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zero-cost-nas/foresight/pruners/measures/jacob_cov.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 numpy as np
+
+from . import measure
+
+
+def get_batch_jacobian(net, x, target, device, split_data):
+    x.requires_grad_(True)
+
+    N = x.shape[0]
+    for sp in range(split_data):
+        st=sp*N//split_data
+        en=(sp+1)*N//split_data
+        y = net(x[st:en])
+        y.backward(torch.ones_like(y))
+
+    jacob = x.grad.detach()
+    x.requires_grad_(False)
+    return jacob, target.detach()
+
+def eval_score(jacob, labels=None):
+    corrs = np.corrcoef(jacob)
+    v, _  = np.linalg.eig(corrs)
+    k = 1e-5
+    return -np.sum(np.log(v + k) + 1./(v + k))
+
+@measure('jacob_cov', bn=True)
+def compute_jacob_cov(net, inputs, targets, split_data=1, loss_fn=None):
+    device = inputs.device
+    # Compute gradients (but don't apply them)
+    net.zero_grad()
+
+    jacobs, labels = get_batch_jacobian(net, inputs, targets, device, split_data=split_data)
+    jacobs = jacobs.reshape(jacobs.size(0), -1).cpu().numpy()
+
+    try:
+        jc = eval_score(jacobs, labels)
+    except Exception as e:
+        print(e)
+        jc = np.nan
+
+    return jc