z_show = sample_Z(3, Z_dim) y_show = show_Y(3) magnify_interval = False for it in range(1000000): if it % 100 == 0: samples = sess.run(G_sample, feed_dict={Y: y_show, Z: z_show}) save_img(dir=dir_name, iter=it, imgs=y_show, boxs=samples, magnify_interval=magnify_interval) Y_mb, X_mb = get_batch(mb_size, crop_img=False, magnify_interval=magnify_interval) # X_mb = np.reshape(X_mb, [-1, 28, 28, 1]) _, D_loss_curr = sess.run([D_optimizer, D_loss], feed_dict={ X: X_mb, Y: Y_mb, Z: sample_Z(mb_size, Z_dim) }) _, G_loss_curr = sess.run([G_optimizer, G_loss], feed_dict={ Y: Y_mb, Z: sample_Z(mb_size, Z_dim) }) if it % 100 == 0:
def show_Y(m): Y_mb, _ = get_batch(m, crop_img=False, magnify_interval=False) return Y_mb
i = 0 mb_size = 128 Z_dim = 100 z_show = sample_Z(16, Z_dim) for it in range(1000000): if it % 100 == 0: samples = sess.run(G_sample, feed_dict={Z: z_show}) fig = plot(samples) plt.savefig(dir_name + '/{}.png'.format(str(i).zfill(3)), bbox_inches='tight') i += 1 plt.close(fig) X_mb = get_batch(mb_size) # X_mb = np.reshape(X_mb, [-1, 28, 28, 1]) _, D_loss_curr = sess.run([D_optimizer, D_loss], feed_dict={ X: X_mb, Z: sample_Z(mb_size, Z_dim) }) _, G_loss_curr = sess.run([G_optimizer, G_loss], feed_dict={Z: sample_Z(mb_size, Z_dim)}) if it % 100 == 0: print('Iter: {}'.format(it)) print('D loss: {:.4}'.format(D_loss_curr)) print('G_loss: {:.4}'.format(G_loss_curr)) print()
Z_dim = 100 z_show = sample_Z(16, Z_dim) for it in range(1000000): if it % 100 == 0: samples = sess.run(G_sample, feed_dict={Z: z_show}) samples = (samples + 1.) / 2. # inverse transform from [-1,1] to [0,1] # fig = plot(samples) # plt.savefig(dir_name + '/{}.png'.format(str(i).zfill(3)), bbox_inches='tight') i += 1 # plt.close(fig) from scipy import misc misc.imsave(dir_name + '/{}.png'.format(str(i)), samples[0]) X_mb, _ = get_batch(mb_size, crop_img=False, magnify_interval=True) # X_mb = np.reshape(X_mb, [-1, 28, 28, 1]) _, D_loss_curr = sess.run([D_optimizer, D_loss], feed_dict={ X: X_mb, Z: sample_Z(mb_size, Z_dim) }) _, G_loss_curr = sess.run([G_optimizer, G_loss], feed_dict={Z: sample_Z(mb_size, Z_dim)}) if it % 100 == 0: print('Iter: {}'.format(it)) print('D loss: {:.4}'.format(D_loss_curr)) print('G_loss: {:.4}'.format(G_loss_curr)) print()
cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2) cv2.imwrite('{}/{}_{}.png'.format(dir, j, iter), img) j += 1 else: for i, img in enumerate(imgs): img = img * 255 img = RGB2BGR(img) cv2.imwrite('{}/{}_{}.png'.format(dir, i, iter), img) if __name__ == '__main__': from datas import get_batch batch_size = 4 imgs_batch, box_batch = get_batch(batch_size, crop_img=False, magnify_interval=False) print(len(box_batch)) # save_img('test', 2, imgs_batch) # import cv2 # from scipy import misc # misc.imsave('001_misc.png', imgs_batch[0]) # img = imgs_batch[0] * 255 # img = img[..., ::-1] # img = img.copy() # cv2.rectangle(img, (212, 300), (290, 300), (0, 255, 0), 2) # cv2.imwrite('001_cv.png', img) save_img('test', 1, imgs_batch, box_batch) save_img('test', 2, imgs_batch)
mb_size = 32 Z_dim = 100 z_show = sample_Z(16, Z_dim) for it in range(1000000): if it % 100 == 0: samples = sess.run(G_sample, feed_dict={Z: z_show}) samples = (samples + 1.) / 2. # inverse transform from [-1,1] to [0,1] fig = plot(samples) plt.savefig(dir_name + '/{}.png'.format(str(i).zfill(3)), bbox_inches='tight') i += 1 plt.close(fig) X_mb = get_batch(mb_size, magnify_interval=True) # X_mb = np.reshape(X_mb, [-1, 28, 28, 1]) _, D_loss_curr = sess.run([D_optimizer, D_loss], feed_dict={ X: X_mb, Z: sample_Z(mb_size, Z_dim) }) _, G_loss_curr = sess.run([G_optimizer, G_loss], feed_dict={Z: sample_Z(mb_size, Z_dim)}) if it % 100 == 0: print('Iter: {}'.format(it)) print('D loss: {:.4}'.format(D_loss_curr)) print('G_loss: {:.4}'.format(G_loss_curr)) print()
mb_size = 128 Z_dim = 100 z_show = sample_Z(16, Z_dim) for it in range(1000000): if it % 100 == 0: samples = sess.run(G_sample, feed_dict={Z: z_show}) samples = (samples + 1.) / 2. # inverse transform from [-1,1] to [0,1] fig = plot(samples) plt.savefig(dir_name + '/{}.png'.format(str(i).zfill(3)), bbox_inches='tight') i += 1 plt.close(fig) X_mb = get_batch(mb_size, resize=True) # X_mb = np.reshape(X_mb, [-1, 28, 28, 1]) _, D_loss_curr = sess.run([D_optimizer, D_loss], feed_dict={ X: X_mb, Z: sample_Z(mb_size, Z_dim) }) _, G_loss_curr = sess.run([G_optimizer, G_loss], feed_dict={Z: sample_Z(mb_size, Z_dim)}) if it % 100 == 0: print('Iter: {}'.format(it)) print('D loss: {:.4}'.format(D_loss_curr)) print('G_loss: {:.4}'.format(G_loss_curr)) print()