increase validate interval (#382)

configs/pix2pix_cityscapes.yaml

@@ -61,7 +61,7 @@ dataset:
             keys: [image, image]
   test:
     name: PairedDataset
-    dataroot: data/cityscapes/test
+    dataroot: data/cityscapes/val
     num_workers: 4
     batch_size: 1
     preprocess:
@@ -112,11 +112,9 @@ snapshot_config:
   interval: 5
 
 validate:
-  interval: 500
+  interval: 29750
   save_img: false
   metrics:
     fid: # metric name, can be arbitrary
         name: FID
         batch_size: 8
-
-

configs/pix2pix_cityscapes_2gpus.yaml

@@ -58,7 +58,7 @@ dataset:
             keys: [image, image]
   test:
     name: PairedDataset
-    dataroot: data/cityscapes/test
+    dataroot: data/cityscapes/val
     num_workers: 4
     batch_size: 1
     preprocess:
@@ -109,11 +109,9 @@ snapshot_config:
   interval: 5
 
 validate:
-  interval: 500
+  interval: 29750
   save_img: false
   metrics:
     fid: # metric name, can be arbitrary
         name: FID
         batch_size: 8
-
-

configs/pix2pix_facades.yaml

@@ -109,11 +109,9 @@ snapshot_config:
   interval: 5
 
 validate:
-  interval: 500
+  interval: 4000
   save_img: false
   metrics:
     fid: # metric name, can be arbitrary
         name: FID
         batch_size: 8
-
-

configs/stylegan_v2_256_ffhq.yaml

@@ -90,7 +90,7 @@ snapshot_config:
   interval: 5000
 
 validate:
-  interval: 5000
+  interval: 50000
   save_imig: False
   metrics:
     fid: # metric name, can be arbitrary

ppgan/metrics/fid.py

@@ -39,9 +39,15 @@ inceptionV3 pretrain model is convert from pytorch, pretrain_model url is https:
 """
 INCEPTIONV3_WEIGHT_URL = "https://paddlegan.bj.bcebos.com/InceptionV3.pdparams"
 
+
 @METRICS.register()
 class FID(paddle.metric.Metric):
-    def __init__(self, batch_size=1, use_GPU=True, dims = 2048, premodel_path=None, model=None):
+    def __init__(self,
+                 batch_size=1,
+                 use_GPU=True,
+                 dims=2048,
+                 premodel_path=None,
+                 model=None):
         self.batch_size = batch_size
         self.use_GPU = use_GPU
         self.dims = dims
@@ -55,8 +61,8 @@ class FID(paddle.metric.Metric):
         param_dict = paddle.load(premodel_path)
         self.model.load_dict(param_dict)
         self.model.eval()
-        self.reset()   
-        
+        self.reset()
+
     def reset(self):
         self.preds = []
         self.gts = []
@@ -72,7 +78,7 @@ class FID(paddle.metric.Metric):
         self.preds = np.concatenate(self.preds, axis=0)
         self.gts = np.concatenate(self.gts, axis=0)
         value = calculate_fid_given_img(self.preds, self.gts)
-        self.reset() 
+        self.reset()
         return value
 
     def name(self):
@@ -115,10 +121,10 @@ def _calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
 def _get_activations_from_ims(img, model, batch_size, dims, use_gpu):
     n_batches = (len(img) + batch_size - 1) // batch_size
     n_used_img = len(img)
-    
+
     pred_arr = np.empty((n_used_img, dims))
-    
-    for i in tqdm(range(n_batches)):
+
+    for i in range(n_batches):
         start = i * batch_size
         end = start + batch_size
         if end > len(img):
@@ -126,7 +132,7 @@ def _get_activations_from_ims(img, model, batch_size, dims, use_gpu):
         images = img[start:end]
         if images.shape[1] != 3:
             images = images.transpose((0, 3, 1, 2))
-        
+
         images = paddle.to_tensor(images)
         pred = model(images)[0][0]
         pred_arr[start:end] = pred.reshape([end - start, -1]).cpu().numpy()
@@ -138,16 +144,20 @@ def _compute_statistic_of_img(act):
     sigma = np.cov(act, rowvar=False)
     return mu, sigma
 
+
 def calculate_inception_val(img_fake,
                             img_real,
                             batch_size,
                             model,
-                            use_gpu = True,
-                            dims = 2048):
-    act_fake = _get_activations_from_ims(img_fake, model, batch_size, dims, use_gpu)
-    act_real = _get_activations_from_ims(img_real, model, batch_size, dims, use_gpu)
+                            use_gpu=True,
+                            dims=2048):
+    act_fake = _get_activations_from_ims(img_fake, model, batch_size, dims,
+                                         use_gpu)
+    act_real = _get_activations_from_ims(img_real, model, batch_size, dims,
+                                         use_gpu)
     return act_fake, act_real
 
+
 def calculate_fid_given_img(act_fake, act_real):
 
     m1, s1 = _compute_statistic_of_img(act_fake)
@@ -299,4 +309,3 @@ def calculate_fid_given_paths(paths,
 
     fid_value = _calculate_frechet_distance(m1, s1, m2, s2)
     return fid_value
-