alexmr09
138 lines
6.6 KiB
Python
138 lines
6.6 KiB
Python
import mpq_quantize
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import pareto_sols
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import configure_ibex
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import simulate_ibex
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import os
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import sys
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import numpy as np
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def create_ibex_qnn(net, name, device, X_train, y_train, X_test, y_test, X_val = None, y_val = None,
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BATCH_SIZE = 32, epochs = 20, lr = 0.0001, max_acc_drop = None):
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net = net.to(device)
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in_shape = mpq_quantize.net_input_size(X_train)
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macc_per_layer, _ = mpq_quantize.display_model_info(net, in_shape)
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train_loader, val_loader, test_loader = mpq_quantize.create_dataloaders(BATCH_SIZE, X_train, y_train, X_test, y_test,
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X_val, y_val)
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if isinstance(epochs, (int, float)):
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tr_epochs = [epochs]
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else:
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tr_epochs = epochs
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if isinstance(lr, (int, float)):
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tr_lr = [lr]
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else:
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tr_lr = lr
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if len(tr_epochs) != len(tr_lr):
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print("Error: The lengths of 'epochs' and 'lr' are not equal.")
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sys.exit(1)
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print('\nFULL PRECISION MODEL TRAINING ...')
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for i in range(len(tr_epochs)):
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print(f'Round No{i+1}')
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fp_net = mpq_quantize.fp_train(net, train_loader, val_loader, device,
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epochs = tr_epochs[i], lr = tr_lr[i])
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fp_accuracy = mpq_quantize.fp_evaluate(net, test_loader, device)
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weights_per_layer, total_macc_opt_sorted = mpq_quantize.create_weight_confs(macc_per_layer)
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if(max_acc_drop is None):
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quant_net, accuracy = mpq_quantize.dse(fp_net, max_acc_drop, weights_per_layer, fp_accuracy, train_loader,
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test_loader, val_loader, device, tr_epochs, tr_lr)
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optimal_config = pareto_sols.pareto_space(fp_accuracy, accuracy, weights_per_layer, macc_per_layer,
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total_macc_opt_sorted, name)
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layer_mapping = mpq_quantize.create_layer_mapping(optimal_config)
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quant_net = mpq_quantize.Quant_Model(fp_net, optimal_config, layer_mapping,
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mpq_quantize.calculate_minimum(train_loader) >= 0)
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quant_net = mpq_quantize.train_quant_model(quant_net, train_loader, val_loader, device, epochs, lr)
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mpq_quantize.quant_net_evaluation(quant_net, test_loader, device)
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else:
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quant_net, optimal_config = mpq_quantize.dse(fp_net, max_acc_drop, weights_per_layer, fp_accuracy, train_loader,
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test_loader, val_loader, device, tr_epochs, tr_lr)
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print('\nCREATING FILES FOR SIMULATION ON IBEX CORE ...')
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mode_per_layer, layer_type = configure_ibex.decide_mode(fp_net,
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optimal_config, mpq_quantize.calculate_minimum(train_loader) >= 0)
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int_weights, int_og_bias, int_biases, shift_biases, mul_vals, shift_vals = configure_ibex.get_int_params(quant_net)
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input, padded_input, padded_w, padded_b, padded_sb_v, padded_mv, padded_sv = configure_ibex.pad_inputs_weights(quant_net,
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test_loader, mode_per_layer, int_weights,
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int_biases, shift_biases, mul_vals, shift_vals)
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combined_input, new_int_w, new_int_b, shift_b, mul_v, shift_v = configure_ibex.concat_inputs_weights(mode_per_layer,
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padded_input, padded_w, padded_b,
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padded_sb_v, padded_mv, padded_sv)
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optimized_path = '../inference_codes/' + name + '/optimized'
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original_path = '../inference_codes/' + name + '/original'
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os.makedirs(optimized_path, exist_ok = True)
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os.makedirs(original_path, exist_ok = True)
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configure_ibex.generate_Makefile(original_path, name)
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if np.unique(layer_type)[0] =='Linear':
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configure_ibex.save_1d_inputs(original_path, input)
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configure_ibex.save_1d_inputs(optimized_path, combined_input)
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configure_ibex.save_mlp_net_params(original_path, int_weights, int_og_bias, mul_vals, shift_vals)
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configure_ibex.save_mlp_net_params(optimized_path, new_int_w, new_int_b, mul_v, shift_v, shift_b)
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configure_ibex.generate_og_c_code_mlp(original_path, name, int_weights, optimal_config, layer_type)
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configure_ibex.generate_opt_c_code_mlp(optimized_path, name, new_int_w, optimal_config, layer_type)
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else:
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configure_ibex.save_2d_inputs(original_path, input)
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configure_ibex.save_2d_inputs(optimized_path, combined_input)
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configure_ibex.save_cnn_net_params(original_path, int_weights, int_og_bias, mul_vals, shift_vals)
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configure_ibex.save_cnn_net_params(optimized_path, new_int_w, new_int_b, mul_v, shift_v, shift_b)
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cnn_details = configure_ibex.get_cnn_details(fp_net)
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configure_ibex.generate_og_c_code_cnn(original_path, name, input, cnn_details, int_weights)
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configure_ibex.generate_opt_c_code_cnn(optimized_path, name, combined_input, cnn_details,
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new_int_w, optimal_config)
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print('FINISHED ...')
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if(name == 'elderly_fall'):
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print('\nSIMULATING MODEL ON IBEX CORE\nUSE THE OUTPUTS TO VERIFY THAT THE RESULTS ARE CORRECT !!')
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ibex_model = simulate_ibex.create_fann_model(int_weights, int_og_bias, mul_vals, shift_vals)
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simulate_ibex.eval_sim_model(quant_net, ibex_model, test_loader)
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elif(name == 'uci_fann_net'):
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print('\nSIMULATING MODEL ON IBEX CORE\nUSE THE OUTPUTS TO VERIFY THAT THE RESULTS ARE CORRECT !!')
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ibex_model = simulate_ibex.create_uci_model(int_weights, int_og_bias, mul_vals, shift_vals)
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simulate_ibex.eval_sim_model(quant_net, ibex_model, test_loader)
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elif(name == 'lenet5_mnist'):
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print('\nSIMULATING MODEL ON IBEX CORE\nUSE THE OUTPUTS TO VERIFY THAT THE RESULTS ARE CORRECT !!')
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ibex_model = simulate_ibex.create_lenet_model(int_weights, int_og_bias, mul_vals, shift_vals)
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simulate_ibex.eval_sim_model(quant_net, ibex_model, test_loader)
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elif(name == 'cmsis_cnn'):
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print('\nSIMULATING MODEL ON IBEX CORE\nUSE THE OUTPUTS TO VERIFY THAT THE RESULTS ARE CORRECT !!')
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ibex_model = simulate_ibex.create_cmsis_cnn_model(int_weights, int_og_bias, mul_vals, shift_vals)
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simulate_ibex.eval_sim_model(quant_net, ibex_model, test_loader)
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elif(name == 'cifar10_dws_cnn'):
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print('\nSIMULATING MODEL ON IBEX CORE\nUSE THE OUTPUTS TO VERIFY THAT THE RESULTS ARE CORRECT !!')
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ibex_model = simulate_ibex.create_ibex_dws_model(int_weights, int_og_bias, mul_vals, shift_vals)
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simulate_ibex.eval_sim_model(quant_net, ibex_model, test_loader)
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