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medical_gradcam.py
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medical_gradcam.py
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"""
Created on Thu Oct 26 11:06:51 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
from PIL import Image
import numpy as np
import torch, os, argparse, json, pprint
from config import system_configs
from misc_functions import get_example_params, save_class_activation_images
from nnet.py_factory import NetworkFactory
from db.datasets import datasets
from misc_functions import preprocess_image
from models.py_utils.kp_utils import _exct_decode, _h_aggregate, _v_aggregate
import tqdm
class CamExtractor():
"""
Extracts cam features from the model
"""
def __init__(self, model, nnet):
self.model = model
self.nnet = nnet
self.gradients = None
def save_gradient(self, grad):
self.gradients = grad
# yezheng: there is no return
return grad#add by yezheng
def forward_pass_on_convolutions(self, x):
"""
Does a forward pass on convolutions, hooks the function at given layer
"""
conv_output = None
image = x
inter = self.model.pre(image)
outs = []
layers = zip(
self.model.kps, self.model.cnvs,
self.model.t_heats, self.model.l_heats, self.model.b_heats,
self.model.r_heats, self.model.ct_heats,self.model.t_regrs,
self.model.l_regrs, self.model.b_regrs, self.model.r_regrs,
)
yezheng_l_heats = []
yezheng_r_heats = []
yezheng_t_heats = []
yezheng_b_heats = []
yezheng_ct_heats = []
# print("[CamExtractor forward_pass_on_convolutions] self.model.nstack", self.model.nstack)
for ind, layer in enumerate(layers):
kp_, cnv_ = layer[0:2]
t_heat_, l_heat_, b_heat_, r_heat_ = layer[2:6]
ct_heat_ = layer[6]
t_regr_, l_regr_, b_regr_, r_regr_ = layer[7:11]
kp = kp_(inter)
cnv = cnv_(kp)
t_heat, l_heat = t_heat_(cnv), l_heat_(cnv)
b_heat, r_heat = b_heat_(cnv), r_heat_(cnv)
ct_heat = ct_heat_(cnv)
# #======
# # from exkp.py _debug()
# t_heat = torch.sigmoid(t_heat)
# l_heat = torch.sigmoid(l_heat)
# b_heat = torch.sigmoid(b_heat)
# r_heat = torch.sigmoid(r_heat)
# aggr_weight = 0.1
# t_heat = _h_aggregate(t_heat, aggr_weight=aggr_weight)
# l_heat = _v_aggregate(l_heat, aggr_weight=aggr_weight)
# b_heat = _h_aggregate(b_heat, aggr_weight=aggr_weight)
# r_heat = _v_aggregate(r_heat, aggr_weight=aggr_weight)
# #======
t_regr, l_regr = t_regr_(cnv), l_regr_(cnv)
b_regr, r_regr = b_regr_(cnv), r_regr_(cnv)
# print("ind", ind, "t_heat", t_heat.shape, "l_heat", l_heat.shape)
if ind == 0:#self.model.nstack - 1:# ind == self.model.nstack - 1:
outs += [t_heat, l_heat, b_heat, r_heat, ct_heat,
t_regr, l_regr, b_regr, r_regr]
yezheng_l_heats.append(l_heat[0,...])
yezheng_r_heats.append(r_heat[0,...])
yezheng_t_heats.append(t_heat[0,...])
yezheng_b_heats.append(b_heat[0,...])
yezheng_ct_heats.append(ct_heat[0,...])
# print("self.model.l_heats", self.model.l_heats)
#========
# self.model.l_heats ModuleList(
# (0): Sequential(
# (0): convolution(
# (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
# (bn): Sequential()
# (relu): ReLU(inplace)
# )
# (1): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
# )
# (1): Sequential(
# (0): convolution(
# (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
# (bn): Sequential()
# (relu): ReLU(inplace)
# )
# (1): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
# )
# )
# yezheng_l_heats = [self.model.l_heats]
# yezheng_r_heats = [self.model.r_heats]
# yezheng_t_heats = [self.model.t_heats]
# yezheng_b_heats = [self.model.b_heats]
# yezheng_ct_heats = [self.model.ct_heats]
if ind < self.model.nstack - 1:
inter = self.model.inters_[ind](inter) + self.model.cnvs_[ind](cnv)
inter = self.model.relu(inter)
inter = self.model.inters[ind](inter)
# print("[gradcam.py CamExtractor forward_pass_on_convolutions] outs", len(outs))
# for out in outs:
# print("[gradcam.py CamExtractor forward_pass_on_convolutions] out", out.shape)
# print("yezheng_l_heats", len(yezheng_l_heats))
yezheng_l_heats_torch = torch.stack(yezheng_l_heats,dim = 0)
yezheng_r_heats_torch = torch.stack(yezheng_r_heats,dim = 0)
yezheng_t_heats_torch = torch.stack(yezheng_t_heats,dim = 0)
yezheng_b_heats_torch = torch.stack(yezheng_b_heats,dim = 0)
yezheng_ct_heats_torch = torch.stack(yezheng_ct_heats,dim = 0)
return yezheng_t_heats_torch, yezheng_l_heats_torch, yezheng_b_heats_torch, yezheng_t_heats_torch, yezheng_ct_heats_torch, outs
def forward_pass(self, x):
"""
Does a full forward pass on the model
"""
# Forward pass on the convolutions
t_heat, l_heat, b_heat, r_heat, ct_heat, outs = self.forward_pass_on_convolutions(x)
# x = x.view(x.size(0), -1) # Flatten
# # Forward pass on the classifier
# x = self.model.classifier(x)
#=======
#yezheng
# testing_loss = self.nnet.loss(x, ) #yezheng
testing_loss = None
return t_heat, l_heat, b_heat, r_heat, ct_heat, testing_loss
class GradCam():
"""
Produces class activation map
"""
def __init__(self, nnet, flag):
self.model = nnet.model.module
self.nnet = nnet
# self.model.eval()
# Define extractor
self.extractor = CamExtractor(self.model, self.nnet)
self.flag = flag
def generate_cam(self, input_image, target_class=None):
# Full forward pass
# conv_output is the output of convolutions at specified layer
# model_output is the final output of the model (1, 1000)
# conv_output, model_output = self.extractor.forward_pass(input_image)
#yezheng
# self.model.zero_grad()
t_heat, l_heat, b_heat, r_heat, ct_heat, testing_loss =self.extractor.forward_pass(input_image)
if 't' == self.flag:
cam= t_heat
elif 'l' == self.flag:
cam = l_heat
elif 'r' == self.flag:
cam = r_heat
elif 'b' == self.flag:
cam = b_heat
# print("[GradCam generate_cam] cam",cam.shape)
cam = torch.sum(cam,dim =0)
cam = cam.detach().numpy()
cam = np.sum(cam, axis = 0)
cam = (cam - np.min(cam)) / (np.max(cam) - np.min(cam)) # Normalize between 0-1
cam = np.uint8(cam * 255) # Scale between 0-255 to visualize
# print("[generate_cam] input_image.shape", input_image.shape)
cam = np.uint8(Image.fromarray(cam).resize((input_image.shape[2],
input_image.shape[3]), Image.ANTIALIAS))
# ^ I am extremely unhappy with this line. Originally resizing was done in cv2 which
# supports resizing numpy matrices, however, when I moved the repository to PIL, this
# option is out of the window. So, in order to use resizing with ANTIALIAS feature of PIL,
# I briefly convert matrix to PIL image and then back.
# If there is a more beautiful way, send a PR.
return cam
#--------
if target_class is None:
target_class = np.argmax(model_output.data.numpy())
# Target for backprop
one_hot_output = torch.FloatTensor(1, testing_loss.size()[-1]).zero_()
one_hot_output[0][target_class] = 1
# Zero grads
# self.model.features.zero_grad()
# self.model.classifier.zero_grad()
# Backward pass with specified target
self.nnet.loss.backward(gradient=one_hot_output, retain_graph=True)
# Get hooked gradients
guided_gradients = self.extractor.gradients.data.numpy()[0]
# Get convolution outputs
target = t_heat.data.numpy()[0]
# Get weights from gradients
weights = np.mean(guided_gradients, axis=(1, 2)) # Take averages for each gradient
# Create empty numpy array for cam
cam = np.ones(target.shape[1:], dtype=np.float32)
# Multiply each weight with its conv output and then, sum
for i, w in enumerate(weights):
cam += w * target[i, :, :]
cam = np.maximum(cam, 0)
cam = (cam - np.min(cam)) / (np.max(cam) - np.min(cam)) # Normalize between 0-1
cam = np.uint8(cam * 255) # Scale between 0-255 to visualize
cam = np.uint8(Image.fromarray(cam).resize((input_image.shape[2],
input_image.shape[3]), Image.ANTIALIAS))
# ^ I am extremely unhappy with this line. Originally resizing was done in cv2 which
# supports resizing numpy matrices, however, when I moved the repository to PIL, this
# option is out of the window. So, in order to use resizing with ANTIALIAS feature of PIL,
# I briefly convert matrix to PIL image and then back.
# If there is a more beautiful way, send a PR.
return cam
def parse_args():
parser = argparse.ArgumentParser(description="Demo CornerNet")
parser.add_argument("--cfg_file", help="config file",
default='medical_ExtremeNet', type=str)
parser.add_argument("--demo", help="demo image path or folders",
default="data/medical_img/test2017", type=str)
parser.add_argument("--model_path",
default='cache/nnet/medical_ExtremeNet/medical_ExtremeNet_27600.pkl')
parser.add_argument("--show_mask", action='store_true',
help="Run Deep extreme cut to obtain accurate mask")
args = parser.parse_args()
return args
if __name__ == '__main__':
# Get params
args = parse_args()
cfg_file = os.path.join(
system_configs.config_dir, args.cfg_file + ".json")
print("[demo] cfg_file: {}".format(cfg_file))
with open(cfg_file, "r") as f:
configs = json.load(f)
configs["system"]["snapshot_name"] = args.cfg_file
system_configs.update_config(configs["system"])
print("system config...")
pprint.pprint(system_configs.full)
print("loading parameters: {}".format(args.model_path))
print("building neural network...")
train_split = system_configs.train_split
dataset = system_configs.dataset
training_db = datasets[dataset](configs["db"], train_split)
nnet = NetworkFactory(training_db, configs["cuda_flag"])
print("loading parameters...")
nnet.load_pretrained_params(args.model_path)
if torch.cuda.is_available() and configs["cuda_flag"]:
nnet.cuda()
nnet.eval_mode()
# Grad cam
for ind_img in tqdm.tqdm(range(0,2600)):
for flag in ['l','r','b','t']:
grad_cam = GradCam(nnet,flag)
img_path = "../../medical_img/data/test/img/{:06d}.jpg".format(ind_img)
original_image = Image.open(img_path).convert('RGB').resize((512,512))
target_class = 1
prep_img = preprocess_image(original_image)
# Generate cam mask
cam = grad_cam.generate_cam(prep_img, target_class)
# print("[gradcam.py] cam",cam.shape,cam)
# Save mask
file_name_to_export = "out_heatmap_{}/{:06d}_{}".format(flag, ind_img, flag)
# print("file_name_to_export", file_name_to_export) ##snake
save_class_activation_images(original_image, cam, file_name_to_export)
# print('Grad cam completed')