def main():
argv = sys.argv
print("loading %s ..." % argv[1])
ssvm = SaveLogger(file_name=argv[1]).load()
if hasattr(ssvm, 'problem'):
ssvm.model = ssvm.problem
print(ssvm)
if hasattr(ssvm, 'base_ssvm'):
ssvm = ssvm.base_ssvm
print("Iterations: %d" % len(ssvm.objective_curve_))
print("Objective: %f" % ssvm.objective_curve_[-1])
inference_run = None
if hasattr(ssvm, 'cached_constraint_'):
inference_run = ~np.array(ssvm.cached_constraint_)
print("Gap: %f" %
(np.array(ssvm.primal_objective_curve_)[inference_run][-1] -
ssvm.objective_curve_[-1]))
if len(argv) <= 2:
argv.append("acc")
if len(argv) <= 3:
dataset = 'nyu'
else:
dataset = argv[3]
if argv[2] == 'acc':
ssvm.n_jobs = 1
for data_str, title in zip(["train", "val"],
["TRAINING SET", "VALIDATION SET"]):
print(title)
edge_type = "pairwise"
if dataset == 'msrc':
ds = MSRC21Dataset()
data = msrc_helpers.load_data(data_str, which="piecewise_new")
#data = add_kraehenbuehl_features(data, which="train_30px")
data = msrc_helpers.add_kraehenbuehl_features(data, which="train")
elif dataset == 'pascal':
ds = PascalSegmentation()
data = pascal_helpers.load_pascal(data_str, sp_type="cpmc")
#data = pascal_helpers.load_pascal(data_str)
elif dataset == 'nyu':
ds = NYUSegmentation()
data = nyu_helpers.load_nyu(data_str, n_sp=500, sp='rgbd')
else:
raise ValueError("Excepted dataset to be 'nyu', 'pascal' or 'msrc',"
" got %s." % dataset)
if type(ssvm.model).__name__ == "LatentNodeCRF":
print("making data hierarchical")
data = pascal_helpers.make_cpmc_hierarchy(ds, data)
#data = make_hierarchical_data(
#ds, data, lateral=True, latent=True, latent_lateral=False,
#add_edge_features=False)
else:
data = add_edges(data, edge_type)
if type(ssvm.model).__name__ == 'EdgeFeatureGraphCRF':
data = add_edge_features(ds, data, depth_diff=True, normal_angles=True)
if type(ssvm.model).__name__ == "EdgeFeatureLatentNodeCRF":
data = add_edge_features(ds, data)
data = make_hierarchical_data(
ds, data, lateral=True, latent=True, latent_lateral=False,
add_edge_features=True)
#ssvm.model.inference_method = "qpbo"
Y_pred = ssvm.predict(data.X)
if isinstance(ssvm.model, LatentNodeCRF):
Y_pred = [ssvm.model.label_from_latent(h) for h in Y_pred]
Y_flat = np.hstack(data.Y)
print("superpixel accuracy: %.2f"
% (np.mean((np.hstack(Y_pred) == Y_flat)[Y_flat != ds.void_label]) * 100))
if dataset == 'msrc':
res = msrc_helpers.eval_on_pixels(data, Y_pred,
print_results=True)
print("global: %.2f, average: %.2f" % (res['global'] * 100,
res['average'] * 100))
#msrc_helpers.plot_confusion_matrix(res['confusion'])
else:
hamming, jaccard = eval_on_sp(ds, data, Y_pred,
print_results=True)
print("Jaccard: %.2f, Hamming: %.2f" % (jaccard.mean(),
hamming.mean()))
plt.show()
elif argv[2] == 'plot':
data_str = 'val'
if len(argv) <= 4:
raise ValueError("Need a folder name for plotting.")
if dataset == "msrc":
ds = MSRC21Dataset()
data = msrc_helpers.load_data(data_str, which="piecewise")
data = add_edges(data, independent=False)
data = msrc_helpers.add_kraehenbuehl_features(
data, which="train_30px")
data = msrc_helpers.add_kraehenbuehl_features(
data, which="train")
elif dataset == "pascal":
ds = PascalSegmentation()
data = pascal_helpers.load_pascal("val")
data = add_edges(data)
elif dataset == "nyu":
ds = NYUSegmentation()
data = nyu_helpers.load_nyu("test")
data = add_edges(data)
if type(ssvm.model).__name__ == 'EdgeFeatureGraphCRF':
data = add_edge_features(ds, data, depth_diff=True, normal_angles=True)
Y_pred = ssvm.predict(data.X)
plot_results(ds, data, Y_pred, argv[4])
def main():
argv = sys.argv
print("loading %s ..." % argv[1])
ssvm1 = SaveLogger(file_name=argv[1]).load()
ssvm2 = SaveLogger(file_name=argv[2]).load()
data_str = 'val'
if len(argv) <= 3:
raise ValueError("Need a folder name for plotting.")
print("loading data...")
data = load_nyu(data_str, n_sp=500)
dataset = NYUSegmentation()
print("done")
data1 = add_edges(data, kind="pairwise")
data2 = add_edges(data, kind="pairwise")
data1 = add_edge_features(dataset, data1)
data2 = add_edge_features(dataset, data2, depth_diff=True)
Y_pred1 = ssvm1.predict(data1.X)
Y_pred2 = ssvm2.predict(data2.X)
folder = argv[3]
if not os.path.exists(folder):
os.mkdir(folder)
np.random.seed(0)
for image_name, superpixels, y_pred1, y_pred2 in zip(data.file_names,
data.superpixels,
Y_pred1, Y_pred2):
if np.all(y_pred1 == y_pred2):
continue
gt = dataset.get_ground_truth(image_name)
perf1 = eval_on_pixels(dataset, [gt], [y_pred1[superpixels]],
print_results=False)[0]
perf1 = np.mean(perf1[np.isfinite(perf1)])
perf2 = eval_on_pixels(dataset, [gt], [y_pred2[superpixels]],
print_results=False)[0]
perf2 = np.mean(perf2[np.isfinite(perf2)])
if np.abs(perf1 - perf2) < 2:
continue
image = dataset.get_image(image_name)
fig, axes = plt.subplots(2, 3, figsize=(12, 6))
axes[0, 0].imshow(image)
axes[0, 0].imshow((y_pred1 != y_pred2)[superpixels], vmin=0, vmax=1,
alpha=.7)
axes[0, 1].set_title("ground truth")
axes[0, 1].imshow(image)
axes[0, 1].imshow(gt, alpha=.7, cmap=dataset.cmap, vmin=0,
vmax=dataset.cmap.N)
axes[1, 0].set_title("%.2f" % perf1)
axes[1, 0].imshow(image)
axes[1, 0].imshow(y_pred1[superpixels], vmin=0, vmax=dataset.cmap.N,
alpha=.7, cmap=dataset.cmap)
axes[1, 1].set_title("%.2f" % perf2)
axes[1, 1].imshow(image)
axes[1, 1].imshow(y_pred2[superpixels], alpha=.7, cmap=dataset.cmap,
vmin=0, vmax=dataset.cmap.N)
present_y = np.unique(np.hstack([y_pred1, y_pred2, np.unique(gt)]))
present_y = np.array([y_ for y_ in present_y if y_ !=
dataset.void_label])
axes[0, 2].imshow(present_y[:, np.newaxis], interpolation='nearest',
cmap=dataset.cmap, vmin=0, vmax=dataset.cmap.N)
for i, c in enumerate(present_y):
axes[0, 2].text(1, i, dataset.classes[c])
for ax in axes.ravel():
ax.set_xticks(())
ax.set_yticks(())
axes[1, 2].set_visible(False)
fig.savefig(folder + "/%s.png" % image_name, bbox_inches="tight")
plt.close(fig)