Loading contrib/DiabeticRetinopathy/model.py 0 → 100755 +248 −0 Original line number Diff line number Diff line #!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Mon Sep 10 06:12:10 2018 @author: zqwu """ import numpy as np import tensorflow as tf from deepchem.data import NumpyDataset, pad_features from deepchem.metrics import to_one_hot from deepchem.models.tensorgraph.layers import Layer, Dense, SoftMax, Reshape, \ SparseSoftMaxCrossEntropy, BatchNorm, Conv2D, MaxPool2D, WeightedError, \ Dropout, ReLU, Stack, Flatten, ReduceMax, WeightDecay from deepchem.models.tensorgraph.layers import L2Loss, Label, Weights, Feature from deepchem.models.tensorgraph.tensor_graph import TensorGraph from deepchem.trans import undo_transforms from deepchem.data.data_loader import ImageLoader from sklearn.metrics import confusion_matrix, accuracy_score class DRModel(TensorGraph): def __init__(self, n_tasks=1, image_size=512, n_downsample=6, n_init_kernel=16, n_fully_connected=[1024], n_classes=5, augment=False, **kwargs): """ Parameters ---------- n_tasks: int Number of tasks image_size: int Resolution of the input images(square) n_downsample: int Downsample ratio in power of 2 n_init_kernel: int Kernel size for the first convolutional layer n_fully_connected: list of int Shape of FC layers after convolutions n_classes: int Number of classes to predict (only used in classification mode) augment: bool If to use data augmentation """ self.n_tasks = n_tasks self.image_size = image_size self.n_downsample = n_downsample self.n_init_kernel = n_init_kernel self.n_fully_connected = n_fully_connected self.n_classes = n_classes self.augment = augment super(DRModel, self).__init__(**kwargs) self.build_graph() def build_graph(self): # inputs placeholder self.inputs = Feature(shape=(None, self.image_size, self.image_size, 3), dtype=tf.uint8) # data preprocessing and augmentation in_layer = DRAugment(self.augment, self.batch_size, size=(self.image_size, self.image_size), in_layers=[self.inputs]) # first conv layer in_layer = Conv2D(self.n_init_kernel, kernel_size=7, activation_fn=None, in_layers=[in_layer]) in_layer = BatchNorm(in_layers=[in_layer]) in_layer = ReLU(in_layers=[in_layer]) # downsample by max pooling res_in = MaxPool2D(ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], in_layers=[in_layer]) for ct_module in range(self.n_downsample - 1): # each module is a residual convolutional block # followed by a convolutional downsample layer in_layer = Conv2D(self.n_init_kernel * 2 ** (ct_module - 1), kernel_size=1, activation_fn=None, in_layers=[res_in]) in_layer = BatchNorm(in_layers=[in_layer]) in_layer = ReLU(in_layers=[in_layer]) in_layer = Conv2D(self.n_init_kernel * 2 ** (ct_module - 1), kernel_size=3, activation_fn=None, in_layers=[in_layer]) in_layer = BatchNorm(in_layers=[in_layer]) in_layer = ReLU(in_layers=[in_layer]) in_layer = Conv2D(self.n_init_kernel * 2 ** ct_module, kernel_size=1, activation_fn=None, in_layers=[in_layer]) res_a = BatchNorm(in_layers=[in_layer]) res_out = res_in + res_a res_in = Conv2D(self.n_init_kernel * 2 ** (ct_module + 1), kernel_size=3, stride=2, in_layers=[res_out]) res_in = BatchNorm(in_layers=[res_in]) # max pooling over the final outcome in_layer = ReduceMax(axis=(1, 2), in_layers=[res_in]) for layer_size in self.n_fully_connected: # fully connected layers in_layer = Dense(layer_size, activation_fn=tf.nn.relu, in_layers=[in_layer]) # dropout for dense layers #in_layer = Dropout(0.25, in_layers=[in_layer]) logit_pred = Dense(self.n_tasks * self.n_classes, activation_fn=None, in_layers=[in_layer]) logit_pred = Reshape(shape=(None, self.n_tasks, self.n_classes), in_layers=[logit_pred]) weights = Weights(shape=(None, self.n_tasks)) labels = Label(shape=(None, self.n_tasks), dtype=tf.int32) output = SoftMax(logit_pred) self.add_output(output) loss = SparseSoftMaxCrossEntropy(in_layers=[labels, logit_pred]) weighted_loss = WeightedError(in_layers=[loss, weights]) # weight decay regularizer # weighted_loss = WeightDecay(0.1, 'l2', in_layers=[weighted_loss]) self.set_loss(weighted_loss) def default_generator(self, dataset, epochs=1, predict=False, deterministic=True, pad_batches=True): for epoch in range(epochs): for (X_b, y_b, w_b, ids_b) in dataset.iterbatches( batch_size=self.batch_size, deterministic=deterministic, pad_batches=pad_batches): feed_dict = dict() if None in X_b: # load images on the fly feed_dict[self.features[0]] = ImageLoader.load_img(ids_b) else: feed_dict[self.features[0]] = X_b if y_b is not None and not predict: feed_dict[self.labels[0]] = y_b if w_b is not None and not predict: feed_dict[self.task_weights[0]] = w_b yield feed_dict def DRAccuracy(y, y_pred): y_pred = np.argmax(y_pred, 1) return accuracy_score(y, y_pred) def DRSpecificity(y, y_pred): y_pred = (np.argmax(y_pred, 1)>0)*1 y = (y>0)*1 TN = sum((1 - y_pred) * (1 - y)) N = sum(1-y) return float(TN)/N def DRSensitivity(y, y_pred): y_pred = (np.argmax(y_pred, 1)>0)*1 y = (y>0)*1 TP = sum(y_pred * y) P = sum(y) return float(TP)/P def ConfusionMatrix(y, y_pred): y_pred = np.argmax(y_pred, 1) return confusion_matrix(y, y_pred) class DRAugment(Layer): def __init__(self, augment, batch_size, distort_color=True, central_crop=True, size=(512, 512), **kwargs): """ Parameters ---------- augment: bool If to use data augmentation batch_size: int Number of images in the batch distort_color: bool If to apply random distortion on the color central_crop: bool If to randomly crop the sample around the center size: int Resolution of the input images(square) """ self.augment = augment self.batch_size = batch_size self.distort_color = distort_color self.central_crop = central_crop self.size = size super(DRAugment, self).__init__(**kwargs) def create_tensor(self, in_layers=None, set_tensors=True, **kwargs): inputs = self._get_input_tensors(in_layers) parent_tensor = inputs[0] training = kwargs['training'] if 'training' in kwargs else 1.0 parent_tensor = tf.image.convert_image_dtype(parent_tensor, tf.float32) if not self.augment: out_tensor = parent_tensor else: def preprocess(img): img = tf.image.random_flip_left_right(img) img = tf.image.random_flip_up_down(img) img = tf.image.rot90(img, k=np.random.randint(0, 4)) if self.distort_color: img = tf.image.random_brightness(img, max_delta=32. / 255.) img = tf.image.random_saturation(img, lower=0.5, upper=1.5) img = tf.clip_by_value(img, 0.0, 1.0) if self.central_crop: # sample cut ratio from a clipped gaussian img = tf.image.central_crop(img, np.clip(np.random.normal(1., 0.06), 0.8, 1.)) img = tf.image.resize_bilinear(tf.expand_dims(img, 0), tf.convert_to_tensor(self.size))[0] return img outs = tf.map_fn(preprocess, parent_tensor) # train/valid differences out_tensor = training * outs + (1 - training) * parent_tensor if set_tensors: self.out_tensor = out_tensor return out_tensor contrib/DiabeticRetinopathy/run.py 0 → 100755 +72 −0 Original line number Diff line number Diff line #!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Mon Sep 10 06:12:11 2018 @author: zqwu """ import deepchem as dc import numpy as np import pandas as pd import os import logging from model import DRModel, DRAccuracy, DRSensitivity, DRSpecificity, ConfusionMatrix PATH = './data/train_cut/' # Input images image_names = os.listdir(PATH) labels = dict(zip(*np.transpose(np.array(pd.read_csv('./data/trainLabels.csv'))))) np.random.seed(123) np.random.shuffle(image_names) # Use 80% of the data for training, the rest for validation train_images = image_names[:int(0.8*len(image_names))] test_images = image_names[int(0.8*len(image_names)):] print("Number of training images: %d" % len(train_images)) train_labels = np.array([labels[os.path.splitext(n)[0][4:]] for n in train_images]).reshape((-1, 1)) train_image_paths = [os.path.join(PATH, n) for n in train_images] test_labels = np.array([labels[os.path.splitext(n)[0][4:]] for n in test_images]).reshape((-1, 1)) test_image_paths = [os.path.join(PATH, n) for n in test_images] # Generate class weights according to training set labels distribution classes, cts = np.unique(train_labels, return_counts=True) weight_ratio = dict(zip(classes, np.max(cts) / cts.astype(float))) train_weights = np.array([weight_ratio[l[0]] for l in train_labels]).reshape((-1, 1)) test_weights = np.array([weight_ratio[l[0]] for l in test_labels]).reshape((-1, 1)) loader = dc.data.ImageLoader() train_data = loader.featurize(train_image_paths, labels=train_labels, weights=train_weights, read_img=False) test_data = loader.featurize(test_image_paths, labels=test_labels, weights=test_weights, read_img=False) # Define and build model model = DRModel(n_init_kernel=32, batch_size=32, learning_rate=1e-5, augment=True, model_dir='./test_model') model.build() #model.restore() metrics = [dc.metrics.Metric(DRAccuracy, mode='classification'), dc.metrics.Metric(DRSensitivity, mode='classification'), dc.metrics.Metric(DRSpecificity, mode='classification')] cm = [dc.metrics.Metric(ConfusionMatrix, mode='classification')] logger = logging.getLogger('deepchem.models.tensorgraph.tensor_graph') logger.setLevel(logging.DEBUG) for i in range(10): model.fit(train_data, nb_epoch=10, checkpoint_interval=3512) # Evaluate every 10 epochs model.evaluate(train_data, metrics) model.evaluate(test_data, metrics) model.evaluate(test_data, cm) Loading
contrib/DiabeticRetinopathy/model.py 0 → 100755 +248 −0 Original line number Diff line number Diff line #!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Mon Sep 10 06:12:10 2018 @author: zqwu """ import numpy as np import tensorflow as tf from deepchem.data import NumpyDataset, pad_features from deepchem.metrics import to_one_hot from deepchem.models.tensorgraph.layers import Layer, Dense, SoftMax, Reshape, \ SparseSoftMaxCrossEntropy, BatchNorm, Conv2D, MaxPool2D, WeightedError, \ Dropout, ReLU, Stack, Flatten, ReduceMax, WeightDecay from deepchem.models.tensorgraph.layers import L2Loss, Label, Weights, Feature from deepchem.models.tensorgraph.tensor_graph import TensorGraph from deepchem.trans import undo_transforms from deepchem.data.data_loader import ImageLoader from sklearn.metrics import confusion_matrix, accuracy_score class DRModel(TensorGraph): def __init__(self, n_tasks=1, image_size=512, n_downsample=6, n_init_kernel=16, n_fully_connected=[1024], n_classes=5, augment=False, **kwargs): """ Parameters ---------- n_tasks: int Number of tasks image_size: int Resolution of the input images(square) n_downsample: int Downsample ratio in power of 2 n_init_kernel: int Kernel size for the first convolutional layer n_fully_connected: list of int Shape of FC layers after convolutions n_classes: int Number of classes to predict (only used in classification mode) augment: bool If to use data augmentation """ self.n_tasks = n_tasks self.image_size = image_size self.n_downsample = n_downsample self.n_init_kernel = n_init_kernel self.n_fully_connected = n_fully_connected self.n_classes = n_classes self.augment = augment super(DRModel, self).__init__(**kwargs) self.build_graph() def build_graph(self): # inputs placeholder self.inputs = Feature(shape=(None, self.image_size, self.image_size, 3), dtype=tf.uint8) # data preprocessing and augmentation in_layer = DRAugment(self.augment, self.batch_size, size=(self.image_size, self.image_size), in_layers=[self.inputs]) # first conv layer in_layer = Conv2D(self.n_init_kernel, kernel_size=7, activation_fn=None, in_layers=[in_layer]) in_layer = BatchNorm(in_layers=[in_layer]) in_layer = ReLU(in_layers=[in_layer]) # downsample by max pooling res_in = MaxPool2D(ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], in_layers=[in_layer]) for ct_module in range(self.n_downsample - 1): # each module is a residual convolutional block # followed by a convolutional downsample layer in_layer = Conv2D(self.n_init_kernel * 2 ** (ct_module - 1), kernel_size=1, activation_fn=None, in_layers=[res_in]) in_layer = BatchNorm(in_layers=[in_layer]) in_layer = ReLU(in_layers=[in_layer]) in_layer = Conv2D(self.n_init_kernel * 2 ** (ct_module - 1), kernel_size=3, activation_fn=None, in_layers=[in_layer]) in_layer = BatchNorm(in_layers=[in_layer]) in_layer = ReLU(in_layers=[in_layer]) in_layer = Conv2D(self.n_init_kernel * 2 ** ct_module, kernel_size=1, activation_fn=None, in_layers=[in_layer]) res_a = BatchNorm(in_layers=[in_layer]) res_out = res_in + res_a res_in = Conv2D(self.n_init_kernel * 2 ** (ct_module + 1), kernel_size=3, stride=2, in_layers=[res_out]) res_in = BatchNorm(in_layers=[res_in]) # max pooling over the final outcome in_layer = ReduceMax(axis=(1, 2), in_layers=[res_in]) for layer_size in self.n_fully_connected: # fully connected layers in_layer = Dense(layer_size, activation_fn=tf.nn.relu, in_layers=[in_layer]) # dropout for dense layers #in_layer = Dropout(0.25, in_layers=[in_layer]) logit_pred = Dense(self.n_tasks * self.n_classes, activation_fn=None, in_layers=[in_layer]) logit_pred = Reshape(shape=(None, self.n_tasks, self.n_classes), in_layers=[logit_pred]) weights = Weights(shape=(None, self.n_tasks)) labels = Label(shape=(None, self.n_tasks), dtype=tf.int32) output = SoftMax(logit_pred) self.add_output(output) loss = SparseSoftMaxCrossEntropy(in_layers=[labels, logit_pred]) weighted_loss = WeightedError(in_layers=[loss, weights]) # weight decay regularizer # weighted_loss = WeightDecay(0.1, 'l2', in_layers=[weighted_loss]) self.set_loss(weighted_loss) def default_generator(self, dataset, epochs=1, predict=False, deterministic=True, pad_batches=True): for epoch in range(epochs): for (X_b, y_b, w_b, ids_b) in dataset.iterbatches( batch_size=self.batch_size, deterministic=deterministic, pad_batches=pad_batches): feed_dict = dict() if None in X_b: # load images on the fly feed_dict[self.features[0]] = ImageLoader.load_img(ids_b) else: feed_dict[self.features[0]] = X_b if y_b is not None and not predict: feed_dict[self.labels[0]] = y_b if w_b is not None and not predict: feed_dict[self.task_weights[0]] = w_b yield feed_dict def DRAccuracy(y, y_pred): y_pred = np.argmax(y_pred, 1) return accuracy_score(y, y_pred) def DRSpecificity(y, y_pred): y_pred = (np.argmax(y_pred, 1)>0)*1 y = (y>0)*1 TN = sum((1 - y_pred) * (1 - y)) N = sum(1-y) return float(TN)/N def DRSensitivity(y, y_pred): y_pred = (np.argmax(y_pred, 1)>0)*1 y = (y>0)*1 TP = sum(y_pred * y) P = sum(y) return float(TP)/P def ConfusionMatrix(y, y_pred): y_pred = np.argmax(y_pred, 1) return confusion_matrix(y, y_pred) class DRAugment(Layer): def __init__(self, augment, batch_size, distort_color=True, central_crop=True, size=(512, 512), **kwargs): """ Parameters ---------- augment: bool If to use data augmentation batch_size: int Number of images in the batch distort_color: bool If to apply random distortion on the color central_crop: bool If to randomly crop the sample around the center size: int Resolution of the input images(square) """ self.augment = augment self.batch_size = batch_size self.distort_color = distort_color self.central_crop = central_crop self.size = size super(DRAugment, self).__init__(**kwargs) def create_tensor(self, in_layers=None, set_tensors=True, **kwargs): inputs = self._get_input_tensors(in_layers) parent_tensor = inputs[0] training = kwargs['training'] if 'training' in kwargs else 1.0 parent_tensor = tf.image.convert_image_dtype(parent_tensor, tf.float32) if not self.augment: out_tensor = parent_tensor else: def preprocess(img): img = tf.image.random_flip_left_right(img) img = tf.image.random_flip_up_down(img) img = tf.image.rot90(img, k=np.random.randint(0, 4)) if self.distort_color: img = tf.image.random_brightness(img, max_delta=32. / 255.) img = tf.image.random_saturation(img, lower=0.5, upper=1.5) img = tf.clip_by_value(img, 0.0, 1.0) if self.central_crop: # sample cut ratio from a clipped gaussian img = tf.image.central_crop(img, np.clip(np.random.normal(1., 0.06), 0.8, 1.)) img = tf.image.resize_bilinear(tf.expand_dims(img, 0), tf.convert_to_tensor(self.size))[0] return img outs = tf.map_fn(preprocess, parent_tensor) # train/valid differences out_tensor = training * outs + (1 - training) * parent_tensor if set_tensors: self.out_tensor = out_tensor return out_tensor
contrib/DiabeticRetinopathy/run.py 0 → 100755 +72 −0 Original line number Diff line number Diff line #!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Mon Sep 10 06:12:11 2018 @author: zqwu """ import deepchem as dc import numpy as np import pandas as pd import os import logging from model import DRModel, DRAccuracy, DRSensitivity, DRSpecificity, ConfusionMatrix PATH = './data/train_cut/' # Input images image_names = os.listdir(PATH) labels = dict(zip(*np.transpose(np.array(pd.read_csv('./data/trainLabels.csv'))))) np.random.seed(123) np.random.shuffle(image_names) # Use 80% of the data for training, the rest for validation train_images = image_names[:int(0.8*len(image_names))] test_images = image_names[int(0.8*len(image_names)):] print("Number of training images: %d" % len(train_images)) train_labels = np.array([labels[os.path.splitext(n)[0][4:]] for n in train_images]).reshape((-1, 1)) train_image_paths = [os.path.join(PATH, n) for n in train_images] test_labels = np.array([labels[os.path.splitext(n)[0][4:]] for n in test_images]).reshape((-1, 1)) test_image_paths = [os.path.join(PATH, n) for n in test_images] # Generate class weights according to training set labels distribution classes, cts = np.unique(train_labels, return_counts=True) weight_ratio = dict(zip(classes, np.max(cts) / cts.astype(float))) train_weights = np.array([weight_ratio[l[0]] for l in train_labels]).reshape((-1, 1)) test_weights = np.array([weight_ratio[l[0]] for l in test_labels]).reshape((-1, 1)) loader = dc.data.ImageLoader() train_data = loader.featurize(train_image_paths, labels=train_labels, weights=train_weights, read_img=False) test_data = loader.featurize(test_image_paths, labels=test_labels, weights=test_weights, read_img=False) # Define and build model model = DRModel(n_init_kernel=32, batch_size=32, learning_rate=1e-5, augment=True, model_dir='./test_model') model.build() #model.restore() metrics = [dc.metrics.Metric(DRAccuracy, mode='classification'), dc.metrics.Metric(DRSensitivity, mode='classification'), dc.metrics.Metric(DRSpecificity, mode='classification')] cm = [dc.metrics.Metric(ConfusionMatrix, mode='classification')] logger = logging.getLogger('deepchem.models.tensorgraph.tensor_graph') logger.setLevel(logging.DEBUG) for i in range(10): model.fit(train_data, nb_epoch=10, checkpoint_interval=3512) # Evaluate every 10 epochs model.evaluate(train_data, metrics) model.evaluate(test_data, metrics) model.evaluate(test_data, cm)
