def rand_samples(batch_size, params, rand_type='uniform'):
# randomly sample background locations
rand_feats_orig = torch.rand(batch_size, 3).to(params['device']) * 2 - 1
if rand_type == 'spherical':
theta = (
(rand_feats_orig[:, 1].unsqueeze(1) + 1) / 2.0) * (2 * math.pi)
r_lon = torch.sqrt(
1.0 - rand_feats_orig[:, 0].unsqueeze(1)**2) * torch.cos(theta)
r_lat = torch.sqrt(
1.0 - rand_feats_orig[:, 0].unsqueeze(1)**2) * torch.sin(theta)
rand_feats_orig = torch.cat(
(r_lon, r_lat, rand_feats_orig[:, 2].unsqueeze(1)), 1)
rand_feats = ut.encode_loc_time(rand_feats_orig[:, :2],
rand_feats_orig[:, 2],
concat_dim=1,
params=params)
return rand_feats
def rand_samples(batch_size, params, rand_type='uniform'):
'''
randomly sample background locations, generate (lon, lat, date) and put into pre loc encoder
Note that the generated (lon, lat) are between [-1, 1] for geo_net
But for our spa_enc, they generate real (lat, lon)
Return:
rand_feats: shape (batch_size, input_feat_dim)
'''
spa_enc_type = params['spa_enc_type']
# randomly sample background locations and date
# the generated location and date from [-1, 1]
rand_feats_orig = torch.rand(batch_size, 3).to(params['device']) * 2 - 1
if rand_type == 'spherical':
# theta is between (0, 2*pi), computed based on latitude
theta = (
(rand_feats_orig[:, 1].unsqueeze(1) + 1) / 2.0) * (2 * math.pi)
r_lon = torch.sqrt(
1.0 - rand_feats_orig[:, 0].unsqueeze(1)**2) * torch.cos(theta)
r_lat = torch.sqrt(
1.0 - rand_feats_orig[:, 0].unsqueeze(1)**2) * torch.sin(theta)
# rand_feats_orig: (batch_size, 3)
rand_feats_orig = torch.cat(
(r_lon, r_lat, rand_feats_orig[:, 2].unsqueeze(1)), 1)
if spa_enc_type == "geo_net":
rand_feats = ut.encode_loc_time(rand_feats_orig[:, :2],
rand_feats_orig[:, 2],
concat_dim=1,
params=params)
elif spa_enc_type in ut.get_spa_enc_list():
lon = torch.unsqueeze(rand_feats_orig[:, 0] * 180, dim=1)
lat = torch.unsqueeze(rand_feats_orig[:, 1] * 90, dim=1)
# rand_feats: shape (batch_size, input_feat_dim = 2)
rand_feats = torch.cat((lon, lat), 1).to(params["device"])
else:
raise Exception("spa_enc not defined!!!")
return rand_feats
def rand_samples(batch_size, params, rand_type='uniform'):
# randomly sample background locations
if rand_type == 'spherical':
rand_feats_orig = torch.rand(batch_size, 3).to(params['device'])
rand_feats_orig[:,
2] = rand_feats_orig[:,
2] * 2.0 - 1.0 # make dates between -1 and 1
theta1 = 2.0 * math.pi * rand_feats_orig[:, 0]
theta2 = torch.acos(2.0 * rand_feats_orig[:, 1] - 1.0)
lat = 1.0 - 2.0 * theta2 / math.pi
lon = (theta1 / math.pi) - 1.0
rand_feats = torch.cat((lon.unsqueeze(1), lat.unsqueeze(1),
rand_feats_orig[:, 2].unsqueeze(1)), 1)
elif rand_type == 'uniform':
rand_feats = torch.rand(batch_size, 3).to(params['device']) * 2.0 - 1.0
rand_feats = ut.encode_loc_time(rand_feats[:, :2],
rand_feats[:, 2],
concat_dim=1,
params=params)
return rand_feats
#
# neural network spatio-temporal prior
#
if 'geo_net' in eval_params['algs']:
print('\nNeural net prior')
print(' Model :\t' + os.path.basename(nn_model_path))
net_params = torch.load(nn_model_path)
params = net_params['params']
# construct features
val_locs_scaled = ut.convert_loc_to_tensor(val_locs)
val_dates_scaled = torch.from_numpy(
val_dates.astype(np.float32) * 2 - 1)
val_feats_net = ut.encode_loc_time(val_locs_scaled,
val_dates_scaled,
concat_dim=1,
params=params)
model = models.FCNet(params['num_feats'], params['num_classes'],
params['num_filts'], params['num_users'])
model.load_state_dict(net_params['state_dict'])
model.eval()
pred_geo_net = compute_acc(val_preds,
val_classes,
val_split,
val_feats=val_feats_net,
prior_type='geo_net',
prior=model)
#
# Tang et al ICCV 2015, Improving Image Classification with Location Context
def generate_feats(locs, dates, params):
x_locs = ut.convert_loc_to_tensor(locs, params['device'])
x_dates = torch.from_numpy(dates.astype(np.float32)*2 - 1).to(params['device'])
feats = ut.encode_loc_time(x_locs, x_dates, concat_dim=1, params=params)
return feats