This is the result I get ,Here you are :
...
Pre-processing Successful!
Files already downloaded and verified
prune.py:112: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.
  data, target = Variable(data, volatile=True), Variable(target)

Test set: Accuracy: 1000/10000 (10.0%)

cfg/simpnet
DataParallel(
  (module): simpnet(
    (features): Sequential(
      (0): Conv2d(3, 66, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(66, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): Conv2d(66, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
      (4): BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
      (5): ReLU(inplace)
      (6): Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
      (7): BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
      (8): ReLU(inplace)
      (9): Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
      (10): BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
      (11): ReLU(inplace)
      (12): Conv2d(128, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
      (13): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
      (14): ReLU(inplace)
      (15): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=(1, 1), ceil_mode=False)
      (16): Dropout2d(p=0.05)
      (17): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
      (18): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
      (19): ReLU(inplace)
      (20): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
      (21): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
      (22): ReLU(inplace)
      (23): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
      (24): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
      (25): ReLU(inplace)
      (26): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
      (27): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
      (28): ReLU(inplace)
      (29): Conv2d(192, 288, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
      (30): BatchNorm2d(288, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
      (31): ReLU(inplace)
      (32): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=(1, 1), ceil_mode=False)
      (33): Dropout2d(p=0.05)
      (34): Conv2d(288, 288, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
      (35): BatchNorm2d(288, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
      (36): ReLU(inplace)
      (37): Conv2d(288, 355, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
      (38): BatchNorm2d(355, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
      (39): ReLU(inplace)
      (40): Conv2d(355, 432, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
      (41): BatchNorm2d(432, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
      (42): ReLU(inplace)
    )
    (classifier): Linear(in_features=432, out_features=10, bias=True)
  )
)
simpnet(
  (features): Sequential(
    (0): Conv2d(3, 66, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(66, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (2): ReLU(inplace)
    (3): Conv2d(66, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (5): ReLU(inplace)
    (6): Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (7): BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (8): ReLU(inplace)
    (9): Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (10): BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (11): ReLU(inplace)
    (12): Conv2d(128, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (13): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (14): ReLU(inplace)
    (15): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=(1, 1), ceil_mode=False)
    (16): Dropout2d(p=0.05)
    (17): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (18): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (19): ReLU(inplace)
    (20): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (21): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (22): ReLU(inplace)
    (23): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (24): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (25): ReLU(inplace)
    (26): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (27): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (28): ReLU(inplace)
    (29): Conv2d(192, 288, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (30): BatchNorm2d(288, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (31): ReLU(inplace)
    (32): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=(1, 1), ceil_mode=False)
    (33): Dropout2d(p=0.05)
    (34): Conv2d(288, 288, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (35): BatchNorm2d(288, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (36): ReLU(inplace)
    (37): Conv2d(288, 355, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (38): BatchNorm2d(355, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (39): ReLU(inplace)
    (40): Conv2d(355, 432, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (41): BatchNorm2d(432, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (42): ReLU(inplace)
  )
  (classifier): Linear(in_features=432, out_features=10, bias=True)
)
simpnet(
  (features): Sequential(
    (0): Conv2d(3, 66, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(66, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (2): ReLU(inplace)
    (3): Conv2d(66, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (5): ReLU(inplace)
    (6): Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (7): BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (8): ReLU(inplace)
    (9): Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (10): BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (11): ReLU(inplace)
    (12): Conv2d(128, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (13): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (14): ReLU(inplace)
    (15): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=(1, 1), ceil_mode=False)
    (16): Dropout2d(p=0.05)
    (17): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (18): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (19): ReLU(inplace)
    (20): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (21): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (22): ReLU(inplace)
    (23): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (24): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (25): ReLU(inplace)
    (26): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (27): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (28): ReLU(inplace)
    (29): Conv2d(192, 288, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (30): BatchNorm2d(288, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (31): ReLU(inplace)
    (32): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=(1, 1), ceil_mode=False)
    (33): Dropout2d(p=0.05)
    (34): Conv2d(288, 288, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (35): BatchNorm2d(288, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (36): ReLU(inplace)
    (37): Conv2d(288, 355, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (38): BatchNorm2d(355, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (39): ReLU(inplace)
    (40): Conv2d(355, 432, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
    (41): BatchNorm2d(432, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
    (42): ReLU(inplace)
  )
  (classifier): Linear(in_features=432, out_features=10, bias=True)
)
Sequential(
  (0): Conv2d(3, 66, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (1): BatchNorm2d(66, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (2): ReLU(inplace)
  (3): Conv2d(66, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (4): BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (5): ReLU(inplace)
  (6): Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (7): BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (8): ReLU(inplace)
  (9): Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (10): BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (11): ReLU(inplace)
  (12): Conv2d(128, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (13): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (14): ReLU(inplace)
  (15): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=(1, 1), ceil_mode=False)
  (16): Dropout2d(p=0.05)
  (17): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (18): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (19): ReLU(inplace)
  (20): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (21): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (22): ReLU(inplace)
  (23): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (24): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (25): ReLU(inplace)
  (26): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (27): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (28): ReLU(inplace)
  (29): Conv2d(192, 288, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (30): BatchNorm2d(288, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (31): ReLU(inplace)
  (32): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=(1, 1), ceil_mode=False)
  (33): Dropout2d(p=0.05)
  (34): Conv2d(288, 288, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (35): BatchNorm2d(288, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (36): ReLU(inplace)
  (37): Conv2d(288, 355, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (38): BatchNorm2d(355, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (39): ReLU(inplace)
  (40): Conv2d(355, 432, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (41): BatchNorm2d(432, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (42): ReLU(inplace)
)
Sequential(
  (0): Conv2d(3, 66, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (1): BatchNorm2d(66, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (2): ReLU(inplace)
  (3): Conv2d(66, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (4): BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (5): ReLU(inplace)
  (6): Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (7): BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (8): ReLU(inplace)
  (9): Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (10): BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (11): ReLU(inplace)
  (12): Conv2d(128, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (13): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (14): ReLU(inplace)
  (15): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=(1, 1), ceil_mode=False)
  (16): Dropout2d(p=0.05)
  (17): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (18): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (19): ReLU(inplace)
  (20): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (21): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (22): ReLU(inplace)
  (23): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (24): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (25): ReLU(inplace)
  (26): Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (27): BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (28): ReLU(inplace)
  (29): Conv2d(192, 288, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (30): BatchNorm2d(288, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (31): ReLU(inplace)
  (32): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=(1, 1), ceil_mode=False)
  (33): Dropout2d(p=0.05)
  (34): Conv2d(288, 288, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (35): BatchNorm2d(288, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (36): ReLU(inplace)
  (37): Conv2d(288, 355, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (38): BatchNorm2d(355, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (39): ReLU(inplace)
  (40): Conv2d(355, 432, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
  (41): BatchNorm2d(432, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
  (42): ReLU(inplace)
)
Conv2d(3, 66, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
Conv2d(3, 66, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
BatchNorm2d(66, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
BatchNorm2d(66, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
ReLU(inplace)
ReLU(inplace)
Conv2d(66, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
Conv2d(66, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
ReLU(inplace)
ReLU(inplace)
Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
ReLU(inplace)
ReLU(inplace)
Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
BatchNorm2d(128, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
ReLU(inplace)
ReLU(inplace)
Conv2d(128, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
Conv2d(128, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
ReLU(inplace)
ReLU(inplace)
MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=(1, 1), ceil_mode=False)
MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=(1, 1), ceil_mode=False)
Dropout2d(p=0.05)
Dropout2d(p=0.05)
Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
ReLU(inplace)
ReLU(inplace)
Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
ReLU(inplace)
ReLU(inplace)
Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
ReLU(inplace)
ReLU(inplace)
Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
Conv2d(192, 192, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
BatchNorm2d(192, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
ReLU(inplace)
ReLU(inplace)
Conv2d(192, 288, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
Conv2d(192, 288, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
BatchNorm2d(288, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
BatchNorm2d(288, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
ReLU(inplace)
ReLU(inplace)
MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=(1, 1), ceil_mode=False)
MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=(1, 1), ceil_mode=False)
Dropout2d(p=0.05)
Dropout2d(p=0.05)
Conv2d(288, 288, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
Conv2d(288, 288, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
BatchNorm2d(288, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
BatchNorm2d(288, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
ReLU(inplace)
ReLU(inplace)
Conv2d(288, 355, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
Conv2d(288, 355, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
BatchNorm2d(355, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
BatchNorm2d(355, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
ReLU(inplace)
ReLU(inplace)
Conv2d(355, 432, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
Conv2d(355, 432, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))
BatchNorm2d(432, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
BatchNorm2d(432, eps=1e-05, momentum=0.05, affine=True, track_running_stats=True)
ReLU(inplace)
ReLU(inplace)
Linear(in_features=432, out_features=10, bias=True)
----------------------------------------
idx0 values : [0 1 2] idx1 values :[ 0  1  3  5  8  9 18 20 21 26 27 31 33 34 41 46 50 51 54 57 59 65]
idx0 shape: (3,) idx1 shape:(22,)
In shape: 3 Out shape:22
Traceback (most recent call last):
  File "prune.py", line 142, in <module>
    m1.weight.data = m0.weight.data[idx1].clone()
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 532, in __getattr__
    type(self).__name__, name))
AttributeError: 'ReLU' object has no attribute 'weight'