add RK3 traning script

dcase2020_task2/experiments/classification_experiment.py

@@ -141,18 +141,18 @@ def configuration():
     #####################
 
     machine_type = 0
-    machine_id = 2
+    machine_id = 0
 
     num_mel = 128
     n_fft = 1024
     hop_size = 512
     power = 2.0
     fmin = 0
-    context = 5
+    context = 32
 
-    model_class = 'dcase2020_task2.models.FCNN'
-    hidden_size = 256
-    num_hidden = 3
+    model_class = 'dcase2020_task2.models.CNN'
+    hidden_size = 512
+    num_hidden = 4
     dropout_probability = 0.0
 
     # complement set
@@ -162,11 +162,11 @@ def configuration():
     if debug:
         num_workers = 0
     else:
-        num_workers = 3
+        num_workers = 4
 
     epochs = 100
     loss_class = 'dcase2020_task2.losses.BCE'
-    batch_size = 8192
+    batch_size = 512
     learning_rate = 1e-4
     weight_decay = 0
 

dcase2020_task2/models/__init__.py

@@ -2,4 +2,4 @@ from dcase2020_task2.models.base_model import ClassifierBase, VAEBase
 
 from dcase2020_task2.models.made import MADE
 from dcase2020_task2.models.ae import AE, ConvAE
-from dcase2020_task2.models.classifier import FCNN
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+from dcase2020_task2.models.classifier import FCNN, CNN
\ No newline at end of file

dcase2020_task2/models/classifier.py

@@ -24,7 +24,7 @@ class FCNN(torch.nn.Module):
         activation_fn = activation_dict[activation]
         self.input_shape = input_shape
 
-        sizes = [np.prod(input_shape)] + [hidden_size for _ in range(num_hidden)] + [num_outputs]
+        sizes = [np.prod(input_shape)] + [hidden_size for i in range(num_hidden)] + [num_outputs]
         layers = []
         for i, o in zip(sizes[:-1], sizes[1:]):
             layers.append(torch.nn.Linear(i, o))
@@ -46,4 +46,45 @@ class FCNN(torch.nn.Module):
         x = batch['observations']
         x = x.view(x.shape[0], -1)
         batch['scores'] = self.clf(x)
+        return batch
+
+
+class CNN(torch.nn.Module):
+
+    def __init__(
+            self,
+            input_shape,
+            hidden_size=256,
+            num_hidden=3,
+            num_outputs=1,
+            activation='relu',
+            batch_norm=False,
+            dropout_probability=0.1
+    ):
+        super().__init__()
+
+        activation_fn = activation_dict[activation]
+        self.input_shape = input_shape
+
+        sizes = [input_shape[0]] + [hidden_size for i in range(num_hidden)] + [num_outputs]
+        layers = []
+        for i, o in zip(sizes[:-1], sizes[1:]):
+            layers.append(torch.nn.Conv2d(i, o, kernel_size=(3, 3), stride=2, padding=(1, 1)))
+            # layers.append(torch.nn.Dropout(p=dropout_probability))
+            layers.append(activation_fn())
+
+        _ = layers.pop()
+        layers.append(torch.nn.AdaptiveMaxPool2d(1))
+        # _ = layers.pop()
+
+
+        if batch_norm:
+            _ = layers.pop()
+
+        self.clf = torch.nn.Sequential(*layers)
+        self.apply(init_weights)
+
+    def forward(self, batch):
+        x = batch['observations']
+        batch['scores'] = self.clf(x).view(-1, 1)
         return batch
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scripts/convolutional_classification/convolutional_complement_rk0.sh

+conda activate dcase2020_task2
+
+./scripts/run_all.sh classification_experiment 0 "debug=False num_hidden=3 hidden_size=256 batch_size=512 learning_rate=1e-4 weight_decay=0 model_class=dcase2020_task2.models.CNN loss_class=dcase2020_task2.losses.BCE -m student2.cp.jku.at:27017:dcase2020_task2_conv_complement_classification_gridsearch" &
+./scripts/run_all.sh classification_experiment 1 "debug=False num_hidden=3 hidden_size=256 batch_size=512 learning_rate=1e-5 weight_decay=0 model_class=dcase2020_task2.models.CNN loss_class=dcase2020_task2.losses.BCE -m student2.cp.jku.at:27017:dcase2020_task2_conv_complement_classification_gridsearch" &
+./scripts/run_all.sh classification_experiment 2 "debug=False num_hidden=3 hidden_size=256 batch_size=512 learning_rate=1e-4 weight_decay=1e-5 model_class=dcase2020_task2.models.CNN loss_class=dcase2020_task2.losses.BCE -m student2.cp.jku.at:27017:dcase2020_task2_conv_complement_classification_gridsearch" &
+./scripts/run_all.sh classification_experiment 3 "debug=False num_hidden=3 hidden_size=256 batch_size=512 learning_rate=1e-5 weight_decay=1e-5 model_class=dcase2020_task2.models.CNN loss_class=dcase2020_task2.losses.BCE -m student2.cp.jku.at:27017:dcase2020_task2_conv_complement_classification_gridsearch" &
+
+wait
+
+./scripts/run_all.sh classification_experiment 0 "debug=False num_hidden=3 hidden_size=512 batch_size=512 learning_rate=1e-4 weight_decay=0 model_class=dcase2020_task2.models.CNN loss_class=dcase2020_task2.losses.BCE -m student2.cp.jku.at:27017:dcase2020_task2_conv_complement_classification_gridsearch" &
+./scripts/run_all.sh classification_experiment 1 "debug=False num_hidden=3 hidden_size=512 batch_size=512 learning_rate=1e-5 weight_decay=0 model_class=dcase2020_task2.models.CNN loss_class=dcase2020_task2.losses.BCE -m student2.cp.jku.at:27017:dcase2020_task2_conv_complement_classification_gridsearch" &
+./scripts/run_all.sh classification_experiment 2 "debug=False num_hidden=3 hidden_size=512 batch_size=512 learning_rate=1e-4 weight_decay=1e-5 model_class=dcase2020_task2.models.CNN loss_class=dcase2020_task2.losses.BCE -m student2.cp.jku.at:27017:dcase2020_task2_conv_complement_classification_gridsearch" &
+./scripts/run_all.sh classification_experiment 3 "debug=False num_hidden=3 hidden_size=512 batch_size=512 learning_rate=1e-5 weight_decay=1e-5 model_class=dcase2020_task2.models.CNN loss_class=dcase2020_task2.losses.BCE -m student2.cp.jku.at:27017:dcase2020_task2_conv_complement_classification_gridsearch" &
+
+wait
+
+./scripts/run_all.sh classification_experiment 0 "debug=False num_hidden=4 hidden_size=512 batch_size=512 learning_rate=1e-4 weight_decay=0 model_class=dcase2020_task2.models.CNN loss_class=dcase2020_task2.losses.BCE -m student2.cp.jku.at:27017:dcase2020_task2_conv_complement_classification_gridsearch" &
+./scripts/run_all.sh classification_experiment 1 "debug=False num_hidden=4 hidden_size=512 batch_size=512 learning_rate=1e-5 weight_decay=0 model_class=dcase2020_task2.models.CNN loss_class=dcase2020_task2.losses.BCE -m student2.cp.jku.at:27017:dcase2020_task2_conv_complement_classification_gridsearch" &
+./scripts/run_all.sh classification_experiment 2 "debug=False num_hidden=4 hidden_size=512 batch_size=512 learning_rate=1e-4 weight_decay=1e-5 model_class=dcase2020_task2.models.CNN loss_class=dcase2020_task2.losses.BCE -m student2.cp.jku.at:27017:dcase2020_task2_conv_complement_classification_gridsearch" &
+./scripts/run_all.sh classification_experiment 3 "debug=False num_hidden=4 hidden_size=512 batch_size=512 learning_rate=1e-5 weight_decay=1e-5 model_class=dcase2020_task2.models.CNN loss_class=dcase2020_task2.losses.BCE -m student2.cp.jku.at:27017:dcase2020_task2_conv_complement_classification_gridsearch" &
+
+wait
+
+./scripts/run_all.sh classification_experiment 0 "debug=False num_hidden=4 hidden_size=256 batch_size=512 learning_rate=1e-4 weight_decay=0 model_class=dcase2020_task2.models.CNN loss_class=dcase2020_task2.losses.BCE -m student2.cp.jku.at:27017:dcase2020_task2_conv_complement_classification_gridsearch" &
+./scripts/run_all.sh classification_experiment 1 "debug=False num_hidden=4 hidden_size=256 batch_size=512 learning_rate=1e-5 weight_decay=0 model_class=dcase2020_task2.models.CNN loss_class=dcase2020_task2.losses.BCE -m student2.cp.jku.at:27017:dcase2020_task2_conv_complement_classification_gridsearch" &
+./scripts/run_all.sh classification_experiment 2 "debug=False num_hidden=4 hidden_size=256 batch_size=512 learning_rate=1e-4 weight_decay=1e-5 model_class=dcase2020_task2.models.CNN loss_class=dcase2020_task2.losses.BCE -m student2.cp.jku.at:27017:dcase2020_task2_conv_complement_classification_gridsearch" &
+./scripts/run_all.sh classification_experiment 3 "debug=False num_hidden=4 hidden_size=256 batch_size=512 learning_rate=1e-5 weight_decay=1e-5 model_class=dcase2020_task2.models.CNN loss_class=dcase2020_task2.losses.BCE -m student2.cp.jku.at:27017:dcase2020_task2_conv_complement_classification_gridsearch" &
+
+wait
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