last batch of live-fixes and improvments

api_server.py

@@ -22,7 +22,8 @@ from train import inference as ctd_inference
 app = FastAPI()
 
 # reference_pattern_path = '/home/nils/kinect_reference_cropped.png'
-reference_pattern_path = '/home/nils/kinect_reference_far.png'
+# reference_pattern_path = '/home/nils/kinect_reference_far.png'
+reference_pattern_path = '/home/nils/mpc/kinect_downshift_rotate_left-1.png'
 # reference_pattern_path = '/home/nils/kinect_diff_ref.png'
 print(reference_pattern_path)
 reference_pattern = cv2.imread(reference_pattern_path)

cfgs/train.yaml

@@ -1,12 +1,12 @@
 seed: 0
 mixed_precision: true
-base_lr: 4.0e-4
+base_lr: 0.00025
-# base_lr: 0.00001
 t_max: 16100
+scheduler: "cosineannealing"
 
 nr_gpus: 3
 batch_size: 3
-n_total_epoch: 100
+n_total_epoch: 64
 minibatch_per_epoch: 500
 
 loadmodel: ~
@@ -17,17 +17,9 @@ model_save_freq_epoch: 1
 max_disp: 256
 image_width: 640
 image_height: 480
-# dataset: "blender"
-# training_data_path: "./stereo_trainset/crestereo"
-# training_data_path: "/media/Data1/connecting_the_dots_data/ctd_data/"
-# training_data_path: "/media/Data1/connecting_the_dots_data/blender_renders/data"
 training_data_path: "/media/Data1/connecting_the_dots_data/blender_renders_ctd_randomize_light/data"
-
+test_data_path: "./eval_kinect"
-
+data_limit: 1.
-# FIXME any of this??
-pattern_attention: false
-scene_attention: true
-ignore_pattern_completely: false
 
 
 log_level: "logging.INFO"

dataset.py

@@ -239,17 +239,22 @@ class CREStereoDataset(Dataset):
 
 
 class CTDDataset(Dataset):
-    def __init__(self, root, pattern_path: str, data_type: str = 'syn', augment=True, resize_pattern=True, blur=False, split=0.9, test_set=False, use_lightning=True):
+    def __init__(self, root, pattern_path: str, data_type: str = 'syn', augment=True, resize_pattern=True, blur=False, split=0.9, test_set=False, use_lightning=True, data_limit=1.):
         super().__init__()
         self.rng = np.random.RandomState(0)
         self.augment = augment
         self.blur = blur
         self.use_lightning = use_lightning
-        imgs = glob.glob(os.path.join(root, f"{data_type}/*/im0_*.npy"), recursive=True)
+        self.data_type = data_type
-        if test_set:
+        
+        imgs = glob.glob(os.path.join(root, f"{data_type if not 'syn' in root else ''}/*/im0_0*.npy"), recursive=True)
+        if not test_set:
             self.imgs = imgs[:int(split * len(imgs))]
         else:
             self.imgs = imgs[int(split * len(imgs)):]
+
+        self.imgs = self.imgs[:int(data_limit * len(self.imgs))]
+
         self.pattern = cv2.imread(pattern_path)#, cv2.IMREAD_GRAYSCALE)
 
         if resize_pattern and self.pattern.shape != (480, 640, 3):
@@ -325,9 +330,30 @@ class CTDDataset(Dataset):
 
 
 class BlenderDataset(CTDDataset):
-    def __init__(self, root, pattern_path: str, data_type: str = 'syn', augment=True, resize_pattern=True, blur=False, split=0.9, test_set=False, use_lightning=False):
+    def __init__(self, root, pattern_path: str, data_type: str = 'syn', augment=True, resize_pattern=True, blur=False, split=0.9, test_set=False, use_lightning=False, disp_avail=False, data_limit=1.):
-        super().__init__(root, pattern_path)
+        super().__init__(root, pattern_path, augment=augment)
         self.use_lightning = use_lightning
+        self.disp_avail = disp_avail
+        self.data_type = data_type
+        
+        self.get_imgs(root, test_set, split)
+        self.imgs = self.imgs[:int(data_limit * len(self.imgs))]
+
+        self.pattern = cv2.imread(pattern_path)#, cv2.IMREAD_GRAYSCALE)
+
+        if resize_pattern and self.pattern.shape != (480, 640, 3):
+            self.pattern = downsample(self.pattern)
+
+        self.augmentor = Augmentor(
+            image_height=480,
+            image_width=640,
+            max_disp=256,
+            scale_min=0.6,
+            scale_max=1.0,
+            seed=0,
+        )
+        
+    def get_imgs(self, root, test_set, split):
         additional_img_types = {
                 'depth',
                 'disp',
@@ -348,24 +374,12 @@ class BlenderDataset(CTDDataset):
         else:
             self.imgs = imgs[int(split * len(imgs)):]
 
-        self.pattern = cv2.imread(pattern_path)#, cv2.IMREAD_GRAYSCALE)
-
-        if resize_pattern and self.pattern.shape != (480, 640, 3):
-            self.pattern = downsample(self.pattern)
-
-        self.augmentor = Augmentor(
-            image_height=480,
-            image_width=640,
-            max_disp=256,
-            scale_min=0.6,
-            scale_max=1.0,
-            seed=0,
-        )
-
     def __getitem__(self, index):
         # find path
         left_path = self.imgs[index]
-        left_disp_path = left_path.split('.')[0] + '_disp0001.png'
+        left_disp_path = left_path.rsplit('.', maxsplit=1)[0] + '_disp0001.png'
+        if not self.disp_avail:
+            left_depth_path = left_path.rsplit('.', maxsplit=1)[0] + '_depth0001.png'
 
         # read img, disp
         left_img = cv2.imread(left_path)
@@ -379,9 +393,23 @@ class BlenderDataset(CTDDataset):
                 left_img = cv2.merge([left_img, left_img, left_img]).reshape((480, 640, 3))
 
             right_img = self.pattern
-        # left_disp = self.get_disp(left_disp_path)
+
-        disp = cv2.imread(left_disp_path, cv2.IMREAD_UNCHANGED)
+        # In some cases, we have disparity as floats in the range [0..1]. Thus we need to upscale the values.
-        left_disp = downsample(disp)
+        # 64 has been arbitrarily chosen as max_disp for this case, as this is roughly the max disparity of the CTD dataset
+        max_disp = 64
+        if not self.disp_avail:
+            left_disp = self.get_disp(left_depth_path)
+            if left_disp.max() <= 1.:
+                left_disp = (left_disp * max_disp).astype('uint8')
+        else:
+            try:
+                left_disp = cv2.imread(left_disp_path, cv2.IMREAD_UNCHANGED)
+                if left_disp.max() <= 1.:
+                    left_disp = (left_disp * max_disp).astype('uint8')
+                if left_disp.shape != (480, 640, 3): 
+                    left_disp = downsample(left_disp)
+            except:
+                print(f'something happened, probably couldn\'t find {left_disp_path}')
 
         if False: # self.rng.binomial(1, 0.5):
             left_img, right_img = np.fliplr(right_img), np.fliplr(left_img)
@@ -398,6 +426,9 @@ class BlenderDataset(CTDDataset):
             _left_img, _right_img, _left_disp, disp_mask = self.augmentor(
                 left_img, right_img, left_disp
             )
+            left_img = left_img.astype('float32')
+            right_img = right_img.astype('float32')
+            left_disp = left_disp.astype('float32')
         else:
             left_img, right_img, left_disp, disp_mask = self.augmentor(
                 left_img, right_img, left_disp
@@ -418,14 +449,13 @@ class BlenderDataset(CTDDataset):
     def get_disp(self, path):
         baseline = 0.075    # meters
         fl = 560.           # as per CTD
-        depth = cv2.imread(path, cv2.IMREAD_UNCHANGED)
+        # depth = cv2.imread(path, cv2.IMREAD_UNCHANGED)
+        depth = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
+        if depth.shape != (480, 640):
             depth = downsample(depth)
-        # disp = np.load(path).transpose(1,2,0)
+            
-        # disp = baseline * fl / depth
+        disp = (baseline * fl) / depth
-        # return disp.astype(np.float32) / 32
+            
-        # FIXME temporarily increase disparity until new data with better depth values is generated
+        disp[disp == np.inf] = 0
-        #       higher values seem to speedup convergence, but introduce much stronger artifacting
+        
-        mystery_factor = 35
-        # mystery_factor = 1
-        disp = (baseline * fl * mystery_factor) / depth
         return disp.astype(np.float32)

nets/attention/transformer.py

@@ -76,7 +76,7 @@ class LocalFeatureTransformer(nn.Module):
             if p.dim() > 1:
                 nn.init.xavier_uniform_(p)
 
-    def forward(self, feat0, feat1, mask0=None, mask1=None):
+    def forward(self, feat0, feat1, mask0=None, mask1=None, ignore_second_feat=False):
         """
         Args:
             feat0 (torch.Tensor): [N, L, C]
@@ -97,6 +97,9 @@ class LocalFeatureTransformer(nn.Module):
             name = self.layer_names[i]
             if name == 'self':
                 feat0 = layer(feat0, feat0, mask0, mask0)
+                if ignore_second_feat:
+                    # save some compute
+                    continue
                 feat1 = layer(feat1, feat1, mask1, mask1)
             elif name == 'cross':
                 feat0 = layer(feat0, feat1, mask0, mask1)
@@ -104,4 +107,6 @@ class LocalFeatureTransformer(nn.Module):
             else:
                 raise KeyError
 
+        if ignore_second_feat:
+            return feat0 
         return feat0, feat1

nets/crestereo.py

@@ -151,7 +151,8 @@ class CREStereo(nn.Module):
 
             # FIXME experimental ! no self-attention for pattern
             if not self_attend_right:
-                fmap1_dw16, _ = self.self_att_fn(fmap1_dw16, fmap2_dw16)
+                print('skipping right attention')
+                fmap1_dw16 = self.self_att_fn(fmap1_dw16, fmap2_dw16, ignore_second_feat=True)
             else:
                 fmap1_dw16, fmap2_dw16 = self.self_att_fn(fmap1_dw16, fmap2_dw16)
 

train_lightning.py

@@ -31,8 +31,22 @@ import numpy as np
 import cv2
 
 
-def normalize_and_colormap(img):
+def normalize_and_colormap(img, reduce_dynamic_range=False):
+    # print(img.min())
+    # print(img.max())
+    # print(img.mean())
     ret = (img - img.min()) / (img.max() - img.min()) * 255.0
+    # print(ret.min())
+    # print(ret.max())
+    # print(ret.mean())
+    
+    # FIXME do I need to compress dynamic range somehow or something?
+    if reduce_dynamic_range and img.max() > 5*img.mean():
+        ret = (img - img.min()) / (5*img.mean() - img.min()) * 255.0
+    # print(ret.min())
+    # print(ret.max())
+    # print(ret.mean())
+        
     if isinstance(ret, torch.Tensor):
         ret = ret.cpu().detach().numpy()
     ret = ret.astype("uint8")
@@ -47,20 +61,56 @@ def log_images(left, right, pred_disp, gt_disp):
     if isinstance(pred_disp, list):
         pred_disp = pred_disp[-1]
 
-    pred_disp = torch.squeeze(pred_disp[:, 0, :, :])
-    gt_disp = torch.squeeze(gt_disp[:, 0, :, :])
     left = torch.squeeze(left[:, 0, :, :])
     right = torch.squeeze(right[:, 0, :, :])
+    pred_disp = torch.squeeze(pred_disp[:, 0, :, :])
+    gt_disp = torch.squeeze(gt_disp[:, 0, :, :])
     
-    disp = pred_disp
+    # print('gt_disp debug')
-    disp_error = gt_disp - disp
+    # print(gt_disp.shape)
     
+    singular_batch = False
+    if len(left.shape) == 2:
+        singular_batch = True
+        print('batch_size seems to be 1')
+        input_left = left.cpu().detach().numpy()
+        input_right = right.cpu().detach().numpy()
+    else:
         input_left = left[batch_idx].cpu().detach().numpy()# .transpose(1,2,0)
         input_right = right[batch_idx].cpu().detach().numpy()# .transpose(1,2,0)
         
+    disp = pred_disp
+    disp_error = gt_disp - disp
+    
+    # print('gt_disp debug normalize')
+    # print(gt_disp.max(), gt_disp.min())
+    # print(gt_disp.dtype)
+
+    if singular_batch:
         wandb_log =  dict(
             key='samples', 
             images=[
+                pred_disp,
+                normalize_and_colormap(pred_disp),
+                normalize_and_colormap(abs(disp_error), reduce_dynamic_range=True),
+                normalize_and_colormap(gt_disp, reduce_dynamic_range=True),
+                input_left,
+                input_right,
+            ],
+            caption=[
+                f"Disparity \n{pred_disp.min():.{2}f}/{pred_disp.max():.{2}f}",
+                f"Disparity (vis) \n{pred_disp.min():.{2}f}/{pred_disp.max():.{2}f}",
+                f"Disp. Error\n{disp_error.min():.{2}f}/{disp_error.max():.{2}f}\n{abs(disp_error).mean():.{2}f}",
+                f"GT Disp Vis \n{gt_disp.min():.{2}f}/{gt_disp.max():.{2}f}",
+                "Input Left",
+                "Input Right"
+            ],
+        )
+    else:
+        wandb_log =  dict(
+            key='samples', 
+            images=[
+                # pred_disp.cpu().detach().numpy().transpose(1,2,0),
                 normalize_and_colormap(pred_disp[batch_idx]),
                 normalize_and_colormap(abs(disp_error[batch_idx])),
                 normalize_and_colormap(gt_disp[batch_idx]),
@@ -68,7 +118,8 @@ def log_images(left, right, pred_disp, gt_disp):
                 input_right,
             ],
             caption=[
-            f"Disparity \n{pred_disp[batch_idx].min():.{2}f}/{pred_disp[batch_idx].max():.{2}f}",
+                # f"Disparity \n{pred_disp[batch_idx].min():.{2}f}/{pred_disp[batch_idx].max():.{2}f}",
+                f"Disparity (vis)\n{pred_disp[batch_idx].min():.{2}f}/{pred_disp[batch_idx].max():.{2}f}",
                 f"Disp. Error\n{disp_error[batch_idx].min():.{2}f}/{disp_error[batch_idx].max():.{2}f}\n{abs(disp_error[batch_idx]).mean():.{2}f}",
                 f"GT Disp Vis \n{gt_disp[batch_idx].min():.{2}f}/{gt_disp[batch_idx].max():.{2}f}",
                 "Input Left",
@@ -103,6 +154,9 @@ def outlier_fraction(estimate, target, mask=None, threshold=0):
         mask = np.ones(estimate.shape, dtype=np.bool)
       else:
         mask = mask != 0
+      if estimate.shape != mask.shape:
+        if len(mask.shape) == 3:
+          mask = mask[0]
           if estimate.shape != mask.shape:
            raise Exception(f'estimate and mask have to be same shape (expected {estimate.shape} == {mask.shape})')
       return estimate, target, mask
@@ -131,27 +185,9 @@ def sequence_loss(flow_preds, flow_gt, valid, gamma=0.8, test=False):
     flow_preds[0]: (B, 2, H, W)
     flow_gt: (B, 2, H, W)
     '''
-    """
-    if test:
-        # print('sequence loss')
-        if valid.shape != (2, 480, 640):
-            valid = torch.stack([valid, valid])#.transpose(0,1)#.transpose(1,2)
-            # print(valid.shape)
-            #valid = torch.stack([valid, valid])
-            # print(valid.shape)
-            if valid.shape != (2, 480, 640):
-                valid = valid.transpose(0,1)
-                # print(valid.shape)
-    """
-    # print(valid.shape)
-    # print(flow_preds[0].shape)
-    # print(flow_gt.shape)
     n_predictions = len(flow_preds)
     flow_loss = 0.0
     
-    # TEST
-    # flow_gt = torch.squeeze(flow_gt, dim=-1)
-
     for i in range(n_predictions):
         i_weight = gamma ** (n_predictions - i - 1)
         i_loss = torch.abs(flow_preds[i] - flow_gt)
@@ -161,38 +197,50 @@ def sequence_loss(flow_preds, flow_gt, valid, gamma=0.8, test=False):
 
 
 class CREStereoLightning(LightningModule):
-    def __init__(self, args, logger=None, pattern_path='', data_path=''):
+    def __init__(self, args, logger=None, pattern_path=''):
         super().__init__()
         self.batch_size = args.batch_size
         self.wandb_logger = logger
-        self.data_type = 'blender' if 'blender' in data_path else 'ctd'
+        self.imwidth = args.image_width
+        self.imheight = args.image_height
+        self.data_type = 'blender' if 'blender' in args.training_data_path else 'ctd'
+        self.eval_type = 'kinect' if 'kinect' in args.test_data_path else args.training_data_path
         self.lr = args.base_lr
-        print(f'lr = {self.lr}')
         self.T_max = args.t_max if args.t_max else None
         self.pattern_attention = args.pattern_attention
         self.pattern_path = pattern_path
-        self.data_path = data_path
+        self.data_path = args.training_data_path
+        self.test_data_path = args.test_data_path
+        self.data_limit = args.data_limit       # between 0 and 1.
         self.model = Model(
             max_disp=args.max_disp, mixed_precision=args.mixed_precision, test_mode=False
         )
+        
+        if args.scheduler == 'default':
+            self.automatic_optimization = False
             # so I can access it in adjust learn rate more easily
+            
         self.n_total_epoch = args.n_total_epoch
         self.base_lr = args.base_lr
 
-        self.automatic_optimization = False
-
     def train_dataloader(self):
+        # we never train on kinect
+        is_kinect = False
         if self.data_type == 'blender':
             dataset = BlenderDataset(
                     root=self.data_path,
                     pattern_path=self.pattern_path,
                     use_lightning=True,
+                    data_type='kinect' if is_kinect else 'blender',
+                    disp_avail=not is_kinect,
+                    data_limit = self.data_limit,
                 )
         elif self.data_type == 'ctd':
             dataset = CTDDataset(
                     root=self.data_path,
                     pattern_path=self.pattern_path,
                     use_lightning=True,
+                    data_limit = self.data_limit,
                     )
         dataloader = DataLoader(
                 dataset,
@@ -203,16 +251,20 @@ class CREStereoLightning(LightningModule):
                 persistent_workers=True,
                 pin_memory=True,
             )
-                # num_workers=0, drop_last=True, persistent_workers=False, pin_memory=True)
         return dataloader
 
     def val_dataloader(self):
+        # we also don't want to validate on kinect data
+        is_kinect = False
         if self.data_type == 'blender':
             test_dataset = BlenderDataset(
                     root=self.data_path,
                     pattern_path=self.pattern_path,
                     test_set=True,
                     use_lightning=True,
+                    data_type='kinect' if is_kinect else 'blender',
+                    disp_avail=not is_kinect,
+                    data_limit = self.data_limit,
                 )
         elif self.data_type == 'ctd':
             test_dataset = CTDDataset(
@@ -220,6 +272,7 @@ class CREStereoLightning(LightningModule):
                     pattern_path=self.pattern_path,
                     test_set=True,
                     use_lightning=True,
+                    data_limit = self.data_limit,
                 )
 
         test_dataloader = DataLoader(
@@ -231,29 +284,35 @@ class CREStereoLightning(LightningModule):
                 persistent_workers=True,
                 pin_memory=True
             )
-                # num_workers=0, drop_last=True, persistent_workers=False, pin_memory=True)
         return test_dataloader
 
     def test_dataloader(self):
-        # TODO change this to use IRL data?
+        is_kinect = self.eval_type == 'kinect'
         if self.data_type == 'blender':
-            test_dataset = CTDDataset(
+            test_dataset = BlenderDataset(
-                    root=self.data_path,
+                    root=self.test_data_path,
                     pattern_path=self.pattern_path,
                     test_set=True,
+                    split=0. if is_kinect else 0.9,     # if we test on kinect data, use all available samples for test set
                     use_lightning=True,
+                    augment=False,
+                    disp_avail=not is_kinect,
+                    data_type='kinect' if is_kinect else 'blender',
+                    data_limit = self.data_limit,
                 )
         elif self.data_type == 'ctd':
-            test_dataset = BlenderDataset(
+            test_dataset = CTDDataset(
-                    root=self.data_path,
+                    root=self.test_data_path,
                     pattern_path=self.pattern_path,
                     test_set=True,
                     use_lightning=True,
+                    augment=False,
+                    data_limit = self.data_limit,
                 )
 
         test_dataloader = DataLoader(
                 test_dataset,
-                self.batch_size,
+                1 if is_kinect else self.batch_size,
                 shuffle=False,
                 num_workers=4,
                 drop_last=False,
@@ -307,7 +366,8 @@ class CREStereoLightning(LightningModule):
         self.log("outlier_fraction", of)
         # print(', '.join(f'of{thr}={val}' for thr, val in of.items()))
         if batch_idx % 8 == 0:
-            self.wandb_logger.log_image(**log_images(left, right, flow_predictions, gt_disp))
+            images = log_images(left, right, flow_predictions, gt_disp)
+            self.wandb_logger.log_image(**images)
 
     def test_step(self, batch, batch_idx):
         left, right, gt_disp, valid_mask = batch
@@ -318,20 +378,28 @@ class CREStereoLightning(LightningModule):
             flow_predictions, gt_flow, valid_mask, gamma=0.8
         )
         self.log("test_loss", test_loss)
-        self.wandb_logger.log_image(**log_images(left, right, flow_predictions, gt_disp))
+        of = {}
+        for threshold in [0.1, 0.5, 1, 2, 5]:
+            of[str(threshold)] = outlier_fraction(flow_predictions[0], gt_flow, valid_mask, threshold)
+        self.log("outlier_fraction", of)
+        images = log_images(left, right, flow_predictions, gt_disp)
+        images['images'].append(gt_disp)
+        images['caption'].append('GT Disp')
+        self.wandb_logger.log_image(**images)
         
     def predict_step(self, batch, batch_idx, dataloader_idx=0):
         return self(batch)
     
     def configure_optimizers(self):
         optimizer = optim.Adam(self.model.parameters(), lr=self.lr, betas=(0.9, 0.999))
-        print('len(self.train_dataloader)', len(self.train_dataloader()))
+        if not self.automatic_optimization:
+            return optimizer
         lr_scheduler = {
                 'scheduler': torch.optim.lr_scheduler.CosineAnnealingLR(
                     optimizer,
                     T_max=self.T_max if self.T_max else len(self.train_dataloader())/self.batch_size,
                     ),
-                'name': 'CosineAnnealingLRScheduler',
+                'name': 'LR Scheduler',
                 }
         return [optimizer], [lr_scheduler]
     
@@ -356,18 +424,21 @@ class CREStereoLightning(LightningModule):
 
         for param_group in optimizer.param_groups:
             param_group['lr'] = lr
+        self.log('train/lr', lr)
 
 
 if __name__ == "__main__":
+    # wandb.init(project='crestereo-lightning')
     wandb_logger = WandbLogger(project="crestereo-lightning", log_model=True)
     # train configuration
     args = parse_yaml("cfgs/train.yaml")
     wandb_logger.experiment.config.update(args._asdict())
     config = wandb.config
+    data_limit = config.data_limit
     if 'blender' in config.training_data_path:
         # this was used for our blender renders
         pattern_path = '/home/nils/miniprojekt/kinect_syn_ref.png'
-    if 'ctd' in config.training_data_path:
+    elif 'ctd' in config.training_data_path:
         # this one is used (i hope) for ctd
         pattern_path = '/home/nils/kinect_from_settings.png'
     
@@ -381,7 +452,6 @@ if __name__ == "__main__":
             config,
             wandb_logger, 
             pattern_path, 
-            config.training_data_path,
             # lr=0.00017378008287493763,          # found with auto_lr_find=True
             )
     # NOTE turn this down once it's working, this might use too much space
@@ -394,6 +464,7 @@ if __name__ == "__main__":
             save_last=True,
             )
 
+    if config.scheduler == 'default': 
         trainer = Trainer(
                 accelerator='gpu', 
                 devices=devices,
@@ -402,7 +473,7 @@ if __name__ == "__main__":
                     EarlyStopping(
                             monitor="val_loss", 
                             mode="min",
-                        patience=16,
+                            patience=8,
                         ),
                     LearningRateMonitor(),
                     model_checkpoint,
@@ -418,7 +489,34 @@ if __name__ == "__main__":
                 logger=wandb_logger,
                 default_root_dir=config.log_dir_lightning,
             )
+    else:
+        trainer = Trainer(
+                accelerator='gpu', 
+                devices=devices,
+                max_epochs=config.n_total_epoch, 
+                callbacks=[
+                    EarlyStopping(
+                            monitor="val_loss", 
+                            mode="min",
+                            patience=8,
+                        ),
+                    LearningRateMonitor(),
+                    model_checkpoint,
+                    ],
+                strategy=DDPSpawnStrategy(find_unused_parameters=False),
+                # auto_scale_batch_size='binsearch',
+                # auto_lr_find=True,
+                accumulate_grad_batches=4,           # needed to disable for manual optimization
+                deterministic=True,
+                check_val_every_n_epoch=1,
+                limit_val_batches=64,
+                limit_test_batches=256,
+                logger=wandb_logger,
+                default_root_dir=config.log_dir_lightning,
+            )
+
 
     # trainer.tune(model)
     trainer.fit(model)
     # trainer.validate(chkpt_path=model_checkpoint.best_model_path)
+    trainer.test(model)