def predict_softmax(img1, preproess=False):
if preproess:
img1 = do_preprocess(img1, crop_size=PREPROCESS_IMG_SIZE)
prob_np = []
prob = []
pred = [
] # only one class, multi_models multi-class multi-label has multiple classes
for dict1 in dicts_models:
#real time image aug
img_tensor = my_gen_img_tensor(img1, image_shape=dict1['input_shape'])
prob1 = dict1['model'].predict_on_batch(img_tensor)
prob1 = np.mean(prob1, axis=0) # batch mean, test time img aug
pred1 = prob1.argmax(axis=-1)
prob_np.append(prob1) # numpy weight avg prob_total
prob.append(prob1.tolist()) #担心XMLRPC numpy
pred.append(int(pred1)) # numpy int64, int XMLRPC
list_weights = [] # the prediction weights of models
for dict1 in dicts_models:
list_weights.append(dict1['model_weight'])
prob_total = np.average(prob_np, axis=0, weights=list_weights)
pred_total = prob_total.argmax(axis=-1)
prob_total = prob_total.tolist() #RPC Service can not pass numpy variable
pred_total = int(pred_total) # 'numpy.int64' XMLRPC
# correct_model_no is used for choosing which model to generate CAM
# on extreme condition: average softmax prediction class is not in every model's prediction class
correct_model_no = 0
for i, pred1 in enumerate(pred):
if pred1 == pred_total:
correct_model_no = i #start from 0
break
return prob, pred, prob_total, pred_total, correct_model_no
save_dir = os.path.join(dir_dest, heatmap_type)
df = pd.read_csv(filename_csv)
for _, row in df.iterrows():
image_file = row['images']
image_label = int(row['labels'])
assert dir_preprocess in image_file, 'preprocess directory error'
preprocess = False
image_size = 299
if preprocess:
img_preprocess = my_preprocess.do_preprocess(image_file,
crop_size=384)
img_input = my_gen_img_tensor(img_preprocess,
image_shape=(image_size, image_size,
3))
else:
img_source = image_file
img_input = my_gen_img_tensor(image_file,
image_shape=(image_size, image_size,
3))
probs = model1.predict(img_input)
class_predict = np.argmax(probs)
if class_predict == 1:
# Apply analyzer w.r.t. maximum activated output-neuron
data_heatmap = analyzer.analyze(img_input)
# Aggregate along color channels and normalize to [-1, 1]
import pandas as pd
df = pd.read_csv(filename_csv)
for _, row in df.iterrows():
image_file = row['images']
image_label = int(row['labels'])
preprocess = False
input_shape = (299, 299, 3)
img_source = image_file
file_dir, filename = os.path.split(img_source)
file_basename, file_ext = os.path.splitext(filename)
from LIBS.ImgPreprocess.my_image_helper import my_gen_img_tensor
img_input = my_gen_img_tensor(image_file, image_shape=input_shape)
for model_no in range(len(dicts_models)):
model1 = dicts_models[model_no]['model_original']
probs = model1.predict(img_input)
for j in range(len(probs[0])): # number of classes
if probs[0][j] > 0.5:
print(j)
if heatmap_type == 'grad_cam':
filename_CAM1 = server_grad_cam(dicts_models=dicts_models, model_no=model_no,
img_source=img_source, pred=j,
preprocess=False, blend_original_image=blend_original_image,
base_dir_save='/tmp/temp_cam/')
if heatmap_type == 'CAM':