def ranking(types, model, distfn):
lt = th.from_numpy(model.embedding())
embedding = Variable(lt, requires_grad=True)
with torch.no_grad():
ranks = []
ap_scores = []
for s, s_types in types.items():
s_e = Variable(lt[s].expand_as(embedding), requires_grad=True)
with torch.no_grad():
_dists = model.dist()(s_e, embedding).data.cpu().numpy().flatten()
_dists[s] = 1e+12
_labels = np.zeros(embedding.size(0))
_dists_masked = _dists.copy()
_ranks = []
for o in s_types:
_dists_masked[o] = np.Inf
_labels[o] = 1
ap_scores.append(average_precision_score(_labels, -_dists))
for o in s_types:
d = _dists_masked.copy()
d[o] = _dists[o]
r = np.argsort(d)
_ranks.append(np.where(r == o)[0][0] + 1)
ranks += _ranks
return np.mean(ranks), np.mean(ap_scores)
def plot_emb(types, model, epoch, objects, ap_scores, loss, best_rank,
best_mAP): # TODO : SB
fig = plt.figure(figsize=(10, 10))
ax = plt.gca()
ax.cla()
fig.patch.set_visible(False)
ax.axis('off')
ax.set_xlim((-1.1, 1.1))
ax.set_ylim((-1.1, 1.1))
ax.add_artist(plt.Circle((0, 0), 1., color='lightgrey', fill=False))
emb = model.embedding()
# visible_ents = np.unique(np.random.randint(low=0, high=len(ap_scores)-1, size=(int(.05*len(ap_scores)))))
# visible_ents = [th.LongTensor([5])]
printed_ents = []
prob_u = np.random.uniform(0, 1, len(ap_scores))
i = 0
for s, s_types in tqdm(types.items()):
# if s in visible_ents and s not in printed_ents:
# printed_ents.append(s)
if prob_u[i] < .01:
plt.plot(emb[s][0], emb[s][1], marker='b',
color='black') # plot the dot
ax.text(
emb[s][0] + .01,
emb[s][1] + .01,
objects[s].split('.')[0],
color='g',
bbox=dict(
facecolor='none',
edgecolor='black',
boxstyle='round,pad=1')) # add text with box dodgerblue
# m = 0
for o in s_types:
# if o not in visible_ents and m <= 4:
# visible_ents.append(o)
plt.plot([emb[s][0], emb[o][0]], [emb[s][1], emb[o][1]],
c='cornflowerblue',
linewidth=ap_scores[i] / max(ap_scores) * 5)
# m += 1
i += 1
plt.text(-1,
1,
'epoch = ' + str(epoch + 1) + '\nloss = ' + str(loss)[:5],
bbox=dict(facecolor='red',
alpha=0.5,
edgecolor='black',
boxstyle='round,pad=1'))
plt.text(-1,
-1,
'best_rank = ' + str(best_rank)[:5] + '\nbest_mAP = ' +
str(best_mAP)[:5],
bbox=dict(facecolor='red',
alpha=0.5,
edgecolor='black',
boxstyle='round,pad=1'))
plt.show(block=False)
fig.savefig(model.model_name + '_e' + str(epoch + 1) + '.png', dpi=fig.dpi)
left_images_path = os.path.join('C:/Users/Hazem/', 'left')
right_images_path = os.path.join('C:/Users/Hazem/', 'right')
img_shape = (245, 200, 3)
dataset = SiameseDatasetGenerator(left_images_path, right_images_path)
siamese_model.fit(dataset, epochs=2)
#here we just load from the dataset an example
#we should NOT test the performace of the model
#using training data but here we are just see how did it learn
example_prediction = dataset[9]
anchor_example = image.array_to_img(example_prediction[:, 0][0])
positive_example = image.array_to_img(example_prediction[:, 1][0])
negative_example = image.array_to_img(example_prediction[:, 2][0])
#we add an extra dimension (batch_size dimension) in the first axis by using expand dims.
anchor_tensor = np.expand_dims(example_prediction[:, 0][0], axis=0)
positive_tensor = np.expand_dims(example_prediction[:, 1][0], axis=0)
negative_tensor = np.expand_dims(example_prediction[:, 2][0], axis=0)
anchor_embedding, positive_embedding = embedding(anchor_tensor), embedding(positive_tensor)
positive_similarity = CosineDistance()(anchor_embedding, positive_embedding)
print("Similarity:", positive_similarity)
anchor_embedding, negative_embedding = embedding(anchor_tensor), embedding(negative_tensor)
negative_similarity = CosineDistance()(anchor_embedding, negative_embedding)
print("Similarity:", negative_similarity)
