# Fileset [generate_geometies_using_trained_cgan.py](https://mdr.nims.go.jp/filesets/e608fde8-8c44-4c83-88d1-cfa2ab793f75/download) ## Creator [Xiaoyang Zheng](https://orcid.org/0000-0002-9004-9216), Ta-Te Chen, Xiaofeng Guo, Sadaki Samitsu, Ikumu Watanabe ## Rights [Creative Commons BY Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) ## Other metadata [Tutorial for conditional generative adversarial network](https://mdr.nims.go.jp/datasets/14268605-d006-4212-b2d4-c2274ca555de) ## Fulltext """Python code for conditional generative adversarial networkver. 20230310coded by Xiaoyang ZhengCopyright (C) Xiaoyang Zheng and Ikumu WatanabeEmail: ZHENG.Xiaoyang@nims.go.jp; WATANABE.Ikumu@nims.go.jp"""import osos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'import numpy as npimport tensorflow as tffrom PIL import Imageimport glob, datetimeimport matplotlib.pyplot as pltfrom solver import Solverfrom tensorflow import kerasfrom tensorflow.keras import layers## for circular paddingdef pad_dim(x, n=1): x = tf.concat((x[:,:,-n:,:], x, x[:,:,:n,:]), axis=-2) x = tf.concat((x[:,-n:,:,:], x, x[:,:n,:,:]), axis=1) return xdef delete_dim(x): x = x[:, 3:-3, 3:-3, :] return x## architecture of generatorclass Generator(keras.Model): def __init__(self): super(Generator, self).__init__() self.fc = layers.Dense(4*4*512) self.bn0 = layers.BatchNormalization(momentum=0.9, epsilon=1e-5) self.conv1 = layers.Conv2DTranspose(384, kernel_size=[4,4], strides=[2,2], padding='valid') self.bn1 = layers.BatchNormalization(momentum=0.9, epsilon=1e-5) self.conv2 = layers.Conv2DTranspose(256, kernel_size=4, strides=2, padding='valid') self.bn2 = layers.BatchNormalization(momentum=0.9, epsilon=1e-5) self.conv3 = layers.Conv2DTranspose(128, kernel_size=4, strides=2, padding='valid') self.bn3 = layers.BatchNormalization(momentum=0.9, epsilon=1e-5) self.conv4 = layers.Conv2DTranspose(64, kernel_size=4, strides=2, padding='valid') self.bn4 = layers.BatchNormalization(momentum=0.9, epsilon=1e-5) self.conv5 = layers.Conv2DTranspose(32, kernel_size=4, strides=2, padding='valid') self.bn5 = layers.BatchNormalization(momentum=0.9, epsilon=1e-5) self.conv6 = layers.Conv2DTranspose(1, kernel_size=4, strides=2, padding='valid') def call(self, inputs_noise, input_condition, training=None): # inputs_noise: (b, 128), inputs_condition: (b, 2) inputs_noise = tf.cast(inputs_noise, dtype=tf.float32) input_condition = tf.cast(input_condition, dtype=tf.float32) net = tf.concat((inputs_noise, input_condition), axis=-1) net = self.fc(net) # (b, 4*4*512) net = self.bn0(net, training=training) net = tf.reshape(net, [-1, 4, 4, 512]) # (b, 4, 4, 512) net = pad_dim(net, n=1) net = tf.nn.leaky_relu(self.bn1(self.conv1(net), training=training)) net = delete_dim(net) net = pad_dim(net) net = tf.nn.leaky_relu(self.bn2(self.conv2(net), training=training)) net = delete_dim(net) net = pad_dim(net) net = tf.nn.leaky_relu(self.bn3(self.conv3(net), training=training)) net = delete_dim(net) net = pad_dim(net) net = tf.nn.leaky_relu(self.bn4(self.conv4(net), training=training)) net = delete_dim(net) net = pad_dim(net) net = tf.nn.leaky_relu(self.bn5(self.conv5(net), training=training)) net = delete_dim(net) net = pad_dim(net) net = self.conv6(net) net = delete_dim(net) net = tf.tanh(net) # (b, 256, 256, 1) return net## draw figures for comparing the input and output valuesdef new_bi_plot(): lims_property1 = [0, 1] lims_property2 = [0, 1] fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[8, 4], constrained_layout=True) ax1.plot(lims_property1, lims_property1, 'gray') ax1.set_xlim(lims_property1) ax1.set_ylim(lims_property1) ax1.set_aspect(1) ax1.set_xlabel("Input property 1") ax1.set_ylabel("Output property 1") ax2.plot(lims_property2, lims_property2, 'gray') ax2.set_xlim(lims_property2) ax2.set_ylim(lims_property2) ax2.set_aspect(1) ax2.set_xlabel("Input property 2") ax2.set_ylabel("Output property 2") return ax1, ax2def bi_plot(pre, ref, ax1, ax2): x2 = pre[:, 1] y2 = ref[:, 1] x1 = pre[:, 0] y1 = ref[:, 0] ax1.scatter(x1, y1, c='C0', alpha=0.2) ax2.scatter(x2, y2, c='C1', alpha=0.2)## generate the images of generated 2D geometriesdef generate_and_save_images(fake_image, epoch): fig = plt.figure(figsize=(4, 4), dpi=300) for i in range(fake_image.shape[0]): plt.subplot(4, 4, i + 1) plt.imshow(fake_image[i, :, :, 0] * 127.5 + 127.5, cmap='gray') plt.axis('off') plt.savefig(r'fake_images/image_at_epoch_{:04d}.png'.format(epoch)) plt.close()def main(): tf.random.set_seed(222) np.random.seed(222) assert tf.__version__.startswith('2.') # hyper parameters z_dim = 128 generator = Generator() generator.load_weights(r'ckpt/generator_199.ckpt') ## replace the source file solver = Solver() solver.load_weights(r"/ckpt/solver_199.ckpt") ## replace the source file # calculate MSE mse_1024 = 0 # plot test ax1, ax2 = new_bi_plot() for i in range(64): noise_test = tf.random.normal([batch_size, z_dim]) ## you can replace the condition using your target values condition = np.stack([np.random.uniform(lims_property1[0], lims_property1[1], batch_size), np.random.uniform(lims_property2[0], lims_property2[1], batch_size)], axis=1) # get a condition from available data space fake_image = generator(noise_test, condition, training=False) reference = solver(fake_image) # cal mse mse = tf.reduce_mean(tf.losses.mean_squared_error(condition, reference)) mse_1024 += mse bi_plot(condition, reference, ax1, ax2) mse_1024 = mse_1024/64 print(mse_1024) plt.savefig('results/%d_test.png' % 0) ## save the comparison between input and output values plt.close() generate_and_save_images(fake_image, 0) ## save generated imagesif __name__ == '__main__': main()