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zero-cost-nas/foresight/pruners/measures/synflow.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 torch
+
+from . import measure
+from ..p_utils import get_layer_metric_array
+
+
+@measure('synflow', bn=False, mode='param')
+@measure('synflow_bn', bn=True, mode='param')
+def compute_synflow_per_weight(net, inputs, targets, mode, split_data=1, loss_fn=None):
+
+    device = inputs.device
+
+    #convert params to their abs. Keep sign for converting it back.
+    @torch.no_grad()
+    def linearize(net):
+        signs = {}
+        for name, param in net.state_dict().items():
+            signs[name] = torch.sign(param)
+            param.abs_()
+        return signs
+
+    #convert to orig values
+    @torch.no_grad()
+    def nonlinearize(net, signs):
+        for name, param in net.state_dict().items():
+            if 'weight_mask' not in name:
+                param.mul_(signs[name])
+
+    # keep signs of all params
+    signs = linearize(net)
+    
+    # Compute gradients with input of 1s 
+    net.zero_grad()
+    net.double()
+    input_dim = list(inputs[0,:].shape)
+    inputs = torch.ones([1] + input_dim).double().to(device)
+    output = net.forward(inputs)
+    torch.sum(output).backward() 
+
+    # select the gradients that we want to use for search/prune
+    def synflow(layer):
+        if layer.weight.grad is not None:
+            return torch.abs(layer.weight * layer.weight.grad)
+        else:
+            return torch.zeros_like(layer.weight)
+
+    grads_abs = get_layer_metric_array(net, synflow, mode)
+
+    # apply signs of all params
+    nonlinearize(net, signs)
+
+    return grads_abs
+
+