Move to xautodl

xautodl/models/cell_searchs/search_model_setn_nasnet.py

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+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+######################################################################################
+# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 #
+######################################################################################
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from typing import List, Text, Dict
+from .search_cells import NASNetSearchCell as SearchCell
+
+
+# The macro structure is based on NASNet
+class NASNetworkSETN(nn.Module):
+    def __init__(
+        self,
+        C: int,
+        N: int,
+        steps: int,
+        multiplier: int,
+        stem_multiplier: int,
+        num_classes: int,
+        search_space: List[Text],
+        affine: bool,
+        track_running_stats: bool,
+    ):
+        super(NASNetworkSETN, 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)
+        ):
+            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 (
+                    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, 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_normal_parameters = nn.Parameter(
+            1e-3 * torch.randn(num_edge, len(search_space))
+        )
+        self.arch_reduce_parameters = nn.Parameter(
+            1e-3 * torch.randn(num_edge, len(search_space))
+        )
+        self.mode = "urs"
+        self.dynamic_cell = None
+
+    def set_cal_mode(self, mode, dynamic_cell=None):
+        assert mode in ["urs", "joint", "select", "dynamic"]
+        self.mode = mode
+        if mode == "dynamic":
+            self.dynamic_cell = deepcopy(dynamic_cell)
+        else:
+            self.dynamic_cell = None
+
+    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 get_alphas(self):
+        return [self.arch_normal_parameters, self.arch_reduce_parameters]
+
+    def show_alphas(self):
+        with torch.no_grad():
+            A = "arch-normal-parameters :\n{:}".format(
+                nn.functional.softmax(self.arch_normal_parameters, dim=-1).cpu()
+            )
+            B = "arch-reduce-parameters :\n{:}".format(
+                nn.functional.softmax(self.arch_reduce_parameters, dim=-1).cpu()
+            )
+        return "{:}\n{:}".format(A, B)
+
+    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 dync_genotype(self, use_random=False):
+        genotypes = []
+        with torch.no_grad():
+            alphas_cpu = nn.functional.softmax(self.arch_parameters, dim=-1)
+        for i in range(1, self.max_nodes):
+            xlist = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                if use_random:
+                    op_name = random.choice(self.op_names)
+                else:
+                    weights = alphas_cpu[self.edge2index[node_str]]
+                    op_index = torch.multinomial(weights, 1).item()
+                    op_name = self.op_names[op_index]
+                xlist.append((op_name, j))
+            genotypes.append(tuple(xlist))
+        return Structure(genotypes)
+
+    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_normal_parameters, dim=-1).cpu().numpy()
+            )
+            gene_reduce = _parse(
+                torch.softmax(self.arch_reduce_parameters, dim=-1).cpu().numpy()
+            )
+        return {
+            "normal": gene_normal,
+            "normal_concat": list(
+                range(2 + self._steps - self._multiplier, self._steps + 2)
+            ),
+            "reduce": gene_reduce,
+            "reduce_concat": list(
+                range(2 + self._steps - self._multiplier, self._steps + 2)
+            ),
+        }
+
+    def forward(self, inputs):
+        normal_hardwts = nn.functional.softmax(self.arch_normal_parameters, dim=-1)
+        reduce_hardwts = nn.functional.softmax(self.arch_reduce_parameters, dim=-1)
+
+        s0 = s1 = self.stem(inputs)
+        for i, cell in enumerate(self.cells):
+            # [TODO]
+            raise NotImplementedError
+            if cell.reduction:
+                hardwts, index = reduce_hardwts, reduce_index
+            else:
+                hardwts, index = normal_hardwts, normal_index
+            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