Merge 001cb0d into b44b680

vanvalenlab · Apr 20, 2020 · 9bcde44 · 9bcde44
2 parents b44b680 + 001cb0d
commit 9bcde44
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Showing 4 changed files with 19 additions and 12 deletions.
diff --git a/deepcell/applications/cytoplasm_segmentation.py b/deepcell/applications/cytoplasm_segmentation.py
@@ -41,7 +41,7 @@
 
 
 WEIGHTS_PATH = ('https://deepcell-data.s3-us-west-1.amazonaws.com/'
-                'model-weights/general_cyto_9c7b79e6238d72c14ea8f87023ac3af9.h5')
+                'model-weights/general_cyto.h5')
 
 
 class CytoplasmSegmentation(Application):
@@ -96,7 +96,7 @@ class CytoplasmSegmentation(Application):
     #: Metadata for the model and training process
     model_metadata = {
         'batch_size': 2,
-        'lr': 1e-5,
+        'lr': 1e-4,
         'lr_decay': 0.95,
         'training_seed': 0,
         'n_epochs': 8,
@@ -114,14 +114,16 @@ def __init__(self,
                             num_semantic_heads=3,
                             num_semantic_classes=[1, 1, 2],
                             location=True,
-                            include_top=True)
+                            include_top=True,
+                            lite=True,
+                            interpolation='bilinear')
 
         if use_pretrained_weights:
             weights_path = get_file(
                 os.path.basename(WEIGHTS_PATH),
                 WEIGHTS_PATH,
                 cache_subdir='models',
-                md5_hash='4e9136df5071930a66365b2229fc358b'
+                md5_hash='50614f04d5dbc4b3eadd897fa5fb0e23'
             )
 
             model.load_weights(weights_path)

diff --git a/deepcell/applications/nuclear_segmentation.py b/deepcell/applications/nuclear_segmentation.py
@@ -40,8 +40,8 @@
 
 
 WEIGHTS_PATH = ('https://deepcell-data.s3-us-west-1.amazonaws.com/'
-                'model-weights/general_nuclear_train_batch_size_82800_resnet50_'
-                '8_epochs_c4b2167eb754923856bc84fb29074413.h5')
+                'model-weights/nuclear_0_82800_resnet50_watershed_'
+                '076bb10d832089b6a77faed1e63ad375.h5')
 
 
 class NuclearSegmentation(Application):
@@ -114,14 +114,16 @@ def __init__(self,
                             num_semantic_heads=3,
                             num_semantic_classes=[1, 1, 2],
                             location=True,
-                            include_top=True)
+                            include_top=True,
+                            lite=True,
+                            interpolation='bilinear')
 
         if use_pretrained_weights:
             weights_path = get_file(
                 os.path.basename(WEIGHTS_PATH),
                 WEIGHTS_PATH,
                 cache_subdir='models',
-                md5_hash='eb29808ef2f662fb3bcda6986e47f91a'
+                md5_hash='62b6ac21807d34dd42734f43ed20756f'
             )
 
             model.load_weights(weights_path)

diff --git a/deepcell/model_zoo/fpn.py b/deepcell/model_zoo/fpn.py
@@ -282,8 +282,9 @@ def __create_pyramid_features(backbone_dict,
         P_minus_1 = Activation('relu', name=N + '_relu')(P_minus_2)
 
         if ndim == 2:
-            P_minus_1 = Conv2D(feature_size, kernel_size=(3, 3), strides=(2, 2),
-                               padding='same', name=P_minus_1_name)(P_minus_1)
+            P_minus_1 = Conv2D(feature_size, kernel_size=(3, 3),
+                               strides=(2, 2), padding='same',
+                               name=P_minus_1_name)(P_minus_1)
         else:
             P_minus_1 = Conv3D(feature_size, kernel_size=(1, 3, 3),
                                strides=(1, 2, 2), padding='same',

diff --git a/deepcell/model_zoo/panopticnet.py b/deepcell/model_zoo/panopticnet.py
@@ -396,7 +396,8 @@ def PanopticNet(backbone,
     pyramid_dict = create_pyramid_features(backbone_dict_reduced,
                                            ndim=ndim,
                                            lite=lite,
-                                           upsample_type='upsampling2d')
+                                           upsample_type='upsampling2d',
+                                           interpolation=interpolation)
 
     features = [pyramid_dict[key] for key in pyramid_levels]
 
@@ -414,7 +415,8 @@ def PanopticNet(backbone,
         semantic_head_list.append(create_semantic_head(
             pyramid_dict, n_classes=num_semantic_classes[i],
             input_target=inputs, target_level=target_level,
-            semantic_id=i, ndim=ndim, **kwargs))
+            semantic_id=i, ndim=ndim, interpolation=interpolation,
+            **kwargs))
 
     outputs = semantic_head_list