Fix mprnet train bug and add docs (#506)

* fix mprnet train and add docs

* update config

configs/mprnet_deblurring.yaml

-total_iters: 100000
+# epoch: 3000 for total batch size=16
+total_iters: 400000
 output_dir: output_dir
 
 model:
@@ -15,38 +16,38 @@ dataset:
   train:
     name: MPRTrain
     rgb_dir: 'data/GoPro/train'
-    num_workers: 16
-    batch_size: 4
+    num_workers: 4
+    batch_size: 2
     img_options:
       patch_size: 256
   test:
-    name: MPRTrain
+    name: MPRVal
     rgb_dir: 'data/GoPro/test'
-    num_workers: 16
-    batch_size: 4
+    num_workers: 4
+    batch_size: 2
     img_options:
       patch_size: 256
 
 lr_scheduler:
   name: CosineAnnealingRestartLR
-  learning_rate: !!float 2e-4
-  periods: [25000, 25000, 25000, 25000]
-  restart_weights: [1, 1, 1, 1]
+  learning_rate: !!float 1e-4
+  periods: [400000]
+  restart_weights: [1]
   eta_min: !!float 1e-6
 
 validate:
-  interval: 10
+  interval: 5000
   save_img: false
 
   metrics:
     psnr: # metric name, can be arbitrary
       name: PSNR
       crop_border: 4
-      test_y_channel: True
+      test_y_channel: false
     ssim:
       name: SSIM
       crop_border: 4
-      test_y_channel: True
+      test_y_channel: false
 
 optimizer:
   name: Adam
@@ -59,7 +60,7 @@ optimizer:
   epsilon: 1e-8
 
 log_config:
-  interval: 10
+  interval: 100
   visiual_interval: 5000
 
 snapshot_config:

docs/en_US/tutorials/single_image_super_resolution.md

@@ -130,6 +130,10 @@ The metrics are PSNR / SSIM.
 | pan_x4  | 30.4574 / 0.8643 | 26.7204 / 0.7434 | 28.9187 / 0.8176 |
 | drns_x4  | 32.6684 / 0.8999 | 28.9037 / 0.7885 | - |
 
+Deblur models zoo
+| model | GoPro | Download Link |
+|---|---|---|
+| MPRNet  | 33.4360 / 0.9410 | [link](https://paddlegan.bj.bcebos.com/models/MPR_Deblurring.pdparams) |
 
 <!-- ![](../../imgs/horse2zebra.png) -->
 

docs/zh_CN/tutorials/single_image_super_resolution.md

@@ -120,6 +120,10 @@ paddle模型使用DIV2K数据集训练，torch模型使用df2k和DIV2K数据集
 | paddle  | 30.4574 / 0.8643 | 26.7204 / 0.7434 |
 | torch  | 30.2183 / 0.8643 | 26.8035 / 0.7445 |
 
+去模糊模型
+| 模型 | GoPro | 下载地址 |
+|---|---|---|
+| MPRNet  | 33.4360 / 0.9410 | [链接](https://paddlegan.bj.bcebos.com/models/MPR_Deblurring.pdparams) |
 
 <!-- ![](../../imgs/horse2zebra.png) -->
 

ppgan/engine/trainer.py

@@ -29,6 +29,7 @@ from ..utils.filesystem import makedirs, save, load
 from ..utils.timer import TimeAverager
 from ..utils.profiler import add_profiler_step
 
+
 class IterLoader:
     def __init__(self, dataloader):
         self._dataloader = dataloader
@@ -429,15 +430,35 @@ class Trainer:
     def load(self, weight_path):
         state_dicts = load(weight_path)
 
-        for net_name, net in self.model.nets.items():
-            if net_name in state_dicts:
-                net.set_state_dict(state_dicts[net_name])
-                self.logger.info(
-                    'Loaded pretrained weight for net {}'.format(net_name))
+        def is_dict_in_dict_weight(state_dict):
+            if isinstance(state_dict, dict) and len(state_dict) > 0:
+                val = list(state_dict.values())[0]
+                if isinstance(val, dict):
+                    return True
+                else:
+                    return False
             else:
-                self.logger.warning(
-                    'Can not find state dict of net {}. Skip load pretrained weight for net {}'
-                    .format(net_name, net_name))
+                return False
+
+        if is_dict_in_dict_weight(state_dicts):
+            for net_name, net in self.model.nets.items():
+                if net_name in state_dicts:
+                    net.set_state_dict(state_dicts[net_name])
+                    self.logger.info(
+                        'Loaded pretrained weight for net {}'.format(net_name))
+                else:
+                    self.logger.warning(
+                        'Can not find state dict of net {}. Skip load pretrained weight for net {}'
+                        .format(net_name, net_name))
+        else:
+            assert len(self.model.nets
+                       ) == 1, 'checkpoint only contain weight of one net, \
+                                                but model contains more than one net!'
+
+            net_name, net = list(self.model.nets.items())[0]
+            net.set_state_dict(state_dicts)
+            self.logger.info(
+                'Loaded pretrained weight for net {}'.format(net_name))
 
     def close(self):
         """

ppgan/models/criterions/pixel_loss.py

@@ -249,23 +249,25 @@ class CalcStyleLoss():
 class EdgeLoss():
     def __init__(self):
         k = paddle.to_tensor([[.05, .25, .4, .25, .05]])
-        self.kernel = paddle.matmul(k.t(),k).unsqueeze(0).tile([3,1,1,1])
+        self.kernel = paddle.matmul(k.t(), k).unsqueeze(0).tile([3, 1, 1, 1])
         self.loss = CharbonnierLoss()
 
     def conv_gauss(self, img):
         n_channels, _, kw, kh = self.kernel.shape
-        img = F.pad(img, [kw//2, kh//2, kw//2, kh//2], mode='replicate')
+        img = F.pad(img, [kw // 2, kh // 2, kw // 2, kh // 2], mode='replicate')
         return F.conv2d(img, self.kernel, groups=n_channels)
 
     def laplacian_kernel(self, current):
-        filtered    = self.conv_gauss(current)    # filter
-        down        = filtered[:,:,::2,::2]               # downsample
-        new_filter  = paddle.zeros_like(filtered)
-        new_filter[:,:,::2,::2] = down*4                  # upsample
-        filtered    = self.conv_gauss(new_filter) # filter
+        filtered = self.conv_gauss(current)  # filter
+        down = filtered[:, :, ::2, ::2]  # downsample
+        new_filter = paddle.zeros_like(filtered)
+        new_filter.stop_gradient = True
+        new_filter[:, :, ::2, ::2] = down * 4  # upsample
+        filtered = self.conv_gauss(new_filter)  # filter
         diff = current - filtered
         return diff
 
     def __call__(self, x, y):
+        y.stop_gradient = True
         loss = self.loss(self.laplacian_kernel(x), self.laplacian_kernel(y))
-        return loss
\ No newline at end of file
+        return loss

ppgan/models/mpr_model.py

@@ -20,6 +20,7 @@ from .base_model import BaseModel
 from .generators.builder import build_generator
 from .criterions.builder import build_criterion
 from ..modules.init import reset_parameters, init_weights
+from ..utils.visual import tensor2img
 
 
 @MODELS.register()
@@ -50,12 +51,12 @@ class MPRModel(BaseModel):
 
     def setup_input(self, input):
         self.target = input[0]
-        self.input_ = input[1]
+        self.lq = input[1]
 
     def train_iter(self, optims=None):
         optims['optim'].clear_gradients()
 
-        restored = self.nets['generator'](self.input_)
+        restored = self.nets['generator'](self.lq)
 
         loss_char = []
         loss_edge = []
@@ -75,5 +76,21 @@ class MPRModel(BaseModel):
         self.losses['loss'] = loss.numpy()
 
     def forward(self):
-        """Run forward pass; called by both functions <train_iter> and <test_iter>."""
         pass
+
+    def test_iter(self, metrics=None):
+        self.nets['generator'].eval()
+        with paddle.no_grad():
+            self.output = self.nets['generator'](self.lq)[0]
+            self.visual_items['output'] = self.output
+        self.nets['generator'].train()
+
+        out_img = []
+        gt_img = []
+        for out_tensor, gt_tensor in zip(self.output, self.target):
+            out_img.append(tensor2img(out_tensor, (0., 1.)))
+            gt_img.append(tensor2img(gt_tensor, (0., 1.)))
+
+        if metrics is not None:
+            for metric in metrics.values():
+                metric.update(out_img, gt_img)