Change NSFW Model (#307)

* Change download for NSFW model

Signed-off-by: Ryan Wolf <[email protected]>

* Fix model init

Signed-off-by: Ryan Wolf <[email protected]>

* Fix embedding size

Signed-off-by: Ryan Wolf <[email protected]>

---------

Signed-off-by: Ryan Wolf <[email protected]>

nemo_curator/image/classifiers/nsfw.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
+import zipfile
 from typing import Optional
 
 import requests
@@ -23,33 +24,35 @@
 
 
 # MLP code taken from LAION's CLIP-based-NSFW-Detector
-# https://github.com/LAION-AI/CLIP-based-NSFW-Detector/blob/main/h14_nsfw_model.py
-class H14_NSFW_Detector(nn.Module):
-    def __init__(self, input_size=1024):
+# https://github.com/LAION-AI/CLIP-based-NSFW-Detector/issues/7
+class Normalization(nn.Module):
+    def __init__(self, shape):
         super().__init__()
-        self.input_size = input_size
-        self.layers = nn.Sequential(
-            nn.Linear(self.input_size, 1024),
-            nn.ReLU(),
-            nn.Dropout(0.2),
-            nn.Linear(1024, 2048),
-            nn.ReLU(),
-            nn.Dropout(0.2),
-            nn.Linear(2048, 1024),
-            nn.ReLU(),
-            nn.Dropout(0.2),
-            nn.Linear(1024, 256),
-            nn.ReLU(),
-            nn.Dropout(0.2),
-            nn.Linear(256, 128),
-            nn.ReLU(),
-            nn.Dropout(0.2),
-            nn.Linear(128, 16),
-            nn.Linear(16, 1),
-        )
+        self.register_buffer("mean", torch.zeros(shape))
+        self.register_buffer("variance", torch.ones(shape))
+
+    def forward(self, x):
+        return (x - self.mean) / self.variance.sqrt()
+
+
+class NSFWModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.norm = Normalization([768])
+        self.linear_1 = nn.Linear(768, 64)
+        self.linear_2 = nn.Linear(64, 512)
+        self.linear_3 = nn.Linear(512, 256)
+        self.linear_4 = nn.Linear(256, 1)
+        self.act = nn.ReLU()
+        self.act_out = nn.Sigmoid()
 
     def forward(self, x):
-        return self.layers(x)
+        x = self.norm(x)
+        x = self.act(self.linear_1(x))
+        x = self.act(self.linear_2(x))
+        x = self.act(self.linear_3(x))
+        x = self.act_out(self.linear_4(x))
+        return x
 
 
 class NsfwClassifier(ImageClassifier):
@@ -66,7 +69,7 @@ def __init__(
             pred_column=pred_column,
             pred_type=float,
             batch_size=batch_size,
-            embedding_size=1024,
+            embedding_size=768,
         )
 
         if model_path is None:
@@ -76,21 +79,24 @@ def __init__(
 
     @staticmethod
     def _get_default_model():
-        weights_name = "h14_nsfw.pth"
+        weights_name = "clip_autokeras_binary_nsfw.pth"
         model_path = os.path.join(NEMO_CURATOR_HOME, weights_name)
         os.makedirs(NEMO_CURATOR_HOME, exist_ok=True)
 
         if not os.path.exists(model_path):
-            url = f"https://github.com/LAION-AI/CLIP-based-NSFW-Detector/blob/main/{weights_name}?raw=true"
+            url = "https://github.com/LAION-AI/CLIP-based-NSFW-Detector/files/10250461/clip_autokeras_binary_nsfw.zip"
             r = requests.get(url)
 
-            with open(model_path, "wb") as f:
+            raw_zip_path = os.path.join(NEMO_CURATOR_HOME, "nsfw.zip")
+            with open(raw_zip_path, "wb") as f:
                 f.write(r.content)
+            with zipfile.ZipFile(raw_zip_path, "r") as f:
+                f.extractall(NEMO_CURATOR_HOME)
 
         return model_path
 
     def load_model(self, device):
-        model = H14_NSFW_Detector(input_size=self.embedding_size).to(device)
+        model = NSFWModel().to(device)
         weights = torch.load(self.model_path, map_location=torch.device("cpu"))
         model.load_state_dict(weights)
         model.eval()