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Source code for mmedit.models.backbones.encoder_decoders.gl_encoder_decoder

# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import auto_fp16, load_checkpoint

from mmedit.models.builder import build_component
from mmedit.models.registry import BACKBONES
from mmedit.utils import get_root_logger


[docs]@BACKBONES.register_module() class GLEncoderDecoder(nn.Module): """Encoder-Decoder used in Global&Local model. This implementation follows: Globally and locally Consistent Image Completion The architecture of the encoder-decoder is:\ (conv2d x 6) --> (dilated conv2d x 4) --> (conv2d or deconv2d x 7) Args: encoder (dict): Config dict to encoder. decoder (dict): Config dict to build decoder. dilation_neck (dict): Config dict to build dilation neck. """ def __init__(self, encoder=dict(type='GLEncoder'), decoder=dict(type='GLDecoder'), dilation_neck=dict(type='GLDilationNeck')): super().__init__() self.encoder = build_component(encoder) self.decoder = build_component(decoder) self.dilation_neck = build_component(dilation_neck) # support fp16 self.fp16_enabled = False
[docs] @auto_fp16() def forward(self, x): """Forward Function. Args: x (torch.Tensor): Input tensor with shape of (n, c, h, w). Returns: torch.Tensor: Output tensor with shape of (n, c, h', w'). """ x = self.encoder(x) if isinstance(x, dict): x = x['out'] x = self.dilation_neck(x) x = self.decoder(x) return x
[docs] def init_weights(self, pretrained=None): """Init weights for models. Args: pretrained (str, optional): Path for pretrained weights. If given None, pretrained weights will not be loaded. Defaults to None. """ if isinstance(pretrained, str): logger = get_root_logger() load_checkpoint(self, pretrained, strict=False, logger=logger) elif pretrained is None: # Here, we just use the default initialization in `ConvModule`. pass else: raise TypeError('pretrained must be a str or None')
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