"_data/mean_shape.pkl" w, h = 50, 50 bx, dx = 0.5, 0.001 border_x = bx tags_oc = None with open(path_test, 'r') as f: l_samples = pkl.load(f) input = T.fmatrix("x_input") output = T.fmatrix("y_output") ds = FaceDataset() x, y, l_infos = ds.sample_from_list_to_test(l_samples, w, h) ts_batch_size = 1000 x, y, l_infos, tags_oc = artificiel_occlusion(x, y, l_infos) list_minibatchs_vl = split_data_to_minibatchs_eval( {"x": x, "y": y}, ts_batch_size) fold_exp = "../../exps/" + sys.argv[1] with open(fold_exp+"/model.pkl", 'r') as f: stuff = pkl.load(f) layers_infos, params_vl = stuff["layers_infos"], stuff["params_vl"] print layers_infos tag = stuff["tag"] dropout = stuff["dropout"] rng = np.random.RandomState(23455) input = T.fmatrix("x_input") for l in layers_infos: l["W"], l["b"], l["rng"] = None, None, rng model = ModelMLP(layers_infos, input, dropout=dropout) model.set_params_vals(fold_exp+"/model.pkl") eval_fn = get_eval_fn(model)
id_deep_conv_ae = id_deep_conv_ae.replace("(", "") id_deep_conv_ae = id_deep_conv_ae.replace(")", "") print len(id_deep_conv_ae), id_deep_conv_ae path_ini_params_deep_conv_ae = init_w_deep_conv_ae_path +\ "deep_conv_ae_init_" + id_deep_conv_ae + ".pkl" if not os.path.isfile(path_ini_params_deep_conv_ae): deep_conv_ae.save_params(path_ini_params_deep_conv_ae) else: deep_conv_ae.set_params_vals(path_ini_params_deep_conv_ae) with open(path_valid, 'r') as f: l_samples_vl = pkl.load(f) # convert to 3D nbr_xx = l_samples_vl["x"].shape[0] l_samples_vl["x"] = l_samples_vl["x"].reshape((nbr_xx, 1, h, w)) list_minibatchs_vl = split_data_to_minibatchs_eval(l_samples_vl, vl_batch_size) max_epochs = int(1000) lr_vl = 1e-4 lr = sharedX_value(lr_vl, name="lr") optimizer = "momentum" if optimizer == "adadelta": updater = AdaDelta(decay=0.95) elif optimizer == "momentum": updater = Momentum(0.9, nesterov_momentum=True, imagenet=False, imagenetDecay=5e-4, max_colm_norm=False) else: raise ValueError("Optimizer not recognized.")
# number of the hidden layer just before the output ae. Default: None id_code = None model = ModelMLP(layers, input, l1_reg=0., l2_reg=0., reg_bias=False, dropout=dropout, id_code=id_code) aes_in = [] aes_out = [] if id_code is not None: assert aes_out != [] # Train # Data tr_batch_size = 10 vl_batch_size = 8000 with open(path_valid, 'r') as f: l_samples_vl = pkl.load(f) list_minibatchs_vl = split_data_to_minibatchs_eval( l_samples_vl, vl_batch_size) max_epochs = int(1000) lr_vl = 1e-3 lr = sharedX_value(lr_vl, name="lr") # cost weights separate = True l_in = [sharedX_value(0., name="l_in"), sharedX_value(0.0, name="l_in2")] l_out = [sharedX_value(0., name="l_out")] l_sup = sharedX_value(1., name="l_sup") l_code = sharedX_value(0.0, name="l_code") if not separate: assert l_sup.get_value() + l_in.get_value() + l_out.get_value() == 1. if l_in[0].get_value() != 0. and aes_in == []: raise ValueError("You setup the l_in but no aes in found.") if l_out[0].get_value() != 0. and aes_out == []: raise ValueError("You setup the l_out but no aes out found.")
# x, y, l_infos = ds.sample_from_list_to_test(l_samples, w, h) ts_batch_size = 1000 with open("../../inout/data/face/" + faceset + "_data/test.pkl", 'r') as fx: dumped = pkl.load(fx) with open("../../inout/data/face/" + faceset + "_data/valid.pkl", 'r') as fx: dumped_vl = pkl.load(fx) x = dumped["x"] y = dumped["y"] nfids = dumped["nfids"] bboxesT = dumped["bboxesT"] bboxesT_original = dumped["bboxesT_original"] base_name = dumped["base_name"] list_minibatchs_vl = split_data_to_minibatchs_eval({ "x": x, "y": y }, ts_batch_size) fold_exp = "../../exps/" + sys.argv[1] with open(fold_exp + "/model.pkl", 'r') as f: stuff = pkl.load(f) layers_infos, params_vl = stuff["layers_infos"], stuff["params_vl"] print layers_infos tag = stuff["tag"] dropout = stuff["dropout"] rng = np.random.RandomState(23455) input = T.fmatrix("x_input") for l in layers_infos: l["W"], l["b"], l["rng"] = None, None, rng model = ModelMLP(layers_infos, input, dropout=dropout) model.set_params_vals(fold_exp + "/model.pkl")