def test_general():
gc = pygco.gco()
gc.createGeneralGraph(n_sites, n_labels, True)
print gc.handle
gc.destroyGraph()
u = np.array([[2, 8, 8], [7, 3, 7], [8, 8, 2], [6, 4, 6]], dtype=np.intc)
e = np.array([[0, 1], [1, 2], [2, 3]], dtype=np.intc)
ew = np.array([3, 10, 1], dtype=np.intc)
s = (1 - np.eye(3)).astype(np.intc)
print pygco.cut_general_graph(e, ew, u, s, n_iter=1)
def test_float():
unary_cost = np.array([[0.0, 1.0, 2.0],
[4.0, 1.0, 0.0],
[0.0, 1.0, 2.0]])
edges = np.array([[0, 1],
[1, 2],
[0, 2]]).astype(np.int32)
pairwise_cost = np.array([[0.0, 1.0, 1.0],
[1.0, 0.0, 1.0],
[1.0, 1.0, 0.0]])
edge_weights = np.array([2.0, 0.0, 0.0])
n_sites = 3
n_labels = 3
n_edges = 3
print pygco.cut_general_graph(edges, edge_weights, unary_cost, pairwise_cost, n_iter=-1, algorithm="swap")
def test_float():
unary_cost = np.array([[0.0, 1.0, 2.0], [4.0, 1.0, 0.0], [0.0, 1.0, 2.0]])
edges = np.array([[0, 1], [1, 2], [0, 2]]).astype(np.int32)
pairwise_cost = np.array([[0.0, 1.0, 1.0], [1.0, 0.0, 1.0],
[1.0, 1.0, 0.0]])
edge_weights = np.array([2.0, 0.0, 0.0])
n_sites = 3
n_labels = 3
n_edges = 3
print pygco.cut_general_graph(edges,
edge_weights,
unary_cost,
pairwise_cost,
n_iter=-1,
algorithm="swap")
def test_general():
gc = pygco.gco()
gc.createGeneralGraph(n_sites, n_labels, True)
print gc.handle
gc.destroyGraph()
u = np.array([
[2, 8, 8],
[7, 3, 7],
[8, 8, 2],
[6, 4, 6]
], dtype=np.intc)
e = np.array([
[0, 1],
[1, 2],
[2, 3]
], dtype=np.intc)
ew = np.array([3, 10, 1], dtype=np.intc)
s = (1 - np.eye(3)).astype(np.intc)
print pygco.cut_general_graph(e, ew, u, s, n_iter=1)
def test_integer():
""" """
unary = np.array([[2, 8, 8],
[7, 3, 7],
[8, 8, 2],
[6, 4, 6]])
edges = np.array([[0, 1], [1, 2], [2, 3]])
edge_weight = np.array([3, 10, 1])
smooth = 1 - np.eye(3)
labels = pygco.cut_general_graph(edges, edge_weight, unary, smooth,
n_iter=1)
np.array_equal(labels, np.array([0, 2, 2, 1]))
def test_integer():
""" """
unary = np.array([[2, 8, 8],
[7, 3, 7],
[8, 8, 2],
[6, 4, 6]])
edges = np.array([[0, 1], [1, 2], [2, 3]])
edge_weight = np.array([3, 10, 1])
smooth = 1 - np.eye(3)
labels = pygco.cut_general_graph(edges, edge_weight, unary, smooth,
n_iter=1)
np.array_equal(labels, np.array([0, 2, 2, 1]))
def test_float():
""" """
unary = np.array([[0.0, 1.0, 2.0],
[4.0, 1.0, 0.0],
[1.0, 0.0, 2.0]])
edges = np.array([[0, 1],
[1, 2],
[0, 2]]).astype(np.int32)
smooth = (1 - np.eye(3)).astype(np.float)
edge_weights = np.array([2.0, 0.0, 0.0])
labels = pygco.cut_general_graph(edges, edge_weights, unary, smooth,
n_iter=-1, algorithm="swap")
np.array_equal(labels, np.array([0, 2, 1]))
def test_float():
""" """
unary = np.array([[0.0, 1.0, 2.0],
[4.0, 1.0, 0.0],
[1.0, 0.0, 2.0]])
edges = np.array([[0, 1],
[1, 2],
[0, 2]]).astype(np.int32)
smooth = (1 - np.eye(3)).astype(np.float)
edge_weights = np.array([2.0, 0.0, 0.0])
labels = pygco.cut_general_graph(edges, edge_weights, unary, smooth,
n_iter=-1, algorithm="swap")
np.array_equal(labels, np.array([0, 2, 1]))
def unary_pairwise_predict_single_image(unary, edges, edge_weights, pw_weight):
"""
Run graph cuts to make predictions for segmentation of a single image.
The pairwise model is a Potts model.
unary: N*K, N is the number of pixels, K is the number of classes
edges, edge_weights: pairwise potentials generated by one of the get_pw
functions, or in the same format.
pw_weight: a parameter for relative importance of pairwise potentials
compared to unary potentials.
return: y, a N-d label vector
"""
return pygco.cut_general_graph(edges, edge_weights, unary,
pw_weight * (1 - np.eye(2)), n_iter=-1, algorithm='expansion')
def unary_pairwise_predict_single_image(unary, edges, edge_weights, pw_weight):
"""
Run graph cuts to make predictions for segmentation of a single image.
The pairwise model is a Potts model.
unary: N*K, N is the number of pixels, K is the number of classes
edges, edge_weights: pairwise potentials generated by one of the get_pw
functions, or in the same format.
pw_weight: a parameter for relative importance of pairwise potentials
compared to unary potentials.
return: y, a N-d label vector
"""
return pygco.cut_general_graph(edges,
edge_weights,
unary,
pw_weight * (1 - np.eye(2)),
n_iter=-1,
algorithm='expansion')
def detect_paraphrase(k=.8,
clutter=50,
region_num=5,
split='val',
data_path=None,
max_n=None,
saveto=None,
verbose=False,
visual=False):
'''
k: float. parameter to balance the data term and the smooth term.
clutter: float in range of [0,100]. control the number of clusters. larger number results in more clusters.
'''
t2t_df = pd.read_csv(data_path + '/alignment_%s.csv' % split)
t2r_df = pd.read_csv(data_path + '/entity_region_scores_%s.csv' % split)
if split == 'test':
with open(data_path + '/filtered_test_id.txt', 'r') as f:
img_list = [int(line.strip()) for line in f]
else:
img_list = t2t_df.image.unique()
# test first max_n images
img_list = img_list[:max_n]
# img_list = [102851549]
result = []
# setup graph
bar = progressbar.ProgressBar()
N_gt = 0 # number of ground truth paraphrases
for img in bar(img_list):
p_detector = ParaphraseDetector(img,
t2t_df,
t2r_df,
region_num,
clutter=clutter,
visual=visual)
Gtw = p_detector.get_phrase_omega_graph()
edges, weights, unary, pairwise, node2int = p_detector.cvrt2gco(Gtw)
init_labels = unary.argmin(axis=1)
# optimize
if edges.size: # if there is no edges between any phrases
labels = pygco.cut_general_graph(edges,
weights,
unary,
k * pairwise,
init_labels=init_labels,
n_iter=-1,
algorithm='swap')
else:
labels = init_labels
detected_phrase = []
for l in np.unique(labels):
nodes = [n for n in node2int if labels[node2int[n]] == l]
detected = p_detector.iscandidates(combinations(
nodes, 2)) # remove phrase pair from the same sentence
detected_phrase += detected
# check if each detected paraphrase is correct
df = t2t_df.query('image == %i' % img)
for phrase_pair in detected_phrase:
res = df.query('(phrase1 == """%s""") and (phrase2 == """%s""")' %
phrase_pair)
occurrence = len(res)
gt_count = sum(res.region1 == res.region2)
res = df.query('(phrase2 == """%s""") and (phrase1 == """%s""")' %
phrase_pair)
occurrence += len(res)
gt_count += sum(res.region1 == res.region2)
result.append({
'image': img,
'phrase': phrase_pair,
'occurrence': occurrence,
'score': gt_count
})
# count ground truth paraphrase
if visual:
N_gt += len(
df.query(
'(phrase1 != phrase2) and (region1 == region2) and (region1 != 0) and (region2 != 0)'
))
else:
N_gt += len(
df.query('(phrase1 != phrase2) and (region1 == region2)'))
# compute evaluation scores
N_ans = sum([r['occurrence'] for r in result])
N_tp = sum([r['score'] for r in result])
prec = N_tp * 1. / N_ans
rec = N_tp * 1. / N_gt
f1 = 2. * (prec * rec) / (prec + rec)
if verbose:
print('No. prediction:', N_ans, ', No. true positive', N_tp,
', No. gt positives', N_gt)
print('prec = %.4f rec = %.4f f1 = %.4f' % (prec, rec, f1))
if saveto is not None:
res_df = pd.DataFrame({
'image': [r['image'] for r in result],
'phrase1': [r['phrase'][0] for r in result],
'phrase2': [r['phrase'][1] for r in result],
'occurrence': [r['occurrence'] for r in result],
'score': [r['score'] for r in result]
})
res_df.to_csv(saveto)
return f1, prec, rec
def detect_paraphrase(phrase_pair_file,
graph_dir,
p2r_dir,
rfeat_dir,
p2r_method,
r2r_method,
top_k=5,
clutter=90,
damping=0.5,
k=.1,
thresh=.58,
saveto=None,
wo_phrase_connection=False):
phrase_pair_df = pd.read_csv(phrase_pair_file, encoding="utf-8")
bar = progressbar.ProgressBar()
y_true = []
y_pred = []
items = []
for fn in bar(os.listdir(graph_dir)):
graph_data = json.load(open(os.path.join(graph_dir, fn)))
edges = np.asarray(graph_data['edges'])
weights = np.asarray(graph_data['weights'])
weights = weights / 2. + .5
phrase = graph_data['phrases']
# preprocess graph
edges = edges[weights > thresh]
weights = weights[weights > thresh]
Mp2r = np.load(os.path.join(p2r_dir, fn[:-4] + 'npy'))
Xr = np.load(os.path.join(rfeat_dir, fn[:-4] + 'npy'))
r_ids = np.arange(len(Xr))
Mp2r, smpl = preprocess_Mp2r(Mp2r, top_k)
Xr = Xr[smpl]
Xr /= np.linalg.norm(Xr, axis=1, keepdims=True)
r_ids[smpl]
# cluster regions
Tcr = get_region2label_table(Xr, clutter, damping, metric=r2r_method)
# p(l | i)
# Table. size N_label x N_phrase
Tli = np.dot(Tcr, Mp2r.T)
unary = -np.log(Tli.T)
pairwise = (1 - np.eye(unary.shape[-1]))
# optimize labels
init_labels = unary.argmin(axis=1)
N_label = len(Tcr)
if wo_phrase_connection:
labels = init_labels
elif (len(edges) > 0) and (N_label > 1):
labels = pygco.cut_general_graph(edges,
k * weights,
unary,
pairwise,
n_iter=-1,
algorithm='swap')
else: # if there is no edges between any phrases or only one cluster is found
labels = init_labels
# eval results
p2i = {p: i for i, p in enumerate(phrase)}
sub_df = phrase_pair_df[phrase_pair_df.image == int(fn[:-5])]
for p in phrase:
items.append((int(fn[:-5]), p, labels[p2i[p]]))
for _, row in sub_df.iterrows():
p1 = row['phrase1']
p2 = row['phrase2']
y_true.append(row['ytrue'])
y_pred.append(labels[p2i[p1]] == labels[p2i[p2]])
if saveto:
res_df = pd.DataFrame(items, columns=['image', 'phrase', 'label'])
res_df.to_csv(saveto)
prec = precision_score(y_true, y_pred)
rec = recall_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred)
return f1, prec, rec
