Well tested gpu mode of Fastcells and Bonsai

pytorch/examples/FastCells/fastcell_example.py

@@ -46,7 +46,6 @@ def main():
     assert dataDimension % inputDims == 0, "Infeasible per step input, " + \
         "Timesteps have to be integer"
 
-    timeSteps = int(dataDimension/inputDims)
 
     currDir = helpermethods.createTimeStampDir(dataDir, cell)
 
@@ -77,7 +76,7 @@ def main():
     else:
         sys.exit('Exiting: No Such Cell as ' + cell)
 
-    FastCellTrainer = FastTrainer(FastCell, timeSteps, numClasses, sW=sW, sU=sU,
+    FastCellTrainer = FastTrainer(FastCell, numClasses, sW=sW, sU=sU,
                                   learningRate=learningRate, outFile=outFile, device=device)
 
     FastCellTrainer.train(batchSize, totalEpochs,

pytorch/pytorch_edgeml/graph/rnn.py

@@ -43,23 +43,22 @@ class BaseRNN(nn.Module):
     [batchSize, timeSteps, inputDims]
     '''
 
-    def __init__(self, RNNCell, timeSteps, device=None):
+    def __init__(self, RNNCell, device=None):
         super(BaseRNN, self).__init__()
         self.RNNCell = RNNCell
         if device is None:
             self.device = "cpu"
         else:
             self.device = device
-        self.timeSteps = timeSteps
 
     def forward(self, input):
         hiddenStates = torch.zeros(
-            [input.shape[0], self.timeSteps, self.RNNCell.output_size]).to(self.device)
+            [input.shape[0], input.shape[1], self.RNNCell.output_size]).to(self.device)
 
         hiddenState = torch.zeros([input.shape[0], self.RNNCell.output_size]).to(self.device)
         if self.RNNCell.cellType == "LSTMLR":
             cellStates = torch.zeros(
-                [input.shape[0], self.timeSteps, self.RNNCell.output_size]).to(self.device)
+                [input.shape[0], input.shape[1], self.RNNCell.output_size]).to(self.device)
             cellState = torch.zeros([input.shape[0], self.RNNCell.output_size]).to(self.device)
             for i in range(0, input.shape[1]):
                 hiddenState, cellState = self.RNNCell(

pytorch/pytorch_edgeml/trainer/fastTrainer.py

@@ -11,7 +11,7 @@ import numpy as np
 
 class FastTrainer:
 
-    def __init__(self, FastObj, timeSteps, numClasses, sW=1.0, sU=1.0, learningRate=0.01, outFile=None, device=None):
+    def __init__(self, FastObj, numClasses, sW=1.0, sU=1.0, learningRate=0.01, outFile=None, device=None):
         '''
         FastObj - Can be either FastRNN or FastGRNN or any of the RNN cells 
         in graph.rnn with proper initialisations
@@ -44,14 +44,13 @@ class FastTrainer:
         else:
             self.isDenseTraining = False
 
-        self.timeSteps = timeSteps
         self.assertInit()
         self.numMatrices = self.FastObj.num_weight_matrices
         self.totalMatrices = self.numMatrices[0] + self.numMatrices[1]
 
         self.optimizer = self.optimizer()
 
-        self.RNN = BaseRNN(self.FastObj, self.timeSteps, self.device).to(device)
+        self.RNN = BaseRNN(self.FastObj, self.device).to(device)
 
         self.FC = nn.Parameter(torch.randn([self.FastObj.output_size, self.numClasses])).to(device)
         self.FCbias = nn.Parameter(torch.randn([self.numClasses])).to(device)
@@ -353,7 +352,7 @@ class FastTrainer:
             ihtDone = 1
             maxTestAcc = -10000
         header = '*' * 20
-
+        self.timeSteps = int(Xtest.shape[1]/self.inputDims)
         Xtest = Xtest.reshape((-1, self.timeSteps, self.inputDims))
         Xtrain = Xtrain.reshape((-1, self.timeSteps, self.inputDims))