{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training of modified U-Net for Optic Disc on DRISHTI-GS database (cross-validation fold #0).\n",
"\n",
"512 px cropped by Optic Disc area and resized to 128 px images were used.\n",
"\n",
"You can either train your model or upload a pre-trained one from:\n",
"*../models_weights/02.03,13:57,OD Cup, U-Net light on DRISHTI-GS 512 px cropped to OD 128 px fold 0, SGD, log_dice loss/last_checkpoint.hdf5*"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import os\n",
"import glob\n",
"from datetime import datetime\n",
"#import warnings\n",
"#warnings.simplefilter('ignore')\n",
"import scipy as sp\n",
"import scipy.ndimage\n",
"import numpy as np\n",
"import pandas as pd\n",
"import tensorflow as tf\n",
"import skimage\n",
"import skimage.exposure\n",
"import skimage.transform\n",
"import mahotas as mh\n",
"from sklearn.model_selection import KFold\n",
"from PIL import Image\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import h5py\n",
"from tqdm import tqdm_notebook\n",
"from IPython.display import display\n",
"from dual_IDG import DualImageDataGenerator"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import keras\n",
"from keras.models import Sequential, Model\n",
"from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization, \\\n",
" Conv2D, MaxPooling2D, ZeroPadding2D, Input, Embedding, \\\n",
" Lambda, UpSampling2D, Cropping2D, Concatenate\n",
"from keras.utils import np_utils\n",
"from keras.optimizers import SGD, Adam\n",
"from keras.callbacks import ModelCheckpoint, LearningRateScheduler, ReduceLROnPlateau, CSVLogger\n",
"from keras.preprocessing.image import ImageDataGenerator\n",
"from keras import backend as K"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Keras version: 2.3.1\n",
"TensorFlow version: 2.0.0\n"
]
}
],
"source": [
"print('Keras version:', keras.__version__)\n",
"print('TensorFlow version:', tf.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"K.set_image_data_format('channels_first')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def mean_IOU_gpu(X, Y):\n",
" \"\"\"Computes mean Intersection-over-Union (IOU) for two arrays of binary images.\n",
" Assuming X and Y are of shape (n_images, w, h).\"\"\"\n",
" \n",
" #X_fl = K.clip(K.batch_flatten(X), K.epsilon(), 1.)\n",
" #Y_fl = K.clip(K.batch_flatten(Y), K.epsilon(), 1.)\n",
" X_fl = K.clip(K.batch_flatten(X), 0., 1.)\n",
" Y_fl = K.clip(K.batch_flatten(Y), 0., 1.)\n",
" X_fl = K.cast(K.greater(X_fl, 0.5), 'float32')\n",
" Y_fl = K.cast(K.greater(Y_fl, 0.5), 'float32')\n",
"\n",
" intersection = K.sum(X_fl * Y_fl, axis=1)\n",
" union = K.sum(K.maximum(X_fl, Y_fl), axis=1)\n",
" # if union == 0, it follows that intersection == 0 => score should be 0.\n",
" union = K.switch(K.equal(union, 0), K.ones_like(union), union)\n",
" return K.mean(intersection / K.cast(union, 'float32'))\n",
"\n",
"\n",
"def mean_IOU_gpu_loss(X, Y):\n",
" return -mean_IOU_gpu(X, Y)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def dice(y_true, y_pred):\n",
" # Workaround for shape bug. For some reason y_true shape was not being set correctly\n",
" #y_true.set_shape(y_pred.get_shape())\n",
"\n",
" # Without K.clip, K.sum() behaves differently when compared to np.count_nonzero()\n",
" #y_true_f = K.clip(K.batch_flatten(y_true), K.epsilon(), 1.)\n",
" #y_pred_f = K.clip(K.batch_flatten(y_pred), K.epsilon(), 1.)\n",
" y_true_f = K.clip(K.batch_flatten(y_true), 0., 1.)\n",
" y_pred_f = K.clip(K.batch_flatten(y_pred), 0., 1.)\n",
" #y_pred_f = K.greater(y_pred_f, 0.5)\n",
"\n",
" intersection = 2 * K.sum(y_true_f * y_pred_f, axis=1)\n",
" union = K.sum(y_true_f * y_true_f, axis=1) + K.sum(y_pred_f * y_pred_f, axis=1)\n",
" return K.mean(intersection / union)\n",
"\n",
"\n",
"def dice_loss(y_true, y_pred):\n",
" return -dice(y_true, y_pred)\n",
"\n",
"\n",
"def log_dice_loss(y_true, y_pred):\n",
" return -K.log(dice(y_true, y_pred))\n",
"\n",
"\n",
"def dice_metric(y_true, y_pred):\n",
" \"\"\"An exact Dice score for binary tensors.\"\"\"\n",
" y_true_f = K.cast(K.greater(y_true, 0.5), 'float32')\n",
" y_pred_f = K.cast(K.greater(y_pred, 0.5), 'float32')\n",
" return dice(y_true_f, y_pred_f)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def tf_to_th_encoding(X):\n",
" return np.rollaxis(X, 3, 1)\n",
"\n",
"\n",
"def th_to_tf_encoding(X):\n",
" return np.rollaxis(X, 1, 4)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# h5f = h5py.File(os.path.join(os.path.dirname(os.getcwd()), 'data', 'hdf5_datasets', 'all_data.hdf5'), 'r')\n",
"h5f = h5py.File(os.path.join(os.path.dirname(os.getcwd()), 'data', 'hdf5_datasets', 'DRISHTI_GS.hdf5'), 'r')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### U-Net architecture\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def get_unet_light(img_rows=256, img_cols=256):\n",
" inputs = Input((3, img_rows, img_cols))\n",
" conv1 = Conv2D(32, kernel_size=3, activation='relu', padding='same')(inputs)\n",
" conv1 = Dropout(0.3)(conv1)\n",
" conv1 = Conv2D(32, kernel_size=3, activation='relu', padding='same')(conv1)\n",
" pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
"\n",
" conv2 = Conv2D(64, kernel_size=3, activation='relu', padding='same')(pool1)\n",
" conv2 = Dropout(0.3)(conv2)\n",
" conv2 = Conv2D(64, kernel_size=3, activation='relu', padding='same')(conv2)\n",
" pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
"\n",
" conv3 = Conv2D(64, kernel_size=3, activation='relu', padding='same')(pool2)\n",
" conv3 = Dropout(0.3)(conv3)\n",
" conv3 = Conv2D(64, kernel_size=3, activation='relu', padding='same')(conv3)\n",
" pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n",
"\n",
" conv4 = Conv2D(64, kernel_size=3, activation='relu', padding='same')(pool3)\n",
" conv4 = Dropout(0.3)(conv4)\n",
" conv4 = Conv2D(64, kernel_size=3, activation='relu', padding='same')(conv4)\n",
" pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)\n",
"\n",
" conv5 = Conv2D(64, kernel_size=3, activation='relu', padding='same')(pool4)\n",
" conv5 = Dropout(0.3)(conv5)\n",
" conv5 = Conv2D(64, kernel_size=3, activation='relu', padding='same')(conv5)\n",
"\n",
" up6 = Concatenate(axis=1)([UpSampling2D(size=(2, 2))(conv5), conv4])\n",
" conv6 = Conv2D(64, kernel_size=3, activation='relu', padding='same')(up6)\n",
" conv6 = Dropout(0.3)(conv6)\n",
" conv6 = Conv2D(64, kernel_size=3, activation='relu', padding='same')(conv6)\n",
"\n",
" up7 = Concatenate(axis=1)([UpSampling2D(size=(2, 2))(conv6), conv3])\n",
" conv7 = Conv2D(64, kernel_size=3, activation='relu', padding='same')(up7)\n",
" conv7 = Dropout(0.3)(conv7)\n",
" conv7 = Conv2D(64, kernel_size=3, activation='relu', padding='same')(conv7)\n",
"\n",
" up8 = Concatenate(axis=1)([UpSampling2D(size=(2, 2))(conv7), conv2])\n",
" conv8 = Conv2D(64, kernel_size=3, activation='relu', padding='same')(up8)\n",
" conv8 = Dropout(0.3)(conv8)\n",
" conv8 = Conv2D(64, kernel_size=3, activation='relu', padding='same')(conv8)\n",
"\n",
" up9 = Concatenate(axis=1)([UpSampling2D(size=(2, 2))(conv8), conv1])\n",
" conv9 = Conv2D(32, kernel_size=3, activation='relu', padding='same')(up9)\n",
" conv9 = Dropout(0.3)(conv9)\n",
" conv9 = Conv2D(32, kernel_size=3, activation='relu', padding='same')(conv9)\n",
"\n",
" conv10 = Conv2D(1, kernel_size=1, activation='sigmoid', padding='same')(conv9)\n",
" #conv10 = Flatten()(conv10)\n",
"\n",
" model = Model(input=inputs, output=conv10)\n",
"\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/artem/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:50: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"co...)`\n"
]
}
],
"source": [
"model = get_unet_light(img_rows=128, img_cols=128)\n",
"model.compile(optimizer=SGD(lr=1e-4, momentum=0.95),\n",
" loss=log_dice_loss,\n",
" metrics=[mean_IOU_gpu, dice_metric])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"model_1\"\n",
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"input_1 (InputLayer) (None, 3, 128, 128) 0 \n",
"__________________________________________________________________________________________________\n",
"conv2d_1 (Conv2D) (None, 32, 128, 128) 896 input_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_1 (Dropout) (None, 32, 128, 128) 0 conv2d_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_2 (Conv2D) (None, 32, 128, 128) 9248 dropout_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"max_pooling2d_1 (MaxPooling2D) (None, 32, 64, 64) 0 conv2d_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_3 (Conv2D) (None, 64, 64, 64) 18496 max_pooling2d_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_2 (Dropout) (None, 64, 64, 64) 0 conv2d_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_4 (Conv2D) (None, 64, 64, 64) 36928 dropout_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"max_pooling2d_2 (MaxPooling2D) (None, 64, 32, 32) 0 conv2d_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_5 (Conv2D) (None, 64, 32, 32) 36928 max_pooling2d_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_3 (Dropout) (None, 64, 32, 32) 0 conv2d_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_6 (Conv2D) (None, 64, 32, 32) 36928 dropout_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"max_pooling2d_3 (MaxPooling2D) (None, 64, 16, 16) 0 conv2d_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_7 (Conv2D) (None, 64, 16, 16) 36928 max_pooling2d_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_4 (Dropout) (None, 64, 16, 16) 0 conv2d_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_8 (Conv2D) (None, 64, 16, 16) 36928 dropout_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"max_pooling2d_4 (MaxPooling2D) (None, 64, 8, 8) 0 conv2d_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_9 (Conv2D) (None, 64, 8, 8) 36928 max_pooling2d_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_5 (Dropout) (None, 64, 8, 8) 0 conv2d_9[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_10 (Conv2D) (None, 64, 8, 8) 36928 dropout_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"up_sampling2d_1 (UpSampling2D) (None, 64, 16, 16) 0 conv2d_10[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_1 (Concatenate) (None, 128, 16, 16) 0 up_sampling2d_1[0][0] \n",
" conv2d_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_11 (Conv2D) (None, 64, 16, 16) 73792 concatenate_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_6 (Dropout) (None, 64, 16, 16) 0 conv2d_11[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_12 (Conv2D) (None, 64, 16, 16) 36928 dropout_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"up_sampling2d_2 (UpSampling2D) (None, 64, 32, 32) 0 conv2d_12[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_2 (Concatenate) (None, 128, 32, 32) 0 up_sampling2d_2[0][0] \n",
" conv2d_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_13 (Conv2D) (None, 64, 32, 32) 73792 concatenate_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_7 (Dropout) (None, 64, 32, 32) 0 conv2d_13[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_14 (Conv2D) (None, 64, 32, 32) 36928 dropout_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"up_sampling2d_3 (UpSampling2D) (None, 64, 64, 64) 0 conv2d_14[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_3 (Concatenate) (None, 128, 64, 64) 0 up_sampling2d_3[0][0] \n",
" conv2d_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_15 (Conv2D) (None, 64, 64, 64) 73792 concatenate_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_8 (Dropout) (None, 64, 64, 64) 0 conv2d_15[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_16 (Conv2D) (None, 64, 64, 64) 36928 dropout_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"up_sampling2d_4 (UpSampling2D) (None, 64, 128, 128) 0 conv2d_16[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_4 (Concatenate) (None, 96, 128, 128) 0 up_sampling2d_4[0][0] \n",
" conv2d_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_17 (Conv2D) (None, 32, 128, 128) 27680 concatenate_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_9 (Dropout) (None, 32, 128, 128) 0 conv2d_17[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_18 (Conv2D) (None, 32, 128, 128) 9248 dropout_9[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_19 (Conv2D) (None, 1, 128, 128) 33 conv2d_18[0][0] \n",
"==================================================================================================\n",
"Total params: 656,257\n",
"Trainable params: 656,257\n",
"Non-trainable params: 0\n",
"__________________________________________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### DRISHTI-GS\n",
"\n",
"Accessing data, preparing train/validation sets division:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# Loading full images of desired resolution:\n",
"X = h5f['DRISHTI-GS/512 px/images']\n",
"Y = h5f['DRISHTI-GS/512 px/cup']\n",
"disc_locations = h5f['DRISHTI-GS/512 px/disc_locations']"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(,\n",
" )"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X, Y"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"#train_idx = h5f['RIM-ONE v3/train_idx_driu']\n",
"#test_idx = h5f['RIM-ONE v3/test_idx_driu']\n",
"\n",
"train_idx = h5f['DRISHTI-GS/train_idx_cv'][0]\n",
"test_idx = h5f['DRISHTI-GS/test_idx_cv'][0]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"50 40 10\n",
"[ 0 1 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25\n",
" 26 28 30 32 33 34 36 37 39 41 42 43 44 45 47 49] [ 2 3 27 29 31 35 38 40 46 48]\n"
]
}
],
"source": [
"print(len(X), len(train_idx), len(test_idx))\n",
"print(train_idx, test_idx)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Generator of augmented data:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"train_idg = DualImageDataGenerator(#rescale=1/255.0,\n",
" #samplewise_center=True, samplewise_std_normalization=True,\n",
" horizontal_flip=True, vertical_flip=True,\n",
" rotation_range=20, width_shift_range=0.1, height_shift_range=0.1,\n",
" zoom_range=(0.8, 1.2),\n",
" fill_mode='constant', cval=0.0)\n",
"test_idg = DualImageDataGenerator()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Preprocessing function and data generator:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"def preprocess(batch_X, batch_y, train_or_test='train'):\n",
" batch_X = batch_X / 255.0\n",
" # the following line thresholds segmentation mask for DRISHTI-GS, since it contains averaged soft maps:\n",
" batch_y = batch_y >= 0.5\n",
" \n",
" if train_or_test == 'train':\n",
" batch_X, batch_y = next(train_idg.flow(batch_X, batch_y, batch_size=len(batch_X), shuffle=False))\n",
" elif train_or_test == 'test':\n",
" batch_X, batch_y = next(test_idg.flow(batch_X, batch_y, batch_size=len(batch_X), shuffle=False))\n",
" batch_X = th_to_tf_encoding(batch_X)\n",
" batch_X = [skimage.exposure.equalize_adapthist(batch_X[i])\n",
" for i in range(len(batch_X))]\n",
" batch_X = np.array(batch_X)\n",
" batch_X = tf_to_th_encoding(batch_X)\n",
" return batch_X, batch_y\n",
"\n",
"\n",
"def data_generator(X, y, resize_to=128, train_or_test='train', batch_size=3, return_orig=False, stationary=False):\n",
" \"\"\"Gets random batch of data, \n",
" divides by 255,\n",
" feeds it to DualImageDataGenerator.\"\"\"\n",
" \n",
" while True:\n",
" if train_or_test == 'train':\n",
" idx = np.random.choice(train_idx, size=batch_size)\n",
" elif train_or_test == 'test':\n",
" if stationary:\n",
" idx = test_idx[:batch_size]\n",
" else:\n",
" idx = np.random.choice(test_idx, size=batch_size)\n",
" batch_X = [X[i][disc_locations[i][0]:disc_locations[i][2], disc_locations[i][1]:disc_locations[i][3]] \n",
" for i in idx]\n",
" batch_X = [np.rollaxis(img, 2) for img in batch_X]\n",
" batch_X = [skimage.transform.resize(np.rollaxis(img, 0, 3), (resize_to, resize_to))\n",
" for img in batch_X]\n",
" batch_X = np.array(batch_X).copy()\n",
" \n",
" batch_y = [y[i][disc_locations[i][0]:disc_locations[i][2], disc_locations[i][1]:disc_locations[i][3]] \n",
" for i in idx]\n",
" batch_y = [img[..., 0] for img in batch_y]\n",
" batch_y = [skimage.transform.resize(img, (resize_to, resize_to))[..., None] for img in batch_y]\n",
" batch_y = np.array(batch_y).copy()\n",
" batch_X = tf_to_th_encoding(batch_X)\n",
" batch_y = tf_to_th_encoding(batch_y)\n",
" if return_orig:\n",
" batch_X_orig, batch_Y_orig = batch_X.copy(), batch_y.copy()\n",
" \n",
" batch_X, batch_y = preprocess(batch_X, batch_y, train_or_test)\n",
" \n",
" if not return_orig:\n",
" yield batch_X, batch_y\n",
" else:\n",
" yield batch_X, batch_y, batch_X_orig, batch_Y_orig"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Testing the data generator and generator for augmented data:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(1, 3, 128, 128)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gen = data_generator(X, Y, 128, 'train', batch_size=1)\n",
"batch = next(gen)\n",
"batch[0].shape"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
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