def main(args):
transform_fn = data_utils.make_transform_fn(128,
128,
args.input_dim,
1.0,
normalize=True)
snapshot = os.path.join('../experiment/save',
'{}.h5'.format(args.timestamp))
test_list = os.path.join('../experiment/test_lists',
'{}.txt'.format(args.timestamp))
model = MilkEager(encoder_args=encoder_args,
mil_type=args.mil,
deep_classifier=True)
x_dummy = tf.zeros(
shape=[1, args.batch_size, args.input_dim, args.input_dim, 3],
dtype=tf.float32)
retvals = model(x_dummy, verbose=True)
model.load_weights(snapshot, by_name=True)
model.summary()
test_list = read_test_list(test_list)
yhats, ytrues = [], []
features_case, features_classifier = [], []
for test_case in test_list:
case_name = os.path.basename(test_case).replace('.npy', '')
print(test_case, case_name)
# case_path = os.path.join('../dataset/tiles_reduced', '{}.npy'.format(case_name))
case_x = np.load(test_case)
case_x = np.stack([transform_fn(x) for x in case_x], 0)
print(case_x.shape)
if args.sample:
case_x = case_x[
np.random.choice(range(case_x.shape[0]), args.sample), ...]
print(case_x.shape)
features = model.encode_bag(case_x,
batch_size=args.batch_size,
training=True,
return_z=True)
print('features:', features.shape)
# features_att, attention = model.mil_attention(features, return_att=True, training=False)
# print('features:', features_att.shape, 'attention:', attention.shape)
features_att = tf.reduce_mean(features, axis=0, keep_dims=True)
yhat_instances = model.apply_classifier(features, training=False)
print('yhat instances:', yhat_instances.shape)
yhat = model.apply_classifier(features_att, training=False)
print('yhat:', yhat.shape)
yhat_1 = yhat_instances[:, 1].numpy()
# yhat, attention, features, feat_case, feat_class = retvals
# attention = np.squeeze(attention.numpy(), axis=0)
high_att_idx, high_att_imgs, low_att_idx, low_att_imgs = get_attention_extremes(
yhat_1, case_x, n=5)
print('Case {}: predicted={} ({})'.format(test_case,
np.argmax(yhat, axis=-1),
yhat.numpy()))
features = features.numpy()
savepath = '{}_{}_{:3.2f}_yhat.png'.format(args.savebase, case_name,
yhat[0, 1])
print('Saving figure {}'.format(savepath))
z = draw_projection(features, features_att, yhat_1, savepath=savepath)
savepath = '{}_{}_{:3.2f}_yhat_img.png'.format(args.savebase,
case_name, yhat[0, 1])
print('Saving figure {}'.format(savepath))
draw_projection_with_images(z,
yhat_1,
high_att_idx,
high_att_imgs,
low_att_idx,
low_att_imgs,
savepath=savepath)
def main(args):
transform_fn = data_utils.make_transform_fn(128,
128,
args.input_dim,
1.0,
normalize=True)
snapshot = os.path.join(args.snapshot_dir, '{}.h5'.format(args.timestamp))
test_list = os.path.join(args.test_list_dir,
'{}.txt'.format(args.timestamp))
encoder_args = get_encoder_args(args.encoder)
model = MilkEager(encoder_args=encoder_args,
deep_classifier=True,
batch_size=args.batch_size,
temperature=args.temperature,
cls_normalize=args.cls_normalize)
x_dummy = tf.zeros(
shape=[1, args.batch_size, args.input_dim, args.input_dim, 3],
dtype=tf.float32)
retvals = model(x_dummy, verbose=True)
model.load_weights(snapshot, by_name=True)
model.summary()
test_list = read_test_list(test_list)
savebase = os.path.join(args.odir, args.timestamp)
if os.path.exists(savebase):
shutil.rmtree(savebase)
os.makedirs(savebase)
yhats, ytrues = [], []
features_case, features_classifier = [], []
for test_case in test_list:
case_name = os.path.basename(test_case).replace('.npy', '')
print(test_case, case_name)
# case_path = os.path.join('../dataset/tiles_reduced', '{}.npy'.format(case_name))
case_x = np.load(test_case)
case_x = np.stack([transform_fn(x) for x in case_x], 0)
ytrue = case_dict[case_name]
print(case_x.shape, ytrue)
ytrues.append(ytrue)
if args.sample:
case_x = case_x[
np.random.choice(range(case_x.shape[0]), args.sample), ...]
print(case_x.shape)
features = model.encode_bag(case_x, training=True, return_z=True)
print('features:', features.shape)
features_att, attention = model.mil_attention(features,
return_att=True,
training=False)
print('features:', features_att.shape, 'attention:', attention.shape)
features_avg = np.mean(features, axis=0, keepdims=True)
yhat_instances = model.apply_classifier(features, training=False)
print('yhat instances:', yhat_instances.shape)
yhat = model.apply_classifier(features_att, training=False)
print('yhat:', yhat.shape)
yhats.append(yhat)
# yhat, attention, features, feat_case, feat_class = retvals
# attention = np.squeeze(attention.numpy(), axis=0)
high_att_idx, high_att_imgs, low_att_idx, low_att_imgs = get_attention_extremes(
attention, case_x, n=5)
print('Case {}: predicted={} ({})'.format(test_case,
np.argmax(yhat, axis=-1),
yhat.numpy()))
attention = np.squeeze(attention.numpy())
features = features.numpy()
savepath = os.path.join(savebase,
'{}_{:3.2f}.png'.format(case_name, yhat[0, 1]))
print('Saving figure {}'.format(savepath))
z = draw_projection(features,
features_avg,
features_att,
attention,
savepath=savepath)
# savepath = os.path.join(savebase, '{}_{:3.2f}_ys.png'.format(case_name, yhat[0,1]))
# print('Saving figure {}'.format(savepath))
# draw_projection_with_images(z, yhat_instances[:,1].numpy(),
# high_att_idx, high_att_imgs,
# low_att_idx, low_att_imgs,
# savepath=savepath)
savepath = os.path.join(
savebase, '{}_{:3.2f}_imgs.png'.format(case_name, yhat[0, 1]))
print('Saving figure {}'.format(savepath))
draw_projection_with_images(z,
attention,
high_att_idx,
high_att_imgs,
low_att_idx,
low_att_imgs,
savepath=savepath)
savepath = os.path.join(savebase, '{}_atns.npy'.format(case_name))
np.save(savepath, attention)
savepath = os.path.join(savebase, '{}_feat.npy'.format(case_name))
np.save(savepath, features)
yhats = np.concatenate(yhats, axis=0)
yhats = np.argmax(yhats, axis=1)
ytrues = np.array(ytrues)
acc = (yhats == ytrues).mean()
print(acc)
cm = confusion_matrix(y_true=ytrues, y_pred=yhats)
print(cm)