How to use skip_path method in tempest

Best Python code snippet using tempest_python

goml_ext.py

Source:goml_ext.py

...46    if 'SUB_CODE_PATH_LIST' in stream_info and stream_info['SUB_CODE_PATH_LIST'] != '':47        sub_code_path_list = stream_info['SUB_CODE_PATH_LIST'].split(',')48        sub_path_list = [''.join(stream_code_path+'/'+path).replace('//','/') for path in sub_code_path_list]49        find_path = stream_code_path50        stream_info['SKIP_PATHS'] += util.add_skip_path('', stream_code_path, find_path, sub_path_list)51    if "SKIP_PATHS" in stream_info:52        skip_path_list = stream_info['SKIP_PATHS'].split(';')53        for skip_path in skip_path_list:54            skip_path = skip_path.replace(".*/",'').replace("/.*",'').replace(".*",'').replace("*",'')55            if  skip_path.replace(' ', '') == "":56                continue57            if re.search("^src/", skip_path):58                skip_path = skip_path[4:]59            skip_paths += " --skip=\""+skip_path+"\" "60    61    if 'CHECKER_OPTIONS' in stream_info and stream_info['CHECKER_OPTIONS'] != '':62        checker_options = json.loads(stream_info['CHECKER_OPTIONS'])63        for checker_option in checker_options.values():64            checker_option = json.loads(checker_option)...

tree_pruner.py

Source:tree_pruner.py

1from IPython import embed2from utility.tree import Node, Tree3import claripy4import json5SKIP      = 16SKIP_PATH = 27ADD       = 38class Filter(object):9    def __init__(self, blocks_white, edges_white, blocks_black, edges_black, max_fork, fork_choices, range_symbols):10        self.blocks_white  = blocks_white11        self.edges_white   = edges_white12        self.blocks_black  = blocks_black13        self.edges_black   = edges_black14        self.max_fork      = max_fork15        self.fork_choices  = fork_choices16        self.range_symbols = range_symbols17class TreePruner(object):18    def __init__(self, symb_tree, address_to_block, symbols):19        self.symb_tree = symb_tree20        self.symbols   = symbols21        if isinstance(address_to_block, dict):22            self.address_to_block = address_to_block23        else:24            with open(address_to_block, "r") as fin:25                self.address_to_block = json.load(fin)26    27    def parse_options(self, filter_opts):28        res = Filter(29            dict(), dict(), dict(), dict(), dict(), dict(), dict()30        )31        32        for opt in filter_opts:33            if opt["type"] == "filter_block" and opt["mode"] == "black":34                count = 035                if "count" in opt:36                    count = int(opt["count"])37                res.blocks_black[opt["block_id"]] = count38            elif opt["type"] == "filter_block" and opt["mode"] == "white":39                count = 140                if "count" in opt:41                    count = int(opt["count"])42                res.blocks_white[opt["block_id"]] = count43            elif opt["type"] == "filter_edge" and opt["mode"] == "black":44                count = 045                if "count" in opt:46                    count = int(opt["count"])47                res.edges_black[48                    opt["src_id"], opt["dst_id"]49                ] = count50            elif opt["type"] == "filter_edge" and opt["mode"] == "white":51                count = 152                if "count" in opt:53                    count = int(opt["count"])54                res.edges_white[(55                    opt["src_id"], opt["dst_id"]56                )] = count57            elif opt["type"] == "limit_fork":58                assert opt["block_id"] not in res.max_fork59                res.max_fork[opt["block_id"]] = [int(opt["num_fork"]), True]60                if "fork_choice" in opt:61                    res.fork_choices[opt["block_id"]] = opt["fork_choice"]62                if "inverted" in opt:63                    assert opt["inverted"] in {True, False}64                    if opt["inverted"]:65                        res.max_fork[opt["block_id"]] = [int(opt["num_fork"]), False]66            elif opt["type"] == "limit_symbol":67                assert opt["symbol_id"] not in res.range_symbols68                assert opt["symbol_id"] in self.symbols69                symb_obj = self.symbols[opt["symbol_id"]].claripy_obj70                res.range_symbols[opt["symbol_id"]] = (71                    symb_obj, int(opt["min"]), int(opt["max"])72                )73        return res74    75    @staticmethod76    def check_blacklisted_blocks(path, blocks_black):77        """ check whether all black-list blocks do not exceed max count """78        tmp_blocks_black = dict(blocks_black)79        for b in path:80            if b in tmp_blocks_black:81                tmp_blocks_black[b] -= 182                if tmp_blocks_black[b] < 0:83                    return False84        return True85    86    @staticmethod87    def check_blacklisted_edges(path, edges_black):88        """ check whether all black-list edges do not exceed max count """89        edges = list()90        for b1, b2 in zip(path, path[1:]):91            edges.append((b1, b2))92        tmp_edges_black = dict(edges_black)93        for b1, b2 in edges:94            if (b1, b2) in tmp_edges_black:95                tmp_edges_black[(b1, b2)] -= 196                if tmp_edges_black[(b1, b2)] < 0:97                    return False98        return True99    @staticmethod100    def check_whitelisted_blocks(path, blocks_white):101        """ check whether all white-list blocks are in path """102        tmp_blocks_white = dict(blocks_white)103        for b in path:104            if b in tmp_blocks_white:105                tmp_blocks_white[b] -= 1106        107        for b in tmp_blocks_white:108            if tmp_blocks_white[b] > 0:109                return False110        return True111    112    @staticmethod113    def check_whitelisted_edges(path, edges_white):114        """ check whether all white-list edges are in path """115        edges = list()116        for b1, b2 in zip(path, path[1:]):117            edges.append((b1, b2))118        tmp_edges_white = dict(edges_white)119        for e in edges:120            if e in tmp_edges_white:121                tmp_edges_white[e] -= 1122        123        for e in tmp_edges_white:124            if tmp_edges_white[e] > 0:125                return False126        return True127    128    @staticmethod129    def check_symbol_range(state, range_symbols):130        for sid in range_symbols:131            symbol, rmin, rmax = range_symbols[sid]132            if not state.solver.satisfiable(extra_constraints=[133                claripy.And(134                    claripy.SGE(symbol, rmin),135                    claripy.SLE(symbol, rmax)136                )137            ]):138                return False139        return True140    141    def get_blocks_state(self, state):142        blocks = []143        for addr in state.block_addresses:144            if str(addr) in self.address_to_block:145                block_id = self.address_to_block[str(addr)]146                if block_id not in blocks:147                    blocks.append(block_id)148        return blocks149    150    def choose_next(self, block_id, children, fork_choices):151        assert children152        if block_id not in fork_choices:153            return children[0]154        for child in children:155            lwState = child.data156            blocks = self.get_blocks_state(lwState)157            if set(blocks).intersection(set([fork_choices[block_id]])):158                return child159        return children[0]160    def __filter_tree(self, node, prec_id, path, filter_data, tree):161        lwState  = node.data162        blocks_id = self.get_blocks_state(lwState)163        path = path + blocks_id164        165        # block black check166        if not TreePruner.check_blacklisted_blocks(path, filter_data.blocks_black):167            return SKIP_PATH, None168        # edge black check169        if not TreePruner.check_blacklisted_edges(path, filter_data.edges_black):170            return SKIP_PATH, None171        if len(node.children) == 0:172            # block white check173            if not TreePruner.check_whitelisted_blocks(path, filter_data.blocks_white):174                return SKIP_PATH, None175            # edge white check176            if not TreePruner.check_whitelisted_edges(path, filter_data.edges_white):177                return SKIP_PATH, None178            # symbol range check. Weakref should be good since it is a leaf179            if not TreePruner.check_symbol_range(lwState.state_weakref, filter_data.range_symbols):180                return SKIP_PATH, None181        182        # limit fork check183        limit_child_number = False184        for block_id in blocks_id:185            if block_id in filter_data.max_fork:186                filter_data.max_fork[block_id][0] -= 1187                count, normal = filter_data.max_fork[block_id]188                if (189                    (normal and count < 0) or190                    (not normal and count >= 0)191                ):192                    limit_child_number = True193        # recursive call to children194        new_node  = Node(node.id, node.data, node.father_id)195        skip_path = False196        children = node.children197        # limit forks198        if children and limit_child_number:199            children = [self.choose_next(block_id, node.children, filter_data.fork_choices)]200        for child in children:201            cmd, res_node = self.__filter_tree(child, blocks_id[-1] if blocks_id else "", path, filter_data, tree)202            if cmd == ADD:203                new_node.children.append(res_node)204            elif cmd == SKIP_PATH:205                skip_path = True206        207        if skip_path and len(new_node.children) == 0:208            return SKIP_PATH, None209        tree.add_node(new_node)210        return ADD, new_node211    212    def filter_tree(self, filter_opts):213        filter_data = self.parse_options(filter_opts)214        new_tree = Tree()215        if self.symb_tree.is_empty():216            return new_tree217        node = self.symb_tree.root.children[0]218        self.__filter_tree(node, "", list(), filter_data, new_tree)...

model.py

Source:model.py

1import torch.nn as nn2import torch.nn.functional as F3class Swish(nn.Module):4    def __init__(self, swish=1.0) -> None:5        super(Swish, self).__init__()6        self.swish = swish7    def forward(self, x):8        if self.swish == 1.0:9            return F.silu(x)10        else:11            return x * F.sigmoid(x * float(self.swish))12class ResNetBlock(nn.Module):13    def __init__(self, channels, num_groups = 32, skip_connection_scale=1, swish=1.0, skip_path=False):14        super(ResNetBlock, self).__init__()15        16        if num_groups != 0:17            self.main_path = nn.Sequential(18                nn.GroupNorm(num_groups, channels),19                Swish(swish),20                nn.Conv2d(channels, channels, 3, 1, 1),21                nn.GroupNorm(num_groups, channels),22                Swish(swish),23                nn.Conv2d(channels, channels, 3, 1, 1)24            )25        else:26            self.main_path = nn.Sequential(27                nn.BatchNorm2d(channels),28                Swish(swish),29                nn.Conv2d(channels, channels, 3, 1, 1),30                nn.BatchNorm2d(channels),31                Swish(swish),32                nn.Conv2d(channels, channels, 3, 1, 1)33            )34        self.skip_path = skip_path35        if skip_path:36            self.skip = nn.Conv2d(channels, channels, 1, 1, 0)37        self.skip_conn_scale = skip_connection_scale38    39    def forward(self, x):40        x_main = self.main_path(x)41        if self.skip_path:42            x_skip = self.skip(x)43        else:44            x_skip = x45        return x_main + x_skip * self.skip_conn_scale46class DBlock(nn.Module):47    def __init__(self, in_channels, out_channels, numResNetBlocks, num_groups = 32, skip_connection_scale=1, swish=1.0, use_conv=False, skip_path=False) -> None:48        super(DBlock, self).__init__()49        self.max_pool = nn.MaxPool2d(2, 2)50        self.use_conv = use_conv51        if use_conv:52            self.down_sample_conv = nn.Conv2d(in_channels, out_channels, 3, 2, 1)53        else:54            self.down_sample_conv = nn.Conv2d(in_channels, out_channels, 3, 1, 1)55        self.resblocks = nn.ModuleList([ResNetBlock(out_channels, num_groups, skip_connection_scale, swish, skip_path=skip_path) for i in range(numResNetBlocks)])56    def forward(self, x):57        if not self.use_conv:58            x = self.max_pool(x)59        x = self.down_sample_conv(x)60        for resblock in self.resblocks:61            x = resblock(x)62        return x63class UBlock(nn.Module):64    def __init__(self, in_channels, out_channels, numResNetBlocks, num_groups = 32, skip_connection_scale=1, swish=1.0, skip_path=False) -> None:65        super(UBlock, self).__init__()66        self.upblock = nn.Sequential(67            nn.Upsample(scale_factor=2),68            nn.Conv2d(in_channels, out_channels, 3, 1, 1)69        )70        self.resblocks = nn.ModuleList([ResNetBlock(in_channels, num_groups, skip_connection_scale, swish, skip_path=skip_path) for i in range(numResNetBlocks)])71    def forward(self, x):72        for resblock in self.resblocks:73            x = resblock(x) 74        x = self.upblock(x)    75        return x 76class Unet(nn.Module):77    def __init__(self, config) -> None:78        super(Unet, self).__init__()79        #init model with config80        base_channels = config["base_channels"]81        res1, res2, res3, res4 = config["resblocks"]82        swish = config["swish"]83        num_groups = config["num_groups"]84        skip_connection_scale = config["skip_connection_scale"]85        downsample_conv = config["down_conv"]86        skip_conv = config["skip_conv"]87        #downsample blocks88        if num_groups!=0:89            self.inp_conv = nn.Sequential(nn.Conv2d(3, base_channels, 3, 1, 1),90                                        ResNetBlock(base_channels),91                                        nn.GroupNorm(num_groups, base_channels),92                                        Swish(swish))93        else:94            self.inp_conv = nn.Sequential(nn.Conv2d(3, base_channels, 3, 1, 1),95                                        ResNetBlock(base_channels),96                                        nn.BatchNorm2d(base_channels),97                                        Swish(swish))98        self.down_1 = DBlock(base_channels, base_channels*2, res1, num_groups, skip_connection_scale, swish, use_conv=downsample_conv, skip_path=skip_conv) 99        self.down_2 = DBlock(base_channels*2, base_channels*4, res2, num_groups, skip_connection_scale, swish, use_conv=downsample_conv, skip_path=skip_conv)100        self.down_3 = DBlock(base_channels*4, base_channels*8, res3, num_groups, skip_connection_scale, swish, use_conv=downsample_conv, skip_path=skip_conv)101        self.down_4 = DBlock(base_channels*8, base_channels*16, res4, num_groups, skip_connection_scale, swish, use_conv=downsample_conv, skip_path=skip_conv)102        #upsample blocks103        self.up_1 = UBlock(base_channels*16, base_channels*8, res4, num_groups, skip_connection_scale, swish, skip_path=skip_conv)104        self.up_2 = UBlock(base_channels*8, base_channels*4, res3, num_groups, skip_connection_scale, swish, skip_path=skip_conv)105        self.up_3 = UBlock(base_channels*4, base_channels*2, res2, num_groups, skip_connection_scale, swish, skip_path=skip_conv)106        self.up_4 = UBlock(base_channels*2, base_channels, res1, num_groups, skip_connection_scale, swish, skip_path=skip_conv)107        self.up_5 = UBlock(base_channels, base_channels//2, 1, num_groups, skip_connection_scale, swish, skip_path=skip_conv)108        109        if num_groups!=0:110            self.out_conv = nn.Sequential(nn.GroupNorm(num_groups, base_channels//2),111                                        Swish(swish),112                                        nn.Conv2d(base_channels//2, 3, 3, 1, 1))113        else:114            self.out_conv = nn.Sequential(nn.BatchNorm2d(base_channels//2),115                                        Swish(swish),116                                        nn.Conv2d(base_channels//2, 3, 3, 1, 1))117    118    def forward(self, x):119        # assuming 3 x 256 x 256 input120        x = self.inp_conv(x) # output: 64 x 256 x 256121        #down sample122        x_d1 = self.down_1(x) # output : 128 x 128 x 128123        x_d2 = self.down_2(x_d1) # output : 256 x 64 x 64124        x_d3 = self.down_3(x_d2) # output : 512 x 32 x 32125        x_d4 = self.down_4(x_d3) # output : 1024 x 16 x 16126        127        #upsample128        x_up1 = self.up_1(x_d4) # output : 512 x 32 x 32129        x_up2 = self.up_2(x_up1 + x_d3) #output : 256 x 64 x 64130        x_up3 = self.up_3(x_up2 + x_d2) # output : 128 x 128 x 128131        x_up4 = self.up_4(x_up3 + x_d1) # output : 64 x 256 x 256132        x_up5 = self.up_5(x_up4) # output : 32 x 512 x 512133        out = self.out_conv(x_up5)...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.