prototorch_models/prototorch/models/vis.py

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"""Visualization Callbacks."""
import numpy as np
import pytorch_lightning as pl
import torch
import torchvision
from matplotlib import pyplot as plt
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from torch.utils.data import DataLoader, Dataset
class Vis2DAbstract(pl.Callback):
def __init__(self,
data,
title="Prototype Visualization",
cmap="viridis",
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border=0.1,
resolution=100,
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flatten_data=True,
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axis_off=False,
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show_protos=True,
show=True,
tensorboard=False,
show_last_only=False,
pause_time=0.1,
block=False):
super().__init__()
if isinstance(data, Dataset):
x, y = next(iter(DataLoader(data, batch_size=len(data))))
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elif isinstance(data, torch.utils.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
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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
self.y_train = y
self.title = title
self.fig = plt.figure(self.title)
self.cmap = cmap
self.border = border
self.resolution = resolution
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self.axis_off = axis_off
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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
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def precheck(self, trainer):
if self.show_last_only:
if trainer.current_epoch != trainer.max_epochs - 1:
return False
return True
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def setup_ax(self, xlabel=None, ylabel=None):
ax = self.fig.gca()
ax.cla()
ax.set_title(self.title)
if xlabel:
ax.set_xlabel("Data dimension 1")
if ylabel:
ax.set_ylabel("Data dimension 2")
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if self.axis_off:
ax.axis("off")
return ax
def get_mesh_input(self, x):
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x_shift = self.border * np.ptp(x[:, 0])
y_shift = self.border * np.ptp(x[:, 1])
x_min, x_max = x[:, 0].min() - x_shift, x[:, 0].max() + x_shift
y_min, y_max = x[:, 1].min() - y_shift, x[:, 1].max() + y_shift
xx, yy = np.meshgrid(np.linspace(x_min, x_max, self.resolution),
np.linspace(y_min, y_max, self.resolution))
mesh_input = np.c_[xx.ravel(), yy.ravel()]
return mesh_input, xx, yy
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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:
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plt.show(block=self.block)
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def on_train_end(self, trainer, pl_module):
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plt.close()
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class VisGLVQ2D(Vis2DAbstract):
def on_epoch_end(self, trainer, pl_module):
if not self.precheck(trainer):
return True
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protos = pl_module.prototypes
plabels = pl_module.prototype_labels
x_train, y_train = self.x_train, self.y_train
ax = self.setup_ax(xlabel="Data dimension 1",
ylabel="Data dimension 2")
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self.plot_data(ax, x_train, y_train)
self.plot_protos(ax, protos, plabels)
x = np.vstack((x_train, protos))
mesh_input, xx, yy = self.get_mesh_input(x)
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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)
ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
self.log_and_display(trainer, pl_module)
class VisSiameseGLVQ2D(Vis2DAbstract):
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def __init__(self, *args, map_protos=True, **kwargs):
super().__init__(*args, **kwargs)
self.map_protos = map_protos
def on_epoch_end(self, trainer, pl_module):
if not self.precheck(trainer):
return True
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protos = pl_module.prototypes
plabels = pl_module.prototype_labels
x_train, y_train = self.x_train, self.y_train
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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()
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if self.map_protos:
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with torch.no_grad():
protos = pl_module.backbone(torch.Tensor(protos).to(device))
protos = protos.cpu().detach()
ax = self.setup_ax()
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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 = self.get_mesh_input(x)
else:
mesh_input, xx, yy = self.get_mesh_input(x_train)
_components = pl_module.proto_layer._components
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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)
self.log_and_display(trainer, pl_module)
class VisCBC2D(Vis2DAbstract):
def on_epoch_end(self, trainer, pl_module):
if not self.precheck(trainer):
return True
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x_train, y_train = self.x_train, self.y_train
protos = pl_module.components
ax = self.setup_ax(xlabel="Data dimension 1",
ylabel="Data dimension 2")
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self.plot_data(ax, x_train, y_train)
self.plot_protos(ax, protos, "w")
x = np.vstack((x_train, protos))
mesh_input, xx, yy = self.get_mesh_input(x)
_components = pl_module.components_layer._components
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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)
self.log_and_display(trainer, pl_module)
class VisNG2D(Vis2DAbstract):
def on_epoch_end(self, trainer, pl_module):
if not self.precheck(trainer):
return True
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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(xlabel="Data dimension 1",
ylabel="Data dimension 2")
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self.plot_data(ax, x_train, y_train)
self.plot_protos(ax, protos, "w")
# Draw connections
for i in range(len(protos)):
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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-",
)
self.log_and_display(trainer, pl_module)
class VisImgComp(Vis2DAbstract):
def __init__(self,
*args,
random_data=0,
dataformats="CHW",
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num_columns=2,
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add_embedding=False,
embedding_data=100,
**kwargs):
super().__init__(*args, **kwargs)
self.random_data = random_data
self.dataformats = dataformats
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self.num_columns = num_columns
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self.add_embedding = add_embedding
self.embedding_data = embedding_data
def on_train_start(self, trainer, pl_module):
tb = pl_module.logger.experiment
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if self.add_embedding:
ind = np.random.choice(len(self.x_train),
size=self.embedding_data,
replace=False)
data = self.x_train[ind]
# print(f"{data.shape=}")
# print(f"{self.y_train[ind].shape=}")
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)
if self.random_data:
ind = np.random.choice(len(self.x_train),
size=self.random_data,
replace=False)
data = self.x_train[ind]
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grid = torchvision.utils.make_grid(data, nrow=self.num_columns)
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tb.add_image(tag="Data",
img_tensor=grid,
global_step=None,
dataformats=self.dataformats)
def add_to_tensorboard(self, trainer, pl_module):
tb = pl_module.logger.experiment
components = pl_module.components
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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,
)
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def on_epoch_end(self, trainer, pl_module):
if not self.precheck(trainer):
return True
if self.show:
components = pl_module.components
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grid = torchvision.utils.make_grid(components,
nrow=self.num_columns)
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plt.imshow(grid.permute((1, 2, 0)).cpu(), cmap=self.cmap)
self.log_and_display(trainer, pl_module)