示例#1
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else:
    init = "/Users/salvatorecapuozzo/Desktop/"

if mode == 0:
    #model = fcn_32(n_classes=38 ,  input_height=224, input_width=320  )
    # model = vgg_unet(n_classes=38 ,  input_height=416, input_width=608  )
    #model = vgg_unet(n_classes=38 ,  input_height=416, input_width=608  )

    if trainingFromInit:
        #model = pspnet_50_slim( n_classes=38 ) # accuracy: 0.5348 10 epochs
        #model = pspnet_50_ADE_20K_SUNRGB()
        #model = convertToSunRgb(model)
        #print(model.summary())
        #prun_schedule = PolynomialDecay(initial_sparsity=0.0, final_sparsity=0.5,begin_step=2000,end_step=4000)
        #model = prune_low_magnitude(model, pruning_schedule=prun_schedule)
        model = pspnet_50(n_classes=38)
        pretrained_model = pspnet_50_ADE_20K()

        tf.keras.utils.plot_model(
            model,
            to_file=init + "pspnet_50.png",
            show_shapes=True,
            show_layer_names=True,
            rankdir="TB",
            expand_nested=False,
            dpi=96,
        )
        tf.keras.utils.plot_model(
            pretrained_model,
            to_file=init + "pspnet_50_ade_20k.png",
            show_shapes=True,
示例#2
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import os
#os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
#os.environ["CUDA_VISIBLE_DEVICES"] = ""

from keras_segmentation.models.model_utils import transfer_weights
from keras_segmentation.pretrained import pspnet_50_ADE_20K
from keras_segmentation.models.pspnet import pspnet_50

pretrained_model = pspnet_50_ADE_20K()

keji_model1 = pspnet_50(n_classes=150)

transfer_weights(
    pretrained_model,
    keji_model1)  # transfer weights from pre-trained model to your model

keji_model1.train(train_images="../VGdata/images_prepped_train_png/",
                  train_annotations="../VGdata/annotations_prepped_train_png/",
                  checkpoints_path="./keji1check",
                  epochs=5)
示例#3
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gc.collect()

#rifare training con data augmentation, provare altre reti, transfer learning, fare valutazione

gpu_devices = tf.config.experimental.list_physical_devices('GPU')
for device in gpu_devices: tf.config.experimental.set_memory_growth(device, True)

print(f"[INFO] Building the model..")
#model = vgg_unet(n_classes=2 ,  input_height=512, input_width=512  )


pretrained_model =  pspnet_50_ADE_20K()

#model = pspnet_50( n_classes=2 ) #new model

model = pspnet_50( n_classes=2 ) #new model

transfer_weights( pretrained_model , model  ) # transfer weights from pre-trained model to your model


print(f"[INFO] Training the model..")
model.train(
    train_images =  r"D:\FISICA MEDICA\radiomics_eco\Dataset_BUSI_with_GT\segnet\images_train",
    train_annotations = r"D:\FISICA MEDICA\radiomics_eco\Dataset_BUSI_with_GT\segnet\masks_train",
    checkpoints_path =r"C:\Users\matte\PycharmProjects\ecographic_breast_nn\segnet\checkpoints\psp_unet", epochs=5,batch_size=1)



print(f"[INFO] Running predictions")
pdr=model.predict_multiple(
	inp_dir=r"D:\FISICA MEDICA\radiomics_eco\Dataset_BUSI_with_GT\segnet\images_val",
示例#4
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from imgaug import augmenters as iaa
import imageio
import numpy as np
import datetime
import imutils
from skimage import measure, color
from sklearn.decomposition import PCA

from keras_segmentation.train import find_latest_checkpoint
from keras_segmentation.models.model_utils import transfer_weights
from keras_segmentation.pretrained import pspnet_50_ADE_20K
from keras_segmentation.models.pspnet import pspnet_50  #,resnet50_pspnet
#from cv2 import imresize

# In[model]:
new_model = pspnet_50(n_classes=2, input_height=473, input_width=473)
file_model = 'new_model.299'
#file_model = 'new_model_val.50'
new_model.load_weights(file_model)

# In[val 测试集（不用先增强，直接拆分）]:
### 原始图像和分割的路径
image_dir = r"dataset\val"
#
## 切分块后保存图像和分割的路径
image_block_dir = r"dataset\block_val"
image_predict_dir = r"dataset\val_predict"

# In[proc]:

# 指定子块的宽和高
示例#5
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from keras_segmentation.models.model_utils import transfer_weights
from keras_segmentation.pretrained import pspnet_50_ADE_20K
from keras_segmentation.models.pspnet import pspnet_50

from tensorflow.compat.v1 import InteractiveSession
from tensorflow.compat.v1 import ConfigProto

config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)

pretrained_model = pspnet_50_ADE_20K()

new_model = pspnet_50(n_classes=2)

transfer_weights(
    pretrained_model,
    new_model)  # transfer weights from pre-trained model to your model

new_model.train(train_images="/home/klz/food_training_images/",
                train_annotations="/home/klz/food_training_annotations/",
                checkpoints_path="/home/klz/checkpoints/vgg_unet_1",
                epochs=5)