Merge ce6e4eb into 7e3aacc

configs/cyclegan/cyclegan_lsgan_resnet_in_1x1_270k_horse2zebra.py

@@ -79,14 +79,51 @@
         keys=[f'img_{domain_a}', f'img_{domain_b}'],
         meta_keys=[f'img_{domain_a}_path', f'img_{domain_b}_path'])
 ]
+test_pipeline = [
+    dict(
+        type='LoadImageFromFile',
+        io_backend='disk',
+        key=f'img_{domain_a}',
+        flag='color'),
+    dict(
+        type='LoadImageFromFile',
+        io_backend='disk',
+        key=f'img_{domain_b}',
+        flag='color'),
+    dict(
+        type='Resize',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        scale=(256, 256),
+        interpolation='bicubic'),
+    dict(type='RescaleToZeroOne', keys=[f'img_{domain_a}', f'img_{domain_b}']),
+    dict(
+        type='Normalize',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        to_rgb=False,
+        mean=[0.5, 0.5, 0.5],
+        std=[0.5, 0.5, 0.5]),
+    dict(type='ImageToTensor', keys=[f'img_{domain_a}', f'img_{domain_b}']),
+    dict(
+        type='Collect',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        meta_keys=[f'img_{domain_a}_path', f'img_{domain_b}_path'])
+]
 data = dict(
     train=dict(
         dataroot=dataroot,
         pipeline=train_pipeline,
         domain_a=domain_a,
         domain_b=domain_b),
-    val=dict(dataroot=dataroot, domain_a=domain_a, domain_b=domain_b),
-    test=dict(dataroot=dataroot, domain_a=domain_a, domain_b=domain_b))
+    val=dict(
+        dataroot=dataroot,
+        domain_a=domain_a,
+        domain_b=domain_b,
+        pipeline=test_pipeline),
+    test=dict(
+        dataroot=dataroot,
+        domain_a=domain_a,
+        domain_b=domain_b,
+        pipeline=test_pipeline))
 
 optimizer = dict(
     generators=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999)),
@@ -115,3 +152,14 @@
 metrics = dict(
     FID=dict(type='FID', num_images=140, image_shape=(3, 256, 256)),
     IS=dict(type='IS', num_images=140, image_shape=(3, 256, 256)))
+
+# inception_pkl = None
+evaluation = dict(
+    type='TranslationEvalHook',
+    target_domain='zebra',
+    interval=200,
+    metrics=[
+        dict(type='FID', num_images=140, bgr2rgb=True),
+        dict(type='IS', num_images=140)
+    ],
+    best_metric=['fid', 'is'])

mmgen/apis/train.py

@@ -172,9 +172,9 @@ def train_model(model,
         val_dataloader = build_dataloader(
             val_dataset, dist=distributed, **val_loader_cfg)
         eval_cfg = deepcopy(cfg.get('evaluation'))
+        priority = eval_cfg.pop('priority', 'LOW')
         eval_cfg.update(dict(dist=distributed, dataloader=val_dataloader))
         eval_hook = build_from_cfg(eval_cfg, HOOKS)
-        priority = eval_cfg.pop('priority', 'NORMAL')
         runner.register_hook(eval_hook, priority=priority)
 
     # user-defined hooks

mmgen/core/evaluation/__init__.py

@@ -1,23 +1,14 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from .eval_hooks import GenerativeEvalHook
+from .eval_hooks import GenerativeEvalHook, TranslationEvalHook
 from .evaluation import (make_metrics_table, make_vanilla_dataloader,
                          single_gpu_evaluation, single_gpu_online_evaluation)
 from .metric_utils import slerp
 from .metrics import (IS, MS_SSIM, PR, SWD, GaussianKLD, ms_ssim,
                       sliced_wasserstein)
 
 __all__ = [
-    'MS_SSIM',
-    'SWD',
-    'ms_ssim',
-    'sliced_wasserstein',
-    'single_gpu_evaluation',
-    'single_gpu_online_evaluation',
-    'PR',
-    'IS',
-    'slerp',
-    'GenerativeEvalHook',
-    'make_metrics_table',
-    'make_vanilla_dataloader',
-    'GaussianKLD',
+    'MS_SSIM', 'SWD', 'ms_ssim', 'sliced_wasserstein', 'single_gpu_evaluation',
+    'single_gpu_online_evaluation', 'PR', 'IS', 'slerp', 'GenerativeEvalHook',
+    'make_metrics_table', 'make_vanilla_dataloader', 'GaussianKLD',
+    'TranslationEvalHook'
 ]