def for_pixel_input():
# load test data
print('Load test data..')
test_imgs = load_raw_images_data(data_path,
rescale_ratio=0.25,
preserve_range_after_rescale=True)
img_height, img_width = test_imgs[0].shape
test_dataset, channel_len = load_patch_dataset_from_imgs(test_imgs,
patch_size=3)
model = models.conv_hybrid(channel_len, 3)
model.load_state_dict(
torch.load(f'{model_path}/conv_hybrid_on_{data_class}.pth',
map_location='cpu'))
model.eval()
for name, param in model.named_parameters():
print(name, param.shape)
# Visualize feature maps
activation = {}
model.residual[0].register_forward_hook(get_activation(
'conv1', activation))
output = models.get_model_output(test_dataset, model)
act = activation['conv1']
filter_num = act.size(1)
reconstruct_feature_map(act, filter_num, img_height - 2, img_width - 2)
# Visualize conv filter
kernels = model.residual[0].weight.detach()
# normalize filter values to 0-1 so we can visualize them
kernels = kernels - kernels.min()
kernels = kernels / kernels.max()
def for_img_input():
# load test data
print('Load test data..')
imgs_norm = load_raw_images_data(data_path, rescale_ratio=0.25)
sample_img = get_sample_image(data_path, rescale_ratio=0.25)
img_height, img_width = sample_img.shape
model = models.conv2d_net(23, img_width, img_height, 3)
model.load_state_dict(
torch.load(
f'{model_path}/conv{conv_nd}d_on_{data_class}_{model_data_id}.pth',
map_location='cpu'))
model.eval()
for name, param in model.named_parameters():
print(name, param.shape)
# Visualize feature maps
activation = {}
model.conv[0].register_forward_hook(get_activation('conv1', activation))
output = model(torch.FloatTensor(imgs_norm))
act = activation['conv1'].squeeze()
view_feature_map(act)
# Visualize conv filter
kernels = model.conv[0].weight.detach()
# normalize filter values to 0-1 so we can visualize them
kernels = kernels - kernels.min()
kernels = kernels / kernels.max()
view_filter(kernels)
def enhance_roi(data_id):
data_class = 'allClass'
data_type = 'cropped_roi'
# file paths
data_path = f'networks/data/sgp/{data_id}/cropped_roi/*'
model_path = 'networks/model'
img_save_path = 'networks/reconstructed_roi/conv_resid'
# mkdir if not exists
Path(f'{img_save_path}').mkdir(parents=True, exist_ok=True)
# load test data
print('Load test data..')
test_imgs = load_raw_images_data(data_path,
rescale_ratio=0.25,
preserve_range_after_rescale=True)
sample_img = test_imgs[0]
test_dataset, channel_len = load_patch_dataset_from_imgs(test_imgs,
patch_size=3)
# load model
print('Load model..')
model = models.conv_resid(channel_len, 3)
model.load_state_dict(
torch.load(f'{model_path}/conv_resid_on_{data_class}.pth',
map_location='cpu'))
model.eval()
print('Model predict..')
predictions = models.predict_class(test_dataset, model)
predictions = pad_prediction(predictions, sample_img,
test_dataset.patch_size)
print('Reconstruct..')
sample_img = reconstruct_image(sample_img,
predictions,
enhance_intensity=20,
count_note=True)
imsave(f'{img_save_path}/{data_id}_conv_resid.png', sample_img)
data_path = f'networks/data/sgp/{data_id}/cropped_roi/*'
model_path = 'networks/model'
img_save_path = 'networks/reconstructed_roi'
log_path = f'networks/training_log/conv{conv_nd}ds/sgd'
# mkdir if not exists
Path(f'{model_path}').mkdir(parents=True, exist_ok=True)
Path(f'{log_path}').mkdir(parents=True, exist_ok=True)
Path(f'{img_save_path}').mkdir(parents=True, exist_ok=True)
# load training data
print('Load training data..')
# load images
print('-load images..')
imgs_norm = load_raw_images_data(data_path, rescale_ratio=0.25)
test_dataset, sample_img = load_images_data(data_path, rescale_ratio=0.25)
img_height, img_width = sample_img.shape
channel_len = 23
pxl_num = img_width * img_height
# load estimator
print('-load estimator..')
autoencoder = models.sdae(dimensions=[channel_len, 10, 10, 20, 3])
ae_model = models.sdae_lr(autoencoder)
ae_model.load_state_dict(
torch.load(f'{model_path}/ae_on_{data_class}.pth', map_location='cpu'))
ae_model.eval()
# predict labels
channel_train, y_true, channel_len = load_raw_labeled_data()
# fit lda
print('Model training..')
classifier = lda()
classifier.fit(channel_train, y_true)
precision_clf = classifier.score(channel_train, y_true)
prediction = classifier.predict(channel_train)
balanced_acc = balanced_accuracy_score(y_true, prediction)
kappa = cohen_kappa_score(y_true, prediction)
# plot learning curve
plot_learning_curve(classifier, 'learning curve of LDA', channel_train, y_true)
print('train-accuracy: ', precision_clf)
print('balanced-accuracy: ', balanced_acc)
print('kappa: ', kappa)
# prepare test data
print('Prepare test data..')
imgs = load_raw_images_data(test_data_path,
rescale_ratio=0.25,
preserve_range_after_rescale=True)
channel_test, _ = flatten_images(imgs)
print('Model predict..')
predictions = classifier.predict(channel_test)
print('Reconstruct..')
sample_img = imgs[0]
sample_img = reconstruct_image(sample_img, predictions)
imsave(f'{img_save_path}/{data_id}_lda.png', sample_img)
center_pxl_num = len(center_pxls)
# add new column: center_pxl_id
center_pxls['center_pxl_id'] = pd.Series(range(1, center_pxl_num + 1),
index=center_pxls.index)
target_folio = center_pxls['folio_name'].unique()
extend_pxls = pd.DataFrame(columns=center_pxls.columns)
for folio_name in target_folio:
print('process on', folio_name, '..')
# load images
file_path = f'~/Desktop/sgp-imgs/{folio_name}/tif/'
filenames = get_filenames(file_path)
imgs = load_raw_images_data(filenames, preserve_range_after_rescale=True)
channel_len = len(imgs)
# find neighbors: [(5x5): radius-2, (3x3): radius-1]
radius = 1
pxls_index = center_pxls.loc[center_pxls['folio_name'] == folio_name].index
for i in pxls_index:
label = center_pxls.loc[i]['class_name']
x_l = center_pxls.loc[i]['x_loc'] - 1
y_l = center_pxls.loc[i]['y_loc'] - 1
center_id = center_pxls.loc[i]['center_pxl_id']
neighbors_locs = get_window(radius, x=x_l, y=y_l)
for (neigh_x, neigh_y) in neighbors_locs:
# intensity data
channel_data = []
for c in range(channel_len):
channel_data.append(imgs[c][neigh_y][neigh_x])
def enhanced_roi(data_id):
data_class = 'allClass'
folio_ids = ['024r_029v', '102v_107r', '214v_221r']
model_data_id = folio_ids[0]
data_type = 'cropped_roi'
conv_nd = 2
# net_style {'normal': 0, 'fconv': 1, 'hybrid': 2}
net_style = 2
# file paths
data_path = f'networks/data/sgp/{data_id}/cropped_roi/*'
model_path = 'networks/model'
img_save_path = 'networks/reconstructed_roi'
# mkdir if not exists
Path(f'{img_save_path}').mkdir(parents=True, exist_ok=True)
# load images
print('-load images..')
imgs_norm = load_raw_images_data(data_path, rescale_ratio=0.25)
sample_img = get_sample_image(data_path, rescale_ratio=0.25)
img_height, img_width = sample_img.shape
channel_len = 23
# conv model
if net_style == 2:
if conv_nd == 2:
conv_model = models.conv2d_hyb_net(channel_len, img_width,
img_height, 3)
elif conv_nd == 3:
conv_model = models.conv3d_hyb_net(channel_len, img_width,
img_height, 3)
model_name = f'{model_path}/conv{conv_nd}d_hyb_on_{data_class}_{model_data_id}.pth'
elif net_style == 1:
if conv_nd == 2:
conv_model = models.fconv2d_net(channel_len, img_width, img_height,
3)
elif conv_nd == 3:
conv_model = models.fconv3d_net(channel_len, img_width, img_height,
3)
model_name = f'{model_path}/fconv{conv_nd}d_on_{data_class}_{model_data_id}.pth'
elif net_style == 0:
if conv_nd == 2:
conv_model = models.conv2d_net(channel_len, img_width, img_height,
3)
elif conv_nd == 3:
conv_model = models.conv3d_net(channel_len, img_width, img_height,
3)
model_name = f'{model_path}/conv{conv_nd}d_on_{data_class}_{model_data_id}.pth'
conv_model.load_state_dict(torch.load(model_name, map_location='cpu'))
conv_model.eval()
print('Reconstruct..')
with torch.no_grad():
output = conv_model(torch.FloatTensor(imgs_norm))
_, conv_pred = torch.max(output.data, 1)
print('-max in conv_pred: ', torch.max(conv_pred.data).item())
print('-min in conv_pred: ', torch.min(conv_pred.data).item())
imsave(f'{img_save_path}/{data_id}_orig_eval.png', sample_img)
sample_img_conv = reconstruct_image(sample_img, conv_pred, count_note=True)
if net_style == 2:
img_name = f'{img_save_path}/conv{conv_nd}d_hyb/{data_id}_conv{conv_nd}d_hyb_eval_model_{model_data_id}.png'
elif net_style == 1:
img_name = f'{img_save_path}/fconv{conv_nd}d/{data_id}_fconv{conv_nd}d_eval_model_{model_data_id}.png'
elif net_style == 0:
img_name = f'{img_save_path}/conv{conv_nd}d/{data_id}_conv{conv_nd}d_eval_model_{model_data_id}.png'
imsave(img_name, sample_img_conv)