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torchsummary() extensions by input_initializer, dtype #89

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11 changes: 4 additions & 7 deletions torchsummary/torchsummary.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
import numpy as np


def summary(model, input_size, batch_size=-1, device="cuda"):
def summary(model, input_size, batch_size=2, input_initializer=torch.rand, dtype=torch.float64, device="cuda"):

def register_hook(module):

Expand Down Expand Up @@ -47,17 +47,14 @@ def hook(module, input, output):
"cpu",
], "Input device is not valid, please specify 'cuda' or 'cpu'"

if device == "cuda" and torch.cuda.is_available():
dtype = torch.cuda.FloatTensor
else:
dtype = torch.FloatTensor
device = torch.device(device) if torch.cuda.is_available() else torch.device('cpu')

# multiple inputs to the network
if isinstance(input_size, tuple):
input_size = [input_size]

# batch_size of 2 for batchnorm
x = [torch.rand(2, *in_size).type(dtype) for in_size in input_size]
x = [input_initializer((batch_size, *in_size)).type(dtype).to(device) for in_size in input_size]
# print(type(x[0]))

# create properties
Expand Down Expand Up @@ -97,7 +94,7 @@ def hook(module, input, output):
print(line_new)

# assume 4 bytes/number (float on cuda).
total_input_size = abs(np.prod(input_size) * batch_size * 4. / (1024 ** 2.))
total_input_size = abs(np.prod([dimension for tensor_size in input_size for dimension in tensor_size]) * batch_size * 4. / (1024 ** 2.))
total_output_size = abs(2. * total_output * 4. / (1024 ** 2.)) # x2 for gradients
total_params_size = abs(total_params.numpy() * 4. / (1024 ** 2.))
total_size = total_params_size + total_output_size + total_input_size
Expand Down