update

correlation/foresight/pruners/measures/meco.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
+import copy
+import time
+
+#     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
+from torch import nn
+
+from . import measure
+
+
+def get_score(net, x, target, device, split_data):
+    result_list = []
+    x = torch.randn(size=(1, 3, 64, 64)).to(device)
+    net.to(device)
+    def forward_hook(module, data_input, data_output):
+
+        fea = data_output[0].detach()
+        fea = fea.reshape(fea.shape[0], -1)
+        n = fea.shape[0]
+        corr = torch.corrcoef(fea)
+        corr[torch.isnan(corr)] = 0
+        corr[torch.isinf(corr)] = 0
+        values = torch.linalg.eig(corr)[0]
+        # result = np.real(np.min(values)) / np.real(np.max(values))
+        result = torch.min(torch.real(values))
+        result_list.append(result)
+
+    for name, modules in net.named_modules():
+        modules.register_forward_hook(forward_hook)
+
+
+
+    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])
+        # break
+    results = torch.tensor(result_list)
+    results = results[torch.logical_not(torch.isnan(results))]
+    v = torch.sum(results)
+    result_list.clear()
+    return v.item()
+
+
+
+@measure('meco', bn=True)
+def compute_meco(net, inputs, targets, split_data=1, loss_fn=None):
+    device = inputs.device
+    # Compute gradients (but don't apply them)
+    net.zero_grad()
+
+
+    try:
+        meco = get_score(net, inputs, targets, device, split_data=split_data)
+    except Exception as e:
+        print(e)
+        meco = np.nan, None
+    return meco