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zero-cost-nas/foresight/pruners/measures/snip.py

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+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import copy
+import types
+
+from . import measure
+from ..p_utils import get_layer_metric_array
+
+
+def snip_forward_conv2d(self, x):
+        return F.conv2d(x, self.weight * self.weight_mask, self.bias,
+                        self.stride, self.padding, self.dilation, self.groups)
+
+def snip_forward_linear(self, x):
+        return F.linear(x, self.weight * self.weight_mask, self.bias)
+
+@measure('snip', bn=True, mode='param')
+def compute_snip_per_weight(net, inputs, targets, mode, loss_fn, split_data=1):
+    for layer in net.modules():
+        if isinstance(layer, nn.Conv2d) or isinstance(layer, nn.Linear):
+            layer.weight_mask = nn.Parameter(torch.ones_like(layer.weight))
+            layer.weight.requires_grad = False
+
+        # Override the forward methods:
+        if isinstance(layer, nn.Conv2d):
+            layer.forward = types.MethodType(snip_forward_conv2d, layer)
+
+        if isinstance(layer, nn.Linear):
+            layer.forward = types.MethodType(snip_forward_linear, layer)
+
+    # Compute gradients (but don't apply them)
+    net.zero_grad()
+    N = inputs.shape[0]
+    for sp in range(split_data):
+        st=sp*N//split_data
+        en=(sp+1)*N//split_data
+    
+        outputs = net.forward(inputs[st:en])
+        loss = loss_fn(outputs, targets[st:en])
+        loss.backward()
+
+    # select the gradients that we want to use for search/prune
+    def snip(layer):
+        if layer.weight_mask.grad is not None:
+            return torch.abs(layer.weight_mask.grad)
+        else:
+            return torch.zeros_like(layer.weight)
+    
+    grads_abs = get_layer_metric_array(net, snip, mode)
+
+    return grads_abs