PyTorch-StudioGAN
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POSTECH-CVLab
Can't run any type of analysis after training
For example, when I try to execute FA I get this error
[INFO] 2022-04-04 05:47:59 > Run frequency analysis (use 8 fake and valid images).
Traceback (most recent call last):
File "src/main.py", line 186, in <module>
hdf5_path=hdf5_path)
File "/root/gan-experiments/src/loader.py", line 438, in load_worker
worker.run_frequency_analysis(dataloader=eval_dataloader)
File "/root/gan-experiments/src/worker.py", line 1253, in run_frequency_analysis
N, C, H, W = np.shape(real_array)
UnboundLocalError: local variable 'real_array' referenced before assignment
My command is python src/main.py -cfg CFG -hdf5 -l -save SAVE -fa -ckpt CKPT_DIR -data DATA -ref="valid"
tsne
[t-SNE] Mean sigma: 2.670893
[t-SNE] KL divergence after 250 iterations with early exaggeration: 73.358437
[t-SNE] KL divergence after 300 iterations: 1.787954
Traceback (most recent call last):
File "src/main.py", line 186, in
For me, it works well.
Could you please give me more information about your situation?
By the way, I used the following command:
CUDA_VISIBLE_DEVICES=0 python src/main.py -cfg "./src/configs/CIFAR10/SAGAN.yaml" -hdf5 -l -save "/root/code/studiogan" -fa -tsne -knn -ckpt "/root/ext_data/studiogan_ckpt/CIFAR10/checkpoints/CIFAR10-SAGAN-train-2022_01_13_01_21_17"
-data "/root/code/data/CIFAR10" -ref="test" -metrics "none"
And the results are
Oh, I see that you set metrics to none. Could it be the reason that it worked?
Hmm, interesting. So the commands are almost the same but it doesn't work in my case. Ok, I'll give it a try a bit later with your command and let you know.
Nope, doesn't work
Command
CUDA_VISIBLE_DEVICES=0 python src/main.py -cfg "./src/configs/Custom/BIGGAN_CYT.yaml" -hdf5 -l -save "/root/gan-experiments/save/biggan_cyt" -v -knn -fa -tsne -sf -sf_num 300 -ckpt "/root/gan-experiments/save/biggan_cyt/checkpoints/cyt_64-BIGGAN_CYT-train-2022_04_04_06_02_59" -data "/root/gan-experiments/data/cyt_64" -ref="valid" -metrics="none"
Which Python version are you using? Mine is 3.7.6
Mine is 3.7.10.
[INFO] 2022-04-04 05:47:59 > Run frequency analysis (use 8 fake and valid images).
Traceback (most recent call last):
File "src/main.py", line 186, in <module>
hdf5_path=hdf5_path)
File "/root/gan-experiments/src/loader.py", line 438, in load_worker
worker.run_frequency_analysis(dataloader=eval_dataloader)
File "/root/gan-experiments/src/worker.py", line 1253, in run_frequency_analysis
N, C, H, W = np.shape(real_array)
UnboundLocalError: local variable 'real_array' referenced before assignment
Let see the first comment. The error message tells us the local variable "real_array" is not defined. This indicates that the variable "number of batches" is under 1, so the for loop is not executed. Could you tell me the structure of the dataset you used ?
for i in range(num_batches):
real_images, real_labels = next(data_iter)
fake_images, fake_labels, _, _, _, _, _ = sample.generate_images(z_prior=self.MODEL.z_prior,
truncation_factor=self.RUN.truncation_factor,
batch_size=self.OPTIMIZATION.batch_size,
z_dim=self.MODEL.z_dim,
num_classes=self.DATA.num_classes,
y_sampler="totally_random",
radius="N/A",
generator=generator,
discriminator=self.Dis,
is_train=False,
LOSS=self.LOSS,
RUN=self.RUN,
MODEL=self.MODEL,
device=self.local_rank,
is_stylegan=self.is_stylegan,
generator_mapping=generator_mapping,
generator_synthesis=generator_synthesis,
style_mixing_p=0.0,
stylegan_update_emas=False,
cal_trsp_cost=False)
fake_images = fake_images.detach().cpu().numpy()
real_images = np.asarray((real_images + 1) * 127.5, np.uint8)
fake_images = np.asarray((fake_images + 1) * 127.5, np.uint8)
if i == 0:
real_array = real_images
fake_array = fake_images
else:
real_array = np.concatenate([real_array, real_images], axis=0)
fake_array = np.concatenate([fake_array, fake_images], axis=0)
Thank you.
Hmm... Could you print "num_batches" variable ?
def run_frequency_analysis(self, dataloader):
if self.global_rank == 0:
self.logger.info("Run frequency analysis (use {num} fake and {ref} images).".\
format(num=len(dataloader), ref=self.RUN.ref_dataset))
if self.gen_ctlr.standing_statistics:
self.gen_ctlr.std_stat_counter += 1
requires_grad = self.LOSS.apply_lo or self.RUN.langevin_sampling
with torch.no_grad() if not requires_grad else misc.dummy_context_mgr() as ctx:
misc.make_GAN_untrainable(self.Gen, self.Gen_ema, self.Dis)
generator, generator_mapping, generator_synthesis = self.gen_ctlr.prepare_generator()
data_iter = iter(dataloader)
num_batches = len(dataloader) // self.OPTIMIZATION.batch_size
for i in range(num_batches):
real_images, real_labels = next(data_iter)
fake_images, fake_labels, _, _, _, _, _ = sample.generate_images(z_prior=self.MODEL.z_prior,
truncation_factor=self.RUN.truncation_factor,
batch_size=self.OPTIMIZATION.batch_size,
z_dim=self.MODEL.z_dim,
num_classes=self.DATA.num_classes,
y_sampler="totally_random",
radius="N/A",
generator=generator,
discriminator=self.Dis,
is_train=False,
LOSS=self.LOSS,
RUN=self.RUN,
MODEL=self.MODEL,
device=self.local_rank,
is_stylegan=self.is_stylegan,
generator_mapping=generator_mapping,
generator_synthesis=generator_synthesis,
style_mixing_p=0.0,
stylegan_update_emas=False,
cal_trsp_cost=False)
fake_images = fake_images.detach().cpu().numpy()
real_images = np.asarray((real_images + 1) * 127.5, np.uint8)
fake_images = np.asarray((fake_images + 1) * 127.5, np.uint8)
if i == 0:
real_array = real_images
fake_array = fake_images
else:
real_array = np.concatenate([real_array, real_images], axis=0)
fake_array = np.concatenate([fake_array, fake_images], axis=0)
N, C, H, W = np.shape(real_array)
real_r, real_g, real_b = real_array[:, 0, :, :], real_array[:, 1, :, :], real_array[:, 2, :, :]
real_gray = 0.2989 * real_r + 0.5870 * real_g + 0.1140 * real_b
fake_r, fake_g, fake_b = fake_array[:, 0, :, :], fake_array[:, 1, :, :], fake_array[:, 2, :, :]
fake_gray = 0.2989 * fake_r + 0.5870 * fake_g + 0.1140 * fake_b
for j in tqdm(range(N)):
real_gray_f = np.fft.fft2(real_gray[j] - ndimage.median_filter(real_gray[j], size=H // 8))
fake_gray_f = np.fft.fft2(fake_gray[j] - ndimage.median_filter(fake_gray[j], size=H // 8))
real_gray_f_shifted = np.fft.fftshift(real_gray_f)
fake_gray_f_shifted = np.fft.fftshift(fake_gray_f)
if j == 0:
real_gray_spectrum = 20 * np.log(np.abs(real_gray_f_shifted)) / N
fake_gray_spectrum = 20 * np.log(np.abs(fake_gray_f_shifted)) / N
else:
real_gray_spectrum += 20 * np.log(np.abs(real_gray_f_shifted)) / N
fake_gray_spectrum += 20 * np.log(np.abs(fake_gray_f_shifted)) / N
misc.plot_spectrum_image(real_spectrum=real_gray_spectrum,
fake_spectrum=fake_gray_spectrum,
directory=join(self.RUN.save_dir, "figures", self.run_name),
logger=self.logger,
logging=self.global_rank == 0 and self.logger)
misc.make_GAN_trainable(self.Gen, self.Gen_ema, self.Dis)
I'll try a bit later. Could it be the consequence that my dataset is unbalanced? I mean each class consists of different count of images.
Could it be the consequence that my dataset is unbalanced? ==> I do not think so. Hope the problem is resolved.
Thank you.
Best,