How to use _forward method in fMBT

Best Python code snippet using fMBT_python

variations.py

Source:variations.py

...72    def psi(cls, x):73        return torch.rand_like(x)74    @torch.no_grad()75    def forward(self, points: torch.Tensor):76        v = self._forward(points)77        if isinstance(v, tuple):78            v = torch.stack(list(v), dim=1)79        return v80    def __getstate__(self):81        return dict(82            p=self.p83        )84class Linear(Variation):85    def _forward(self, points):86        return points87class Sinusoidal(Variation):88    def _forward(self, points):89        return torch.sin(points)90class Spherical1(Variation):91    def _forward(self, points):92        return points / self.r(points).unsqueeze(1)93class Spherical2(Variation):94    def _forward(self, points):95        return points / self.r2(points).unsqueeze(1)96class Swirl(Variation):97    def _forward(self, points):98        x = self.x(points)99        y = self.y(points)100        r2 = self.r2(x, y)101        sin_r2 = torch.sin(r2)102        cos_r2 = torch.cos(r2)103        return (104            x * sin_r2 - y * cos_r2,105            x * cos_r2 + y * sin_r2106        )107class Swirl2(Variation):108    def _forward(self, points):109        r = self.r(points)110        theta = self.theta(points)111        return (112            r * torch.cos(theta + r),113            r * torch.sin(theta + r)114        )115class Horseshoe(Variation):116    def _forward(self, points):117        r = self.r(points)118        theta = self.theta(points)119        return (120            r * torch.cos(2 * theta),121            r * torch.sin(2 * theta)122        )123class Horseshoe2(Variation):124    def _forward(self, points):125        x = self.x(points)126        y = self.y(points)127        r = self.r(x, y)128        return (129            (x - y) * (x + y) / r,130            2 * x * y / r131        )132class Polar(Variation):133    def _forward(self, points):134        return (135            self.theta(points) / np.pi,136            self.r(points) - 1137        )138class Handkerchief(Variation):139    def _forward(self, points):140        r = self.r(points)141        theta = self.theta(points)142        return (143            r * torch.sin(theta + r),144            r * torch.cos(theta - r)145        )146class Heart(Variation):147    def _forward(self, points):148        r = self.r(points)149        theta = self.theta(points)150        return (151            r * torch.sin(theta * r),152            -r * torch.cos(theta * r)153        )154class Spiral(Variation):155    def _forward(self, points):156        r = self.r(points)157        theta = self.theta(points)158        return (159            (torch.cos(theta) + torch.sin(r)) / r,160            (torch.sin(theta) - torch.cos(r)) / r161        )162class Hyperbolic(Variation):163    def _forward(self, points):164        r = self.r(points)165        theta = self.theta(points)166        return (167            torch.sin(theta) / r,168            torch.cos(theta) * r169        )170class Diamond(Variation):171    def _forward(self, points):172        r = self.r(points)173        theta = self.theta(points)174        return (175            torch.sin(theta) * torch.cos(r),176            torch.cos(theta) * torch.sin(r)177        )178class Ex(Variation):179    def _forward(self, points):180        r = self.r(points)181        theta = self.theta(points)182        return (183            r * torch.pow(torch.sin(theta + r), 3),184            r * torch.pow(torch.cos(theta - r), 3)185        )186class Ex2(Variation):187    def _forward(self, points):188        r = self.r(points)189        theta = self.theta(points)190        p0 = torch.sin(theta + r) ** 3191        p1 = torch.cos(theta - r) ** 3192        return (193            r * (p0 + p1),194            r * (p0 - p1)195        )196class Julia(Variation):197    def _forward(self, points):198        sqrt_r = torch.sqrt(self.r(points))199        theta = self.theta(points)200        omega = self.omega(sqrt_r)201        return (202            sqrt_r * torch.cos(theta / 2 + omega),203            sqrt_r * torch.sin(theta / 2 + omega)204        )205    206    207class Disc(Variation):208    def _forward(self, points):209        theta_pi = np.pi * self.theta(points)210        r_pi = np.pi * self.r(points)211        return (212            theta_pi * torch.sin(r_pi),213            theta_pi * torch.cos(r_pi)214        )215    216    217class Bent(Variation):218    def _forward(self, points):219        x = self.x(points)220        y = self.y(points)221        return torch.where(x >= 0, x, 2 * x), torch.where(y >= 0, y, y / 2)222# class Waves(Variation):223#     pass224class Fisheye(Variation):225    def _forward(self, points):226        scale = 2 / (self.r(points) + 1)227        return (  # Yes, this ordering is intentional. See Eyefish for the "fixed" version...228            self.y(points) * scale,229            self.x(points) * scale230        )231# class Popcorn(Variation):232#     pass233class Exponential(Variation):234    def _forward(self, points):235        x = self.x(points)236        y = self.y(points)237        scale = torch.exp(x - 1)238        return (239            scale * torch.cos(np.pi * y),240            scale * torch.sin(np.pi * y)241        )242class Power(Variation):243    def _forward(self, points):244        x = self.x(points)245        y = self.y(points)246        r = self.r(x, y)247        theta = self.theta(x, y)248        scale = r ** torch.sin(theta)249        return (250            scale * torch.cos(theta),251            scale * torch.sin(theta)252        )253class Cosine(Variation):254    def _forward(self, points):255        x = self.x(points)256        y = self.y(points)257        r = self.r(x, y)258        theta = self.theta(x, y)259        return (260            torch.cos(np.pi * x) * torch.cosh(y),261            -torch.sin(np.pi * x) * torch.sinh(y)262        )263# class Rings(Variation):264#     def _forward(self, points):265#         x = self.x(points)266#         y = self.y(points)267#         r = self.r(x, y)268#         theta = self.theta(x, y)269#         return (270#271#         )272# class Fan(Variation):273#     def _forward(self, points):274#         x = self.x(points)275#         y = self.y(points)276#         r = self.r(x, y)277#         theta = self.theta(x, y)278#         return (279#280#         )281class Blob(Variation):282    num_p = 3283    def _forward(self, points):284        x = self.x(points)285        y = self.y(points)286        r = self.r(x, y)287        theta = self.theta(x, y)288        high, low, waves = points.new_tensor(self.p)289        scale = r * (low + (high - low) / 2 * (torch.sin(waves * theta) + 1))290        return (291            scale * torch.cos(theta),292            scale * torch.sin(theta)293        )294class PDJ(Variation):295    num_p = 4296    def _forward(self, points):297        x = self.x(points)298        y = self.y(points)299        return (300            torch.sin(self.p[0] * y) - torch.cos(self.p[1] * x),301            torch.sin(self.p[2] * x) - torch.cos(self.p[3] * y)302        )303class Fan2(Variation):304    num_p = 2305    def _forward(self, points):306        x = self.x(points)307        y = self.y(points)308        r = self.r(x, y)309        theta = self.theta(x, y)310        j = np.pi * self.p[0] ** 2311        t = theta + self.p[1] - j * torch.trunc(2 * theta * self.p[1] / j)312        condition = t > j / 2313        theta_plus = theta + j / 2314        theta_minus = theta - j / 2315        return (316            r * torch.where(condition, torch.sin(theta_minus), torch.sin(theta_plus)),317            r * torch.where(condition, torch.cos(theta_minus), torch.cos(theta_plus))318        )319class Rings2(Variation):320    num_p = 1321    def _forward(self, points):322        x = self.x(points)323        y = self.y(points)324        r = self.r(x, y)325        theta = self.theta(x, y)326        p = self.p[0] ** 2327        t = r - 2 * p * torch.trunc((r + p) / (2 * p)) + r * (1 - p)328        return (329            t * torch.sin(theta),330            t * torch.cos(theta)331        )332class Eyefish(Variation):333    def _forward(self, points):334        scale = 2 / (self.r(points) + 1)335        return scale.unsqueeze(1) * points336class Bubble(Variation):337    def _forward(self, points):338        scale = 4 / (self.r2(points) + 4)339        return scale.unsqueeze(1) * points340class Cylinder(Variation):341    def _forward(self, points):342        return (343            torch.sin(self.x(points)),344            self.y(points)345        )346class Perspective(Variation):347    num_p = 2348    def _forward(self, points):349        x = self.x(points)350        y = self.y(points)351        angle, dist = points.new_tensor(self.p)352        scale = dist / (dist - y * torch.sin(angle))353        return (354            scale * x,355            scale * y * torch.cos(angle)356        )357class Noise(Variation):358    def _forward(self, points):359        x = self.x(points)360        y = self.y(points)361        r1 = self.psi(x)362        r2 = self.psi(x)363        return (364            r1 * x * torch.cos(2 * np.pi * r2),365            r1 * y * torch.sin(2 * np.pi * r2)366        )367class JuliaN(Variation):368    num_p = 2369    def _forward(self, points):370        x = self.x(points)371        y = self.y(points)372        r = self.r(x, y)373        power, dist = points.new_tensor(self.p)374        p3 = torch.trunc(torch.abs(power) * self.psi(x))375        t = (self.phi(points) + 2 * np.pi * p3) / power376        scale = r ** (dist / power)377        return (378            scale * torch.cos(t),379            scale * torch.sin(t)380        )381class JuliaScope(Variation):382    num_p = 2383    def _forward(self, points):384        x = self.x(points)385        y = self.y(points)386        r = self.r(x, y)387        power, dist = points.new_tensor(self.p)388        p3 = torch.trunc(torch.abs(power) * self.psi(x))389        t = (self.lambda_(x) * self.phi(points) + 2 * np.pi * p3) / power390        scale = r ** (dist / power)391        return (392            scale * torch.cos(t),393            scale * torch.sin(t)394        )395class Blur(Variation):396    def _forward(self, points):397        x = self.x(points)398        r1 = self.psi(x)399        r2 = self.psi(x)400        return (401            r1 * torch.cos(2 * np.pi * r2),402            r1 * torch.sin(2 * np.pi * r2)403        )404class Gaussian(Variation):405    # I deviate slightly here; they do a gaussian approximation, I'll just use randn406    def _forward(self, points):407        x = self.x(points)408        r1 = torch.randn_like(x)409        r2 = self.psi(x)410        return (411            r1 * torch.cos(2 * np.pi * r2),412            r1 * torch.sin(2 * np.pi * r2)413        )414# class RadialBlur(Variation):415#     def _forward(self, points):416#         x = self.x(points)417#         y = self.y(points)418#         r = self.r(x, y)419#         theta = self.theta(x, y)420#         return (421#422#         )423class Pie(Variation):424    num_p = 3425    def _forward(self, points):426        x = self.x(points)427        y = self.y(points)428        t1 = torch.trunc(self.psi(x) * self.p[0] + 0.5)429        t2 = self.p[1] + 2 * np.pi / self.p[0] * (t1 + self.psi(x) * self.p[2])430        scale = self.psi(x)431        return (432            scale * torch.cos(t2),433            scale * torch.sin(t2)434        )435# class NAME(Variation):436#     def _forward(self, points):437#         x = self.x(points)438#         y = self.y(points)439#         r = self.r(x, y)440#         theta = self.theta(x, y)441#         return (442#443#         )444#445#446# class NAME(Variation):447#     def _forward(self, points):448#         x = self.x(points)449#         y = self.y(points)450#         r = self.r(x, y)451#         theta = self.theta(x, y)452#         return (453#454#         )455#456#457# class NAME(Variation):458#     def _forward(self, points):459#         x = self.x(points)460#         y = self.y(points)461#         r = self.r(x, y)462#         theta = self.theta(x, y)463#         return (464#465#         )466#467#468# class NAME(Variation):469#     def _forward(self, points):470#         x = self.x(points)471#         y = self.y(points)472#         r = self.r(x, y)473#         theta = self.theta(x, y)474#         return (475#476#         )477#478#479# class NAME(Variation):480#     def _forward(self, points):481#         x = self.x(points)482#         y = self.y(points)483#         r = self.r(x, y)484#         theta = self.theta(x, y)485#         return (486#487#         )488#489#490# class NAME(Variation):491#     def _forward(self, points):492#         x = self.x(points)493#         y = self.y(points)494#         r = self.r(x, y)495#         theta = self.theta(x, y)496#         return (497#498#         )499#500#501# class NAME(Variation):502#     def _forward(self, points):503#         x = self.x(points)504#         y = self.y(points)505#         r = self.r(x, y)506#         theta = self.theta(x, y)507#         return (508#509#         )510#511#512# class NAME(Variation):513#     def _forward(self, points):514#         x = self.x(points)515#         y = self.y(points)516#         r = self.r(x, y)517#         theta = self.theta(x, y)518#         return (519#520#         )521#522#523# class NAME(Variation):524#     def _forward(self, points):525#         x = self.x(points)526#         y = self.y(points)527#         r = self.r(x, y)528#         theta = self.theta(x, y)529#         return (530#531#         )532#533#534# class NAME(Variation):535#     def _forward(self, points):536#         x = self.x(points)537#         y = self.y(points)538#         r = self.r(x, y)539#         theta = self.theta(x, y)540#         return (541#542#         )543#544#545# class NAME(Variation):546#     def _forward(self, points):547#         x = self.x(points)548#         y = self.y(points)549#         r = self.r(x, y)550#         theta = self.theta(x, y)551#         return (552#553#         )554if __name__ == '__main__':555    from . import grid_plot556    import argparse557    parser = argparse.ArgumentParser()558    parser.add_argument('variation', choices=list(sorted(Variation.all_variations.keys())))559    args = parser.parse_args()560    variation = Variation.all_variations[args.variation]()...

start_points.py

Source:start_points.py

...17        self.linear = nn.Linear(1024, vocab_size)18        self.device = device19    def forward(self, input, gts=None):20        if self.training:21            return self._forward(input, gts)22        else:23            with torch.no_grad():24                return self._forward(input, gts)25    def _forward(self, input, gts=None):26        cnn_output = self.cnn(input)27        _, hidden = self.encoder(cnn_output)  # width, batch, alphabet28        _, b, _ = hidden[0].shape29        outputs = []30        output = torch.zeros((1, b, 1024)).to(self.device)31        for i in range(MAX_LENGTH):32            output, hidden = self.decoder(output, hidden)33            #output = nn.functional.relu(output)34            outputs.append(self.linear(output))35        # sigmoids are done in the loss36        outputs = torch.cat(outputs, dim=0)37        return outputs38class StartPointModel2(nn.Module):39    def __init__(self, vocab_size=3, device="cuda", cnn_type="default", first_conv_op=CoordConv, first_conv_opts=None, **kwargs):40        super().__init__()41        self.__dict__.update(kwargs)42        if first_conv_op:43            first_conv_op = CoordConv44        self.decoder_size = 25645        self.cnn = CNN(nc=1, first_conv_op=first_conv_op, cnn_type=cnn_type, first_conv_opts=first_conv_opts)46        self.encoder = nn.LSTM(input_size=1024, hidden_size=self.decoder_size, bidirectional=True, dropout=.5, num_layers=1)47        self.decoder = nn.LSTM(input_size=self.decoder_size, hidden_size=self.decoder_size, num_layers=2, dropout=.5)48        self.linear = nn.Linear(self.decoder_size, vocab_size)49        self.device = device50    def forward(self, input, gts=None):51        if self.training:52            return self._forward(input, gts)53        else:54            with torch.no_grad():55                return self._forward(input, gts)56    def _forward(self, input, gts=None):57        cnn_output = self.cnn(input)58        _, hidden = self.encoder(cnn_output)  # width, batch, alphabet59        _, b, _ = hidden[0].shape60        outputs = []61        output = torch.zeros((1, b, self.decoder_size)).to(self.device)62        for i in range(MAX_LENGTH):63            output, hidden = self.decoder(output, hidden)64            #output = nn.functional.relu(output)65            outputs.append(self.linear(output))66        # sigmoids are done in the loss67        outputs = torch.cat(outputs, dim=0)68        return outputs69class StartPointAttnModel(nn.Module):70    def __init__(self, vocab_size=3, device="cuda", cnn_type="default", first_conv_op=CoordConv, first_conv_opts=None, **kwargs):71        super().__init__()72        self.__dict__.update(kwargs)73        if first_conv_op:74            first_conv_op = CoordConv75        self.cnn = CNN(nc=1, first_conv_op=first_conv_op, cnn_type=cnn_type, first_conv_opts=first_conv_opts)76        self.encoder = nn.LSTM(input_size=1024, hidden_size=256)77        self.attn = nn.MultiheadAttention(embed_dim=256, num_heads=1)78        self.decoder = nn.LSTM(input_size=512, hidden_size=256, num_layers=1)79        self.linear = nn.Linear(256, vocab_size)80        self.device = device81    def forward(self, input, gts=None):82        if self.training:83            return self._forward(input, gts)84        else:85            with torch.no_grad():86                return self._forward(input, gts)87    def _forward(self, input, gts=None):88        cnn_output = self.cnn(input)89        encoding, hidden = self.encoder(cnn_output)  # width, batch, alphabet90        _, b, _ = hidden[0].shape91        outputs = []92        output = torch.zeros((1, b, 256)).to(self.device)93        for i in range(MAX_LENGTH):94            context, _ = self.attn(output, encoding, encoding)95            output, hidden = self.decoder(torch.cat([output, context], dim=-1), hidden)96            output = nn.functional.relu(output)97            outputs.append(self.linear(output))98        # sigmoids are done in the loss99        outputs = torch.cat(outputs, dim=0)100        return outputs101class StartPointAttnModelDeep(nn.Module):102    def __init__(self, vocab_size=3, device="cuda", cnn_type="default", first_conv_op=CoordConv, first_conv_opts=None, **kwargs):103        super().__init__()104        self.__dict__.update(kwargs)105        if first_conv_op:106            first_conv_op = CoordConv107        self.decoder_layers = 2108        self.encoder_layers = 2109        self.context_dim = 128110        self.vocab_size = vocab_size111        self.decoder_dim = 128112        self.cnn = CNN(nc=1, first_conv_op=first_conv_op, cnn_type=cnn_type, first_conv_opts=first_conv_opts)113        self.encoder = nn.LSTM(input_size=1024, hidden_size=128, num_layers=self.encoder_layers)114        self.attn = nn.MultiheadAttention(embed_dim=128, num_heads=4)115        self.decoder = nn.LSTM(input_size=self.context_dim+self.decoder_dim, hidden_size=self.decoder_dim, num_layers=self.decoder_layers)116        self.linear = nn.Linear(self.decoder_dim, vocab_size)117        self.device = device118    def forward(self, input, gts=None):119        if self.training:120            return self._forward(input, gts)121        else:122            with torch.no_grad():123                return self._forward(input, gts)124    def _forward(self, input, gts=None):125        cnn_output = self.cnn(input)126        encoding, hidden = self.encoder(cnn_output)  # width, batch, alphabet127        _, b, _ = hidden[0].shape128        outputs = []129        output = torch.zeros((1, b, self.decoder_dim)).to(self.device)130        hidden = 2 * [torch.zeros((2, b, self.decoder_dim)).to(self.device)]131        for i in range(MAX_LENGTH):132            context, _ = self.attn(output, encoding, encoding)133            output, hidden = self.decoder(torch.cat([output, context], dim=-1), hidden)134            output = nn.functional.relu(output)135            outputs.append(self.linear(output))136        # sigmoids are done in the loss137        outputs = torch.cat(outputs, dim=0)138        return outputs139class StartPointAttnModelFull(nn.Module):140    def __init__(self, vocab_size=4, device="cuda", cnn_type="default", first_conv_op=CoordConv, first_conv_opts=None, **kwargs):141        super().__init__()142        self.__dict__.update(kwargs)143        if first_conv_op:144            first_conv_op = CoordConv145        self.decoder_layers = 2146        self.encoder_layers = 2147        self.context_dim = 128148        self.vocab_size = vocab_size149        self.decoder_dim = 128150        self.cnn = CNN(nc=1, first_conv_op=first_conv_op, cnn_type=cnn_type, first_conv_opts=first_conv_opts)151        self.encoder = nn.LSTM(input_size=1024, hidden_size=128, num_layers=self.encoder_layers)152        self.attn = nn.MultiheadAttention(embed_dim=128, num_heads=4)153        self.decoder = nn.LSTM(input_size=self.context_dim+vocab_size, hidden_size=self.decoder_dim, num_layers=self.decoder_layers)154        self.linear = nn.Linear(self.context_dim+self.decoder_dim, vocab_size)155        self.device = device156    def forward(self, input, gts=None):157        if self.training:158            return self._forward(input, gts)159        else:160            with torch.no_grad():161                return self._forward(input, gts)162    def _forward(self, input, gts=None):163        print(input.shape)164        cnn_output = self.cnn(input)165        encoding, hidden = self.encoder(cnn_output)  # width, batch, alphabet166        #assert encoding[-1,-1,-1] == hidden[0][-1,-1,-1])167        # https://stackoverflow.com/questions/48302810/whats-the-difference-between-hidden-and-output-in-pytorch-lstm168        _, b, _ = encoding.shape169        outputs = []170        hidden = [torch.zeros(2, b, self.decoder_dim).to(self.device)] * self.decoder_layers171        y = torch.zeros((1, b, self.vocab_size)).to(self.device)172        c_t = torch.zeros((1, b, self.context_dim)).to(self.device)173        for i in range(MAX_LENGTH):174            s1, hidden = self.decoder(torch.cat([y, c_t], dim=-1), hidden)175            c_t, _ = self.attn(s1, encoding, encoding)176            y = self.linear(torch.cat([s1, c_t], dim=-1))...

forwarding_canvas.py

Source:forwarding_canvas.py

1from .canvas import Canvas2def _forward(method):3    def func(self, *args, **kwargs):4        # pylint: disable=W02125        return getattr(self._canvas, method)(*args, **kwargs)6    func.name = method7    return func8class ForwardingCanvas(Canvas):9    """10    Canvas that dispatches all calls to a contained canvas11    """12    def __init__(self, canvas):13        super().__init__(canvas.width, canvas.height)14        self._canvas = canvas15    @property16    def turtle(self):17        return self._canvas.turtle18    @turtle.setter19    def turtle(self, turtle):20        self._canvas.turtle = turtle21    def set_canvas(self, canvas):22        canvas.turtle = self._canvas.turtle23        self._canvas = canvas24        self.width = canvas.width25        self.height = canvas.height26    draw_rectangular_line = _forward("draw_rectangular_line")27    draw_circle = _forward("draw_circle")28    fill_path = _forward("fill_path")29    axis_aligned_rectangle = _forward("axis_aligned_rectangle")30    set_bgcolor = _forward("set_bgcolor")31    clear = _forward("clear")32    refreshed_turtle = _forward("refreshed_turtle")33    set_speed = _forward("set_speed")...

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