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import numpy as np
import torch
import torch.nn as nn
from collections import OrderedDict
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
def tensor2numpy(tensor, rgb_range=1.):
rgb_coefficient = 255 / rgb_range
img = tensor.mul(rgb_coefficient).clamp(0, 255).round()
img = img[0].data if img.ndim==4 else img.data
img = np.transpose(img.cpu().numpy(), (1, 2, 0)).astype(np.uint8)
return img
def center_crop(img, size):
h,w = img.shape[-2:]
cut_h, cut_w = h-size[0], w-size[1]
lh = cut_h // 2
rh = h - (cut_h - lh)
lw = cut_w // 2
rw = w - (cut_w - lw)
img = img[:,:, lh:rh, lw:rw]
return img
def make_coord(shape, ranges=None, flatten=True, device='cpu'):
# Make coordinates at grid centers.
coord_seqs = []
for i, n in enumerate(shape):
if ranges is None:
v0, v1 = -1, 1
else:
v0, v1 = ranges[i]
r = (v1 - v0) / (2 * n)
seq = v0 + r + (2 * r) * torch.arange(n, device=device).float()
coord_seqs.append(seq)
ret = torch.stack(torch.meshgrid(*coord_seqs), dim=-1)
if flatten:
ret = ret.view(-1, ret.shape[-1])
return ret
def compute_num_params(model, text=False):
tot = int(sum([np.prod(p.shape) for p in model.parameters()]))
if text:
if tot >= 1e6:
return '{:.3f}M'.format(tot / 1e6)
elif tot >= 1e3:
return '{:.2f}K'.format(tot / 1e3)
else:
return '{}'.format(tot)
else:
return tot
def get_names_dict(model):
"""Recursive walk to get names including path."""
names = {}
def _get_names(module, parent_name=""):
for key, m in module.named_children():
cls_name = str(m.__class__).split(".")[-1].split("'")[0]
num_named_children = len(list(m.named_children()))
if num_named_children > 0:
name = parent_name + "." + key if parent_name else key
else:
name = parent_name + "." + cls_name + "_"+ key if parent_name else key
names[name] = m
if isinstance(m, nn.Module):
_get_names(m, parent_name=name)
_get_names(model)
return names
# https://github.com/chenbong/ARM-Net/blob/main/utils/util.py
def get_model_flops(model, x, *args, **kwargs):
"""Summarize the given input model.
Summarized information are 1) output shape, 2) kernel shape,
3) number of the parameters and 4) operations (Mult-Adds)
Args:
model (Module): Model to summarize
x (Tensor): Input tensor of the model with [N, C, H, W] shape
dtype and device have to match to the model
args, kwargs: Other argument used in `model.forward` function
"""
model.eval()
if hasattr(model, 'module'):
model = model.module
#x = torch.zeros(input_size).to(next(model.parameters()).device)
def register_hook(module):
def hook(module, inputs, outputs):
cls_name = str(module.__class__).split(".")[-1].split("'")[0]
module_idx = len(summary)
key = None
for name, item in module_names.items():
if item == module:
key = "{}_{}".format(module_idx, name)
break
assert key
info = OrderedDict()
info["id"] = id(module)
if isinstance(outputs, (list, tuple)):
try:
info["out"] = list(outputs[0].size())
except AttributeError:
info["out"] = list(outputs[0].data.size())
else:
info["out"] = list(outputs.size())
info["ksize"] = "-"
info["inner"] = OrderedDict()
info["params_nt"], info["params"], info["flops"] = 0, 0, 0
for name, param in module.named_parameters():
info["params"] += param.nelement() * param.requires_grad
info["params_nt"] += param.nelement() * (not param.requires_grad)
if name == "weight":
ksize = list(param.size())
if len(ksize) > 1:
ksize[0], ksize[1] = ksize[1], ksize[0]
info["ksize"] = ksize
if isinstance(module, nn.Conv2d) or isinstance(module, nn.ConvTranspose2d):
assert len(inputs[0].size()) == 4 and len(inputs[0].size()) == len(outputs[0].size())+1
in_c, in_h, in_w = inputs[0].size()[1:]
k_h, k_w = module.kernel_size
out_c, out_h, out_w = outputs[0].size()
groups = module.groups
kernel_mul = k_h * k_w * (in_c // groups)
kernel_mul_group = kernel_mul * out_h * out_w * (out_c // groups)
total_mul = kernel_mul_group * groups
info["flops"] += 2 * total_mul * inputs[0].size()[0] # total
elif isinstance(module, nn.BatchNorm2d):
info["flops"] += 2 * inputs[0].numel()
elif isinstance(module, nn.InstanceNorm2d):
info["flops"] += 6 * inputs[0].numel()
elif isinstance(module, nn.LayerNorm):
info["flops"] += 8 * inputs[0].numel()
elif isinstance(module, nn.Linear):
q = inputs[0].numel() // inputs[0].shape[-1]
info["flops"] += 2*q * module.in_features * module.out_features # total
elif isinstance(module, nn.PReLU) or isinstance(module, nn.ReLU):
info["flops"] += inputs[0].numel()
else:
print('not supported:', module)
exit()
info["flops"] += param.nelement()
elif "weight" in name:
info["inner"][name] = list(param.size())
info["flops"] += param.nelement()
if list(module.named_parameters()):
for v in summary.values():
if info["id"] == v["id"]:
info["params"] = "(recursive)"
#if info["params"] == 0:
# info["params"], info["flops"] = "-", "-"
summary[key] = info
if not module._modules:
hooks.append(module.register_forward_hook(hook))
module_names = get_names_dict(model)
hooks = []
summary = OrderedDict()
model.apply(register_hook)
try:
with torch.no_grad():
model(x) if not (kwargs or args) else model(x, *args, **kwargs)
finally:
for hook in hooks:
hook.remove()
# Use pandas to align the columns
df = pd.DataFrame(summary).T
df["Mult-Adds"] = pd.to_numeric(df["flops"], errors="coerce")
df["Params"] = pd.to_numeric(df["params"], errors="coerce")
df["Non-trainable params"] = pd.to_numeric(df["params_nt"], errors="coerce")
df = df.rename(columns=dict(
ksize="Kernel Shape",
out="Output Shape",
))
return df['Mult-Adds'].sum()
'''
with warnings.catch_warnings():
warnings.filterwarnings('ignore')
df_sum = df.sum()
df.index.name = "Layer"
df = df[["Kernel Shape", "Output Shape", "Params", "Mult-Adds"]]
max_repr_width = max([len(row) for row in df.to_string().split("\n")])
return df_sum["Mult-Adds"]
''' |