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