From 6e7ba51d37e36333ac0760f3d022035f2475e574 Mon Sep 17 00:00:00 2001 From: hej Date: Wed, 26 Oct 2022 17:11:12 +0200 Subject: [PATCH] Initial commit: added skeleton --- README.md | 53 +++++++ code/evaluate | 13 ++ code/networks.py | 129 +++++++++++++++++ code/resnet.py | 222 +++++++++++++++++++++++++++++ code/verifier.py | 78 ++++++++++ examples/gt.txt | 20 +++ examples/net1/img1_0.0500.txt | 1 + examples/net1/img2_0.0550.txt | 1 + examples/net10/img1_0.0014.txt | 1 + examples/net10/img2_0.0015.txt | 1 + examples/net2/img1_0.2400.txt | 1 + examples/net2/img2_0.2360.txt | 1 + examples/net3/img1_0.0065.txt | 1 + examples/net3/img2_0.0081.txt | 1 + examples/net4/img1_0.2240.txt | 1 + examples/net4/img2_0.2290.txt | 1 + examples/net5/img1_0.0260.txt | 1 + examples/net5/img2_0.0470.txt | 1 + examples/net6/img1_0.0140.txt | 1 + examples/net6/img2_0.0147.txt | 1 + examples/net7/img1_0.0540.txt | 1 + examples/net7/img2_0.1440.txt | 1 + examples/net8/img1_0.0096.txt | 1 + examples/net8/img2_0.0100.txt | 1 + examples/net9/img1_0.0040.txt | 1 + examples/net9/img2_0.0040.txt | 1 + nets/net10_cifar10_resnet_4b.pt | Bin 0 -> 383383 bytes nets/net1_mnist_fc1.pt | Bin 0 -> 160827 bytes nets/net2_mnist_fc2.pt | Bin 0 -> 338561 bytes nets/net3_cifar10_fc3.pt | Bin 0 -> 1275969 bytes nets/net4_mnist_conv1.pt | Bin 0 -> 1261889 bytes nets/net5_mnist_conv2.pt | Bin 0 -> 668615 bytes nets/net6_cifar10_conv2.pt | Bin 0 -> 862663 bytes nets/net7_mnist_conv3.pt | Bin 0 -> 1369743 bytes nets/net8_cifar10_resnet_2b.pt | Bin 0 -> 453631 bytes nets/net9_cifar10_resnet_2b2_bn.pt | Bin 0 -> 721819 bytes requirements.txt | 3 + 37 files changed, 538 insertions(+) create mode 100644 README.md create mode 100755 code/evaluate create mode 100644 code/networks.py create mode 100644 code/resnet.py create mode 100644 code/verifier.py create mode 100644 examples/gt.txt create mode 100644 examples/net1/img1_0.0500.txt create mode 100644 examples/net1/img2_0.0550.txt create mode 100644 examples/net10/img1_0.0014.txt create mode 100644 examples/net10/img2_0.0015.txt create mode 100644 examples/net2/img1_0.2400.txt create mode 100644 examples/net2/img2_0.2360.txt create mode 100644 examples/net3/img1_0.0065.txt create mode 100644 examples/net3/img2_0.0081.txt create mode 100644 examples/net4/img1_0.2240.txt create mode 100644 examples/net4/img2_0.2290.txt create mode 100644 examples/net5/img1_0.0260.txt create mode 100644 examples/net5/img2_0.0470.txt create mode 100644 examples/net6/img1_0.0140.txt create mode 100644 examples/net6/img2_0.0147.txt create mode 100644 examples/net7/img1_0.0540.txt create mode 100644 examples/net7/img2_0.1440.txt create mode 100644 examples/net8/img1_0.0096.txt create mode 100644 examples/net8/img2_0.0100.txt create mode 100644 examples/net9/img1_0.0040.txt create mode 100644 examples/net9/img2_0.0040.txt create mode 100644 nets/net10_cifar10_resnet_4b.pt create mode 100644 nets/net1_mnist_fc1.pt create mode 100644 nets/net2_mnist_fc2.pt create mode 100644 nets/net3_cifar10_fc3.pt create mode 100644 nets/net4_mnist_conv1.pt create mode 100644 nets/net5_mnist_conv2.pt create mode 100644 nets/net6_cifar10_conv2.pt create mode 100644 nets/net7_mnist_conv3.pt create mode 100644 nets/net8_cifar10_resnet_2b.pt create mode 100644 nets/net9_cifar10_resnet_2b2_bn.pt create mode 100644 requirements.txt diff --git a/README.md b/README.md new file mode 100644 index 0000000..8dd9259 --- /dev/null +++ b/README.md @@ -0,0 +1,53 @@ +# ReliableAI 2022 Course Project + +This is the project for Reliable and Trustworthy Artificial Intelligence course at ETH Zurich. + +## Folder structure +In the directory `code` you can find several files. +File `networks.py` and `resnet.py` contain encodings of fully connected, convolutional and residual neural network architectures as PyTorch classes. +The architectures extend `nn.Module` object and consist of standard PyTorch layers (e.g. `Linear`, `Flatten`, `ReLU`, `Conv2d`). Please note that first layer of each network performs normalization of the input image. +File `verifier.py` contains a template of verifier. Loading of the stored networks and test cases is already implemented in `main` function. If you decide to modify `main` function, please ensure that parsing of the test cases works correctly. Your task is to modify `analyze` function by building upon DeepPoly convex relaxation. Note that provided verifier template is guaranteed to achieve **0** points (by always outputting `not verified`). + +In folder `nets` you can find 10 neural networks (3 fully connected, 4 convolutional, and 3 residual). These networks are loaded using PyTorch in `verifier.py`. +You can find architectures of these networks in `networks.py`. +Note that for ResNet we prepend Normalization layer after loading the network (see `get_net` function in `verifier.py`). +Name of each network contains the dataset the network is trained used on, e.g. `net3_cifar10_fc3.pt` is network which receives CIFAR-10 images as inputs. +In folder `examples` you can find 10 subfolders. Each subfolder is associated with one of the 10 networks. In a subfolder corresponding to a network, you can find 2 example test cases for this network. +As explained in the lecture, these test cases **are not** part of the set of test cases which we will use for the final evaluation, and they are only here for you to develop your verifier. + +## Setup instructions + +We recommend you to install Python virtual environment to ensure dependencies are same as the ones we will use for evaluation. +To evaluate your solution, we are going to use Python 3.7. +You can create virtual environment and install the dependencies using the following commands: + +```bash +$ virtualenv venv --python=python3.7 +$ source venv/bin/activate +$ pip install -r requirements.txt +``` + +## Running the verifier + +We will run your verifier from `code` directory using the command: + +```bash +$ python verifier.py --net {net} --spec ../examples/{net}/img{test_idx}_{eps}.txt +``` + +In this command, `{net}` is equal to one of the following values (each representing one of the networks we want to verify): `net1, net2, net3, net4, net5, net6, net7, net8, net9, net10`. +`test_idx` is an integer representing index of the test case, while `eps` is perturbation that verifier should certify in this test case. + +To test your verifier, you can run for example: + +```bash +$ python verifier.py --net net1 --spec ../examples/net1/img1_0.0500.txt +``` + +To evaluate the verifier on all networks and sample test cases, we provide the evaluation script. +You can run this script using the following commands: + +```bash +chmod +x evaluate +./evaluate ../examples +``` diff --git a/code/evaluate b/code/evaluate new file mode 100755 index 0000000..34f1e28 --- /dev/null +++ b/code/evaluate @@ -0,0 +1,13 @@ +#!/bin/bash + +rm $1/res.txt +for net in {1..10} +do + echo Evaluating network net${net}... + for spec in `ls $1/net${net}/` + do + echo ${spec} + res=$(python verifier.py --net net${net} --spec $1/net${net}/${spec}) + echo net${k}_${net},$spec,$res >> $1/res.txt + done +done diff --git a/code/networks.py b/code/networks.py new file mode 100644 index 0000000..8ea6ac9 --- /dev/null +++ b/code/networks.py @@ -0,0 +1,129 @@ +import torch +import torch.nn as nn +from resnet import ResNet, BasicBlock + +class Normalization(nn.Module): + + def __init__(self, device, dataset): + super(Normalization, self).__init__() + if dataset == 'mnist': + self.mean = torch.FloatTensor([0.1307]).view((1, 1, 1, 1)).to(device) + self.sigma = torch.FloatTensor([0.3081]).view((1, 1, 1, 1)).to(device) + elif dataset == 'cifar10': + self.mean = torch.FloatTensor([0.4914, 0.4822, 0.4465]).view((1, 3, 1, 1)).to(device) + self.sigma = torch.FloatTensor([0.2023, 0.1994, 0.201]).view((1, 3, 1, 1)).to(device) + else: + assert False + + def forward(self, x): + return (x - self.mean) / self.sigma + + +class FullyConnected(nn.Module): + + def __init__(self, device, dataset, input_size, input_channels, fc_layers, act='relu'): + super(FullyConnected, self).__init__() + + layers = [Normalization(device, dataset), nn.Flatten()] + prev_fc_size = input_size * input_size * input_channels + for i, fc_size in enumerate(fc_layers): + layers += [nn.Linear(prev_fc_size, fc_size)] + if i + 1 < len(fc_layers): + if act == 'relu': + layers += [nn.ReLU()] + else: + assert False + prev_fc_size = fc_size + self.layers = nn.Sequential(*layers) + + def forward(self, x): + return self.layers(x) + + +class Conv(nn.Module): + + def __init__(self, device, dataset, input_size, input_channels, conv_layers, fc_layers, n_class=10): + super(Conv, self).__init__() + + self.input_size = input_size + self.n_class = n_class + + layers = [Normalization(device, dataset)] + prev_channels = input_channels + img_dim = input_size + + for n_channels, kernel_size, stride, padding in conv_layers: + layers += [ + nn.Conv2d(prev_channels, n_channels, kernel_size, stride=stride, padding=padding), + nn.ReLU(), + ] + prev_channels = n_channels + img_dim = img_dim // stride + layers += [nn.Flatten()] + + prev_fc_size = prev_channels * img_dim * img_dim + for i, fc_size in enumerate(fc_layers): + layers += [nn.Linear(prev_fc_size, fc_size)] + if i + 1 < len(fc_layers): + layers += [nn.ReLU()] + prev_fc_size = fc_size + self.layers = nn.Sequential(*layers) + + def forward(self, x): + return self.layers(x) + + +class NormalizedResnet(nn.Module): + + def __init__(self, device, resnet): + super(NormalizedResnet, self).__init__() + + self.normalization = Normalization(device, 'cifar10') + self.resnet = resnet + + def forward(self, x): + x = self.normalization(x) + x = self.resnet(x) + return x + + +def get_net_name(net): + net_names = { + 'net1': 'net1_mnist_fc1.pt', + 'net2': 'net2_mnist_fc2.pt', + 'net3': 'net3_cifar10_fc3.pt', + 'net4': 'net4_mnist_conv1.pt', + 'net5': 'net5_mnist_conv2.pt', + 'net6': 'net6_cifar10_conv2.pt', + 'net7': 'net7_mnist_conv3.pt', + 'net8': 'net8_cifar10_resnet_2b.pt', + 'net9': 'net9_cifar10_resnet_2b2_bn.pt', + 'net10': 'net10_cifar10_resnet_4b.pt', + } + return net_names[net] + +def get_network(device, net): + if net == 'net1': + return FullyConnected(device, 'mnist', 28, 1, [50, 10]) + elif net == 'net2': + return FullyConnected(device, 'mnist', 28, 1, [100, 50, 10]) + elif net == 'net3': + return FullyConnected(device, 'cifar10', 32, 3, [100, 100, 10]) + elif net == 'net4': + return Conv(device, 'mnist', 28, 1, [(16, 3, 2, 1)], [100, 10], 10) + elif net == 'net5': + return Conv(device, 'mnist', 28, 1, [(16, 4, 2, 1), (32, 4, 2, 1)], [100, 10], 10) + elif net == 'net6': + return Conv(device, 'cifar10', 32, 3, [(16, 4, 2, 1), (32, 4, 2, 1)], [100, 10], 10) + elif net == 'net7': + return Conv(device, 'mnist', 28, 1, [(16, 4, 2, 1), (64, 4, 2, 1)], [100, 100, 10], 10) + elif net == 'net8': + return ResNet(BasicBlock, num_stages=1, num_blocks=2, in_planes=8, bn=False, last_layer="dense") + elif net == 'net9': + return ResNet( + BasicBlock, num_stages=2, num_blocks=1, in_planes=16, + bn=True, last_layer="dense", stride=[2, 2, 2]) + elif net == 'net10': + return ResNet(BasicBlock, num_stages=2, num_blocks=2, in_planes=8, bn=False, last_layer="dense") + assert False + diff --git a/code/resnet.py b/code/resnet.py new file mode 100644 index 0000000..d1c9041 --- /dev/null +++ b/code/resnet.py @@ -0,0 +1,222 @@ +import torch +import torch.nn as nn +from torch import Tensor +from typing import List, Optional, Sequence, Tuple, Type + + +class ResidualBlock(nn.Module): + def __init__( + self, + path_a: nn.Sequential, + path_b: nn.Sequential, + ) -> None: + super(ResidualBlock, self).__init__() + self.path_a = path_a + self.path_b = path_b + + def forward(self, x: Tensor) -> Tensor: + out = self.path_a(x) + self.path_b(x) + return out + + +class BasicBlock(ResidualBlock, nn.Module): + expansion = 1 + + in_planes: int + planes: int + stride: int + bn: bool + kernel: int + + out_dim: int + + def __init__( + self, + in_planes: int, + planes: int, + stride: int = 1, + bn: bool = True, + kernel: int = 3, + in_dim: int = -1, + ) -> None: + self.in_planes = in_planes + self.planes = planes + self.stride = stride + self.bn = bn + self.kernel = kernel + + kernel_size = kernel + assert kernel_size in [1, 2, 3], "kernel not supported!" + p_1 = 1 if kernel_size > 1 else 0 + p_2 = 1 if kernel_size > 2 else 0 + + layers_b: List[nn.Module] = [] + layers_b.append( + nn.Conv2d( + in_planes, + planes, + kernel_size=kernel_size, + stride=stride, + padding=p_1, + bias=(not bn), + ) + ) + _, _, in_dim = self._getShapeConv( + (in_planes, in_dim, in_dim), + (self.in_planes, kernel_size, kernel_size), + stride=stride, + padding=p_1, + ) + + if bn: + layers_b.append(nn.BatchNorm2d(planes)) + layers_b.append(nn.ReLU()) + layers_b.append( + nn.Conv2d( + planes, + self.expansion * planes, + kernel_size=kernel_size, + stride=1, + padding=p_2, + bias=(not bn), + ) + ) + _, _, in_dim = self._getShapeConv( + (planes, in_dim, in_dim), + (self.in_planes, kernel_size, kernel_size), + stride=1, + padding=p_2, + ) + if bn: + layers_b.append(nn.BatchNorm2d(self.expansion * planes)) + path_b = nn.Sequential(*layers_b) + + layers_a: List[nn.Module] = [torch.nn.Identity()] + if stride != 1 or in_planes != self.expansion * planes: + layers_a.append( + nn.Conv2d( + in_planes, + self.expansion * planes, + kernel_size=1, + stride=stride, + bias=(not bn), + ) + ) + if bn: + layers_a.append(nn.BatchNorm2d(self.expansion * planes)) + path_a = nn.Sequential(*layers_a) + self.out_dim = in_dim + super(BasicBlock, self).__init__(path_a, path_b) + + def _getShapeConv( + self, + in_shape: Tuple[int, int, int], + conv_shape: Tuple[int, ...], + stride: int = 1, + padding: int = 0, + ) -> Tuple[int, int, int]: + inChan, inH, inW = in_shape + outChan, kH, kW = conv_shape[:3] + + outH = 1 + int((2 * padding + inH - kH) / stride) + outW = 1 + int((2 * padding + inW - kW) / stride) + return (outChan, outH, outW) + +def getShapeConv( + in_shape: Tuple[int, int, int], + conv_shape: Tuple[int, ...], + stride: int = 1, + padding: int = 0, +) -> Tuple[int, int, int]: + inChan, inH, inW = in_shape + outChan, kH, kW = conv_shape[:3] + + outH = 1 + int((2 * padding + inH - kH) / stride) + outW = 1 + int((2 * padding + inW - kW) / stride) + return (outChan, outH, outW) + + +class ResNet(nn.Sequential): + def __init__( + self, + block: Type[BasicBlock], + in_ch: int = 3, + num_stages: int = 1, + num_blocks: int = 2, + num_classes: int = 10, + in_planes: int = 64, + bn: bool = True, + last_layer: str = "dense", + in_dim: int = 32, + stride: Optional[Sequence[int]] = None, + ): + layers: List[nn.Module] = [] + self.in_planes = in_planes + if stride is None: + stride = (num_stages + 1) * [2] + + layers.append( + nn.Conv2d( + in_ch, + self.in_planes, + kernel_size=3, + stride=stride[0], + padding=1, + bias=not bn, + ) + ) + + _, _, in_dim = getShapeConv( + (in_ch, in_dim, in_dim), (self.in_planes, 3, 3), stride=stride[0], padding=1 + ) + + if bn: + layers.append(nn.BatchNorm2d(self.in_planes)) + + layers.append(nn.ReLU()) + + for s in stride[1:]: + block_layers, in_dim = self._make_layer( + block, + self.in_planes * 2, + num_blocks, + stride=s, + bn=bn, + kernel=3, + in_dim=in_dim, + ) + layers.append(block_layers) + + if last_layer == "dense": + layers.append(nn.Flatten()) + layers.append( + nn.Linear(self.in_planes * block.expansion * in_dim**2, 100) + ) + layers.append(nn.ReLU()) + layers.append(nn.Linear(100, num_classes)) + else: + exit("last_layer type not supported!") + + super(ResNet, self).__init__(*layers) + + def _make_layer( + self, + block: Type[BasicBlock], + planes: int, + num_layers: int, + stride: int, + bn: bool, + kernel: int, + in_dim: int, + ) -> Tuple[nn.Sequential, int]: + strides = [stride] + [1] * (num_layers - 1) + cur_dim: int = in_dim + layers: List[nn.Module] = [] + for stride in strides: + layer = block(self.in_planes, planes, stride, bn, kernel, in_dim=cur_dim) + layers.append(layer) + cur_dim = layer.out_dim + layers.append(nn.ReLU()) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers), cur_dim + diff --git a/code/verifier.py b/code/verifier.py new file mode 100644 index 0000000..1d720d8 --- /dev/null +++ b/code/verifier.py @@ -0,0 +1,78 @@ +import argparse +import csv +import torch +import torch.nn.functional as F +from networks import get_network, get_net_name, NormalizedResnet + + +DEVICE = 'cpu' +DTYPE = torch.float32 + +def transform_image(pixel_values, input_dim): + normalized_pixel_values = torch.tensor([float(p) / 255.0 for p in pixel_values]) + if len(input_dim) > 1: + input_dim_in_hwc = (input_dim[1], input_dim[2], input_dim[0]) + image_in_hwc = normalized_pixel_values.view(input_dim_in_hwc) + image_in_chw = image_in_hwc.permute(2, 0, 1) + image = image_in_chw + else: + image = normalized_pixel_values + + assert (image >= 0).all() + assert (image <= 1).all() + return image + +def get_spec(spec, dataset): + input_dim = [1, 28, 28] if dataset == 'mnist' else [3, 32, 32] + eps = float(spec[:-4].split('/')[-1].split('_')[-1]) + test_file = open(spec, "r") + test_instances = csv.reader(test_file, delimiter=",") + for i, (label, *pixel_values) in enumerate(test_instances): + inputs = transform_image(pixel_values, input_dim) + inputs = inputs.to(DEVICE).to(dtype=DTYPE) + true_label = int(label) + inputs = inputs.unsqueeze(0) + return inputs, true_label, eps + + +def get_net(net, net_name): + net = get_network(DEVICE, net) + state_dict = torch.load('../nets/%s' % net_name, map_location=torch.device(DEVICE)) + if "state_dict" in state_dict.keys(): + state_dict = state_dict["state_dict"] + net.load_state_dict(state_dict) + net = net.to(dtype=DTYPE) + net.eval() + if 'resnet' in net_name: + net = NormalizedResnet(DEVICE, net) + return net + + +def analyze(net, inputs, eps, true_label): + return 0 + + +def main(): + parser = argparse.ArgumentParser(description='Neural network verification using DeepPoly relaxation') + parser.add_argument('--net', type=str, required=True, help='Neural network architecture to be verified.') + parser.add_argument('--spec', type=str, required=True, help='Test case to verify.') + args = parser.parse_args() + + net_name = get_net_name(args.net) + dataset = 'mnist' if 'mnist' in net_name else 'cifar10' + + inputs, true_label, eps = get_spec(args.spec, dataset) + net = get_net(args.net, net_name) + + outs = net(inputs) + pred_label = outs.max(dim=1)[1].item() + assert pred_label == true_label + + if analyze(net, inputs, eps, true_label): + print('verified') + else: + print('not verified') + + +if __name__ == '__main__': + main() diff --git a/examples/gt.txt b/examples/gt.txt new file mode 100644 index 0000000..7db2b6d --- /dev/null +++ b/examples/gt.txt @@ -0,0 +1,20 @@ +net_1,img1_0.0500.txt,verified +net_1,img2_0.0550.txt,verified +net_2,img1_0.2400.txt,verified +net_2,img2_0.2360.txt,verified +net_3,img1_0.0065.txt,not verified +net_3,img2_0.0081.txt,verified +net_4,img1_0.2240.txt,verified +net_4,img2_0.2290.txt,not verified +net_5,img1_0.0260.txt,verified +net_5,img2_0.0470.txt,verified +net_6,img1_0.0140.txt,verified +net_6,img2_0.0147.txt,not verified +net_7,img1_0.0540.txt,verified +net_7,img2_0.1440.txt,not verified +net_8,img1_0.0096.txt,verified +net_8,img2_0.0100.txt,verified +net_9,img1_0.0040.txt,verified +net_9,img2_0.0040.txt,verified +net_10,img1_0.0014.txt,verified +net_10,img2_0.0015.txt,not verified diff --git a/examples/net1/img1_0.0500.txt b/examples/net1/img1_0.0500.txt new file mode 100644 index 0000000..7de9913 --- /dev/null +++ b/examples/net1/img1_0.0500.txt @@ -0,0 +1 @@ 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diff --git a/examples/net1/img2_0.0550.txt b/examples/net1/img2_0.0550.txt new file mode 100644 index 0000000..65fb44f --- /dev/null +++ b/examples/net1/img2_0.0550.txt @@ -0,0 +1 @@ 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diff --git a/examples/net10/img1_0.0014.txt b/examples/net10/img1_0.0014.txt new file mode 100644 index 0000000..29da4c2 --- /dev/null +++ b/examples/net10/img1_0.0014.txt @@ -0,0 +1 @@ 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diff --git a/examples/net10/img2_0.0015.txt b/examples/net10/img2_0.0015.txt new file mode 100644 index 0000000..ecf91be --- /dev/null +++ b/examples/net10/img2_0.0015.txt @@ -0,0 +1 @@ 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diff --git a/examples/net2/img1_0.2400.txt b/examples/net2/img1_0.2400.txt new file mode 100644 index 0000000..488b405 --- /dev/null +++ b/examples/net2/img1_0.2400.txt @@ -0,0 +1 @@ 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diff --git a/examples/net3/img1_0.0065.txt b/examples/net3/img1_0.0065.txt new file mode 100644 index 0000000..9d70112 --- /dev/null +++ b/examples/net3/img1_0.0065.txt @@ -0,0 +1 @@ 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diff --git a/examples/net3/img2_0.0081.txt b/examples/net3/img2_0.0081.txt new file mode 100644 index 0000000..6d44f49 --- /dev/null +++ b/examples/net3/img2_0.0081.txt @@ -0,0 +1 @@ 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diff --git a/examples/net4/img1_0.2240.txt b/examples/net4/img1_0.2240.txt new file mode 100644 index 0000000..c7c6c42 --- /dev/null +++ b/examples/net4/img1_0.2240.txt @@ -0,0 +1 @@ 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diff --git a/examples/net5/img1_0.0260.txt b/examples/net5/img1_0.0260.txt new file mode 100644 index 0000000..cc99051 --- /dev/null +++ b/examples/net5/img1_0.0260.txt @@ -0,0 +1 @@ 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diff --git a/examples/net6/img1_0.0140.txt b/examples/net6/img1_0.0140.txt new file mode 100644 index 0000000..9629dd0 --- /dev/null +++ b/examples/net6/img1_0.0140.txt @@ -0,0 +1 @@ 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diff --git a/examples/net6/img2_0.0147.txt b/examples/net6/img2_0.0147.txt new file mode 100644 index 0000000..7721929 --- /dev/null +++ b/examples/net6/img2_0.0147.txt @@ -0,0 +1 @@ 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diff --git a/examples/net7/img1_0.0540.txt b/examples/net7/img1_0.0540.txt new file mode 100644 index 0000000..61074ab --- /dev/null +++ b/examples/net7/img1_0.0540.txt @@ -0,0 +1 @@ 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diff --git a/examples/net7/img2_0.1440.txt b/examples/net7/img2_0.1440.txt new file mode 100644 index 0000000..4c5f319 --- /dev/null +++ b/examples/net7/img2_0.1440.txt @@ -0,0 +1 @@ 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diff --git a/examples/net8/img1_0.0096.txt b/examples/net8/img1_0.0096.txt new file mode 100644 index 0000000..5eb037e --- /dev/null +++ b/examples/net8/img1_0.0096.txt @@ -0,0 +1 @@ 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diff --git a/examples/net8/img2_0.0100.txt b/examples/net8/img2_0.0100.txt new file mode 100644 index 0000000..cbeecc6 --- /dev/null +++ b/examples/net8/img2_0.0100.txt @@ -0,0 +1 @@ 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diff --git a/examples/net9/img1_0.0040.txt b/examples/net9/img1_0.0040.txt new file mode 100644 index 0000000..0843b8d --- /dev/null +++ b/examples/net9/img1_0.0040.txt @@ -0,0 +1 @@ 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diff --git a/examples/net9/img2_0.0040.txt b/examples/net9/img2_0.0040.txt new file mode 100644 index 0000000..7d508b5 --- /dev/null +++ b/examples/net9/img2_0.0040.txt @@ -0,0 +1 @@ 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