New config for data selection.

Author: erak

creator/dvc.lock

@@ -4,13 +4,13 @@ stages:
     cmd: python src/train.py
     deps:
     - path: src/train.py
-      md5: 02335565db7f658fd2e4dd3c6c3cf743
-      size: 4108
+      md5: afe44f9001ad3e56cfd8aa9441fa092d
+      size: 4184
     params:
       params.yaml:
-        train.batch_size: 128
+        train.batch_size: 32
         train.custom_loss: true
-        train.dancers: maria
+        train.dancers: all
         train.epochs: 1
         train.look_back: 10
         train.lstm_layer: 32
@@ -19,34 +19,35 @@ stages:
         train.validation_split: 0.7
     outs:
     - path: ../data/metrics/deep-dance-loss.json
-      md5: 13dc782f07133da6903e55d6b475bc67
-      size: 128
+      md5: fc40a323d5909d64bdb8b4581b989fbd
+      size: 123
     - path: ../data/metrics/deep-dance-scores.json
-      md5: 2e6574f2d78111cc86cac591f409438b
-      size: 80
+      md5: 4c30eb3ff6a3dbab13c2f9445c3684b3
+      size: 75
     - path: ../data/models
-      md5: e5883246f4f38a059485d6f6472b9555.dir
+      md5: 9024a2e0efc48d91e7a4ca82a71ad2fe.dir
       size: 535704
       nfiles: 1
   generate:
     cmd: python src/generate.py
     deps:
     - path: ../data/models
-      md5: 70be98e833d4e6a3617faf798247a601.dir
+      md5: 9024a2e0efc48d91e7a4ca82a71ad2fe.dir
       size: 535704
       nfiles: 1
     - path: src/generate.py
-      md5: b0128c019efb4bd1a0ff17ee4c554ac2
-      size: 2293
+      md5: 923a628ff5fcbaed9e01f35f397ebb27
+      size: 2816
     params:
       params.yaml:
         generate.custom_loss: true
         generate.look_back: 10
         generate.random_state: 42
         generate.seed: 1500
         generate.steps_limit: 1500
+        generate.validation_split: 0.7
     outs:
     - path: ../data/generated
-      md5: 6016096bad13b978bb560bc52d668b77.dir
-      size: 1623899
+      md5: 9d3d915de708ebaeb2521fe7847e8306.dir
+      size: 1620568
       nfiles: 1

creator/exp_generate.py

@@ -24,12 +24,11 @@
 -S train.lstm_layer={3} \
 -S train.mdn_layer={4} \
 -S train.dancers={5} \
--S train.tags={6} \
--S generate.look_back={7}'
+-S generate.look_back={6} \
+-S generate.dancers={7}'
 
 def run_exp_suite_01(call = False):
-    dancers = 'girish,maria,mark,marlen,raymond,tinyeung'
-    tags = 'impro'
+    dancers = 'all'
     for batch_size in [32]:
         for look_back in [10, 100]:
             for lstm_layer in [32, 64, 128]:
@@ -41,8 +40,8 @@ def run_exp_suite_01(call = False):
                         lstm_layer,
                         mdn_layer,
                         dancers,
-                        tags,
-                        look_back)
+                        look_back,
+                        dancers)
                     print(sh_command)
                     if call:
                         stream = os.popen(sh_command)

creator/exp_run.sh

@@ -1,5 +1,6 @@
 #!/bin/bash
 
+
 # --------------------------
 # Smoke test(s)
 # --------------------------
@@ -8,36 +9,32 @@
 #     -S train.custom_loss=False \
 #     -S generate.custom_loss=False
 
-
 # --------------------------
 # Queue up experiments. Please see 'exp_generate.py' on how to generate these DVC calls.
 # --------------------------
-echo "Training started..."
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=32 -S train.mdn_layer=2 -S train.dancers=all -S generate.look_back=10 -S generate.dancers=all
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=32 -S train.mdn_layer=3 -S train.dancers=all -S generate.look_back=10 -S generate.dancers=all
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=32 -S train.mdn_layer=5 -S train.dancers=all -S generate.look_back=10 -S generate.dancers=all
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=64 -S train.mdn_layer=2 -S train.dancers=all -S generate.look_back=10 -S generate.dancers=all
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=64 -S train.mdn_layer=3 -S train.dancers=all -S generate.look_back=10 -S generate.dancers=all
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=64 -S train.mdn_layer=5 -S train.dancers=all -S generate.look_back=10 -S generate.dancers=all
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=128 -S train.mdn_layer=2 -S train.dancers=all -S generate.look_back=10 -S generate.dancers=all
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=128 -S train.mdn_layer=3 -S train.dancers=all -S generate.look_back=10 -S generate.dancers=all
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=128 -S train.mdn_layer=5 -S train.dancers=all -S generate.look_back=10 -S generate.dancers=all
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=32 -S train.mdn_layer=2 -S train.dancers=all -S generate.look_back=100 -S generate.dancers=all
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=32 -S train.mdn_layer=3 -S train.dancers=all -S generate.look_back=100 -S generate.dancers=all
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=32 -S train.mdn_layer=5 -S train.dancers=all -S generate.look_back=100 -S generate.dancers=all
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=64 -S train.mdn_layer=2 -S train.dancers=all -S generate.look_back=100 -S generate.dancers=all
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=64 -S train.mdn_layer=3 -S train.dancers=all -S generate.look_back=100 -S generate.dancers=all
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=64 -S train.mdn_layer=5 -S train.dancers=all -S generate.look_back=100 -S generate.dancers=all
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=128 -S train.mdn_layer=2 -S train.dancers=all -S generate.look_back=100 -S generate.dancers=all
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=128 -S train.mdn_layer=3 -S train.dancers=all -S generate.look_back=100 -S generate.dancers=all
+dvc exp run --queue -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=128 -S train.mdn_layer=5 -S train.dancers=all -S generate.look_back=100 -S generate.dancers=all
 
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=32 -S train.mdn_layer=2 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=10
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=32 -S train.mdn_layer=3 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=10
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=32 -S train.mdn_layer=5 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=10
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=64 -S train.mdn_layer=2 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=10
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=64 -S train.mdn_layer=3 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=10
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=64 -S train.mdn_layer=5 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=10
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=128 -S train.mdn_layer=2 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=10
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=128 -S train.mdn_layer=3 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=10
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=10 -S train.lstm_layer=128 -S train.mdn_layer=5 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=10
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=32 -S train.mdn_layer=2 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=100
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=32 -S train.mdn_layer=3 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=100
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=32 -S train.mdn_layer=5 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=100
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=64 -S train.mdn_layer=2 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=100
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=64 -S train.mdn_layer=3 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=100
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=64 -S train.mdn_layer=5 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=100
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=128 -S train.mdn_layer=2 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=100
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=128 -S train.mdn_layer=3 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=100
-dvc exp run -S train.epochs=10 -S train.batch_size=32 -S train.look_back=100 -S train.lstm_layer=128 -S train.mdn_layer=5 -S train.dancers=girish,maria,mark,marlen,raymond,tinyeung -S train.tags=impro -S generate.look_back=100
 
-echo "Training finished."
-echo "$now"
 # ---------------------------
 # Run queued experiments
 # ---------------------------
-# dvc exp run --run-all
+dvc exp run --run-all --jobs 1
 # Or run in parallel
 # dvc exp run --run-all --jobs 2

creator/params.yaml

@@ -2,25 +2,29 @@ prepare:
   look_back: 10
   split: 0.05
   random_state: 42
-  dancers: 'maria'
-  tags: 'impro'
+  dancers: all
+  tags: impro
 
 train:
   epochs: 1
-  batch_size: 128
+  batch_size: 32
   look_back: 10
   lstm_layer: 32
   mdn_layer: 3
-  custom_loss: True
+  custom_loss: true
   random_state: 42
-  dancers: 'maria'
-  tags: 'impro'
+  dancers: all
+  tags: impro
+  test_size: 0.05
   validation_split: 0.7
-
+  
 generate:
   look_back: 10
   random_state: 42
   seed: 1500
   steps_limit: 1500
-  custom_loss: True
-  validation_split: 0.7
+  custom_loss: true
+  dancers: all
+  tags: impro
+  test_size: 0.05
+  validation_split: 0.7

creator/src/generate.py

@@ -33,12 +33,15 @@ def get_parser():
 
 if __name__ == '__main__':
     params = yaml.safe_load(open('params.yaml'))['generate']
+    dancers = params['dancers']
+    tags = params['tags']
     seed = params['seed']
     steps_limit = params['steps_limit']
     look_back = params['look_back']
     random_state = params['random_state']
     custom_loss = params['custom_loss']
     validation_split = params['validation_split']
+    test_size = params['test_size']
 
     args = get_parser().parse_args()
 
@@ -53,9 +56,19 @@ def get_parser():
 
     # TODO Remove after NPZ export fix
     print("Loading prepared data. This might take a while..")
-    x, y = get_training_data(look_back = look_back)
+    # x, y = get_training_data(look_back = look_back)
+    selected_dancers = dancers.split(',')
+    for d in selected_dancers:
+        d = d.strip()
+
+    selected_tags = tags.split(',')
+    for t in selected_tags:
+        t = d.strip()
+
+    x, y = get_training_data(dancers = selected_dancers, tags = selected_tags,
+        look_back = look_back)
     x_train, x_test, y_train, y_test = train_test_split(
-        x, y, test_size=validation_split, shuffle=True, random_state=random_state)
+        x, y, test_size=test_size, shuffle=True, random_state=random_state)
 
     os.makedirs(os.path.join('../', 'data', 'generated'), exist_ok=True)
 

creator/src/model.py

@@ -39,13 +39,13 @@ class DeepDanceModel:
     def __init__(
             self,
             look_back = 10, lstm_layers = 32,
-            mdn_layers = 3, custom_loss = False):
+            mdn_layers = 3, validation_split = 0.10, custom_loss = False):
         self.look_back = look_back
         self.mdn_layers = mdn_layers
         self.custom_loss = custom_loss
         self.trained = False
 
-        self.validation_split = 0.10
+        self.validation_split = validation_split
 
         self.model = keras.Sequential()
         self.model.add(layers.LSTM(

creator/src/train.py

@@ -60,6 +60,7 @@ def get_parser():
     mdn_layer = params['mdn_layer']
     custom_loss = params['custom_loss']
     validation_split = params['validation_split']
+    test_size = params['test_size']
 
     args = get_parser().parse_args()
 
@@ -95,7 +96,7 @@ def get_parser():
     x, y = get_training_data(dancers = selected_dancers, tags = selected_tags,
         look_back = look_back)
     x_train, x_test, y_train, y_test = train_test_split(
-        x, y, test_size=validation_split, shuffle= True, random_state=42)
+        x, y, test_size=test_size, shuffle= True, random_state=42)
 
     print("Data shape(s):")
     print("x: ", np.shape(x_train))
@@ -112,7 +113,8 @@ def get_parser():
 
     model = DeepDanceModel(
         look_back = look_back, lstm_layers = lstm_layer,
-        mdn_layers = mdn_layer, custom_loss=custom_loss)
+        mdn_layers = mdn_layer, validation_split = validation_split,
+        custom_loss = custom_loss)
     model.train(x, y, epochs = epochs, batch_size = batch_size)
 
     print('Saving model and metrics...')

data/metrics/deep-dance-loss.json

@@ -1,8 +1,8 @@
 {
     "perf": [
         {
-            "loss": -46.17393493652344,
-            "val_loss": -51.5170783996582
+            "loss": -66.89143371582031,
+            "val_loss": -81.72607421875
         }
     ]
 }

data/metrics/deep-dance-scores.json

@@ -1,4 +1,4 @@
 {
-    "end_loss": -46.17393493652344,
-    "end_val_loss": -51.5170783996582
+    "end_loss": -66.89143371582031,
+    "end_val_loss": -81.72607421875
 }

data/train/video/deep-dance.dvc

@@ -1,5 +1,5 @@
 outs:
-- md5: 35a0f4dd4f0fbe71dc538cdf93f01af9.dir
-  size: 3566601036
-  nfiles: 1273
+- md5: 25ead98518a2747bf1252ffde07256a9.dir
+  size: 3923719785
+  nfiles: 1281
   path: deep-dance