[Introduction to sinGAN-Tensorflow] I played with the super-resolution "Challenge Big Imayuyu" ♬
Continuing from the last time, sinGAN has been rewritten by Tensorflow with the following reference, and the code is easy to understand, so SRGAN (I haven't written it yet. .) And Last sinGAN I played with "Challenge Big Imayuyu" which failed. 【reference】 ① I implemented some SinGAN ②ppooiiuuyh/SinGAN-tensorflow2.0 This time, I played while trying the implementation of Reference ①.
- Reference ② is complicated as it is, so read it slowly.
The output example of SR was as follows. (Maybe the same as the above reference)
- A sufficiently magnified super-resolution image has been obtained.
| 1 | 2 |
|---|---|
 |
 |
This article starts with the following figure comparing the above output example with the original Pytorch output. In other words, they are doing the same thing, but the accuracy obtained seems to be different. This starts from the fact that it is probably due to the detailed hyperparameter differences. Anyway, I can't read the code with Pytorch, but the code for this reference is easy to read and modify, so it's still not enough, but I played with it.
| Pytorch version | Tensorflow version |
|---|---|
 |
 |
What i did
·environment ・ Check hyperparameters ・ Try to output the image of the middle layer ・ Challenge the big Mayu Yu
·environment
Reference above ① Download the Zip file from the Github site of the site and extract it. Install the following dependencies in a normal conda environment.
python -m pip install -r requirements.txt
I think you can do it below. However, since the storage location of the input image and the result storage directory are different, change it according to your environment.
(base) C:\Users\user\SinGAN_tf_impl-master>python main.py "SR" "Input/images/33039_LR.png "
・ Check hyperparameters
It is difficult for Uwan to just decipher what values and how the hyperparameters of both codes are used.
However, this time both codes are summarized below and are relatively easy to read.
Certainly this is not all, especially both in SR.py etc.
parser.add_argument('--sr_factor', type=int, default=4)
Has been redefined.
So, the hyperparameters of the Tensorflow version are main.py and are defined as follows.
[email protected]
import argparse
from train import *
#from SinGAN.train import *
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('mode')
parser.add_argument('input_image')
parser.add_argument('--save_dir', default='models')
#Network hyper parameter
parser.add_argument('--hidden_channels', type=int, default=32)
parser.add_argument('--k_size', type=int, default=3)
parser.add_argument('--n_layers', type=int, default=5)
#train settings
parser.add_argument('--n_iter', type=int, default=2000)
parser.add_argument('--lr_g', type=float, default=0.0005)
parser.add_argument('--lr_d', type=float, default=0.0005)
parser.add_argument('--beta1', type=float, default=0.5)
parser.add_argument('--g_times', type=int, default=3)
parser.add_argument('--d_times', type=int, default=3)
parser.add_argument('--gp_weight', type=float, default=0.1)
parser.add_argument('--alpha', type=float, default=10)
#data manipulation
parser.add_argument('--scale_factor', type=float, default=0.75)
parser.add_argument('--noise_weight', type=float, default=0.1)
parser.add_argument('--min_size', type=int, default=18)
#SR params
parser.add_argument('--sr_factor', type=int, default=4)
args = parser.parse_args()
if args.mode == 'SR':
train_SR(args)
On the other hand, the Pytorch version is defined in config.py as follows.
So
** Here, it was confirmed that all the parameters match. ** **
However,
parser.add_argument('--scale_factor', type=float, default=0.75)
Actually, I don't understand yet, but in the Pytorch version, it may be the following parameters from the result directory name.
Example: scale_factor = 0.793701, alpha = 100
[email protected]
import argparse
def get_arguments():
parser = argparse.ArgumentParser()
#parser.add_argument('--mode', help='task to be done', default='train')
#workspace:
parser.add_argument('--not_cuda', action='store_true', help='disables cuda', default=0)
#load, input, save configurations:
parser.add_argument('--netG', default='', help="path to netG (to continue training)")
parser.add_argument('--netD', default='', help="path to netD (to continue training)")
parser.add_argument('--manualSeed', type=int, help='manual seed')
parser.add_argument('--nc_z',type=int,help='noise # channels',default=3)
parser.add_argument('--nc_im',type=int,help='image # channels',default=3)
parser.add_argument('--out',help='output folder',default='Output')
#networks hyper parameters:
parser.add_argument('--nfc', type=int, default=32)
parser.add_argument('--min_nfc', type=int, default=32)
parser.add_argument('--ker_size',type=int,help='kernel size',default=3)
parser.add_argument('--num_layer',type=int,help='number of layers',default=5)
parser.add_argument('--stride',help='stride',default=1)
parser.add_argument('--padd_size',type=int,help='net pad size',default=0)#math.floor(opt.ker_size/2)
#pyramid parameters:
parser.add_argument('--scale_factor',type=float,help='pyramid scale factor',default=0.75)#pow(0.5,1/6))
parser.add_argument('--noise_amp',type=float,help='addative noise cont weight',default=0.1)
parser.add_argument('--min_size',type=int,help='image minimal size at the coarser scale',default=25)
parser.add_argument('--max_size', type=int,help='image minimal size at the coarser scale', default=250)
#optimization hyper parameters:
parser.add_argument('--niter', type=int, default=2000, help='number of epochs to train per scale')
parser.add_argument('--gamma',type=float,help='scheduler gamma',default=0.1)
parser.add_argument('--lr_g', type=float, default=0.0005, help='learning rate, default=0.0005')
parser.add_argument('--lr_d', type=float, default=0.0005, help='learning rate, default=0.0005')
parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')
parser.add_argument('--Gsteps',type=int, help='Generator inner steps',default=3)
parser.add_argument('--Dsteps',type=int, help='Discriminator inner steps',default=3)
parser.add_argument('--lambda_grad',type=float, help='gradient penelty weight',default=0.1)
parser.add_argument('--alpha',type=float, help='reconstruction loss weight',default=10)
return parser
・ Try to output the image of the middle layer
Next, I doubted the structure of the model. This has not been solved either, but since the Tensorflow version is easy to understand, I made the following modifications and output it to the standard output. In addition, the output before and after modification is shown.
- The part specified by None is a numerical value. In terms of code, the relevant part has been changed as follows.
train.py
for i in range(n_blocks):
scale = math.pow(scale_factor, n_blocks-i-1)
cur_h, cur_w = int(h*scale), int(w*scale)
img = tf.image.resize(real_image, (cur_h, cur_w))
resolutions.append((cur_h, cur_w))
#inp = tf.keras.Input(shape=(None, None, 3))
#noise = tf.keras.Input(shape=(None, None, 3))
inp = tf.keras.Input(shape=(cur_h, cur_w, 3))
noise = tf.keras.Input(shape=(cur_h, cur_w, 3))
G = tf.keras.Model(inputs=[inp, noise], outputs=model.G_block(inp, noise, name='G_block_%d'%i, hidden_maps=args.hidden_channels, num_layers=args.n_layers))
D = tf.keras.Model(inputs=inp, outputs=model.D_block(inp, name='D_block_%d'%i, hidden_maps=args.hidden_channels, num_layers=args.n_layers))
lr_g = tf.Variable(args.lr_g, trainable=False)
lr_d = tf.Variable(args.lr_d, trainable=False)
opt_G = tf.keras.optimizers.Adam(lr_g, args.beta1)
opt_D = tf.keras.optimizers.Adam(lr_d, args.beta1)
G.summary()
D.summary()
This should determine if the network is the same as the Pytorch version. The Pytorch version changes a little, but it has a certain structure and changes after a while, so it seems that the logic is a little different.
- This is also just an output comparison, but it has not been confirmed in the source. So, this time, I tried to output the state of the intermediate state of learning as an image. The continuation of the above code has been changed as follows.
train.py
if i > 0:
for (prev, cur) in zip(Gs[-1].layers, G.layers):
cur.set_weights(prev.get_weights())
for (prev, cur) in zip(Ds[-1].layers, D.layers):
cur.set_weights(prev.get_weights())
init_opt(opt_G, G)
init_opt(opt_D, D)
with tqdm(range(args.n_iter)) as bar:
bar.set_description('Block %d / %d'%(i+1, n_blocks))
for iteration in bar:
if i == 0:
prev_img = tf.zeros_like(img)
else:
prev_img = proc_image(tf.zeros([1, resolutions[0][0], resolutions[0][1], 3]), Gs, args.noise_weight, resolutions)
g_loss, d_loss = train_step(img, prev_img, args.noise_weight, G, D, opt_G, opt_D, args.g_times, args.d_times, args.alpha)
bar.set_postfix(ordered_dict=OrderedDict(
g_loss=g_loss.numpy(), d_loss=d_loss.numpy()
))
if iteration == int(args.n_iter*0.8):
lr_d.assign(args.lr_d*0.1)
lr_g.assign(args.lr_g*0.1)
Gs.append(G)
Ds.append(D)
G.save(os.path.join(save_dir, 'SR_G_%d_res_%dx%d.h5'%(i+1, cur_h, cur_w)))
D.save(os.path.join(save_dir, 'SR_D_%d_res_%dx%d.h5'%(i+1, cur_h, cur_w)))
scale_factor = math.pow(1/2, 1/3)
target_res = 4
scale = 1.0 / scale_factor
n, h, w, c = real_image.shape
t_h, t_w = h*target_res, w*target_res
iter_times = int(math.log(target_res, scale))
img = real_image
os.makedirs(os.path.join(save_dir, 'result'), exist_ok=True)
for j in range(1, iter_times+1, 1):
res = (int(h*math.pow(scale, j)), int(w*math.pow(scale, j)))
img = tf.image.resize(img, size=res)
img = G([img, tf.zeros_like(img)])
image = np.squeeze(img)
image = (np.clip(image, -1.0, 1.0) + 1.0) * 127.5
image = Image.fromarray(image.astype(np.uint8))
image.save(save_dir+'/result/'+str(i)+'_%dx%d.jpg'%res)
In other words, I changed the last image output part to be one step inside and output the image in the intermediate state.
So you can get the following figure.
Looking at this, this occasional learning is as shown in reference ③ below.
"→ Hierarchical Patch-GANs
Capture features of various scales while gradually increasing the resolution from a coarse image
Set a small receptive field so that the entire image is not memorized. "
You can see that.
【reference】
③ Explanation of SinGAN's dissertation
・ Challenge the big Mayu Yu
So, I tried to challenge Mayu Watanabe. Below are the results. ⇒ Last time, if you expanded it too much, a line would appear, but it looks calm, and I think it's a success.
| Mayuyu | |
|---|---|
| 128 |
 |
| 161 |  |
| 203 |  |
| 255 |  |
| 322 |  |
| 406 |  |
| 511 |  |
| 645 |  |
| 812 |  |
| 1023 |  |
Summary
・ I tried sinGAN-Tensorflow version ・ I tried to output an image of intermediate learning ・ "Challenge the big Mayuyu" and got relatively good results
・ I want to master sinGAN a little more
bonus
The modified version of Tensorflow version is as follows
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 30, 20, 3)] 0
__________________________________________________________________________________________________
input_2 (InputLayer) [(None, 30, 20, 3)] 0
__________________________________________________________________________________________________
tf_op_layer_add (TensorFlowOpLa [(None, 30, 20, 3)] 0 input_1[0][0]
input_2[0][0]
__________________________________________________________________________________________________
conv_block_0_conv_0 (Conv2D) (None, 30, 20, 32) 896 tf_op_layer_add[0][0]
__________________________________________________________________________________________________
conv_block_0_BN_0 (BatchNormali (None, 30, 20, 32) 128 conv_block_0_conv_0[0][0]
__________________________________________________________________________________________________
leaky_re_lu (LeakyReLU) (None, 30, 20, 32) 0 conv_block_0_BN_0[0][0]
conv_block_1_BN_1[0][0]
conv_block_2_BN_2[0][0]
conv_block_3_BN_3[0][0]
__________________________________________________________________________________________________
conv_block_1_conv_1 (Conv2D) (None, 30, 20, 32) 9248 leaky_re_lu[0][0]
__________________________________________________________________________________________________
conv_block_1_BN_1 (BatchNormali (None, 30, 20, 32) 128 conv_block_1_conv_1[0][0]
__________________________________________________________________________________________________
conv_block_2_conv_2 (Conv2D) (None, 30, 20, 32) 9248 leaky_re_lu[1][0]
__________________________________________________________________________________________________
conv_block_2_BN_2 (BatchNormali (None, 30, 20, 32) 128 conv_block_2_conv_2[0][0]
__________________________________________________________________________________________________
conv_block_3_conv_3 (Conv2D) (None, 30, 20, 32) 9248 leaky_re_lu[2][0]
__________________________________________________________________________________________________
conv_block_3_BN_3 (BatchNormali (None, 30, 20, 32) 128 conv_block_3_conv_3[0][0]
__________________________________________________________________________________________________
conv_block_4_conv_4 (Conv2D) (None, 30, 20, 3) 867 leaky_re_lu[3][0]
__________________________________________________________________________________________________
tf_op_layer_Tanh (TensorFlowOpL [(None, 30, 20, 3)] 0 conv_block_4_conv_4[0][0]
__________________________________________________________________________________________________
tf_op_layer_add_1 (TensorFlowOp [(None, 30, 20, 3)] 0 tf_op_layer_Tanh[0][0]
input_1[0][0]
==================================================================================================
Total params: 30,019
Trainable params: 29,763
Non-trainable params: 256
__________________________________________________________________________________________________
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 30, 20, 3)] 0
__________________________________________________________________________________________________
conv_block_0_conv_0 (Conv2D) (None, 30, 20, 32) 896 input_1[0][0]
__________________________________________________________________________________________________
conv_block_0_BN_0 (BatchNormali (None, 30, 20, 32) 128 conv_block_0_conv_0[0][0]
__________________________________________________________________________________________________
leaky_re_lu (LeakyReLU) (None, 30, 20, 32) 0 conv_block_0_BN_0[0][0]
conv_block_1_BN_1[0][0]
conv_block_2_BN_2[0][0]
conv_block_3_BN_3[0][0]
__________________________________________________________________________________________________
conv_block_1_conv_1 (Conv2D) (None, 30, 20, 32) 9248 leaky_re_lu[4][0]
__________________________________________________________________________________________________
conv_block_1_BN_1 (BatchNormali (None, 30, 20, 32) 128 conv_block_1_conv_1[0][0]
__________________________________________________________________________________________________
conv_block_2_conv_2 (Conv2D) (None, 30, 20, 32) 9248 leaky_re_lu[5][0]
__________________________________________________________________________________________________
conv_block_2_BN_2 (BatchNormali (None, 30, 20, 32) 128 conv_block_2_conv_2[0][0]
__________________________________________________________________________________________________
conv_block_3_conv_3 (Conv2D) (None, 30, 20, 32) 9248 leaky_re_lu[6][0]
__________________________________________________________________________________________________
conv_block_3_BN_3 (BatchNormali (None, 30, 20, 32) 128 conv_block_3_conv_3[0][0]
__________________________________________________________________________________________________
conv_block_4_conv_4 (Conv2D) (None, 30, 20, 1) 289 leaky_re_lu[7][0]
==================================================================================================
Total params: 29,441
Trainable params: 29,185
Non-trainable params: 256
__________________________________________________________________________________________________
The original of Tensorflow version is as follows
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, None, None, 0
__________________________________________________________________________________________________
input_2 (InputLayer) [(None, None, None, 0
__________________________________________________________________________________________________
tf_op_layer_add (TensorFlowOpLa [(None, None, None, 0 input_1[0][0]
input_2[0][0]
__________________________________________________________________________________________________
conv_block_0_conv_0 (Conv2D) (None, None, None, 3 896 tf_op_layer_add[0][0]
__________________________________________________________________________________________________
conv_block_0_BN_0 (BatchNormali (None, None, None, 3 128 conv_block_0_conv_0[0][0]
__________________________________________________________________________________________________
leaky_re_lu (LeakyReLU) (None, None, None, 3 0 conv_block_0_BN_0[0][0]
conv_block_1_BN_1[0][0]
conv_block_2_BN_2[0][0]
conv_block_3_BN_3[0][0]
__________________________________________________________________________________________________
conv_block_1_conv_1 (Conv2D) (None, None, None, 3 9248 leaky_re_lu[0][0]
__________________________________________________________________________________________________
conv_block_1_BN_1 (BatchNormali (None, None, None, 3 128 conv_block_1_conv_1[0][0]
__________________________________________________________________________________________________
conv_block_2_conv_2 (Conv2D) (None, None, None, 3 9248 leaky_re_lu[1][0]
__________________________________________________________________________________________________
conv_block_2_BN_2 (BatchNormali (None, None, None, 3 128 conv_block_2_conv_2[0][0]
__________________________________________________________________________________________________
conv_block_3_conv_3 (Conv2D) (None, None, None, 3 9248 leaky_re_lu[2][0]
__________________________________________________________________________________________________
conv_block_3_BN_3 (BatchNormali (None, None, None, 3 128 conv_block_3_conv_3[0][0]
__________________________________________________________________________________________________
conv_block_4_conv_4 (Conv2D) (None, None, None, 3 867 leaky_re_lu[3][0]
__________________________________________________________________________________________________
tf_op_layer_Tanh (TensorFlowOpL [(None, None, None, 0 conv_block_4_conv_4[0][0]
__________________________________________________________________________________________________
tf_op_layer_add_1 (TensorFlowOp [(None, None, None, 0 tf_op_layer_Tanh[0][0]
input_1[0][0]
==================================================================================================
Total params: 30,019
Trainable params: 29,763
Non-trainable params: 256
__________________________________________________________________________________________________
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, None, None, 0
__________________________________________________________________________________________________
conv_block_0_conv_0 (Conv2D) (None, None, None, 3 896 input_1[0][0]
__________________________________________________________________________________________________
conv_block_0_BN_0 (BatchNormali (None, None, None, 3 128 conv_block_0_conv_0[0][0]
__________________________________________________________________________________________________
leaky_re_lu (LeakyReLU) (None, None, None, 3 0 conv_block_0_BN_0[0][0]
conv_block_1_BN_1[0][0]
conv_block_2_BN_2[0][0]
conv_block_3_BN_3[0][0]
__________________________________________________________________________________________________
conv_block_1_conv_1 (Conv2D) (None, None, None, 3 9248 leaky_re_lu[4][0]
__________________________________________________________________________________________________
conv_block_1_BN_1 (BatchNormali (None, None, None, 3 128 conv_block_1_conv_1[0][0]
__________________________________________________________________________________________________
conv_block_2_conv_2 (Conv2D) (None, None, None, 3 9248 leaky_re_lu[5][0]
__________________________________________________________________________________________________
conv_block_2_BN_2 (BatchNormali (None, None, None, 3 128 conv_block_2_conv_2[0][0]
__________________________________________________________________________________________________
conv_block_3_conv_3 (Conv2D) (None, None, None, 3 9248 leaky_re_lu[6][0]
__________________________________________________________________________________________________
conv_block_3_BN_3 (BatchNormali (None, None, None, 3 128 conv_block_3_conv_3[0][0]
__________________________________________________________________________________________________
conv_block_4_conv_4 (Conv2D) (None, None, None, 1 289 leaky_re_lu[7][0]
==================================================================================================
Total params: 29,441
Trainable params: 29,185
Non-trainable params: 256
__________________________________________________________________________________________________
Block 1 / 7: 100%|████████████████████████████████████████████████████████| 2000/2000 [01:32<00:00, 21.58it/s, g_loss=[0.9006956], d_loss=[-0.02634283]]
Bonus 2
In fact, the Tensorflow version of the model has a tensor size that increases as the input image increases, as shown below. And each model does not change the number of parameters as the size increases.
(base) C:\Users\user\SinGAN_tf_impl-master>python main.py "SR" "Input/images/mayuyu128.jpg "
2019-12-30 23:23:33.694956: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 20, 20, 3)] 0
__________________________________________________________________________________________________
input_2 (InputLayer) [(None, 20, 20, 3)] 0
__________________________________________________________________________________________________
tf_op_layer_add (TensorFlowOpLa [(None, 20, 20, 3)] 0 input_1[0][0]
input_2[0][0]
__________________________________________________________________________________________________
conv_block_0_conv_0 (Conv2D) (None, 20, 20, 32) 896 tf_op_layer_add[0][0]
__________________________________________________________________________________________________
conv_block_0_BN_0 (BatchNormali (None, 20, 20, 32) 128 conv_block_0_conv_0[0][0]
__________________________________________________________________________________________________
leaky_re_lu (LeakyReLU) (None, 20, 20, 32) 0 conv_block_0_BN_0[0][0]
conv_block_1_BN_1[0][0]
conv_block_2_BN_2[0][0]
conv_block_3_BN_3[0][0]
__________________________________________________________________________________________________
conv_block_1_conv_1 (Conv2D) (None, 20, 20, 32) 9248 leaky_re_lu[0][0]
__________________________________________________________________________________________________
conv_block_1_BN_1 (BatchNormali (None, 20, 20, 32) 128 conv_block_1_conv_1[0][0]
__________________________________________________________________________________________________
conv_block_2_conv_2 (Conv2D) (None, 20, 20, 32) 9248 leaky_re_lu[1][0]
__________________________________________________________________________________________________
conv_block_2_BN_2 (BatchNormali (None, 20, 20, 32) 128 conv_block_2_conv_2[0][0]
__________________________________________________________________________________________________
conv_block_3_conv_3 (Conv2D) (None, 20, 20, 32) 9248 leaky_re_lu[2][0]
__________________________________________________________________________________________________
conv_block_3_BN_3 (BatchNormali (None, 20, 20, 32) 128 conv_block_3_conv_3[0][0]
__________________________________________________________________________________________________
conv_block_4_conv_4 (Conv2D) (None, 20, 20, 3) 867 leaky_re_lu[3][0]
__________________________________________________________________________________________________
tf_op_layer_Tanh (TensorFlowOpL [(None, 20, 20, 3)] 0 conv_block_4_conv_4[0][0]
__________________________________________________________________________________________________
tf_op_layer_add_1 (TensorFlowOp [(None, 20, 20, 3)] 0 tf_op_layer_Tanh[0][0]
input_1[0][0]
==================================================================================================
Total params: 30,019
Trainable params: 29,763
Non-trainable params: 256
__________________________________________________________________________________________________
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 20, 20, 3)] 0
__________________________________________________________________________________________________
conv_block_0_conv_0 (Conv2D) (None, 20, 20, 32) 896 input_1[0][0]
__________________________________________________________________________________________________
conv_block_0_BN_0 (BatchNormali (None, 20, 20, 32) 128 conv_block_0_conv_0[0][0]
__________________________________________________________________________________________________
leaky_re_lu (LeakyReLU) (None, 20, 20, 32) 0 conv_block_0_BN_0[0][0]
conv_block_1_BN_1[0][0]
conv_block_2_BN_2[0][0]
conv_block_3_BN_3[0][0]
__________________________________________________________________________________________________
conv_block_1_conv_1 (Conv2D) (None, 20, 20, 32) 9248 leaky_re_lu[4][0]
__________________________________________________________________________________________________
conv_block_1_BN_1 (BatchNormali (None, 20, 20, 32) 128 conv_block_1_conv_1[0][0]
__________________________________________________________________________________________________
conv_block_2_conv_2 (Conv2D) (None, 20, 20, 32) 9248 leaky_re_lu[5][0]
__________________________________________________________________________________________________
conv_block_2_BN_2 (BatchNormali (None, 20, 20, 32) 128 conv_block_2_conv_2[0][0]
__________________________________________________________________________________________________
conv_block_3_conv_3 (Conv2D) (None, 20, 20, 32) 9248 leaky_re_lu[6][0]
__________________________________________________________________________________________________
conv_block_3_BN_3 (BatchNormali (None, 20, 20, 32) 128 conv_block_3_conv_3[0][0]
__________________________________________________________________________________________________
conv_block_4_conv_4 (Conv2D) (None, 20, 20, 1) 289 leaky_re_lu[7][0]
==================================================================================================
Total params: 29,441
Trainable params: 29,185
Non-trainable params: 256
__________________________________________________________________________________________________
Block 1 / 9: 100%|███████████████████████████████████████████████████████████████████████████████████████████████| 2000/2000 [01:13<00:00, 27.10it/s, g_loss=[7.9145103], d_loss=[-0.0302126]]
Model: "model_2"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_3 (InputLayer) [(None, 25, 25, 3)] 0
__________________________________________________________________________________________________
input_4 (InputLayer) [(None, 25, 25, 3)] 0
__________________________________________________________________________________________________
tf_op_layer_add_2 (TensorFlowOp [(None, 25, 25, 3)] 0 input_3[0][0]
input_4[0][0]
__________________________________________________________________________________________________
conv_block_0_conv_0 (Conv2D) (None, 25, 25, 32) 896 tf_op_layer_add_2[0][0]
__________________________________________________________________________________________________
conv_block_0_BN_0 (BatchNormali (None, 25, 25, 32) 128 conv_block_0_conv_0[0][0]
__________________________________________________________________________________________________
leaky_re_lu (LeakyReLU) multiple 0 conv_block_0_BN_0[0][0]
conv_block_1_BN_1[0][0]
conv_block_2_BN_2[0][0]
conv_block_3_BN_3[0][0]
__________________________________________________________________________________________________
conv_block_1_conv_1 (Conv2D) (None, 25, 25, 32) 9248 leaky_re_lu[8][0]
__________________________________________________________________________________________________
conv_block_1_BN_1 (BatchNormali (None, 25, 25, 32) 128 conv_block_1_conv_1[0][0]
__________________________________________________________________________________________________
conv_block_2_conv_2 (Conv2D) (None, 25, 25, 32) 9248 leaky_re_lu[9][0]
__________________________________________________________________________________________________
conv_block_2_BN_2 (BatchNormali (None, 25, 25, 32) 128 conv_block_2_conv_2[0][0]
__________________________________________________________________________________________________
conv_block_3_conv_3 (Conv2D) (None, 25, 25, 32) 9248 leaky_re_lu[10][0]
__________________________________________________________________________________________________
conv_block_3_BN_3 (BatchNormali (None, 25, 25, 32) 128 conv_block_3_conv_3[0][0]
__________________________________________________________________________________________________
conv_block_4_conv_4 (Conv2D) (None, 25, 25, 3) 867 leaky_re_lu[11][0]
__________________________________________________________________________________________________
tf_op_layer_Tanh_1 (TensorFlowO [(None, 25, 25, 3)] 0 conv_block_4_conv_4[0][0]
__________________________________________________________________________________________________
tf_op_layer_add_3 (TensorFlowOp [(None, 25, 25, 3)] 0 tf_op_layer_Tanh_1[0][0]
input_3[0][0]
==================================================================================================
Total params: 30,019
Trainable params: 29,763
Non-trainable params: 256
__________________________________________________________________________________________________
Model: "model_3"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_3 (InputLayer) [(None, 25, 25, 3)] 0
__________________________________________________________________________________________________
conv_block_0_conv_0 (Conv2D) (None, 25, 25, 32) 896 input_3[0][0]
__________________________________________________________________________________________________
conv_block_0_BN_0 (BatchNormali (None, 25, 25, 32) 128 conv_block_0_conv_0[0][0]
__________________________________________________________________________________________________
leaky_re_lu (LeakyReLU) multiple 0 conv_block_0_BN_0[0][0]
conv_block_1_BN_1[0][0]
conv_block_2_BN_2[0][0]
conv_block_3_BN_3[0][0]
__________________________________________________________________________________________________
conv_block_1_conv_1 (Conv2D) (None, 25, 25, 32) 9248 leaky_re_lu[12][0]
__________________________________________________________________________________________________
conv_block_1_BN_1 (BatchNormali (None, 25, 25, 32) 128 conv_block_1_conv_1[0][0]
__________________________________________________________________________________________________
conv_block_2_conv_2 (Conv2D) (None, 25, 25, 32) 9248 leaky_re_lu[13][0]
__________________________________________________________________________________________________
conv_block_2_BN_2 (BatchNormali (None, 25, 25, 32) 128 conv_block_2_conv_2[0][0]
__________________________________________________________________________________________________
conv_block_3_conv_3 (Conv2D) (None, 25, 25, 32) 9248 leaky_re_lu[14][0]
__________________________________________________________________________________________________
conv_block_3_BN_3 (BatchNormali (None, 25, 25, 32) 128 conv_block_3_conv_3[0][0]
__________________________________________________________________________________________________
conv_block_4_conv_4 (Conv2D) (None, 25, 25, 1) 289 leaky_re_lu[15][0]
==================================================================================================
Total params: 29,441
Trainable params: 29,185
Non-trainable params: 256
__________________________________________________________________________________________________
Block 2 / 9: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 2000/2000 [01:42<00:00, 19.48it/s, g_loss=[0.7259917], d_loss=[-0.00471149]]
Model: "model_4"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_5 (InputLayer) [(None, 32, 32, 3)] 0
__________________________________________________________________________________________________
．．．
Model: "model_5"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_5 (InputLayer) [(None, 32, 32, 3)] 0
__________________________________________________________________________________________________
．．．
Block 3 / 9: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 2000/2000 [03:49<00:00, 8.71it/s, g_loss=[0.05491346], d_loss=[-0.00068739]]
Model: "model_6"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_7 (InputLayer) [(None, 40, 40, 3)] 0
__________________________________________________________________________________________________
．．．
Model: "model_7"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_7 (InputLayer) [(None, 40, 40, 3)] 0
__________________________________________________________________________________________________
．．．
Block 4 / 9: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 2000/2000 [05:04<00:00, 6.56it/s, g_loss=[0.13994163], d_loss=[-0.00033907]]
Model: "model_8"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_9 (InputLayer) [(None, 50, 50, 3)] 0
__________________________________________________________________________________________________
．．．
Model: "model_9"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_9 (InputLayer) [(None, 50, 50, 3)] 0
__________________________________________________________________________________________________
．．．
Block 5 / 9: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 2000/2000 [07:46<00:00, 4.29it/s, g_loss=[0.1438144], d_loss=[-0.00011725]]
Model: "model_10"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_11 (InputLayer) [(None, 64, 64, 3)] 0
__________________________________________________________________________________________________
．．．
Model: "model_11"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_11 (InputLayer) [(None, 64, 64, 3)] 0
__________________________________________________________________________________________________
．．．
Block 6 / 9: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 2000/2000 [12:43<00:00, 2.62it/s, g_loss=[0.09378527], d_loss=[-7.251864e-05]]
Model: "model_12"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_13 (InputLayer) [(None, 80, 80, 3)] 0
__________________________________________________________________________________________________
．．．
Model: "model_13"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_13 (InputLayer) [(None, 80, 80, 3)] 0
__________________________________________________________________________________________________
．．．
Block 7 / 9: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 2000/2000 [19:51<00:00, 1.68it/s, g_loss=[0.1352475], d_loss=[-0.00010792]]
Model: "model_14"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_15 (InputLayer) [(None, 101, 101, 3) 0
__________________________________________________________________________________________________
．．．
Block 8 / 9: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 2000/2000 [32:49<00:00, 1.02it/s, g_loss=[0.12389164], d_loss=[-2.1162363e-05]]
Model: "model_16"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_17 (InputLayer) [(None, 128, 128, 3) 0
__________________________________________________________________________________________________
．．．
Model: "model_17"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_17 (InputLayer) [(None, 128, 128, 3) 0
__________________________________________________________________________________________________
．．．
Block 9 / 9: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 2000/2000 [50:12<00:00, 1.51s/it, g_loss=[0.13538168], d_loss=[2.7423768e-05]]
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