remove download

paddlex/interpret/__init__.py

@@ -15,4 +15,5 @@
 from __future__ import absolute_import
 from . import visualize
 
-visualize = visualize.visualize
+lime = visualize.lime
\ No newline at end of file
+normlime = visualize.normlime

paddlex/interpret/core/_session_preparation.py

@@ -28,17 +28,6 @@ def gen_user_home():
     return os.path.expanduser('~')
 
 
-root_path = gen_user_home()
-root_path = osp.join(root_path, '.paddlex')
-h_pre_models = osp.join(root_path, "pre_models")
-if not osp.exists(h_pre_models):
-    if not osp.exists(root_path):
-        os.makedirs(root_path)
-    url = "https://bj.bcebos.com/paddlex/interpret/pre_models.tar.gz"
-    pdx.utils.download_and_decompress(url, path=root_path)
-h_pre_models_kmeans = osp.join(h_pre_models, "kmeans_model.pkl")
-
-
 def paddle_get_fc_weights(var_name="fc_0.w_0"):
     fc_weights = fluid.global_scope().find_var(var_name).get_tensor()
     return np.array(fc_weights)
@@ -50,6 +39,14 @@ def paddle_resize(extracted_features, outsize):
 
 
 def compute_features_for_kmeans(data_content):
+    root_path = gen_user_home()
+    root_path = osp.join(root_path, '.paddlex')
+    h_pre_models = osp.join(root_path, "pre_models")
+    if not osp.exists(h_pre_models):
+        if not osp.exists(root_path):
+            os.makedirs(root_path)
+        url = "https://bj.bcebos.com/paddlex/interpret/pre_models.tar.gz"
+        pdx.utils.download_and_decompress(url, path=root_path)
     def conv_bn_layer(input,
                       num_filters,
                       filter_size,

paddlex/interpret/core/interpretation_algorithms.py

@@ -13,11 +13,12 @@
 #limitations under the License.
 
 import os
+import os.path as osp
 import numpy as np
 import time
 
 from . import lime_base
-from ._session_preparation import paddle_get_fc_weights, compute_features_for_kmeans, h_pre_models_kmeans
+from ._session_preparation import paddle_get_fc_weights, compute_features_for_kmeans, gen_user_home
 from .normlime_base import combine_normlime_and_lime, get_feature_for_kmeans, load_kmeans_model
 from paddlex.interpret.as_data_reader.readers import read_image
 
@@ -215,6 +216,15 @@ class LIME(object):
 class NormLIME(object):
     def __init__(self, predict_fn, label_names, num_samples=3000, batch_size=50,
                  kmeans_model_for_normlime=None, normlime_weights=None):
+        root_path = gen_user_home()
+        root_path = osp.join(root_path, '.paddlex')
+        h_pre_models = osp.join(root_path, "pre_models")
+        if not osp.exists(h_pre_models):
+            if not osp.exists(root_path):
+                os.makedirs(root_path)
+            url = "https://bj.bcebos.com/paddlex/interpret/pre_models.tar.gz"
+            pdx.utils.download_and_decompress(url, path=root_path)
+        h_pre_models_kmeans = osp.join(h_pre_models, "kmeans_model.pkl")
         if kmeans_model_for_normlime is None:
             try:
                 self.kmeans_model = load_kmeans_model(h_pre_models_kmeans)
@@ -242,7 +252,13 @@ class NormLIME(object):
         self.label_names = label_names
 
     def predict_cluster_labels(self, feature_map, segments):
-        return self.kmeans_model.predict(get_feature_for_kmeans(feature_map, segments))
+        X = get_feature_for_kmeans(feature_map, segments)
+        try:
+            cluster_labels = self.kmeans_model.predict(X)
+        except AttributeError:
+            from sklearn.metrics import pairwise_distances_argmin_min
+            cluster_labels, _ = pairwise_distances_argmin_min(X, self.kmeans_model.cluster_centers_)
+        return cluster_labels
 
     def predict_using_normlime_weights(self, pred_labels, predicted_cluster_labels):
         # global weights

paddlex/interpret/core/normlime_base.py

@@ -13,12 +13,14 @@
 #limitations under the License.
 
 import os
+import os.path as osp
 import numpy as np
 import glob
 
 from paddlex.interpret.as_data_reader.readers import read_image
+import paddlex.utils.logging as logging
 from . import lime_base
-from ._session_preparation import compute_features_for_kmeans, h_pre_models_kmeans
+from ._session_preparation import compute_features_for_kmeans, gen_user_home
 
 
 def load_kmeans_model(fname):
@@ -102,6 +104,15 @@ def save_one_lime_predict_and_kmean_labels(lime_all_weights, image_pred_labels,
 
 
 def precompute_lime_weights(list_data_, predict_fn, num_samples, batch_size, save_dir):
+    root_path = gen_user_home()
+    root_path = osp.join(root_path, '.paddlex')
+    h_pre_models = osp.join(root_path, "pre_models")
+    if not osp.exists(h_pre_models):
+        if not osp.exists(root_path):
+            os.makedirs(root_path)
+        url = "https://bj.bcebos.com/paddlex/interpret/pre_models.tar.gz"
+        pdx.utils.download_and_decompress(url, path=root_path)
+    h_pre_models_kmeans = osp.join(h_pre_models, "kmeans_model.pkl")
     kmeans_model = load_kmeans_model(h_pre_models_kmeans)
 
     for data_index, each_data_ in enumerate(list_data_):
@@ -113,11 +124,10 @@ def precompute_lime_weights(list_data_, predict_fn, num_samples, batch_size, sav
             save_path = os.path.join(save_dir, save_path)
 
         if os.path.exists(save_path):
-            print(f'{save_path} exists, not computing this one.')
+            logging.info(save_path + ' exists, not computing this one.', use_color=True)
             continue
-
-        print('processing', each_data_ if isinstance(each_data_, str) else data_index,
-              f', {data_index}/{len(list_data_)}')
+        img_file_name = each_data_ if isinstance(each_data_, str) else data_index
+        logging.info('processing '+ img_file_name + ' [{}/{}]'.format(data_index, len(list_data_)), use_color=True)
 
         image_show = read_image(each_data_)
         result = predict_fn(image_show)
@@ -149,9 +159,12 @@ def precompute_lime_weights(list_data_, predict_fn, num_samples, batch_size, sav
         interpreter = algo.interpret_instance(image_show[0], predict_fn, pred_label, 0,
                                           num_samples=num_samples, batch_size=batch_size)
 
-        cluster_labels = kmeans_model.predict(
-            get_feature_for_kmeans(compute_features_for_kmeans(image_show).transpose((1, 2, 0)), interpreter.segments)
-        )
+        X = get_feature_for_kmeans(compute_features_for_kmeans(image_show).transpose((1, 2, 0)), interpreter.segments)
+        try:
+            cluster_labels = kmeans_model.predict(X)
+        except AttributeError:
+            from sklearn.metrics import pairwise_distances_argmin_min
+            cluster_labels, _ = pairwise_distances_argmin_min(X, kmeans_model.cluster_centers_)
         save_one_lime_predict_and_kmean_labels(
             interpreter.local_weights, pred_label,
             cluster_labels,