Removed scipy dependency. Apex is not optional.

mnist_cifar/main.py

@@ -15,7 +15,6 @@
 from sparselearning.models import AlexNet, VGG16, LeNet_300_100, LeNet_5_Caffe, WideResNet
 from sparselearning.utils import get_mnist_dataloaders, get_cifar10_dataloaders
 
-from apex.fp16_utils import FP16_Optimizer
 
 cudnn.benchmark = True
 cudnn.deterministic = True
@@ -134,6 +133,14 @@ def main():
     sparselearning.core.add_sparse_args(parser)
 
     args = parser.parse_args()
+
+    if args.fp16:
+        try:
+            from apex.fp16_utils import FP16_Optimizer
+        except:
+            print('WARNING: apex not installed, ignoring --fp16 option')
+            args.fp16 = False
+
     use_cuda = not args.no_cuda and torch.cuda.is_available()
     device = torch.device("cuda" if use_cuda else "cpu")
 

requirements.txt

@@ -1,3 +1,2 @@
 numpy
 matplotlib
-apex

results/MNIST_sparse_summary.csv

@@ -1,5 +1,5 @@
 Sparsity,Full Dense,error1,Dynamic Sparse,error2,Sparse Momentum,error3,SET,error4,DEEP-R,error5
-0.99,0.9869,0.000106094,0.9754,0.000157,0.9753,0.000469515,0.958,0.000451
+0.99,0.9869,0.000106094,0.9754,0.000157,0.9753,0.000469515,0.958,0.000451, 0.94, 0.0001
 0.98,0.9869,0.000106094,0.9814,0.00064,0.982,0.00017966,0.9774096,0.0007068877551,0.9821,0.00135
 0.97,0.9869,0.000106094,0.9820,0.00023,0.983,0.000546148,0.9806024,0.0003380612245,0.9822,0.000451
 0.96,0.9869,0.000106094,0.9823,0.00023,0.9842,0.00023202,0.9809639,0.0003688265306,0.983,0.000574

sparselearning/core.py

@@ -6,7 +6,6 @@
 import torch.optim as optim
 from torch.utils.data.sampler import SubsetRandomSampler
 
-from scipy.sparse import coo_matrix, csr_matrix
 import numpy as np
 import math
 import os