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jblitzar
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github-python

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paths = json.load(f)
# load model
train_params = torch.load(eval_params['model_path'], map_location='cpu')
model = models.get_model(train_params['params'])
model.load_state_dict(train_params['state_dict'], strict=True)
model = model.to(eval_params['device'])
model.eval()
if train_params['params']['input_enc'] in ['env', 'sin_cos_env']:
raster = datasets.load_env()
else:
raster = None
enc = utils.CoordEncoder(train_params['params']['input_enc'], raster=raster)
# user specified random taxa
if eval_params['rand_taxa']:
print('Selecting random taxa')
eval_params['taxa_id'] = np.random.choice(train_params['params']['class_to_taxa'])
# load taxa of interest
if eval_params['taxa_id'] in train_params['params']['class_to_taxa']:
class_of_interest = train_params['params']['class_to_taxa'].index(eval_params['taxa_id'])
else:
print(f'Error: Taxa specified that is not in the model: {eval_params["taxa_id"]}')
return False
print(f'Loading taxa: {eval_params["taxa_id"]}')
# load ocean mask
if eval_params['high_res']:
mask = np.load(os.path.join(paths['masks'], 'ocean_mask_hr.npy'))
else:
mask = np.load(os.path.join(paths['masks'], 'ocean_mask.npy'))
mask_inds = np.where(mask.reshape(-1) == 1)[0]
# generate input features
locs = utils.coord_grid(mask.shape)
if not eval_params['disable_ocean_mask']:
locs = locs[mask_inds, :]
locs = torch.from_numpy(locs)
locs_enc = enc.encode(locs).to(eval_params['device'])
# make prediction
with torch.no_grad():
preds = model(locs_enc, return_feats=False, class_of_interest=class_of_interest).cpu().numpy()
# threshold predictions
if eval_params['threshold'] > 0:
print(f'Applying threshold of {eval_params["threshold"]} to the predictions.')
preds[preds<eval_params['threshold']] = 0.0
preds[preds>=eval_params['threshold']] = 1.0
# mask data
if not eval_params['disable_ocean_mask']:
op_im = np.ones((mask.shape[0] * mask.shape[1])) * np.nan # set to NaN
op_im[mask_inds] = preds
else:
op_im = preds
# reshape and create masked array for visualization
op_im = op_im.reshape((mask.shape[0], mask.shape[1]))
op_im = np.ma.masked_invalid(op_im)
# set color for masked values
cmap = plt.cm.plasma
cmap.set_bad(color='none')
if eval_params['set_max_cmap_to_1']:
vmax = 1.0
else:
vmax = np.max(op_im)
# save image
save_loc = os.path.join(eval_params['op_path'], str(eval_params['taxa_id']) + '_map.png')
print(f'Saving image to {save_loc}')
plt.imsave(fname=save_loc, arr=op_im, vmin=0, vmax=vmax, cmap=cmap)
return True
if __name__ == '__main__':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
info_str = '\nDemo that takes an iNaturalist taxa ID as input and ' + \
'generates a predicted range for each location on the globe ' + \
'and saves the ouput as an image.\n\n' + \
'Warning: these estimated ranges should be validated before use.'
parser = argparse.ArgumentParser(usage=info_str)
parser.add_argument('--model_path', type=str, default='./pretrained_models/model_an_full_input_enc_sin_cos_hard_cap_num_per_class_1000.pt')
parser.add_argument('--taxa_id', type=int, default=130714, help='iNaturalist taxon ID.')
parser.add_argument('--threshold', type=float, default=-1, help='Threshold the range map [0, 1].')
parser.add_argument('--op_path', type=str, default='./images/', help='Location where the output image will be saved.')
parser.add_argument('--rand_taxa', action='store_true', help='Select a random taxa.')
parser.add_argument('--high_res', action='store_true', help='Generate higher resolution output.')
parser.add_argument('--disable_ocean_mask', action='store_true', help='Do not use an ocean mask.')
parser.add_argument('--set_max_cmap_to_1', action='store_true', help='Consistent maximum intensity ouput.')
parser.add_argument('--device', type=str, default=device, help='cpu or cuda')
eval_params = vars(parser.parse_args())