update quant analysis (#1455)

example/post_training_quantization/pytorch_yolo_series/analysis.py

@@ -37,6 +37,12 @@ def argsparser():
         type=str,
         default='gpu',
         help="which device used to compress.")
+    parser.add_argument(
+        '--resume',
+        type=bool,
+        default=False,
+        help="When break off while ananlyzing, could resume analysis program and load already analyzed information."
+    )
     return parser
 
 
@@ -104,6 +110,7 @@ def main():
         eval_function=eval_function,
         data_loader=data_loader,
         save_dir=config['save_dir'],
+        resume=FLAGS.resume,
         ptq_config=ptq_config)
 
     # plot the boxplot of activations of quantizable weights

example/post_training_quantization/pytorch_yolo_series/configs/yolov7_analysis.yaml

 arch: YOLOv7
 model_dir: ./yolov7.onnx
 save_dir: ./analysis_results
-dataset_dir: /dataset/coco/
+dataset_dir: dataset/coco/
 val_image_dir: val2017
 val_anno_path: annotations/instances_val2017.json
 # Small Dataset to accelerate analysis
@@ -15,5 +15,6 @@ PTQ:
   weight_quantize_type: 'abs_max'
   activation_quantize_type: 'moving_average_abs_max'
   is_full_quantize: False
+  onnx_format: False
   batch_size: 10
   batch_nums: 10
\ No newline at end of file

example/post_training_quantization/pytorch_yolo_series/post_process.py

@@ -197,12 +197,15 @@ def coco_metric(anno_file, bboxes_list, bbox_nums_list, image_id_list):
     with open(output, 'w') as f:
         json.dump(results, f)
 
-    coco_dt = coco_gt.loadRes(output)
-    coco_eval = COCOeval(coco_gt, coco_dt, 'bbox')
-    coco_eval.evaluate()
-    coco_eval.accumulate()
-    coco_eval.summarize()
-    return coco_eval.stats
+    try:
+        coco_dt = coco_gt.loadRes(output)
+        coco_eval = COCOeval(coco_gt, coco_dt, 'bbox')
+        coco_eval.evaluate()
+        coco_eval.accumulate()
+        coco_eval.summarize()
+        return coco_eval.stats
+    except:
+        return [0.]
 
 
 def _get_det_res(bboxes, bbox_nums, image_id, label_to_cat_id_map):

paddleslim/quant/analysis.py

@@ -21,6 +21,7 @@ import matplotlib.pyplot as plt
 from matplotlib.backends.backend_pdf import PdfPages
 import numpy as np
 import random
+import tempfile
 import paddle
 from paddle.fluid import core
 from paddle.fluid import framework
@@ -46,8 +47,8 @@ class AnalysisQuant(object):
                  eval_function=None,
                  data_loader=None,
                  save_dir='analysis_results',
-                 checkpoint_name='analysis_checkpoint.pkl',
                  num_histogram_plots=10,
+                 resume=False,
                  ptq_config=None):
         """
         AnalysisQuant provides to analysis the sensitivity of each op in the model.
@@ -61,7 +62,7 @@ class AnalysisQuant(object):
                 Generator or Dataloader provides calibrate data, and it could
                 return a batch every time
             save_dir(str, optional): the output dir that stores the analyzed information
-            checkpoint_name(str, optional): the name of checkpoint file that saves analyzed information and avoids break off while ananlyzing
+            resume(bool, optional): When break off while ananlyzing, could resume analysis program and load already analyzed information.
             ptq_config(dict, optional): the args that can initialize PostTrainingQuantization
             
         """
@@ -76,15 +77,24 @@ class AnalysisQuant(object):
         self.save_dir = save_dir
         self.eval_function = eval_function
         self.quant_layer_names = []
-        self.checkpoint_name = os.path.join(save_dir, checkpoint_name)
+        self.checkpoint_name = os.path.join(save_dir, 'analysis_checkpoint.pkl')
         self.quant_layer_metrics = {}
         self.num_histogram_plots = num_histogram_plots
         self.ptq_config = ptq_config
         self.batch_nums = ptq_config[
             'batch_nums'] if 'batch_nums' in ptq_config else 10
+        self.is_full_quantize = ptq_config[
+            'is_full_quantize'] if 'is_full_quantize' in ptq_config else False
+        self.onnx_format = ptq_config[
+            'onnx_format'] if 'onnx_format' in ptq_config else False
 
         if not os.path.exists(self.save_dir):
             os.mkdir(self.save_dir)
+        if self.onnx_format:
+            self.temp_root_path = tempfile.TemporaryDirectory(dir=self.save_dir)
+            self.temp_save_path = os.path.join(self.temp_root_path.name, "ptq")
+            if not os.path.exists(self.temp_save_path):
+                os.makedirs(self.temp_save_path)
 
         devices = paddle.device.get_device().split(':')[0]
         self.places = paddle.device._convert_to_place(devices)
@@ -117,8 +127,19 @@ class AnalysisQuant(object):
             params_filename=self.params_filename,
             skip_tensor_list=None,
             algo='avg',  #fastest
+            onnx_format=self.onnx_format,
             **self.ptq_config)
         program = post_training_quantization.quantize()
+        if self.onnx_format:
+            post_training_quantization.save_quantized_model(
+                self.temp_save_path,
+                model_filename='model.pdmodel',
+                params_filename='model.pdiparams')
+            program, _, _ = load_inference_model(
+                self.temp_save_path,
+                executor,
+                model_filename='model.pdmodel',
+                params_filename='model.pdiparams')
         self.quant_metric = self.eval_function(executor, program,
                                                self.feed_list, self.fetch_list)
         _logger.info('After quantized, the accuracy of the model is: {}'.format(
@@ -127,11 +148,14 @@ class AnalysisQuant(object):
         # get quantized weight and act var name
         self.quantized_weight_var_name = post_training_quantization._quantized_weight_var_name
         self.quantized_act_var_name = post_training_quantization._quantized_act_var_name
+        self.support_quant_val_name_list = self.quantized_weight_var_name if not self.is_full_quantize else list(
+            self.quantized_act_var_name)
         executor.close()
 
-        # load tobe_analyized_layer from checkpoint 
-        self.load_checkpoint()
-        self.tobe_analyized_layer = self.quantized_weight_var_name - set(
+        # load tobe_analyized_layer from checkpoint
+        if resume:
+            self.load_checkpoint()
+        self.tobe_analyized_layer = set(self.support_quant_val_name_list) - set(
             list(self.quant_layer_metrics.keys()))
         self.tobe_analyized_layer = sorted(list(self.tobe_analyized_layer))
 
@@ -194,8 +218,6 @@ class AnalysisQuant(object):
 
         scope = global_scope()
 
-        graph = IrGraph(core.Graph(program.desc), for_test=False)
-
         persistable_var_names = []
         for var in program.list_vars():
             if var.persistable:
@@ -249,7 +271,7 @@ class AnalysisQuant(object):
         for i, layer_name in enumerate(self.tobe_analyized_layer):
             _logger.info('Checking {}/{} quant model: quant layer {}'.format(
                 i + 1, len(self.tobe_analyized_layer), layer_name))
-            skip_list = copy.copy(list(self.quantized_weight_var_name))
+            skip_list = copy.copy(list(self.support_quant_val_name_list))
             skip_list.remove(layer_name)
 
             executor = paddle.static.Executor(self.places)
@@ -260,20 +282,33 @@ class AnalysisQuant(object):
                 model_filename=self.model_filename,
                 params_filename=self.params_filename,
                 skip_tensor_list=skip_list,
+                onnx_format=self.onnx_format,
                 algo='avg',  #fastest
                 **self.ptq_config)
             program = post_training_quantization.quantize()
-
             _logger.info('Evaluating...')
+            if self.onnx_format:
+                post_training_quantization.save_quantized_model(
+                    self.temp_save_path,
+                    model_filename='model.pdmodel',
+                    params_filename='model.pdiparams')
+                program, _, _ = load_inference_model(
+                    self.temp_save_path,
+                    executor,
+                    model_filename='model.pdmodel',
+                    params_filename='model.pdiparams')
             quant_metric = self.eval_function(executor, program, self.feed_list,
                                               self.fetch_list)
             executor.close()
             _logger.info(
                 "Quantized layer name: {}, eval metric: {}, the loss caused by this layer: {}".
-                format(layer_name, quant_metric, self.base_metric -
-                       quant_metric))
+                format(layer_name,
+                       round(quant_metric, 4),
+                       round(self.base_metric - quant_metric, 4)))
             self.quant_layer_metrics[layer_name] = quant_metric
             self.save_checkpoint()
+        if self.onnx_format:
+            self.temp_root_path.cleanup()
 
     def get_weight_act_map(self, program, weight_names, persistable_var_names):
         act_names = {}
@@ -408,6 +443,7 @@ class AnalysisQuant(object):
                 model_dir=self.model_dir,
                 model_filename=self.model_filename,
                 params_filename=self.params_filename,
+                onnx_format=self.onnx_format,
                 skip_tensor_list=skip_list,
                 **self.ptq_config)
             program = post_training_quantization.quantize()