How to use xy method in fMBT

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Bidirection.py

Source:Bidirection.py

1import torch.nn as nn2from Attention import AttentionModel,AttentionModel_without_Residual,AttentionModel_for_single_side_attention3import torch4from Models import GC_Block, PostGCN5from torch.nn.parameter import Parameter6class MergeNet(nn.Module):7    def __init__(self, seq_len, pose_dim):8        super(MergeNet, self).__init__()9        self.block1 = GC_Block(pose_dim, p_dropout=0.3, bias=True, node_n=seq_len)10        self.block2 = GC_Block(pose_dim, p_dropout=0.3, bias=True, node_n=seq_len)11        self.block3 = GC_Block(pose_dim, p_dropout=0.3, bias=True, node_n=seq_len)12        self.post_net = PostGCN(pose_dim, 66, node_n=seq_len)13        #self.lin1 = nn.Linear(pose_dim, 256)14        self.lin1 = nn.Linear(66, 66)15        self.dp1 = nn.Dropout(0.3)16        self.act1 = nn.LeakyReLU(0.2)17        # self.lin2 = nn.Linear(256, 66)18        # self.dp2 = nn.Dropout(0.3)19        # self.act2 = nn.LeakyReLU(0.2)20        self.W1 = Parameter(torch.FloatTensor(25, 1))21        self.W2 = Parameter(torch.FloatTensor(25, 1))22        self.reset_parameters()23    def reset_parameters(self):24        self.W1.data.fill_(0.5);25        self.W2.data.fill_(0.5);26    def forward(self, x, y, for_resid):27        # x = torch.mul(x,self.W1)28        # y = torch.mul(y,self.W2)29        xy = torch.add(x, y)30        xy = xy/231        #xy = torch.cat((x, y), dim=-1)32        # xy = self.lin1(xy)33        # xy = self.dp1(xy)34        #xy = self.act1(xy)35        # xy = self.lin2(xy)36        # xy = self.dp2(xy)37        # xy = self.act2(xy)38        # xy = torch.cat((x, y), dim=-1)39        # xy = self.block1(xy)40        # xy = self.block2(xy)41        # xy = self.block3(xy)42        # xy = self.post_net(xy)43        #xy = xy + for_resid44        return xy45class MergeNet_without_Residual(nn.Module):46    def __init__(self, seq_len, pose_dim):47        super(MergeNet_without_Residual, self).__init__()48        self.block1 = GC_Block(pose_dim, p_dropout=0.3, bias=True, node_n=seq_len)49        self.block2 = GC_Block(pose_dim, p_dropout=0.3, bias=True, node_n=seq_len)50        self.block3 = GC_Block(pose_dim, p_dropout=0.3, bias=True, node_n=seq_len)51        self.post_net = PostGCN(pose_dim, 66, node_n=seq_len)52    def forward(self, x, y, for_resid):53        # xy = torch.add(x, y)54        # xy = xy/255        xy = torch.cat((x, y), dim=-1)56        xy = self.block1(xy)57        xy = self.block2(xy)58        xy = self.block3(xy)59        xy = self.post_net(xy)60       # xy = xy + for_resid61        return xy62class BidirectionModel(nn.Module):63    def __init__(self):64        super(BidirectionModel, self).__init__()65        self.model = AttentionModel()66        self.inverse_model = AttentionModel()67        self.merge = MergeNet(25, 66 * 2)68    def forward(self, x, z, for_resid, invx, invz, inv_for_resid):69        out = self.model(x, z, for_resid)70        inv_out = self.inverse_model(invz, invx, inv_for_resid)71        inv_inv_out = inv_out.flip(1)72        ret = self.merge(out, inv_inv_out, for_resid)73        return out, inv_out, ret74class BidirectionModel_for_single_side_attention(nn.Module):75    def __init__(self):76        super(BidirectionModel_for_single_side_attention, self).__init__()77        self.model = AttentionModel_for_single_side_attention()78        self.inverse_model = AttentionModel_for_single_side_attention()79        self.merge = MergeNet(25, 66 * 2)80    def forward(self, x, z, for_resid, invx, invz, inv_for_resid):81        out = self.model(x, z, for_resid)82        inv_out = self.inverse_model(invz, invx, inv_for_resid)83        inv_inv_out = inv_out.flip(1)84        ret = self.merge(out, inv_inv_out, for_resid)85        return out, inv_out, ret86class BidirectionModel_without_Residual(nn.Module):87    def __init__(self):88        super(BidirectionModel_without_Residual, self).__init__()89        self.model = AttentionModel_without_Residual()90        self.inverse_model = AttentionModel_without_Residual()91        self.merge = MergeNet_without_Residual(25, 66 * 2)92    def forward(self, x, z, for_resid, invx, invz):93        out = self.model(x, z,)94        inv_out = self.inverse_model(invz, invx)95        inv_inv_out = inv_out.flip(1)96        ret = self.merge(out, inv_inv_out, for_resid)...

test_pairs.py

Source:test_pairs.py

...3import networkx as nx4from base_test import BaseTestAttributeMixing,BaseTestDegreeMixing5class TestAttributeMixingXY(BaseTestAttributeMixing):6    def test_node_attribute_xy_undirected(self):7        attrxy=sorted(nx.node_attribute_xy(self.G,'fish'))8        attrxy_result=sorted([('one','one'),9                              ('one','one'),10                              ('two','two'),11                              ('two','two'),12                              ('one','red'),13                              ('red','one'),14                              ('blue','two'),15                              ('two','blue')16                              ])17        assert_equal(attrxy,attrxy_result)18    def test_node_attribute_xy_undirected_nodes(self):19        attrxy=sorted(nx.node_attribute_xy(self.G,'fish',20                                           nodes=['one','yellow']))21        attrxy_result=sorted( [22                              ])23        assert_equal(attrxy,attrxy_result)24    def test_node_attribute_xy_directed(self):25        attrxy=sorted(nx.node_attribute_xy(self.D,'fish'))26        attrxy_result=sorted([('one','one'),27                              ('two','two'),28                              ('one','red'),29                              ('two','blue')30                              ])31        assert_equal(attrxy,attrxy_result)32    def test_node_attribute_xy_multigraph(self):33        attrxy=sorted(nx.node_attribute_xy(self.M,'fish'))34        attrxy_result=[('one','one'),35                       ('one','one'),36                       ('one','one'),37                       ('one','one'),38                       ('two','two'),39                       ('two','two')40                       ]41        assert_equal(attrxy,attrxy_result)42    def test_node_attribute_xy_selfloop(self):43        attrxy=sorted(nx.node_attribute_xy(self.S,'fish'))44        attrxy_result=[('one','one'),45                       ('two','two')46                       ]47        assert_equal(attrxy,attrxy_result)48class TestDegreeMixingXY(BaseTestDegreeMixing):49    def test_node_degree_xy_undirected(self):50        xy=sorted(nx.node_degree_xy(self.P4))51        xy_result=sorted([(1,2),52                          (2,1),53                          (2,2),54                          (2,2),55                          (1,2),56                          (2,1)])57        assert_equal(xy,xy_result)58    def test_node_degree_xy_undirected_nodes(self):59        xy=sorted(nx.node_degree_xy(self.P4,nodes=[0,1,-1]))60        xy_result=sorted([(1,2),61                          (2,1),])62        assert_equal(xy,xy_result)63    def test_node_degree_xy_directed(self):64        xy=sorted(nx.node_degree_xy(self.D))65        xy_result=sorted([(2,1),66                          (2,3),67                          (1,3),68                          (1,3)])69        assert_equal(xy,xy_result)70    def test_node_degree_xy_multigraph(self):71        xy=sorted(nx.node_degree_xy(self.M))72        xy_result=sorted([(2,3),73                          (2,3),74                          (3,2),75                          (3,2),76                          (2,3),77                          (3,2),78                          (1,2),79                          (2,1)])80        assert_equal(xy,xy_result)81    def test_node_degree_xy_selfloop(self):82        xy=sorted(nx.node_degree_xy(self.S))83        xy_result=sorted([(2,2),84                          (2,2)])85        assert_equal(xy,xy_result)86    def test_node_degree_xy_weighted(self):87        G = nx.Graph()88        G.add_edge(1,2,weight=7)89        G.add_edge(2,3,weight=10)90        xy=sorted(nx.node_degree_xy(G,weight='weight'))91        xy_result=sorted([(7,17),92                          (17,10),93                          (17,7),94                          (10,17)])...

convert-rgb-space-xyz.py

Source:convert-rgb-space-xyz.py

1#!/usr/bin/env python2#3# This program allows to generate matrices to convert between RGB spaces and XYZ4# All hardcoded values are directly taken from the ITU-R documents5#6# NOTE: When trying to convert from one space to another, make sure the whitepoint is the same,7# otherwise math gets more complicated (see Bradford transform).8#9# See also:10# http://www.brucelindbloom.com/index.html?Eqn_RGB_XYZ_Matrix.html11# https://ninedegreesbelow.com/photography/xyz-rgb.html12import numpy13def xy_to_XYZ(xy):14    return [xy[0] / xy[1], 1.0, (1.0 - xy[0] - xy[1]) / xy[1]]15def compute_rgb_to_zyx_matrix(whitepoint_XYZ, R, G, B):16    Xr = R[0] / R[1]17    Yr = 118    Zr = (1 - R[0] - R[1]) / R[1]19    Xg = G[0] / G[1]20    Yg = 121    Zg = (1 - G[0] - G[1]) / G[1]22    Xb = B[0] / B[1]23    Yb = 124    Zb = (1 - B[0] - B[1]) / B[1]25    m = numpy.array([26        [Xr, Xg, Xb],27        [Yr, Yg, Yb],28        [Zr, Zg, Zb]])29    m_inverse = numpy.linalg.inv(m)30    S = numpy.dot(m_inverse, whitepoint_XYZ)31    m = numpy.array([32        [S[0] * Xr, S[1] * Xg, S[2] * Xb],33        [S[0] * Yr, S[1] * Yg, S[2] * Yb],34        [S[0] * Zr, S[1] * Zg, S[2] * Zb]])35    return m36def d65_XYZ():37    d65_xy = [0.3127, 0.3290]38    return xy_to_XYZ(d65_xy)39def compute_srgb_to_xyz():40    R_xy = [0.640, 0.330]41    G_xy = [0.300, 0.600]42    B_xy = [0.150, 0.060]43    return compute_rgb_to_zyx_matrix(d65_XYZ(), R_xy, G_xy, B_xy)44def compute_rec709_rgb_to_xyz():45    R_xy = [0.640, 0.330]46    G_xy = [0.300, 0.600]47    B_xy = [0.150, 0.060]48    return compute_rgb_to_zyx_matrix(d65_XYZ(), R_xy, G_xy, B_xy)49def compute_rec2020_rgb_to_xyz():50    R_xy = [0.708, 0.292]51    G_xy = [0.170, 0.797]52    B_xy = [0.131, 0.046]53    return compute_rgb_to_zyx_matrix(d65_XYZ(), R_xy, G_xy, B_xy)54def compute_display_p3_rgb_to_xyz():55    R_xy = [0.680, 0.320]56    G_xy = [0.265, 0.690]57    B_xy = [0.150, 0.060]58    return compute_rgb_to_zyx_matrix(d65_XYZ(), R_xy, G_xy, B_xy)59def compute_full_transform(A_to_XYZ, B_to_XYZ):60    XYZ_to_B = numpy.linalg.inv(B_to_XYZ)61    A_to_B = numpy.matmul(XYZ_to_B, A_to_XYZ)62    print(f'M\n{A_to_B}')63    B_to_A = numpy.linalg.inv(A_to_B)64    print(f'M-1\n{B_to_A}')65if __name__ == '__main__':66    numpy.set_printoptions(precision = 10, suppress = True, floatmode = 'fixed')67    rgb_xyz = compute_srgb_to_xyz()68    xyz_rgb = numpy.linalg.inv(rgb_xyz)69    print(f'M\n{xyz_rgb}')...

5.1.py

Source:5.1.py

1# Import the data2f = open("5_data.txt")3data = []4# Process the data to a valid input array5for index, line in enumerate(f):6    # Split on the arrow part, the first part is from, second to.7    processed = line.rstrip('\n').split(' -> ')8    data.append(processed)9    # For debug purposes, limit the for loop.10    # if index > 10:11    #     break12grid = {}13for item in data:14    # Process the from.15    # @todo Changes map() the order?16    xyFrom = list(map(lambda x: int(x), item[0].split(',')))17    xyTo = list(map(lambda x: int(x), item[1].split(',')))18    # Only process the items where x/y from and to are equal.19    if (xyFrom[0] == xyTo[0] or xyFrom[1] == xyTo[1]):20        # Do something with the data.21        if xyFrom[0] == xyTo[0]:22            startY = xyFrom[1] if xyFrom[1] < xyTo[1] else xyTo[1]23            endY = xyFrom[1] if xyFrom[1] > xyTo[1] else xyTo[1]24            # The y changes, while loop until values matches.25            while startY <= endY:26                i = ','.join([str(xyFrom[0]), str(startY)])27                if i not in grid:28                    grid[i] = 029                grid[i] += 130                startY += 131        if xyFrom[1] == xyTo[1]:32            startX = xyFrom[0] if xyFrom[0] < xyTo[0] else xyTo[0]33            endX = xyFrom[0] if xyFrom[0] > xyTo[0] else xyTo[0]34            # The y changes, while loop until values matches.35            while startX <= endX:36                i = ','.join([str(startX), str(xyFrom[1])])37                if i not in grid:38                    grid[i] = 039                grid[i] += 140                startX += 141# Filter out where the count is 142test = dict(filter(lambda x: x[1] > 1, grid.items()))43print('answer: ', len(test))44# 8022 (to low)...

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