.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_train_side_loss_unet.py: Train Side Loss UNet Example ================================ In this example a UNet with side supervision and auxiliary loss implemented Imports needed for this example .. code-block:: python import torch import torch.nn as nn from inferno.io.box.binary_blobs import get_binary_blob_loaders from inferno.trainers.basic import Trainer from inferno.extensions.layers.convolutional import Conv2D from inferno.extensions.layers.building_blocks import ResBlock from inferno.extensions.layers import ResBlockUNet from inferno.utils.torch_utils import unwrap from inferno.utils.python_utils import ensure_dir import pylab To create a UNet with side loss we create a new nn.Module class which has a ResBlockUNet as member. The ResBlockUNet is configured such that the results of the bottom convolution and all the results of the up-stream convolutions are returned as (side)-output. a 1x1 convolutions is used to give the side outputs the right number of out_channels and UpSampling is used to resize all side-outputs to the full resolution of the input. These side `side-predictions` are returned by our MySideLossUNet. Furthermore, all `side-predictions` are concatenated and feed trough another two residual blocks to make the final prediction. .. code-block:: python class MySideLossUNet(nn.Module): def __init__(self, in_channels, out_channels, depth=3): super(MySideLossUNet, self).__init__() self.depth = depth self.unet = ResBlockUNet(in_channels=in_channels, out_channels=in_channels*2, dim=2, unet_kwargs=dict(depth=depth), side_out_parts=['bottom', 'up']) # number of out channels self.n_channels_per_output = self.unet.n_channels_per_output # 1x1 conv to give the side outs of the unet # the right number of channels # and a Upsampling to give the right shape upscale_factor = 2**self.depth conv_and_scale = [] for n_channels in self.n_channels_per_output: # conv blocks conv = Conv2D(in_channels=n_channels, out_channels=out_channels, kernel_size=1) if upscale_factor > 1: upsample = nn.Upsample(scale_factor=upscale_factor) conv_and_scale.append(nn.Sequential(conv, upsample)) else: conv_and_scale.append(conv) upscale_factor //= 2 self.conv_and_scale = nn.ModuleList(conv_and_scale) # combined number of channels after concat # concat side output predictions with main output of unet self.n_channels_combined = (self.depth + 1)* out_channels + in_channels*2 self.final_block = nn.Sequential( ResBlock(dim=2,in_channels=self.n_channels_combined, out_channels=self.n_channels_combined), ResBlock(in_channels=self.n_channels_combined, out_channels=out_channels, dim=2, activated=False), ) def forward(self, input): outs = self.unet(input) assert len(outs) == len(self.n_channels_per_output) # convert the unet output into the right number of preds = [None] * len(outs) for i,out in enumerate(outs): preds[i] = self.conv_and_scale[i](out) # this is the side output preds = tuple(preds) # concat side output predictions with main output of unet combined = torch.cat(preds + (outs[-1],), 1) final_res = self.final_block(combined) # return everything return preds + (final_res,) We use a custom loss functions which applied CrossEntropyLoss to all side outputs. The side outputs are weighted in a quadratic fashion and added up into a single value .. code-block:: python class MySideLoss(nn.Module): """Wrap a criterion. Collect regularization losses from model and combine with wrapped criterion. """ def __init__(self): super(MySideLoss, self).__init__() self.criterion = nn.CrossEntropyLoss(reduce=True) w = 1.0 l = None def forward(self, predictions, target): w = 1.0 l = None for p in predictions: ll = self.criterion(p, target)*w if l is None: l = ll else: l += ll w *= 2 return l Training boilerplate (see :ref:`sphx_glr_auto_examples_trainer.py`) .. code-block:: python LOG_DIRECTORY = ensure_dir('log') SAVE_DIRECTORY = ensure_dir('save') DATASET_DIRECTORY = ensure_dir('dataset') USE_CUDA = True # Build a residual unet where the last layer is not activated sl_unet = MySideLossUNet(in_channels=5, out_channels=2) model = nn.Sequential( ResBlock(dim=2, in_channels=1, out_channels=5), sl_unet ) train_loader, test_loader, validate_loader = get_binary_blob_loaders( train_batch_size=3, length=512, # <= size of the images gaussian_noise_sigma=1.5 # <= how noise are the images ) # Build trainer trainer = Trainer(model) trainer.build_criterion(MySideLoss()) trainer.build_optimizer('Adam') trainer.validate_every((10, 'epochs')) #trainer.save_every((10, 'epochs')) #trainer.save_to_directory(SAVE_DIRECTORY) trainer.set_max_num_epochs(40) # Bind loaders trainer \ .bind_loader('train', train_loader)\ .bind_loader('validate', validate_loader) if USE_CUDA: trainer.cuda() # Go! trainer.fit() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none [+][2018-08-15 16:29:37.317833] [PROGRESS] Training iteration 0 (batch 0 of epoch 0). 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Predict with the trained network and visualize the results .. code-block:: python # predict: #trainer.load(best=True) trainer.bind_loader('train', train_loader) trainer.bind_loader('validate', validate_loader) trainer.eval_mode() if USE_CUDA: trainer.cuda() # look at an example for img,target in test_loader: if USE_CUDA: img = img.cuda() # softmax on each of the prediction preds = trainer.apply_model(img) preds = [nn.functional.softmax(pred,dim=1) for pred in preds] preds = [unwrap(pred, as_numpy=True, to_cpu=True) for pred in preds] img = unwrap(img, as_numpy=True, to_cpu=True) target = unwrap(target, as_numpy=True, to_cpu=True) n_plots = len(preds) + 2 batch_size = preds[0].shape[0] for b in range(batch_size): fig = pylab.figure() ax1 = fig.add_subplot(2,4,1) ax1.set_title('image') ax1.imshow(img[b,0,...]) ax2 = fig.add_subplot(2,4,2) ax2.set_title('ground truth') ax2.imshow(target[b,...]) for i,pred in enumerate(preds): axn = fig.add_subplot(2,4, 3+i) axn.imshow(pred[b,1,...]) if i + 1 < len(preds): axn.set_title('side prediction %d'%i) else: axn.set_title('combined prediction') pylab.show() break .. image:: /auto_examples/images/sphx_glr_plot_train_side_loss_unet_001.png :class: sphx-glr-single-img **Total running time of the script:** ( 1 minutes 31.868 seconds) .. _sphx_glr_download_auto_examples_plot_train_side_loss_unet.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: plot_train_side_loss_unet.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_train_side_loss_unet.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_