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get_cmaps_het_dimer.py
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get_cmaps_het_dimer.py
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import copy
#https://pytorch.org/tutorials/intermediate/ddp_tutorial.html
# https://github.com/horovod/horovod/blob/1b24d0f23d4a9965341851d935ebc2eff8bf886b/examples/adasum/adasum_small_model.py#L38
# https://github.com/horovod/horovod/blob/master/examples/pytorch/pytorch_imagenet_resnet50.py
import torch
# (442)
import torch.utils.data as data
import torch.nn as nn
import torchvision.transforms as transforms
import math
import os
import sys
import numpy as np
# READ ALL THESE FROM A FILE
import rough_copy
import torch.optim as optim
project_root = os.path.dirname(os.path.abspath(sys.argv[0]))
#feature_path = '/gpfs/alpine/proj-shared/bif135/raj/hetero_data/features/'
#label_path ='/gpfs/alpine/proj-shared/bif135/raj/hetero_data/labels/'
#feature_path = "/gpfs/alpine/proj-shared/bif135/raj/hetero_data/CASP/monomer_trRosetta_bfd/"
#feature_path = "/gpfs/alpine/proj-shared/bif135/raj/hetero_data/CASP/glinter_npz/"
feature_path= "/gpfs/alpine/proj-shared/bif135/raj/hetero_data/CASP/new_extracted_trRoss/"
#feature_path = '/gpfs/alpine/proj-shared/bif135/raj/hetero_data/CASP/trRossetta_monomer/'
label_path = "/gpfs/alpine/proj-shared/bif135/raj/hetero_data/CASP/Y-8A/"
val_file = "/gpfs/alpine/proj-shared/bif135/raj/hetero_data/CASP/casp_list.txt"
#feature_path = '/gpfs/alpine/proj-shared/bif132/farhan/data/HETERO_STD/trRosetta_monomer/'
#label_path = "/gpfs/alpine/proj-shared/bif132/farhan/data/HETERO_STD/Y-8A_including_transpose/"
#val_file = "../dataset/monomer_no_aug/monomer_het_val_list.txt"
#intra_cmap = "/gpfs/alpine/proj-shared/bif132/farhan/data/HETERO_STD/het_intra_cmap/"
#feature_path="/gpfs/alpine/proj-shared/bif132/farhan/data/HETERO_STD/selected_hetero/selected_trRosetta_joined/"
#label_path = "/gpfs/alpine/proj-shared/bif132/farhan/data/HETERO_STD/selected_hetero/Y-8A/"
#val_file="/gpfs/alpine/proj-shared/bif135/raj/hetero_data/latest_pdb.txt"
#feature_path = '/gpfs/alpine/proj-shared/bif132/farhan/data/HETERO_STD/new_extracted_trRoss/'
#label_path = "/gpfs/alpine/proj-shared/bif132/farhan/data/HETERO_STD/Y-8A_including_transpose/"
#val_file = "/gpfs/alpine/proj-shared/bif135/raj/Hetero/monomerhet/het_pytorch_aug/dataset/cdhit_no_aug_monomer/aug/cdhit_aug_test_list.txt"
#val_file = "../dataset/initial/het_std_val_list.txt"
intra_cmap = "/gpfs/alpine/proj-shared/bif135/raj/hetero_data/CASP/intra_cmap/"
weighth_list =sys.argv[1]
output_folder =sys.argv[2]
if not os.path.exists(output_folder):
os.system("mkdir -p "+ output_folder)
MAX_LENGTH = 500
EPOCH =100
FEATURES = 442
RESNET_DEPTH = 36
err_list = []
BATCH_SIZE = 1
device = torch.cuda.device(0)
torch.cuda.get_device_name(0)
def check_single_exists(_file):
if os.path.exists(_file):
return True
else:
print("missing "+str(_file))
return False
def check_path_exists(_feat, _labels):
if check_single_exists(_feat) and check_single_exists(_labels) :
return True
else:
return False
def filter_files(_feat_path, _label_path, file_list):
_feat_files = []
_labels = []
for val in file_list:
feat_file_name = _feat_path + str(val) + ".npz"
label_name = val.replace("Y-","").split("_")
name_1 = label_name[0]
name_2 = label_name[1].replace(".txt","")
# labels_path_file_name = _label_path +"Y-"+ str(name_2)+"_"+str(name_1) + ".txt"
labels_path_file_name = _label_path +"Y-"+ str(name_1)+"_"+str(name_2) + ".txt"
if check_path_exists(_feat=feat_file_name,_labels=labels_path_file_name):
_feat_files.append(feat_file_name)
_labels.append(labels_path_file_name)
return sorted(_feat_files), sorted(_labels)
def append_file_information(_filename, _info, _epoch, _type):
exists_status = 0
if os.path.exists(_filename):
exists_status = 1
if _type == "loss":
with open(_filename, "a+") as f:
f.write(str(_epoch) + " " + str(_info[0]) + "\n")
else:
if exists_status == 1:
with open(_filename, "a+") as f:
f.write(str(_epoch) + "\t\t\t" + str(_info[0]) + "\t\t\t" + str(_info[1]) + "\t\t\t" + str(
_info[2]) + "\t\t\t" + str(_info[3]) + "\t\t\t" + str(_info[4]) + "\t\t\t" + str(
_info[5]) + "\t\t\t" + str(_info[6]) + "\t\t\t" + str(_info[7]) + "\t\t\t" + str(
_info[8]) + "\t\t\t" + str(_info[9]) + "\n")
else:
with open(_filename, "a+") as f:
f.write("Epoch\tSample_Size\tPrec-T5\tPrec-T10\tPrec-T20\tPrec-T30\tPrec-T50\tL/30\tL/20\tL/10\tL/5\n")
f.write(str(_epoch) + "\t\t\t" + str(len(_info)) + "\t\t\t" + str(_info[1]) + "\t\t\t" + str(
_info[2]) + "\t\t\t" + str(_info[3]) + "\t\t\t" + str(_info[4]) + "\t\t\t" + str(
_info[5]) + "\t\t\t" + str(_info[6]) + "\t\t\t" + str(_info[7]) + "\t\t\t" + str(
_info[8]) + "\t\t\t" + str(_info[9]) + "\n")
def calculateEvaluationStats(_pred_cmap, _true_cmap, _L):
prec_T5, prec_T10, prec_T20, prec_T30, prec_T50, prec_L30, prec_L20, prec_L10, prec_L5, con_num = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
pred_cmap = copy.deepcopy(_pred_cmap).squeeze()
true_cmap = copy.deepcopy(_true_cmap).squeeze()
L = _L
if true_cmap.shape != pred_cmap.shape: print("True and predicted contact maps do not match in shape!")
print("&&" * 30)
max_Top = int((L / 5) + 0.5)
if 50 > max_Top: max_Top = 50
for i in range(1, max_Top + 1):
(x, y) = np.unravel_index(np.argmax(pred_cmap, axis=None), pred_cmap.shape)
pred_cmap[x][y] = 0
if true_cmap[x][y] == 1:
con_num += 1
if i == 5:
prec_T5 = con_num * 20
if prec_T5 > 100: prec_T5 = 100
print("L=", L, "Val=", 5, "Con_num=", con_num)
if i == 10:
prec_T10 = con_num * 10
if prec_T10 > 100: prec_T10 = 100
print("L=", L, "Val=", 10, "Con_num=", con_num)
if i == 20:
prec_T20 = con_num * 5
if prec_T20 > 100: prec_T20 = 100
print("L=", L, "Val=", 20, "Con_num=", con_num)
if i == 30:
prec_T30 = con_num * 100 / 30
if prec_T30 > 100: prec_T30 = 100
print("L=", L, "Val=", 30, "Con_num=", con_num)
if i == 50:
prec_T50 = con_num * 2
if prec_T50 > 100: prec_T50 = 100
print("L=", L, "Val=", 50, "Con_num=", con_num)
if i == int((L / 30) + 0.5):
prec_L30 = con_num * 100 / i
if prec_L30 > 100: prec_L30 = 100
print("L=", L, "Val=", i, "Con_num=", con_num)
if i == int((L / 20) + 0.5):
prec_L20 = con_num * 100 / i
if prec_L20 > 100: prec_L20 = 100
print("L=", L, "Val=", i, "Con_num=", con_num)
if i == int((L / 10) + 0.5):
prec_L10 = con_num * 100 / i
if prec_L10 > 100: prec_L10 = 100
print("L=", L, "Val=", i, "Con_num=", con_num)
if i == int((L / 5) + 0.5):
prec_L5 = con_num * 100 / i
if prec_L5 > 100: prec_L5 = 100
print("L=", L, "Val=", i, "Con_num=", con_num)
del pred_cmap, true_cmap
return prec_T5, prec_T10, prec_T20, prec_T30, prec_T50, prec_L30, prec_L20, prec_L10, prec_L5
def text_file_reader(_file):
err_list =[]
file = open(_file, "r")
output_array = []
if file.mode == 'r':
output_array = file.read().splitlines()
file.close()
for error in err_list:
if error.strip() in output_array:
output_array.remove(error.strip())
return output_array
class my_dataset(data.Dataset):
def initialize(self, _feat_path, _label_path, _file_list, _max_len):
file_list = _file_list
self.features, self.labels = filter_files(_feat_path, _label_path, file_list)
self.size = len(self.features)
self.size_labels = len(self.labels)
self.MaxLen = _max_len
self.name = ""
self.len_a = 0
self.len_b = 0
self.transform = transforms.Compose([transforms.ToTensor()])
def __getitem__(self, index):
feature_path = self.features[index % self.size]
path_labels = self.labels[index % self.size_labels]
print(os.path.basename(path_labels))
content = np.load(feature_path, allow_pickle=True)
tr_feature = content.f.arr_0.squeeze()
ground_truth = np.loadtxt(path_labels)
lab_a_len,lab_b_len= ground_truth.shape
self.name = os.path.basename(path_labels)
len_a,len_b,fetures = tr_feature.shape
self.len_a = len_a
self.len_b = len_b
# print(len_a,len_b)
#print(lab_a_len,lab_b_len)
if len_a != lab_a_len or len_b!=lab_b_len:
print(feature_path)
print(path_labels)
print(len_a, len_b)
print(lab_a_len, lab_b_len)
print("mistmatch"+str(os.path.basename(feature_path))+","+str(len_a)+","+str(len_b)+","+str(lab_a_len)+","+str(lab_b_len))
# PADDING FEATURES
final_feature = np.zeros((self.MaxLen, self.MaxLen, FEATURES))
for val in range(0, len_a):
final_feature[val, 0:len_b, 0:FEATURES] = tr_feature[val]
dist = self.transform(final_feature)
padded_labels = np.zeros((self.MaxLen, self.MaxLen))
for val in range(0, len_a):
padded_labels[val][0:len_b] = ground_truth[val]
return {'feat': dist, 'ground_truth': padded_labels, 'len_a': self.len_a, 'len_b': self.len_b,
'name': self.name}
def __len__(self):
return self.size
model = rough_copy.ResNet_custom(img_channel=FEATURES, num_classes=MAX_LENGTH * MAX_LENGTH, _depth=RESNET_DEPTH)
model.cuda()
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=0.0001, weight_decay=0)
Current_EPOCH = 0
weights =weighth_list
if len(weights) > 0:
# resume_from_epoch = hvd.broadcast(torch.tensor(weights), root_rank=0).item()
last_weight = weights
print("Weight loading "+str(weights))
checkpoint = torch.load(last_weight)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
epoch = checkpoint['epoch']
Current_EPOCH = epoch
#print(last_weight)
nThreads=6
print("VALIDATION DATA LOADER AREA ")
val_file_list = text_file_reader(val_file)
print(len(val_file_list))
val_dataset = my_dataset()
val_dataset.initialize(_feat_path=feature_path, _label_path=label_path, _file_list=val_file_list,_max_len=MAX_LENGTH)
#val_dataset.initialize(_feat_path=feature_path, _label_path=label_path,_file_list=val_file_list, _max_len=MAX_LENGTH)
#
val_dataloader = torch.utils.data.DataLoader(val_dataset, batch_size=BATCH_SIZE, num_workers=nThreads)
print(len(val_dataloader))
print("MODELLOADER AREA ")
def Average(lst):
return round(sum(lst) / len(lst), 5)
last_epoch =1
print("HVD AREA ")
with torch.no_grad():
list_acc_T5, list_acc_T10, list_acc_l30, list_acc_l20, list_acc_l10, list_acc_l5, list_acc_T20, list_acc_T30, list_acc_T50 = [], [], [], [], [],[], [], [], []
for i, data in enumerate(val_dataloader):
print(data['name'])
features = data['feat'].float().cuda()
label = data['ground_truth'].float().cuda()
real_sequence_length_a = data['len_a']
real_sequence_length_b = data['len_b']
model.eval()
# print(data['name'])
output_val = model(features)
output_final = torch.squeeze(output_val, -1).detach().cpu().clone().numpy().squeeze()
print(output_final)
loss = criterion(torch.squeeze(output_val), torch.squeeze(label))
print(loss)
# running_validation_loss += loss.item()
# val_loss.append(loss.item())
# val_loss.update(loss)
mini_batch_size = len(label)
np_label = torch.squeeze(label, -1).detach().cpu().clone().numpy().squeeze()
# i_len = math.floor(math.sqrt(real_sequence_length_b*real_sequence_length_a))
for counter in range(0, mini_batch_size):
if mini_batch_size == 1:
#shape_label = shape_label_array
padded_predicted_label = output_final
padded_true_label_label = np_label
else:
shape_label = shape_label_array[counter]
padded_predicted_label = output_final[counter]
padded_true_label_label = np_label[counter]
unpadded_predicted_label = np.zeros((real_sequence_length_a, real_sequence_length_b))
unpadded_true_label = np.zeros((real_sequence_length_a, real_sequence_length_b))
for val in range(0, real_sequence_length_a):
unpadded_predicted_label[val] = padded_predicted_label[val][0:real_sequence_length_b]
unpadded_true_label[val] = padded_true_label_label[val][0:real_sequence_length_b]
i_len = math.floor(math.sqrt(real_sequence_length_b * real_sequence_length_a))
np.savetxt( output_folder+"/"+str(data['name'])+".cmap" ,unpadded_predicted_label)
(prec_T5, prec_T10, prec_T20, prec_T30, prec_T50, prec_L30, prec_L20, prec_L10, prec_L5) = calculateEvaluationStats(_pred_cmap=unpadded_predicted_label, _true_cmap=unpadded_true_label, _L=i_len)
list_acc_T5.append(prec_T5)
list_acc_T10.append(prec_T10)
list_acc_T20.append(prec_T20)
list_acc_T30.append(prec_T30)
list_acc_T50.append(prec_T50)
list_acc_l30.append(prec_L30)
list_acc_l20.append(prec_L20)
list_acc_l10.append(prec_L10)
list_acc_l5.append(prec_L5)
# val_acc_info = [len(list_acc_T5), Average(list_acc_T5), Average(list_acc_T10), Average(list_acc_T20), Average(list_acc_T30), Average(list_acc_T50) , Average(list_acc_l30), Average(list_acc_l20), Average(list_acc_l10), Average(list_acc_l5)]
# append_file_information(_filename=LOG_DIR + "/validation_acc" + str(Current_EPOCH) + "txt", _epoch=Current_EPOCH, _info=val_acc_info, _type="Accuracy")