This is a CNTK implementation of U-net, which is a deep learning segmentation method proposed by Ronneberger et al.
U-Net: Convolutional Networks for Biomedical Image Segmentation
Tested with cntk-2.1-gpu-python3.5 docker
import numpy as np
import cntk_unet
import simulation
%matplotlib inline
import helper
import cntk as C
from cntk.learners import learning_rate_schedule, UnitType# Generate some random images
input_images, target_masks = simulation.generate_random_data(192, 192, count=3)
print(input_images.shape, target_masks.shape)
# Change channel-order and make 3 channels for matplot
input_images_rgb = [(x.swapaxes(0, 2).swapaxes(0,1).repeat(3, axis=2) * -255 + 255).astype(np.uint8) for x in input_images]
# Map each channel (i.e. class) to each color
target_masks_rgb = [helper.masks_to_colorimg(x) for x in target_masks]
# Left: Input image, Right: Target mask
helper.plot_side_by_side([input_images_rgb, target_masks_rgb])(3, 1, 192, 192) (3, 6, 192, 192)
from cntk.device import try_set_default_device, gpu
try_set_default_device(gpu(0))
def slice_minibatch(data_x, data_y, i, minibatch_size):
sx = data_x[i * minibatch_size:(i + 1) * minibatch_size]
sy = data_y[i * minibatch_size:(i + 1) * minibatch_size]
return sx, sy
def measure_error(data_x, data_y, x, y, trainer, minibatch_size):
errors = []
for i in range(0, int(len(data_x) / minibatch_size)):
data_sx, data_sy = slice_minibatch(data_x, data_y, i, minibatch_size)
errors.append(trainer.test_minibatch({x: data_sx, y: data_sy}))
return np.mean(errors)
def train(images, masks, use_existing=False):
shape = input_images[0].shape
data_size = input_images.shape[0]
# Split data
test_portion = int(data_size * 0.1)
indices = np.random.permutation(data_size)
test_indices = indices[:test_portion]
training_indices = indices[test_portion:]
test_data = (images[test_indices], masks[test_indices])
training_data = (images[training_indices], masks[training_indices])
# Create model
x = C.input_variable(shape)
y = C.input_variable(masks[0].shape)
z = cntk_unet.create_model(x, masks.shape[1])
dice_coef = cntk_unet.dice_coefficient(z, y)
# Load the saved model if specified
checkpoint_file = "cntk-unet.dnn"
if use_existing:
z.load_model(checkpoint_file)
# Prepare model and trainer
lr = learning_rate_schedule(0.00001, UnitType.sample)
momentum = C.learners.momentum_as_time_constant_schedule(0.9)
trainer = C.Trainer(z, (-dice_coef, -dice_coef), C.learners.adam(z.parameters, lr=lr, momentum=momentum))
# Get minibatches of training data and perform model training
minibatch_size = 8
num_epochs = 75
training_errors = []
test_errors = []
for e in range(0, num_epochs):
for i in range(0, int(len(training_data[0]) / minibatch_size)):
data_x, data_y = slice_minibatch(training_data[0], training_data[1], i, minibatch_size)
trainer.train_minibatch({x: data_x, y: data_y})
# Measure training error
training_error = measure_error(training_data[0], training_data[1], x, y, trainer, minibatch_size)
training_errors.append(training_error)
# Measure test error
test_error = measure_error(test_data[0], test_data[1], x, y, trainer, minibatch_size)
test_errors.append(test_error)
print("epoch #{}: training_error={}, test_error={}".format(e, training_errors[-1], test_errors[-1]))
trainer.save_checkpoint(checkpoint_file)
return trainer, training_errors, test_errorsinput_images, target_masks = input_images, target_masks = simulation.generate_random_data(192, 192, count=1024)
trainer, training_errors, test_errors = train(input_images, target_masks)epoch #0: training_error=-0.019768690868564275, test_error=-0.020717654066781204
epoch #1: training_error=-0.14359762888887653, test_error=-0.14444618796308836
epoch #2: training_error=-0.16253145324147267, test_error=-0.16211109856764475
epoch #3: training_error=-0.19506786180579144, test_error=-0.19313516840338707
epoch #4: training_error=-0.22743441371814063, test_error=-0.2299772041539351
epoch #5: training_error=-0.22651763014171433, test_error=-0.2276181268195311
epoch #6: training_error=-0.25330155660276826, test_error=-0.2570686799784501
epoch #7: training_error=-0.27093734209952147, test_error=-0.2745807444055875
epoch #8: training_error=-0.32097409859947534, test_error=-0.3199269076188405
epoch #9: training_error=-0.32212045840595077, test_error=-0.3231043294072151
epoch #10: training_error=-0.4180802334909854, test_error=-0.41908330221970874
epoch #11: training_error=-0.5081395947414896, test_error=-0.5119023198882738
epoch #12: training_error=-0.5355584644753, test_error=-0.5396901319424311
epoch #13: training_error=-0.5596165833265885, test_error=-0.5651968071858088
epoch #14: training_error=-0.6433821154677349, test_error=-0.6459879775842031
epoch #15: training_error=-0.6609660775765128, test_error=-0.6660722494125366
epoch #16: training_error=-0.6821304870688397, test_error=-0.6858411679665247
epoch #17: training_error=-0.6975538927575816, test_error=-0.7021944771210352
epoch #18: training_error=-0.7028526124746903, test_error=-0.7015786518653234
epoch #19: training_error=-0.7167597547821377, test_error=-0.720397874712944
epoch #20: training_error=-0.7156574731287749, test_error=-0.7193131099144617
epoch #21: training_error=-0.7252500445946403, test_error=-0.7287015269200007
epoch #22: training_error=-0.7334559886351876, test_error=-0.7369491656621298
epoch #23: training_error=-0.7400820079057113, test_error=-0.74381023645401
epoch #24: training_error=-0.7529412295507348, test_error=-0.7539715717236201
epoch #25: training_error=-0.7605505870736163, test_error=-0.7639513413111368
epoch #26: training_error=-0.7628906576529794, test_error=-0.7664126455783844
epoch #27: training_error=-0.7715898026590762, test_error=-0.7751793265342712
epoch #28: training_error=-0.7752938420876213, test_error=-0.7786444971958796
epoch #29: training_error=-0.7786822977273361, test_error=-0.7816734810670217
epoch #30: training_error=-0.782146718191064, test_error=-0.7854048907756805
epoch #31: training_error=-0.7842959367710611, test_error=-0.7869394868612289
epoch #32: training_error=-0.7898872852325439, test_error=-0.7923590739568075
epoch #33: training_error=-0.7931342622508173, test_error=-0.7958166400591532
epoch #34: training_error=-0.7994443261105081, test_error=-0.8019372572501501
epoch #35: training_error=-0.7979994908623074, test_error=-0.8004395614067713
epoch #36: training_error=-0.7975412876709648, test_error=-0.8000765045483907
epoch #37: training_error=-0.8006633597871532, test_error=-0.8025749226411184
epoch #38: training_error=-0.8104041752607926, test_error=-0.812293271223704
epoch #39: training_error=-0.8078771876252215, test_error=-0.8101015339295069
epoch #40: training_error=-0.8104296383650407, test_error=-0.8116654952367147
epoch #41: training_error=-0.812252676486969, test_error=-0.8135889967282613
epoch #42: training_error=-0.8167183248893074, test_error=-0.8186343262592951
epoch #43: training_error=-0.8183319314666416, test_error=-0.8183851490418116
epoch #44: training_error=-0.8180797893068065, test_error=-0.8185829023520151
epoch #45: training_error=-0.8236719385437343, test_error=-0.8233668257792791
epoch #46: training_error=-0.8258916523145593, test_error=-0.8249997198581696
epoch #47: training_error=-0.8239949169366256, test_error=-0.8246004184087118
epoch #48: training_error=-0.8278672368630119, test_error=-0.8268091479937235
epoch #49: training_error=-0.832696767475294, test_error=-0.831729864080747
epoch #50: training_error=-0.8263022370960401, test_error=-0.8262786467870077
epoch #51: training_error=-0.842771373624387, test_error=-0.8406341224908829
epoch #52: training_error=-0.857192787916764, test_error=-0.8564214507738749
epoch #53: training_error=-0.8568549975104954, test_error=-0.8593804885943731
epoch #54: training_error=-0.86151617091635, test_error=-0.8591275463501612
epoch #55: training_error=-0.8698923546334971, test_error=-0.8672014276186625
epoch #56: training_error=-0.899405440558558, test_error=-0.8971130202213923
epoch #57: training_error=-0.8627254413521808, test_error=-0.8596462905406952
epoch #58: training_error=-0.9112692019213801, test_error=-0.9090506235758463
epoch #59: training_error=-0.9077493258144544, test_error=-0.9010724077622095
epoch #60: training_error=-0.9106351442958998, test_error=-0.9055953522523245
epoch #61: training_error=-0.9199885093647501, test_error=-0.913592591881752
epoch #62: training_error=-0.924489686800086, test_error=-0.9214814503987631
epoch #63: training_error=-0.9274574082830678, test_error=-0.9210271586974462
epoch #64: training_error=-0.9304829752963523, test_error=-0.9223643392324448
epoch #65: training_error=-0.9349522372950678, test_error=-0.9265173921982447
epoch #66: training_error=-0.9366193714349166, test_error=-0.9279018491506577
epoch #67: training_error=-0.9376024194385695, test_error=-0.9277462114890417
epoch #68: training_error=-0.9381804207096929, test_error=-0.928676704565684
epoch #69: training_error=-0.9367524255876956, test_error=-0.9292152722676595
epoch #70: training_error=-0.9380349750104158, test_error=-0.9298784782489141
epoch #71: training_error=-0.9406253742135089, test_error=-0.9317039549350739
epoch #72: training_error=-0.944081843935925, test_error=-0.9340653717517853
epoch #73: training_error=-0.9468802089276521, test_error=-0.9356546898682913
epoch #74: training_error=-0.9458474998888762, test_error=-0.9347378015518188
helper.plot_errors({"training": training_errors, "test": test_errors}, title="Simulation Learning Curve")
# Generate some random images
input_images, target_masks = input_images, target_masks = simulation.generate_random_data(192, 192, count=10)
# Predict
pred = trainer.model.eval(input_images)
print(input_images.shape, target_masks.shape, pred.shape)(10, 1, 192, 192) (10, 6, 192, 192) (10, 6, 192, 192)
# Change channel-order and make 3 channels for matplot
input_images_rgb = [(x.swapaxes(0, 2).swapaxes(0,1).repeat(3, axis=2) * -255 + 255).astype(np.uint8) for x in input_images]
# Map each channel (i.e. class) to each color
target_masks_rgb = [helper.masks_to_colorimg(x) for x in target_masks]
pred_rgb = [helper.masks_to_colorimg(x) for x in pred]
# Left: Input image, Middle: Correct mask (Ground-truth), Rigth: Predicted mask
helper.plot_side_by_side([input_images_rgb, target_masks_rgb, pred_rgb])
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