Add demo for combined strategies. (#1954)

* Add demo for combined strategies.

* Fix quan.

* Restore args in run.sh

PaddleSlim/configs/quantization.yaml

@@ -3,7 +3,7 @@ strategies:
     quantization_strategy:
         class: 'QuantizationStrategy'
         start_epoch: 0
-        end_epoch: 0
+        end_epoch: 20
         float_model_save_path: './output/float'
         mobile_model_save_path: './output/mobile'
         int8_model_save_path: './output/int8'
@@ -14,7 +14,7 @@ strategies:
         save_in_nodes: ['image']
         save_out_nodes: ['fc_0.tmp_2']
 compressor:
-    epoch: 6
+    epoch: 21
     checkpoint_path: './checkpoints_quan/'
     strategies:
         - quantization_strategy

PaddleSlim/run.sh

@@ -25,7 +25,7 @@ fi
 cd -
 
 # for distillation
-#--------------------
+#-----------------
 export CUDA_VISIBLE_DEVICES=0
 python compress.py \
 --model "MobileNet" \
@@ -35,7 +35,7 @@ python compress.py \
 
 
 # for sensitivity filter pruning
-#---------------------------
+#-------------------------------
 #export CUDA_VISIBLE_DEVICES=0
 #python compress.py \
 #--model "MobileNet" \
@@ -51,7 +51,7 @@ python compress.py \
 #--compress_config ./configs/filter_pruning_uniform.yaml
 
 # for quantization
-#---------------------------
+#-----------------
 #export CUDA_VISIBLE_DEVICES=0
 #python compress.py \
 #--batch_size 64 \
@@ -59,3 +59,21 @@ python compress.py \
 #--pretrained_model ./pretrain/MobileNetV1_pretrained \
 #--compress_config ./configs/quantization.yaml
 
+# for distillation with quantization
+#-----------------------------------
+#export CUDA_VISIBLE_DEVICES=0
+#python compress.py \
+#--model "MobileNet" \
+#--teacher_model "ResNet50" \
+#--teacher_pretrained_model ./data/pretrain/ResNet50_pretrained \
+#--compress_config ./configs/quantization_dist.yaml
+
+
+# for uniform filter pruning with quantization
+#---------------------------------------------
+#export CUDA_VISIBLE_DEVICES=0
+#python compress.py \
+#--model "MobileNet" \
+#--pretrained_model ./data/pretrain/MobileNetV1_pretrained \
+#--compress_config ./configs/quantization_pruning.yaml
+

fluid/PaddleSlim/configs/quantization_dist.yaml

+# Step1: distillation training from epoch-0 to epoch-120
+# Step2: quantization training from epoch-121 to epoch-141
+version: 1.0
+distillers:
+    fsp_distiller:
+        class: 'FSPDistiller'
+        teacher_pairs: [['res2a_branch2a.conv2d.output.1.tmp_0', 'res3a_branch2a.conv2d.output.1.tmp_0']]
+        student_pairs: [['depthwise_conv2d_1.tmp_0', 'conv2d_3.tmp_0']]
+        distillation_loss_weight: 1
+    l2_distiller:
+        class: 'L2Distiller'
+        teacher_feature_map: 'fc_1.tmp_0'
+        student_feature_map: 'fc_0.tmp_0'
+        distillation_loss_weight: 1
+strategies:
+    distillation_strategy:
+        class: 'DistillationStrategy'
+        distillers: ['fsp_distiller', 'l2_distiller']
+        start_epoch: 0
+        end_epoch: 120
+    quantization_strategy:
+        class: 'QuantizationStrategy'
+        start_epoch: 121
+        end_epoch: 141
+        float_model_save_path: './output/float'
+        mobile_model_save_path: './output/mobile'
+        int8_model_save_path: './output/int8'
+        weight_bits: 8
+        activation_bits: 8
+        weight_quantize_type: 'abs_max'
+        activation_quantize_type: 'abs_max'
+
+compressor:
+    epoch: 142
+    checkpoint_path: './checkpoints/'
+    strategies:
+        - distillation_strategy
+        - quantization_strategy

fluid/PaddleSlim/configs/quantization_pruning.yaml

+# step1: Pruning at epoch-0
+# step2: Fine-tune from epoch-0 to epoch-120
+# step3: Quantization training from epoch-121 to epoch-141
+version: 1.0
+pruners:
+    pruner_1:
+        class: 'StructurePruner'
+        pruning_axis:
+            '*': 0
+        criterions:
+            '*': 'l1_norm'
+strategies:
+    uniform_pruning_strategy:
+        class: 'UniformPruneStrategy'
+        pruner: 'pruner_1'
+        start_epoch: 0
+        target_ratio: 0.5
+        pruned_params: '.*_sep_weights'
+        metric_name: 'acc_top1'
+    quantization_strategy:
+        class: 'QuantizationStrategy'
+        start_epoch: 121
+        end_epoch: 141
+        float_model_save_path: './output/float'
+        mobile_model_save_path: './output/mobile'
+        int8_model_save_path: './output/int8'
+        weight_bits: 8
+        activation_bits: 8
+        weight_quantize_type: 'abs_max'
+        activation_quantize_type: 'abs_max'
+compressor:
+    epoch: 142
+    checkpoint_path: './checkpoints/'
+    strategies:
+        - uniform_pruning_strategy
+        - quantization_strategy