From e0d46c6056e659549a6619f6a31016d8cce13efd Mon Sep 17 00:00:00 2001 From: LielinJiang Date: Thu, 22 Oct 2020 01:14:46 +0000 Subject: [PATCH] fix apps --- ppgan/apps/base_predictor.py | 6 +++--- ppgan/apps/deoldify_predictor.py | 2 +- ppgan/apps/realsr_predictor.py | 2 +- ppgan/models/generators/deoldify.py | 11 +++++------ ppgan/models/generators/hook.py | 2 +- ppgan/models/generators/mobile_resnet.py | 4 ++-- ppgan/models/generators/remaster.py | 4 ++-- 7 files changed, 15 insertions(+), 16 deletions(-) diff --git a/ppgan/apps/base_predictor.py b/ppgan/apps/base_predictor.py index 9fcdff1..d92573b 100644 --- a/ppgan/apps/base_predictor.py +++ b/ppgan/apps/base_predictor.py @@ -62,13 +62,13 @@ class BasePredictor(object): return out - def is_video(self, input): + def is_image(self, input): try: img = Image.open(input) _ = img.size - return False - except: return True + except: + return False def run(self): raise NotImplementedError diff --git a/ppgan/apps/deoldify_predictor.py b/ppgan/apps/deoldify_predictor.py index d6d3371..df54c9c 100644 --- a/ppgan/apps/deoldify_predictor.py +++ b/ppgan/apps/deoldify_predictor.py @@ -128,7 +128,7 @@ class DeOldifyPredictor(BasePredictor): return frame_pattern_combined, vid_out_path def run(self, input): - if self.is_video(input): + if not self.is_image(input): return self.run_video(input) else: pred_img = self.run_image(input) diff --git a/ppgan/apps/realsr_predictor.py b/ppgan/apps/realsr_predictor.py index bbddbbf..e7bd32b 100644 --- a/ppgan/apps/realsr_predictor.py +++ b/ppgan/apps/realsr_predictor.py @@ -101,7 +101,7 @@ class RealSRPredictor(BasePredictor): if not os.path.exists(self.output): os.makedirs(self.output) - if self.is_video(input): + if not self.is_image(input): return self.run_video(input) else: pred_img = self.run_image(input) diff --git a/ppgan/models/generators/deoldify.py b/ppgan/models/generators/deoldify.py index f5699ea..ee4b950 100644 --- a/ppgan/models/generators/deoldify.py +++ b/ppgan/models/generators/deoldify.py @@ -2,10 +2,9 @@ import numpy as np import paddle import paddle.nn as nn import paddle.nn.functional as F +from paddle.vision.models import resnet101 from .hook import hook_outputs, model_sizes, dummy_eval -from ..backbones import resnet34, resnet101 -# from paddle.vision.models import resnet101 from ...modules.nn import Spectralnorm @@ -273,7 +272,7 @@ class PixelShuffle_ICNR(nn.Layer): self.shuf = PixelShuffle(scale) - self.pad = ReplicationPad2d((1, 0, 1, 0)) + self.pad = ReplicationPad2d([1, 0, 1, 0]) self.blur = nn.Pool2D(2, pool_stride=1, pool_type='avg') self.relu = relu(True, leaky=leaky) @@ -339,7 +338,7 @@ class CustomPixelShuffle_ICNR(nn.Layer): self.shuf = PixelShuffle(scale) - self.pad = ReplicationPad2d((1, 0, 1, 0)) + self.pad = ReplicationPad2d([1, 0, 1, 0]) self.blur = nn.Pool2D(2, pool_stride=1, pool_type='avg') self.relu = nn.LeakyReLU( leaky) if leaky is not None else nn.ReLU() #relu(True, leaky=leaky) @@ -410,7 +409,7 @@ class ReplicationPad2d(nn.Layer): self.size = size def forward(self, x): - return F.pad2d(x, self.size, mode="edge") + return F.pad(x, self.size, mode="replicate") def conv1d(ni: int, @@ -420,7 +419,7 @@ def conv1d(ni: int, padding: int = 0, bias: bool = False): "Create and initialize a `nn.Conv1d` layer with spectral normalization." - conv = nn.Conv1d(ni, no, ks, stride=stride, padding=padding, bias_attr=bias) + conv = nn.Conv1D(ni, no, ks, stride=stride, padding=padding, bias_attr=bias) return Spectralnorm(conv) diff --git a/ppgan/models/generators/hook.py b/ppgan/models/generators/hook.py index a9ad7d4..0a7fe37 100644 --- a/ppgan/models/generators/hook.py +++ b/ppgan/models/generators/hook.py @@ -77,7 +77,7 @@ class Hooks(): def _hook_inner(m, i, o): return o if isinstance( - o, paddle.framework.Variable) else o if is_listy(o) else list(o) + o, paddle.fluid.framework.Variable) else o if is_listy(o) else list(o) def hook_output(module, detach=True, grad=False): diff --git a/ppgan/models/generators/mobile_resnet.py b/ppgan/models/generators/mobile_resnet.py index eb617f0..6b178ab 100644 --- a/ppgan/models/generators/mobile_resnet.py +++ b/ppgan/models/generators/mobile_resnet.py @@ -109,9 +109,9 @@ class MobileResnetBlock(nn.Layer): p = 0 if self.padding_type == 'reflect': - self.conv_block.extend([nn.ReflectionPad2d([1, 1, 1, 1])]) + self.conv_block.extend([nn.Pad2D([1, 1, 1, 1], mode='reflect')]) elif self.padding_type == 'replicate': - self.conv_block.extend([nn.ReplicationPad2d([1, 1, 1, 1])]) + self.conv_block.extend([nn.Pad2D([1, 1, 1, 1], mode='replicate')]) elif self.padding_type == 'zero': p = 1 else: diff --git a/ppgan/models/generators/remaster.py b/ppgan/models/generators/remaster.py index 42abb39..f81f1be 100644 --- a/ppgan/models/generators/remaster.py +++ b/ppgan/models/generators/remaster.py @@ -128,7 +128,7 @@ class NetworkR(nn.Layer): super(NetworkR, self).__init__() self.layers = nn.Sequential( - nn.ReplicationPad3d((1, 1, 1, 1, 1, 1)), + nn.Pad3D((1, 1, 1, 1, 1, 1), mode='replicate'), TempConv(1, 64, kernel_size=(3, 3, 3), @@ -165,7 +165,7 @@ class NetworkC(nn.Layer): super(NetworkC, self).__init__() self.down1 = nn.Sequential( - nn.ReplicationPad3d((1, 1, 1, 1, 0, 0)), + nn.Pad3D((1, 1, 1, 1, 0, 0), mode='replicate'), TempConv(1, 64, stride=(1, 2, 2), padding=(0, 0, 0)), TempConv(64, 128), TempConv(128, 128), TempConv(128, 256, stride=(1, 2, 2)), TempConv(256, 256), -- GitLab