Initial commit

Tensorflow/tutoriel27-3/map.py

@@ -0,0 +1,111 @@
+import tensorflow as tf
+import sys
+import time
+import cv2
+import numpy as np
+import math
+import common
+import config
+import model
+
+images, labels, labels2=common.read_json('test.json', 10)
+images=np.array(images, dtype=np.float32)/255
+labels=np.array(labels, dtype=np.float32)
+
+model=model.model(config.nbr_classes, config.nbr_boxes, config.cellule_y, config.cellule_x)
+
+checkpoint=tf.train.Checkpoint(model=model)
+checkpoint.restore(tf.train.latest_checkpoint("./training/"))
+
+dataset=tf.data.Dataset.from_tensor_slices((images, labels)).batch(config.batch_size)
+
+def calcul_map(model, dataset, beta=1., seuil=0.5):
+  grid=np.meshgrid(np.arange(config.cellule_x, dtype=np.float32), np.arange(config.cellule_y, dtype=np.float32))
+  grid=np.expand_dims(np.stack(grid, axis=-1), axis=2)
+  grid=np.tile(grid, (1, 1, 1, config.nbr_boxes, 1))
+
+  index_labels2=0
+  labels2_=labels2*[config.r_x, config.r_y, config.r_x, config.r_y, 1, 1, 1]
+  score=[]
+  tab_nbr_reponse=[]
+  tab_tp=[]
+  tab_true_boxes=[]
+  
+  for images, labels in dataset:
+    predictions=np.array(model(images))
+
+    pred_conf=common.sigmoid(predictions[:, :, :, :, 4])
+    pred_classes=common.softmax(predictions[:, :, :, :, 5:])
+    pred_ids=np.argmax(pred_classes, axis=-1)
+    
+    x_center=((grid[:, :, :, :, 0]+common.sigmoid(predictions[:, :, :, :, 0]))*config.r_x)
+    y_center=((grid[:, :, :, :, 1]+common.sigmoid(predictions[:, :, :, :, 1]))*config.r_y)
+    w=(np.exp(predictions[:, :, :, :, 2])*config.anchors[:, 0]*config.r_x)
+    h=(np.exp(predictions[:, :, :, :, 3])*config.anchors[:, 1]*config.r_y)
+
+    x_min=x_center-w/2
+    y_min=y_center-h/2
+    x_max=x_center+w/2
+    y_max=y_center+h/2
+
+    tab_boxes=np.stack([y_min, x_min, y_max, x_max], axis=-1).astype(np.float32)
+    tab_boxes=tab_boxes.reshape(-1, config.cellule_y*config.cellule_x*config.nbr_boxes, 4)
+    pred_conf=pred_conf.reshape(-1, config.cellule_y*config.cellule_x*config.nbr_boxes)
+    pred_ids=pred_ids.reshape(-1, config.cellule_y*config.cellule_x*config.nbr_boxes)
+
+    for p in range(len(predictions)):
+      nbr_reponse=np.zeros(config.nbr_classes)
+      tp=np.zeros(config.nbr_classes)
+      nbr_true_boxes=np.zeros(config.nbr_classes)
+      tab_index=tf.image.non_max_suppression(tab_boxes[p], pred_conf[p], 100)      
+      for id in tab_index:
+        if pred_conf[p, id]>0.10:
+          nbr_reponse[pred_ids[p, id]]+=1
+          for box in labels2_[index_labels2]:
+            if not box[5]:
+              break
+            b1=[tab_boxes[p, id, 1], tab_boxes[p, id, 0], tab_boxes[p, id, 3], tab_boxes[p, id, 2]]
+            iou=common.intersection_over_union(b1, box)
+            if iou>seuil and box[6]==pred_ids[p, id]:
+              tp[pred_ids[p, id]]+=1
+
+      for box in labels2[index_labels2]:
+        if not box[5]:
+          break
+        nbr_true_boxes[int(box[6])]+=1
+
+      tab_nbr_reponse.append(nbr_reponse)
+      tab_tp.append(tp)
+      tab_true_boxes.append(nbr_true_boxes)
+      
+      index_labels2=index_labels2+1
+
+  tab_nbr_reponse=np.array(tab_nbr_reponse)
+  tab_tp=np.array(tab_tp)
+  tab_true_boxes=np.array(tab_true_boxes)
+
+  ########################
+  precision_globule_rouge=tab_tp[:, 1]/(tab_nbr_reponse[:, 1]+1E-7)
+  precision_trophozoite=tab_tp[:, 4]/(tab_nbr_reponse[:, 4]+1E-7)
+
+  rappel_globule_rouge=tab_tp[:, 1]/(tab_true_boxes[:, 1]+1E-7)
+  rappel_trophozoite=tab_tp[:, 4]/(tab_true_boxes[:, 4]+1E-7)
+    
+  print("F1 score globule rouge", np.mean(2*precision_globule_rouge*rappel_globule_rouge/(precision_globule_rouge+rappel_globule_rouge+1E-7)))
+  print("F1 score trophozoite", np.mean(2*precision_trophozoite*rappel_trophozoite/(precision_trophozoite+rappel_trophozoite+1E-7)))
+  
+  precision=(precision_globule_rouge+precision_trophozoite)/2
+  rappel=(rappel_globule_rouge+rappel_trophozoite)/2
+
+  score=np.mean((1+beta*beta)*precision*rappel/(beta*beta*precision+rappel+1E-7))
+  print("SCORE (globule rouge/trophozoite)", score)
+  ########################
+
+  precision=tab_tp/(tab_nbr_reponse+1E-7)
+  rappel=tab_tp/(tab_true_boxes+1E-7)
+  score=np.mean((1+beta*beta)*precision*rappel/(beta*beta*precision+rappel+1E-7))
+  
+  return score
+
+score=calcul_map(model, dataset)
+print("Resultat", score)