revert prune

deploy/slim/prune/sensitivity_anal.py

@@ -24,14 +24,6 @@ sys.path.append(__dir__)
 sys.path.append(os.path.join(__dir__, '..', '..', '..'))
 sys.path.append(os.path.join(__dir__, '..', '..', '..', 'tools'))
 
-import json
-import cv2
-import paddle
-from paddle import fluid
-import paddleslim as slim
-from copy import deepcopy
-from tools import program
-
 import paddle
 import paddle.distributed as dist
 from ppocr.data import build_dataloader
@@ -46,28 +38,14 @@ import tools.program as program
 dist.get_world_size()
 
 
-def get_pruned_params(parameters, mode="det"):
-    if mode == "det":
-        skip_prune_params = [
-            "conv2d_56.w_0", "conv2d_54.w_0", "conv2d_51.w_0",
-            "conv_last_weights", "conv14_linear_weights",
-            "conv13_expand_weights", "conv12_linear_weights",
-            "conv12_expand_weights", "conv7_expand_weights",
-            "conv8_expand_weights", "conv8_linear_weights",
-            "conv5_linear_weights", "conv5_expand_weights",
-            "conv3_linear_weights"
-        ]
-        skip_prune_params = skip_prune_params + ['conv2d_53.w_0']
-    else:
-        skip_prune_params = None
+def get_pruned_params(parameters):
     params = []
 
     for param in parameters:
         if len(
                 param.shape
         ) == 4 and 'depthwise' not in param.name and 'transpose' not in param.name and "conv2d_57" not in param.name and "conv2d_56" not in param.name:
-            if param.name not in skip_prune_params:
-                params.append(param.name)
+            params.append(param.name)
     return params
 
 
@@ -118,6 +96,11 @@ def main(config, device, logger, vdl_writer):
     # load pretrain model
     pre_best_model_dict = init_model(config, model, logger, optimizer)
 
+    logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
+                format(len(train_dataloader), len(valid_dataloader)))
+    # build metric
+    eval_class = build_metric(config['Metric'])
+
     logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
                 format(len(train_dataloader), len(valid_dataloader)))
 
@@ -127,20 +110,22 @@ def main(config, device, logger, vdl_writer):
         logger.info(f"metric['hmean']: {metric['hmean']}")
         return metric['hmean']
 
-    pruner.sensitive(
+    params_sensitive = pruner.sensitive(
         eval_func=eval_fn,
         sen_file="./sen.pickle",
         skip_vars=[
             "conv2d_57.w_0", "conv2d_transpose_2.w_0", "conv2d_transpose_3.w_0"
         ])
 
-    params = get_pruned_params(model.parameters())
-    ratios = {}
-    # set the prune ratio is 0.2
-    for param in params:
-        ratios[param] = 0.2
+    logger.info(
+        "The sensitivity analysis results of model parameters saved in sen.pickle"
+    )
+    # calculate pruned params's ratio
+    params_sensitive = pruner._get_ratios_by_loss(params_sensitive, loss=0.02)
+    for key in params_sensitive.keys():
+        logger.info(f"{key}, {params_sensitive[key]}")
 
-    plan = pruner.prune_vars(ratios, [0])
+    plan = pruner.prune_vars(params_sensitive, [0])
     for param in model.parameters():
         if ("weights" in param.name and "conv" in param.name) or (
                 "w_0" in param.name and "conv2d" in param.name):
@@ -150,6 +135,7 @@ def main(config, device, logger, vdl_writer):
     logger.info(f"FLOPs after pruning: {flops}")
 
     # start train
+
     program.train(config, train_dataloader, valid_dataloader, device, model,
                   loss_class, optimizer, lr_scheduler, post_process_class,
                   eval_class, pre_best_model_dict, logger, vdl_writer)