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torch_summary.py
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torch_summary.py
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"""
Utility function to generate a Keras-like summary for Pytorch models.
Adapted from https://github.com/sksq96/pytorch-summary
"""
import torch
import torch.nn as nn
from collections import OrderedDict
import numpy as np
torch_classes = [x for x in dir(torch.nn) if x[0].isupper()]
def summary(model, input_size, batch_size=-1, device=torch.device('cuda:0'), dtypes=None, accept_classes=None):
result, params_info = summary_string(
model, input_size, batch_size, device, dtypes, accept_classes)
print(result)
return params_info
def summary_string(model, input_size, batch_size=-1, device=torch.device('cuda:0'), dtypes=None, accept_classes=None):
if dtypes == None:
dtypes = [torch.FloatTensor]*len(input_size)
if accept_classes is None:
accept_classes = torch_classes
summary_str = ''
def register_hook(module):
def hook(module, input, output):
class_name = str(module.__class__).split(".")[-1].split("'")[0]
if class_name not in accept_classes:
return output
module_idx = len(summary)
m_key = "%s-%i" % (class_name, module_idx + 1)
summary[m_key] = OrderedDict()
summary[m_key]["input_shape"] = list(input[0].size())
summary[m_key]["input_shape"][0] = batch_size
if isinstance(output, (list, tuple)):
for o in output:
print('output type', type(o), m_key)
summary[m_key]["output_shape"] = [
[-1] + list(o.size())[1:] for o in output
]
else:
summary[m_key]["output_shape"] = list(output.size())
summary[m_key]["output_shape"][0] = batch_size
params = 0
if hasattr(module, "weight") and hasattr(module.weight, "size"):
params += torch.prod(torch.LongTensor(list(module.weight.size())))
summary[m_key]["trainable"] = module.weight.requires_grad
if hasattr(module, "bias") and hasattr(module.bias, "size"):
params += torch.prod(torch.LongTensor(list(module.bias.size())))
summary[m_key]["nb_params"] = params
if (
not isinstance(module, nn.Sequential)
and not isinstance(module, nn.ModuleList)
):
hooks.append(module.register_forward_hook(hook))
# 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).to(device=device)
for in_size, dtype in zip(input_size, dtypes)]
# create properties
summary = OrderedDict()
hooks = []
# register hook
model.apply(register_hook)
# make a forward pass
model(*x)
# remove these hooks
for h in hooks:
h.remove()
summary_str += "----------------------------------------------------------------" + "\n"
line_new = "{:>20} {:>25} {:>15}".format(
"Layer (type)", "Output Shape", "Param #")
summary_str += line_new + "\n"
summary_str += "================================================================" + "\n"
total_params = 0
total_output = 0
trainable_params = 0
for layer in summary:
# input_shape, output_shape, trainable, nb_params
line_new = "{:>20} {:>25} {:>15}".format(
layer,
str(summary[layer]["output_shape"]),
"{0:,}".format(summary[layer]["nb_params"]),
)
total_params += summary[layer]["nb_params"]
total_output += np.prod(summary[layer]["output_shape"])
if "trainable" in summary[layer]:
if summary[layer]["trainable"] == True:
trainable_params += summary[layer]["nb_params"]
summary_str += line_new + "\n"
# assume 4 bytes/number (float on cuda).
total_input_size = abs(np.prod(sum(input_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 * 4. / (1024 ** 2.))
total_size = total_params_size + total_output_size + total_input_size
summary_str += "================================================================" + "\n"
summary_str += "Total params: {0:,}".format(total_params) + "\n"
summary_str += "Trainable params: {0:,}".format(trainable_params) + "\n"
summary_str += "Non-trainable params: {0:,}".format(total_params -
trainable_params) + "\n"
summary_str += "----------------------------------------------------------------" + "\n"
summary_str += "Input size (MB): %0.2f" % total_input_size + "\n"
summary_str += "Forward/backward pass size (MB): %0.2f" % total_output_size + "\n"
summary_str += "Params size (MB): %0.2f" % total_params_size + "\n"
summary_str += "Estimated Total Size (MB): %0.2f" % total_size + "\n"
summary_str += "----------------------------------------------------------------" + "\n"
return summary_str, (total_params, trainable_params)
def print_FOV(model, input_size, device=torch.device('cuda:0'), dtypes=None, upsample_list=None):
if dtypes == None:
dtypes = [torch.FloatTensor]*len(input_size)
if upsample_list is None:
upsample_list = ['ConvTranspose', 'Upsample']
def hook(module, input, output):
if res['keep_going'] and hasattr(module, 'kernel_size'):
if any(map(lambda e: e in str(module.__class__), upsample_list)):
res['keep_going'] = False
return
k = np.array(module.kernel_size)
s = np.array(module.stride)
d = np.array(module.dilation)
res['r'] += (k - 1) * res['j'] * d
res['j'] *= s
# 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).to(device=device)
for in_size, dtype in zip(input_size, dtypes)]
# create properties
res = {'j': 1, 'r': 1, 'keep_going': True}
hooks = []
# register hooks
model.apply(lambda module: hooks.append(module.register_forward_hook(hook)))
# make a forward pass
model(*x)
# remove these hooks
for h in hooks:
h.remove()
print('Receptive field of the network:', res['r'])