update

correlation/foresight/pruners/measures/pearson.py

@@ -0,0 +1,71 @@
+# 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
+from torch import nn
+# import pandas as pd
+
+from . import measure
+
+
+def get_score(net, x, target, device, split_data):
+    result_list = []
+    result_t = []
+    def forward_hook(module, data_input, data_output):
+        s = time.time()
+        fea = data_output[0].detach().cpu().numpy()
+        fea = fea.reshape(fea.shape[0], -1)
+        # result = 1 / np.var(np.corrcoef(fea))
+        result = np.var(np.corrcoef(fea))
+        e = time.time()
+        t = e - s
+        result_list.append(result)
+        result_t.append(t)
+
+    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])
+        # print(y)
+    results = np.array(result_list)
+    results = results[np.logical_not(np.isnan(results))]
+    v = np.sum(results)
+    t = sum(result_t)
+    result_list.clear()
+    result_t.clear()
+    return v, t
+
+
+
+@measure('pearson', bn=True)
+def compute_pearson(net, inputs, targets, split_data=1, loss_fn=None):
+    device = inputs.device
+    # Compute gradients (but don't apply them)
+    net.zero_grad()
+
+    try:
+        pearson, t = get_score(net, inputs, targets, device, split_data=split_data)
+    except Exception as e:
+        print(e)
+        pearson, t = np.nan, None
+    return pearson, t