Use correct type and fix documentation (#36)

* Use correct type and fix documentation * Fix PEP8
Lightning-Universe · Jun 15, 2020 · 4642b32 · 4642b32
1 parent 76e54c1
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Showing 7 changed files with 13 additions and 12 deletions.
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -72,3 +72,4 @@ Indices and tables
    api/pl_bolts.loggers
    api/pl_bolts.optimizers
    api/pl_bolts.transforms
+   api/pl_bolts.utils
diff --git a/docs/source/readme.md b/docs/source/readme.md
@@ -1,6 +1,7 @@
 # PyTorchLightning Bolts
 
 
+![CI testing](https://github.com/PyTorchLightning/pytorch-lightning-bolts/workflows/CI%20testing/badge.svg?branch=master)
 
 
 

diff --git a/pl_bolts/losses/self_supervised_learning.py b/pl_bolts/losses/self_supervised_learning.py
@@ -94,10 +94,10 @@ class AmdimNceLoss(nn.Module):
     def forward(self, anchor_representations, positive_representations, mask_mat):
         """
         Compute the NCE scores for predicting r_src->r_trg.
-        Input:
-          r_src    : (batch_size, emb_dim)
-          r_trg    : (emb_dim, n_batch * w* h) (ie: nb_feat_vectors x embedding_dim)
-          mask_mat : (n_batch_gpu, n_batch)
+        Args:
+          anchor_representations   : (batch_size, emb_dim)
+          positive_representations : (emb_dim, n_batch * w* h) (ie: nb_feat_vectors x embedding_dim)
+          mask_mat                 : (n_batch_gpu, n_batch)
 
         Output:
           raw_scores : (n_batch_gpu, n_locs)

diff --git a/pl_bolts/metrics/aggregation.py b/pl_bolts/metrics/aggregation.py
@@ -15,8 +15,6 @@ def accuracy(preds, labels):
 
     return acc
 
-    result.log('key', val)
-    result.to_pbar()
 
 def precision_at_k(output, target, top_k=(1,)):
     """Computes the accuracy over the k top predictions for the specified values of k"""

diff --git a/pl_bolts/models/gans/basic/basic_gan_module.py b/pl_bolts/models/gans/basic/basic_gan_module.py
@@ -93,7 +93,7 @@ def discriminator_loss(self, x):
 
         # how well can it label as fake?
         fake = torch.zeros(x.size(0), 1)
-        fake = fake.type_as(fake)
+        fake = fake.type_as(x)
 
         fake_loss = self.adversarial_loss(
             self.discriminator(self.generated_imgs.detach()), fake)

diff --git a/pl_bolts/models/self_supervised/resnets.py b/pl_bolts/models/self_supervised/resnets.py
@@ -112,6 +112,7 @@ def forward(self, x):
 
         return out
 
+
 class BottleneckBN(nn.Module):
     expansion = 4
 
@@ -434,12 +435,12 @@ def __init__(self, expansion=1, num_classes=1000, zero_init_residual=False,
         self.bn1 = norm_layer(self.inplanes)
         self.relu = nn.ReLU(inplace=True)
         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = self._make_layer(BottleneckBN, 64*expansion, layers[0])
-        self.layer2 = self._make_layer(BottleneckBN, 128*expansion, layers[1], stride=2,
+        self.layer1 = self._make_layer(BottleneckBN, 64 * expansion, layers[0])
+        self.layer2 = self._make_layer(BottleneckBN, 128 * expansion, layers[1], stride=2,
                                        dilate=replace_stride_with_dilation[0])
-        self.layer3 = self._make_layer(BottleneckBN, 256*expansion, layers[2], stride=2,
+        self.layer3 = self._make_layer(BottleneckBN, 256 * expansion, layers[2], stride=2,
                                        dilate=replace_stride_with_dilation[1])
-        self.layer4 = self._make_layer(BottleneckBN, 512*expansion, layers[3], stride=2,
+        self.layer4 = self._make_layer(BottleneckBN, 512 * expansion, layers[3], stride=2,
                                        dilate=replace_stride_with_dilation[2])
         self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
         self.fc = nn.Linear(512 * BottleneckBN.expansion * expansion, num_classes)

diff --git a/pl_bolts/utils/ssl_utils.py b/pl_bolts/utils/ssl_utils.py
@@ -14,4 +14,4 @@ def torchvision_ssl_encoder(name, pretrained=False, return_all_feature_maps=Fals
     pretrained_model = getattr(resnets, name)(pretrained=pretrained, return_all_feature_maps=return_all_feature_maps)
 
     pretrained_model.fc = Identity()
-    return pretrained_model
+    return pretrained_model