Add Lesrcnn model (#136)

* add lesrcnn model

configs/esrgan_psnr_x4_div2k.yaml

@@ -91,11 +91,11 @@ validate:
     psnr: # metric name, can be arbitrary
       name: PSNR
       crop_border: 4
-      test_y_channel: false
+      test_y_channel: True
     ssim:
       name: SSIM
       crop_border: 4
-      test_y_channel: false
+      test_y_channel: True
 
 log_config:
   interval: 10

configs/lesrcnn_psnr_x4_div2k.yaml

+total_iters: 1000000
+output_dir: output_dir
+# tensor range for function tensor2img
+min_max:
+  (0., 1.)
+
+model:
+  name: BaseSRModel
+  generator:
+    name: LESRCNNGenerator
+  pixel_criterion:
+    name: L1Loss
+
+dataset:
+  train:
+    name: SRDataset
+    gt_folder: data/DIV2K/DIV2K_train_HR_sub
+    lq_folder: data/DIV2K/DIV2K_train_LR_bicubic/X4_sub
+    num_workers: 4
+    batch_size: 16
+    scale: 4
+    preprocess:
+      - name: LoadImageFromFile
+        key: lq
+      - name: LoadImageFromFile
+        key: gt
+      - name: Transforms
+        input_keys: [lq, gt]
+        pipeline:
+          - name: SRPairedRandomCrop
+            gt_patch_size: 128
+            scale: 4
+            keys: [image, image]
+          - name: PairedRandomHorizontalFlip
+            keys: [image, image]
+          - name: PairedRandomVerticalFlip
+            keys: [image, image]
+          - name: PairedRandomTransposeHW
+            keys: [image, image]
+          - name: Transpose
+            keys: [image, image]
+          - name: Normalize
+            mean: [0., .0, 0.]
+            std: [255., 255., 255.]
+            keys: [image, image]
+  test:
+    name: SRDataset
+    gt_folder: data/DIV2K/val_set14/Set14
+    lq_folder: data/DIV2K/val_set14/Set14_bicLRx4
+    scale: 4
+    preprocess:
+      - name: LoadImageFromFile
+        key: lq
+      - name: LoadImageFromFile
+        key: gt
+      - name: Transforms
+        input_keys: [lq, gt]
+        pipeline:
+          - name: Transpose
+            keys: [image, image]
+          - name: Normalize
+            mean: [0., .0, 0.]
+            std: [255., 255., 255.]
+            keys: [image, image]
+
+lr_scheduler:
+  name: CosineAnnealingRestartLR
+  learning_rate: 0.0002
+  periods: [250000, 250000, 250000, 250000]
+  restart_weights: [1, 1, 1, 1]
+  eta_min: !!float 1e-7
+
+optimizer:
+  name: Adam
+  # add parameters of net_name to optim
+  # name should in self.nets
+  net_names:
+    - generator
+  beta1: 0.9
+  beta2: 0.99
+
+validate:
+  interval: 5000
+  save_img: false
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      name: PSNR
+      crop_border: 4
+      test_y_channel: True
+    ssim:
+      name: SSIM
+      crop_border: 4
+      test_y_channel: True
+
+log_config:
+  interval: 100
+  visiual_interval: 5000
+
+snapshot_config:
+  interval: 5000

ppgan/datasets/preprocess/transforms.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import sys
+import cv2
 import random
 import numbers
 import collections
@@ -40,8 +41,9 @@ TRANSFORMS.register(T.Transpose)
 
 @PREPROCESS.register()
 class Transforms():
-    def __init__(self, pipeline, input_keys):
+    def __init__(self, pipeline, input_keys, output_keys=None):
         self.input_keys = input_keys
+        self.output_keys = output_keys
         self.transforms = []
         for transform_cfg in pipeline:
             self.transforms.append(build_from_config(transform_cfg, TRANSFORMS))
@@ -58,6 +60,11 @@ class Transforms():
                     transform.params, dict):
                 datas.update(transform.params)
 
+        if self.output_keys is not None:
+            for i, k in enumerate(self.output_keys):
+                datas[k] = data[i]
+            return datas
+
         for i, k in enumerate(self.input_keys):
             datas[k] = data[i]
 
@@ -183,10 +190,11 @@ class SRPairedRandomCrop(T.BaseTransform):
         scale (int): model upscale factor.
         gt_patch_size (int): cropped gt patch size.
     """
-    def __init__(self, scale, gt_patch_size, keys=None):
+    def __init__(self, scale, gt_patch_size, scale_list=False, keys=None):
         self.gt_patch_size = gt_patch_size
         self.scale = scale
         self.keys = keys
+        self.scale_list = scale_list
 
     def __call__(self, inputs):
         """inputs must be (lq_img, gt_img)"""
@@ -214,5 +222,11 @@ class SRPairedRandomCrop(T.BaseTransform):
         gt = gt[top_gt:top_gt + self.gt_patch_size,
                 left_gt:left_gt + self.gt_patch_size, ...]
 
+        if self.scale_list and self.scale == 4:
+            lqx2 = F.resize(gt, (lq_patch_size * 2, lq_patch_size * 2),
+                            'bicubic')
+            outputs = (lq, lqx2, gt)
+            return outputs
+
         outputs = (lq, gt)
         return outputs

ppgan/datasets/transforms/__init__.py

-from .transforms import ResizeToScale, PairedRandomCrop, PairedRandomHorizontalFlip, Add
+from .transforms import ResizeToScale, PairedRandomCrop, PairedRandomHorizontalFlip, Add
\ No newline at end of file

ppgan/engine/trainer.py

@@ -386,7 +386,6 @@ class Trainer:
                 self.logger.warning(
                     'Can not find state dict of net {}. Skip load pretrained weight for net {}'
                     .format(net_name, net_name))
-            net.set_state_dict(state_dicts[net_name])
 
     def close(self):
         """

ppgan/metrics/psnr_ssim.py

@@ -270,6 +270,48 @@ def bgr2ycbcr(img, y_only=False):
     return out_img
 
 
+def rgb2ycbcr(img, y_only=False):
+    """Convert a RGB image to YCbCr image.
+
+    The RGB version of rgb2ycbcr.
+    It implements the ITU-R BT.601 conversion for standard-definition
+    television. See more details in
+    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
+
+    It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`.
+    In OpenCV, it implements a JPEG conversion. See more details in
+    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
+
+    Args:
+        img (ndarray): The input image. It accepts:
+            1. np.uint8 type with range [0, 255];
+            2. np.float32 type with range [0, 1].
+        y_only (bool): Whether to only return Y channel. Default: False.
+
+    Returns:
+        ndarray: The converted YCbCr image. The output image has the same type
+            and range as input image.
+    """
+    img_type = img.dtype
+
+    if img_type != np.uint8:
+        img *= 255.
+
+    if y_only:
+        out_img = np.dot(img, [65.481, 128.553, 24.966]) / 255. + 16.0
+    else:
+        out_img = np.matmul(
+            img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
+                  [65.481, -37.797, 112.0]]) + [16, 128, 128]
+
+    if img_type != np.uint8:
+        out_img /= 255.
+    else:
+        out_img = out_img.round()
+
+    return out_img
+
+
 def to_y_channel(img):
     """Change to Y channel of YCbCr.
 
@@ -281,6 +323,6 @@ def to_y_channel(img):
     """
     img = img.astype(np.float32) / 255.
     if img.ndim == 3 and img.shape[2] == 3:
-        img = bgr2ycbcr(img, y_only=True)
+        img = rgb2ycbcr(img, y_only=True)
         img = img[..., None]
     return img * 255.

ppgan/models/esrgan_model.py

@@ -61,7 +61,6 @@ class ESRGAN(BaseSRModel):
             self.gan_criterion = build_criterion(gan_criterion)
 
     def train_iter(self, optimizers=None):
-        self.set_requires_grad(self.nets['discriminator'], False)
         optimizers['optimG'].clear_grad()
         l_total = 0
         self.output = self.nets['generator'](self.lq)
@@ -83,41 +82,48 @@ class ESRGAN(BaseSRModel):
                 self.losses['loss_style'] = l_g_style
 
         # gan loss (relativistic gan)
-        real_d_pred = self.nets['discriminator'](self.gt).detach()
-        fake_g_pred = self.nets['discriminator'](self.output)
-        l_g_real = self.gan_criterion(real_d_pred - paddle.mean(fake_g_pred),
-                                      False,
-                                      is_disc=False)
-        l_g_fake = self.gan_criterion(fake_g_pred - paddle.mean(real_d_pred),
-                                      True,
-                                      is_disc=False)
-        l_g_gan = (l_g_real + l_g_fake) / 2
-
-        l_total += l_g_gan
-        self.losses['l_g_gan'] = l_g_gan
-
-        l_total.backward()
-        optimizers['optimG'].step()
-
-        self.set_requires_grad(self.nets['discriminator'], True)
-        optimizers['optimD'].clear_grad()
-        # real
-        fake_d_pred = self.nets['discriminator'](self.output).detach()
-        real_d_pred = self.nets['discriminator'](self.gt)
-        l_d_real = self.gan_criterion(
-            real_d_pred - paddle.mean(fake_d_pred), True, is_disc=True) * 0.5
-
-        # fake
-        fake_d_pred = self.nets['discriminator'](self.output.detach())
-        l_d_fake = self.gan_criterion(
-            fake_d_pred - paddle.mean(real_d_pred.detach()),
-            False,
-            is_disc=True) * 0.5
-
-        (l_d_real + l_d_fake).backward()
-        optimizers['optimD'].step()
-
-        self.losses['l_d_real'] = l_d_real
-        self.losses['l_d_fake'] = l_d_fake
-        self.losses['out_d_real'] = paddle.mean(real_d_pred.detach())
-        self.losses['out_d_fake'] = paddle.mean(fake_d_pred.detach())
+        if hasattr(self, 'gan_criterion'):
+            self.set_requires_grad(self.nets['discriminator'], False)
+            real_d_pred = self.nets['discriminator'](self.gt).detach()
+            fake_g_pred = self.nets['discriminator'](self.output)
+            l_g_real = self.gan_criterion(real_d_pred -
+                                          paddle.mean(fake_g_pred),
+                                          False,
+                                          is_disc=False)
+            l_g_fake = self.gan_criterion(fake_g_pred -
+                                          paddle.mean(real_d_pred),
+                                          True,
+                                          is_disc=False)
+            l_g_gan = (l_g_real + l_g_fake) / 2
+
+            l_total += l_g_gan
+            self.losses['l_g_gan'] = l_g_gan
+            l_total.backward()
+            optimizers['optimG'].step()
+
+            self.set_requires_grad(self.nets['discriminator'], True)
+            optimizers['optimD'].clear_grad()
+            # real
+            fake_d_pred = self.nets['discriminator'](self.output).detach()
+            real_d_pred = self.nets['discriminator'](self.gt)
+            l_d_real = self.gan_criterion(
+                real_d_pred - paddle.mean(fake_d_pred), True,
+                is_disc=True) * 0.5
+
+            # fake
+            fake_d_pred = self.nets['discriminator'](self.output.detach())
+            l_d_fake = self.gan_criterion(
+                fake_d_pred - paddle.mean(real_d_pred.detach()),
+                False,
+                is_disc=True) * 0.5
+
+            (l_d_real + l_d_fake).backward()
+            optimizers['optimD'].step()
+
+            self.losses['l_d_real'] = l_d_real
+            self.losses['l_d_fake'] = l_d_fake
+            self.losses['out_d_real'] = paddle.mean(real_d_pred.detach())
+            self.losses['out_d_fake'] = paddle.mean(fake_d_pred.detach())
+        else:
+            l_total.backward()
+            optimizers['optimG'].step()

ppgan/models/generators/__init__.py

@@ -21,6 +21,7 @@ from .resnet_ugatit import ResnetUGATITGenerator
 from .dcgenerator import DCGenerator
 from .generater_animegan import AnimeGenerator, AnimeGeneratorLite
 from .wav2lip import Wav2Lip
+from .lesrcnn import LESRCNNGenerator
 from .resnet_ugatit_p2c import ResnetUGATITP2CGenerator
 from .generator_styleganv2 import StyleGANv2Generator
 from .generator_pixel2style2pixel import Pixel2Style2Pixel

ppgan/models/generators/lesrcnn.py

+import math
+import numpy as np
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+from .builder import GENERATORS
+
+
+class MeanShift(nn.Layer):
+    def __init__(self, mean_rgb, sub):
+        super(MeanShift, self).__init__()
+
+        sign = -1 if sub else 1
+        r = mean_rgb[0] * sign
+        g = mean_rgb[1] * sign
+        b = mean_rgb[2] * sign
+
+        self.shifter = nn.Conv2D(3, 3, 1, 1, 0)
+        self.shifter.weight.set_value(paddle.eye(3).reshape([3, 3, 1, 1]))
+        self.shifter.bias.set_value(np.array([r, g, b]).astype('float32'))
+        # Freeze the mean shift layer
+        for params in self.shifter.parameters():
+            params.trainable = False
+
+    def forward(self, x):
+        x = self.shifter(x)
+        return x
+
+
+class UpsampleBlock(nn.Layer):
+    def __init__(self, n_channels, scale, multi_scale, group=1):
+        super(UpsampleBlock, self).__init__()
+
+        if multi_scale:
+            self.up2 = _UpsampleBlock(n_channels, scale=2, group=group)
+            self.up3 = _UpsampleBlock(n_channels, scale=3, group=group)
+            self.up4 = _UpsampleBlock(n_channels, scale=4, group=group)
+        else:
+            self.up = _UpsampleBlock(n_channels, scale=scale, group=group)
+
+        self.multi_scale = multi_scale
+
+    def forward(self, x, scale):
+        if self.multi_scale:
+            if scale == 2:
+                return self.up2(x)
+            elif scale == 3:
+                return self.up3(x)
+            elif scale == 4:
+                return self.up4(x)
+        else:
+            return self.up(x)
+
+
+class _UpsampleBlock(nn.Layer):
+    def __init__(self, n_channels, scale, group=1):
+        super(_UpsampleBlock, self).__init__()
+
+        modules = []
+        if scale == 2 or scale == 4 or scale == 8:
+            for _ in range(int(math.log(scale, 2))):
+                modules += [
+                    nn.Conv2D(n_channels, 4 * n_channels, 3, 1, 1, groups=group)
+                ]
+                modules += [nn.PixelShuffle(2)]
+        elif scale == 3:
+            modules += [
+                nn.Conv2D(n_channels, 9 * n_channels, 3, 1, 1, groups=group)
+            ]
+            modules += [nn.PixelShuffle(3)]
+
+        self.body = nn.Sequential(*modules)
+
+    def forward(self, x):
+        out = self.body(x)
+        return out
+
+
+@GENERATORS.register()
+class LESRCNNGenerator(nn.Layer):
+    """Construct a Resnet-based generator that consists of residual blocks
+    between a few downsampling/upsampling operations.
+
+    Args:
+        scale (int): scale of upsample.
+        multi_scale (bool): Whether to train multi scale model.
+        group (int): group option for convolution.
+    """
+    def __init__(
+        self,
+        scale=4,
+        multi_scale=False,
+        group=1,
+    ):
+        super(LESRCNNGenerator, self).__init__()
+
+        kernel_size = 3
+        kernel_size1 = 1
+        padding1 = 0
+        padding = 1
+        features = 64
+        groups = 1
+        channels = 3
+        features1 = 64
+        self.scale = scale
+        self.sub_mean = MeanShift((0.4488, 0.4371, 0.4040), sub=True)
+        self.add_mean = MeanShift((0.4488, 0.4371, 0.4040), sub=False)
+
+        self.conv1 = nn.Sequential(
+            nn.Conv2D(in_channels=channels,
+                      out_channels=features,
+                      kernel_size=kernel_size,
+                      padding=padding,
+                      groups=1,
+                      bias_attr=False))
+        self.conv2 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size,
+                      padding=1,
+                      groups=1,
+                      bias_attr=False), nn.ReLU())
+        self.conv3 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size1,
+                      padding=0,
+                      groups=groups,
+                      bias_attr=False))
+        self.conv4 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size,
+                      padding=1,
+                      groups=1,
+                      bias_attr=False), nn.ReLU())
+        self.conv5 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size1,
+                      padding=0,
+                      groups=groups,
+                      bias_attr=False))
+        self.conv6 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size,
+                      padding=1,
+                      groups=1,
+                      bias_attr=False), nn.ReLU())
+        self.conv7 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size1,
+                      padding=0,
+                      groups=groups,
+                      bias_attr=False))
+        self.conv8 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size,
+                      padding=1,
+                      groups=1,
+                      bias_attr=False), nn.ReLU())
+        self.conv9 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size1,
+                      padding=0,
+                      groups=groups,
+                      bias_attr=False))
+        self.conv10 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size,
+                      padding=1,
+                      groups=1,
+                      bias_attr=False), nn.ReLU())
+        self.conv11 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size1,
+                      padding=0,
+                      groups=groups,
+                      bias_attr=False))
+        self.conv12 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size,
+                      padding=1,
+                      groups=1,
+                      bias_attr=False), nn.ReLU())
+        self.conv13 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size1,
+                      padding=0,
+                      groups=groups,
+                      bias_attr=False))
+        self.conv14 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size,
+                      padding=1,
+                      groups=1,
+                      bias_attr=False), nn.ReLU())
+        self.conv15 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size1,
+                      padding=0,
+                      groups=groups,
+                      bias_attr=False))
+        self.conv16 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size,
+                      padding=1,
+                      groups=1,
+                      bias_attr=False), nn.ReLU())
+        self.conv17 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size1,
+                      padding=0,
+                      groups=groups,
+                      bias_attr=False))
+        self.conv17_1 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size,
+                      padding=1,
+                      groups=1,
+                      bias_attr=False), nn.ReLU())
+        self.conv17_2 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size,
+                      padding=1,
+                      groups=1,
+                      bias_attr=False), nn.ReLU())
+        self.conv17_3 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size,
+                      padding=1,
+                      groups=1,
+                      bias_attr=False), nn.ReLU())
+        self.conv17_4 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=features,
+                      kernel_size=kernel_size,
+                      padding=1,
+                      groups=1,
+                      bias_attr=False), nn.ReLU())
+        self.conv18 = nn.Sequential(
+            nn.Conv2D(in_channels=features,
+                      out_channels=3,
+                      kernel_size=kernel_size,
+                      padding=padding,
+                      groups=groups,
+                      bias_attr=False))
+
+        self.ReLU = nn.ReLU()
+        self.upsample = UpsampleBlock(64,
+                                      scale=scale,
+                                      multi_scale=multi_scale,
+                                      group=1)
+
+    def forward(self, x, scale=None):
+        if scale is None:
+            scale = self.scale
+
+        x = self.sub_mean(x)
+
+        x1 = self.conv1(x)
+        x1_1 = self.ReLU(x1)
+        x2 = self.conv2(x1_1)
+        x3 = self.conv3(x2)
+
+        x2_3 = x1 + x3
+        x2_4 = self.ReLU(x2_3)
+        x4 = self.conv4(x2_4)
+        x5 = self.conv5(x4)
+        x3_5 = x2_3 + x5
+
+        x3_6 = self.ReLU(x3_5)
+        x6 = self.conv6(x3_6)
+        x7 = self.conv7(x6)
+        x7_1 = x3_5 + x7
+
+        x7_2 = self.ReLU(x7_1)
+        x8 = self.conv8(x7_2)
+        x9 = self.conv9(x8)
+        x9_2 = x7_1 + x9
+
+        x9_1 = self.ReLU(x9_2)
+        x10 = self.conv10(x9_1)
+        x11 = self.conv11(x10)
+        x11_1 = x9_2 + x11
+
+        x11_2 = self.ReLU(x11_1)
+        x12 = self.conv12(x11_2)
+        x13 = self.conv13(x12)
+        x13_1 = x11_1 + x13
+
+        x13_2 = self.ReLU(x13_1)
+        x14 = self.conv14(x13_2)
+        x15 = self.conv15(x14)
+        x15_1 = x15 + x13_1
+
+        x15_2 = self.ReLU(x15_1)
+        x16 = self.conv16(x15_2)
+        x17 = self.conv17(x16)
+        x17_2 = x17 + x15_1
+
+        x17_3 = self.ReLU(x17_2)
+        temp = self.upsample(x17_3, scale=scale)
+        x1111 = self.upsample(x1_1, scale=scale)
+        temp1 = x1111 + temp
+        temp2 = self.ReLU(temp1)
+        temp3 = self.conv17_1(temp2)
+        temp4 = self.conv17_2(temp3)
+        temp5 = self.conv17_3(temp4)
+        temp6 = self.conv17_4(temp5)
+        x18 = self.conv18(temp6)
+        out = self.add_mean(x18)
+
+        return out