def example_plot(rinfo,
b=0,
t=-1,
mask_components=False,
size=2,
column_titles=True):
image = rinfo["data"]["image"][b, 0]
recons = rinfo["outputs"]["recons"][b, t, 0]
pred_mask = rinfo["outputs"]["pred_mask"][b, t]
components = rinfo["outputs"]["components"][b, t]
K, H, W, C = components.shape
colors = get_mask_plot_colors(K)
nrows = 1
ncols = 3 + K
fig, axes = plt.subplots(ncols=ncols, figsize=(ncols * size, nrows * size))
show_img(image, ax=axes[0], color="#000000")
show_img(recons, ax=axes[1], color="#000000")
show_mask(pred_mask[Ellipsis, 0], ax=axes[2], color="#000000")
for k in range(K):
mask = pred_mask[k] if mask_components else None
show_img(components[k], ax=axes[k + 3], color=colors[k], mask=mask)
if column_titles:
labels = ["Image", "Recons.", "Mask"
] + ["Component {}".format(k + 1) for k in range(K)]
for ax, title in zip(axes, labels):
ax.set_title(title)
plt.subplots_adjust(hspace=0.03, wspace=0.035)
return fig
def inputs_plot(rinfo, b=0, t=0, size=2):
B, T, K, H, W, C = rinfo["outputs"]["components"].shape
colors = get_mask_plot_colors(K)
inputs = rinfo["inputs"]["spatial"]
rows = [
("image", show_img, False),
("components", show_img, False),
("dcomponents", functools.partial(show_img, norm=True), False),
("mask", show_mat, True),
("pred_mask", show_mat, True),
("dmask", functools.partial(show_mat, cmap="coolwarm"), True),
("posterior", show_mat, True),
("log_prob", show_mat, True),
("counterfactual", show_mat, True),
("coordinates", show_coords, False),
]
rows = [(n, f, mcb) for n, f, mcb in rows if n in inputs]
nrows = len(rows)
ncols = K + 1
fig, axes = plt.subplots(
nrows=nrows,
ncols=ncols,
figsize=(ncols * size - size * 0.9, nrows * size),
gridspec_kw={"width_ratios": [1] * K + [0.1]},
)
for r, (name, plot_fn, make_cbar) in enumerate(rows):
axes[r, 0].set_ylabel(name)
if make_cbar:
vmin = np.min(inputs[name][b, t])
vmax = np.max(inputs[name][b, t])
if np.abs(vmin - vmax) < 1e-6:
vmin -= 0.1
vmax += 0.1
plot_fn = functools.partial(plot_fn, vmin=vmin, vmax=vmax)
# print("range of {:<16}: [{:0.2f}, {:0.2f}]".format(name, vmin, vmax))
for k in range(K):
if inputs[name].shape[2] == 1:
m = inputs[name][b, t, 0]
color = (0.0, 0.0, 0.0)
else:
m = inputs[name][b, t, k]
color = colors[k]
mappable = plot_fn(m, ax=axes[r, k], color=color)
if make_cbar:
fig.colorbar(mappable, cax=axes[r, K])
else:
axes[r, K].set_visible(False)
for k in range(K):
axes[0,
k].set_title("Component {}".format(k + 1)) # , color=colors[k])
plt.subplots_adjust(hspace=0.05, wspace=0.05)
return fig
def iterations_plot(rinfo, b=0, mask_components=False, size=2):
image = rinfo["data"]["image"][b]
true_mask = rinfo["data"]["true_mask"][b]
recons = rinfo["outputs"]["recons"][b]
pred_mask = rinfo["outputs"]["pred_mask"][b]
pred_mask_logits = rinfo["outputs"]["pred_mask_logits"][b]
components = rinfo["outputs"]["components"][b]
T, K, H, W, C = components.shape
colors = get_mask_plot_colors(K)
nrows = T + 1
ncols = 2 + K
fig, axes = plt.subplots(nrows=nrows,
ncols=ncols,
figsize=(ncols * size, nrows * size))
for t in range(T):
show_img(recons[t, 0], ax=axes[t, 0])
show_mask(pred_mask[t, Ellipsis, 0], ax=axes[t, 1])
axes[t, 0].set_ylabel("iter {}".format(t))
for k in range(K):
mask = pred_mask[t, k] if mask_components else None
show_img(components[t, k],
ax=axes[t, k + 2],
color=colors[k],
mask=mask)
axes[0, 0].set_title("Reconstruction")
axes[0, 1].set_title("Mask")
show_img(image[0], ax=axes[T, 0])
show_mask(true_mask[0, Ellipsis, 0], ax=axes[T, 1])
vmin = np.min(pred_mask_logits[T - 1])
vmax = np.max(pred_mask_logits[T - 1])
for k in range(K):
axes[0, k + 2].set_title(
"Component {}".format(k + 1)) # , color=colors[k])
show_mat(pred_mask_logits[T - 1, k],
ax=axes[T, k + 2],
vmin=vmin,
vmax=vmax)
axes[T, k + 2].set_xlabel(
"Mask Logits for\nComponent {}".format(k +
1)) # , color=colors[k])
axes[T, 0].set_xlabel("Input Image")
axes[T, 1].set_xlabel("Ground Truth Mask")
plt.subplots_adjust(wspace=0.05, hspace=0.05)
return fig
def show_coords(m, ax):
vmin, vmax = np.min(m), np.max(m)
m = (m - vmin) / (vmax - vmin)
color_conv = get_mask_plot_colors(m.shape[-1])
color_mask = np.dot(m, color_conv)
return ax.imshow(color_mask, interpolation="nearest")
def show_mask(m, ax):
color_conv = get_mask_plot_colors(m.shape[0])
color_mask = np.dot(np.transpose(m, [1, 2, 0]), color_conv)
return ax.imshow(color_mask.clip(0.0, 1.0), interpolation="nearest")
