revise conflict

demo/colorization/predict.py

@@ -2,9 +2,8 @@ import paddle
 import paddlehub as hub
 import paddle.nn as nn
 
-
 if __name__ == '__main__':
     paddle.disable_static()
-    model = hub.Module(directory='user_guided_colorization')
+    model = hub.Module(name='user_guided_colorization')
     model.eval()
-    result = model.predict(images='sea.jpg')
+    result = model.predict(images='house.png')
\ No newline at end of file

demo/colorization/train.py

@@ -3,18 +3,24 @@ import paddlehub as hub
 import paddle.nn as nn
 from paddlehub.finetune.trainer import Trainer
 
-from paddlehub.datasets.colorizedataset import  Colorizedataset
-from paddlehub.process.transforms import Compose, Resize, RandomPaddingCrop, ConvertColorSpace,  ColorizePreprocess
-
+from paddlehub.datasets.colorizedataset import Colorizedataset
+from paddlehub.process.transforms import Compose, Resize, RandomPaddingCrop, ConvertColorSpace, ColorizePreprocess
 
 if __name__ == '__main__':
     is_train = True
     paddle.disable_static()
-    model = hub.Module(directory='user_guided_colorization')
-    transform = Compose([Resize((256,256),interp="RANDOM"),RandomPaddingCrop(crop_size=176), ConvertColorSpace(mode='RGB2LAB'), ColorizePreprocess(ab_thresh=0, p=1)], stay_rgb=True)
-    color_set = Colorizedataset(transform=transform, mode=is_train)
+    model = hub.Module(name='user_guided_colorization')
+    transform = Compose([
+        Resize((256, 256), interp='NEAREST'),
+        RandomPaddingCrop(crop_size=176),
+        ConvertColorSpace(mode='RGB2LAB'),
+        ColorizePreprocess(ab_thresh=0, is_train=is_train),
+    ],
+                        stay_rgb=True,
+                        is_permute=False)
+    color_set = Colorizedataset(transform=transform, mode='train')
     if is_train:
         model.train()
         optimizer = paddle.optimizer.Adam(learning_rate=0.0001, parameters=model.parameters())
         trainer = Trainer(model, optimizer, checkpoint_dir='test_ckpt_img_cls')
-        trainer.train(color_set, epochs=3, batch_size=1, eval_dataset=color_set, save_interval=1)
+        trainer.train(color_set, epochs=101, batch_size=5, eval_dataset=color_set, log_interval=10, save_interval=10)

demo/image_classification/train.py

-import paddle.fluid as fluid
+import paddle
 import paddlehub as hub
-from paddle.fluid.dygraph.parallel import ParallelEnv
+from paddle.distributed import ParallelEnv
 from paddlehub.finetune.trainer import Trainer
 from paddlehub.datasets.flowers import Flowers
 from paddlehub.process.transforms import Compose, Resize, Normalize
 from paddlehub.module.cv_module import ImageClassifierModule
 
 if __name__ == '__main__':
-    with fluid.dygraph.guard(fluid.CUDAPlace(ParallelEnv().dev_id)):
-        transforms = Compose([Resize((224, 224)), Normalize()])
-        flowers = Flowers(transforms)
-        flowers_validate = Flowers(transforms, mode='val')
+    paddle.disable_static(paddle.CUDAPlace(ParallelEnv().dev_id))
+    transforms = Compose([Resize((224, 224)), Normalize()])
+    flowers = Flowers(transforms)
+    flowers_validate = Flowers(transforms, mode='val')
 
-        model = hub.Module(directory='mobilenet_v2_animals', class_dim=flowers.num_classes)
-        # model = hub.Module(name='mobilenet_v2_animals', class_dim=flowers.num_classes)
+    model = hub.Module(name='mobilenet_v2_imagenet', class_dim=flowers.num_classes)
 
-        optimizer = fluid.optimizer.AdamOptimizer(learning_rate=0.001, parameter_list=model.parameters())
-        trainer = Trainer(model, optimizer, checkpoint_dir='test_ckpt_img_cls')
+    optimizer = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
+    trainer = Trainer(model, optimizer, checkpoint_dir='test_ckpt_img_cls')
 
-        trainer.train(flowers, epochs=100, batch_size=32, eval_dataset=flowers_validate, save_interval=1)
+    trainer.train(flowers, epochs=100, batch_size=32, eval_dataset=flowers_validate, save_interval=1)

demo/style_transfer/predict.py

+import paddle
+import paddlehub as hub
+
+if __name__ == '__main__':
+    place = paddle.CUDAPlace(0)
+    paddle.disable_static()
+    model = hub.Module(name='msgnet')
+    model.eval()
+    result = model.predict("venice-boat.jpg", "candy.jpg")

demo/style_transfer/train.py

+import paddle
+import paddlehub as hub
+
+from paddlehub.finetune.trainer import Trainer
+from paddlehub.datasets.styletransfer import StyleTransferData
+from paddlehub.process.transforms import Compose, Resize, CenterCrop, SetType
+
+if __name__ == "__main__":
+    place = paddle.CUDAPlace(0)
+    paddle.disable_static()
+    model = hub.Module(name='msgnet')
+    transform = Compose([Resize((256, 256), interp='LINEAR'), CenterCrop(crop_size=256)], SetType(datatype='float32'))
+    styledata = StyleTransferData(transform)
+    model.train()
+    optimizer = paddle.optimizer.Adam(learning_rate=0.0001, parameters=model.parameters())
+    trainer = Trainer(model, optimizer, checkpoint_dir='test_ckpt_img_cls')
+    trainer.train(styledata, epochs=5, batch_size=1, eval_dataset=styledata, log_interval=1, save_interval=1)

hub_module/modules/image/colorization/user_guided_colorization/module.py

@@ -12,12 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
+
 import paddle
 import numpy
 import paddle.nn as nn
 from paddle.nn import Conv2d, ConvTranspose2d
 from paddlehub.module.module import moduleinfo
-
 from paddlehub.process.transforms import Compose, Resize, RandomPaddingCrop, ConvertColorSpace, ColorizePreprocess
 from paddlehub.module.cv_module import ImageColorizeModule
 
@@ -178,24 +179,31 @@ class UserGuidedColorization(nn.Layer):
         if load_checkpoint is not None:
             model_dict = paddle.load(load_checkpoint)[0]
             self.set_dict(model_dict)
-            print("load pretrained model success")
+            print("load custom checkpoint success")
+        else:
+            checkpoint = os.path.join(self.directory, 'user_guided.pdparams')
+            model_dict = paddle.load(checkpoint)[0]
+            self.set_dict(model_dict)
+            print("load pretrained checkpoint success")
 
     def transforms(self, images: str, is_train: bool = True) -> callable:
         if is_train:
             transform = Compose([
-                Resize((256, 256), interp="RANDOM"),
+                Resize((256, 256), interp='NEAREST'),
                 RandomPaddingCrop(crop_size=176),
                 ConvertColorSpace(mode='RGB2LAB'),
                 ColorizePreprocess(ab_thresh=0, is_train=is_train)
             ],
-                                stay_rgb=True)
+                                stay_rgb=True,
+                                is_permute=False)
         else:
             transform = Compose([
-                Resize((256, 256), interp="RANDOM"),
+                Resize((256, 256), interp='NEAREST'),
                 ConvertColorSpace(mode='RGB2LAB'),
                 ColorizePreprocess(ab_thresh=0, is_train=is_train)
             ],
-                                stay_rgb=True)
+                                stay_rgb=True,
+                                is_permute=False)
         return transform(images)
 
     def forward(self,

hub_module/modules/image/style_transfer/msgnet/module.py

+import os
+
+import paddle
+import paddle.nn as nn
+import numpy as np
+import paddle.nn.functional as F
+
+from paddlehub.module.module import moduleinfo
+from paddlehub.process.transforms import Compose, Resize, CenterCrop, SetType
+from paddlehub.module.cv_module import StyleTransferModule
+
+
+class GramMatrix(nn.Layer):
+    """Calculate gram matrix"""
+    def forward(self, y):
+        (b, ch, h, w) = y.size()
+        features = y.reshape((b, ch, w * h))
+        features_t = features.transpose((0, 2, 1))
+        gram = features.bmm(features_t) / (ch * h * w)
+        return gram
+
+
+class ConvLayer(nn.Layer):
+    """Basic conv layer with reflection padding layer"""
+    def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride: int):
+        super(ConvLayer, self).__init__()
+        pad = int(np.floor(kernel_size / 2))
+        self.reflection_pad = nn.ReflectionPad2d([pad, pad, pad, pad])
+        self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride)
+
+    def forward(self, x: paddle.Tensor):
+        out = self.reflection_pad(x)
+        out = self.conv2d(out)
+        return out
+
+
+class UpsampleConvLayer(nn.Layer):
+    """
+    Upsamples the input and then does a convolution. This method gives better results compared to ConvTranspose2d.
+    ref: http://distill.pub/2016/deconv-checkerboard/
+
+    Args:
+       in_channels(int): Number of input channels.
+       out_channels(int): Number of output channels.
+       kernel_size(int): Number of kernel size.
+       stride(int): Number of stride.
+       upsample(int): Scale factor for upsample layer, default is None.
+
+    Return:
+        img(paddle.Tensor): UpsampleConvLayer output.
+    """
+    def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride: int, upsample=None):
+        super(UpsampleConvLayer, self).__init__()
+        self.upsample = upsample
+        if upsample:
+            self.upsample_layer = nn.UpSample(scale_factor=upsample)
+        self.pad = int(np.floor(kernel_size / 2))
+        if self.pad != 0:
+            self.reflection_pad = nn.ReflectionPad2d([self.pad, self.pad, self.pad, self.pad])
+        self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride)
+
+    def forward(self, x):
+        if self.upsample:
+            x = self.upsample_layer(x)
+        if self.pad != 0:
+            x = self.reflection_pad(x)
+        out = self.conv2d(x)
+        return out
+
+
+class Bottleneck(nn.Layer):
+    """ Pre-activation residual block
+        Identity Mapping in Deep Residual Networks
+        ref https://arxiv.org/abs/1603.05027
+
+    Args:
+       inplanes(int): Number of input channels.
+       planes(int): Number of output channels.
+       stride(int): Number of stride.
+       downsample(int): Scale factor for downsample layer, default is None.
+       norm_layer(nn.Layer): Batch norm layer, default is nn.BatchNorm2d.
+
+    Return:
+        img(paddle.Tensor): Bottleneck output.
+    """
+    def __init__(self,
+                 inplanes: int,
+                 planes: int,
+                 stride: int = 1,
+                 downsample: int = None,
+                 norm_layer: nn.Layer = nn.BatchNorm2d):
+        super(Bottleneck, self).__init__()
+        self.expansion = 4
+        self.downsample = downsample
+        if self.downsample is not None:
+            self.residual_layer = nn.Conv2d(inplanes, planes * self.expansion, kernel_size=1, stride=stride)
+
+        conv_block = (norm_layer(inplanes), nn.ReLU(), nn.Conv2d(inplanes, planes, kernel_size=1, stride=1),
+                      norm_layer(planes), nn.ReLU(), ConvLayer(planes, planes, kernel_size=3, stride=stride),
+                      norm_layer(planes), nn.ReLU(), nn.Conv2d(planes, planes * self.expansion, kernel_size=1,
+                                                               stride=1))
+
+        self.conv_block = nn.Sequential(*conv_block)
+
+    def forward(self, x: paddle.Tensor):
+        if self.downsample is not None:
+            residual = self.residual_layer(x)
+        else:
+            residual = x
+        m = self.conv_block(x)
+        return residual + self.conv_block(x)
+
+
+class UpBottleneck(nn.Layer):
+    """ Up-sample residual block (from MSG-Net paper)
+    Enables passing identity all the way through the generator
+    ref https://arxiv.org/abs/1703.06953
+
+    Args:
+       inplanes(int): Number of input channels.
+       planes(int): Number of output channels.
+       stride(int): Number of stride, default is 2.
+       norm_layer(nn.Layer): Batch norm layer, default is nn.BatchNorm2d.
+
+    Return:
+        img(paddle.Tensor): UpBottleneck output.
+    """
+    def __init__(self, inplanes: int, planes: int, stride: int = 2, norm_layer: nn.Layer = nn.BatchNorm2d):
+        super(UpBottleneck, self).__init__()
+        self.expansion = 4
+        self.residual_layer = UpsampleConvLayer(inplanes,
+                                                planes * self.expansion,
+                                                kernel_size=1,
+                                                stride=1,
+                                                upsample=stride)
+        conv_block = []
+        conv_block += [norm_layer(inplanes), nn.ReLU(), nn.Conv2d(inplanes, planes, kernel_size=1, stride=1)]
+
+        conv_block += [
+            norm_layer(planes),
+            nn.ReLU(),
+            UpsampleConvLayer(planes, planes, kernel_size=3, stride=1, upsample=stride)
+        ]
+
+        conv_block += [
+            norm_layer(planes),
+            nn.ReLU(),
+            nn.Conv2d(planes, planes * self.expansion, kernel_size=1, stride=1)
+        ]
+
+        self.conv_block = nn.Sequential(*conv_block)
+
+    def forward(self, x: paddle.Tensor):
+        return self.residual_layer(x) + self.conv_block(x)
+
+
+class Inspiration(nn.Layer):
+    """ Inspiration Layer (from MSG-Net paper)
+    tuning the featuremap with target Gram Matrix
+    ref https://arxiv.org/abs/1703.06953
+
+    Args:
+       C(int): Number of input channels.
+       B(int):  B is equal to 1 or input mini_batch, default is 1.
+
+    Return:
+        img(paddle.Tensor): UpBottleneck output.
+    """
+    def __init__(self, C: int, B: int = 1):
+        super(Inspiration, self).__init__()
+
+        self.weight = self.weight = paddle.create_parameter(shape=[1, C, C], dtype='float32')
+        # non-parameter buffer
+        self.G = paddle.to_tensor(np.random.rand(B, C, C))
+        self.C = C
+
+    def setTarget(self, target: paddle.Tensor):
+        self.G = target
+
+    def forward(self, X: paddle.Tensor):
+        # input X is a 3D feature map
+        self.P = paddle.bmm(self.weight.expand_as(self.G), self.G)
+
+        x = paddle.bmm(
+            self.P.transpose((0, 2, 1)).expand((X.shape[0], self.C, self.C)), X.reshape(
+                (X.shape[0], X.shape[1], -1))).reshape(X.shape)
+        return x
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(' \
+               + 'N x ' + str(self.C) + ')'
+
+
+class Vgg16(nn.Layer):
+    """ First four layers from Vgg16."""
+    def __init__(self):
+        super(Vgg16, self).__init__()
+        self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
+        self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
+
+        self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
+        self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
+
+        self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
+        self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
+        self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
+
+        self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
+        self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+
+        self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+
+        checkpoint = os.path.join(self.directory, 'vgg16.pdparams')
+        if not os.path.exists(checkpoint):
+            os.system('wget https://bj.bcebos.com/paddlehub/model/image/image_editing/vgg_paddle.pdparams -O ' +
+                      checkpoint)
+        model_dict = paddle.load(checkpoint)[0]
+        self.set_dict(model_dict)
+        print("load pretrained vgg16 checkpoint success")
+
+    def forward(self, X):
+        h = F.relu(self.conv1_1(X))
+        h = F.relu(self.conv1_2(h))
+        relu1_2 = h
+        h = F.max_pool2d(h, kernel_size=2, stride=2)
+
+        h = F.relu(self.conv2_1(h))
+        h = F.relu(self.conv2_2(h))
+        relu2_2 = h
+        h = F.max_pool2d(h, kernel_size=2, stride=2)
+
+        h = F.relu(self.conv3_1(h))
+        h = F.relu(self.conv3_2(h))
+        h = F.relu(self.conv3_3(h))
+        relu3_3 = h
+        h = F.max_pool2d(h, kernel_size=2, stride=2)
+
+        h = F.relu(self.conv4_1(h))
+        h = F.relu(self.conv4_2(h))
+        h = F.relu(self.conv4_3(h))
+        relu4_3 = h
+
+        return [relu1_2, relu2_2, relu3_3, relu4_3]
+
+
+@moduleinfo(
+    name="msgnet",
+    type="CV/image_editing",
+    author="paddlepaddle",
+    author_email="",
+    summary="Msgnet is a image colorization style transfer model, this module is trained with COCO2014 dataset.",
+    version="1.0.0",
+    meta=StyleTransferModule)
+class MSGNet(nn.Layer):
+    """ MSGNet (from MSG-Net paper)
+    Enables passing identity all the way through the generator
+    ref https://arxiv.org/abs/1703.06953
+
+    Args:
+       input_nc(int): Number of input channels, default is 3.
+       output_nc(int): Number of output channels, default is 3.
+       ngf(int): Number of input channel for middle layer, default is 128.
+       n_blocks(int): Block number, default is 6.
+       norm_layer(nn.Layer): Batch norm layer, default is nn.InstanceNorm2d.
+       load_checkpoint(str): Pretrained checkpoint path, default is None.
+
+    Return:
+        img(paddle.Tensor): MSGNet output.
+    """
+    def __init__(self,
+                 input_nc=3,
+                 output_nc=3,
+                 ngf=128,
+                 n_blocks=6,
+                 norm_layer=nn.InstanceNorm2d,
+                 load_checkpoint=None):
+        super(MSGNet, self).__init__()
+        self.gram = GramMatrix()
+        block = Bottleneck
+        upblock = UpBottleneck
+        expansion = 4
+
+        model1 = [
+            ConvLayer(input_nc, 64, kernel_size=7, stride=1),
+            norm_layer(64),
+            nn.ReLU(),
+            block(64, 32, 2, 1, norm_layer),
+            block(32 * expansion, ngf, 2, 1, norm_layer)
+        ]
+
+        self.model1 = nn.Sequential(*tuple(model1))
+
+        model = []
+        model += model1
+
+        self.ins = Inspiration(ngf * expansion)
+        model.append(self.ins)
+        for i in range(n_blocks):
+            model += [block(ngf * expansion, ngf, 1, None, norm_layer)]
+
+        model += [
+            upblock(ngf * expansion, 32, 2, norm_layer),
+            upblock(32 * expansion, 16, 2, norm_layer),
+            norm_layer(16 * expansion),
+            nn.ReLU(),
+            ConvLayer(16 * expansion, output_nc, kernel_size=7, stride=1)
+        ]
+        model = tuple(model)
+        self.model = nn.Sequential(*model)
+
+        if load_checkpoint is not None:
+            model_dict = paddle.load(load_checkpoint)[0]
+            self.set_dict(model_dict)
+            print("load custom checkpoint success")
+
+        else:
+            checkpoint = os.path.join(self.directory, 'style_paddle.pdparams')
+            if not os.path.exists(checkpoint):
+                os.system('wget https://bj.bcebos.com/paddlehub/model/image/image_editing/style_paddle.pdparams -O ' +
+                          checkpoint)
+            model_dict = paddle.load(checkpoint)[0]
+            model_dict_clone = model_dict.copy()
+            for key, value in model_dict_clone.items():
+                if key.endswith(("scale")):
+                    name = key.rsplit('.', 1)[0] + '.bias'
+                    model_dict[name] = paddle.zeros(shape=model_dict[name].shape, dtype='float32')
+                    model_dict[key] = paddle.ones(shape=model_dict[key].shape, dtype='float32')
+            self.set_dict(model_dict)
+            print("load pretrained checkpoint success")
+
+        self._vgg = None
+
+    def transform(self, path: str):
+        transform = Compose([Resize(
+            (256, 256), interp='LINEAR'), CenterCrop(crop_size=256)], SetType(datatype='float32'))
+        return transform(path)
+
+    def setTarget(self, Xs: paddle.Tensor):
+        """Calculate feature gram matrix"""
+        F = self.model1(Xs)
+        G = self.gram(F)
+        self.ins.setTarget(G)
+
+    def getFeature(self, input: paddle.Tensor):
+        if not self._vgg:
+            self._vgg = Vgg16()
+        return self._vgg(input)
+
+    def forward(self, input: paddle.Tensor):
+        return self.model(input)

paddlehub/commands/install.py

@@ -36,5 +36,8 @@ class InstallCommand:
             elif os.path.exists(_arg) and xarfile.is_xarfile(_arg):
                 manager.install(archive=_arg)
             else:
-                manager.install(name=_arg)
+                _arg = _arg.split('==')
+                name = _arg[0]
+                version = None if len(_arg) == 1 else _arg[1]
+                manager.install(name=name, version=version)
         return True

paddlehub/commands/run.py

+# coding:utf-8
+# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import ast
+import os
+from typing import Any, List
+
+from paddlehub.compat.module.module_v1 import ModuleV1
+from paddlehub.commands import register
+from paddlehub.module.manager import LocalModuleManager
+from paddlehub.module.module import Module, InvalidHubModule
+
+
+@register(name='hub.run', description='Run the specific module.')
+class RunCommand:
+    def execute(self, argv: List) -> bool:
+        if not argv:
+            print('ERROR: You must give one module to run.')
+            return False
+        module_name = argv[0]
+
+        if os.path.exists(module_name) and os.path.isdir(module_name):
+            try:
+                module = Module.load(module_name)
+            except InvalidHubModule:
+                print('{} is not a valid HubModule'.format(module_name))
+                return False
+            except:
+                print('Some exception occurred while loading the {}'.format(module_name))
+                return False
+        else:
+            module = Module(name=module_name)
+
+        if not module.is_runnable:
+            print('ERROR! Module {} is not executable.'.format(module_name))
+            return False
+
+        if isinstance(module, ModuleV1):
+            result = self.run_module_v1(module, argv[1:])
+        else:
+            result = module._run_func(argv[1:])
+
+        print(result)
+        return True
+
+    def run_module_v1(self, module, argv: List) -> Any:
+        parser = argparse.ArgumentParser(prog='hub run {}'.format(module.name), add_help=False)
+
+        arg_input_group = parser.add_argument_group(title='Input options', description='Data feed into the module.')
+        arg_config_group = parser.add_argument_group(
+            title='Config options', description='Run configuration for controlling module behavior, optional.')
+
+        arg_config_group.add_argument(
+            '--use_gpu', type=ast.literal_eval, default=False, help='whether use GPU for prediction')
+        arg_config_group.add_argument('--batch_size', type=int, default=1, help='batch size for prediction')
+
+        module_type = module.type.lower()
+        if module_type.startswith('cv'):
+            arg_input_group.add_argument(
+                '--input_path', type=str, default=None, help='path of image/video to predict', required=True)
+        else:
+            arg_input_group.add_argument('--input_text', type=str, default=None, help='text to predict', required=True)
+
+        args = parser.parse_args(argv)
+
+        except_data_format = module.processor.data_format(module.default_signature)
+        key = list(except_data_format.keys())[0]
+        input_data = {key: [args.input_path] if module_type.startswith('cv') else [args.input_text]}
+
+        return module(
+            sign_name=module.default_signature, data=input_data, use_gpu=args.use_gpu, batch_size=args.batch_size)

paddlehub/commands/search.py

+#coding:utf-8
+# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import os
+from typing import List
+
+from paddlehub.commands import register
+from paddlehub.module.manager import LocalModuleManager
+from paddlehub.server.server import module_server
+from paddlehub.utils import log, platform
+
+
+@register(name='hub.search', description='Search PaddleHub pretrained model through model keywords.')
+class SearchCommand:
+    def execute(self, argv: List) -> bool:
+        argv = '.*' if not argv else argv[0]
+
+        widths = [20, 8, 30] if platform.is_windows() else [30, 8, 40]
+        table = log.Table(widths=widths)
+        table.append(*['ModuleName', 'Version', 'Summary'], aligns=['^', '^', '^'], colors=["blue", "blue", "blue"])
+
+        results = module_server.search_module(name=argv)
+        for result in results:
+            table.append(result['name'], result['version'], result['summary'])
+
+        print(table)
+        return True

paddlehub/commands/show.py

@@ -47,7 +47,7 @@ class ShowCommand:
 
         widths = [15, 40] if platform.is_windows else [15, 50]
         aligns = ['^', '<']
-        colors = ['yellow', '']
+        colors = ['cyan', '']
         table = log.Table(widths=widths, colors=colors, aligns=aligns)
 
         table.append('ModuleName', module.name)

paddlehub/compat/module/module_v1.py

@@ -37,6 +37,7 @@ class ModuleV1(object):
         self.desc = module_v1_utils.convert_module_desc(desc_file)
         self.helper = self
         self.signatures = self.desc.signatures
+        self.default_signature = self.desc.default_signature
 
         self.directory = directory
         self._load_model()
@@ -185,6 +186,7 @@ class ModuleV1(object):
         cls.type = module_info.type
         cls.summary = module_info.summary
         cls.version = utils.Version(module_info.version)
+        cls.directory = directory
         return cls
 
     @classmethod
@@ -195,3 +197,7 @@ class ModuleV1(object):
 
     def assets_path(self):
         return os.path.join(self.directory, 'assets')
+
+    @property
+    def is_runnable(self):
+        return self.default_signature != None

paddlehub/datasets/colorizedataset.py

@@ -22,6 +22,7 @@ from paddlehub.process.functional import get_img_file
 from paddlehub.env import DATA_HOME
 from typing import Callable
 
+
 class Colorizedataset(paddle.io.Dataset):
     """
     Dataset for colorization.
@@ -34,14 +35,12 @@ class Colorizedataset(paddle.io.Dataset):
     def __init__(self, transform: Callable, mode: str = 'train'):
         self.mode = mode
         self.transform = transform
-        
+
         if self.mode == 'train':
             self.file = 'train'
         elif self.mode == 'test':
             self.file = 'test'
-        else:
-            self.file = 'validation'
-            
+
         self.file = os.path.join(DATA_HOME, 'canvas', self.file)
         self.data = get_img_file(self.file)
 
@@ -51,4 +50,4 @@ class Colorizedataset(paddle.io.Dataset):
         return im['A'], im['hint_B'], im['mask_B'], im['B'], im['real_B_enc']
 
     def __len__(self):
-        return len(self.data)
+        return len(self.data)
\ No newline at end of file

paddlehub/datasets/styletransfer.py

+# coding:utf-8
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from typing import Callable
+
+import paddle
+from paddlehub.process.functional import get_img_file
+from paddlehub.env import DATA_HOME
+
+
+class StyleTransferData(paddle.io.Dataset):
+    """
+    Dataset for Style transfer.
+
+    Args:
+       transform(callmethod) : The method of preprocess images.
+       mode(str): The mode for preparing dataset.
+
+    Returns:
+        DataSet: An iterable object for data iterating
+    """
+    def __init__(self, transform: Callable, mode: str = 'train'):
+        self.mode = mode
+        self.transform = transform
+
+        if self.mode == 'train':
+            self.file = 'train'
+        elif self.mode == 'test':
+            self.file = 'test'
+        self.file = os.path.join(DATA_HOME, 'minicoco', self.file)
+        self.style_file = os.path.join(DATA_HOME, 'minicoco', '21styles')
+        self.data = get_img_file(self.file)
+        self.style = get_img_file(self.style_file)
+
+    def __getitem__(self, idx: int):
+
+        img_path = self.data[idx]
+        im = self.transform(img_path)
+        style_idx = idx % len(self.style)
+        style_path = self.style[style_idx]
+        style = self.transform(style_path)
+        return im, style
+
+    def __len__(self):
+        return len(self.data)

paddlehub/module/cv_module.py

@@ -18,6 +18,7 @@ import os
 from typing import List
 from collections import OrderedDict
 
+import cv2
 import numpy as np
 import paddle
 import paddle.nn as nn
@@ -137,6 +138,7 @@ class ImageColorizeModule(RunModule, ImageServing):
         psnrs = []
         lab2rgb = T.ConvertColorSpace(mode='LAB2RGB')
         process = T.ColorPostprocess()
+
         for i in range(batch[0].numpy().shape[0]):
             real = lab2rgb(np.concatenate((batch[0].numpy(), batch[3].numpy()), axis=1))[i]
             visual_ret['real'] = process(real)
@@ -146,6 +148,7 @@ class ImageColorizeModule(RunModule, ImageServing):
             psnr_value = 20 * np.log10(255. / np.sqrt(mse))
             psnrs.append(psnr_value)
         psnr = paddle.to_variable(np.array(psnrs))
+
         return {'loss': loss, 'metrics': {'psnr': psnr}}
 
     def predict(self, images: str, visualization: bool = True, save_path: str = 'result'):
@@ -309,3 +312,87 @@ class Yolov3Module(RunModule, ImageServing):
             Func.draw_boxes_on_image(imgpath, boxes, scores, labels, label_names, 0.5)
 
         return boxes, scores, labels
+
+
+class StyleTransferModule(RunModule, ImageServing):
+    def training_step(self, batch: int, batch_idx: int) -> dict:
+        '''
+        One step for training, which should be called as forward computation.
+
+        Args:
+            batch(list[paddle.Tensor]): The one batch data, which contains images and labels.
+            batch_idx(int): The index of batch.
+
+        Returns:
+            results(dict) : The model outputs, such as loss and metrics.
+        '''
+        return self.validation_step(batch, batch_idx)
+
+    def validation_step(self, batch: int, batch_idx: int) -> dict:
+        '''
+        One step for validation, which should be called as forward computation.
+
+        Args:
+            batch(list[paddle.Tensor]): The one batch data, which contains images and labels.
+            batch_idx(int): The index of batch.
+
+        Returns:
+            results(dict) : The model outputs, such as metrics.
+        '''
+        mse_loss = nn.MSELoss()
+        N, C, H, W = batch[0].shape
+        batch[1] = batch[1][0].unsqueeze(0)
+        self.setTarget(batch[1])
+
+        y = self(batch[0])
+        xc = paddle.to_tensor(batch[0].numpy().copy())
+        y = Func.subtract_imagenet_mean_batch(y)
+        xc = Func.subtract_imagenet_mean_batch(xc)
+        features_y = self.getFeature(y)
+        features_xc = self.getFeature(xc)
+        f_xc_c = paddle.to_tensor(features_xc[1].numpy(), stop_gradient=True)
+        content_loss = mse_loss(features_y[1], f_xc_c)
+
+        batch[1] = Func.subtract_imagenet_mean_batch(batch[1])
+        features_style = self.getFeature(batch[1])
+        gram_style = [Func.gram_matrix(y) for y in features_style]
+        style_loss = 0.
+        for m in range(len(features_y)):
+            gram_y = Func.gram_matrix(features_y[m])
+            gram_s = paddle.to_tensor(np.tile(gram_style[m].numpy(), (N, 1, 1, 1)))
+            style_loss += mse_loss(gram_y, gram_s[:N, :, :])
+
+        loss = content_loss + style_loss
+
+        return {'loss': loss, 'metrics': {'content gap': content_loss, 'style gap': style_loss}}
+
+    def predict(self, origin_path: str, style_path: str, visualization: bool = True, save_path: str = 'result'):
+        '''
+        Colorize images
+
+        Args:
+            origin_path(str): Content image path .
+            style_path(str): Style image path.
+            visualization(bool): Whether to save colorized images.
+            save_path(str) : Path to save colorized images.
+
+        Returns:
+            output(np.ndarray) : The style transformed images with bgr mode.
+        '''
+        content = paddle.to_tensor(self.transform(origin_path))
+        style = paddle.to_tensor(self.transform(style_path))
+        content = content.unsqueeze(0)
+        style = style.unsqueeze(0)
+
+        self.setTarget(style)
+        output = self(content)
+        output = paddle.clip(output[0].transpose((1, 2, 0)), 0, 255).numpy()
+
+        if visualization:
+            output = output.astype(np.uint8)
+            style_name = "style_" + str(time.time()) + ".png"
+            if not os.path.exists(save_path):
+                os.mkdir(save_path)
+            path = os.path.join(save_path, style_name)
+            cv2.imwrite(path, output)
+        return output

paddlehub/module/manager.py

@@ -58,7 +58,7 @@ class HubModuleNotFoundError(Exception):
                 hub_version = 'Any' if not info['hub_version'] else ', '.join(info['hub_version'])
                 table.append(self.name, _ver, paddle_version, hub_version, aligns=['^', '^', '^', '^'])
 
-            tips += ',  \n{}'.format(table)
+            tips += ':\n{}'.format(table)
         return tips
 
 
@@ -104,7 +104,7 @@ class EnvironmentMismatchError(Exception):
 
                 table.append(self.name, _ver, paddle_version, hub_version, aligns=['^', '^', '^', '^'])
 
-            tips += ',  \n{}'.format(table)
+            tips += ':\n{}'.format(table)
         return tips
 
 
@@ -238,28 +238,29 @@ class LocalModuleManager(object):
                 return self._local_modules[name]
 
         result = module_server.search_module(name=name, version=version, source=source)
-        if not result:
-            module_infos = module_server.get_module_info(name=name, source=source)
-            # The HubModule with the specified name cannot be found
-            if not module_infos:
-                raise HubModuleNotFoundError(name=name, version=version, source=source)
-
-            valid_infos = {}
-            if version:
-                for _ver, _info in module_infos.items():
-                    if utils.Version(_ver).match(version):
-                        valid_infos[_ver] = _info
-            else:
-                valid_infos = list(module_infos.keys())
-
-            # Cannot find a HubModule that meets the version
-            if valid_infos:
-                raise EnvironmentMismatchError(name=name, info=valid_infos, version=version)
-            raise HubModuleNotFoundError(name=name, info=module_infos, version=version, source=source)
-
-        if source or 'source' in result:
-            return self._install_from_source(result)
-        return self._install_from_url(result['url'])
+        for item in result:
+            if name.lower() == item['name'].lower() and utils.Version(item['version']).match(version):
+                if source or 'source' in item:
+                    return self._install_from_source(result)
+                return self._install_from_url(item['url'])
+
+        module_infos = module_server.get_module_info(name=name, source=source)
+        # The HubModule with the specified name cannot be found
+        if not module_infos:
+            raise HubModuleNotFoundError(name=name, version=version, source=source)
+
+        valid_infos = {}
+        if version:
+            for _ver, _info in module_infos.items():
+                if utils.Version(_ver).match(version):
+                    valid_infos[_ver] = _info
+        else:
+            valid_infos = module_infos.copy()
+
+        # Cannot find a HubModule that meets the version
+        if valid_infos:
+            raise EnvironmentMismatchError(name=name, info=valid_infos, version=version)
+        raise HubModuleNotFoundError(name=name, info=module_infos, version=version, source=source)
 
     def _install_from_source(self, source: str) -> HubModule:
         '''Install a HubModule from Git Repo'''

paddlehub/module/module.py

@@ -148,6 +148,10 @@ class Module(object):
             user_module = user_module_cls(directory=directory)
             user_module._initialize(**kwargs)
             return user_module
+
+        if user_module_cls == ModuleV1:
+            return user_module_cls(directory=directory, **kwargs)
+
         user_module_cls.directory = directory
         return user_module_cls(**kwargs)
 
@@ -166,6 +170,10 @@ class Module(object):
             user_module = user_module_cls(directory=directory)
             user_module._initialize(**kwargs)
             return user_module
+
+        if user_module_cls == ModuleV1:
+            return user_module_cls(directory=directory, **kwargs)
+
         user_module_cls.directory = directory
         return user_module_cls(**kwargs)
 

paddlehub/process/functional.py

@@ -119,7 +119,6 @@ def get_img_file(dir_name: str) -> list:
             if not is_image_file(filename):
                 continue
             img_path = os.path.join(parent, filename)
-            print(img_path)
             images.append(img_path)
     images.sort()
     return images
@@ -246,3 +245,22 @@ def get_label_infos(file_list: str):
     for category in categories:
         label_names.append(category['name'])
     return label_names
+
+
+def subtract_imagenet_mean_batch(batch: paddle.Tensor) -> paddle.Tensor:
+    """Subtract ImageNet mean pixel-wise from a BGR image."""
+    mean = np.zeros(shape=batch.shape, dtype='float32')
+    mean[:, 0, :, :] = 103.939
+    mean[:, 1, :, :] = 116.779
+    mean[:, 2, :, :] = 123.680
+    mean = paddle.to_tensor(mean)
+    return batch - mean
+
+
+def gram_matrix(data: paddle.Tensor) -> paddle.Tensor:
+    """Get gram matrix"""
+    b, ch, h, w = data.shape
+    features = data.reshape((b, ch, w * h))
+    features_t = features.transpose((0, 2, 1))
+    gram = features.bmm(features_t) / (ch * h * w)
+    return gram

paddlehub/process/transforms.py

@@ -26,15 +26,16 @@ from paddlehub.process.functional import *
 
 
 class Compose:
-    def __init__(self, transforms, to_rgb=True, stay_rgb=False):
+    def __init__(self, transforms, to_rgb=True, stay_rgb=False, is_permute=True):
         if not isinstance(transforms, list):
             raise TypeError('The transforms must be a list!')
         if len(transforms) < 1:
             raise ValueError('The length of transforms ' + \
-                            'must be equal or larger than 1!')
+                             'must be equal or larger than 1!')
         self.transforms = transforms
         self.to_rgb = to_rgb
         self.stay_rgb = stay_rgb
+        self.is_permute = is_permute
 
     def __call__(self, im):
         if isinstance(im, str):
@@ -49,6 +50,9 @@ class Compose:
             im = op(im)
 
         if not self.stay_rgb:
+            im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
+
+        if self.is_permute:
             im = permute(im)
 
         return im
@@ -570,17 +574,7 @@ class ColorizeHint:
         self.use_avg = use_avg
 
     def __call__(self, data: np.ndarray, hint: np.ndarray, mask: np.ndarray):
-        sample_Ps = [
-            1,
-            2,
-            3,
-            4,
-            5,
-            6,
-            7,
-            8,
-            9,
-        ]
+        sample_Ps = [1, 2, 3, 4, 5, 6, 7, 8, 9]
         self.data = data
         self.hint = hint
         self.mask = mask
@@ -591,7 +585,7 @@ class ColorizeHint:
             while cont_cond:
                 if self.num_points is None:  # draw from geometric
                     # embed()
-                    cont_cond = np.random.rand() < (1 - self.percent)
+                    cont_cond = np.random.rand() > (1 - self.percent)
                 else:  # add certain number of points
                     cont_cond = pp < self.num_points
                 if not cont_cond:  # skip out of loop if condition not met
@@ -659,7 +653,7 @@ class ColorizePreprocess:
     """
     def __init__(self,
                  ab_thresh: float = 0.,
-                 p: float = .125,
+                 p: float = 0.,
                  num_points: int = None,
                  samp: str = 'normal',
                  use_avg: bool = True,
@@ -733,3 +727,41 @@ class ColorPostprocess:
         img = np.clip(img, 0, 1) * 255
         img = img.astype(self.type)
         return img
+
+
+class CenterCrop:
+    """
+        Crop the middle part of the image to the specified size.
+
+        Args:
+           crop_size(int): Crop size.
+
+        Return:
+            img(np.ndarray): Croped image.
+    """
+    def __init__(self, crop_size: int):
+        self.crop_size = crop_size
+
+    def __call__(self, img: np.ndarray):
+        img_width, img_height, chanel = img.shape
+        crop_top = int((img_height - self.crop_size) / 2.)
+        crop_left = int((img_width - self.crop_size) / 2.)
+        return img[crop_left:crop_left + self.crop_size, crop_top:crop_top + self.crop_size, :]
+
+
+class SetType:
+    """
+    Set image type.
+
+    Args:
+       type(type): Type of Image value.
+
+    Return:
+        img(np.ndarray): Transformed image.
+    """
+    def __init__(self, datatype: type = 'float32'):
+        self.type = datatype
+
+    def __call__(self, img: np.ndarray):
+        img = img.astype(self.type)
+        return img

paddlehub/server/git_source.py

@@ -18,6 +18,7 @@ import importlib
 import os
 import sys
 from collections import OrderedDict
+from typing import List
 from urllib.parse import urlparse
 
 import git
@@ -74,7 +75,7 @@ class GitSource(object):
             log.logger.warning('An error occurred while loading {}'.format(self.path))
         sys.path.remove(self.path)
 
-    def search_module(self, name: str, version: str = None) -> dict:
+    def search_module(self, name: str, version: str = None) -> List[dict]:
         '''
         Search PaddleHub module
 
@@ -84,7 +85,7 @@ class GitSource(object):
         '''
         return self.search_resource(type='module', name=name, version=version)
 
-    def search_resource(self, type: str, name: str, version: str = None) -> dict:
+    def search_resource(self, type: str, name: str, version: str = None) -> List[dict]:
         '''
         Search PaddleHub Resource
 
@@ -95,13 +96,13 @@ class GitSource(object):
         '''
         module = self.hub_modules.get(name, None)
         if module and module.version.match(version):
-            return {
+            return [{
                 'version': module.version,
                 'name': module.name,
                 'path': self.path,
                 'class': module.__name__,
                 'source': self.url
-            }
+            }]
         return None
 
     @classmethod

paddlehub/server/server.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 
 from collections import OrderedDict
+from typing import List
 
 from paddlehub.server import ServerSource, GitSource
 
@@ -44,7 +45,7 @@ class HubServer(object):
         '''Remove a module source'''
         self.sources.pop(key)
 
-    def search_module(self, name: str, version: str = None, source: str = None) -> dict:
+    def search_module(self, name: str, version: str = None, source: str = None) -> List[dict]:
         '''
         Search PaddleHub module
 
@@ -54,7 +55,7 @@ class HubServer(object):
         '''
         return self.search_resource(type='module', name=name, version=version, source=source)
 
-    def search_resource(self, type: str, name: str, version: str = None, source: str = None) -> dict:
+    def search_resource(self, type: str, name: str, version: str = None, source: str = None) -> List[dict]:
         '''
         Search PaddleHub Resource
 
@@ -68,7 +69,7 @@ class HubServer(object):
             result = source.search_resource(name=name, type=type, version=version)
             if result:
                 return result
-        return {}
+        return []
 
     def get_module_info(self, name: str, source: str = None) -> dict:
         '''

paddlehub/server/server_source.py

@@ -43,7 +43,7 @@ class ServerSource(object):
         self._url = url
         self._timeout = timeout
 
-    def search_module(self, name: str, version: str = None) -> dict:
+    def search_module(self, name: str, version: str = None) -> List[dict]:
         '''
         Search PaddleHub module
 
@@ -53,7 +53,7 @@ class ServerSource(object):
         '''
         return self.search_resource(type='module', name=name, version=version)
 
-    def search_resource(self, type: str, name: str, version: str = None) -> dict:
+    def search_resource(self, type: str, name: str, version: str = None) -> List[dict]:
         '''
         Search PaddleHub Resource
 
@@ -76,9 +76,7 @@ class ServerSource(object):
 
         result = self.request(path='search', params=params)
         if result['status'] == 0 and len(result['data']) > 0:
-            for item in result['data']:
-                if name.lower() == item['name'].lower() and utils.Version(item['version']).match(version):
-                    return item
+            return result['data']
         return None
 
     def get_module_info(self, name: str) -> dict:

paddlehub/utils/paddlex.py

@@ -41,11 +41,17 @@ def download(name: str, save_path: str, version: str = None):
     if os.path.exists(file):
         return
 
-    resource = module_server.search_resouce(name=name, version=version, type='Model')
-    if not resource:
+    resources = module_server.search_resouce(name=name, version=version, type='Model')
+    if not resources:
+        raise ResourceNotFoundError(name, version)
+
+    for item in resources:
+        if item['name'] == name and utils.Version(item['version']).match(version):
+            url = item['url']
+            break
+    else:
         raise ResourceNotFoundError(name, version)
 
-    url = resource['url']
     with utils.generate_tempdir() as _dir:
         if not os.path.exists(save_path):
             os.makedirs(save_path)