From c4d7aa76bf91a4427d7caf3c9d59e18fab4252d9 Mon Sep 17 00:00:00 2001 From: Eric Date: Fri, 9 Apr 2021 14:25:17 -0400 Subject: [PATCH] Remove commented code --- mindspore/dataset/engine/datasets.py | 2 -- mindspore/lite/minddata/example/testlenet.cpp | 1 - .../official/cv/cnn_direction_model/src/cnn_direction_model.py | 2 -- 3 files changed, 5 deletions(-) diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py index 6ae65c7779..d91e8d6c12 100644 --- a/mindspore/dataset/engine/datasets.py +++ b/mindspore/dataset/engine/datasets.py @@ -1300,8 +1300,6 @@ class Dataset: file_type (str, optional): Dataset format (default='mindrecord'). """ - # todo(CRC) warning("Used shuffle, repeat, batch before save operator.") - ir_tree, api_tree = self.create_ir_tree() runtime_context = cde.PythonRuntimeContext() diff --git a/mindspore/lite/minddata/example/testlenet.cpp b/mindspore/lite/minddata/example/testlenet.cpp index 59dda1495d..cb7b2ed5d5 100644 --- a/mindspore/lite/minddata/example/testlenet.cpp +++ b/mindspore/lite/minddata/example/testlenet.cpp @@ -53,7 +53,6 @@ int main(int argc, char **argv) { uint64_t i = 0; while (row.size() != 0) { i++; - // auto image = row["image"]; iter->GetNextRow(&row); } diff --git a/model_zoo/official/cv/cnn_direction_model/src/cnn_direction_model.py b/model_zoo/official/cv/cnn_direction_model/src/cnn_direction_model.py index 82ea8d61a5..d3cf8c7d4a 100644 --- a/model_zoo/official/cv/cnn_direction_model/src/cnn_direction_model.py +++ b/model_zoo/official/cv/cnn_direction_model/src/cnn_direction_model.py @@ -233,9 +233,7 @@ class CNNDirectionModel(nn.Cell): # 5 previous layers have mp=2. Height and width of the image would become 1/32. self.avg_pool = nn.AvgPool2d(kernel_size=(int(self.image_h / 32), int(self.image_w / 32))) - # sqrt(6 / (fan_in + fan_out)) scale = math.sqrt(6 / (out_channels[-1] + dense_layers[0])) - # weight_init='glorot_uniform' self.dense1 = nn.Dense(out_channels[-1], dense_layers[0], weight_init=Uniform(scale=scale), activation='relu') scale = math.sqrt(6 / (dense_layers[0] + dense_layers[1]))