiscretizedSpace`26                Image domain.27            `'observation_space'` : :class:`odl.DiscretizedSpace`28                Observation domain.29    save_val_sub_path : str30        Path to append to `cfg.save_histories_path` and31        `cfg.save_iterates_path` (if those are specified, respectively).32        This allows to store the results for multiple reconstructions within33        the validation run path.34    cfg : :class:`omegaconf.OmegaConf`35        Full configuration of the run. Note that ``cfg.mdl`` is ignored and36        instead `cfg_mdl_val` is used as the model configuration.37    cfg_mdl_val : :class:`omegaconf.OmegaConf`38        Configuration of the model.39    Returns40    -------41    reco :  :class:`numpy.ndarray`42        The reconstruction.43    psnr_history : list of scalar values44        PSNR history.45    """46    reconstructor = DeepImagePriorReconstructor(**ray_trafo, cfg=cfg_mdl_val)47    reco, *optional_out = reconstructor.reconstruct(48            noisy_obs, fbp, gt,49            return_histories=True,50            return_iterates=cfg.save_iterates_path is not None)51    psnr_history = optional_out[0]['psnr']52    if cfg.save_histories_path is not None:53        histories = {k: np.array(v, dtype=np.float32)54                     for k, v in optional_out[0].items()}55        save_histories_path = os.path.join(56                cfg.save_histories_path, save_val_sub_path)57        os.makedirs(save_histories_path, exist_ok=True)58        np.savez(os.path.join(save_histories_path, 'histories.npz'),59                 **histories)60    if cfg.save_iterates_path is not None:61        iterates = optional_out[1]62        iterates_iters = optional_out[2]63        save_iterates_path = os.path.join(64                cfg.save_iterates_path, save_val_sub_path)65        os.makedirs(save_iterates_path, exist_ok=True)66        np.savez_compressed(67                os.path.join(save_iterates_path, 'iterates.npz'),68                iterates=np.asarray(iterates),69                iterates_iters=iterates_iters)70    return reco, psnr_history71def validate_model(val_dataset, ray_trafo, seed, val_sub_path_mdl, baseline_psnr_steady, log_path_base, cfg, cfg_mdl_val):72    """73    Validate a model on a validation dataset with repeated runs.74    Parameters75    ----------76    val_dataset : :class:`torch.utils.data.TensorDataset`77        Validation dataset, as returned by :func:`dataset.get_validation_data`.78    ray_trafo : dict79        Dictionary with the following entries:80            `'ray_trafo_module'` : :class:`torch.nn.Module`81                Ray transform module.82            `'reco_space'` : :class:`odl.DiscretizedSpace`83                Image domain.84            `'observation_space'` : :class:`odl.DiscretizedSpace`85                Observation domain.86    seed : int87        Initial seed for the model.  In each repetition the seed is set by88        ``cfg_mdl_val.torch_manual_seed = seed + i``, where89        ``i in range(cfg.val.num_repeats)``.90    val_sub_path_mdl : str91        Path to append to `cfg.save_histories_path`,92        `cfg.save_iterates_path` (if those are specified, respectively) and93        `log_path_base` to identify the model. This allows to store the results94        for models within the validation run path. Inside the path specific to95        the model, the results of each individual run are saved in96        ``val_sub_sub_path(i, i_sample)``, where97        ``i in range(cfg.val.num_repeats)`` and98        ``i_sample in range(len(val_dataset))``.99    baseline_psnr_steady : float or `'own_PSNR_steady'`100        Steady PSNR of a baseline, by which ``info['rise_time_to_baseline']``101        is determined. If it is `'own_PSNR_steady'`, then102        ``info['PSNR_steady']`` is used. If it is `None`, also103        ``info['rise_time_to_baseline']`` is set to `None`.104    log_path_base : str105        Base path under which to save the tensorboard logs.106    cfg : :class:`omegaconf.OmegaConf`107        Full configuration of the run. Note that ``cfg.mdl`` is ignored and108        instead `cfg_mdl_val` is used as the model configuration.109    cfg_mdl_val : :class:`omegaconf.OmegaConf`110        Configuration of the model. This function will override111        ``cfg_mdl_val.torch_manual_seed`` and ``cfg_mdl_val.log_path`` (in a112        copy of the configuration).113    Returns114    -------115    psnr_histories : list of lists of lists of scalar values116        PSNR histories of all runs.117        The PSNR history of repetition `i` on validation sample `i_sample` is118        given by ``psnr_histories[i][i_sample]``.119    info : dict120        Validation info about the model. It is based on the median PSNR history121        that is the point-wise median w.r.t. all repetitions and validation122        samples, i.e. ``np.median(psnr_histories, axis=(0, 1))``.123            `'rise_time'` : int124                The first iteration where the median PSNR reaches125                ``info['PSNR_steady'] - cfg.val.rise_time_remaining_psnr``.126            `'rise_time_to_baseline'` : int or None127                The first iteration where the median PSNR reaches128                ``baseline_psnr_steady - cfg.val.rise_time_to_baseline_remaining_psnr``,129                or `None` if `baseline_psnr_steady` is `None`.130            `'PSNR_steady'` : scalar131                The steady PSNR, determined as the median of the median PSNR132                history in the configured interval, i.e.133                ``np.median(np.median(psnr_histories, axis=(0, 1))[cfg.val.psnr_steady_start:cfg.val.psnr_steady_stop])``.134            `'PSNR_0'`: scalar135                The initial median PSNR, i.e.136                ``np.median(psnr_histories, axis=(0, 1))[0]``.137    """138    cfg_mdl_val = deepcopy(cfg_mdl_val)139    psnr_histories = []140    for i in range(cfg.val.num_repeats):141        psnr_histories_i = []142        for i_sample, (noisy_obs, fbp, *gt) in enumerate(val_dataset):143            gt = gt[0] if gt else None144            if cfg.val.load_histories_from_run_path is not None:145                load_histories_path = os.path.join(146                        cfg.val.load_histories_from_run_path,147                        cfg.save_histories_path,148                        val_sub_path_mdl,149                        val_sub_sub_path(i=i, i_sample=i_sample))150                psnr_history = np.load(os.path.join(load_histories_path, 'histories.npz'))['psnr'].tolist()151            else:152                cfg_mdl_val.torch_manual_seed = seed + i153                cfg_mdl_val.log_path = os.path.join(154                        log_path_base,155                        val_sub_path_mdl,156                        val_sub_sub_path(i=i, i_sample=i_sample))157                save_val_sub_path = os.path.join(158                        val_sub_path_mdl,159                        val_sub_sub_path(i=i, i_sample=i_sample))160                _, psnr_history = reconstruct(161                        noisy_obs=noisy_obs.float().unsqueeze(dim=0),162                        fbp=fbp.unsqueeze(dim=0), gt=gt.unsqueeze(dim=0),163                        ray_trafo=ray_trafo,164                        save_val_sub_path=save_val_sub_path,165                        cfg=cfg, cfg_mdl_val=cfg_mdl_val)166            psnr_histories_i.append(psnr_history)167        psnr_histories.append(psnr_histories_i)168    median_psnr_output = np.median(psnr_histories, axis=(0, 1))169    psnr_steady = np.median(median_psnr_output[170            cfg.val.psnr_steady_start:cfg.val.psnr_steady_stop])171    rise_time = int(np.argwhere(172        median_psnr_output > psnr_steady - cfg.val.rise_time_remaining_psnr)[0][0])173    if baseline_psnr_steady == 'own_PSNR_steady':174        baseline_psnr_steady = psnr_steady175    if baseline_psnr_steady is None:176        rise_time_to_baseline = None177    else:178        argwhere_close_enough_to_baseline = np.argwhere(179                median_psnr_output > baseline_psnr_steady - cfg.val.rise_time_to_baseline_remaining_psnr)180        rise_time_to_baseline = (181                int(argwhere_close_enough_to_baseline[0][0])182                if len(argwhere_close_enough_to_baseline) >= 1 else None)183    info = {'rise_time': rise_time,184            'rise_time_to_baseline': rise_time_to_baseline,185            'PSNR_steady': psnr_steady,186            'PSNR_0': median_psnr_output[0]}...

blas_test.py

Source:blas_test.py

1#!/usr/bin/env python32# Copyright (c) 2020 Graphcore Ltd. All rights reserved.3import pytest4import torch5import poptorch6import helpers7def blas_op(op, input1, input2, out, trace_model, atol=1e-04, rtol=1e-04):8    class Model(torch.nn.Module):9        def __init__(self, op):10            super(Model, self).__init__()11            self.op = op12        def forward(self, x, y, out=None):13            return self.op(x, y, out=out)14    model = Model(op)15    args = [input1, input2]16    if out is not None:17        args.append(out)18    # Run on CPU.19    native_out = model(*args)20    # Run on IPU.21    options = poptorch.Options()22    options.Jit.traceModel(trace_model)23    poptorch_model = poptorch.inferenceModel(model, options)24    poptorch_out = poptorch_model(*args)25    helpers.assert_allclose(expected=native_out,26                            actual=poptorch_out,27                            atol=atol,28                            rtol=rtol,29                            equal_nan=True)30    if out is not None:31        helpers.assert_allclose(expected=native_out,32                                actual=out,33                                atol=atol,34                                rtol=rtol,35                                equal_nan=True)36@pytest.mark.parametrize("optional_out", [True, False])37@pytest.mark.parametrize("trace_model", [True, False])38def test_matmul(optional_out, trace_model):39    torch.manual_seed(42)40    input1 = torch.randn([10, 200])41    input2 = torch.randn([200, 45])42    out = torch.randn([10, 45]) if optional_out else None43    blas_op(torch.matmul, input1, input2, out, trace_model)44@pytest.mark.parametrize("trace_model", [True, False])45@pytest.mark.parametrize("mode",46                         (poptorch.MatMulSerializationMode.InputChannels,47                          poptorch.MatMulSerializationMode.ReducingDim,48                          poptorch.MatMulSerializationMode.OutputChannels,49                          poptorch.MatMulSerializationMode.Disabled))50@pytest.mark.parametrize("factor", (2, 5, 10))51@pytest.mark.parametrize("keep_precision", [True, False])52def test_serializedMatMul(trace_model, mode, factor, keep_precision):53    torch.manual_seed(42)54    input1 = torch.rand(1, 10, 200)55    input2_dim = 4556    if mode == poptorch.MatMulSerializationMode.OutputChannels:57        # Ensure the value is a multiple of factor58        input2_dim = input2_dim // factor * factor59    input2 = torch.rand(200, input2_dim)60    def serialise_matmal_op(input, other, out):61        assert out is None62        return poptorch.serializedMatMul(input, other, mode, factor,63                                         keep_precision)64    if keep_precision:65        input1 = input1.half()66        input2 = input2.half()67        blas_op(serialise_matmal_op,68                input1,69                input2,70                None,71                trace_model,72                rtol=0.01,73                atol=0.05)74    else:75        blas_op(serialise_matmal_op, input1, input2, None, trace_model)76@pytest.mark.parametrize("optional_out", [True, False])77@pytest.mark.parametrize("trace_model", [True, False])78def test_bmm(optional_out, trace_model):79    input1 = torch.randn([12, 10, 200])80    input2 = torch.randn([12, 200, 33])81    out = torch.randn([12, 10, 33]) if optional_out else None82    blas_op(torch.bmm, input1, input2, out, trace_model)83@pytest.mark.parametrize("bias", [True, False])84@pytest.mark.parametrize("trace_model", [True, False])85def test_matmul_training(bias, trace_model):86    N, M, K, C = 100, 9, 7, 587    class Net(torch.nn.Module):88        def __init__(self):89            super(Net, self).__init__()90            torch.manual_seed(42)91            self.linear = torch.nn.Linear(K, K, bias=bias)92            self.softmax = torch.nn.LogSoftmax(dim=1)93            self.loss = torch.nn.L1Loss(reduction="mean")94        def forward(self, x, y, target):95            x = self.linear(x)96            x = torch.matmul(x, y)97            return x, self.loss(x, target)98    torch.manual_seed(42)99    model = Net()100    opts = poptorch.Options()101    opts.Jit.traceModel(trace_model)102    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)103    torch.manual_seed(42)104    poptorch_model = poptorch.trainingModel(model, opts, optimizer)105    x = torch.randn(N, M, K)106    y = torch.randn(K, K)107    target = torch.empty(N, M, K, dtype=torch.long).random_(0, C)108    for _ in range(0, 400):109        optimizer.zero_grad()110        poptorch_out, poptorch_loss = poptorch_model(x, y, target)111        native_out, native_loss = model(x, y, target)112        native_loss.backward(retain_graph=True)113        optimizer.step()114    helpers.assert_allclose(actual=poptorch_out,115                            expected=native_out,116                            rtol=1e-02,117                            atol=1e-02)118    helpers.assert_allclose(actual=poptorch_loss,119                            expected=native_loss,120                            rtol=1e-03,...

coordinator.py

Source:coordinator.py

1import hydra2import os3import h5py4import numpy as np5from omegaconf import DictConfig6from dataset import get_standard_dataset, get_test_data, get_validation_data7import torch8from torch.utils.data import DataLoader9from deep_image_prior import DeepImagePriorReconstructor10from pre_training import Trainer11from copy import deepcopy12@hydra.main(config_path='cfgs', config_name='config')13def coordinator(cfg : DictConfig) -> None:14    dataset, ray_trafos = get_standard_dataset(cfg.data.name, cfg.data)15    obs_shape = dataset.space[0].shape16    im_shape = dataset.space[1].shape17    if cfg.validation_run:18        if cfg.data.validation_data:19            dataset_test = get_validation_data(cfg.data.name, cfg.data)20        else:21            dataset_test = dataset.create_torch_dataset(22                fold='validation', reshape=((1,) + obs_shape,23                                            (1,) + im_shape,24                                            (1,) + im_shape))25    else:26        if cfg.data.test_data:27            dataset_test = get_test_data(cfg.data.name, cfg.data)28        else:29            dataset_test = dataset.create_torch_dataset(30                fold='test', reshape=((1,) + obs_shape,31                                      (1,) + im_shape,32                                      (1,) + im_shape))33    ray_trafo = {'ray_trafo_module': ray_trafos['ray_trafo_module'],34                 'reco_space': dataset.space[1],35                 'observation_space': dataset.space[0]36                 }37    if cfg.torch_manual_seed_pretrain_init_model:38        torch.random.manual_seed(cfg.torch_manual_seed_pretrain_init_model)39    reconstructor = DeepImagePriorReconstructor(**ray_trafo, cfg=cfg.mdl)40    model = deepcopy(reconstructor.model)41    if cfg.pretraining:42        trn_ray_trafos = ({'smooth_pinv_ray_trafo_module':43                               ray_trafos['smooth_pinv_ray_trafo_module']}44                          if cfg.trn.use_adversarial_attacks else {})45        Trainer(model=model,46                ray_trafos=trn_ray_trafos,47                cfg=cfg.trn).train(dataset)48    os.makedirs(cfg.save_reconstruction_path, exist_ok=True)49    if cfg.save_histories_path is not None:50        os.makedirs(cfg.save_histories_path, exist_ok=True)51    if cfg.save_iterates_path is not None:52        os.makedirs(cfg.save_iterates_path, exist_ok=True)53    if cfg.save_iterates_params_path is not None:54        os.makedirs(cfg.save_iterates_params_path, exist_ok=True)55    filename = os.path.join(cfg.save_reconstruction_path,'recos.hdf5')56    file = h5py.File(filename, 'w')57    recos_dataset = file.create_dataset('recos',58            shape=(1,) + im_shape, maxshape=(1,) + im_shape, dtype=np.float32,59            chunks=True)60    dataloader = DataLoader(dataset_test, batch_size=1, num_workers=0,61                            shuffle=True, pin_memory=True)62    for i, (noisy_obs, fbp, *gt) in enumerate(dataloader):63        gt = gt[0] if gt else None64        reco, *optional_out = reconstructor.reconstruct(65                noisy_obs.float(), fbp, gt,66                return_histories=cfg.save_histories_path is not None,67                return_iterates=cfg.save_iterates_path is not None,68                return_iterates_params=cfg.save_iterates_params_path is not None)69        recos_dataset[i] = reco70        if cfg.save_histories_path is not None:71            histories = optional_out.pop(0)72            histories = {k: np.array(v, dtype=np.float32)73                         for k, v in histories.items()}74            np.savez(os.path.join(cfg.save_histories_path, 'histories.npz'),75                     **histories)76        if cfg.save_iterates_path is not None:77            iterates = optional_out.pop(0)78            iterates_iters = optional_out.pop(0)79            np.savez_compressed(80                    os.path.join(cfg.save_iterates_path, 'iterates.npz'),81                    iterates=np.asarray(iterates),82                    iterates_iters=iterates_iters)83        if cfg.save_iterates_params_path is not None:84            iterates_params = optional_out.pop(0)85            iterates_params_iters = optional_out.pop(0)86            for params, iters in zip(iterates_params, iterates_params_iters):87                torch.save(params,88                           os.path.join(cfg.save_iterates_params_path,89                                        'params_iters{:d}.pt'.format(iters)))90if __name__ == '__main__':...

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