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e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 | # Copyright 2020 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
MONAI Generative Adversarial Networks Workflow Example
Sample script using MONAI to train a GAN to synthesize images from a latent code.
## Get the dataset
MedNIST.tar.gz link: https://www.dropbox.com/s/5wwskxctvcxiuea/MedNIST.tar.gz
Extract tarball and set input_dir variable. GAN script trains using hand CT scan jpg images.
Dataset information available in MedNIST Tutorial
https://github.com/Project-MONAI/Tutorials/blob/master/mednist_tutorial.ipynb
"""
import logging
import os
import sys
import torch
import monai
from monai.apps.utils import download_and_extract
from monai.data import CacheDataset, DataLoader, png_writer
from monai.engines import GanTrainer
from monai.engines.utils import GanKeys as Keys
from monai.engines.utils import default_make_latent as make_latent
from monai.handlers import CheckpointSaver, StatsHandler
from monai.networks import normal_init
from monai.networks.nets import Discriminator, Generator
from monai.transforms import (
AddChannelD,
Compose,
LoadPNGD,
RandFlipD,
RandRotateD,
RandZoomD,
ScaleIntensityD,
ToTensorD,
)
from monai.utils.misc import set_determinism
def main():
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
set_determinism(12345)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# load real data
mednist_url = "https://www.dropbox.com/s/5wwskxctvcxiuea/MedNIST.tar.gz?dl=1"
md5_value = "0bc7306e7427e00ad1c5526a6677552d"
extract_dir = "data"
tar_save_path = os.path.join(extract_dir, "MedNIST.tar.gz")
download_and_extract(mednist_url, tar_save_path, extract_dir, md5_value)
hand_dir = os.path.join(extract_dir, "MedNIST", "Hand")
real_data = [{"hand": os.path.join(hand_dir, filename)} for filename in os.listdir(hand_dir)]
# define real data transforms
train_transforms = Compose(
[
LoadPNGD(keys=["hand"]),
AddChannelD(keys=["hand"]),
ScaleIntensityD(keys=["hand"]),
RandRotateD(keys=["hand"], range_x=15, prob=0.5, keep_size=True),
RandFlipD(keys=["hand"], spatial_axis=0, prob=0.5),
RandZoomD(keys=["hand"], min_zoom=0.9, max_zoom=1.1, prob=0.5),
ToTensorD(keys=["hand"]),
]
)
# create dataset and dataloader
real_dataset = CacheDataset(real_data, train_transforms)
batch_size = 300
real_dataloader = DataLoader(real_dataset, batch_size=batch_size, shuffle=True, num_workers=10)
# define function to process batchdata for input into discriminator
def prepare_batch(batchdata):
"""
Process Dataloader batchdata dict object and return image tensors for D Inferer
"""
return batchdata["hand"]
# define networks
disc_net = Discriminator(
in_shape=(1, 64, 64), channels=(8, 16, 32, 64, 1), strides=(2, 2, 2, 2, 1), num_res_units=1, kernel_size=5
).to(device)
latent_size = 64
gen_net = Generator(
latent_shape=latent_size, start_shape=(latent_size, 8, 8), channels=[32, 16, 8, 1], strides=[2, 2, 2, 1]
)
# initialize both networks
disc_net.apply(normal_init)
gen_net.apply(normal_init)
# input images are scaled to [0,1] so enforce the same of generated outputs
gen_net.conv.add_module("activation", torch.nn.Sigmoid())
gen_net = gen_net.to(device)
# create optimizers and loss functions
learning_rate = 2e-4
betas = (0.5, 0.999)
disc_opt = torch.optim.Adam(disc_net.parameters(), learning_rate, betas=betas)
gen_opt = torch.optim.Adam(gen_net.parameters(), learning_rate, betas=betas)
disc_loss_criterion = torch.nn.BCELoss()
gen_loss_criterion = torch.nn.BCELoss()
real_label = 1
fake_label = 0
def discriminator_loss(gen_images, real_images):
"""
The discriminator loss is calculated by comparing D
prediction for real and generated images.
"""
real = real_images.new_full((real_images.shape[0], 1), real_label)
gen = gen_images.new_full((gen_images.shape[0], 1), fake_label)
realloss = disc_loss_criterion(disc_net(real_images), real)
genloss = disc_loss_criterion(disc_net(gen_images.detach()), gen)
return (genloss + realloss) / 2
def generator_loss(gen_images):
"""
The generator loss is calculated by determining how realistic
the discriminator classifies the generated images.
"""
output = disc_net(gen_images)
cats = output.new_full(output.shape, real_label)
return gen_loss_criterion(output, cats)
# initialize current run dir
run_dir = "model_out"
print("Saving model output to: %s " % run_dir)
# create workflow handlers
handlers = [
StatsHandler(
name="batch_training_loss",
output_transform=lambda x: {Keys.GLOSS: x[Keys.GLOSS], Keys.DLOSS: x[Keys.DLOSS]},
),
CheckpointSaver(
save_dir=run_dir,
save_dict={"g_net": gen_net, "d_net": disc_net},
save_interval=10,
save_final=True,
epoch_level=True,
),
]
# define key metric
key_train_metric = None
# create adversarial trainer
disc_train_steps = 5
num_epochs = 50
trainer = GanTrainer(
device,
num_epochs,
real_dataloader,
gen_net,
gen_opt,
generator_loss,
disc_net,
disc_opt,
discriminator_loss,
d_prepare_batch=prepare_batch,
d_train_steps=disc_train_steps,
latent_shape=latent_size,
key_train_metric=key_train_metric,
train_handlers=handlers,
)
# run GAN training
trainer.run()
# Training completed, save a few random generated images.
print("Saving trained generator sample output.")
test_img_count = 10
test_latents = make_latent(test_img_count, latent_size).to(device)
fakes = gen_net(test_latents)
for i, image in enumerate(fakes):
filename = "gen-fake-final-%d.png" % (i)
save_path = os.path.join(run_dir, filename)
img_array = image[0].cpu().data.numpy()
png_writer.write_png(img_array, save_path, scale=255)
if __name__ == "__main__":
main()
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