def main(): # parse the argument parser = argparse.ArgumentParser() parser.add_argument( 'data_list', help='The path of data list file, which consists of one image path per line' ) parser.add_argument( 'model', help='The model for image classification', choices=[ 'alexnet', 'vgg13', 'vgg16', 'vgg19', 'resnet', 'googlenet', 'inception-resnet-v2', 'inception_v4', 'xception' ]) parser.add_argument( 'params_path', help='The file which stores the parameters') args = parser.parse_args() # PaddlePaddle init paddle.init(use_gpu=True, trainer_count=1) image = paddle.layer.data( name="image", type=paddle.data_type.dense_vector(DATA_DIM)) if args.model == 'alexnet': out = alexnet.alexnet(image, class_dim=CLASS_DIM) elif args.model == 'vgg13': out = vgg.vgg13(image, class_dim=CLASS_DIM) elif args.model == 'vgg16': out = vgg.vgg16(image, class_dim=CLASS_DIM) elif args.model == 'vgg19': out = vgg.vgg19(image, class_dim=CLASS_DIM) elif args.model == 'resnet': out = resnet.resnet_imagenet(image, class_dim=CLASS_DIM) elif args.model == 'googlenet': out, _, _ = googlenet.googlenet(image, class_dim=CLASS_DIM) elif args.model == 'inception-resnet-v2': assert DATA_DIM == 3 * 331 * 331 or DATA_DIM == 3 * 299 * 299 out = inception_resnet_v2.inception_resnet_v2( image, class_dim=CLASS_DIM, dropout_rate=0.5, data_dim=DATA_DIM) elif args.model == 'inception_v4': out = inception_v4.inception_v4(image, class_dim=CLASS_DIM) elif args.model == 'xception': out = xception.xception(image, class_dim=CLASS_DIM) # load parameters with gzip.open(args.params_path, 'r') as f: parameters = paddle.parameters.Parameters.from_tar(f) file_list = [line.strip() for line in open(args.data_list)] test_data = [(paddle.image.load_and_transform(image_file, 256, 224, False) .flatten().astype('float32'), ) for image_file in file_list] probs = paddle.infer( output_layer=out, parameters=parameters, input=test_data) lab = np.argsort(-probs) for file_name, result in zip(file_list, lab): print "Label of %s is: %d" % (file_name, result[0])
def main(): # parse the argument parser = argparse.ArgumentParser() parser.add_argument('-m', '--model', help='The model for image classification', choices=[ 'alexnet', 'vgg13', 'vgg16', 'vgg19', 'resnet', 'googlenet', 'inception-resnet-v2', 'inception_v4', 'xception' ]) parser.add_argument( '-r', '--retrain_file', type=str, default='', help="The model file to retrain, none is for train from scratch") args = parser.parse_args() # PaddlePaddle init paddle.init(use_gpu=True, trainer_count=1) image = paddle.layer.data(name="image", type=paddle.data_type.dense_vector(DATA_DIM)) lbl = paddle.layer.data(name="label", type=paddle.data_type.integer_value(CLASS_DIM)) extra_layers = None learning_rate = 0.0001 if args.model == 'alexnet': out = alexnet.alexnet(image, class_dim=CLASS_DIM) elif args.model == 'vgg13': out = vgg.vgg13(image, class_dim=CLASS_DIM) elif args.model == 'vgg16': out = vgg.vgg16(image, class_dim=CLASS_DIM) elif args.model == 'vgg19': out = vgg.vgg19(image, class_dim=CLASS_DIM) elif args.model == 'resnet': conv, pool, out = resnet.resnet_imagenet(image, class_dim=CLASS_DIM) learning_rate = 0.1 elif args.model == 'googlenet': out, out1, out2 = googlenet.googlenet(image, class_dim=CLASS_DIM) loss1 = paddle.layer.cross_entropy_cost(input=out1, label=lbl, coeff=0.3) paddle.evaluator.classification_error(input=out1, label=lbl) loss2 = paddle.layer.cross_entropy_cost(input=out2, label=lbl, coeff=0.3) paddle.evaluator.classification_error(input=out2, label=lbl) extra_layers = [loss1, loss2] elif args.model == 'inception-resnet-v2': assert DATA_DIM == 3 * 331 * 331 or DATA_DIM == 3 * 299 * 299 out = inception_resnet_v2.inception_resnet_v2(image, class_dim=CLASS_DIM, dropout_rate=0.5, data_dim=DATA_DIM) elif args.model == 'inception_v4': conv, pool, out = inception_v4.inception_v4(image, class_dim=CLASS_DIM) elif args.model == 'xception': out = xception.xception(image, class_dim=CLASS_DIM) cost = paddle.layer.classification_cost(input=out, label=lbl) # Create parameters parameters = paddle.parameters.create(cost) for k, v in parameters.__param_conf__.items(): print(" config key {0}\t\t\tval{1}".format(k, v)) print("-" * 50) #print(parameters.__param_conf__[0]) if args.retrain_file is not None and '' != args.retrain_file: print("restore parameters from {0}".format(args.retrain_file)) exclude_params = [ param for param in parameters.names() if param.startswith('___fc_layer_0__') ] parameters.init_from_tar(gzip.open(args.retrain_file), exclude_params) # Create optimizer optimizer = paddle.optimizer.Momentum( momentum=0.9, regularization=paddle.optimizer.L2Regularization(rate=0.0005 * BATCH_SIZE), learning_rate=learning_rate / BATCH_SIZE, learning_rate_decay_a=0.1, learning_rate_decay_b=128000 * 35, learning_rate_schedule="discexp", ) train_reader = paddle.batch( paddle.reader.shuffle( # flowers.train(), # To use other data, replace the above line with: reader.train_reader('valid_train0.lst'), buf_size=2048), batch_size=BATCH_SIZE) test_reader = paddle.batch( # flowers.valid(), # To use other data, replace the above line with: reader.test_reader('valid_val.lst'), batch_size=BATCH_SIZE) # Create trainer trainer = paddle.trainer.SGD(cost=cost, parameters=parameters, update_equation=optimizer, extra_layers=extra_layers) # End batch and end pass event handler def event_handler(event): global step global start if isinstance(event, paddle.event.EndIteration): if event.batch_id % 10 == 0: print "\nPass %d, Batch %d, Cost %f, %s, %s" % ( event.pass_id, event.batch_id, event.cost, event.metrics, time.time() - start) start = time.time() loss_scalar.add_record(step, event.cost) acc_scalar.add_record( step, 1 - event.metrics['classification_error_evaluator']) start = time.time() step += 1 if event.batch_id % 100 == 0: with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f: trainer.save_parameter_to_tar(f) if isinstance(event, paddle.event.EndPass): with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f: trainer.save_parameter_to_tar(f) result = trainer.test(reader=test_reader) print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics) trainer.train(reader=train_reader, num_passes=200, event_handler=event_handler)
def main(): # parse the argument parser = argparse.ArgumentParser() parser.add_argument( 'model', help='The model for image classification', choices=['alexnet', 'vgg13', 'vgg16', 'vgg19', 'resnet', 'googlenet']) args = parser.parse_args() # PaddlePaddle init paddle.init(use_gpu=True, trainer_count=1) image = paddle.layer.data( name="image", type=paddle.data_type.dense_vector(DATA_DIM)) lbl = paddle.layer.data( name="label", type=paddle.data_type.integer_value(CLASS_DIM)) extra_layers = None learning_rate = 0.01 if args.model == 'alexnet': out = alexnet.alexnet(image, class_dim=CLASS_DIM) elif args.model == 'vgg13': out = vgg.vgg13(image, class_dim=CLASS_DIM) elif args.model == 'vgg16': out = vgg.vgg16(image, class_dim=CLASS_DIM) elif args.model == 'vgg19': out = vgg.vgg19(image, class_dim=CLASS_DIM) elif args.model == 'resnet': out = resnet.resnet_imagenet(image, class_dim=CLASS_DIM) learning_rate = 0.1 elif args.model == 'googlenet': out, out1, out2 = googlenet.googlenet(image, class_dim=CLASS_DIM) loss1 = paddle.layer.cross_entropy_cost( input=out1, label=lbl, coeff=0.3) paddle.evaluator.classification_error(input=out1, label=lbl) loss2 = paddle.layer.cross_entropy_cost( input=out2, label=lbl, coeff=0.3) paddle.evaluator.classification_error(input=out2, label=lbl) extra_layers = [loss1, loss2] cost = paddle.layer.classification_cost(input=out, label=lbl) # Create parameters parameters = paddle.parameters.create(cost) # Create optimizer optimizer = paddle.optimizer.Momentum( momentum=0.9, regularization=paddle.optimizer.L2Regularization(rate=0.0005 * BATCH_SIZE), learning_rate=learning_rate / BATCH_SIZE, learning_rate_decay_a=0.1, learning_rate_decay_b=128000 * 35, learning_rate_schedule="discexp", ) train_reader = paddle.batch( paddle.reader.shuffle( flowers.train(), # To use other data, replace the above line with: # reader.train_reader('train.list'), buf_size=1000), batch_size=BATCH_SIZE) test_reader = paddle.batch( flowers.valid(), # To use other data, replace the above line with: # reader.test_reader('val.list'), batch_size=BATCH_SIZE) # End batch and end pass event handler def event_handler(event): if isinstance(event, paddle.event.EndIteration): if event.batch_id % 1 == 0: print "\nPass %d, Batch %d, Cost %f, %s" % ( event.pass_id, event.batch_id, event.cost, event.metrics) if isinstance(event, paddle.event.EndPass): with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f: parameters.to_tar(f) result = trainer.test(reader=test_reader) print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics) # Create trainer trainer = paddle.trainer.SGD( cost=cost, parameters=parameters, update_equation=optimizer, extra_layers=extra_layers) trainer.train( reader=train_reader, num_passes=200, event_handler=event_handler)
def main(): # parse the argument parser = argparse.ArgumentParser() parser.add_argument( 'model', help='The model for image classification', choices=['alexnet', 'vgg13', 'vgg16', 'vgg19', 'resnet', 'googlenet']) args = parser.parse_args() # PaddlePaddle init paddle.init(use_gpu=True, trainer_count=7) image = paddle.layer.data( name="image", type=paddle.data_type.dense_vector(DATA_DIM)) lbl = paddle.layer.data( name="label", type=paddle.data_type.integer_value(CLASS_DIM)) extra_layers = None learning_rate = 0.01 if args.model == 'alexnet': out = alexnet.alexnet(image, class_dim=CLASS_DIM) elif args.model == 'vgg13': out = vgg.vgg13(image, class_dim=CLASS_DIM) elif args.model == 'vgg16': out = vgg.vgg16(image, class_dim=CLASS_DIM) elif args.model == 'vgg19': out = vgg.vgg19(image, class_dim=CLASS_DIM) elif args.model == 'resnet': out = resnet.resnet_imagenet(image, class_dim=CLASS_DIM) learning_rate = 0.1 elif args.model == 'googlenet': out, out1, out2 = googlenet.googlenet(image, class_dim=CLASS_DIM) loss1 = paddle.layer.cross_entropy_cost( input=out1, label=lbl, coeff=0.3) paddle.evaluator.classification_error(input=out1, label=lbl) loss2 = paddle.layer.cross_entropy_cost( input=out2, label=lbl, coeff=0.3) paddle.evaluator.classification_error(input=out2, label=lbl) extra_layers = [loss1, loss2] cost = paddle.layer.classification_cost(input=out, label=lbl) # Create parameters parameters = paddle.parameters.create(cost) # Create optimizer optimizer = paddle.optimizer.Momentum( momentum=0.9, regularization=paddle.optimizer.L2Regularization(rate=0.0005 * BATCH_SIZE), learning_rate=learning_rate / BATCH_SIZE, learning_rate_decay_a=0.1, learning_rate_decay_b=128000 * 35, learning_rate_schedule="discexp", ) train_reader = paddle.batch( paddle.reader.shuffle( flowers.train(), # To use other data, replace the above line with: # reader.train_reader('train.list'), buf_size=1000), batch_size=BATCH_SIZE) test_reader = paddle.batch( flowers.valid(), # To use other data, replace the above line with: # reader.test_reader('val.list'), batch_size=BATCH_SIZE) # Create trainer trainer = paddle.trainer.SGD( cost=cost, parameters=parameters, update_equation=optimizer, extra_layers=extra_layers) # End batch and end pass event handler def event_handler(event): if isinstance(event, paddle.event.EndIteration): if event.batch_id % 1 == 0: print "\nPass %d, Batch %d, Cost %f, %s" % ( event.pass_id, event.batch_id, event.cost, event.metrics) if isinstance(event, paddle.event.EndPass): with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f: trainer.save_parameter_to_tar(f) result = trainer.test(reader=test_reader) print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics) trainer.train( reader=train_reader, num_passes=200, event_handler=event_handler)