# filter based on num of examples from each user
print('\nTotal number of users ' + str(user_emb.shape[0]))
new_user_inds = np.where(cnt_users >= min_num_exs)[0]
user_emb = user_emb[new_user_inds, :]
train_users = train_users[new_user_inds]
user_class_affinity = user_class_affinity[new_user_inds, :]
print('Total number of users after filtering ' + str(user_emb.shape[0]))
print('Performing TSNE on users with min ' + str(min_num_exs) + ' examples')
user_emb_low = TSNE(n_components=2, random_state=seed).fit_transform(user_emb)
# load ocean mask
mask = np.load(get_paths('mask_dir') + 'ocean_mask.npy')
mask_lines = (np.gradient(mask)[0]**2 + np.gradient(mask)[1]**2)
mask_lines[mask_lines > 0.0] = 1.0
gp = grid.GridPredictor(mask, params, mask_only_pred=True)
loc_emb = gp.dense_prediction_masked_feats(model, 0.5).data.cpu().numpy()
# compute locations where users go
mask_inds = np.where(mask.ravel() == 1)[0]
user_loc = np.zeros((mask.shape[0] * mask.shape[1]))
user_loc[mask_inds] = sig(np.dot(loc_emb, user_emb.T)).sum(1)
user_loc = user_loc.reshape((mask.shape[0], mask.shape[1]))
user_loc = np.log(1 + user_loc)
user_loc[mask_lines == 1] = user_loc.max()
plt.close('all')
cmap_r = cm.afmhot.reversed()
# plot user affinity for each location
plt.figure(1)
def main(args):
download_model(args.model_url, args.model_path)
print("Loading model: " + args.model_path)
net_params = torch.load(args.model_path, map_location="cpu")
params = net_params["params"]
device = params["device"] if torch.cuda.is_available() else "cpu"
model = models.FCNet(
num_inputs=params["num_feats"],
num_classes=params["num_classes"],
num_filts=params["num_filts"],
num_users=params["num_users"],
).to(device)
model.load_state_dict(net_params["state_dict"])
model.eval()
# load class names
with open(args.class_names_path) as da:
class_data = json.load(da)
if args.demo_type == "location":
# convert coords to torch
coords = np.array([args.longitude, args.latitude])[np.newaxis, ...]
obs_coords = utils.convert_loc_to_tensor(coords, params["device"])
obs_time = (torch.ones(coords.shape[0], device=params["device"]) *
args.time_of_year * 2 - 1.0)
loc_time_feats = utils.encode_loc_time(obs_coords,
obs_time,
concat_dim=1,
params=params)
print("Making prediction ...")
with torch.no_grad():
pred = model(loc_time_feats)[0, :]
pred = pred.cpu().numpy()
num_categories = 25
print("\nTop {} likely categories for location {:.4f}, {:.4f}:".format(
num_categories, coords[0, 0], coords[0, 1]))
most_likely = np.argsort(pred)[::-1]
for ii, cls_id in enumerate(most_likely[:num_categories]):
print("{}\t{}\t{:.3f}".format(ii, cls_id, np.round(
pred[cls_id], 3)) + "\t" + class_data[cls_id]["our_name"] +
" - " + class_data[cls_id]["preferred_common_name"])
elif args.demo_type == "map":
# grid predictor - for making dense predictions for each lon/lat location
gp = grid.GridPredictor(np.load("data/ocean_mask.npy"),
params,
mask_only_pred=True)
if args.class_of_interest == -1:
args.class_of_interest = np.random.randint(len(class_data))
print("Selected category: " +
class_data[args.class_of_interest]["our_name"] + " - " +
class_data[args.class_of_interest]["preferred_common_name"])
print("Making prediction ...")
grid_pred = gp.dense_prediction(model,
args.class_of_interest,
time_step=args.time_of_year)
op_file_name = (
class_data[args.class_of_interest]["our_name"].lower().replace(
" ", "_") + ".png")
print("Saving prediction to: " + op_file_name)
plt.imsave(op_file_name,
1.0 - grid_pred,
cmap="afmhot",
vmin=0,
vmax=1)
def main(args):
download_model(args.model_url, args.model_path)
print('Loading model: ' + args.model_path)
net_params = torch.load(args.model_path, map_location='cpu')
params = net_params['params']
model = models.FCNet(num_inputs=params['num_feats'],
num_classes=params['num_classes'],
num_filts=params['num_filts'],
num_users=params['num_users']).to(params['device'])
model.load_state_dict(net_params['state_dict'])
model.eval()
# load class names
with open(args.class_names_path) as da:
class_data = json.load(da)
if args.demo_type == 'location':
# convert coords to torch
coords = np.array([args.longitude, args.latitude])[np.newaxis, ...]
obs_coords = utils.convert_loc_to_tensor(coords, params['device'])
obs_time = torch.ones(coords.shape[0], device=params['device']
) * args.time_of_year * 2 - 1.0
loc_time_feats = utils.encode_loc_time(obs_coords,
obs_time,
concat_dim=1,
params=params)
print('Making prediction ...')
with torch.no_grad():
pred = model(loc_time_feats)[0, :]
pred = pred.cpu().numpy()
num_categories = 25
print('\nTop {} likely categories for location {:.4f}, {:.4f}:'.format(
num_categories, coords[0, 0], coords[0, 1]))
most_likely = np.argsort(pred)[::-1]
for ii, cls_id in enumerate(most_likely[:num_categories]):
print('{}\t{}\t{:.3f}'.format(ii, cls_id, np.round(pred[cls_id], 3)) + \
'\t' + class_data[cls_id]['our_name'] + ' - ' + class_data[cls_id]['preferred_common_name'])
elif args.demo_type == 'map':
# grid predictor - for making dense predictions for each lon/lat location
gp = grid.GridPredictor(np.load('data/ocean_mask.npy'),
params,
mask_only_pred=True)
if args.class_of_interest == -1:
args.class_of_interest = np.random.randint(len(class_data))
print('Selected category: ' + class_data[args.class_of_interest]['our_name'] +\
' - ' + class_data[args.class_of_interest]['preferred_common_name'])
print('Making prediction ...')
grid_pred = gp.dense_prediction(model,
args.class_of_interest,
time_step=args.time_of_year)
op_file_name = class_data[args.class_of_interest]['our_name'].lower(
).replace(' ', '_') + '.png'
print('Saving prediction to: ' + op_file_name)
plt.imsave(op_file_name,
1.0 - grid_pred,
cmap='afmhot',
vmin=0,
vmax=1)
