diff --git a/exps-tf/GDAS.py b/exps-tf/GDAS.py
deleted file mode 100644
index a77511e..0000000
--- a/exps-tf/GDAS.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# [D-X-Y]
-# Run GDAS
-# CUDA_VISIBLE_DEVICES=0 python exps-tf/GDAS.py
-# Run DARTS
-# CUDA_VISIBLE_DEVICES=0 python exps-tf/GDAS.py --tau_max -1 --tau_min -1 --epochs 50
-#
-import os, sys, math, time, random, argparse
-import tensorflow as tf
-from pathlib import Path
-
-lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
-if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
-
-# self-lib
-from tf_models import get_cell_based_tiny_net
-from tf_optimizers import SGDW, AdamW
-from config_utils import dict2config
-from log_utils import time_string
-from models import CellStructure
-
-
-def pre_process(image_a, label_a, image_b, label_b):
- def standard_func(image):
- x = tf.pad(image, [[4, 4], [4, 4], [0, 0]])
- x = tf.image.random_crop(x, [32, 32, 3])
- x = tf.image.random_flip_left_right(x)
- return x
- return standard_func(image_a), label_a, standard_func(image_b), label_b
-
-
-class CosineAnnealingLR(object):
- def __init__(self, warmup_epochs, epochs, initial_lr, min_lr):
- self.warmup_epochs = warmup_epochs
- self.epochs = epochs
- self.initial_lr = initial_lr
- self.min_lr = min_lr
-
- def get_lr(self, epoch):
- if epoch < self.warmup_epochs:
- lr = self.min_lr + (epoch/self.warmup_epochs) * (self.initial_lr-self.min_lr)
- elif epoch >= self.epochs:
- lr = self.min_lr
- else:
- lr = self.min_lr + (self.initial_lr-self.min_lr) * 0.5 * (1 + math.cos(math.pi * epoch / self.epochs))
- return lr
-
-
-
-def main(xargs):
- cifar10 = tf.keras.datasets.cifar10
-
- (x_train, y_train), (x_test, y_test) = cifar10.load_data()
- x_train, x_test = x_train / 255.0, x_test / 255.0
- x_train, x_test = x_train.astype('float32'), x_test.astype('float32')
-
- # Add a channels dimension
- all_indexes = list(range(x_train.shape[0]))
- random.shuffle(all_indexes)
- s_train_idxs, s_valid_idxs = all_indexes[::2], all_indexes[1::2]
- search_train_x, search_train_y = x_train[s_train_idxs], y_train[s_train_idxs]
- search_valid_x, search_valid_y = x_train[s_valid_idxs], y_train[s_valid_idxs]
- #x_train, x_test = x_train[..., tf.newaxis], x_test[..., tf.newaxis]
-
- # Use tf.data
- #train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(64)
- search_ds = tf.data.Dataset.from_tensor_slices((search_train_x, search_train_y, search_valid_x, search_valid_y))
- search_ds = search_ds.map(pre_process).shuffle(1000).batch(64)
-
- test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)
-
- # Create an instance of the model
- config = dict2config({'name': 'GDAS',
- 'C' : xargs.channel, 'N': xargs.num_cells, 'max_nodes': xargs.max_nodes,
- 'num_classes': 10, 'space': 'nas-bench-201', 'affine': True}, None)
- model = get_cell_based_tiny_net(config)
- num_iters_per_epoch = int(tf.data.experimental.cardinality(search_ds).numpy())
- #lr_schedular = tf.keras.experimental.CosineDecay(xargs.w_lr_max, num_iters_per_epoch*xargs.epochs, xargs.w_lr_min / xargs.w_lr_max)
- lr_schedular = CosineAnnealingLR(0, xargs.epochs, xargs.w_lr_max, xargs.w_lr_min)
- # Choose optimizer
- loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
- w_optimizer = SGDW(learning_rate=xargs.w_lr_max, weight_decay=xargs.w_weight_decay, momentum=xargs.w_momentum, nesterov=True)
- a_optimizer = AdamW(learning_rate=xargs.arch_learning_rate, weight_decay=xargs.arch_weight_decay, beta_1=0.5, beta_2=0.999, epsilon=1e-07)
- #w_optimizer = tf.keras.optimizers.SGD(learning_rate=0.025, momentum=0.9, nesterov=True)
- #a_optimizer = tf.keras.optimizers.AdamW(learning_rate=xargs.arch_learning_rate, beta_1=0.5, beta_2=0.999, epsilon=1e-07)
- ####
- # metrics
- train_loss = tf.keras.metrics.Mean(name='train_loss')
- train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
- valid_loss = tf.keras.metrics.Mean(name='valid_loss')
- valid_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='valid_accuracy')
- test_loss = tf.keras.metrics.Mean(name='test_loss')
- test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
-
- @tf.function
- def search_step(train_images, train_labels, valid_images, valid_labels, tf_tau):
- # optimize weights
- with tf.GradientTape() as tape:
- predictions = model(train_images, tf_tau, True)
- w_loss = loss_object(train_labels, predictions)
- net_w_param = model.get_weights()
- gradients = tape.gradient(w_loss, net_w_param)
- w_optimizer.apply_gradients(zip(gradients, net_w_param))
- train_loss(w_loss)
- train_accuracy(train_labels, predictions)
- # optimize alphas
- with tf.GradientTape() as tape:
- predictions = model(valid_images, tf_tau, True)
- a_loss = loss_object(valid_labels, predictions)
- net_a_param = model.get_alphas()
- gradients = tape.gradient(a_loss, net_a_param)
- a_optimizer.apply_gradients(zip(gradients, net_a_param))
- valid_loss(a_loss)
- valid_accuracy(valid_labels, predictions)
-
- # TEST
- @tf.function
- def test_step(images, labels):
- predictions = model(images)
- t_loss = loss_object(labels, predictions)
-
- test_loss(t_loss)
- test_accuracy(labels, predictions)
-
- print('{:} start searching with {:} epochs ({:} batches per epoch).'.format(time_string(), xargs.epochs, num_iters_per_epoch))
-
- for epoch in range(xargs.epochs):
- # Reset the metrics at the start of the next epoch
- train_loss.reset_states() ; train_accuracy.reset_states()
- test_loss.reset_states() ; test_accuracy.reset_states()
- cur_tau = xargs.tau_max - (xargs.tau_max-xargs.tau_min) * epoch / (xargs.epochs-1)
- tf_tau = tf.cast(cur_tau, dtype=tf.float32, name='tau')
- cur_lr = lr_schedular.get_lr(epoch)
- tf.keras.backend.set_value(w_optimizer.lr, cur_lr)
-
- for trn_imgs, trn_labels, val_imgs, val_labels in search_ds:
- search_step(trn_imgs, trn_labels, val_imgs, val_labels, tf_tau)
- genotype = model.genotype()
- genotype = CellStructure(genotype)
-
- #for test_images, test_labels in test_ds:
- # test_step(test_images, test_labels)
-
- cur_lr = float(tf.keras.backend.get_value(w_optimizer.lr))
- template = '{:} Epoch {:03d}/{:03d}, Train-Loss: {:.3f}, Train-Accuracy: {:.2f}%, Valid-Loss: {:.3f}, Valid-Accuracy: {:.2f}% | tau={:.3f} | lr={:.6f}'
- print(template.format(time_string(), epoch+1, xargs.epochs,
- train_loss.result(),
- train_accuracy.result()*100,
- valid_loss.result(),
- valid_accuracy.result()*100,
- cur_tau,
- cur_lr))
- print('{:} genotype : {:}\n{:}\n'.format(time_string(), genotype, model.get_np_alphas()))
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser(description='NAS-Bench-201', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
- # training details
- parser.add_argument('--epochs' , type=int , default= 250 , help='')
- parser.add_argument('--tau_max' , type=float, default= 10 , help='')
- parser.add_argument('--tau_min' , type=float, default= 0.1 , help='')
- parser.add_argument('--w_lr_max' , type=float, default= 0.025, help='')
- parser.add_argument('--w_lr_min' , type=float, default= 0.001, help='')
- parser.add_argument('--w_weight_decay' , type=float, default=0.0005, help='')
- parser.add_argument('--w_momentum' , type=float, default= 0.9 , help='')
- parser.add_argument('--arch_learning_rate', type=float, default=0.0003, help='')
- parser.add_argument('--arch_weight_decay' , type=float, default=0.001, help='')
- # marco structure
- parser.add_argument('--channel' , type=int , default=16, help='')
- parser.add_argument('--num_cells' , type=int , default= 5, help='')
- parser.add_argument('--max_nodes' , type=int , default= 4, help='')
- args = parser.parse_args()
- main( args )
diff --git a/exps-tf/test-invH.py b/exps-tf/test-invH.py
deleted file mode 100644
index b455506..0000000
--- a/exps-tf/test-invH.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import os, sys, math, time, random, argparse
-import tensorflow as tf
-from pathlib import Path
-
-
-def test_a():
- x = tf.Variable([[1.], [2.], [4.0]])
- with tf.GradientTape(persistent=True) as g:
- trn = tf.math.exp(tf.math.reduce_sum(x))
- val = tf.math.cos(tf.math.reduce_sum(x))
- dT_dx = g.gradient(trn, x)
- dV_dx = g.gradient(val, x)
- hess_vector = g.gradient(dT_dx, x, output_gradients=dV_dx)
- print ('calculate ok : {:}'.format(hess_vector))
-
-def test_b():
- cce = tf.keras.losses.SparseCategoricalCrossentropy()
- L1 = tf.convert_to_tensor([0, 1, 2])
- L2 = tf.convert_to_tensor([2, 0, 1])
- B = tf.Variable([[.9, .05, .05], [.5, .89, .6], [.05, .01, .94]])
- with tf.GradientTape(persistent=True) as g:
- trn = cce(L1, B)
- val = cce(L2, B)
- dT_dx = g.gradient(trn, B)
- dV_dx = g.gradient(val, B)
- hess_vector = g.gradient(dT_dx, B, output_gradients=dV_dx)
- print ('calculate ok : {:}'.format(hess_vector))
-
-def test_c():
- cce = tf.keras.losses.CategoricalCrossentropy()
- L1 = tf.convert_to_tensor([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]])
- L2 = tf.convert_to_tensor([[0., 0., 1.], [0., 1., 0.], [1., 0., 0.]])
- B = tf.Variable([[.9, .05, .05], [.5, .89, .6], [.05, .01, .94]])
- with tf.GradientTape(persistent=True) as g:
- trn = cce(L1, B)
- val = cce(L2, B)
- dT_dx = g.gradient(trn, B)
- dV_dx = g.gradient(val, B)
- hess_vector = g.gradient(dT_dx, B, output_gradients=dV_dx)
- print ('calculate ok : {:}'.format(hess_vector))
-
-if __name__ == '__main__':
- print(tf.__version__)
- test_c()
- #test_b()
- #test_a()
diff --git a/exps/NAS-Bench-201/test-nas-api-vis.py b/exps/NAS-Bench-201/test-nas-api-vis.py
index f08ec5f..98ef293 100644
--- a/exps/NAS-Bench-201/test-nas-api-vis.py
+++ b/exps/NAS-Bench-201/test-nas-api-vis.py
@@ -94,11 +94,11 @@ def visualize_sss_info(api, dataset, vis_save_dir):
params.append(info['params'])
flops.append(info['flops'])
# accuracy
- info = api.get_more_info(index, dataset, hp='90')
+ info = api.get_more_info(index, dataset, hp='90', is_random=False)
train_accs.append(info['train-accuracy'])
test_accs.append(info['test-accuracy'])
if dataset == 'cifar10':
- info = api.get_more_info(index, 'cifar10-valid', hp='90')
+ info = api.get_more_info(index, 'cifar10-valid', hp='90', is_random=False)
valid_accs.append(info['valid-accuracy'])
else:
valid_accs.append(info['valid-accuracy'])
@@ -182,11 +182,11 @@ def visualize_tss_info(api, dataset, vis_save_dir):
params.append(info['params'])
flops.append(info['flops'])
# accuracy
- info = api.get_more_info(index, dataset, hp='200')
+ info = api.get_more_info(index, dataset, hp='200', is_random=False)
train_accs.append(info['train-accuracy'])
test_accs.append(info['test-accuracy'])
if dataset == 'cifar10':
- info = api.get_more_info(index, 'cifar10-valid', hp='200')
+ info = api.get_more_info(index, 'cifar10-valid', hp='200', is_random=False)
valid_accs.append(info['valid-accuracy'])
else:
valid_accs.append(info['valid-accuracy'])
@@ -319,6 +319,68 @@ def visualize_rank_info(api, vis_save_dir, indicator):
plt.close('all')
+def calculate_correlation(*vectors):
+ matrix = []
+ for i, vectori in enumerate(vectors):
+ x = []
+ for j, vectorj in enumerate(vectors):
+ x.append( np.corrcoef(vectori, vectorj)[0,1] )
+ matrix.append( x )
+ return np.array(matrix)
+
+
+def visualize_all_rank_info(api, vis_save_dir, indicator):
+ vis_save_dir = vis_save_dir.resolve()
+ # print ('{:} start to visualize {:} information'.format(time_string(), api))
+ vis_save_dir.mkdir(parents=True, exist_ok=True)
+
+ cifar010_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar10', indicator)
+ cifar100_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('cifar100', indicator)
+ imagenet_cache_path = vis_save_dir / '{:}-cache-{:}-info.pth'.format('ImageNet16-120', indicator)
+ cifar010_info = torch.load(cifar010_cache_path)
+ cifar100_info = torch.load(cifar100_cache_path)
+ imagenet_info = torch.load(imagenet_cache_path)
+ indexes = list(range(len(cifar010_info['params'])))
+
+ print ('{:} start to visualize relative ranking'.format(time_string()))
+
+
+ dpi, width, height = 250, 3200, 1400
+ figsize = width / float(dpi), height / float(dpi)
+ LabelSize, LegendFontsize = 14, 14
+
+ fig, axs = plt.subplots(1, 2, figsize=figsize)
+ ax1, ax2 = axs
+
+ sns_size = 15
+ CoRelMatrix = calculate_correlation(cifar010_info['valid_accs'], cifar010_info['test_accs'], cifar100_info['valid_accs'], cifar100_info['test_accs'], imagenet_info['valid_accs'], imagenet_info['test_accs'])
+
+ sns.heatmap(CoRelMatrix, annot=True, annot_kws={'size':sns_size}, fmt='.3f', linewidths=0.5, ax=ax1,
+ xticklabels=['C10-V', 'C10-T', 'C100-V', 'C100-T', 'I120-V', 'I120-T'],
+ yticklabels=['C10-V', 'C10-T', 'C100-V', 'C100-T', 'I120-V', 'I120-T'])
+
+ selected_indexes, acc_bar = [], 92
+ for i, acc in enumerate(cifar010_info['test_accs']):
+ if acc > acc_bar: selected_indexes.append( i )
+ cifar010_valid_accs = np.array(cifar010_info['valid_accs'])[ selected_indexes ]
+ cifar010_test_accs = np.array(cifar010_info['test_accs']) [ selected_indexes ]
+ cifar100_valid_accs = np.array(cifar100_info['valid_accs'])[ selected_indexes ]
+ cifar100_test_accs = np.array(cifar100_info['test_accs']) [ selected_indexes ]
+ imagenet_valid_accs = np.array(imagenet_info['valid_accs'])[ selected_indexes ]
+ imagenet_test_accs = np.array(imagenet_info['test_accs']) [ selected_indexes ]
+ CoRelMatrix = calculate_correlation(cifar010_valid_accs, cifar010_test_accs, cifar100_valid_accs, cifar100_test_accs, imagenet_valid_accs, imagenet_test_accs)
+
+ sns.heatmap(CoRelMatrix, annot=True, annot_kws={'size':sns_size}, fmt='.3f', linewidths=0.5, ax=ax2,
+ xticklabels=['C10-V', 'C10-T', 'C100-V', 'C100-T', 'I120-V', 'I120-T'],
+ yticklabels=['C10-V', 'C10-T', 'C100-V', 'C100-T', 'I120-V', 'I120-T'])
+ ax1.set_title('Correlation coefficient over ALL candidates')
+ ax2.set_title('Correlation coefficient over candidates with accuracy > {:}%'.format(acc_bar))
+ save_path = (vis_save_dir / '{:}-all-relative-rank.png'.format(indicator)).resolve()
+ fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
+ print ('{:} save into {:}'.format(time_string(), save_path))
+ plt.close('all')
+
+
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='NAS-Bench-X', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--save_dir', type=str, default='output/NAS-BENCH-202', help='Folder to save checkpoints and log.')
@@ -326,20 +388,19 @@ if __name__ == '__main__':
# use for train the model
args = parser.parse_args()
- visualize_rank_info(None, Path('output/vis-nas-bench/'), 'tss')
- visualize_rank_info(None, Path('output/vis-nas-bench/'), 'sss')
-
+ datasets = ['cifar10', 'cifar100', 'ImageNet16-120']
api201 = NASBench201API(None, verbose=True)
- visualize_tss_info(api201, 'cifar10', Path('output/vis-nas-bench'))
- visualize_tss_info(api201, 'cifar100', Path('output/vis-nas-bench'))
- visualize_tss_info(api201, 'ImageNet16-120', Path('output/vis-nas-bench'))
+ for xdata in datasets:
+ visualize_tss_info(api201, xdata, Path('output/vis-nas-bench'))
api301 = NASBench301API(None, verbose=True)
- visualize_sss_info(api301, 'cifar10', Path('output/vis-nas-bench'))
- visualize_sss_info(api301, 'cifar100', Path('output/vis-nas-bench'))
- visualize_sss_info(api301, 'ImageNet16-120', Path('output/vis-nas-bench'))
+ for xdata in datasets:
+ visualize_sss_info(api301, xdata, Path('output/vis-nas-bench'))
visualize_info(None, Path('output/vis-nas-bench/'), 'tss')
visualize_info(None, Path('output/vis-nas-bench/'), 'sss')
visualize_rank_info(None, Path('output/vis-nas-bench/'), 'tss')
visualize_rank_info(None, Path('output/vis-nas-bench/'), 'sss')
+
+ visualize_all_rank_info(None, Path('output/vis-nas-bench/'), 'tss')
+ visualize_all_rank_info(None, Path('output/vis-nas-bench/'), 'sss')
diff --git a/exps/vis/test.py b/exps/experimental/test-nas-plot.py
similarity index 100%
rename from exps/vis/test.py
rename to exps/experimental/test-nas-plot.py