Move to xautodl

xautodl/models/cell_searchs/search_model_gdas_frc_nasnet.py

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+###########################################################################
+# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #
+###########################################################################
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from models.cell_searchs.search_cells import NASNetSearchCell as SearchCell
+from models.cell_operations import RAW_OP_CLASSES
+
+
+# The macro structure is based on NASNet
+class NASNetworkGDAS_FRC(nn.Module):
+    def __init__(
+        self,
+        C,
+        N,
+        steps,
+        multiplier,
+        stem_multiplier,
+        num_classes,
+        search_space,
+        affine,
+        track_running_stats,
+    ):
+        super(NASNetworkGDAS_FRC, self).__init__()
+        self._C = C
+        self._layerN = N
+        self._steps = steps
+        self._multiplier = multiplier
+        self.stem = nn.Sequential(
+            nn.Conv2d(3, C * stem_multiplier, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(C * stem_multiplier),
+        )
+
+        # config for each layer
+        layer_channels = (
+            [C] * N + [C * 2] + [C * 2] * (N - 1) + [C * 4] + [C * 4] * (N - 1)
+        )
+        layer_reductions = (
+            [False] * N + [True] + [False] * (N - 1) + [True] + [False] * (N - 1)
+        )
+
+        num_edge, edge2index = None, None
+        C_prev_prev, C_prev, C_curr, reduction_prev = (
+            C * stem_multiplier,
+            C * stem_multiplier,
+            C,
+            False,
+        )
+
+        self.cells = nn.ModuleList()
+        for index, (C_curr, reduction) in enumerate(
+            zip(layer_channels, layer_reductions)
+        ):
+            if reduction:
+                cell = RAW_OP_CLASSES["gdas_reduction"](
+                    C_prev_prev,
+                    C_prev,
+                    C_curr,
+                    reduction_prev,
+                    affine,
+                    track_running_stats,
+                )
+            else:
+                cell = SearchCell(
+                    search_space,
+                    steps,
+                    multiplier,
+                    C_prev_prev,
+                    C_prev,
+                    C_curr,
+                    reduction,
+                    reduction_prev,
+                    affine,
+                    track_running_stats,
+                )
+            if num_edge is None:
+                num_edge, edge2index = cell.num_edges, cell.edge2index
+            else:
+                assert (
+                    reduction
+                    or num_edge == cell.num_edges
+                    and edge2index == cell.edge2index
+                ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges)
+            self.cells.append(cell)
+            C_prev_prev, C_prev, reduction_prev = (
+                C_prev,
+                cell.multiplier * C_curr,
+                reduction,
+            )
+        self.op_names = deepcopy(search_space)
+        self._Layer = len(self.cells)
+        self.edge2index = edge2index
+        self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(C_prev, num_classes)
+        self.arch_parameters = nn.Parameter(
+            1e-3 * torch.randn(num_edge, len(search_space))
+        )
+        self.tau = 10
+
+    def get_weights(self):
+        xlist = list(self.stem.parameters()) + list(self.cells.parameters())
+        xlist += list(self.lastact.parameters()) + list(
+            self.global_pooling.parameters()
+        )
+        xlist += list(self.classifier.parameters())
+        return xlist
+
+    def set_tau(self, tau):
+        self.tau = tau
+
+    def get_tau(self):
+        return self.tau
+
+    def get_alphas(self):
+        return [self.arch_parameters]
+
+    def show_alphas(self):
+        with torch.no_grad():
+            A = "arch-normal-parameters :\n{:}".format(
+                nn.functional.softmax(self.arch_parameters, dim=-1).cpu()
+            )
+        return "{:}".format(A)
+
+    def get_message(self):
+        string = self.extra_repr()
+        for i, cell in enumerate(self.cells):
+            string += "\n {:02d}/{:02d} :: {:}".format(
+                i, len(self.cells), cell.extra_repr()
+            )
+        return string
+
+    def extra_repr(self):
+        return "{name}(C={_C}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+    def genotype(self):
+        def _parse(weights):
+            gene = []
+            for i in range(self._steps):
+                edges = []
+                for j in range(2 + i):
+                    node_str = "{:}<-{:}".format(i, j)
+                    ws = weights[self.edge2index[node_str]]
+                    for k, op_name in enumerate(self.op_names):
+                        if op_name == "none":
+                            continue
+                        edges.append((op_name, j, ws[k]))
+                edges = sorted(edges, key=lambda x: -x[-1])
+                selected_edges = edges[:2]
+                gene.append(tuple(selected_edges))
+            return gene
+
+        with torch.no_grad():
+            gene_normal = _parse(
+                torch.softmax(self.arch_parameters, dim=-1).cpu().numpy()
+            )
+        return {
+            "normal": gene_normal,
+            "normal_concat": list(
+                range(2 + self._steps - self._multiplier, self._steps + 2)
+            ),
+        }
+
+    def forward(self, inputs):
+        def get_gumbel_prob(xins):
+            while True:
+                gumbels = -torch.empty_like(xins).exponential_().log()
+                logits = (xins.log_softmax(dim=1) + gumbels) / self.tau
+                probs = nn.functional.softmax(logits, dim=1)
+                index = probs.max(-1, keepdim=True)[1]
+                one_h = torch.zeros_like(logits).scatter_(-1, index, 1.0)
+                hardwts = one_h - probs.detach() + probs
+                if (
+                    (torch.isinf(gumbels).any())
+                    or (torch.isinf(probs).any())
+                    or (torch.isnan(probs).any())
+                ):
+                    continue
+                else:
+                    break
+            return hardwts, index
+
+        hardwts, index = get_gumbel_prob(self.arch_parameters)
+
+        s0 = s1 = self.stem(inputs)
+        for i, cell in enumerate(self.cells):
+            if cell.reduction:
+                s0, s1 = s1, cell(s0, s1)
+            else:
+                s0, s1 = s1, cell.forward_gdas(s0, s1, hardwts, index)
+        out = self.lastact(s1)
+        out = self.global_pooling(out)
+        out = out.view(out.size(0), -1)
+        logits = self.classifier(out)
+
+        return out, logits