Clean visualization callbacks
This commit is contained in:
Jensun Ravichandran
parent
6e7d80be88
commit
77b7b59bad
@ -6,262 +6,11 @@ import torch
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import torchvision
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from matplotlib import pyplot as plt
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from matplotlib.offsetbox import AnchoredText
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from torch.utils.data import DataLoader, Dataset
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from prototorch.utils.celluloid import Camera
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from prototorch.utils.colors import color_scheme
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from prototorch.utils.utils import (gif_from_dir, make_directory,
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prettify_string)
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class VisWeights(pl.Callback):
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"""Abstract weight visualization callback."""
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def __init__(
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self,
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data=None,
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ignore_last_output_row=False,
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label_map=None,
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project_mesh=False,
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project_protos=False,
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voronoi=False,
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axis_off=True,
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cmap="viridis",
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show=True,
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display_logs=True,
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display_logs_settings={},
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pause_time=0.5,
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border=1,
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resolution=10,
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interval=False,
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save=False,
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snap=True,
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save_dir="./img",
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make_gif=False,
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make_mp4=False,
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verbose=True,
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dpi=500,
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fps=5,
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figsize=(11, 8.5), # standard paper in inches
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prefix="",
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distance_layer_index=-1,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.data = data
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self.ignore_last_output_row = ignore_last_output_row
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self.label_map = label_map
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self.voronoi = voronoi
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self.axis_off = True
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self.project_mesh = project_mesh
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self.project_protos = project_protos
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self.cmap = cmap
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self.show = show
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self.display_logs = display_logs
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self.display_logs_settings = display_logs_settings
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self.pause_time = pause_time
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self.border = border
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self.resolution = resolution
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self.interval = interval
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self.save = save
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self.snap = snap
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self.save_dir = save_dir
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self.make_gif = make_gif
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self.make_mp4 = make_mp4
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self.verbose = verbose
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self.dpi = dpi
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self.fps = fps
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self.figsize = figsize
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self.prefix = prefix
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self.distance_layer_index = distance_layer_index
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self.title = "Weights Visualization"
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make_directory(self.save_dir)
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def _skip_epoch(self, epoch):
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if self.interval:
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if epoch % self.interval != 0:
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return True
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return False
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def _clean_and_setup_ax(self):
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ax = self.ax
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if not self.snap:
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ax.cla()
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ax.set_title(self.title)
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if self.axis_off:
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ax.axis("off")
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def _savefig(self, fignum, orientation="horizontal"):
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figname = f"{self.save_dir}/{self.prefix}{fignum:05d}.png"
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figsize = self.figsize
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if orientation == "vertical":
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figsize = figsize[::-1]
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elif orientation == "horizontal":
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pass
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else:
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pass
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self.fig.set_size_inches(figsize, forward=False)
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self.fig.savefig(figname, dpi=self.dpi)
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def _show_and_save(self, epoch):
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if self.show:
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plt.pause(self.pause_time)
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if self.save:
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self._savefig(epoch)
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if self.snap:
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self.camera.snap()
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def _display_logs(self, ax, epoch, logs):
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if self.display_logs:
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settings = dict(
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loc="lower right",
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# padding between the text and bounding box
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pad=0.5,
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# padding between the bounding box and the axes
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borderpad=1.0,
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# https://matplotlib.org/api/text_api.html#matplotlib.text.Text
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prop=dict(
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fontfamily="monospace",
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fontweight="medium",
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fontsize=12,
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),
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)
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# Override settings with self.display_logs_settings.
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settings = {**settings, **self.display_logs_settings}
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log_string = f"""Epoch: {epoch:04d},
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val_loss: {logs.get('val_loss', np.nan):.03f},
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val_acc: {logs.get('val_acc', np.nan):.03f},
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loss: {logs.get('loss', np.nan):.03f},
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acc: {logs.get('acc', np.nan):.03f}
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"""
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log_string = prettify_string(log_string, end="")
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# https://matplotlib.org/api/offsetbox_api.html#matplotlib.offsetbox.AnchoredText
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anchored_text = AnchoredText(log_string, **settings)
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self.ax.add_artist(anchored_text)
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def on_train_start(self, trainer, pl_module, logs={}):
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self.fig = plt.figure(self.title)
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self.fig.set_size_inches(self.figsize, forward=False)
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self.ax = self.fig.add_subplot(111)
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self.camera = Camera(self.fig)
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def on_train_end(self, trainer, pl_module, logs={}):
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if self.make_gif:
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gif_from_dir(directory=self.save_dir,
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prefix=self.prefix,
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duration=1.0 / self.fps)
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if self.snap and self.make_mp4:
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animation = self.camera.animate()
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vid = os.path.join(self.save_dir, f"{self.prefix}animation.mp4")
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if self.verbose:
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print(f"Saving mp4 under {vid}.")
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animation.save(vid, fps=self.fps, dpi=self.dpi)
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class VisPointProtos(VisWeights):
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"""Visualization of prototypes.
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.. TODO::
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Still in Progress.
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"""
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self.title = "Point Prototypes Visualization"
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self.data_scatter_settings = {
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"marker": "o",
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"s": 30,
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"edgecolor": "k",
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"cmap": self.cmap,
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}
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self.protos_scatter_settings = {
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"marker": "D",
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"s": 50,
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"edgecolor": "k",
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"cmap": self.cmap,
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}
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def on_epoch_start(self, trainer, pl_module, logs={}):
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epoch = trainer.current_epoch
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if self._skip_epoch(epoch):
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return True
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self._clean_and_setup_ax()
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protos = pl_module.prototypes
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labels = pl_module.proto_layer.prototype_labels.detach().cpu().numpy()
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if self.project_protos:
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protos = self.model.projection(protos).numpy()
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color_map = color_scheme(n=len(set(labels)),
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cmap=self.cmap,
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zero_indexed=True)
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# TODO Get rid of the assumption y values in [0, num_of_classes]
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label_colors = [color_map[l] for l in labels]
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if self.data is not None:
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x, y = self.data
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# TODO Get rid of the assumption y values in [0, num_of_classes]
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y_colors = [color_map[l] for l in y]
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# x = self.model.projection(x)
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if not isinstance(x, np.ndarray):
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x = x.numpy()
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# Plot data points.
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self.ax.scatter(x[:, 0],
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x[:, 1],
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c=y_colors,
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**self.data_scatter_settings)
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# Paint decision regions.
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if self.voronoi:
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border = self.border
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resolution = self.resolution
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x = np.vstack((x, protos))
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x_min, x_max = x[:, 0].min(), x[:, 0].max()
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y_min, y_max = x[:, 1].min(), x[:, 1].max()
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x_min, x_max = x_min - border, x_max + border
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y_min, y_max = y_min - border, y_max + border
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try:
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xx, yy = np.meshgrid(
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np.arange(x_min, x_max, (x_max - x_min) / resolution),
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np.arange(y_min, y_max, (x_max - x_min) / resolution),
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)
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except ValueError as ve:
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print(ve)
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raise ValueError(f"x_min: {x_min}, x_max: {x_max}. "
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f"x_min - x_max is {x_max - x_min}.")
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except MemoryError as me:
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print(me)
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raise ValueError("Too many points. "
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"Try reducing the resolution.")
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mesh_input = np.c_[xx.ravel(), yy.ravel()]
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# Predict mesh labels.
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if self.project_mesh:
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mesh_input = self.model.projection(mesh_input)
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y_pred = pl_module.predict(torch.Tensor(mesh_input))
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y_pred = y_pred.reshape(xx.shape)
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# Plot voronoi regions.
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self.ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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self.ax.set_xlim(left=x_min + 0, right=x_max - 0)
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self.ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
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# Plot prototypes.
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self.ax.scatter(protos[:, 0],
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protos[:, 1],
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c=label_colors,
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**self.protos_scatter_settings)
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# self._show_and_save(epoch)
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def on_epoch_end(self, trainer, pl_module, logs={}):
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epoch = trainer.current_epoch
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self._display_logs(self.ax, epoch, logs)
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self._show_and_save(epoch)
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from torch.utils.data import DataLoader, Dataset
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class Vis2DAbstract(pl.Callback):
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@ -269,8 +18,9 @@ class Vis2DAbstract(pl.Callback):
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data,
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title="Prototype Visualization",
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cmap="viridis",
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border=1,
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resolution=50,
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border=0.1,
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resolution=100,
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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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@ -292,6 +42,7 @@ class Vis2DAbstract(pl.Callback):
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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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@ -309,18 +60,21 @@ class Vis2DAbstract(pl.Callback):
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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.axis("off")
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if xlabel:
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ax.set_xlabel("Data dimension 1")
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if ylabel:
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ax.set_ylabel("Data dimension 2")
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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 get_mesh_input(self, x):
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x_min, x_max = x[:, 0].min() - self.border, x[:, 0].max() + self.border
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y_min, y_max = x[:, 1].min() - self.border, x[:, 1].max() + self.border
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xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / self.resolution),
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np.arange(y_min, y_max, 1 / self.resolution))
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x_shift = self.border * np.ptp(x[:, 0])
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y_shift = self.border * np.ptp(x[:, 1])
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x_min, x_max = x[:, 0].min() - x_shift, x[:, 0].max() + x_shift
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y_min, y_max = x[:, 1].min() - y_shift, x[:, 1].max() + y_shift
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xx, yy = np.meshgrid(np.linspace(x_min, x_max, self.resolution),
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np.linspace(y_min, y_max, self.resolution))
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mesh_input = np.c_[xx.ravel(), yy.ravel()]
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return mesh_input, xx, yy
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@ -381,8 +135,8 @@ class VisGLVQ2D(Vis2DAbstract):
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x = np.vstack((x_train, protos))
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mesh_input, xx, yy = self.get_mesh_input(x)
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_components = pl_module.proto_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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mesh_input = torch.Tensor(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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@ -401,11 +155,14 @@ class VisSiameseGLVQ2D(Vis2DAbstract):
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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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x_train = pl_module.backbone(
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torch.Tensor(x_train).to(pl_module.device)).cpu().detach()
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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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protos = pl_module.backbone(
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torch.Tensor(protos).to(pl_module.device)).cpu().detach()
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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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@ -415,8 +172,9 @@ class VisSiameseGLVQ2D(Vis2DAbstract):
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else:
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mesh_input, xx, yy = self.get_mesh_input(x_train)
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_components = pl_module.proto_layer._components
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y_pred = pl_module.predict_latent(
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torch.Tensor(mesh_input).type_as(_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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