def draw_iqa(img, q, target_a, pred_a):
fig, ax = tfplot.subplots(figsize=(6, 6))
ax.imshow(img)
ax.set_title(question2str(q))
ax.set_xlabel(
answer2str(target_a) + answer2str(pred_a, 'Predicted'))
return fig
def draw_trajectory_multiple(gt, *pred_list):
time_step, coord_size = gt.shape
try:
ay_ax_list = [(a[:, 2], a[:, 3]) for a in [gt] + list(pred_list)]
except:
ay_ax_list = [(a[:, 0], a[:, 1]) for a in [gt] + list(pred_list)]
N = len(ay_ax_list)
sqrtN = int(N**0.5 + 1e-9)
H, W = sqrtN, sqrtN
if H * W < N: W += 1
fig, axes = tfplot.subplots(H, W, figsize=(12, 12), squeeze=False)
for h in range(H):
for w in range(W):
k = h * H + w
ax = axes[h, w]
ax.axis([0, 1, 0, 1])
ax.scatter(ay_ax_list[k][1],
ay_ax_list[k][0],
c=range(time_step),
cmap='jet')
if k == 0:
ax.set_title("GT")
ax.set_axis_bgcolor('gray')
else:
ax.set_title("pred %d" % k)
return fig
def waveform_plot(waveform, delta_t):
fig, ax = tfplot.subplots(figsize=(3, 3))
times = np.arange(len(waveform)) * delta_t
ax.plot(times, waveform)
ax.set_ylabel('signal')
ax.set_xlabel('time')
return fig
def _create_bar_stats_figure(labels, probs):
labels = np.array([os.fsdecode(i) for i in labels])
fig, ax = tfplot.subplots()
ax.bar(np.arange(probs.size), probs, 0.35)
ax.set_xticks(np.arange(labels.size))
ax.set_xticklabels(labels, rotation=+90, ha='center')
fig.set_tight_layout(True)
return fig
def draw_act_hist(h, grid_shape):
fig, ax = tfplot.subplots(figsize=(4, 4))
h = np.reshape(h, [grid_shape[0] * grid_shape[1]])
hist, bins = np.histogram(h)
ax.bar(bins[:-1],
hist.astype(np.float32) / hist.sum(),
width=(bins[1] - bins[0]),
color='blue')
ax.plot(x='Activation values', y='Probability')
return fig
def _create_wrong_example_plot(wis, whs, winst, wtypes, wids):
winst = [os.fsdecode(i) for i in winst]
wtypes = [os.fsdecode(i) for i in wtypes]
mb = np.random.randint(0, wis.shape[0])
fig, [ax1, ax2] = tfplot.subplots(2, 1)
ax1.plot(wis[mb, 0, :, 0])
ax1.plot(wis[mb, 0, :, 1])
ax1.set_title(winst[mb] + ' | ' + wtypes[mb] + ' | id: ' + str(wids[mb]))
ax2.imshow(whs[mb, :, :, 0])
fig.set_size_inches(8, 8)
return fig
def figure_prediction(pred_x, pred_y, test_x, test_y):
fig, (ax1, ax2) = tfplot.subplots(1, 2, figsize=(8, 4))
def subfigure(x, y, ax, title):
ax.plot(x, y, '.b', alpha=.05)
p_line = [max(y), min(y)]
ax.plot(p_line, p_line, '--k', alpha=.5)
ax.axis([min(x), max(x), min(y), max(y)])
ax.set_xlabel("Prediction [eV]")
ax.set_title(title)
subfigure(pred_x, pred_y, ax1, "Batch")
subfigure(test_x, test_y, ax2, "Test")
ax2.yaxis.tick_right()
ax1.set_ylabel("DFT value [eV]")
return fig
def draw_trajectory(gt, pred):
a, b = gt, pred
time_step, coord_size = a.shape
try:
ay, ax = a[:, 2], a[:, 3] # just draw the first player
by, bx = b[:, 2], b[:, 3] # just draw the first player
except:
ay, ax = a[:, 0], a[:, 1]
by, bx = b[:, 0], b[:, 1]
fig, axes = tfplot.subplots(1, 2, figsize=(8, 4))
# gt
axes[0].axis([0, 1, 0, 1])
axes[0].scatter(ax, ay, c=range(time_step), cmap='jet')
axes[0].set_title("GT")
# pred
axes[1].axis([0, 1, 0, 1])
axes[1].scatter(bx, by, c=range(time_step), cmap='jet')
axes[1].set_title("pred")
return fig
def draw_act_hist(h, grid_shape):
fig, ax = tfplot.subplots(figsize=(4,4))
h = np.reshape(h, [grid_shape[0]*grid_shape[1]])
# n, bins, patches = ax.hist(h, 50, normed=1, facecolor='blue', alpha=0.75)
hist, bins = np.histogram(h)
ax.bar(bins[:-1], hist.astype(np.float32) / hist.sum(), width=(bins[1]-bins[0]), color='blue')
ax.plot(x='Activation values', y='Probability')
# fig.xlabel('Activation values')
# fig.ylabel('Probability')
# fig.grid(True)
# ax.show()
"""
fig = plt.figure()
n, bins, patches = plt.hist(np.reshape(h, [grid_shape[0]*grid_shape[1]]), 50, normed=1, facecolor='blue', alpha=0.75)
plt.xlabel('Activation values')
plt.ylabel('Probability')
plt.grid(True)
plt.show()
"""
return fig
def heatmap_overlay(data,
overlay_image=None,
cmap='jet',
cbar=False,
show_axis=False,
alpha=0.5,
**kwargs):
fig, ax = tfplot.subplots(figsize=(5, 4) if cbar else (4, 4))
fig.subplots_adjust(0, 0, 1, 1) # use tight layout (no margins)
ax.axis('off')
if overlay_image is None: alpha = 1.0
sns.heatmap(data, ax=ax, alpha=alpha, cmap=cmap, cbar=cbar, **kwargs)
if overlay_image is not None:
h, w = data.shape
ax.imshow(overlay_image, extent=[0, h, 0, w])
if show_axis:
ax.axis('on')
fig.subplots_adjust(left=0.1, bottom=0.1, right=0.95, top=0.95)
return fig
def draw_iqa(img, q, target_a, pred_a, weights):
d = self.d
H, W = img.shape[:2]
weights = weights.reshape(d*d, d*d)
weights_a2b = np.mean(weights, axis=1).reshape(4,4)
weights_b2a = np.mean(np.transpose(weights), axis=1).reshape(4,4)
mean_w = (weights_a2b + weights_b2a) / 2
mean_w = mean_w / np.max(mean_w)
# print(mean_w.shape, img.shape)
# print("===========")
fig, ax = tfplot.subplots(figsize=(6, 6))
ax.imshow(img, extent=[0,H,0,W])
mid = ax.imshow(mean_w, cmap='jet',
alpha=0.5, extent=[0, H, 0, W])
fig.colorbar(mid)
ax.set_title(question2str(q))
ax.set_xlabel(answer2str(target_a)+answer2str(pred_a, 'Predicted'))
return fig
def calc_heat_map(data, cmap='jet'):
fig, ax = tfplot.subplots()
ax.imshow(data, cmap=cmap)
return fig
def _create_confusion_image(confusion_data):
fig, ax = tfplot.subplots()
ax.imshow(confusion_data)
fig.set_size_inches(8, 8)
return fig
def figure_heatmap(data, cmap=matplotlib.cm.Set1):# Hier Farbe anpassbar
fig, ax = tfplot.subplots()
norm = matplotlib.colors.BoundaryNorm(np.arange(-0.5,8.5,1), cmap.N)
ax.imshow(data, cmap=cmap, norm=norm)
return fig
def draw_act_vis(h, grid_shape):
fig, ax = tfplot.subplots(figsize=(4, 4))
i = ax.imshow(h.reshape(grid_shape))
fig.colorbar(i)
return fig
def figure_attention(activation):
fig, ax = tfp.subplots()
im = ax.imshow(activation, cmap='jet')
fig.colorbar(im)
return fig
def plot_num_incorrect_edges_per_graph(n_nodes, n_incorrect):
fig, ax = tfplot.subplots(figsize=(12, 9))
img = ax.scatter(n_nodes, n_incorrect, c='blue')
return img.figure
def figure_attention(attention):
fig, ax = tfplot.subplots(figsize=(4, 3))
im = ax.imshow(attention)
return fig
