382 lines
13 KiB
Python
382 lines
13 KiB
Python
"""Visualization Callbacks."""
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import os
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import warnings
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from typing import Sized
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import numpy as np
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import pytorch_lightning as pl
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import torch
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import torchvision
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from matplotlib import pyplot as plt
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from prototorch.utils.colors import get_colors, get_legend_handles
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from prototorch.utils.utils import mesh2d
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from pytorch_lightning.loggers import TensorBoardLogger
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from torch.utils.data import DataLoader, Dataset
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class Vis2DAbstract(pl.Callback):
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def __init__(self,
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data=None,
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title="Prototype Visualization",
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cmap="viridis",
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xlabel="Data dimension 1",
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ylabel="Data dimension 2",
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legend_labels=None,
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border=0.1,
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resolution=100,
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flatten_data=True,
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axis_off=False,
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show_protos=True,
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show=True,
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tensorboard=False,
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show_last_only=False,
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pause_time=0.1,
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save=False,
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save_dir="./img",
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fig_size=(5, 4),
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dpi=500,
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block=False):
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super().__init__()
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if data:
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if isinstance(data, Dataset):
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if isinstance(data, Sized):
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x, y = next(iter(DataLoader(data, batch_size=len(data))))
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else:
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# TODO: Add support for non-sized datasets
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raise NotImplementedError(
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"Data must be a dataset with a __len__ method.")
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elif isinstance(data, DataLoader):
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x = torch.tensor([])
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y = torch.tensor([])
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for x_b, y_b in data:
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x = torch.cat([x, x_b])
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y = torch.cat([y, y_b])
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else:
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x, y = data
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if flatten_data:
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x = x.reshape(len(x), -1)
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self.x_train = x
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self.y_train = y
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else:
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self.x_train = None
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self.y_train = None
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self.title = title
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self.xlabel = xlabel
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self.ylabel = ylabel
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self.legend_labels = legend_labels
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self.fig = plt.figure(self.title)
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self.cmap = cmap
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self.border = border
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self.resolution = resolution
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self.axis_off = axis_off
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self.show_protos = show_protos
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self.show = show
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self.tensorboard = tensorboard
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self.show_last_only = show_last_only
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self.pause_time = pause_time
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self.save = save
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self.save_dir = save_dir
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self.fig_size = fig_size
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self.dpi = dpi
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self.block = block
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if save:
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if not os.path.exists(save_dir):
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os.makedirs(save_dir)
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def precheck(self, trainer):
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if self.show_last_only:
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if trainer.current_epoch != trainer.max_epochs - 1:
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return False
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return True
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def setup_ax(self):
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ax = self.fig.gca()
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ax.cla()
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ax.set_title(self.title)
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ax.set_xlabel(self.xlabel)
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ax.set_ylabel(self.ylabel)
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if self.axis_off:
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ax.axis("off")
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return ax
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def plot_data(self, ax, x, y):
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ax.scatter(
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x[:, 0],
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x[:, 1],
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c=y,
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cmap=self.cmap,
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edgecolor="k",
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marker="o",
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s=30,
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)
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def plot_protos(self, ax, protos, plabels):
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ax.scatter(
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protos[:, 0],
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protos[:, 1],
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c=plabels,
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cmap=self.cmap,
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edgecolor="k",
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marker="D",
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s=50,
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)
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def add_to_tensorboard(self, trainer, pl_module):
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tb = pl_module.logger.experiment
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tb.add_figure(tag=f"{self.title}",
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figure=self.fig,
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global_step=trainer.current_epoch,
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close=False)
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def log_and_display(self, trainer, pl_module):
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if self.tensorboard:
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self.add_to_tensorboard(trainer, pl_module)
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if self.save:
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plt.tight_layout()
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self.fig.set_size_inches(*self.fig_size, forward=False)
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plt.savefig(f"{self.save_dir}/{trainer.current_epoch}.png",
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dpi=self.dpi)
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if self.show:
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if not self.block:
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plt.pause(self.pause_time)
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else:
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plt.show(block=self.block)
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def on_train_epoch_end(self, trainer, pl_module):
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if not self.precheck(trainer):
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return True
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self.visualize(pl_module)
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self.log_and_display(trainer, pl_module)
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def on_train_end(self, trainer, pl_module):
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plt.close()
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def visualize(self, pl_module):
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raise NotImplementedError
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class VisGLVQ2D(Vis2DAbstract):
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def visualize(self, pl_module):
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protos = pl_module.prototypes
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plabels = pl_module.prototype_labels
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x_train, y_train = self.x_train, self.y_train
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ax = self.setup_ax()
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self.plot_protos(ax, protos, plabels)
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if x_train is not None:
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self.plot_data(ax, x_train, y_train)
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mesh_input, xx, yy = mesh2d(np.vstack([x_train, protos]),
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self.border, self.resolution)
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else:
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mesh_input, xx, yy = mesh2d(protos, self.border, self.resolution)
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_components = pl_module.proto_layer._components
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mesh_input = torch.from_numpy(mesh_input).type_as(_components)
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y_pred = pl_module.predict(mesh_input)
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y_pred = y_pred.cpu().reshape(xx.shape)
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ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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class VisSiameseGLVQ2D(Vis2DAbstract):
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def __init__(self, *args, map_protos=True, **kwargs):
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super().__init__(*args, **kwargs)
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self.map_protos = map_protos
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def visualize(self, pl_module):
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protos = pl_module.prototypes
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plabels = pl_module.prototype_labels
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x_train, y_train = self.x_train, self.y_train
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device = pl_module.device
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with torch.no_grad():
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x_train = pl_module.backbone(torch.Tensor(x_train).to(device))
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x_train = x_train.cpu().detach()
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if self.map_protos:
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with torch.no_grad():
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protos = pl_module.backbone(torch.Tensor(protos).to(device))
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protos = protos.cpu().detach()
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ax = self.setup_ax()
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self.plot_data(ax, x_train, y_train)
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if self.show_protos:
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self.plot_protos(ax, protos, plabels)
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x = np.vstack((x_train, protos))
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mesh_input, xx, yy = mesh2d(x, self.border, self.resolution)
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else:
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mesh_input, xx, yy = mesh2d(x_train, self.border, self.resolution)
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_components = pl_module.proto_layer._components
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mesh_input = torch.Tensor(mesh_input).type_as(_components)
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y_pred = pl_module.predict_latent(mesh_input,
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map_protos=self.map_protos)
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y_pred = y_pred.cpu().reshape(xx.shape)
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ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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class VisGMLVQ2D(Vis2DAbstract):
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def __init__(self, *args, ev_proj=True, **kwargs):
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super().__init__(*args, **kwargs)
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self.ev_proj = ev_proj
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def visualize(self, pl_module):
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protos = pl_module.prototypes
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plabels = pl_module.prototype_labels
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x_train, y_train = self.x_train, self.y_train
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device = pl_module.device
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omega = pl_module._omega.detach()
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lam = omega @ omega.T
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u, _, _ = torch.pca_lowrank(lam, q=2)
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with torch.no_grad():
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x_train = torch.Tensor(x_train).to(device)
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x_train = x_train @ u
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x_train = x_train.cpu().detach()
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if self.show_protos:
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with torch.no_grad():
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protos = torch.Tensor(protos).to(device)
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protos = protos @ u
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protos = protos.cpu().detach()
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ax = self.setup_ax()
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self.plot_data(ax, x_train, y_train)
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if self.show_protos:
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self.plot_protos(ax, protos, plabels)
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class VisCBC2D(Vis2DAbstract):
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def visualize(self, pl_module):
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x_train, y_train = self.x_train, self.y_train
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protos = pl_module.components
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ax = self.setup_ax()
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self.plot_data(ax, x_train, y_train)
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self.plot_protos(ax, protos, "w")
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x = np.vstack((x_train, protos))
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mesh_input, xx, yy = mesh2d(x, self.border, self.resolution)
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_components = pl_module.components_layer._components
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y_pred = pl_module.predict(
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torch.Tensor(mesh_input).type_as(_components))
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y_pred = y_pred.cpu().reshape(xx.shape)
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ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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class VisNG2D(Vis2DAbstract):
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def visualize(self, pl_module):
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x_train, y_train = self.x_train, self.y_train
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protos = pl_module.prototypes
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cmat = pl_module.topology_layer.cmat.cpu().numpy()
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ax = self.setup_ax()
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self.plot_data(ax, x_train, y_train)
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self.plot_protos(ax, protos, "w")
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# Draw connections
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for i in range(len(protos)):
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for j in range(i, len(protos)):
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if cmat[i][j]:
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ax.plot(
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[protos[i, 0], protos[j, 0]],
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[protos[i, 1], protos[j, 1]],
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"k-",
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)
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class VisSpectralProtos(Vis2DAbstract):
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def visualize(self, pl_module):
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protos = pl_module.prototypes
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plabels = pl_module.prototype_labels
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ax = self.setup_ax()
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colors = get_colors(vmax=max(plabels), vmin=min(plabels))
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for p, pl in zip(protos, plabels):
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ax.plot(p, c=colors[int(pl)])
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if self.legend_labels:
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handles = get_legend_handles(
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colors,
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self.legend_labels,
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marker="lines",
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)
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ax.legend(handles=handles)
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class VisImgComp(Vis2DAbstract):
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def __init__(self,
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*args,
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random_data=0,
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dataformats="CHW",
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num_columns=2,
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add_embedding=False,
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embedding_data=100,
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**kwargs):
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super().__init__(*args, **kwargs)
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self.random_data = random_data
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self.dataformats = dataformats
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self.num_columns = num_columns
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self.add_embedding = add_embedding
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self.embedding_data = embedding_data
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def on_train_start(self, _, pl_module):
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if isinstance(pl_module.logger, TensorBoardLogger):
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tb = pl_module.logger.experiment
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# Add embedding
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if self.add_embedding:
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if self.x_train is not None and self.y_train is not None:
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ind = np.random.choice(len(self.x_train),
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size=self.embedding_data,
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replace=False)
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data = self.x_train[ind]
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tb.add_embedding(data.view(len(ind), -1),
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label_img=data,
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global_step=None,
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tag="Data Embedding",
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metadata=self.y_train[ind],
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metadata_header=None)
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else:
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raise ValueError("No data for add embedding flag")
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# Random Data
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if self.random_data:
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if self.x_train is not None:
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ind = np.random.choice(len(self.x_train),
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size=self.random_data,
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replace=False)
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data = self.x_train[ind]
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grid = torchvision.utils.make_grid(data,
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nrow=self.num_columns)
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tb.add_image(tag="Data",
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img_tensor=grid,
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global_step=None,
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dataformats=self.dataformats)
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else:
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raise ValueError("No data for random data flag")
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else:
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warnings.warn(
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f"TensorBoardLogger is required, got {type(pl_module.logger)}")
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def add_to_tensorboard(self, trainer, pl_module):
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tb = pl_module.logger.experiment
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components = pl_module.components
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grid = torchvision.utils.make_grid(components, nrow=self.num_columns)
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tb.add_image(
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tag="Components",
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img_tensor=grid,
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global_step=trainer.current_epoch,
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dataformats=self.dataformats,
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)
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def visualize(self, pl_module):
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if self.show:
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components = pl_module.components
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grid = torchvision.utils.make_grid(components,
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nrow=self.num_columns)
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plt.imshow(grid.permute((1, 2, 0)).cpu(), cmap=self.cmap)
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