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use xkcd style for plots
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@justanhduc
justanhduc committed Feb 10, 2022
1 parent 1f23f68 commit b4273c0
Showing 1 changed file with 109 additions and 111 deletions.
220 changes: 109 additions & 111 deletions neural_monitor/monitor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1251,91 +1251,89 @@ def save_meshes(self, name, meshes=None, verts=None, faces=None, verts_uvs=None,

def _plot(self, nums, prints):
summary = pd.DataFrame()
with plt.xkcd():
fig = plt.figure()
plt.xlabel('iteration')
for name, val in list(nums.items()):
smooth = self._options[name].get('smooth')
filter_outliers = self._options[name].get('filter_outliers')
prec = self._options[name].get('precision')
display = self._options[name].get('display')

# csv summary
tmp = pd.DataFrame(val.values(), index=val.keys(), columns=[name], dtype='float32')
summary = summary.join(tmp, how='outer')

# plot
self._num_since_beginning[name].update(val)
plt.ylabel(name)
x_vals = sorted(self._num_since_beginning[name].keys())
y_vals = [self._num_since_beginning[name][x] for x in x_vals]
max_, min_, med_, mean_ = np.max(y_vals), np.min(y_vals), np.median(y_vals), np.mean(y_vals)
argmax_, argmin_ = np.argmax(y_vals), np.argmin(y_vals)
plt.title('max: {:.8f} at iter {} min: {:.8f} at iter {} \nmedian: {:.8f} mean: {:.8f}'
.format(max_, x_vals[argmax_], min_, x_vals[argmin_], med_, mean_))

x_vals, y_vals = np.array(x_vals), np.array(y_vals)
y_vals_smoothed = utils.smooth(y_vals, smooth)[:x_vals.shape[0]] if smooth else y_vals
plt.plot(x_vals, y_vals_smoothed)
if filter_outliers:
inlier_indices = ~utils.is_outlier(y_vals)
y_vals_filtered = y_vals[inlier_indices]
min_, max_ = np.min(y_vals_filtered), np.max(y_vals_filtered)
interval = (.9 ** np.sign(min_) * min_, 1.1 ** np.sign(max_) * max_)
if not (np.any(np.isnan(interval)) or np.any(np.isinf(interval))):
plt.ylim(interval)

if display:
prints.append(f"{name}\t{np.mean(np.array(list(val.values())), 0):.{prec}f}")

fig.savefig(os.path.join(self.plot_folder, name.replace(' ', '_') + '.jpg'))
fig.clear()
plt.close()
fig = plt.figure()
plt.xlabel('iteration')
for name, val in list(nums.items()):
smooth = self._options[name].get('smooth')
filter_outliers = self._options[name].get('filter_outliers')
prec = self._options[name].get('precision')
display = self._options[name].get('display')

# csv summary
tmp = pd.DataFrame(val.values(), index=val.keys(), columns=[name], dtype='float32')
summary = summary.join(tmp, how='outer')

# plot
self._num_since_beginning[name].update(val)
plt.ylabel(name)
x_vals = sorted(self._num_since_beginning[name].keys())
y_vals = [self._num_since_beginning[name][x] for x in x_vals]
max_, min_, med_, mean_ = np.max(y_vals), np.min(y_vals), np.median(y_vals), np.mean(y_vals)
argmax_, argmin_ = np.argmax(y_vals), np.argmin(y_vals)
plt.title('max: {:.8f} at iter {} min: {:.8f} at iter {} \nmedian: {:.8f} mean: {:.8f}'
.format(max_, x_vals[argmax_], min_, x_vals[argmin_], med_, mean_))

x_vals, y_vals = np.array(x_vals), np.array(y_vals)
y_vals_smoothed = utils.smooth(y_vals, smooth)[:x_vals.shape[0]] if smooth else y_vals
plt.plot(x_vals, y_vals_smoothed)
if filter_outliers:
inlier_indices = ~utils.is_outlier(y_vals)
y_vals_filtered = y_vals[inlier_indices]
min_, max_ = np.min(y_vals_filtered), np.max(y_vals_filtered)
interval = (.9 ** np.sign(min_) * min_, 1.1 ** np.sign(max_) * max_)
if not (np.any(np.isnan(interval)) or np.any(np.isinf(interval))):
plt.ylim(interval)

if display:
prints.append(f"{name}\t{np.mean(np.array(list(val.values())), 0):.{prec}f}")

fig.savefig(os.path.join(self.plot_folder, name.replace(' ', '_') + '.jpg'))
fig.clear()
plt.close()
csv_file = os.path.join(self.current_folder, 'summary.csv')
summary.sort_index() # makes sure the rows are in chronological order
summary.to_csv(csv_file, mode='a', header=True if not os.path.exists(csv_file) else False)

def _plot_matrix(self, mats):
with plt.xkcd():
fig = plt.figure()
for name, val in list(mats.items()):
ax = fig.add_subplot(111)
im = ax.imshow(val)
fig.colorbar(im)

labels = self._options[name].get('labels')
ax.set_xticks(np.arange(len(val)))
ax.set_yticks(np.arange(len(val)))
if labels is not None:
if isinstance(labels[0], (list, tuple)):
ax.set_xticklabels(labels[0])
ax.set_yticklabels(labels[1])
else:
ax.set_xticklabels(labels)
ax.set_yticklabels(labels)
fig = plt.figure()
for name, val in list(mats.items()):
ax = fig.add_subplot(111)
im = ax.imshow(val)
fig.colorbar(im)

labels = self._options[name].get('labels')
ax.set_xticks(np.arange(len(val)))
ax.set_yticks(np.arange(len(val)))
if labels is not None:
if isinstance(labels[0], (list, tuple)):
ax.set_xticklabels(labels[0])
ax.set_yticklabels(labels[1])
else:
ax.set_xticklabels(labels)
ax.set_yticklabels(labels)

# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode='anchor')
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode='anchor')

ax.set_ylim([-.5, len(val) - .5])
ax.set_ylim([-.5, len(val) - .5])

show_values = self._options[name].get('show_values')
if show_values:
# Loop over data dimensions and create text annotations.
for (i, j), z in np.ndenumerate(val):
ax.text(j, i, z, ha='center', va='center', color='w')
show_values = self._options[name].get('show_values')
if show_values:
# Loop over data dimensions and create text annotations.
for (i, j), z in np.ndenumerate(val):
ax.text(j, i, z, ha='center', va='center', color='w')

ax.set_title(name)
fig.savefig(os.path.join(self.plot_folder, name + '-matrix.jpg'), transparent=None)
ax.set_title(name)
fig.savefig(os.path.join(self.plot_folder, name + '-matrix.jpg'), transparent=None)

canvas = FigureCanvas(fig)
canvas.draw()
width, height = fig.get_size_inches() * fig.get_dpi()
img = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)
self.writer.add_image('matrix-' + name.replace(' ', '-'), img,
global_step=self.iter, dataformats='HWC')
fig.clear()
plt.close()
canvas = FigureCanvas(fig)
canvas.draw()
width, height = fig.get_size_inches() * fig.get_dpi()
img = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)
self.writer.add_image('matrix-' + name.replace(' ', '-'), img,
global_step=self.iter, dataformats='HWC')
fig.clear()
plt.close()

def _imwrite(self, imgs):
for name, val in list(imgs.items()):
Expand Down Expand Up @@ -1372,32 +1370,31 @@ def _imwrite(self, imgs):
raise NotImplementedError

def _hist(self, nums):
with plt.xkcd():
fig = plt.figure()
for name, val in list(nums.items()):
n_bins = self._options[name].get('n_bins')
latest_only = self._options[name].get('latest_only')
if latest_only:
k = max(list(nums[name].keys()))
plt.hist(np.array(val[k]).flatten(), bins='auto')
else:
self._hist_since_beginning[name].update(val)

z_vals = np.array(list(self._hist_since_beginning[name].keys()))
vals = [np.array(self._hist_since_beginning[name][i]).flatten() for i in z_vals]
hists = [np.histogram(v, bins=n_bins) for v in vals]
y_vals = np.array([hists[i][0] for i in range(len(hists))])
x_vals = np.array([hists[i][1] for i in range(len(hists))])
x_vals = (x_vals[:, :-1] + x_vals[:, 1:]) / 2.
z_vals = np.tile(z_vals[:, None], (1, n_bins))

ax = fig.gca(projection='3d')
surf = ax.plot_surface(x_vals, z_vals, y_vals, cmap=cm.coolwarm, linewidth=0, antialiased=False)
ax.view_init(45, 90)
fig.colorbar(surf, shrink=0.5, aspect=5)
fig.savefig(os.path.join(self.hist_folder, name.replace(' ', '_') + '_hist.jpg'))
fig.clear()
plt.close()
fig = plt.figure()
for name, val in list(nums.items()):
n_bins = self._options[name].get('n_bins')
latest_only = self._options[name].get('latest_only')
if latest_only:
k = max(list(nums[name].keys()))
plt.hist(np.array(val[k]).flatten(), bins='auto')
else:
self._hist_since_beginning[name].update(val)

z_vals = np.array(list(self._hist_since_beginning[name].keys()))
vals = [np.array(self._hist_since_beginning[name][i]).flatten() for i in z_vals]
hists = [np.histogram(v, bins=n_bins) for v in vals]
y_vals = np.array([hists[i][0] for i in range(len(hists))])
x_vals = np.array([hists[i][1] for i in range(len(hists))])
x_vals = (x_vals[:, :-1] + x_vals[:, 1:]) / 2.
z_vals = np.tile(z_vals[:, None], (1, n_bins))

ax = fig.gca(projection='3d')
surf = ax.plot_surface(x_vals, z_vals, y_vals, cmap=cm.coolwarm, linewidth=0, antialiased=False)
ax.view_init(45, -90)
fig.colorbar(surf, shrink=0.5, aspect=5)
fig.savefig(os.path.join(self.hist_folder, name.replace(' ', '_') + '_hist.jpg'))
fig.clear()
plt.close()

def _scatter(self, points):
fig = plt.figure()
Expand Down Expand Up @@ -1425,20 +1422,21 @@ def _flush(self):
it, epoch, nums, mats, imgs, hists, points = items
prints = []

# plot statistics
self._plot(nums, prints)
with plt.xkcd():
# plot statistics
self._plot(nums, prints)

# plot confusion matrix
self._plot_matrix(mats)
# plot confusion matrix
self._plot_matrix(mats)

# save recorded images
self._imwrite(imgs)
# save recorded images
self._imwrite(imgs)

# make histograms of recorded data
self._hist(hists)
# make histograms of recorded data
self._hist(hists)

# scatter point set(s)
self._scatter(points)
# scatter point set(s)
self._scatter(points)

lock.acquire_write()
with open(os.path.join(self.file_folder, self._log_file), 'wb') as f:
Expand Down

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