add esrgan x2 (#529)

* add esrgan x2

configs/esrgan_psnr_x2_div2k.yaml

+total_iters: 1000000
+output_dir: output_dir
+# tensor range for function tensor2img
+min_max:
+  (0., 1.)
+
+model:
+  name: BaseSRModel
+  generator:
+    name: RRDBNet
+    in_nc: 3
+    out_nc: 3
+    nf: 64
+    nb: 23
+    scale: 2
+  pixel_criterion:
+    name: L1Loss
+
+dataset:
+  train:
+    name: SRDataset
+    gt_folder: data/DIV2K/DIV2K_train_HR_sub
+    lq_folder: data/DIV2K/DIV2K_train_LR_bicubic/X2_sub
+    num_workers: 4
+    batch_size: 8
+    scale: 2
+    preprocess:
+      - name: LoadImageFromFile
+        key: lq
+      - name: LoadImageFromFile
+        key: gt
+      - name: Transforms
+        input_keys: [lq, gt]
+        pipeline:
+          - name: SRPairedRandomCrop
+            gt_patch_size: 128
+            scale: 2
+            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/Set14/GTmod12
+    lq_folder: data/Set14/LRbicx2
+    scale: 2
+    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: 2
+      test_y_channel: True
+    ssim:
+      name: SSIM
+      crop_border: 2
+      test_y_channel: True
+
+log_config:
+  interval: 100
+  visiual_interval: 500
+
+snapshot_config:
+  interval: 5000

ppgan/models/generators/rrdb_net.py

@@ -22,6 +22,26 @@ import paddle.nn.functional as F
 from .builder import GENERATORS
 
 
+def pixel_unshuffle(x, scale):
+    """ Pixel unshuffle function.
+
+    Args:
+        x (paddle.Tensor): Input feature.
+        scale (int): Downsample ratio.
+
+    Returns:
+        paddle.Tensor: the pixel unshuffled feature.
+    """
+    b, c, h, w = x.shape
+    out_channel = c * (scale**2)
+    assert h % scale == 0 and w % scale == 0
+    hh = h // scale
+    ww = w // scale
+    x_reshaped = x.reshape([b, c, hh, scale, ww, scale])
+    return x_reshaped.transpose([0, 1, 3, 5, 2,
+                                 4]).reshape([b, out_channel, hh, ww])
+
+
 class ResidualDenseBlock_5C(nn.Layer):
     def __init__(self, nf=64, gc=32, bias=True):
         super(ResidualDenseBlock_5C, self).__init__()
@@ -66,13 +86,21 @@ def make_layer(block, n_layers):
 
 @GENERATORS.register()
 class RRDBNet(nn.Layer):
-    def __init__(self, in_nc, out_nc, nf, nb, gc=32):
+    def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=4):
         super(RRDBNet, self).__init__()
+
+        self.scale = scale
+        if scale == 2:
+            in_nc = in_nc * 4
+        elif scale == 1:
+            in_nc = in_nc * 16
+
         RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
 
         self.conv_first = nn.Conv2D(in_nc, nf, 3, 1, 1, bias_attr=True)
         self.RRDB_trunk = make_layer(RRDB_block_f, nb)
         self.trunk_conv = nn.Conv2D(nf, nf, 3, 1, 1, bias_attr=True)
+
         #### upsampling
         self.upconv1 = nn.Conv2D(nf, nf, 3, 1, 1, bias_attr=True)
         self.upconv2 = nn.Conv2D(nf, nf, 3, 1, 1, bias_attr=True)
@@ -82,7 +110,14 @@ class RRDBNet(nn.Layer):
         self.lrelu = nn.LeakyReLU(negative_slope=0.2)
 
     def forward(self, x):
-        fea = self.conv_first(x)
+        if self.scale == 2:
+            fea = pixel_unshuffle(x, scale=2)
+        elif self.scale == 1:
+            fea = pixel_unshuffle(x, scale=4)
+        else:
+            fea = x
+
+        fea = self.conv_first(fea)
         trunk = self.trunk_conv(self.RRDB_trunk(fea))
         fea = fea + trunk