Merge pull request #126 from alibaba/refactor_clip

test mul gpu for clip

Author: chywang

easynlp/appzoo/multi_modal/evaluator.py

@@ -62,8 +62,8 @@ def evaluate(self, model):
         mean_recall = (r1 + r5 + r10) / 3.0
         result = [mean_recall, r1, r5, r10]
         result = [item * 100 for item in result]
-        print(r1_stat,r5_stat,r10_stat,query_len)
-        print(result)
+        print('r1_num:'+str(r1_stat),'r5_num:'+str(r5_stat),'r10_num:'+str(r10_stat),'query_num:'+str(query_len))
+        print('r1(%):'+str(result[1]),'r5(%):'+str(result[2]),'r10(%):'+str(result[3]),'mean_recall(%):'+str(result[0]))
         logger.info("Inference time = {:.2f}s, [{:.4f} ms / sample] ".format(
             total_spent_time, total_spent_time * 1000 / query_len))
         eval_outputs = list()

examples/clip_retrieval/run_clip_local.sh

@@ -1,5 +1,11 @@
 export CUDA_VISIBLE_DEVICES=$1
 
+#动态获取GPU数
+OLD_IFS="$IFS"
+IFS="," 
+arr=($1)
+IFS="$OLD_IFS"
+
 if [ ! -f ./MUGE_MR_train_base64_part.tsv ]; then
   wget https://atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com/release/tutorials/CLIP/MUGE_MR_train_base64_part.tsv
 fi
@@ -21,7 +27,7 @@ mode=$2
 if [ "$mode" = "train" ]; then
   easynlp \
   --mode $mode \
-  --worker_gpu=1 \
+  --worker_gpu=${#arr[@]} \
   --tables=./MUGE_MR_train_base64_part.tsv,./MUGE_MR_valid_base64_part.tsv \
   --input_schema=text:str:1,image:str:1 \
   --first_sequence=text \
@@ -41,7 +47,7 @@ if [ "$mode" = "train" ]; then
 elif [ "$mode" = "evaluate" ]; then
   easynlp \
   --mode $mode \
-  --worker_gpu=1 \
+  --worker_gpu=${#arr[@]} \
   --tables=./MUGE_MR_valid_base64_part.tsv \
   --input_schema=text:str:1,image:str:1 \
   --first_sequence=text \
@@ -57,7 +63,7 @@ elif [ "$mode" = "evaluate" ]; then
 elif [ "$mode" = "predict" ]; then
     easynlp \
       --mode $mode \
-      --worker_gpu=1 \
+      --worker_gpu=${#arr[@]} \
       --tables=./MUGE_MR_test_base64_part_text.tsv \
       --input_schema=text:str:1 \
       --output_schema=text_feat \
@@ -74,7 +80,7 @@ elif [ "$mode" = "predict" ]; then
 # elif [ "$mode" = "predict" ]; then
 #     easynlp \
 #       --mode $mode \
-#       --worker_gpu=1 \
+#       --worker_gpu=${#arr[@]} \
 #       --tables=./MUGE_MR_test_base64_part_image.tsv \
 #       --input_schema=image:str:1 \
 #       --output_schema=image_feat \

requirements.txt

@@ -17,4 +17,5 @@ rouge
 tqdm>=4.48.2
 pillow
 albumentations
-einops
+einops
+torchvision