diff --git a/.gitattributes b/.gitattributes
index ac481c8eb05e4d2496fbe076a38a7b4835dd733d..9a731e2ca0bc06a9f28724482011d14fcb61bb90 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.zip filter=lfs diff=lfs merge=lfs -text
*.zstandard filter=lfs diff=lfs merge=lfs -text
*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text
diff --git a/README.md b/README.md
index 7d98c7b3708ac17a1a9e2b2e6235966d579290a9..cbf38c756ffd7b443b0ba82a9194909d1c0881e8 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,10 @@
---
-title: Bigdl Nano Demo
-emoji: 💩
-colorFrom: blue
-colorTo: pink
+title: BigDL-Nano Demo
+emoji: 🦄
+colorFrom: yellow
+colorTo: green
sdk: gradio
-sdk_version: 3.0.20
+sdk_version: 3.0.13
app_file: app.py
pinned: false
---
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..267b59880744e311a82eb4ec043cdd0139e3da8f
--- /dev/null
+++ b/app.py
@@ -0,0 +1,199 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# 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.
+#
+# Part of the code in this file is adapted from
+# https://github.com/rnwzd/FSPBT-Image-Translation/blob/master/eval.py and
+# https://github.com/rnwzd/FSPBT-Image-Translation/blob/master/train.py
+
+# MIT License
+
+# Copyright (c) 2022 Lorenzo Breschi
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import gradio as gr
+import numpy as np
+import time
+from data import write_image_tensor, PatchDataModule, prepare_data, image2tensor, tensor2image
+import torch
+from tqdm import tqdm
+from bigdl.nano.pytorch.trainer import Trainer
+from torch.utils.data import DataLoader
+from pathlib import Path
+from torch.utils.data import Dataset
+import datetime
+import huggingface_hub
+
+
+device = 'cpu'
+dtype = torch.float32
+MODEL_REPO = 'CVPR/FSPBT'
+ckpt_path = huggingface_hub.hf_hub_download(
+ MODEL_REPO, 'generator.pt')
+generator = torch.load(ckpt_path)
+generator.eval()
+generator.to(device, dtype)
+params = {'batch_size': 1,
+ 'num_workers': 0}
+
+
+class ImageDataset(Dataset):
+ def __init__(self, img):
+ self.imgs = [image2tensor(img)]
+ def __getitem__(self, idx: int) -> dict:
+ return self.imgs[idx]
+
+ def __len__(self) -> int:
+ return len(self.imgs)
+
+
+data_path = Path('data/webcam')
+train_image_dd = prepare_data(data_path)
+dm = PatchDataModule(train_image_dd, patch_size=2**6,
+ batch_size=2**3, patch_num=2**6)
+
+# quantize model
+train_loader = dm.train_dataloader()
+train_loader_iter = iter(train_loader)
+quantized_model = Trainer.quantize(generator, accelerator=None,
+ calib_dataloader=train_loader)
+
+
+def original_transfer(input_img):
+ w, h, _ = input_img.shape
+ print(datetime.datetime.now())
+ print("input size: ", w, h)
+ # resize too large image
+ if w > 3000 or h > 3000:
+ ratio = min(3000 / w, 3000 / h)
+ w = int(w * ratio)
+ h = int(h * ratio)
+ if w % 4 != 0 or h % 4 != 0:
+ NW = int((w // 4) * 4)
+ NH = int((h // 4) * 4)
+ input_img = np.resize(input_img,(NW,NH,3))
+ st = time.perf_counter()
+ dataset = ImageDataset(input_img)
+ loader = DataLoader(dataset, **params)
+ with torch.no_grad():
+ for inputs in tqdm(loader):
+ inputs = inputs.to(device, dtype)
+ st = time.perf_counter()
+ outputs = generator(inputs)
+ ori_time = time.perf_counter() - st
+ ori_time = "{:.3f}s".format(ori_time)
+ ori_image = np.array(tensor2image(outputs[0]))
+ del inputs
+ del outputs
+ return ori_image, ori_time
+
+def nano_transfer(input_img):
+ w, h, _ = input_img.shape
+ print(datetime.datetime.now())
+ print("input size: ", w, h)
+ # resize too large image
+ if w > 3000 or h > 3000:
+ ratio = min(3000 / w, 3000 / h)
+ w = int(w * ratio)
+ h = int(h * ratio)
+ if w % 4 != 0 or h % 4 != 0:
+ NW = int((w // 4) * 4)
+ NH = int((h // 4) * 4)
+ input_img = np.resize(input_img,(NW,NH,3))
+ st = time.perf_counter()
+ dataset = ImageDataset(input_img)
+ loader = DataLoader(dataset, **params)
+ with torch.no_grad():
+ for inputs in tqdm(loader):
+ inputs = inputs.to(device, dtype)
+ st = time.perf_counter()
+ outputs = quantized_model(inputs)
+ nano_time = time.perf_counter() - st
+ nano_time = "{:.3f}s".format(nano_time)
+ nano_image = np.array(tensor2image(outputs[0]))
+ del inputs
+ del outputs
+ return nano_image, nano_time
+
+
+def clear():
+ return None, None, None, None
+
+
+demo = gr.Blocks()
+
+with demo:
+ gr.Markdown("BigDL-Nano inference demo
")
+ with gr.Row().style(equal_height=False):
+ with gr.Column():
+ gr.Markdown('''
+ Overview
+
+ BigDL-Nano is a library in [BigDL 2.0](https://github.com/intel-analytics/BigDL) that allows the users to transparently accelerate their deep learning pipelines (including data processing, training and inference) by automatically integrating optimized libraries, best-known configurations, and software optimizations.
+ The animation on the right shows how the user can easily enable training using BigDL-Nano with just one line of change.
+ ''')
+ with gr.Column():
+ gr.Video(value="data/training_api.mp4")
+ gr.Markdown('''
+ The below animation shows how the user can easily enable acceleration and quantization using BigDL-Nano with just a couple of lines of code; you may refer to our [CVPR 2022 demo paper](https://arxiv.org/abs/2204.01715) for more details.
+ ''')
+ with gr.Row().style(equal_height=True):
+ with gr.Column():
+ gr.Video(value="data/openvino_api.mp4")
+ with gr.Column():
+ gr.Video(value="data/quantize_ort_api.mp4")
+ gr.Markdown('''
+ Demo
+
+ This section we show an inference demo by using an image stylization example to demostrate the speedup of the above code when using quantization in BigDL-Nano (about 2~3x inference time speedup).
+ This inference demo is adapted from the original [FSPBT-Image-Translation code](https://github.com/rnwzd/FSPBT-Image-Translation),
+ and the default image is from [the COCO dataset](https://cocodataset.org/#home).
+ ''')
+ with gr.Row().style(equal_height=False):
+ input_img = gr.Image(label="input image", value="data/COCO_image.jpg", source="upload")
+ with gr.Column():
+ ori_but = gr.Button("Standard PyTorch Lightning")
+ nano_but = gr.Button("BigDL-Nano")
+ clear_but = gr.Button("Clear Output")
+ with gr.Row().style(equal_height=False):
+ with gr.Column():
+ ori_time = gr.Text(label="Standard PyTorch Lightning latency")
+ ori_image = gr.Image(label="Standard PyTorch Lightning output image")
+ with gr.Column():
+ nano_time = gr.Text(label="BigDL-Nano latency")
+ nano_image = gr.Image(label="BigDL-Nano output image")
+
+ ori_but.click(original_transfer, inputs=input_img, outputs=[ori_image, ori_time])
+ nano_but.click(nano_transfer, inputs=input_img, outputs=[nano_image, nano_time])
+ clear_but.click(clear, inputs=None, outputs=[ori_image, ori_time, nano_image, nano_time])
+
+
+demo.launch(share=True, enable_queue=True)
\ No newline at end of file
diff --git a/data.py b/data.py
new file mode 100644
index 0000000000000000000000000000000000000000..d301a5d3bbdda1ebc888bd5e823cb1ada41ae15c
--- /dev/null
+++ b/data.py
@@ -0,0 +1,233 @@
+# This file is copied from https://github.com/rnwzd/FSPBT-Image-Translation/blob/master/data.py
+
+# MIT License
+
+# Copyright (c) 2022 Lorenzo Breschi
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from typing import Callable, Dict
+
+import torch
+
+from torch.utils.data import Dataset
+
+import torchvision.transforms.functional as F
+from torchvision import transforms
+import pytorch_lightning as pl
+
+from collections.abc import Iterable
+
+
+# image reader writer
+from pathlib import Path
+from PIL import Image
+from typing import Tuple
+
+
+def read_image(filepath: Path, mode: str = None) -> Image:
+ with open(filepath, 'rb') as file:
+ image = Image.open(file)
+ return image.convert(mode)
+
+
+image2tensor = transforms.ToTensor()
+tensor2image = transforms.ToPILImage()
+
+
+def write_image(image: Image, filepath: Path):
+ filepath.parent.mkdir(parents=True, exist_ok=True)
+ image.save(str(filepath))
+
+
+def read_image_tensor(filepath: Path, mode: str = 'RGB') -> torch.Tensor:
+ return image2tensor(read_image(filepath, mode))
+
+
+def write_image_tensor(input: torch.Tensor, filepath: Path):
+ write_image(tensor2image(input), filepath)
+
+
+def get_valid_indices(H: int, W: int, patch_size: int, random_overlap: int = 0):
+
+ vih = torch.arange(random_overlap, H-patch_size -
+ random_overlap+1, patch_size)
+ viw = torch.arange(random_overlap, W-patch_size -
+ random_overlap+1, patch_size)
+ if random_overlap > 0:
+ rih = torch.randint_like(vih, -random_overlap, random_overlap)
+ riw = torch.randint_like(viw, -random_overlap, random_overlap)
+ vih += rih
+ viw += riw
+ vi = torch.stack(torch.meshgrid(vih, viw)).view(2, -1).t()
+ return vi
+
+
+def cut_patches(input: torch.Tensor, indices: Tuple[Tuple[int, int]], patch_size: int, padding: int = 0):
+ # TODO use slices to get all patches at the same time ?
+
+ patches_l = []
+ for n in range(len(indices)):
+
+ patch = F.crop(input, *(indices[n]-padding),
+ *(patch_size+padding*2,)*2)
+ patches_l.append(patch)
+ patches = torch.cat(patches_l, dim=0)
+
+ return patches
+
+
+def prepare_data(data_path: Path, read_func: Callable = read_image_tensor) -> Dict:
+ """
+ Takes a data_path of a folder which contains subfolders with input, target, etc.
+ lablelled by the same names.
+ :param data_path: Path of the folder containing data
+ :param read_func: function that reads data and returns a tensor
+ """
+ data_dict = {}
+
+ subdir_names = ["target", "input", "mask"] # ,"helper"
+
+ # checks only files for which there is an target
+ # TODO check for images
+ name_ls = [file.name for file in (
+ data_path / "target").iterdir() if file.is_file()]
+
+ subdirs = [data_path / sdn for sdn in subdir_names]
+ for sd in subdirs:
+ if sd.is_dir():
+ data_ls = []
+ files = [sd / name for name in name_ls]
+ for file in files:
+ tensor = read_func(file)
+ H, W = tensor.shape[-2:]
+ data_ls.append(tensor)
+ # TODO check that all sizes match
+ data_dict[sd.name] = torch.stack(data_ls, dim=0)
+
+ data_dict['name'] = name_ls
+ data_dict['len'] = len(data_dict['name'])
+ data_dict['H'] = H
+ data_dict['W'] = W
+ return data_dict
+
+
+# TODO an image is loaded whenever a patch is needed, this may be a bottleneck
+class DataDictLoader():
+ def __init__(self, data_dict: Dict,
+ batch_size: int = 16,
+ max_length: int = 128,
+ shuffle: bool = False):
+ """
+ """
+
+ self.batch_size = batch_size
+ self.shuffle = shuffle
+
+ self.batch_size = batch_size
+
+ self.data_dict = data_dict
+ self.dataset_len = data_dict['len']
+ self.len = self.dataset_len if max_length is None else min(
+ self.dataset_len, max_length)
+ # Calculate # batches
+ num_batches, remainder = divmod(self.len, self.batch_size)
+ if remainder > 0:
+ num_batches += 1
+ self.num_batches = num_batches
+
+ def __iter__(self):
+ if self.shuffle:
+ r = torch.randperm(self.dataset_len)
+ self.data_dict = {k: v[r] if isinstance(
+ v, Iterable) else v for k, v in self.data_dict.items()}
+ self.i = 0
+ return self
+
+ def __next__(self):
+ if self.i >= self.len:
+ raise StopIteration
+ batch = {k: v[self.i:self.i+self.batch_size]
+ if isinstance(v, Iterable) else v for k, v in self.data_dict.items()}
+
+ self.i += self.batch_size
+ return batch
+
+ def __len__(self):
+ return self.num_batches
+
+
+class PatchDataModule(pl.LightningDataModule):
+
+ def __init__(self, data_dict,
+ patch_size: int = 2**5,
+ batch_size: int = 2**4,
+ patch_num: int = 2**6):
+ super().__init__()
+ self.data_dict = data_dict
+ self.H, self.W = data_dict['H'], data_dict['W']
+ self.len = data_dict['len']
+
+ self.batch_size = batch_size
+ self.patch_size = patch_size
+ self.patch_num = patch_num
+
+ def dataloader(self, data_dict, **kwargs):
+ return DataDictLoader(data_dict, **kwargs)
+
+ def train_dataloader(self):
+ patches = self.cut_patches()
+ return self.dataloader(patches, batch_size=self.batch_size, shuffle=True,
+ max_length=self.patch_num)
+
+ def val_dataloader(self):
+ return self.dataloader(self.data_dict, batch_size=1)
+
+ def test_dataloader(self):
+ return self.dataloader(self.data_dict) # TODO batch size
+
+ def cut_patches(self):
+ # TODO cycle once
+ patch_indices = get_valid_indices(
+ self.H, self.W, self.patch_size, self.patch_size//4)
+ dd = {k: cut_patches(
+ v, patch_indices, self.patch_size) for k, v in self.data_dict.items()
+ if isinstance(v, torch.Tensor)
+ }
+ threshold = 0.1
+ mask_p = torch.mean(
+ dd.get('mask', torch.ones_like(dd['input'])), dim=(-1, -2, -3))
+ masked_idx = (mask_p > threshold).nonzero(as_tuple=True)[0]
+ dd = {k: v[masked_idx] for k, v in dd.items()}
+ dd['len'] = len(masked_idx)
+ dd['H'], dd['W'] = (self.patch_size,)*2
+
+ return dd
+
+
+class ImageDataset(Dataset):
+ def __init__(self, file_paths: Iterable, read_func: Callable = read_image_tensor):
+ self.file_paths = file_paths
+
+ def __getitem__(self, idx: int) -> dict:
+ file = self.file_paths[idx]
+ return read_image_tensor(file), file.name
+
+ def __len__(self) -> int:
+ return len(self.file_paths)
\ No newline at end of file
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new file mode 100644
index 0000000000000000000000000000000000000000..0f4084af26f45de681f82c8f85eae96d25179362
--- /dev/null
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diff --git a/data/quantize_ort_api.mp4 b/data/quantize_ort_api.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..047a8d7c2d265aa1f45774c034c80ab471477481
--- /dev/null
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+oid sha256:25c17439b411d912fd7d5ad998c4dab21e07d991c2340f6192bc63265142fac1
+size 2121679
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new file mode 100644
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--- /dev/null
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diff --git a/original_models.py b/original_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..a62c47e88891585683f3a13ce64f14f6b47a321e
--- /dev/null
+++ b/original_models.py
@@ -0,0 +1,359 @@
+# This file is copied from https://github.com/rnwzd/FSPBT-Image-Translation/blob/master/original_models.py
+
+# MIT License
+
+# Copyright (c) 2022 Lorenzo Breschi
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+from torch.nn import functional as F
+
+import torchvision
+from torchvision import models
+
+import pytorch_lightning as pl
+
+class LeakySoftplus(nn.Module):
+ def __init__(self,negative_slope: float = 0.01 ):
+ super().__init__()
+ self.negative_slope=negative_slope
+
+ def forward(self,input):
+ return F.softplus(input)+F.logsigmoid(input)*self.negative_slope
+
+
+grelu = nn.LeakyReLU(0.2)
+#grelu = nn.Softplus()
+#grelu = LeakySoftplus(0.2)
+#####
+# Currently default generator we use
+# conv0 -> conv1 -> conv2 -> resnet_blocks -> upconv2 -> upconv1 -> conv_11 -> (conv_11_a)* -> conv_12 -> (Tanh)*
+# there are 2 conv layers inside conv_11_a
+# * means is optional, model uses skip-connections
+class Generator(pl.LightningModule):
+ def __init__(self, norm_layer='batch_norm', use_bias=False, resnet_blocks=7, tanh=True,
+ filters=[32, 64, 128, 128, 128, 64], input_channels=3, output_channels=3, append_smoothers=False):
+ super().__init__()
+ assert norm_layer in [None, 'batch_norm', 'instance_norm'], \
+ "norm_layer should be None, 'batch_norm' or 'instance_norm', not {}".format(
+ norm_layer)
+ self.norm_layer = None
+ if norm_layer == 'batch_norm':
+ self.norm_layer = nn.BatchNorm2d
+ elif norm_layer == 'instance_norm':
+ self.norm_layer = nn.InstanceNorm2d
+
+ # filters = [f//3 for f in filters]
+ self.use_bias = use_bias
+ self.resnet_blocks = resnet_blocks
+ self.append_smoothers = append_smoothers
+
+ stride1 = 2
+ stride2 = 2
+ self.conv0 = self.relu_layer(in_filters=input_channels, out_filters=filters[0],
+ kernel_size=7, stride=1, padding=3,
+ bias=self.use_bias,
+ norm_layer=self.norm_layer,
+ nonlinearity=grelu)
+
+ self.conv1 = self.relu_layer(in_filters=filters[0],
+ out_filters=filters[1],
+ kernel_size=3, stride=stride1, padding=1,
+ bias=self.use_bias,
+ norm_layer=self.norm_layer,
+ nonlinearity=grelu)
+
+ self.conv2 = self.relu_layer(in_filters=filters[1],
+ out_filters=filters[2],
+ kernel_size=3, stride=stride2, padding=1,
+ bias=self.use_bias,
+ norm_layer=self.norm_layer,
+ nonlinearity=grelu)
+
+ self.resnets = nn.ModuleList()
+ for i in range(self.resnet_blocks):
+ self.resnets.append(
+ self.resnet_block(in_filters=filters[2],
+ out_filters=filters[2],
+ kernel_size=3, stride=1, padding=1,
+ bias=self.use_bias,
+ norm_layer=self.norm_layer,
+ nonlinearity=grelu))
+
+ self.upconv2 = self.upconv_layer_upsample_and_conv(in_filters=filters[3] + filters[2],
+ # in_filters=filters[3], # disable skip-connections
+ out_filters=filters[4],
+ scale_factor=stride2,
+ kernel_size=3, stride=1, padding=1,
+ bias=self.use_bias,
+ norm_layer=self.norm_layer,
+ nonlinearity=grelu)
+
+ self.upconv1 = self.upconv_layer_upsample_and_conv(in_filters=filters[4] + filters[1],
+ # in_filters=filters[4], # disable skip-connections
+ out_filters=filters[4],
+ scale_factor=stride1,
+ kernel_size=3, stride=1, padding=1,
+ bias=self.use_bias,
+ norm_layer=self.norm_layer,
+ nonlinearity=grelu)
+
+ self.conv_11 = nn.Sequential(
+ nn.Conv2d(in_channels=filters[0] + filters[4] + input_channels,
+ # in_channels=filters[4], # disable skip-connections
+ out_channels=filters[5],
+ kernel_size=7, stride=1, padding=3, bias=self.use_bias, padding_mode='zeros'),
+ grelu
+ )
+
+ if self.append_smoothers:
+ self.conv_11_a = nn.Sequential(
+ nn.Conv2d(filters[5], filters[5], kernel_size=3,
+ bias=self.use_bias, padding=1, padding_mode='zeros'),
+ grelu,
+ # replace with variable
+ nn.BatchNorm2d(num_features=filters[5]),
+ nn.Conv2d(filters[5], filters[5], kernel_size=3,
+ bias=self.use_bias, padding=1, padding_mode='zeros'),
+ grelu
+ )
+
+ if tanh:
+ self.conv_12 = nn.Sequential(nn.Conv2d(filters[5], output_channels,
+ kernel_size=1, stride=1,
+ padding=0, bias=True, padding_mode='zeros'),
+ #torchvision.transforms.Grayscale(num_output_channels=3),
+ nn.Sigmoid())
+ else:
+ self.conv_12 = nn.Conv2d(filters[5], output_channels, kernel_size=1, stride=1,
+ padding=0, bias=True, padding_mode='zeros')
+
+ def log_tensors(self, logger, tag, img_tensor):
+ logger.experiment.add_images(tag, img_tensor)
+
+ def forward(self, input, logger=None, **kwargs):
+ # [1, 3, 534, 800]
+ output_d0 = self.conv0(input)
+ output_d1 = self.conv1(output_d0)
+ # comment to disable skip-connections
+ output_d2 = self.conv2(output_d1)
+
+ output = output_d2
+ for layer in self.resnets:
+ output = layer(output) + output
+
+ output_u2 = self.upconv2(torch.cat((output, output_d2), dim=1))
+
+ output_u1 = self.upconv1(torch.cat((output_u2, output_d1), dim=1))
+ output = torch.cat(
+ (output_u1, output_d0, input), dim=1)
+
+ output_11 = self.conv_11(output)
+
+ if self.append_smoothers:
+ output_11_a = self.conv_11_a(output_11)
+ else:
+ output_11_a = output_11
+ output_12 = self.conv_12(output_11_a)
+
+ output = output_12
+
+ return output
+
+ def relu_layer(self, in_filters, out_filters, kernel_size, stride, padding, bias,
+ norm_layer, nonlinearity):
+ out = nn.Sequential()
+ out.add_module('conv', nn.Conv2d(in_channels=in_filters,
+ out_channels=out_filters,
+ kernel_size=kernel_size, stride=stride,
+ padding=padding, bias=bias, padding_mode='zeros'))
+
+ if norm_layer:
+ out.add_module('normalization',
+ norm_layer(num_features=out_filters))
+ if nonlinearity:
+ out.add_module('nonlinearity', nonlinearity)
+ # out.add_module('dropout', nn.Dropout2d(0.25))
+
+ return out
+
+ def resnet_block(self, in_filters, out_filters, kernel_size, stride, padding, bias,
+ norm_layer, nonlinearity):
+ out = nn.Sequential()
+ if nonlinearity:
+ out.add_module('nonlinearity_0', nonlinearity)
+ out.add_module('conv_0', nn.Conv2d(in_channels=in_filters,
+ out_channels=out_filters,
+ kernel_size=kernel_size, stride=stride,
+ padding=padding, bias=bias, padding_mode='zeros'))
+ if norm_layer:
+ out.add_module('normalization',
+ norm_layer(num_features=out_filters))
+ if nonlinearity:
+ out.add_module('nonlinearity_1', nonlinearity)
+ out.add_module('conv_1', nn.Conv2d(in_channels=in_filters,
+ out_channels=out_filters,
+ kernel_size=kernel_size, stride=stride,
+ padding=padding, bias=bias, padding_mode='zeros'))
+ return out
+
+ def upconv_layer_upsample_and_conv(self, in_filters, out_filters, scale_factor, kernel_size, stride, padding, bias,
+ norm_layer, nonlinearity):
+
+ parts = [nn.Upsample(scale_factor=scale_factor),
+ nn.Conv2d(in_filters, out_filters, kernel_size,
+ stride, padding=padding, bias=False, padding_mode='zeros')
+ ]
+
+ if norm_layer:
+ parts.append(norm_layer(num_features=out_filters))
+
+ if nonlinearity:
+ parts.append(nonlinearity)
+
+ return nn.Sequential(*parts)
+
+
+
+
+relu = grelu
+
+#####
+# Default discriminator
+#####
+
+relu = nn.LeakyReLU(0.2)
+
+class Discriminator(nn.Module):
+ def __init__(self, num_filters=12, input_channels=3, n_layers=2,
+ norm_layer='instance_norm', use_bias=True):
+ super().__init__()
+
+ self.num_filters = num_filters
+
+ self.input_channels = input_channels
+ self.use_bias = use_bias
+
+ if norm_layer == 'batch_norm':
+ self.norm_layer = nn.BatchNorm2d
+ else:
+ self.norm_layer = nn.InstanceNorm2d
+ self.net = self.make_net(
+ n_layers, self.input_channels, 1, 4, 2, self.use_bias)
+
+ def make_net(self, n, flt_in, flt_out=1, k=4, stride=2, bias=True):
+ padding = 1
+ model = nn.Sequential()
+
+ model.add_module('conv0', self.make_block(
+ flt_in, self.num_filters, k, stride, padding, bias, None, relu))
+
+ flt_mult, flt_mult_prev = 1, 1
+ # n - 1 blocks
+ for l in range(1, n):
+ flt_mult_prev = flt_mult
+ flt_mult = min(2**(l), 8)
+ model.add_module('conv_%d' % (l), self.make_block(self.num_filters * flt_mult_prev, self.num_filters * flt_mult,
+ k, stride, padding, bias, self.norm_layer, relu))
+
+ flt_mult_prev = flt_mult
+ flt_mult = min(2**n, 8)
+ model.add_module('conv_%d' % (n), self.make_block(self.num_filters * flt_mult_prev, self.num_filters * flt_mult,
+ k, 1, padding, bias, self.norm_layer, relu))
+ model.add_module('conv_out', self.make_block(
+ self.num_filters * flt_mult, 1, k, 1, padding, bias, None, None))
+ return model
+
+ def make_block(self, flt_in, flt_out, k, stride, padding, bias, norm, relu):
+ m = nn.Sequential()
+ m.add_module('conv', nn.Conv2d(flt_in, flt_out, k,
+ stride=stride, padding=padding, bias=bias, padding_mode='zeros'))
+ if norm is not None:
+ m.add_module('norm', norm(flt_out))
+ if relu is not None:
+ m.add_module('relu', relu)
+ return m
+
+ def forward(self, x):
+ output = self.net(x)
+ # output = output.mean((2, 3), True)
+ # output = output.squeeze(-1).squeeze(-1)
+ # output = output.mean(dim=(-1,-2))
+ return output
+
+
+#####
+# Perception VGG19 loss
+#####
+class PerceptualVGG19(nn.Module):
+ def __init__(self, feature_layers=[0, 3, 5], use_normalization=False):
+ super().__init__()
+ # model = models.vgg19(pretrained=True)
+ model = models.squeezenet1_1(pretrained=True)
+ model.float()
+ model.eval()
+
+ self.model = model
+ self.feature_layers = feature_layers
+
+ self.mean = torch.FloatTensor([0.485, 0.456, 0.406])
+ self.mean_tensor = None
+
+ self.std = torch.FloatTensor([0.229, 0.224, 0.225])
+ self.std_tensor = None
+
+ self.use_normalization = use_normalization
+
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def normalize(self, x):
+ if not self.use_normalization:
+ return x
+
+ if self.mean_tensor is None:
+ self.mean_tensor = Variable(
+ self.mean.view(1, 3, 1, 1).expand(x.shape),
+ requires_grad=False)
+ self.std_tensor = Variable(
+ self.std.view(1, 3, 1, 1).expand(x.shape), requires_grad=False)
+
+ x = (x + 1) / 2
+ return (x - self.mean_tensor) / self.std_tensor
+
+ def run(self, x):
+ features = []
+
+ h = x
+
+ for f in range(max(self.feature_layers) + 1):
+ h = self.model.features[f](h)
+ if f in self.feature_layers:
+ not_normed_features = h.clone().view(h.size(0), -1)
+ features.append(not_normed_features)
+
+ return torch.cat(features, dim=1)
+
+ def forward(self, x):
+ h = self.normalize(x)
+ return self.run(h)
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..01171fc5ccf2c3bd73f47923ca8fbda25aacc71c
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,12 @@
+# nano default
+bigdl-nano[pytorch]==2.1.0b20220606
+setuptools==58.0.4
+protobuf==3.20.1
+
+# inference
+neural_compressor
+onnx==1.9.0
+onnxruntime==1.10.0
+onnxruntime-extensions
+
+pathlib
\ No newline at end of file