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zero-cost-nas/foresight/pruners/measures/grad_norm.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
+import torch.nn.functional as F
+
+import copy
+
+from . import measure
+from ..p_utils import get_layer_metric_array
+
+@measure('grad_norm', bn=True)
+def get_grad_norm_arr(net, inputs, targets, loss_fn, split_data=1, skip_grad=False):
+    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()
+
+        grad_norm_arr = get_layer_metric_array(net, lambda l: l.weight.grad.norm() if l.weight.grad is not None else torch.zeros_like(l.weight), mode='param')
+        
+    return grad_norm_arr