""" PyTorch implementation of DualPathNetworks
Based on original MXNet implementation https://github.com/cypw/DPNs with
many ideas from another PyTorch implementation https://github.com/oyam/pytorch-DPNs.

This implementation is compatible with the pretrained weights from cypw's MXNet implementation.

Hacked together by / Copyright 2020 Ross Wightman
"""
from collections import OrderedDict
from functools import partial
from typing import Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F

from timm.data import IMAGENET_DPN_MEAN, IMAGENET_DPN_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import BatchNormAct2d, ConvNormAct, create_conv2d, create_classifier, get_norm_act_layer
from ._builder import build_model_with_cfg
from ._registry import register_model, generate_default_cfgs

__all__ = ['DPN']


class CatBnAct(nn.Module):
    def __init__(self, in_chs, norm_layer=BatchNormAct2d):
        super(CatBnAct, self).__init__()
        self.bn = norm_layer(in_chs, eps=0.001)

    @torch.jit._overload_method  # noqa: F811
    def forward(self, x):
        # type: (Tuple[torch.Tensor, torch.Tensor]) -> (torch.Tensor)
        pass

    @torch.jit._overload_method  # noqa: F811
    def forward(self, x):
        # type: (torch.Tensor) -> (torch.Tensor)
        pass

    def forward(self, x):
        if isinstance(x, tuple):
            x = torch.cat(x, dim=1)
        return self.bn(x)


class BnActConv2d(nn.Module):
    def __init__(self, in_chs, out_chs, kernel_size, stride, groups=1, norm_layer=BatchNormAct2d):
        super(BnActConv2d, self).__init__()
        self.bn = norm_layer(in_chs, eps=0.001)
        self.conv = create_conv2d(in_chs, out_chs, kernel_size, stride=stride, groups=groups)

    def forward(self, x):
        return self.conv(self.bn(x))


class DualPathBlock(nn.Module):
    def __init__(
            self,
            in_chs,
            num_1x1_a,
            num_3x3_b,
            num_1x1_c,
            inc,
            groups,
            block_type='normal',
            b=False,
    ):
        super(DualPathBlock, self).__init__()
        self.num_1x1_c = num_1x1_c
        self.inc = inc
        self.b = b
        if block_type == 'proj':
            self.key_stride = 1
            self.has_proj = True
        elif block_type == 'down':
            self.key_stride = 2
            self.has_proj = True
        else:
            assert block_type == 'normal'
            self.key_stride = 1
            self.has_proj = False

        self.c1x1_w_s1 = None
        self.c1x1_w_s2 = None
        if self.has_proj:
            # Using different member names here to allow easier parameter key matching for conversion
            if self.key_stride == 2:
                self.c1x1_w_s2 = BnActConv2d(
                    in_chs=in_chs, out_chs=num_1x1_c + 2 * inc, kernel_size=1, stride=2)
            else:
                self.c1x1_w_s1 = BnActConv2d(
                    in_chs=in_chs, out_chs=num_1x1_c + 2 * inc, kernel_size=1, stride=1)

        self.c1x1_a = BnActConv2d(in_chs=in_chs, out_chs=num_1x1_a, kernel_size=1, stride=1)
        self.c3x3_b = BnActConv2d(
            in_chs=num_1x1_a, out_chs=num_3x3_b, kernel_size=3, stride=self.key_stride, groups=groups)
        if b:
            self.c1x1_c = CatBnAct(in_chs=num_3x3_b)
            self.c1x1_c1 = create_conv2d(num_3x3_b, num_1x1_c, kernel_size=1)
            self.c1x1_c2 = create_conv2d(num_3x3_b, inc, kernel_size=1)
        else:
            self.c1x1_c = BnActConv2d(in_chs=num_3x3_b, out_chs=num_1x1_c + inc, kernel_size=1, stride=1)
            self.c1x1_c1 = None
            self.c1x1_c2 = None

    @torch.jit._overload_method  # noqa: F811
    def forward(self, x):
        # type: (Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]
        pass

    @torch.jit._overload_method  # noqa: F811
    def forward(self, x):
        # type: (torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]
        pass

    def forward(self, x) -> Tuple[torch.Tensor, torch.Tensor]:
        if isinstance(x, tuple):
            x_in = torch.cat(x, dim=1)
        else:
            x_in = x
        if self.c1x1_w_s1 is None and self.c1x1_w_s2 is None:
            # self.has_proj == False, torchscript requires condition on module == None
            x_s1 = x[0]
            x_s2 = x[1]
        else:
            # self.has_proj == True
            if self.c1x1_w_s1 is not None:
                # self.key_stride = 1
                x_s = self.c1x1_w_s1(x_in)
            else:
                # self.key_stride = 2
                x_s = self.c1x1_w_s2(x_in)
            x_s1 = x_s[:, :self.num_1x1_c, :, :]
            x_s2 = x_s[:, self.num_1x1_c:, :, :]
        x_in = self.c1x1_a(x_in)
        x_in = self.c3x3_b(x_in)
        x_in = self.c1x1_c(x_in)
        if self.c1x1_c1 is not None:
            # self.b == True, using None check for torchscript compat
            out1 = self.c1x1_c1(x_in)
            out2 = self.c1x1_c2(x_in)
        else:
            out1 = x_in[:, :self.num_1x1_c, :, :]
            out2 = x_in[:, self.num_1x1_c:, :, :]
        resid = x_s1 + out1
        dense = torch.cat([x_s2, out2], dim=1)
        return resid, dense


class DPN(nn.Module):
    def __init__(
            self,
            k_sec=(3, 4, 20, 3),
            inc_sec=(16, 32, 24, 128),
            k_r=96,
            groups=32,
            num_classes=1000,
            in_chans=3,
            output_stride=32,
            global_pool='avg',
            small=False,
            num_init_features=64,
            b=False,
            drop_rate=0.,
            norm_layer='batchnorm2d',
            act_layer='relu',
            fc_act_layer='elu',
    ):
        super(DPN, self).__init__()
        self.num_classes = num_classes
        self.drop_rate = drop_rate
        self.b = b
        assert output_stride == 32  # FIXME look into dilation support

        norm_layer = partial(get_norm_act_layer(norm_layer, act_layer=act_layer), eps=.001)
        fc_norm_layer = partial(get_norm_act_layer(norm_layer, act_layer=fc_act_layer), eps=.001, inplace=False)
        bw_factor = 1 if small else 4
        blocks = OrderedDict()

        # conv1
        blocks['conv1_1'] = ConvNormAct(
            in_chans, num_init_features, kernel_size=3 if small else 7, stride=2, norm_layer=norm_layer)
        blocks['conv1_pool'] = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.feature_info = [dict(num_chs=num_init_features, reduction=2, module='features.conv1_1')]

        # conv2
        bw = 64 * bw_factor
        inc = inc_sec[0]
        r = (k_r * bw) // (64 * bw_factor)
        blocks['conv2_1'] = DualPathBlock(num_init_features, r, r, bw, inc, groups, 'proj', b)
        in_chs = bw + 3 * inc
        for i in range(2, k_sec[0] + 1):
            blocks['conv2_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b)
            in_chs += inc
        self.feature_info += [dict(num_chs=in_chs, reduction=4, module=f'features.conv2_{k_sec[0]}')]

        # conv3
        bw = 128 * bw_factor
        inc = inc_sec[1]
        r = (k_r * bw) // (64 * bw_factor)
        blocks['conv3_1'] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'down', b)
        in_chs = bw + 3 * inc
        for i in range(2, k_sec[1] + 1):
            blocks['conv3_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b)
            in_chs += inc
        self.feature_info += [dict(num_chs=in_chs, reduction=8, module=f'features.conv3_{k_sec[1]}')]

        # conv4
        bw = 256 * bw_factor
        inc = inc_sec[2]
        r = (k_r * bw) // (64 * bw_factor)
        blocks['conv4_1'] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'down', b)
        in_chs = bw + 3 * inc
        for i in range(2, k_sec[2] + 1):
            blocks['conv4_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b)
            in_chs += inc
        self.feature_info += [dict(num_chs=in_chs, reduction=16, module=f'features.conv4_{k_sec[2]}')]

        # conv5
        bw = 512 * bw_factor
        inc = inc_sec[3]
        r = (k_r * bw) // (64 * bw_factor)
        blocks['conv5_1'] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'down', b)
        in_chs = bw + 3 * inc
        for i in range(2, k_sec[3] + 1):
            blocks['conv5_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b)
            in_chs += inc
        self.feature_info += [dict(num_chs=in_chs, reduction=32, module=f'features.conv5_{k_sec[3]}')]

        blocks['conv5_bn_ac'] = CatBnAct(in_chs, norm_layer=fc_norm_layer)

        self.num_features = in_chs
        self.features = nn.Sequential(blocks)

        # Using 1x1 conv for the FC layer to allow the extra pooling scheme
        self.global_pool, self.classifier = create_classifier(
            self.num_features, self.num_classes, pool_type=global_pool, use_conv=True)
        self.flatten = nn.Flatten(1) if global_pool else nn.Identity()

    @torch.jit.ignore
    def group_matcher(self, coarse=False):
        matcher = dict(
            stem=r'^features\.conv1',
            blocks=[
                (r'^features\.conv(\d+)' if coarse else r'^features\.conv(\d+)_(\d+)', None),
                (r'^features\.conv5_bn_ac', (99999,))
            ]
        )
        return matcher

    @torch.jit.ignore
    def set_grad_checkpointing(self, enable=True):
        assert not enable, 'gradient checkpointing not supported'

    @torch.jit.ignore
    def get_classifier(self):
        return self.classifier

    def reset_classifier(self, num_classes, global_pool='avg'):
        self.num_classes = num_classes
        self.global_pool, self.classifier = create_classifier(
            self.num_features, self.num_classes, pool_type=global_pool, use_conv=True)
        self.flatten = nn.Flatten(1) if global_pool else nn.Identity()

    def forward_features(self, x):
        return self.features(x)

    def forward_head(self, x, pre_logits: bool = False):
        x = self.global_pool(x)
        if self.drop_rate > 0.:
            x = F.dropout(x, p=self.drop_rate, training=self.training)
        if pre_logits:
            return self.flatten(x)
        x = self.classifier(x)
        return self.flatten(x)

    def forward(self, x):
        x = self.forward_features(x)
        x = self.forward_head(x)
        return x


def _create_dpn(variant, pretrained=False, **kwargs):
    return build_model_with_cfg(
        DPN,
        variant,
        pretrained,
        feature_cfg=dict(feature_concat=True, flatten_sequential=True),
        **kwargs,
    )


def _cfg(url='', **kwargs):
    return {
        'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
        'crop_pct': 0.875, 'interpolation': 'bicubic',
        'mean': IMAGENET_DPN_MEAN, 'std': IMAGENET_DPN_STD,
        'first_conv': 'features.conv1_1.conv', 'classifier': 'classifier',
        **kwargs
    }


default_cfgs = generate_default_cfgs({
    'dpn48b.untrained': _cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
    'dpn68.mx_in1k': _cfg(hf_hub_id='timm/'),
    'dpn68b.ra_in1k': _cfg(
        hf_hub_id='timm/',
        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD,
        crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
    'dpn68b.mx_in1k': _cfg(hf_hub_id='timm/'),
    'dpn92.mx_in1k': _cfg(hf_hub_id='timm/'),
    'dpn98.mx_in1k': _cfg(hf_hub_id='timm/'),
    'dpn131.mx_in1k': _cfg(hf_hub_id='timm/'),
    'dpn107.mx_in1k': _cfg(hf_hub_id='timm/')
})


@register_model
def dpn48b(pretrained=False, **kwargs) -> DPN:
    model_kwargs = dict(
        small=True, num_init_features=10, k_r=128, groups=32,
        b=True, k_sec=(3, 4, 6, 3), inc_sec=(16, 32, 32, 64), act_layer='silu')
    return _create_dpn('dpn48b', pretrained=pretrained, **dict(model_kwargs, **kwargs))


@register_model
def dpn68(pretrained=False, **kwargs) -> DPN:
    model_kwargs = dict(
        small=True, num_init_features=10, k_r=128, groups=32,
        k_sec=(3, 4, 12, 3), inc_sec=(16, 32, 32, 64))
    return _create_dpn('dpn68', pretrained=pretrained, **dict(model_kwargs, **kwargs))


@register_model
def dpn68b(pretrained=False, **kwargs) -> DPN:
    model_kwargs = dict(
        small=True, num_init_features=10, k_r=128, groups=32,
        b=True, k_sec=(3, 4, 12, 3), inc_sec=(16, 32, 32, 64))
    return _create_dpn('dpn68b', pretrained=pretrained, **dict(model_kwargs, **kwargs))


@register_model
def dpn92(pretrained=False, **kwargs) -> DPN:
    model_kwargs = dict(
        num_init_features=64, k_r=96, groups=32,
        k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128))
    return _create_dpn('dpn92', pretrained=pretrained, **dict(model_kwargs, **kwargs))


@register_model
def dpn98(pretrained=False, **kwargs) -> DPN:
    model_kwargs = dict(
        num_init_features=96, k_r=160, groups=40,
        k_sec=(3, 6, 20, 3), inc_sec=(16, 32, 32, 128))
    return _create_dpn('dpn98', pretrained=pretrained, **dict(model_kwargs, **kwargs))


@register_model
def dpn131(pretrained=False, **kwargs) -> DPN:
    model_kwargs = dict(
        num_init_features=128, k_r=160, groups=40,
        k_sec=(4, 8, 28, 3), inc_sec=(16, 32, 32, 128))
    return _create_dpn('dpn131', pretrained=pretrained, **dict(model_kwargs, **kwargs))


@register_model
def dpn107(pretrained=False, **kwargs) -> DPN:
    model_kwargs = dict(
        num_init_features=128, k_r=200, groups=50,
        k_sec=(4, 8, 20, 3), inc_sec=(20, 64, 64, 128))
    return _create_dpn('dpn107', pretrained=pretrained, **dict(model_kwargs, **kwargs))
