def random_eval():
(ss, ls), ims = util.load_data()
test_index = 50
r_l = 10
end_index = test_index + r_l
sample_s, sample_l, sample_i = ss[test_index:end_index], ls[
test_index:end_index], ims[test_index:end_index]
obj_vec_d = 300
model = Scence2Image(encoding_d=9, obj_vec_d=obj_vec_d)
# model = Scence2Image()
if torch.cuda.is_available():
model.cuda()
SAVE_PATH = "/media/easonnie/Seagate Expansion Drive/RL_net/m_32999_4.3951619817199825"
model.load_state_dict(torch.load(SAVE_PATH))
model.eval()
sample_s_v = Variable(sample_s)
sample_l_v = Variable(sample_l)
sample_i_v = Variable(sample_i)
print(sample_s_v)
if torch.cuda.is_available():
sample_s_v = sample_s_v.cuda()
sample_l_v = sample_l_v.cuda()
sample_i_v = sample_i_v.cuda()
vecs = model.scence2vec(sample_s_v, sample_l_v)
vec1 = vecs[0]
vec2 = vecs[2]
ims = util.abstract_changing(vec1, vec2, model)
# print(ims)
g_im = vutil.make_grid(ims.data, nrow=8, padding=15)
Image.fromarray(util.tensor2im(g_im)).save("g_im_changing_8_(0-2).png")
def produce_dev_images():
import util
(ss, ls), ims = util.load_data(mode='dev')
test_index = 50
r_l = 30
end_index = test_index + r_l
sample_s, sample_l, sample_i = ss[test_index:end_index], ls[test_index:end_index], ims[test_index:end_index]
obj_vec_d = 2400
model = AutoEncoder(obj_vec_d=obj_vec_d)
# model = Scence2Image()
if torch.cuda.is_available():
model.cuda()
SAVE_PATH = "/media/easonnie/Seagate Expansion Drive/RL_net/m_82999_0.2122452172755806_d:2400_auto_encoder"
model.load_state_dict(torch.load(SAVE_PATH))
model.eval()
sample_s_v = Variable(sample_s)
sample_l_v = Variable(sample_l)
sample_i_v = Variable(sample_i)
if torch.cuda.is_available():
sample_s_v = sample_s_v.cuda()
sample_l_v = sample_l_v.cuda()
sample_i_v = sample_i_v.cuda()
vecs = model.im2vec(sample_i_v)
im_list = []
vec1 = vecs[0]
vec2 = vecs[2]
ims = util.abstract_changing(vec1, vec2, model)
im_list.append(ims)
vec1 = vecs[1]
vec2 = vecs[3]
ims = util.abstract_changing(vec1, vec2, model)
im_list.append(ims)
vec1 = vecs[4]
vec2 = vecs[5]
ims = util.abstract_changing(vec1, vec2, model)
im_list.append(ims)
vec1 = vecs[12]
vec2 = vecs[6]
ims = util.abstract_changing(vec1, vec2, model)
im_list.append(ims)
vec1 = vecs[21]
vec2 = vecs[9]
ims = util.abstract_changing(vec1, vec2, model)
im_list.append(ims)
vec1 = vecs[14]
vec2 = vecs[20]
ims = util.abstract_changing(vec1, vec2, model)
im_list.append(ims)
ims = torch.cat(im_list, dim=0)
# print(ims)
g_im = vutil.make_grid(ims.data, nrow=8, padding=15)
Image.fromarray(util.tensor2im(g_im)).save("vector_shifting_3_auto.png")
# loss = l1_loss(ims, sample_i_v)
optimizer.zero_grad()
loss.backward()
# print([param for param in model.fc_dcgan._obj2vec.parameters()])
# print(loss)
optimizer.step()
model.eval()
ims = model(sample_s_v, sample_l_v)
vecs = model.scence2vec(sample_s_v, sample_l_v)
vec1 = vecs[0]
vec2 = vecs[2]
ims = util.abstract_changing(vec1, vec2, model)
# print(ims)
g_im = vutil.make_grid(ims.data, nrow=8, padding=15)
Image.fromarray(util.tensor2im(g_im)).save("g_im_changing_8.png")
# print(sample_s_v)
# print(ims[0])
# g_im = vutil.make_grid([ims[0].data, ims[1].data, ims[2].data], nrow=3, padding=15)
#
# Image.fromarray(util.tensor2im(g_im)).save("g_im1_l1.png")
# t_im = Image.fromarray(util.tensor2im(ims[0].data))
# t_im.save("t_im_0.png")
# t_im = Image.fromarray(util.tensor2im(ims[1].data))