Merge branch 'main' of github.com:NINAnor/rare_species_detections

CONFIG.yaml

@@ -10,33 +10,38 @@
 data:
   n_task_train: 100
   n_task_val: 100
-  target_fs: 16000
-  resample: true
-  denoise: true
-  normalize: true
-  frame_length: 25.0
-  tensor_length: 128
+  target_fs: 22050 # used in preprocessing
+  resample: true # used in preprocessing
+  denoise: true # used in preprocessing
+  normalize: true # used in preprocessing
+  frame_length: 25.0 # used in preprocessing
+  tensor_length: 128 # used in preprocessing
   n_shot: 5
   n_query: 10
-  overlap: 0.5
-  n_subsample: 1 # ask Femke what this stands for
-  status: train # train or validate or evaluate
+  overlap: 0.5 # used in preprocessing
+  n_subsample: 1 
+  num_mel_bins: 128 # used in preprocessing
+  max_segment_length: 1.0 # used in preprocessing
+  status: train # used in preprocessing, train or validate or evaluate
+
 
 #################################
 # PARAMETERS FOR MODEL TRAINING #
 #################################
 # Be sure the parameters match the ones in data processing
 
 trainer:
-  max_epochs: 1
+  max_epochs: 25
   default_root_dir: /data
   accelerator: gpu
   gpus: 1
 
 model:
   distance: euclidean # other option is mahalanobis
   lr: 1.0e-05
-  model_path: /data/BEATs/BEATs_iter3_plus_AS2M.pt # Or FOR INFERENCE: /data/lightning_logs/version_X/checkpoints/epoch=X-step=1500.ckpt
+  model_type: beats # beats, pann or baseline
+  state: None # train or validate or None if using beats or baseline // since we remove from layers from the original PANN we can't load the ckpts normally (see _build_model in prototraining.py)
+  model_path: /data/lightning_logs/version_96/checkpoints/epoch=85-step=8600.ckpt #/data/lightning_logs/pann/lightning_logs/version_1/checkpoints/epoch=99-step=10000.ckpt #/data/lightning_logs/pann/lightning_logs/version_1/checkpoints/epoch=99-step=10000.ckpt #/data/model/PANN/Cnn14_mAP=0.431.pth # /data/model/BEATs/BEATs_iter3_plus_AS2M.pt #   # Or FOR INFERENCE: /data/lightning_logs/version_X/checkpoints/epoch=X-step=1500.ckpt
   specaugment_params: null
   # specaugment_params:
   #   application_ratio: 1.0

Models/baseline.py

@@ -0,0 +1,34 @@
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+from collections import OrderedDict
+
+def conv_block(in_channels,out_channels):
+
+    return nn.Sequential(
+        nn.Conv2d(in_channels,out_channels,3,padding=1),
+        nn.BatchNorm2d(out_channels),
+        nn.ReLU(),
+        nn.MaxPool2d(2)
+    )
+
+class ProtoNet(nn.Module):
+    def __init__(self):
+        super(ProtoNet,self).__init__()
+        self.encoder = nn.Sequential(
+            conv_block(1,64),
+            conv_block(64,64),
+            conv_block(64,64),
+            conv_block(64,64)
+        )
+    def forward(self,x):
+        (num_samples,seq_len,mel_bins) = x.shape
+        x = x.view(-1,1,seq_len,mel_bins)
+        print(x.shape)
+        x = self.encoder(x)
+        x = nn.MaxPool2d(2)(x)
+
+        return x.view(x.size(0),-1)
+
+    def extract_features(self, x, padding_mask=None):
+        return self.forward(x)