import torch
import torchvision
from torchvision.models.detection.rpn import AnchorGenerator, RPNHead
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor

import lgp.models.alfred.hlsm.alfworld.alfworld_constants as constants


def get_model_instance_segmentation(num_classes):
    # load an instance segmentation model pre-trained pre-trained on COCO
    model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)

    anchor_generator = AnchorGenerator(
        sizes=tuple([(4, 8, 16, 32, 64, 128, 256, 512) for _ in range(5)]),
        aspect_ratios=tuple([(0.25, 0.5, 1.0, 2.0) for _ in range(5)]))
    model.rpn.anchor_generator = anchor_generator

    # 256 because that's the number of features that FPN returns
    model.rpn.head = RPNHead(256, anchor_generator.num_anchors_per_location()[0])

    # get number of input features for the classifier
    in_features = model.roi_heads.box_predictor.cls_score.in_features
    # replace the pre-trained head with a new one
    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)

    # now get the number of input features for the mask classifier
    in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels
    hidden_layer = 256
    # and replace the mask predictor with a new one
    model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask,
                                                       hidden_layer,
                                                       num_classes)
    return model


def load_pretrained_model(path):
    mask_rcnn = get_model_instance_segmentation(len(constants.OBJECTS_DETECTOR)+1)
    mask_rcnn.load_state_dict(torch.load(path))
    return mask_rcnn


if __name__ == "__main__":
    lgp.paths.get
    self.detector = load_pretrained_model(pretrained_model_path)