## Pipelines Setup
model_dir = os.path.join(
args.save_dir,
'selfsup_{}+{}_{}_epo{}_bs{}_aug{}+{}_lr{}_mom{}_wd{}_gam1{}_gam2{}_eps{}{}'
.format(args.arch, args.fd, args.data, args.epo, args.bs, args.aug,
args.transform, args.lr, args.mom, args.wd, args.gam1, args.gam2,
args.eps, args.tail))
utils.init_pipeline(model_dir)
## Prepare for Training
if args.pretrain_dir is not None:
net, _ = tf.load_checkpoint(args.pretrain_dir, args.pretrain_epo)
utils.update_params(model_dir, args.pretrain_dir)
else:
net = tf.load_architectures(args.arch, args.fd)
transforms = tf.load_transforms(args.transform)
trainset = tf.load_trainset(args.data, path=args.data_dir)
trainloader = AugmentLoader(trainset,
transforms=transforms,
sampler=args.sampler,
batch_size=args.bs,
num_aug=args.aug)
criterion = MaximalCodingRateReduction(gam1=args.gam1,
gam2=args.gam2,
eps=args.eps)
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=args.mom,
weight_decay=args.wd)
def plot_pca_epoch(args):
"""Plot PCA for different epochs in the same plot. """
EPOCHS = [0, 10, 100, 500]
params = utils.load_params(args.model_dir)
transforms = tf.load_transforms('test')
trainset = tf.load_trainset(params['data'], transforms)
trainloader = DataLoader(trainset, batch_size=200, num_workers=4)
sig_vals = []
for epoch in EPOCHS:
epoch_ = epoch - 1
if epoch_ == -1: # randomly initialized
net = tf.load_architectures(params['arch'], params['fd'])
else:
net, epoch = tf.load_checkpoint(args.model_dir,
epoch=epoch_,
eval_=True)
features, labels = tf.get_features(net, trainloader)
if args.class_ is not None:
features_sort, _ = utils.sort_dataset(
features.numpy(),
labels.numpy(),
num_classes=trainset.num_classes,
stack=False)
features_ = features_sort[args.class_]
else:
features_ = features.numpy()
n_comp = np.min([args.comp, features.shape[1]])
pca = PCA(n_components=n_comp).fit(features_)
sig_vals.append(pca.singular_values_)
## plot singular values
plt.rc('text', usetex=True)
plt.rcParams['font.family'] = 'serif'
plt.rcParams['font.serif'] = ['Times New Roman']
fig, ax = plt.subplots(1, 1, figsize=(7, 5), dpi=400)
x_min = np.min([len(sig_val) for sig_val in sig_vals])
if args.class_ is not None:
ax.set_xticks(np.arange(0, x_min, 10))
ax.set_yticks(np.linspace(0, 40, 9))
ax.set_ylim(0, 40)
else:
ax.set_xticks(np.arange(0, x_min, 10))
ax.set_yticks(np.linspace(0, 80, 9))
ax.set_ylim(0, 90)
for epoch, sig_val in zip(EPOCHS, sig_vals):
ax.plot(np.arange(x_min),
sig_val[:x_min],
marker='',
markersize=5,
label=f'epoch - {epoch}',
alpha=0.6)
ax.legend(loc='upper right',
frameon=True,
fancybox=True,
prop={"size": 8},
ncol=1,
framealpha=0.5)
ax.set_xlabel("components")
ax.set_ylabel("sigular values")
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
[tick.label.set_fontsize(12) for tick in ax.xaxis.get_major_ticks()]
[tick.label.set_fontsize(12) for tick in ax.yaxis.get_major_ticks()]
ax.grid(True, color='white')
ax.set_facecolor('whitesmoke')
fig.tight_layout()
## save
save_dir = os.path.join(args.model_dir, 'figures', 'pca')
np.save(os.path.join(save_dir, "sig_vals_epoch.npy"), sig_vals)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
file_name = os.path.join(save_dir, f"pca_class{args.class_}.png")
fig.savefig(file_name)
print("Plot saved to: {}".format(file_name))
file_name = os.path.join(save_dir, f"pca_class{args.class_}.pdf")
fig.savefig(file_name)
print("Plot saved to: {}".format(file_name))
plt.close()
