from collections import OrderedDict

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

from torchvision.ops import misc as misc_nn_ops
from torchvision.ops import MultiScaleRoIAlign

from .faster_rcnn import FasterRCNN
from .backbone_utils import resnet_fpn_backbone

__all__ = [
]

"""

The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each
image, and should be in 0-1 range. Different images can have different sizes.

The behavior of the model changes depending if it is in training or evaluation mode.

During training, the model expects both the input tensors, as well as a targets (list of dictionary),
containing:
- boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1, x2, y2] format, with values of x
between 0 and W and values of y between 0 and H
- labels (Int64Tensor[N]): the class label for each ground-truth box
- masks (UInt8Tensor[N, H, W]): the segmentation binary masks for each instance

The model returns a Dict[Tensor] during training, containing the classification and regression
losses for both the RPN and the R-CNN, and the mask loss.

During inference, the model requires only the input tensors, and returns the post-processed
predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as
follows:
- boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values of x
between 0 and W and values of y between 0 and H
- labels (Int64Tensor[N]): the predicted labels for each image
- scores (Tensor[N]): the scores or each prediction
- masks (UInt8Tensor[N, 1, H, W]): the predicted masks for each instance, in 0-1 range. In order to
obtain the final segmentation masks, the soft masks can be thresholded, generally
with a value of 0.5 (mask >= 0.5)

Arguments:
backbone (nn.Module): the network used to compute the features for the model.
It should contain a out_channels attribute, which indicates the number of output
channels that each feature map has (and it should be the same for all feature maps).
The backbone should return a single Tensor or and OrderedDict[Tensor].
num_classes (int): number of output classes of the model (including the background).
If box_predictor is specified, num_classes should be None.
min_size (int): minimum size of the image to be rescaled before feeding it to the backbone
max_size (int): maximum size of the image to be rescaled before feeding it to the backbone
image_mean (Tuple[float, float, float]): mean values used for input normalization.
They are generally the mean values of the dataset on which the backbone has been trained
on
image_std (Tuple[float, float, float]): std values used for input normalization.
They are generally the std values of the dataset on which the backbone has been trained on
rpn_anchor_generator (AnchorGenerator): module that generates the anchors for a set of feature
maps.
rpn_head (nn.Module): module that computes the objectness and regression deltas from the RPN
rpn_pre_nms_top_n_train (int): number of proposals to keep before applying NMS during training
rpn_pre_nms_top_n_test (int): number of proposals to keep before applying NMS during testing
rpn_post_nms_top_n_train (int): number of proposals to keep after applying NMS during training
rpn_post_nms_top_n_test (int): number of proposals to keep after applying NMS during testing
rpn_nms_thresh (float): NMS threshold used for postprocessing the RPN proposals
rpn_fg_iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be
considered as positive during training of the RPN.
rpn_bg_iou_thresh (float): maximum IoU between the anchor and the GT box so that they can be
considered as negative during training of the RPN.
rpn_batch_size_per_image (int): number of anchors that are sampled during training of the RPN
for computing the loss
rpn_positive_fraction (float): proportion of positive anchors in a mini-batch during training
of the RPN
box_roi_pool (MultiScaleRoIAlign): the module which crops and resizes the feature maps in
the locations indicated by the bounding boxes
box_head (nn.Module): module that takes the cropped feature maps as input
box_predictor (nn.Module): module that takes the output of box_head and returns the
classification logits and box regression deltas.
box_score_thresh (float): during inference, only return proposals with a classification score
greater than box_score_thresh
box_nms_thresh (float): NMS threshold for the prediction head. Used during inference
box_detections_per_img (int): maximum number of detections per image, for all classes.
box_fg_iou_thresh (float): minimum IoU between the proposals and the GT box so that they can be
considered as positive during training of the classification head
box_bg_iou_thresh (float): maximum IoU between the proposals and the GT box so that they can be
considered as negative during training of the classification head
box_batch_size_per_image (int): number of proposals that are sampled during training of the
box_positive_fraction (float): proportion of positive proposals in a mini-batch during training
bbox_reg_weights (Tuple[float, float, float, float]): weights for the encoding/decoding of the
bounding boxes
mask_roi_pool (MultiScaleRoIAlign): the module which crops and resizes the feature maps in
the locations indicated by the bounding boxes, which will be used for the mask head.

Example::

>>> import torch
>>> import torchvision
>>> from torchvision.models.detection.rpn import AnchorGenerator
>>>
>>> # load a pre-trained model for classification and return
>>> # only the features
>>> backbone = torchvision.models.mobilenet_v2(pretrained=True).features
>>> # MaskRCNN needs to know the number of
>>> # output channels in a backbone. For mobilenet_v2, it's 1280
>>> # so we need to add it here
>>> backbone.out_channels = 1280
>>>
>>> # let's make the RPN generate 5 x 3 anchors per spatial
>>> # location, with 5 different sizes and 3 different aspect
>>> # ratios. We have a Tuple[Tuple[int]] because each feature
>>> # map could potentially have different sizes and
>>> # aspect ratios
>>> anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),),
>>>                                    aspect_ratios=((0.5, 1.0, 2.0),))
>>>
>>> # let's define what are the feature maps that we will
>>> # use to perform the region of interest cropping, as well as
>>> # the size of the crop after rescaling.
>>> # if your backbone returns a Tensor, featmap_names is expected to
>>> # be ['0']. More generally, the backbone should return an
>>> # OrderedDict[Tensor], and in featmap_names you can choose which
>>> # feature maps to use.
>>> roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'],
>>>                                                 output_size=7,
>>>                                                 sampling_ratio=2)
>>>
>>>                                                      output_size=14,
>>>                                                      sampling_ratio=2)
>>> # put the pieces together inside a MaskRCNN model
>>>                  num_classes=2,
>>>                  rpn_anchor_generator=anchor_generator,
>>>                  box_roi_pool=roi_pooler,
>>> model.eval()
>>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
>>> predictions = model(x)
"""
def __init__(self, backbone, num_classes=None,
# transform parameters
min_size=800, max_size=1333,
image_mean=None, image_std=None,
# RPN parameters
rpn_pre_nms_top_n_train=2000, rpn_pre_nms_top_n_test=1000,
rpn_post_nms_top_n_train=2000, rpn_post_nms_top_n_test=1000,
rpn_nms_thresh=0.7,
rpn_fg_iou_thresh=0.7, rpn_bg_iou_thresh=0.3,
rpn_batch_size_per_image=256, rpn_positive_fraction=0.5,
# Box parameters
box_score_thresh=0.05, box_nms_thresh=0.5, box_detections_per_img=100,
box_fg_iou_thresh=0.5, box_bg_iou_thresh=0.5,
box_batch_size_per_image=512, box_positive_fraction=0.25,
bbox_reg_weights=None,

if num_classes is not None:
raise ValueError("num_classes should be None when mask_predictor is specified")

out_channels = backbone.out_channels

featmap_names=['0', '1', '2', '3'],
output_size=14,
sampling_ratio=2)

mask_layers = (256, 256, 256, 256)

backbone, num_classes,
# transform parameters
min_size, max_size,
image_mean, image_std,
# RPN-specific parameters
rpn_pre_nms_top_n_train, rpn_pre_nms_top_n_test,
rpn_post_nms_top_n_train, rpn_post_nms_top_n_test,
rpn_nms_thresh,
rpn_fg_iou_thresh, rpn_bg_iou_thresh,
rpn_batch_size_per_image, rpn_positive_fraction,
# Box parameters
box_score_thresh, box_nms_thresh, box_detections_per_img,
box_fg_iou_thresh, box_bg_iou_thresh,
box_batch_size_per_image, box_positive_fraction,
bbox_reg_weights)

def __init__(self, in_channels, layers, dilation):
"""
Arguments:
in_channels (int): number of input channels
layers (list): feature dimensions of each FCN layer
dilation (int): dilation rate of kernel
"""
d = OrderedDict()
next_feature = in_channels
for layer_idx, layer_features in enumerate(layers, 1):
next_feature, layer_features, kernel_size=3,
d["relu{}".format(layer_idx)] = nn.ReLU(inplace=True)
next_feature = layer_features

for name, param in self.named_parameters():
if "weight" in name:
nn.init.kaiming_normal_(param, mode="fan_out", nonlinearity="relu")
# elif "bias" in name:
#     nn.init.constant_(param, 0)

def __init__(self, in_channels, dim_reduced, num_classes):
("conv5_mask", nn.ConvTranspose2d(in_channels, dim_reduced, 2, 2, 0)),
("relu", nn.ReLU(inplace=True)),
("mask_fcn_logits", nn.Conv2d(dim_reduced, num_classes, 1, 1, 0)),
]))

for name, param in self.named_parameters():
if "weight" in name:
nn.init.kaiming_normal_(param, mode="fan_out", nonlinearity="relu")
# elif "bias" in name:
#     nn.init.constant_(param, 0)

model_urls = {
}

num_classes=91, pretrained_backbone=True, trainable_backbone_layers=3, **kwargs):
"""
Constructs a Mask R-CNN model with a ResNet-50-FPN backbone.

The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each
image, and should be in 0-1 range. Different images can have different sizes.

The behavior of the model changes depending if it is in training or evaluation mode.

During training, the model expects both the input tensors, as well as a targets (list of dictionary),
containing:
- boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1, x2, y2] format,  with values of x
between 0 and W and values of y between 0 and H
- labels (Int64Tensor[N]): the class label for each ground-truth box
- masks (UInt8Tensor[N, H, W]): the segmentation binary masks for each instance

The model returns a Dict[Tensor] during training, containing the classification and regression
losses for both the RPN and the R-CNN, and the mask loss.

During inference, the model requires only the input tensors, and returns the post-processed
predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as
follows:
- boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format,  with values of x
between 0 and W and values of y between 0 and H
- labels (Int64Tensor[N]): the predicted labels for each image
- scores (Tensor[N]): the scores or each prediction
- masks (UInt8Tensor[N, 1, H, W]): the predicted masks for each instance, in 0-1 range. In order to
obtain the final segmentation masks, the soft masks can be thresholded, generally
with a value of 0.5 (mask >= 0.5)

Mask R-CNN is exportable to ONNX for a fixed batch size with inputs images of fixed size.

Example::

>>> model.eval()
>>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
>>> predictions = model(x)
>>>
>>> # optionally, if you want to export the model to ONNX:
>>> torch.onnx.export(model, x, "mask_rcnn.onnx", opset_version = 11)

Arguments:
pretrained (bool): If True, returns a model pre-trained on COCO train2017
progress (bool): If True, displays a progress bar of the download to stderr
pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet
num_classes (int): number of output classes of the model (including the background)
trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.
"""
assert trainable_backbone_layers <= 5 and trainable_backbone_layers >= 0
# dont freeze any layers if pretrained model or backbone is not used
if not (pretrained or pretrained_backbone):
trainable_backbone_layers = 5
if pretrained: