diff --git a/demo/image_matting/Inference_with_ONNX/README.md b/demo/image_matting/Inference_with_ONNX/README.md deleted file mode 100644 index 8e1e4d9..0000000 --- a/demo/image_matting/Inference_with_ONNX/README.md +++ /dev/null @@ -1,22 +0,0 @@ -# Inference with onnxruntime - -### 1. Export onnx model - -Run the following command: -```shell -python export_modnet_onnx.py \ - --ckpt-path=pretrained/modnet_photographic_portrait_matting.ckpt \ - --output-path=modnet.onnx -``` - - -### 2. Inference - -Run the following command: -```shell -python inference_onnx.py \ - --image-path=PATH_TO_IMAGE \ - --output-path=matte.png \ - --model-path=modnet.onnx -``` - diff --git a/demo/image_matting/Inference_with_ONNX/export_modnet_onnx.py b/demo/image_matting/Inference_with_ONNX/export_modnet_onnx.py deleted file mode 100644 index fe5ced7..0000000 --- a/demo/image_matting/Inference_with_ONNX/export_modnet_onnx.py +++ /dev/null @@ -1,55 +0,0 @@ -""" -Export onnx model - -Arguments: - --ckpt-path --> Path of last checkpoint to load - --output-path --> path of onnx model to be saved - -example: -python export_modnet_onnx.py \ - --ckpt-path=modnet_photographic_portrait_matting.ckpt \ - --output-path=modnet.onnx - -output: -ONNX model with dynamic input shape: (batch_size, 3, height, width) & - output shape: (batch_size, 1, height, width) -""" -import os -import argparse -import torch -import torch.nn as nn -from torch.autograd import Variable -from src.models.onnx_modnet import MODNet - - - -if __name__ == '__main__': - # define cmd arguments - parser = argparse.ArgumentParser() - parser.add_argument('--ckpt-path', type=str, required=True, help='path of pre-trained MODNet') - parser.add_argument('--output-path', type=str, required=True, help='path of output onnx model') - args = parser.parse_args() - - # check input arguments - if not os.path.exists(args.ckpt_path): - print('Cannot find checkpoint path: {0}'.format(args.ckpt_path)) - exit() - - # define model & load checkpoint - modnet = MODNet(backbone_pretrained=False) - modnet = nn.DataParallel(modnet).cuda() - state_dict = torch.load(args.ckpt_path) - modnet.load_state_dict(state_dict) - modnet.eval() - - # prepare dummy_input - batch_size = 1 - height = 512 - width = 512 - dummy_input = Variable(torch.randn(batch_size, 3, height, width)).cuda() - - # export to onnx model - torch.onnx.export(modnet.module, dummy_input, args.output_path, export_params = True, opset_version=11, - input_names = ['input'], output_names = ['output'], - dynamic_axes = {'input': {0:'batch_size', 2:'height', 3:'width'}, - 'output': {0: 'batch_size', 2: 'height', 3: 'width'}}) diff --git a/demo/image_matting/Inference_with_ONNX/inference_onnx.py b/demo/image_matting/Inference_with_ONNX/inference_onnx.py deleted file mode 100644 index f19827e..0000000 --- a/demo/image_matting/Inference_with_ONNX/inference_onnx.py +++ /dev/null @@ -1,116 +0,0 @@ -""" -Inference with onnxruntime - -Arguments: - --image-path --> path to single input image - --output-path --> paht to save generated matte - --model-path --> path to onnx model file - -example: -python inference_onnx.py \ - --image-path=demo.jpg \ - --output-path=matte.png \ - --model-path=modnet.onnx - -Optional: -Generate transparent image without background -""" -import os -import argparse -import cv2 -import numpy as np -import onnx -import onnxruntime -from onnx import helper -from PIL import Image - -if __name__ == '__main__': - # define cmd arguments - parser = argparse.ArgumentParser() - parser.add_argument('--image-path', type=str, help='path of input image') - parser.add_argument('--output-path', type=str, help='path of output image') - parser.add_argument('--model-path', type=str, help='path of onnx model') - args = parser.parse_args() - - # check input arguments - if not os.path.exists(args.image_path): - print('Cannot find input path: {0}'.format(args.image_path)) - exit() - if not os.path.exists(args.model_path): - print('Cannot find model path: {0}'.format(args.model_path)) - exit() - - ref_size = 512 - - # Get x_scale_factor & y_scale_factor to resize image - def get_scale_factor(im_h, im_w, ref_size): - - if max(im_h, im_w) < ref_size or min(im_h, im_w) > ref_size: - if im_w >= im_h: - im_rh = ref_size - im_rw = int(im_w / im_h * ref_size) - elif im_w < im_h: - im_rw = ref_size - im_rh = int(im_h / im_w * ref_size) - else: - im_rh = im_h - im_rw = im_w - - im_rw = im_rw - im_rw % 32 - im_rh = im_rh - im_rh % 32 - - x_scale_factor = im_rw / im_w - y_scale_factor = im_rh / im_h - - return x_scale_factor, y_scale_factor - - ############################################## - # Main Inference part - ############################################## - - # read image - im = cv2.imread(args.image_path) - im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) - - # unify image channels to 3 - if len(im.shape) == 2: - im = im[:, :, None] - if im.shape[2] == 1: - im = np.repeat(im, 3, axis=2) - elif im.shape[2] == 4: - im = im[:, :, 0:3] - - # normalize values to scale it between -1 to 1 - im = (im - 127.5) / 127.5 - - im_h, im_w, im_c = im.shape - x, y = get_scale_factor(im_h, im_w, ref_size) - - # resize image - im = cv2.resize(im, None, fx = x, fy = y, interpolation = cv2.INTER_AREA) - - # prepare input shape - im = np.transpose(im) - im = np.swapaxes(im, 1, 2) - im = np.expand_dims(im, axis = 0).astype('float32') - - # Initialize session and get prediction - session = onnxruntime.InferenceSession(args.model_path, None) - input_name = session.get_inputs()[0].name - output_name = session.get_outputs()[0].name - result = session.run([output_name], {input_name: im}) - - # refine matte - matte = (np.squeeze(result[0]) * 255).astype('uint8') - matte = cv2.resize(matte, dsize=(im_w, im_h), interpolation = cv2.INTER_AREA) - - cv2.imwrite(args.output_path, matte) - - ############################################## - # Optional - save png image without background - ############################################## - - im_PIL = Image.open(args.image_path) - matte = Image.fromarray(matte) - im_PIL.putalpha(matte) # add alpha channel to keep transparency - im_PIL.save('without_background.png') \ No newline at end of file diff --git a/demo/image_matting/Inference_with_ONNX/requirements.txt b/demo/image_matting/Inference_with_ONNX/requirements.txt deleted file mode 100644 index 3dfd20d..0000000 --- a/demo/image_matting/Inference_with_ONNX/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -onnx==1.8.1 -onnxruntime==1.6.0 -opencv-python==4.5.1.48 -torch==1.7.1 \ No newline at end of file diff --git a/src/models/onnx_modnet.py b/src/models/onnx_modnet.py deleted file mode 100644 index 6fa5a41..0000000 --- a/src/models/onnx_modnet.py +++ /dev/null @@ -1,254 +0,0 @@ -""" -This file is a modified version of the original file modnet.py without -"pred_semantic" and "pred_details" as these both returns None when "inference = True" - -And it does not contain "inference" argument which will make it easier to -convert checkpoint into onnx model. - -Refer: 'demo/image_matting/inference_with_ONNX/export_modnet_onnx.py' to export model. -""" - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from .backbones import SUPPORTED_BACKBONES - - -#------------------------------------------------------------------------------ -# MODNet Basic Modules -#------------------------------------------------------------------------------ - -class IBNorm(nn.Module): - """ Combine Instance Norm and Batch Norm into One Layer - """ - - def __init__(self, in_channels): - super(IBNorm, self).__init__() - in_channels = in_channels - self.bnorm_channels = int(in_channels / 2) - self.inorm_channels = in_channels - self.bnorm_channels - - self.bnorm = nn.BatchNorm2d(self.bnorm_channels, affine=True) - self.inorm = nn.InstanceNorm2d(self.inorm_channels, affine=False) - - def forward(self, x): - bn_x = self.bnorm(x[:, :self.bnorm_channels, ...].contiguous()) - in_x = self.inorm(x[:, self.bnorm_channels:, ...].contiguous()) - - return torch.cat((bn_x, in_x), 1) - - -class Conv2dIBNormRelu(nn.Module): - """ Convolution + IBNorm + ReLu - """ - - def __init__(self, in_channels, out_channels, kernel_size, - stride=1, padding=0, dilation=1, groups=1, bias=True, - with_ibn=True, with_relu=True): - super(Conv2dIBNormRelu, self).__init__() - - layers = [ - nn.Conv2d(in_channels, out_channels, kernel_size, - stride=stride, padding=padding, dilation=dilation, - groups=groups, bias=bias) - ] - - if with_ibn: - layers.append(IBNorm(out_channels)) - if with_relu: - layers.append(nn.ReLU(inplace=True)) - - self.layers = nn.Sequential(*layers) - - def forward(self, x): - return self.layers(x) - - -class SEBlock(nn.Module): - """ SE Block Proposed in https://arxiv.org/pdf/1709.01507.pdf - """ - - def __init__(self, in_channels, out_channels, reduction=1): - super(SEBlock, self).__init__() - self.pool = nn.AdaptiveAvgPool2d(1) - self.fc = nn.Sequential( - nn.Linear(in_channels, int(in_channels // reduction), bias=False), - nn.ReLU(inplace=True), - nn.Linear(int(in_channels // reduction), out_channels, bias=False), - nn.Sigmoid() - ) - - def forward(self, x): - b, c, _, _ = x.size() - w = self.pool(x).view(b, c) - w = self.fc(w).view(b, c, 1, 1) - - return x * w.expand_as(x) - - -#------------------------------------------------------------------------------ -# MODNet Branches -#------------------------------------------------------------------------------ - -class LRBranch(nn.Module): - """ Low Resolution Branch of MODNet - """ - - def __init__(self, backbone): - super(LRBranch, self).__init__() - - enc_channels = backbone.enc_channels - - self.backbone = backbone - self.se_block = SEBlock(enc_channels[4], enc_channels[4], reduction=4) - self.conv_lr16x = Conv2dIBNormRelu(enc_channels[4], enc_channels[3], 5, stride=1, padding=2) - self.conv_lr8x = Conv2dIBNormRelu(enc_channels[3], enc_channels[2], 5, stride=1, padding=2) - self.conv_lr = Conv2dIBNormRelu(enc_channels[2], 1, kernel_size=3, stride=2, padding=1, with_ibn=False, with_relu=False) - - def forward(self, img): - enc_features = self.backbone.forward(img) - enc2x, enc4x, enc32x = enc_features[0], enc_features[1], enc_features[4] - - enc32x = self.se_block(enc32x) - lr16x = F.interpolate(enc32x, scale_factor=2, mode='bilinear', align_corners=False) - lr16x = self.conv_lr16x(lr16x) - lr8x = F.interpolate(lr16x, scale_factor=2, mode='bilinear', align_corners=False) - lr8x = self.conv_lr8x(lr8x) - - return lr8x, [enc2x, enc4x] - - -class HRBranch(nn.Module): - """ High Resolution Branch of MODNet - """ - - def __init__(self, hr_channels, enc_channels): - super(HRBranch, self).__init__() - - self.tohr_enc2x = Conv2dIBNormRelu(enc_channels[0], hr_channels, 1, stride=1, padding=0) - self.conv_enc2x = Conv2dIBNormRelu(hr_channels + 3, hr_channels, 3, stride=2, padding=1) - - self.tohr_enc4x = Conv2dIBNormRelu(enc_channels[1], hr_channels, 1, stride=1, padding=0) - self.conv_enc4x = Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1) - - self.conv_hr4x = nn.Sequential( - Conv2dIBNormRelu(3 * hr_channels + 3, 2 * hr_channels, 3, stride=1, padding=1), - Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1), - Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1), - ) - - self.conv_hr2x = nn.Sequential( - Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1), - Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1), - Conv2dIBNormRelu(hr_channels, hr_channels, 3, stride=1, padding=1), - Conv2dIBNormRelu(hr_channels, hr_channels, 3, stride=1, padding=1), - ) - - self.conv_hr = nn.Sequential( - Conv2dIBNormRelu(hr_channels + 3, hr_channels, 3, stride=1, padding=1), - Conv2dIBNormRelu(hr_channels, 1, kernel_size=1, stride=1, padding=0, with_ibn=False, with_relu=False), - ) - - def forward(self, img, enc2x, enc4x, lr8x): - img2x = F.interpolate(img, scale_factor=1/2, mode='bilinear', align_corners=False) - img4x = F.interpolate(img, scale_factor=1/4, mode='bilinear', align_corners=False) - - enc2x = self.tohr_enc2x(enc2x) - hr4x = self.conv_enc2x(torch.cat((img2x, enc2x), dim=1)) - - enc4x = self.tohr_enc4x(enc4x) - hr4x = self.conv_enc4x(torch.cat((hr4x, enc4x), dim=1)) - - lr4x = F.interpolate(lr8x, scale_factor=2, mode='bilinear', align_corners=False) - hr4x = self.conv_hr4x(torch.cat((hr4x, lr4x, img4x), dim=1)) - - hr2x = F.interpolate(hr4x, scale_factor=2, mode='bilinear', align_corners=False) - hr2x = self.conv_hr2x(torch.cat((hr2x, enc2x), dim=1)) - - return hr2x - - -class FusionBranch(nn.Module): - """ Fusion Branch of MODNet - """ - - def __init__(self, hr_channels, enc_channels): - super(FusionBranch, self).__init__() - self.conv_lr4x = Conv2dIBNormRelu(enc_channels[2], hr_channels, 5, stride=1, padding=2) - - self.conv_f2x = Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1) - self.conv_f = nn.Sequential( - Conv2dIBNormRelu(hr_channels + 3, int(hr_channels / 2), 3, stride=1, padding=1), - Conv2dIBNormRelu(int(hr_channels / 2), 1, 1, stride=1, padding=0, with_ibn=False, with_relu=False), - ) - - def forward(self, img, lr8x, hr2x): - lr4x = F.interpolate(lr8x, scale_factor=2, mode='bilinear', align_corners=False) - lr4x = self.conv_lr4x(lr4x) - lr2x = F.interpolate(lr4x, scale_factor=2, mode='bilinear', align_corners=False) - - f2x = self.conv_f2x(torch.cat((lr2x, hr2x), dim=1)) - f = F.interpolate(f2x, scale_factor=2, mode='bilinear', align_corners=False) - f = self.conv_f(torch.cat((f, img), dim=1)) - pred_matte = torch.sigmoid(f) - - return pred_matte - - -#------------------------------------------------------------------------------ -# MODNet -#------------------------------------------------------------------------------ - -class MODNet(nn.Module): - """ Architecture of MODNet - """ - - def __init__(self, in_channels=3, hr_channels=32, backbone_arch='mobilenetv2', backbone_pretrained=True): - super(MODNet, self).__init__() - - self.in_channels = in_channels - self.hr_channels = hr_channels - self.backbone_arch = backbone_arch - self.backbone_pretrained = backbone_pretrained - - self.backbone = SUPPORTED_BACKBONES[self.backbone_arch](self.in_channels) - - self.lr_branch = LRBranch(self.backbone) - self.hr_branch = HRBranch(self.hr_channels, self.backbone.enc_channels) - self.f_branch = FusionBranch(self.hr_channels, self.backbone.enc_channels) - - for m in self.modules(): - if isinstance(m, nn.Conv2d): - self._init_conv(m) - elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.InstanceNorm2d): - self._init_norm(m) - - if self.backbone_pretrained: - self.backbone.load_pretrained_ckpt() - - def forward(self, img): - lr8x, [enc2x, enc4x] = self.lr_branch(img) - hr2x = self.hr_branch(img, enc2x, enc4x, lr8x) - pred_matte = self.f_branch(img, lr8x, hr2x) - - return pred_matte - - def freeze_norm(self): - norm_types = [nn.BatchNorm2d, nn.InstanceNorm2d] - for m in self.modules(): - for n in norm_types: - if isinstance(m, n): - m.eval() - continue - - def _init_conv(self, conv): - nn.init.kaiming_uniform_( - conv.weight, a=0, mode='fan_in', nonlinearity='relu') - if conv.bias is not None: - nn.init.constant_(conv.bias, 0) - - def _init_norm(self, norm): - if norm.weight is not None: - nn.init.constant_(norm.weight, 1) - nn.init.constant_(norm.bias, 0)