def get_code_for_data_three(model, data, opt):
options = opt['train']
dci_num_comp_indices = int(options['dci_num_comp_indices'])
dci_num_simp_indices = int(options['dci_num_simp_indices'])
dci_num_levels = int(options['dci_num_levels'])
dci_construction_field_of_view = int(options['dci_construction_field_of_view'])
dci_query_field_of_view = int(options['dci_query_field_of_view'])
dci_prop_to_visit = float(options['dci_prop_to_visit'])
dci_prop_to_retrieve = float(options['dci_prop_to_retrieve'])
sample_perturbation_magnitude = float(options['sample_perturbation_magnitude'])
code_nc = int(opt['network_G']['in_code_nc'])
pull_num_sample_per_img = int(options['num_code_per_img'])
pull_gen_img = data['LR']
d1_gen_img = data['D1']
d2_gen_img = data['D2']
real_gen_img = data['HR']
pull_gen_code_0 = torch.empty(pull_gen_img.shape[0], code_nc, pull_gen_img.shape[2] * 2, pull_gen_img.shape[3] * 2)
forward_bs = 10
print("Generating Pull Samples")
data_length = pull_gen_img.shape[0]
out_feature_shape = model.netF(data['D1'][:1])[-1].shape[1:]
# ============ ADD POINT ==================
pull_samples_dci_db = DCI(np.prod(out_feature_shape), dci_num_comp_indices, dci_num_simp_indices)
for sample_index in range(data_length):
if (sample_index + 1) % 10 == 0:
print_without_newline(
'\rFinding first stack code: Processed %d out of %d instances' % (
sample_index + 1, data_length))
pull_gen_code_pool_0 = torch.randn(pull_num_sample_per_img, code_nc, pull_gen_img.shape[2] * 2, pull_gen_img.shape[3] * 2)
pull_gen_code_pool_1 = torch.randn(pull_num_sample_per_img, code_nc, pull_gen_img.shape[2] * 4,
pull_gen_img.shape[3] * 4)
pull_gen_code_pool_2 = torch.randn(pull_num_sample_per_img, code_nc, pull_gen_img.shape[2] * 8,
pull_gen_img.shape[3] * 8)
pull_gen_features_pool = []
for i in range(0, pull_num_sample_per_img, forward_bs):
pull_img = pull_gen_img[sample_index].expand(forward_bs, -1, -1, -1)
pull_target = real_gen_img[sample_index].expand(forward_bs, -1, -1, -1)
pull_d1 = d1_gen_img[sample_index].expand(forward_bs, -1, -1, -1)
pull_d2 = d2_gen_img[sample_index].expand(forward_bs, -1, -1, -1)
cur_data = {'LR': pull_img, 'HR': pull_target, 'D1': pull_d1, 'D2': pull_d2}
start = i
end = i + forward_bs
model.feed_data(cur_data, code=[pull_gen_code_pool_0[start:end], pull_gen_code_pool_1[start:end],
pull_gen_code_pool_2[start:end]])
feature_output = model.get_features()
pull_gen_features_pool.append(feature_output['gen_feat_D1'].double().numpy())
pull_gen_features_pool = np.concatenate(pull_gen_features_pool, axis=0)
pull_gen_features_pool = pull_gen_features_pool.reshape(-1, np.prod(pull_gen_features_pool.shape[1:]))
pull_samples_dci_db.add(pull_gen_features_pool.copy(),
num_levels=dci_num_levels,
field_of_view=dci_construction_field_of_view,
prop_to_visit=dci_prop_to_visit,
prop_to_retrieve=dci_prop_to_retrieve)
target_feature = feature_output['real_feat_D1']
target_feature = target_feature[0].reshape(1, np.prod(target_feature.shape[1:])).double().numpy().copy()
pull_sample_idx_for_img, _ = pull_samples_dci_db.query(
target_feature,
num_neighbours=1,
field_of_view=dci_query_field_of_view,
prop_to_visit=dci_prop_to_visit,
prop_to_retrieve=dci_prop_to_retrieve)
pull_gen_code_0[sample_index, :] = pull_gen_code_pool_0[int(pull_sample_idx_for_img[0][0]), :]
# clear the db
pull_samples_dci_db.clear()
print('\rFinding first stack code: Processed %d out of %d instances' % (
data_length, data_length))
# ============ ADD POINT ==================
pull_gen_code_1 = torch.empty(pull_gen_img.shape[0], code_nc, pull_gen_img.shape[2] * 4, pull_gen_img.shape[3] * 4)
out_feature_shape = model.netF(data['D2'][:1])[-1].shape[1:]
pull_samples_dci_db = DCI(np.prod(out_feature_shape), dci_num_comp_indices, dci_num_simp_indices)
for sample_index in range(data_length):
if (sample_index + 1) % 10 == 0:
print_without_newline(
'\rFinding second stack code: Processed %d out of %d instances' % (
sample_index + 1, data_length))
# ============ ADD POINT ==================
pull_gen_code_pool_0 = pull_gen_code_0[sample_index].expand(pull_num_sample_per_img, -1, -1, -1)
pull_gen_code_pool_1 = torch.randn(pull_num_sample_per_img, code_nc, pull_gen_img.shape[2] * 4,
pull_gen_img.shape[3] * 4)
pull_gen_code_pool_2 = torch.randn(pull_num_sample_per_img, code_nc, pull_gen_img.shape[2] * 8,
pull_gen_img.shape[3] * 8)
pull_gen_features_pool = []
for i in range(0, pull_num_sample_per_img, forward_bs):
pull_img = pull_gen_img[sample_index].expand(forward_bs, -1, -1, -1)
pull_target = real_gen_img[sample_index].expand(forward_bs, -1, -1, -1)
pull_d1 = d1_gen_img[sample_index].expand(forward_bs, -1, -1, -1)
pull_d2 = d2_gen_img[sample_index].expand(forward_bs, -1, -1, -1)
cur_data = {'LR': pull_img, 'HR': pull_target, 'D1': pull_d1, 'D2': pull_d2}
start = i
end = i + forward_bs
model.feed_data(cur_data, code=[pull_gen_code_pool_0[start:end], pull_gen_code_pool_1[start:end],
pull_gen_code_pool_2[start:end]])
feature_output = model.get_features()
pull_gen_features_pool.append(feature_output['gen_feat_D2'].double().numpy())
pull_gen_features_pool = np.concatenate(pull_gen_features_pool, axis=0)
pull_gen_features_pool = pull_gen_features_pool.reshape(-1, np.prod(pull_gen_features_pool.shape[1:]))
pull_samples_dci_db.add(pull_gen_features_pool.copy(),
num_levels=dci_num_levels,
field_of_view=dci_construction_field_of_view,
prop_to_visit=dci_prop_to_visit,
prop_to_retrieve=dci_prop_to_retrieve)
target_feature = feature_output['real_feat_D2'].double().numpy().copy()
target_feature = target_feature[0].reshape(1, np.prod(target_feature.shape[1:]))
pull_sample_idx_for_img, _ = pull_samples_dci_db.query(
target_feature,
num_neighbours=1,
field_of_view=dci_query_field_of_view,
prop_to_visit=dci_prop_to_visit,
prop_to_retrieve=dci_prop_to_retrieve)
pull_gen_code_1[sample_index, :] = pull_gen_code_pool_1[int(pull_sample_idx_for_img[0][0]), :]
# clear the db
pull_samples_dci_db.clear()
print('\rFinding second stack code: Processed %d out of %d instances' % (
data_length, data_length))
# ============ ADD POINT ==================
pull_gen_code_2 = torch.empty(pull_gen_img.shape[0], code_nc, pull_gen_img.shape[2] * 8, pull_gen_img.shape[3] * 8)
out_feature_shape = model.netF(data['HR'][:1])[-1].shape[1:]
pull_samples_dci_db = DCI(np.prod(out_feature_shape), dci_num_comp_indices, dci_num_simp_indices)
for sample_index in range(data_length):
if (sample_index + 1) % 10 == 0:
print_without_newline(
'\rFinding third stack code: Processed %d out of %d instances' % (
sample_index + 1, data_length))
# ============ ADD POINT ==================
pull_gen_code_pool_0 = pull_gen_code_0[sample_index].expand(pull_num_sample_per_img, -1, -1, -1)
pull_gen_code_pool_1 = pull_gen_code_1[sample_index].expand(pull_num_sample_per_img, -1, -1, -1)
pull_gen_code_pool_2 = torch.randn(pull_num_sample_per_img, code_nc, pull_gen_img.shape[2] * 8,
pull_gen_img.shape[3] * 8)
pull_gen_features_pool = []
for i in range(0, pull_num_sample_per_img, forward_bs):
pull_img = pull_gen_img[sample_index].expand(forward_bs, -1, -1, -1)
pull_target = real_gen_img[sample_index].expand(forward_bs, -1, -1, -1)
pull_d1 = d1_gen_img[sample_index].expand(forward_bs, -1, -1, -1)
pull_d2 = d2_gen_img[sample_index].expand(forward_bs, -1, -1, -1)
cur_data = {'LR': pull_img, 'HR': pull_target, 'D1': pull_d1, 'D2': pull_d2}
start = i
end = i + forward_bs
model.feed_data(cur_data, code=[pull_gen_code_pool_0[start:end], pull_gen_code_pool_1[start:end],
pull_gen_code_pool_2[start:end]])
feature_output = model.get_features()
pull_gen_features_pool.append(feature_output['gen_feat'].double().numpy())
pull_gen_features_pool = np.concatenate(pull_gen_features_pool, axis=0)
pull_gen_features_pool = pull_gen_features_pool.reshape(-1, np.prod(pull_gen_features_pool.shape[1:]))
pull_samples_dci_db.add(pull_gen_features_pool.copy(),
num_levels=dci_num_levels,
field_of_view=dci_construction_field_of_view,
prop_to_visit=dci_prop_to_visit,
prop_to_retrieve=dci_prop_to_retrieve)
target_feature = feature_output['real_feat'].double().numpy().copy()
target_feature = target_feature[0].reshape(1, np.prod(target_feature.shape[1:]))
pull_sample_idx_for_img, _ = pull_samples_dci_db.query(
target_feature,
num_neighbours=1,
field_of_view=dci_query_field_of_view,
prop_to_visit=dci_prop_to_visit,
prop_to_retrieve=dci_prop_to_retrieve)
pull_gen_code_2[sample_index, :] = pull_gen_code_pool_2[int(pull_sample_idx_for_img[0][0]), :]
# clear the db
pull_samples_dci_db.clear()
print('\rFinding third stack code: Processed %d out of %d instances' % (
data_length, data_length))
pull_gen_code_0 += sample_perturbation_magnitude * torch.randn(pull_gen_img.shape[0], code_nc,
pull_gen_img.shape[2] * 2,
pull_gen_img.shape[3] * 2)
pull_gen_code_1 += sample_perturbation_magnitude * torch.randn(pull_gen_img.shape[0], code_nc,
pull_gen_img.shape[2] * 4,
pull_gen_img.shape[3] * 4)
pull_gen_code_2 += sample_perturbation_magnitude * torch.randn(pull_gen_img.shape[0], code_nc,
pull_gen_img.shape[2] * 8,
pull_gen_img.shape[3] * 8)
return [pull_gen_code_0, pull_gen_code_1, pull_gen_code_2]
def get_code_for_data(model, data, opt):
options = opt['train']
dci_num_comp_indices = int(options['dci_num_comp_indices'])
dci_num_simp_indices = int(options['dci_num_simp_indices'])
dci_num_levels = int(options['dci_num_levels'])
dci_construction_field_of_view = int(options['dci_construction_field_of_view'])
dci_query_field_of_view = int(options['dci_query_field_of_view'])
dci_prop_to_visit = float(options['dci_prop_to_visit'])
dci_prop_to_retrieve = float(options['dci_prop_to_retrieve'])
sample_perturbation_magnitude = float(options['sample_perturbation_magnitude'])
code_nc = int(opt['network_G']['in_code_nc'])
pull_num_sample_per_img = int(options['num_code_per_img'])
show_message = False if 'show_message' not in options else options['show_message']
pull_gen_img = data['LR']
real_gen_img = data['HR']
pull_gen_code_0 = torch.empty(pull_gen_img.shape[0], code_nc, pull_gen_img.shape[2], pull_gen_img.shape[3])
if show_message:
print("Generating Pull Samples")
data_length = pull_gen_img.shape[0]
out_feature_shape = model.netF(data['HR'][:1]).shape[1:]
# initialize dci db
pull_samples_dci_db = DCI(np.prod(out_feature_shape), dci_num_comp_indices, dci_num_simp_indices)
for sample_index in range(data_length):
if (sample_index + 1) % 10 == 0 and show_message:
print_without_newline(
'\rFinding first stack code: Processed %d out of %d instances' % (
sample_index + 1, data_length))
if 'zero_code' in options and options['zero_code']:
pull_gen_code_pool_0 = torch.zeros(pull_num_sample_per_img, code_nc, pull_gen_img.shape[2],
pull_gen_img.shape[3])
elif 'rand_code' in options and options['rand_code']:
pull_gen_code_pool_0 = torch.rand(pull_num_sample_per_img, code_nc, pull_gen_img.shape[2],
pull_gen_img.shape[3])
else:
pull_gen_code_pool_0 = torch.randn(pull_num_sample_per_img, code_nc, pull_gen_img.shape[2],
pull_gen_img.shape[3])
pull_img = pull_gen_img[sample_index].expand(pull_num_sample_per_img, -1, -1, -1)
# cur_data = {'LR': pull_img, 'HR': real_gen_img[sample_index: sample_index + 1]}
cur_data = {'LR': pull_img, 'HR': real_gen_img[sample_index: sample_index + 1].expand(
(pull_num_sample_per_img,) + real_gen_img.shape[1:])}
model.feed_data(cur_data, code=pull_gen_code_pool_0)
feature_output = model.get_features()
pull_gen_features_pool = feature_output['gen_feat']
target_feature = feature_output['gen_feat']
pull_gen_features_pool = pull_gen_features_pool.reshape(-1, np.prod(
pull_gen_features_pool.shape[1:])).double().numpy().copy()
target_feature = target_feature.reshape(-1, np.prod(target_feature.shape[1:]))
pull_samples_dci_db.add(pull_gen_features_pool,
num_levels=dci_num_levels,
field_of_view=dci_construction_field_of_view,
prop_to_visit=dci_prop_to_visit,
prop_to_retrieve=dci_prop_to_retrieve)
pull_sample_idx_for_img, _ = pull_samples_dci_db.query(
target_feature.numpy(),
num_neighbours=1,
field_of_view=dci_query_field_of_view,
prop_to_visit=dci_prop_to_visit,
prop_to_retrieve=dci_prop_to_retrieve)
pull_gen_code_0[sample_index, :] = pull_gen_code_pool_0[int(pull_sample_idx_for_img[0][0]), :]
# clear the db for next query
pull_samples_dci_db.clear()
if show_message:
print('\rFinding first stack code: Processed %d out of %d instances' % (
data_length, data_length))
if 'zero_code' in options and options['zero_code']:
pull_gen_code_0 += sample_perturbation_magnitude * torch.zeros(pull_gen_img.shape[0], code_nc,
pull_gen_img.shape[2],
pull_gen_img.shape[3])
elif 'rand_code' in options and options['rand_code']:
pull_gen_code_0 += sample_perturbation_magnitude * torch.rand(pull_gen_img.shape[0], code_nc,
pull_gen_img.shape[2],
pull_gen_img.shape[3])
else:
pull_gen_code_0 += sample_perturbation_magnitude * torch.randn(pull_gen_img.shape[0], code_nc,
pull_gen_img.shape[2],
pull_gen_img.shape[3])
return pull_gen_code_0
def runknn(sz=4,
num_points=202599,
num_queries=1,
num_comp_indices=2,
num_simp_indices=7,
num_outer_iterations=202599,
max_num_candidates=5000,
num_neighbours=10,
patch_size=5):
num_levels = 3
construction_field_of_view = 10
construction_prop_to_retrieve = 0.02
query_field_of_view = 12000
query_prop_to_retrieve = 1.0
dim = patch_size * patch_size * 5
#setmodulename('\"../church_npy/church_%d.npy\"'%sz)
#data_and_queries = get_img(dim, num_points + num_queries)
#data = np.load("../church_npy/church_%d.npy"%sz,mmap_mode='r')#get_img(dim, num_points)#np.copy(data_and_queries[:num_points,:])
down = int(math.log(256 / sz, 2))
dci_db = DCI(dim, num_comp_indices, num_simp_indices)
st = time.time()
print("before adding")
dci_db.add(num_points,
"../church_npy/church_%d_vgg_12_5.npy" % sz,
num_levels=num_levels,
field_of_view=construction_field_of_view,
prop_to_retrieve=construction_prop_to_retrieve,
load_from_file=1)
print("construction time:", time.time() - st)
imgid = 270
flip = 0
datamat = np.load("../church_npy/church_%d.npy" % 128, mmap_mode='r')
rawimg = imread("../results_churchoutdoor/fake_%04d.png" % imgid)
if flip:
rawimg = np.flip(rawimg, 1)
#im = Image.fromarray(target, 'RGB')
for j in range(1):
rawimg = np.mean(np.concatenate([
rawimg[0::2, 0::2, None], rawimg[0::2, 1::2, None],
rawimg[1::2, 0::2, None], rawimg[1::2, 1::2, None]
],
axis=2),
axis=2)
rawimg = rawimg.astype(np.uint8)
if flip:
fakeimgs = np.load("../church_npy/fakechurch_%d_vgg_12_flip.npy" % sz,
mmap_mode='r')
else:
fakeimgs = np.load("../church_npy/fakechurch_%d_vgg_12.npy" % sz,
mmap_mode='r')
mularr = np.load("../church_npy/rpvec_64_5.npy")
fakeimgs_rp = fakeimgs[imgid].copy()
print(fakeimgs_rp.shape)
fakeimg_rp = np.dot(fakeimgs_rp, mularr)
del fakeimgs
del mularr
minx = 0
miny = 0
maxx = 128
maxy = 128
ambient_dim = 32 * 32 * 5
numx = (max(minx + 1, maxx - 32 + 1) - minx + 32 - 1) / 32
numy = (max(miny + 1, maxy - 32 + 1) - miny + 32 - 1) / 32
print(numx, numy)
queries = np.empty([25 * 25, ambient_dim], dtype=np.float32)
d = 32
st = 0
for j in range(miny, max(miny + 1, maxy - 32 + 1), 4):
for k in range(minx, max(minx + 1, maxx - 32 + 1), 4):
eyeimg = fakeimg_rp[j:j + d,
k:k + d, :].flatten().astype(np.float32)
queries[st] = eyeimg / np.linalg.norm(eyeimg) * 255
st += 1
print(queries.shape, queries.dtype, st)
st = time.time()
num_neighbours = 200
query_field_of_view = 10000 #11000
query_prop_to_retrieve = 1.0
nearest_neighbour_idx, nearest_neighbour_dists = dci_db.query(
queries,
num_neighbours,
field_of_view=query_field_of_view,
prop_to_retrieve=query_prop_to_retrieve,
blind=False)
print("query time:", time.time() - st)
finaldist = np.array(nearest_neighbour_dists)
rawidx = np.array(nearest_neighbour_idx)
print(rawidx.shape)
finalidx = rawidx / (25 * 25)
finalpos = np.empty([rawidx.shape[0], 200, 2], dtype=np.int)
for i in range(rawidx.shape[0]):
for j in range(200):
offset = rawidx[i, j] % (25 * 25)
finalpos[i, j, 0] = offset / 25 * 4
finalpos[i, j, 1] = offset % 25 * 4
np.savez("fake_%04d_all_overlap.npz" % imgid,
finalidx=finalidx,
finaldist=finaldist,
finalpos=finalpos)
return
queries = get_query(dim, 1, down, sz, patch_size,
patch_size) #data_and_queries[num_points:,:]
print(queries.shape, queries.dtype)
queries = queries[0:2]
st = time.time()
nearest_neighbour_idx, nearest_neighbour_dists = dci_db.query(
queries,
num_neighbours,
field_of_view=query_field_of_view,
prop_to_retrieve=query_prop_to_retrieve,
blind=False)
print("query time:", time.time() - st)
#gen_res(num_points, queries, nearest_neighbour_idx, down, sz)
#resfile = "../save_%d/res.txt"%(sz)
#f = open(resfile,'w')
print(np.array(nearest_neighbour_idx)[:, 0])
print(np.array(nearest_neighbour_dists)[:, 0])
np.save("churchidx_18_v7.npy", np.array(nearest_neighbour_idx))
np.save("churchdist_18_v7.npy", np.array(nearest_neighbour_dists))
dci_db.clear()
