LeViT-UNet-128s.py

# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.

# Modified from
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# Copyright 2020 Ross Wightman, Apache-2.0 License

from timm.models.layers import activations
import torch
import itertools
import utils
import torch.nn as nn
import torch.nn.functional as F

from timm.models.vision_transformer import trunc_normal_
from timm.models.registry import register_model



specification = {
    'LeViT_128S': {
        'C': '128_256_384', 'D': 16, 'N': '4_6_8', 'X': '2_3_4', 'drop_path': 0,
        'weights': 'https://dl.fbaipublicfiles.com/LeViT/LeViT-128S-96703c44.pth'},
}

__all__ = [specification.keys()]



@register_model  ## No Mask
def Build_LeViT_UNet_128s(num_classes=4, distillation=True,
              pretrained=False, fuse=False):
    return model_factory_v3_NM(**specification['LeViT_128S'], num_classes=num_classes,
                         distillation=distillation, pretrained=pretrained, fuse=fuse)


FLOPS_COUNTER = 0


class Conv2d_BN(torch.nn.Sequential):
    def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1,
                 groups=1, bn_weight_init=1, resolution=-10000):
        super().__init__()
        self.add_module('c', torch.nn.Conv2d(
            a, b, ks, stride, pad, dilation, groups, bias=False))
        bn = torch.nn.BatchNorm2d(b)
        torch.nn.init.constant_(bn.weight, bn_weight_init)
        torch.nn.init.constant_(bn.bias, 0)
        self.add_module('bn', bn)

        global FLOPS_COUNTER
        output_points = ((resolution + 2 * pad - dilation *
                          (ks - 1) - 1) // stride + 1)**2
        FLOPS_COUNTER += a * b * output_points * (ks**2) // groups

    @torch.no_grad()
    def fuse(self):
        c, bn = self._modules.values()
        w = bn.weight / (bn.running_var + bn.eps)**0.5
        w = c.weight * w[:, None, None, None]
        b = bn.bias - bn.running_mean * bn.weight / \
            (bn.running_var + bn.eps)**0.5
        m = torch.nn.Conv2d(w.size(1), w.size(
            0), w.shape[2:], stride=self.c.stride, padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups)
        m.weight.data.copy_(w)
        m.bias.data.copy_(b)
        return m


class Linear_BN(torch.nn.Sequential):
    def __init__(self, a, b, bn_weight_init=1, resolution=-100000):
        super().__init__()
        self.add_module('c', torch.nn.Linear(a, b, bias=False))
        bn = torch.nn.BatchNorm1d(b)
        torch.nn.init.constant_(bn.weight, bn_weight_init)
        torch.nn.init.constant_(bn.bias, 0)
        self.add_module('bn', bn)

        global FLOPS_COUNTER
        output_points = resolution**2
        FLOPS_COUNTER += a * b * output_points

    @torch.no_grad()
    def fuse(self):
        l, bn = self._modules.values()
        w = bn.weight / (bn.running_var + bn.eps)**0.5
        w = l.weight * w[:, None]
        b = bn.bias - bn.running_mean * bn.weight / \
            (bn.running_var + bn.eps)**0.5
        m = torch.nn.Linear(w.size(1), w.size(0))
        m.weight.data.copy_(w)
        m.bias.data.copy_(b)
        return m

    def forward(self, x):
        l, bn = self._modules.values()
        x = l(x)
        return bn(x.flatten(0, 1)).reshape_as(x)


class BN_Linear(torch.nn.Sequential):
    def __init__(self, a, b, bias=True, std=0.02):
        super().__init__()
        self.add_module('bn', torch.nn.BatchNorm1d(a))
        l = torch.nn.Linear(a, b, bias=bias)
        trunc_normal_(l.weight, std=std)
        if bias:
            torch.nn.init.constant_(l.bias, 0)
        self.add_module('l', l)
        global FLOPS_COUNTER
        FLOPS_COUNTER += a * b

    @torch.no_grad()
    def fuse(self):
        bn, l = self._modules.values()
        w = bn.weight / (bn.running_var + bn.eps)**0.5
        b = bn.bias - self.bn.running_mean * \
            self.bn.weight / (bn.running_var + bn.eps)**0.5
        w = l.weight * w[None, :]
        if l.bias is None:
            b = b @ self.l.weight.T
        else:
            b = (l.weight @ b[:, None]).view(-1) + self.l.bias
        m = torch.nn.Linear(w.size(1), w.size(0))
        m.weight.data.copy_(w)
        m.bias.data.copy_(b)
        return m

def b16(n, activation, resolution=224):
    return torch.nn.Sequential(
        Conv2d_BN(1, n // 8, 3, 2, 1, resolution=resolution), # aping num_channel
        activation(),
        Conv2d_BN(n // 8, n // 4, 3, 2, 1, resolution=resolution // 2),
        activation(),
        Conv2d_BN(n // 4, n // 2, 3, 2, 1, resolution=resolution // 4),
        activation(),
        Conv2d_BN(n // 2, n, 3, 2, 1, resolution=resolution // 8))


class Residual(torch.nn.Module):
    def __init__(self, m, drop):
        super().__init__()
        self.m = m
        self.drop = drop

    def forward(self, x):
        if self.training and self.drop > 0:
            return x + self.m(x) * torch.rand(x.size(0), 1, 1,
                                              device=x.device).ge_(self.drop).div(1 - self.drop).detach()
        else:
            return x + self.m(x)


class Attention(torch.nn.Module):
    def __init__(self, dim, key_dim, num_heads=8,
                 attn_ratio=4,
                 activation=None,
                 resolution=14):
        super().__init__()
        self.num_heads = num_heads
        self.scale = key_dim ** -0.5
        self.key_dim = key_dim
        self.nh_kd = nh_kd = key_dim * num_heads
        self.d = int(attn_ratio * key_dim)
        self.dh = int(attn_ratio * key_dim) * num_heads
        self.attn_ratio = attn_ratio
        h = self.dh + nh_kd * 2
        self.qkv = Linear_BN(dim, h, resolution=resolution)
        self.proj = torch.nn.Sequential(activation(), Linear_BN(
            self.dh, dim, bn_weight_init=0, resolution=resolution))

        points = list(itertools.product(range(resolution), range(resolution)))
        N = len(points)
        attention_offsets = {}
        idxs = []
        for p1 in points:
            for p2 in points:
                offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
                if offset not in attention_offsets:
                    attention_offsets[offset] = len(attention_offsets)
                idxs.append(attention_offsets[offset])
        self.attention_biases = torch.nn.Parameter(
            torch.zeros(num_heads, len(attention_offsets)))
        self.register_buffer('attention_bias_idxs',
                             torch.LongTensor(idxs).view(N, N))

        global FLOPS_COUNTER
        #queries * keys
        FLOPS_COUNTER += num_heads * (resolution**4) * key_dim
        # softmax
        FLOPS_COUNTER += num_heads * (resolution**4)
        #attention * v
        FLOPS_COUNTER += num_heads * self.d * (resolution**4)

    @torch.no_grad()
    def train(self, mode=True):
        super().train(mode)
        if mode and hasattr(self, 'ab'):
            del self.ab
        else:
            self.ab = self.attention_biases[:, self.attention_bias_idxs]

    def forward(self, x):  # x (B,N,C)
        B, N, C = x.shape
        qkv = self.qkv(x)
        q, k, v = qkv.view(B, N, self.num_heads, -
                           1).split([self.key_dim, self.key_dim, self.d], dim=3)
        q = q.permute(0, 2, 1, 3)
        k = k.permute(0, 2, 1, 3)
        v = v.permute(0, 2, 1, 3)

        attn = (
            (q @ k.transpose(-2, -1)) * self.scale
            +
            (self.attention_biases[:, self.attention_bias_idxs]
             if self.training else self.ab)
        )
        attn = attn.softmax(dim=-1)
        x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh)
        x = self.proj(x)
        return x


class Subsample(torch.nn.Module):
    def __init__(self, stride, resolution):
        super().__init__()
        self.stride = stride
        self.resolution = resolution

    def forward(self, x):
        B, N, C = x.shape
        x = x.view(B, self.resolution, self.resolution, C)[
            :, ::self.stride, ::self.stride].reshape(B, -1, C)
        return x


class AttentionSubsample(torch.nn.Module):
    def __init__(self, in_dim, out_dim, key_dim, num_heads=8,
                 attn_ratio=2,
                 activation=None,
                 stride=2,
                 resolution=14, resolution_=7):
        super().__init__()
        self.num_heads = num_heads
        self.scale = key_dim ** -0.5
        self.key_dim = key_dim
        self.nh_kd = nh_kd = key_dim * num_heads
        self.d = int(attn_ratio * key_dim)
        self.dh = int(attn_ratio * key_dim) * self.num_heads
        self.attn_ratio = attn_ratio
        self.resolution_ = resolution_
        self.resolution_2 = resolution_**2
        h = self.dh + nh_kd
        self.kv = Linear_BN(in_dim, h, resolution=resolution)

        self.q = torch.nn.Sequential(
            Subsample(stride, resolution),
            Linear_BN(in_dim, nh_kd, resolution=resolution_))
        self.proj = torch.nn.Sequential(activation(), Linear_BN(
            self.dh, out_dim, resolution=resolution_))

        self.stride = stride
        self.resolution = resolution
        points = list(itertools.product(range(resolution), range(resolution)))
        points_ = list(itertools.product(
            range(resolution_), range(resolution_)))
        N = len(points)
        N_ = len(points_)
        attention_offsets = {}
        idxs = []
        for p1 in points_:
            for p2 in points:
                size = 1
                offset = (
                    abs(p1[0] * stride - p2[0] + (size - 1) / 2),
                    abs(p1[1] * stride - p2[1] + (size - 1) / 2))
                if offset not in attention_offsets:
                    attention_offsets[offset] = len(attention_offsets)
                idxs.append(attention_offsets[offset])
        self.attention_biases = torch.nn.Parameter(
            torch.zeros(num_heads, len(attention_offsets)))
        self.register_buffer('attention_bias_idxs',
                             torch.LongTensor(idxs).view(N_, N))

        global FLOPS_COUNTER
        #queries * keys
        FLOPS_COUNTER += num_heads * \
            (resolution**2) * (resolution_**2) * key_dim
        # softmax
        FLOPS_COUNTER += num_heads * (resolution**2) * (resolution_**2)
        #attention * v
        FLOPS_COUNTER += num_heads * \
            (resolution**2) * (resolution_**2) * self.d

    @torch.no_grad()
    def train(self, mode=True):
        super().train(mode)
        if mode and hasattr(self, 'ab'):
            del self.ab
        else:
            self.ab = self.attention_biases[:, self.attention_bias_idxs]

    def forward(self, x):
        B, N, C = x.shape
        k, v = self.kv(x).view(B, N, self.num_heads, -
                               1).split([self.key_dim, self.d], dim=3)
        k = k.permute(0, 2, 1, 3)  # BHNC
        v = v.permute(0, 2, 1, 3)  # BHNC
        q = self.q(x).view(B, self.resolution_2, self.num_heads,
                           self.key_dim).permute(0, 2, 1, 3)

        attn = (q @ k.transpose(-2, -1)) * self.scale + \
            (self.attention_biases[:, self.attention_bias_idxs]
             if self.training else self.ab)
        attn = attn.softmax(dim=-1)

        x = (attn @ v).transpose(1, 2).reshape(B, -1, self.dh)
        x = self.proj(x)
        return x

class Conv2dReLU(nn.Sequential):
    def __init__(
            self,
            in_channels,
            out_channels,
            kernel_size,
            padding=0,
            stride=1,
            use_batchnorm=True,
    ):
        conv = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            bias=not (use_batchnorm),
        )
        relu = nn.ReLU(inplace=True)

        bn = nn.BatchNorm2d(out_channels)

        super(Conv2dReLU, self).__init__(conv, bn, relu)

class DecoderBlock(nn.Module):
    def __init__(
            self,
            in_channels,
            out_channels,
            #skip_channels=0,
            use_batchnorm=True,
    ):
        super().__init__()
        self.conv1 = Conv2dReLU(
            #in_channels + skip_channels,
            in_channels,
            out_channels,
            kernel_size=3,
            padding=1,
            use_batchnorm=use_batchnorm,
        )
        self.conv2 = Conv2dReLU(
            out_channels,
            out_channels,
            kernel_size=3,
            padding=1,
            use_batchnorm=use_batchnorm,
        )
        self.up = nn.UpsamplingBilinear2d(scale_factor=2)

    def forward(self, x, skip=None):
        x = self.up(x)
        if skip is not None:
            x = torch.cat([x, skip], dim=1)
        x = self.conv1(x)
        x = self.conv2(x)
        return x

class SegmentationHead(nn.Sequential):

    def __init__(self, in_channels, out_channels, kernel_size=3, upsampling=1):
        conv2d = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=kernel_size // 2)
        upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampling) if upsampling > 1 else nn.Identity()
        super().__init__(conv2d, upsampling)

##################################
##################################
def model_factory_v3_NM(C, D, X, N, drop_path, weights,
                  num_classes, distillation, pretrained, fuse):
    embed_dim = [int(x) for x in C.split('_')]
    num_heads = [int(x) for x in N.split('_')]
    depth = [int(x) for x in X.split('_')]
    act = torch.nn.Hardswish
    model = LeViT_UNet_128s(
        patch_size=16,
        embed_dim=embed_dim,
        num_heads=num_heads,
        key_dim=[D] * 3,
        depth=depth,
        attn_ratio=[2, 2, 2],
        mlp_ratio=[2, 2, 2],
        down_ops=[
            #('Subsample',key_dim, num_heads, attn_ratio, mlp_ratio, stride)
            ['Subsample', D, embed_dim[0] // D, 4, 2, 2],
            ['Subsample', D, embed_dim[1] // D, 4, 2, 2],
        ],
        attention_activation=act,
        mlp_activation=act,
        hybrid_backbone=None,
        num_classes=num_classes,  ### aping: set number class
        drop_path=drop_path,
        distillation=distillation
    )
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            weights, map_location='cpu')
        ## aping: load according the model items
        checkpoint_model = checkpoint['model']
        # checkpoint_model['cnn_b1']=checkpoint_model.pop('patch_embed.0.c.weight')
        all_pre_keys = checkpoint_model.keys()
        re_str = ['patch_embed.2', 'patch_embed.2', 'patch_embed.4', 'patch_embed.6']
        new_str = ['cnn_b2.0', 'cnn_b2.0', 'cnn_b3.0', 'cnn_b4.0']

        for i, search_str in enumerate(re_str):
            for item in list(all_pre_keys):
                if search_str in item:
                    replace_name = item.replace(re_str[i], new_str[i])
                    print(replace_name)
                    checkpoint_model[replace_name]=checkpoint_model.pop(item)

        re_trans = ['blocks']
        for i, search_str in enumerate(re_trans):
            for item in list(all_pre_keys):
                if search_str in item:
                    idx = int(item.split('.')[1])
                    if (idx < 4):
                        replace_name = item.replace('blocks', 'block_1')
                        print('1: ', replace_name)
                        checkpoint_model[replace_name]=checkpoint_model.pop(item)
                    elif (4 <= idx < 12):
                        replace_name = item.replace('blocks', 'block_2')
                        print('2: ',replace_name)
                        checkpoint_model[replace_name]=checkpoint_model.pop(item)
                    else:
                        replace_name = item.replace('blocks', 'block_3')
                        print('3: ',replace_name)
                        checkpoint_model[replace_name]=checkpoint_model.pop(item)                        

        ## preposcess the pretrained model
        model_dict = model.state_dict()
        pretrained_dict = {k:v for k, v in checkpoint_model.items() if k in model_dict}
        model_dict.update(pretrained_dict)
        model.load_state_dict(model_dict)
        
    if fuse:
        utils.replace_batchnorm(model)

    return model

class LeViT_UNet_128s(torch.nn.Module):
    """ Vision Transformer with support for patch or hybrid CNN input stage
    """

    def __init__(self, img_size=224, ## aping: should change
                 patch_size=16,
                 in_chans=3,
                 num_classes=9,
                 embed_dim=[192],
                 key_dim=[64],
                 depth=[12],
                 num_heads=[3],
                 attn_ratio=[2],
                 mlp_ratio=[2],
                 hybrid_backbone=None,
                 down_ops=[],
                 attention_activation=torch.nn.Hardswish,
                 mlp_activation=torch.nn.Hardswish,
                 distillation=True,
                 drop_path=0):
        super().__init__()
        global FLOPS_COUNTER

        self.num_classes = num_classes
        self.num_features = embed_dim[-1]
        self.embed_dim = embed_dim
        self.distillation = distillation

        # self.patch_embed = hybrid_backbone ## aping: CNN  # aping num_channel
        n = 128 ## cnn num channel
        activation = torch.nn.Hardswish
        self.cnn_b1 = torch.nn.Sequential(
            Conv2d_BN(1, n // 8, 3, 2, 1, resolution=img_size), activation())
        self.cnn_b2 = torch.nn.Sequential(
            Conv2d_BN(n // 8, n // 4, 3, 2, 1, resolution=img_size // 2), activation())
        self.cnn_b3 = torch.nn.Sequential(
            Conv2d_BN(n // 4, n // 2, 3, 2, 1, resolution=img_size // 4), activation())
        self.cnn_b4 = torch.nn.Sequential(    
            Conv2d_BN(n // 2, n, 3, 2, 1, resolution=img_size // 8))


        self.decoderBlock_1 = DecoderBlock(n + (128+256+384), 512) #->att+cnn:128+768=896  '128_256_384'
        self.decoderBlock_2 = DecoderBlock(n//2 + 512, 256) #->28->56, 512+9+192=713
        self.decoderBlock_3 = DecoderBlock(n//4 + 256, 128) #->56->112, 256+9+96=361
        self.segmentation_head = SegmentationHead(n//8 + 128, self.num_classes, kernel_size=3, upsampling=2)


        self.blocks = [] ## attention block
        down_ops.append([''])
        resolution = img_size // patch_size
        for i, (ed, kd, dpth, nh, ar, mr, do) in enumerate(
                zip(embed_dim, key_dim, depth, num_heads, attn_ratio, mlp_ratio, down_ops)):
            print(i)
            for _ in range(dpth):
                self.blocks.append(
                    Residual(Attention(
                        ed, kd, nh,
                        attn_ratio=ar,
                        activation=attention_activation,
                        resolution=resolution,
                    ), drop_path))
                if mr > 0:
                    h = int(ed * mr)
                    self.blocks.append(
                        Residual(torch.nn.Sequential(
                            Linear_BN(ed, h, resolution=resolution),
                            mlp_activation(),
                            Linear_BN(h, ed, bn_weight_init=0,
                                      resolution=resolution),
                        ), drop_path))
            if do[0] == 'Subsample':
                #('Subsample',key_dim, num_heads, attn_ratio, mlp_ratio, stride)
                resolution_ = (resolution - 1) // do[5] + 1
                self.blocks.append(
                    AttentionSubsample(
                        *embed_dim[i:i + 2], key_dim=do[1], num_heads=do[2],
                        attn_ratio=do[3],
                        activation=attention_activation,
                        stride=do[5],
                        resolution=resolution,
                        resolution_=resolution_))
                resolution = resolution_
                if do[4] > 0:  # mlp_ratio
                    h = int(embed_dim[i + 1] * do[4])
                    self.blocks.append(
                        Residual(torch.nn.Sequential(
                            Linear_BN(embed_dim[i + 1], h,
                                      resolution=resolution),
                            mlp_activation(),
                            Linear_BN(
                                h, embed_dim[i + 1], bn_weight_init=0, resolution=resolution),
                        ), drop_path))
        self.blocks = torch.nn.Sequential(*self.blocks)
        ## divid the blocks
        self.block_1 = self.blocks[0:4]
        self.block_2 = self.blocks[4:12]
        self.block_3 = self.blocks[12:]

        del self.blocks

        ## aping: upsampling
        self.up = nn.UpsamplingBilinear2d(scale_factor=2)


        self.FLOPS = FLOPS_COUNTER
        FLOPS_COUNTER = 0
        

    @torch.jit.ignore
    def no_weight_decay(self):
        return {x for x in self.state_dict().keys() if 'attention_biases' in x}

    def forward(self, x):
        x_cnn_1 = self.cnn_b1(x) # channel num 1->16
        x_cnn_2 = self.cnn_b2(x_cnn_1) #->  16->32
        x_cnn_3 = self.cnn_b3(x_cnn_2) # 32->64
        x_cnn = self.cnn_b4(x_cnn_3) # 64->128  : torch.Size([1, 128, 14, 14])

        x = x_cnn.flatten(2).transpose(1, 2) # torch.Size([4, 196, 256])
        ## aping 
        x = self.block_1(x) # torch.Size([1, 196, 128])--> torch.Size([1, 196, 128]) : CNN->Trans
        x_num, x_len = x.shape[0], x.shape[1]
        # reshape: torch.Size([4, 14, 14, 256])
        x_r_1 = x.reshape(x_num, int(x_len**0.5), int(x_len**0.5), -1)
        x_r_1 = x_r_1.permute(0,3,1,2)

        x = self.block_2(x) # downsample + att torch.Size([1, 196, 128])-> torch.Size([1, 49, 256])
        x_num, x_len = x.shape[0], x.shape[1]
        # reshape: torch.Size([4, 14, 14, 256])
        x_r_2 = x.reshape(x_num, int(x_len**0.5), int(x_len**0.5), -1)
        x_r_2 = x_r_2.permute(0,3,1,2)
        ## upsampling
        x_r_2_up = self.up(x_r_2)
        

        x = self.block_3(x) # torch.Size([1, 49, 256])->torch.Size([1, 16, 384])
        x_num, x_len = x.shape[0], x.shape[1]
        # reshape: torch.Size([4, 4, 4, 512])
        x_r_3 = x.reshape(x_num, int(x_len**0.5), int(x_len**0.5), -1)
        x_r_3 = x_r_3.permute(0,3,1,2)
        ## upsampling
        x_r_3_up = self.up(x_r_3)
        x_r_3_up = self.up(x_r_3_up)
        
        ## aping: resize the feature maps
        if (x_r_2_up.shape  != x_r_3_up.shape):
            x_r_3_up = F.interpolate(x_r_3_up, size=x_r_2_up.shape[2:], mode="bilinear",  align_corners=True)
        att_all = torch.cat([ x_r_1, x_r_2_up, x_r_3_up], dim=1) # 128+256+384=768

        x_att_all = torch.cat([x_cnn, att_all], dim=1) ## 768+128=896
        decoder_feature = self.decoderBlock_1(x_att_all) # x_att_all: ([4, 896, 32, 32])->512

        decoder_feature = torch.cat([decoder_feature, x_cnn_3], dim=1) #:(512+9)x64x64
        decoder_feature = self.decoderBlock_2(decoder_feature)

        decoder_feature = torch.cat([decoder_feature, x_cnn_2], dim=1) # ([4, (320+9), 128, 128])
        decoder_feature = self.decoderBlock_3(decoder_feature)
        decoder_feature = torch.cat([decoder_feature, x_cnn_1], dim=1) # ([4, 169, 256, 256])

        
        logits = self.segmentation_head(decoder_feature) ## torch.Size([4, 2, 224, 224])

        return logits

##################################
##################################


#################################
def netParams(model):
    """
    computing total network parameters
    args:
       model: model
    return: the number of parameters
    """
    total_paramters = 0
    for parameter in model.parameters():
        i = len(parameter.size())
        p = 1
        for j in range(i):
            p *= parameter.size(j)
        total_paramters += p

    return total_paramters


from ptflops import get_model_complexity_info

if __name__ == '__main__':
    for name in specification:
        # net = globals()[name](fuse=True, pretrained=False)
        net = Build_LeViT_UNet_128s(num_classes=9, pretrained=True)
        net.eval()
        net(torch.randn(1, 1, 224, 224)) #NCHW
        # print(net)
        print(name,
              net.FLOPS, 'FLOPs',
              sum(p.numel() for p in net.parameters() if p.requires_grad), 'parameters')
        total_paramters = netParams(net)
        print("the number of parameters: %d ==> %.2f M" % (total_paramters, (total_paramters / 1e6)))
        macs, params = get_model_complexity_info(net, (1, 224, 224), as_strings=True,
                                                print_per_layer_stat=False, verbose=False)
        print('{:<30}  {:<8}'.format('Computational complexity: ', macs ))
        print('{:<30}  {:<8}'.format('Number of parameters: ', params ))