KPConv-PyTorch/plot_convergence.py

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2020-03-31 19:42:35 +00:00
#
#
# 0=================================0
# | Kernel Point Convolutions |
# 0=================================0
#
#
# ----------------------------------------------------------------------------------------------------------------------
#
# Callable script to test any model on any dataset
#
# ----------------------------------------------------------------------------------------------------------------------
#
# Hugues THOMAS - 11/06/2018
#
# ----------------------------------------------------------------------------------------------------------------------
#
# Imports and global variables
# \**********************************/
#
# Common libs
import os
import torch
import numpy as np
import matplotlib.pyplot as plt
from os.path import isfile, join, exists
from os import listdir, remove, getcwd
from sklearn.metrics import confusion_matrix
import time
# My libs
from utils.config import Config
from utils.metrics import IoU_from_confusions, smooth_metrics, fast_confusion
from utils.ply import read_ply
# Datasets
from datasets.ModelNet40 import ModelNet40Dataset
from datasets.S3DIS import S3DISDataset
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from datasets.SemanticKitti import SemanticKittiDataset
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# ----------------------------------------------------------------------------------------------------------------------
#
# Utility functions
# \***********************/
#
def running_mean(signal, n, axis=0, stride=1):
signal = np.array(signal)
torch_conv = torch.nn.Conv1d(1, 1, kernel_size=2*n+1, stride=stride, bias=False)
torch_conv.weight.requires_grad_(False)
torch_conv.weight *= 0
torch_conv.weight += 1 / (2*n+1)
if signal.ndim == 1:
torch_signal = torch.from_numpy(signal.reshape([1, 1, -1]).astype(np.float32))
return torch_conv(torch_signal).squeeze().numpy()
elif signal.ndim == 2:
print('TODO implement with torch and stride here')
smoothed = np.empty(signal.shape)
if axis == 0:
for i, sig in enumerate(signal):
sig_sum = np.convolve(sig, np.ones((2*n+1,)), mode='same')
sig_num = np.convolve(sig*0+1, np.ones((2*n+1,)), mode='same')
smoothed[i, :] = sig_sum / sig_num
elif axis == 1:
for i, sig in enumerate(signal.T):
sig_sum = np.convolve(sig, np.ones((2*n+1,)), mode='same')
sig_num = np.convolve(sig*0+1, np.ones((2*n+1,)), mode='same')
smoothed[:, i] = sig_sum / sig_num
else:
print('wrong axis')
return smoothed
else:
print('wrong dimensions')
return None
def IoU_class_metrics(all_IoUs, smooth_n):
# Get mean IoU per class for consecutive epochs to directly get a mean without further smoothing
smoothed_IoUs = []
for epoch in range(len(all_IoUs)):
i0 = max(epoch - smooth_n, 0)
i1 = min(epoch + smooth_n + 1, len(all_IoUs))
smoothed_IoUs += [np.mean(np.vstack(all_IoUs[i0:i1]), axis=0)]
smoothed_IoUs = np.vstack(smoothed_IoUs)
smoothed_mIoUs = np.mean(smoothed_IoUs, axis=1)
return smoothed_IoUs, smoothed_mIoUs
def load_confusions(filename, n_class):
with open(filename, 'r') as f:
lines = f.readlines()
confs = np.zeros((len(lines), n_class, n_class))
for i, line in enumerate(lines):
C = np.array([int(value) for value in line.split()])
confs[i, :, :] = C.reshape((n_class, n_class))
return confs
def load_training_results(path):
filename = join(path, 'training.txt')
with open(filename, 'r') as f:
lines = f.readlines()
epochs = []
steps = []
L_out = []
L_p = []
acc = []
t = []
for line in lines[1:]:
line_info = line.split()
if (len(line) > 0):
epochs += [int(line_info[0])]
steps += [int(line_info[1])]
L_out += [float(line_info[2])]
L_p += [float(line_info[3])]
acc += [float(line_info[4])]
t += [float(line_info[5])]
else:
break
return epochs, steps, L_out, L_p, acc, t
def load_single_IoU(filename, n_parts):
with open(filename, 'r') as f:
lines = f.readlines()
# Load all IoUs
all_IoUs = []
for i, line in enumerate(lines):
all_IoUs += [np.reshape([float(IoU) for IoU in line.split()], [-1, n_parts])]
return all_IoUs
def load_snap_clouds(path, dataset, only_last=False):
cloud_folders = np.array([join(path, f) for f in listdir(path) if f.startswith('val_preds')])
cloud_epochs = np.array([int(f.split('_')[-1]) for f in cloud_folders])
epoch_order = np.argsort(cloud_epochs)
cloud_epochs = cloud_epochs[epoch_order]
cloud_folders = cloud_folders[epoch_order]
Confs = np.zeros((len(cloud_epochs), dataset.num_classes, dataset.num_classes), dtype=np.int32)
for c_i, cloud_folder in enumerate(cloud_folders):
if only_last and c_i < len(cloud_epochs) - 1:
continue
# Load confusion if previously saved
conf_file = join(cloud_folder, 'conf.txt')
if isfile(conf_file):
Confs[c_i] += np.loadtxt(conf_file, dtype=np.int32)
else:
for f in listdir(cloud_folder):
if f.endswith('.ply') and not f.endswith('sub.ply'):
data = read_ply(join(cloud_folder, f))
labels = data['class']
preds = data['preds']
Confs[c_i] += fast_confusion(labels, preds, dataset.label_values).astype(np.int32)
np.savetxt(conf_file, Confs[c_i], '%12d')
# Erase ply to save disk memory
if c_i < len(cloud_folders) - 1:
for f in listdir(cloud_folder):
if f.endswith('.ply'):
remove(join(cloud_folder, f))
# Remove ignored labels from confusions
for l_ind, label_value in reversed(list(enumerate(dataset.label_values))):
if label_value in dataset.ignored_labels:
Confs = np.delete(Confs, l_ind, axis=1)
Confs = np.delete(Confs, l_ind, axis=2)
return cloud_epochs, IoU_from_confusions(Confs)
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# ----------------------------------------------------------------------------------------------------------------------
#
# Plot functions
# \********************/
#
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def compare_trainings(list_of_paths, list_of_labels=None):
# Parameters
# **********
plot_lr = False
smooth_epochs = 0.5
stride = 2
if list_of_labels is None:
list_of_labels = [str(i) for i in range(len(list_of_paths))]
# Read Training Logs
# ******************
all_epochs = []
all_loss = []
all_lr = []
all_times = []
all_RAMs = []
for path in list_of_paths:
print(path)
if ('val_IoUs.txt' in [f for f in listdir(path)]) or ('val_confs.txt' in [f for f in listdir(path)]):
config = Config()
config.load(path)
else:
continue
# Load results
epochs, steps, L_out, L_p, acc, t = load_training_results(path)
epochs = np.array(epochs, dtype=np.int32)
epochs_d = np.array(epochs, dtype=np.float32)
steps = np.array(steps, dtype=np.float32)
# Compute number of steps per epoch
max_e = np.max(epochs)
first_e = np.min(epochs)
epoch_n = []
for i in range(first_e, max_e):
bool0 = epochs == i
e_n = np.sum(bool0)
epoch_n.append(e_n)
epochs_d[bool0] += steps[bool0] / e_n
smooth_n = int(np.mean(epoch_n) * smooth_epochs)
smooth_loss = running_mean(L_out, smooth_n, stride=stride)
all_loss += [smooth_loss]
all_epochs += [epochs_d[smooth_n:-smooth_n:stride]]
all_times += [t[smooth_n:-smooth_n:stride]]
# Learning rate
if plot_lr:
lr_decay_v = np.array([lr_d for ep, lr_d in config.lr_decays.items()])
lr_decay_e = np.array([ep for ep, lr_d in config.lr_decays.items()])
max_e = max(np.max(all_epochs[-1]) + 1, np.max(lr_decay_e) + 1)
lr_decays = np.ones(int(np.ceil(max_e)), dtype=np.float32)
lr_decays[0] = float(config.learning_rate)
lr_decays[lr_decay_e] = lr_decay_v
lr = np.cumprod(lr_decays)
all_lr += [lr[np.floor(all_epochs[-1]).astype(np.int32)]]
# Plots learning rate
# *******************
if plot_lr:
# Figure
fig = plt.figure('lr')
for i, label in enumerate(list_of_labels):
plt.plot(all_epochs[i], all_lr[i], linewidth=1, label=label)
# Set names for axes
plt.xlabel('epochs')
plt.ylabel('lr')
plt.yscale('log')
# Display legends and title
plt.legend(loc=1)
# Customize the graph
ax = fig.gca()
ax.grid(linestyle='-.', which='both')
# ax.set_yticks(np.arange(0.8, 1.02, 0.02))
# Plots loss
# **********
# Figure
fig = plt.figure('loss')
for i, label in enumerate(list_of_labels):
plt.plot(all_epochs[i], all_loss[i], linewidth=1, label=label)
# Set names for axes
plt.xlabel('epochs')
plt.ylabel('loss')
plt.yscale('log')
# Display legends and title
plt.legend(loc=1)
plt.title('Losses compare')
# Customize the graph
ax = fig.gca()
ax.grid(linestyle='-.', which='both')
# ax.set_yticks(np.arange(0.8, 1.02, 0.02))
# Plot Times
# **********
# Figure
fig = plt.figure('time')
for i, label in enumerate(list_of_labels):
plt.plot(all_epochs[i], np.array(all_times[i]) / 3600, linewidth=1, label=label)
# Set names for axes
plt.xlabel('epochs')
plt.ylabel('time')
# plt.yscale('log')
# Display legends and title
plt.legend(loc=0)
# Customize the graph
ax = fig.gca()
ax.grid(linestyle='-.', which='both')
# ax.set_yticks(np.arange(0.8, 1.02, 0.02))
# Show all
plt.show()
def compare_convergences_segment(dataset, list_of_paths, list_of_names=None):
# Parameters
# **********
smooth_n = 10
if list_of_names is None:
list_of_names = [str(i) for i in range(len(list_of_paths))]
# Read Logs
# *********
all_pred_epochs = []
all_mIoUs = []
all_class_IoUs = []
all_snap_epochs = []
all_snap_IoUs = []
# Load parameters
config = Config()
config.load(list_of_paths[0])
class_list = [dataset.label_to_names[label] for label in dataset.label_values
if label not in dataset.ignored_labels]
s = '{:^10}|'.format('mean')
for c in class_list:
s += '{:^10}'.format(c)
print(s)
print(10*'-' + '|' + 10*config.num_classes*'-')
for path in list_of_paths:
# Get validation IoUs
file = join(path, 'val_IoUs.txt')
val_IoUs = load_single_IoU(file, config.num_classes)
# Get mean IoU
class_IoUs, mIoUs = IoU_class_metrics(val_IoUs, smooth_n)
# Aggregate results
all_pred_epochs += [np.array([i for i in range(len(val_IoUs))])]
all_mIoUs += [mIoUs]
all_class_IoUs += [class_IoUs]
s = '{:^10.1f}|'.format(100*mIoUs[-1])
for IoU in class_IoUs[-1]:
s += '{:^10.1f}'.format(100*IoU)
print(s)
# Get optional full validation on clouds
snap_epochs, snap_IoUs = load_snap_clouds(path, dataset)
all_snap_epochs += [snap_epochs]
all_snap_IoUs += [snap_IoUs]
print(10*'-' + '|' + 10*config.num_classes*'-')
for snap_IoUs in all_snap_IoUs:
if len(snap_IoUs) > 0:
s = '{:^10.1f}|'.format(100*np.mean(snap_IoUs[-1]))
for IoU in snap_IoUs[-1]:
s += '{:^10.1f}'.format(100*IoU)
else:
s = '{:^10s}'.format('-')
for _ in range(config.num_classes):
s += '{:^10s}'.format('-')
print(s)
# Plots
# *****
# Figure
fig = plt.figure('mIoUs')
for i, name in enumerate(list_of_names):
p = plt.plot(all_pred_epochs[i], all_mIoUs[i], '--', linewidth=1, label=name)
plt.plot(all_snap_epochs[i], np.mean(all_snap_IoUs[i], axis=1), linewidth=1, color=p[-1].get_color())
plt.xlabel('epochs')
plt.ylabel('IoU')
# Set limits for y axis
#plt.ylim(0.55, 0.95)
# Display legends and title
plt.legend(loc=4)
# Customize the graph
ax = fig.gca()
ax.grid(linestyle='-.', which='both')
#ax.set_yticks(np.arange(0.8, 1.02, 0.02))
displayed_classes = [0, 1, 2, 3, 4, 5, 6, 7]
displayed_classes = []
for c_i, c_name in enumerate(class_list):
if c_i in displayed_classes:
# Figure
fig = plt.figure(c_name + ' IoU')
for i, name in enumerate(list_of_names):
plt.plot(all_pred_epochs[i], all_class_IoUs[i][:, c_i], linewidth=1, label=name)
plt.xlabel('epochs')
plt.ylabel('IoU')
# Set limits for y axis
#plt.ylim(0.8, 1)
# Display legends and title
plt.legend(loc=4)
# Customize the graph
ax = fig.gca()
ax.grid(linestyle='-.', which='both')
#ax.set_yticks(np.arange(0.8, 1.02, 0.02))
# Show all
plt.show()
def compare_convergences_classif(list_of_paths, list_of_labels=None):
# Parameters
# **********
steps_per_epoch = 0
smooth_n = 12
if list_of_labels is None:
list_of_labels = [str(i) for i in range(len(list_of_paths))]
# Read Logs
# *********
all_pred_epochs = []
all_val_OA = []
all_train_OA = []
all_vote_OA = []
all_vote_confs = []
for path in list_of_paths:
# Load parameters
config = Config()
config.load(list_of_paths[0])
# Get the number of classes
n_class = config.num_classes
# Load epochs
epochs, _, _, _, _, _ = load_training_results(path)
first_e = np.min(epochs)
# Get validation confusions
file = join(path, 'val_confs.txt')
val_C1 = load_confusions(file, n_class)
val_PRE, val_REC, val_F1, val_IoU, val_ACC = smooth_metrics(val_C1, smooth_n=smooth_n)
# Get vote confusions
file = join(path, 'vote_confs.txt')
if exists(file):
vote_C2 = load_confusions(file, n_class)
vote_PRE, vote_REC, vote_F1, vote_IoU, vote_ACC = smooth_metrics(vote_C2, smooth_n=2)
else:
vote_C2 = val_C1
vote_PRE, vote_REC, vote_F1, vote_IoU, vote_ACC = (val_PRE, val_REC, val_F1, val_IoU, val_ACC)
# Aggregate results
all_pred_epochs += [np.array([i+first_e for i in range(len(val_ACC))])]
all_val_OA += [val_ACC]
all_vote_OA += [vote_ACC]
all_vote_confs += [vote_C2]
print()
# Best scores
# ***********
for i, label in enumerate(list_of_labels):
print('\n' + label + '\n' + '*' * len(label) + '\n')
print(list_of_paths[i])
best_epoch = np.argmax(all_vote_OA[i])
print('Best Accuracy : {:.1f} % (epoch {:d})'.format(100 * all_vote_OA[i][best_epoch], best_epoch))
confs = all_vote_confs[i]
"""
s = ''
for cc in confs[best_epoch]:
for c in cc:
s += '{:.0f} '.format(c)
s += '\n'
print(s)
"""
TP_plus_FN = np.sum(confs, axis=-1, keepdims=True)
class_avg_confs = confs.astype(np.float32) / TP_plus_FN.astype(np.float32)
diags = np.diagonal(class_avg_confs, axis1=-2, axis2=-1)
class_avg_ACC = np.sum(diags, axis=-1) / np.sum(class_avg_confs, axis=(-1, -2))
print('Corresponding mAcc : {:.1f} %'.format(100 * class_avg_ACC[best_epoch]))
# Plots
# *****
for fig_name, OA in zip(['Validation', 'Vote'], [all_val_OA, all_vote_OA]):
# Figure
fig = plt.figure(fig_name)
for i, label in enumerate(list_of_labels):
plt.plot(all_pred_epochs[i], OA[i], linewidth=1, label=label)
plt.xlabel('epochs')
plt.ylabel(fig_name + ' Accuracy')
# Set limits for y axis
#plt.ylim(0.55, 0.95)
# Display legends and title
plt.legend(loc=4)
# Customize the graph
ax = fig.gca()
ax.grid(linestyle='-.', which='both')
#ax.set_yticks(np.arange(0.8, 1.02, 0.02))
#for i, label in enumerate(list_of_labels):
# print(label, np.max(all_train_OA[i]), np.max(all_val_OA[i]))
# Show all
plt.show()
def compare_convergences_SLAM(dataset, list_of_paths, list_of_names=None):
# Parameters
# **********
smooth_n = 10
if list_of_names is None:
list_of_names = [str(i) for i in range(len(list_of_paths))]
# Read Logs
# *********
all_pred_epochs = []
all_val_mIoUs = []
all_val_class_IoUs = []
all_subpart_mIoUs = []
all_subpart_class_IoUs = []
# Load parameters
config = Config()
config.load(list_of_paths[0])
class_list = [dataset.label_to_names[label] for label in dataset.label_values
if label not in dataset.ignored_labels]
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s = '{:^6}|'.format('mean')
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for c in class_list:
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s += '{:^6}'.format(c[:4])
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print(s)
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print(6*'-' + '|' + 6*config.num_classes*'-')
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for path in list_of_paths:
# Get validation IoUs
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nc_model = dataset.num_classes - len(dataset.ignored_labels)
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file = join(path, 'val_IoUs.txt')
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val_IoUs = load_single_IoU(file, nc_model)
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# Get Subpart IoUs
file = join(path, 'subpart_IoUs.txt')
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subpart_IoUs = load_single_IoU(file, nc_model)
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# Get mean IoU
val_class_IoUs, val_mIoUs = IoU_class_metrics(val_IoUs, smooth_n)
subpart_class_IoUs, subpart_mIoUs = IoU_class_metrics(subpart_IoUs, smooth_n)
# Aggregate results
all_pred_epochs += [np.array([i for i in range(len(val_IoUs))])]
all_val_mIoUs += [val_mIoUs]
all_val_class_IoUs += [val_class_IoUs]
all_subpart_mIoUs += [subpart_mIoUs]
all_subpart_class_IoUs += [subpart_class_IoUs]
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s = '{:^6.1f}|'.format(100*subpart_mIoUs[-1])
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for IoU in subpart_class_IoUs[-1]:
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s += '{:^6.1f}'.format(100*IoU)
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print(s)
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print(6*'-' + '|' + 6*config.num_classes*'-')
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for snap_IoUs in all_val_class_IoUs:
if len(snap_IoUs) > 0:
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s = '{:^6.1f}|'.format(100*np.mean(snap_IoUs[-1]))
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for IoU in snap_IoUs[-1]:
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s += '{:^6.1f}'.format(100*IoU)
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else:
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s = '{:^6s}'.format('-')
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for _ in range(config.num_classes):
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s += '{:^6s}'.format('-')
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print(s)
# Plots
# *****
# Figure
fig = plt.figure('mIoUs')
for i, name in enumerate(list_of_names):
p = plt.plot(all_pred_epochs[i], all_subpart_mIoUs[i], '--', linewidth=1, label=name)
plt.plot(all_pred_epochs[i], all_val_mIoUs[i], linewidth=1, color=p[-1].get_color())
plt.xlabel('epochs')
plt.ylabel('IoU')
# Set limits for y axis
#plt.ylim(0.55, 0.95)
# Display legends and title
plt.legend(loc=4)
# Customize the graph
ax = fig.gca()
ax.grid(linestyle='-.', which='both')
#ax.set_yticks(np.arange(0.8, 1.02, 0.02))
displayed_classes = [0, 1, 2, 3, 4, 5, 6, 7]
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#displayed_classes = []
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for c_i, c_name in enumerate(class_list):
if c_i in displayed_classes:
# Figure
fig = plt.figure(c_name + ' IoU')
for i, name in enumerate(list_of_names):
plt.plot(all_pred_epochs[i], all_val_class_IoUs[i][:, c_i], linewidth=1, label=name)
plt.xlabel('epochs')
plt.ylabel('IoU')
# Set limits for y axis
#plt.ylim(0.8, 1)
# Display legends and title
plt.legend(loc=4)
# Customize the graph
ax = fig.gca()
ax.grid(linestyle='-.', which='both')
#ax.set_yticks(np.arange(0.8, 1.02, 0.02))
# Show all
plt.show()
# ----------------------------------------------------------------------------------------------------------------------
#
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# Experiments
# \*****************/
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#
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def experiment_name_1():
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"""
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In this function you choose the results you want to plot together, to compare them as an experiment.
Just return the list of log paths (like 'results/Log_2020-04-04_10-04-42' for example), and the associated names
of these logs.
Below an example of how to automatically gather all logs between two dates, and name them.
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"""
# Using the dates of the logs, you can easily gather consecutive ones. All logs should be of the same dataset.
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start = 'Log_2020-04-22_11-52-58'
end = 'Log_2020-05-22_11-52-58'
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# Name of the result path
res_path = 'results'
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# Gather logs and sort by date
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logs = np.sort([join(res_path, l) for l in listdir(res_path) if start <= l <= end])
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# Give names to the logs (for plot legends)
logs_names = ['name_log_1',
'name_log_2',
'name_log_3']
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# safe check log names
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logs_names = np.array(logs_names[:len(logs)])
return logs, logs_names
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def experiment_name_2():
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"""
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In this function you choose the results you want to plot together, to compare them as an experiment.
Just return the list of log paths (like 'results/Log_2020-04-04_10-04-42' for example), and the associated names
of these logs.
Below an example of how to automatically gather all logs between two dates, and name them.
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"""
# Using the dates of the logs, you can easily gather consecutive ones. All logs should be of the same dataset.
start = 'Log_2020-04-22_11-52-58'
end = 'Log_2020-05-22_11-52-58'
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# Name of the result path
res_path = 'results'
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# Gather logs and sort by date
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logs = np.sort([join(res_path, l) for l in listdir(res_path) if start <= l <= end])
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# Optionally add a specific log at a specific place in the log list
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logs = logs.astype('<U50')
logs = np.insert(logs, 0, 'results/Log_2020-04-04_10-04-42')
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# Give names to the logs (for plot legends)
logs_names = ['name_log_inserted',
'name_log_1',
'name_log_2',
'name_log_3']
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# safe check log names
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logs_names = np.array(logs_names[:len(logs)])
return logs, logs_names
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# ----------------------------------------------------------------------------------------------------------------------
#
# Main Call
# \***************/
#
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if __name__ == '__main__':
######################################################
# Choose a list of log to plot together for comparison
######################################################
# My logs: choose the logs to show
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logs, logs_names = experiment_name_1()
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################
# Plot functions
################
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# Check that all logs are of the same dataset. Different object can be compared
plot_dataset = None
config = None
for log in logs:
config = Config()
config.load(log)
if 'ShapeNetPart' in config.dataset:
this_dataset = 'ShapeNetPart'
else:
this_dataset = config.dataset
if plot_dataset:
if plot_dataset == this_dataset:
continue
else:
raise ValueError('All logs must share the same dataset to be compared')
else:
plot_dataset = this_dataset
# Plot the training loss and accuracy
compare_trainings(logs, logs_names)
# Plot the validation
if config.dataset_task == 'classification':
compare_convergences_classif(logs, logs_names)
elif config.dataset_task == 'cloud_segmentation':
if config.dataset.startswith('S3DIS'):
dataset = S3DISDataset(config, load_data=False)
compare_convergences_segment(dataset, logs, logs_names)
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elif config.dataset_task == 'slam_segmentation':
if config.dataset.startswith('SemanticKitti'):
dataset = SemanticKittiDataset(config)
compare_convergences_SLAM(dataset, logs, logs_names)
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else:
raise ValueError('Unsupported dataset : ' + plot_dataset)