non-legacy PyTorch

examples/Assamese_handwriting/VGGplus.py

@@ -5,34 +5,38 @@
 # LICENSE file in the root directory of this source tree.
 
 import torch
-import torch.legacy.nn as nn
-import sparseconvnet.legacy as scn
+import torch.nn as nn
+import sparseconvnet as scn
 from data import getIterators
 
-# Use the GPU if there is one, otherwise CPU
-dtype = 'torch.cuda.FloatTensor' if torch.cuda.is_available() else 'torch.FloatTensor'
-
 # two-dimensional SparseConvNet
-model = nn.Sequential()
-sparseModel = scn.Sequential()
-denseModel = nn.Sequential()
-model.add(sparseModel).add(denseModel)
-sparseModel.add(scn.SparseVggNet(2, 3, [
+class Model(nn.Module):
+    def __init__(self):
+        nn.Module.__init__(self)
+        self.sparseModel = scn.SparseVggNet(2, 3, [
             ['C', 8, ], ['C', 8], 'MP',
             ['C', 16], ['C', 16], 'MP',
             ['C', 16, 8], ['C', 16, 8], 'MP',
-    ['C', 24, 8], ['C', 24, 8], 'MP']))
-sparseModel.add(scn.Convolution(2, 32, 64, 5, 1, False))
-sparseModel.add(scn.BatchNormReLU(64))
-sparseModel.add(scn.SparseToDense(2))
-denseModel.add(nn.View(-1, 64))
-denseModel.add(nn.Linear(64, 183))
-model.type(dtype)
-print(model)
+            ['C', 24, 8], ['C', 24, 8], 'MP']
+            ).add(scn.Convolution(2, 32, 64, 5, 1, False)
+            ).add(scn.BatchNormReLU(64)
+            ).add(scn.SparseToDense(2,64))
+        self.linear = nn.Linear(64, 183)
+    def forward(self, x):
+        x = self.sparseModel(x)
+        x = x.view(-1,64)
+        x = self.linear(x)
+        return x
 
-spatial_size = sparseModel.suggestInputSize(torch.LongTensor([1, 1]))
-print('input spatial size', spatial_size)
+model=Model()
+spatial_size = model.sparseModel.input_spatial_size(torch.LongTensor([1, 1]))
+print('Input spatial size:', spatial_size)
 dataset = getIterators(spatial_size, 63, 3)
 scn.ClassificationTrainValidate(
     model, dataset,
-    {'nEpochs': 100, 'initial_LR': 0.1, 'LR_decay': 0.05, 'weightDecay': 1e-4, 'checkPoint': False})
+    {'n_epochs': 100,
+    'initial_lr': 0.1,
+    'lr_decay': 0.05,
+    'weight_decay': 1e-4,
+    'use_gpu':  torch.cuda.is_available(),
+    'check_point': True,})

examples/Assamese_handwriting/VGGplus_legacy.py

+# Copyright 2016-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.legacy.nn as nn
+import sparseconvnet.legacy as scn
+from data import getIterators
+
+# Use the GPU if there is one, otherwise CPU
+dtype = 'torch.cuda.FloatTensor' if torch.cuda.is_available() else 'torch.FloatTensor'
+
+# two-dimensional SparseConvNet
+model = nn.Sequential()
+sparseModel = scn.Sequential()
+denseModel = nn.Sequential()
+model.add(sparseModel).add(denseModel)
+sparseModel.add(scn.SparseVggNet(2, 3, [
+    ['C', 8, ], ['C', 8], 'MP',
+    ['C', 16], ['C', 16], 'MP',
+    ['C', 16, 8], ['C', 16, 8], 'MP',
+    ['C', 24, 8], ['C', 24, 8], 'MP']))
+sparseModel.add(scn.Convolution(2, 32, 64, 5, 1, False))
+sparseModel.add(scn.BatchNormReLU(64))
+sparseModel.add(scn.SparseToDense(2))
+denseModel.add(nn.View(-1, 64))
+denseModel.add(nn.Linear(64, 183))
+model.type(dtype)
+print(model)
+
+spatial_size = sparseModel.suggestInputSize(torch.LongTensor([1, 1]))
+print('input spatial size', spatial_size)
+dataset = getIterators(spatial_size, 63, 3)
+scn.ClassificationTrainValidate(
+    model, dataset,
+    {'nEpochs': 100, 'initial_LR': 0.1, 'LR_decay': 0.05, 'weightDecay': 1e-4, 'checkPoint': False})

examples/hello-world.py

@@ -5,11 +5,10 @@
 # LICENSE file in the root directory of this source tree.
 
 import torch
-import torch.legacy.nn as nn
-import sparseconvnet.legacy as scn
+import sparseconvnet as scn
 
 # Use the GPU if there is one, otherwise CPU
-dtype = 'torch.cuda.FloatTensor' if torch.cuda.is_available() else 'torch.FloatTensor'
+use_gpu = torch.cuda.is_available()
 
 model = scn.Sequential().add(
     scn.SparseVggNet(2, 1,
@@ -21,11 +20,13 @@ model = scn.Sequential().add(
 ).add(
     scn.BatchNormReLU(32)
 ).add(
-    scn.SparseToDense(2)
-).type(dtype)
+    scn.SparseToDense(2,32)
+)
+if use_gpu:
+    model.cuda()
 
 # output will be 10x10
-inputSpatialSize = model.suggestInputSize(torch.LongTensor([10, 10]))
+inputSpatialSize = model.input_spatial_size(torch.LongTensor([10, 10]))
 input = scn.InputBatch(2, inputSpatialSize)
 
 msg = [
@@ -65,10 +66,11 @@ input.setLocations(locations, features, 0)
 #    2 if using MP2 pooling for downsizing.
 input.precomputeMetadata(3)
 
-model.evaluate()
-input.type(dtype)
+model.train()
+if use_gpu:
+    input.cuda()
 output = model.forward(input)
 
 # Output is 2x32x10x10: our minibatch has 2 samples, the network has 32 output
 # feature planes, and 10x10 is the spatial size of the output.
-print(output.size(), output.type())
+print(output.size(), output.data.type())