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lotus_plots.py
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import os
import shutil
import pandas as pd
import numpy as np
import cv2 as cv
import torch
import matplotlib.pyplot as plt
import seaborn as sns
class trainingLog() :
def __init__(self, outPath) :
self.path = os.path.join(os.getcwd(), 'plots', outPath)
if not os.path.exists(self.path) :
os.mkdir(self.path)
self.scalar_log = dict()
def epochLog(self, tag, y):
self.__addLog(self.scalar_log, tag.split('/'), y)
def flush(self):
self.fig = plt.figure('log')
for log_tag, log_values in self.scalar_log.items() :
log_array = np.zeros(shape=(
len(log_values[list(log_values.keys())[0]]),
len(log_values)), dtype=np.float32)
for i, (_, plot_values) in enumerate(log_values.items()) :
log_array[:, i] = np.array(plot_values)
df_log = pd.DataFrame(log_array, columns=list(log_values.keys()))
self.fig.clf()
ax = plt.axes()
ax.grid(True)
for column in log_values.keys() :
sns.lineplot(ax=ax, data=df_log, x=df_log.index, y=column)
ax.legend(labels=list(log_values.keys()))
self.fig.savefig(os.path.join(self.path, log_tag + '.png'), bbox_inches='tight')
df_log.to_csv(os.path.join(self.path, log_tag + '.csv'))
plt.close(self.fig)
def __addLog(self, logDict, tokens, y) :
token = tokens[0]
if token in logDict :
if len(tokens) > 1 :
self.__addLog(logDict[token], tokens[1:], y)
else :
logDict[token] += [y,]
else :
if len(tokens) > 1 :
tmp = dict()
self.__addLog(tmp, tokens[1:], y)
logDict[token] = tmp
else :
logDict[token] = [y,]
class batchPlot() :
def __init__(self, modelName, rootFolderName, clearPath=True, folderName=None) :
self.rootFolderName = rootFolderName
self.name = folderName
self.path = os.path.join(os.getcwd(), 'plots', modelName, rootFolderName)
if not os.path.exists(self.path) :
os.mkdir(self.path)
if self.name != None :
self.path = os.path.join(self.path, self.name)
if clearPath :
if os.path.exists(self.path) :
shutil.rmtree(self.path)
os.mkdir(self.path)
self.fig = plt.figure('plots')
def tonemap(self, x, rescale=True) :
norm_factor = 20. if rescale else 1.
x = np.log(norm_factor * x + 1)
a = 0.055
return np.where(x <= 0.0031308, 12.92*x, (1 + a) * np.power(x, 1/2.4) - a)
def plotGANBatch(self, x, y, numSamples, suffix, folderName) :
outPath = os.path.join(self.path, folderName)
if not os.path.exists(outPath) :
os.mkdir(outPath)
x = x[:numSamples, ...].clone().detach()
y = y[:numSamples, ...].clone().detach()
pred_stacked = torch.stack([x, y], dim=1).cpu()
for sample_i, sample in enumerate(pred_stacked) :
x = sample[0, ::].permute(2, 1, 0)
y = sample[1, ::].permute(2, 1, 0)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.set_title('Generated')
ax1.imshow(self.tonemap(x), cmap='gray')
ax2.set_title('Reference')
ax2.imshow(self.tonemap(y), cmap='gray')
fig.savefig(os.path.join(outPath, f'{sample_i}_{suffix}.png'))
plt.close(fig)
def plotPostProcess(self, sampleName,
gen_goalErr, clustered_goalErr, snapped_goalErr, real_goalErr,
gen_blobs_list, clustered_blobs_list, snapped_blobs_list, real_blobs,
gen_direct_list, clustered_direct_list, snapped_direct_list, real_direct,
goal, seg) :
def plotAxis(axisList, samples, goals) :
for ax_i, ax in enumerate(axisList) :
if ax_i == len(axisList) - 3 :
ax.set_title(f'Ref-{real_goalErr[0]:.4f} / {real_goalErr[4]:.4f}')
elif ax_i == len(axisList) - 2 :
ax.set_title('goal')
elif ax_i == len(axisList) - 1 :
ax.set_title('mask')
else :
ax.set_title(f'{goals[ax_i][0]:.4f} / {goals[ax_i][4]:.4f}')
if ax_i == len(axisList) - 1 :
ax.imshow(seg[0].cpu().permute(2, 1, 0))
else :
img = samples[ax_i, ::].permute(2, 1, 0)
ax.imshow(self.tonemap(img), cmap='gray')
fig, axisList = plt.subplots(6, len(gen_blobs_list) + 2, figsize=(30, 20))
plotAxis(axisList[0, :], torch.stack(gen_direct_list + [real_direct, goal], dim=1).cpu()[0], gen_goalErr)
plotAxis(axisList[1, :], torch.stack(gen_blobs_list + [real_blobs, goal], dim=1).cpu()[0], gen_goalErr)
plotAxis(axisList[2, :], torch.stack(clustered_direct_list + [real_direct, goal], dim=1).cpu()[0], clustered_goalErr)
plotAxis(axisList[3, :], torch.stack(clustered_blobs_list + [real_blobs, goal], dim=1).cpu()[0], clustered_goalErr)
plotAxis(axisList[4, :], torch.stack(snapped_direct_list + [real_direct, goal], dim=1).cpu()[0], snapped_goalErr)
plotAxis(axisList[5, :], torch.stack(snapped_blobs_list + [real_blobs, goal], dim=1).cpu()[0], snapped_goalErr)
fig.savefig(os.path.join(self.path, f'{sampleName[0]}.png'))
plt.close(fig)
def plotGenerator(self, sampleNames, goalErr, fake_placement_list, real_placement, fake_direct_list, real_direct, goal) :
pred_stacked = torch.stack(
fake_direct_list + [real_direct, goal,] +
fake_placement_list + [real_placement, goal,], dim=1).cpu()
def plotAxis(axisList, samples) :
for ax_i, ax in enumerate(axisList) :
if ax_i == len(axisList) - 2 :
ax.set_title(f'Ref-{goalErr[ax_i][0]:.4f} / {goalErr[ax_i][1]:.4f}')
elif ax_i == len(axisList) - 1 :
ax.set_title('goal')
else :
ax.set_title(f'{goalErr[ax_i][0]:.4f} / {goalErr[ax_i][1]:.4f}')
img = samples[ax_i, ::].permute(2, 1, 0)
ax.imshow(self.tonemap(img), cmap='gray')
for sample_i, sample in enumerate(pred_stacked) :
numRowPlots = int(sample.size(0) / 2)
fig, axisList = plt.subplots(2, numRowPlots, figsize=(30, 5))
axisList_direct = axisList[0, :]
axisList_placement = axisList[1, :]
samples_direct = sample[:numRowPlots, ::]
samples_gi = sample[numRowPlots:, ::]
plotAxis(axisList_direct, samples_direct)
plotAxis(axisList_placement, samples_gi)
fig.savefig(os.path.join(self.path, f'{sampleNames[sample_i]}.png'))
#plt.show()
plt.close(fig)
def plotTrainingBatch(self, sampleNames, fake_blobs, fake_direct, real_gi, real_direct, real_placement, seg, goal, folderName) :
outPath = os.path.join(self.path, folderName)
if not os.path.exists(outPath) :
os.mkdir(outPath)
pred_stacked = torch.stack([fake_blobs, fake_direct, real_direct, real_placement, seg, goal[:, 0:1, ...], goal[:, 1:2, ...]], dim=1).cpu()
for sample_i, sample in enumerate(pred_stacked) :
fake_blobs_i = sample[0, ::].permute(2, 1, 0)
fake_direct_i = sample[1, ::].permute(2, 1, 0)
real_direct = sample[2, ::].permute(2, 1, 0)
real_blobs = sample[3, ::].permute(2, 1, 0)
seg_i = sample[4, ::].permute(2, 1, 0)
meanGoal_i = sample[5, ::].permute(2, 1, 0)
fig, (ax1, ax2, ax3, ax4, ax5, ax6,) = plt.subplots(1, 6, figsize=(4 * 6, 4))
ax1.set_title('fake - direct')
ax1.imshow(self.tonemap(fake_direct_i, True), cmap='gray')
ax2.set_title('fake - blobs')
ax2.imshow(self.tonemap(fake_blobs_i, True), cmap='gray')
ax3.set_title('real - direct')
ax3.imshow(self.tonemap(real_direct, True), cmap='gray')
ax4.set_title('real - blobs')
ax4.imshow(self.tonemap(real_blobs, True), cmap='gray')
ax5.set_title('real mask')
ax5.imshow(seg_i, cmap='gray')
ax6.set_title('real goal')
ax6.imshow(meanGoal_i, cmap='gray')
fig.savefig(os.path.join(outPath, sampleNames[sample_i] + '.png'))
plt.close(fig)
def plotTrainingBatchN(self, sampleNames, samples, folderName) :
outPath = os.path.join(self.path, folderName)
if not os.path.exists(outPath) :
os.mkdir(outPath)
for sample_i, sample in enumerate(samples) :
fig, axisList = plt.subplots(1, samples.size(1), figsize=(samples.size(1) * 5, 5))
for ax_i, ax in enumerate(axisList) :
if ax_i == len(axisList) - 1 :
ax.set_title('goal')
elif ax_i == len(axisList) - 2 :
ax.set_title(f'road')
elif ax_i == len(axisList) - 3 :
ax.set_title(f'ref')
else :
ax.set_title(f'seed')
img = sample[ax_i, ::].permute(1, 0)
ax.imshow(self.tonemap(img), cmap='gray')
fig.savefig(os.path.join(outPath, sampleNames[sample_i] + '.png'))
plt.close(fig)