-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathapp.py
121 lines (104 loc) · 7.2 KB
/
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
import gradio as gr
import torch
from PIL import Image
from models.r2gen import R2GenModel
from modules.tokenizers import Tokenizer
import argparse
# Assuming you have a predefined configuration function for model args
def get_model_args():
parser = argparse.ArgumentParser()
# Model loader settings
parser.add_argument('--load', type=str, default='ckpts/few-shot.pth', help='the path to the model weights.')
parser.add_argument('--prompt', type=str, default='prompt/prompt.pth', help='the path to the prompt weights.')
# Data input settings
parser.add_argument('--image_path', type=str, default='example_figs/example_fig1.jpg', help='the path to the test image.')
parser.add_argument('--image_dir', type=str, default='data/images/', help='the path to the directory containing the data.')
parser.add_argument('--ann_path', type=str, default='data/annotation.json', help='the path to the directory containing the data.')
# Data loader settings
parser.add_argument('--dataset_name', type=str, default='mimic_cxr', help='the dataset to be used.')
parser.add_argument('--max_seq_length', type=int, default=60, help='the maximum sequence length of the reports.')
parser.add_argument('--threshold', type=int, default=3, help='the cut off frequency for the words.')
parser.add_argument('--num_workers', type=int, default=2, help='the number of workers for dataloader.')
parser.add_argument('--batch_size', type=int, default=16, help='the number of samples for a batch')
# Model settings (for visual extractor)
parser.add_argument('--visual_extractor', type=str, default='resnet101', help='the visual extractor to be used.')
parser.add_argument('--visual_extractor_pretrained', type=bool, default=True, help='whether to load the pretrained visual extractor')
# Model settings (for Transformer)
parser.add_argument('--d_model', type=int, default=512, help='the dimension of Transformer.')
parser.add_argument('--d_ff', type=int, default=512, help='the dimension of FFN.')
parser.add_argument('--d_vf', type=int, default=2048, help='the dimension of the patch features.')
parser.add_argument('--num_heads', type=int, default=8, help='the number of heads in Transformer.')
parser.add_argument('--num_layers', type=int, default=3, help='the number of layers of Transformer.')
parser.add_argument('--dropout', type=float, default=0.1, help='the dropout rate of Transformer.')
parser.add_argument('--logit_layers', type=int, default=1, help='the number of the logit layer.')
parser.add_argument('--bos_idx', type=int, default=0, help='the index of <bos>.')
parser.add_argument('--eos_idx', type=int, default=0, help='the index of <eos>.')
parser.add_argument('--pad_idx', type=int, default=0, help='the index of <pad>.')
parser.add_argument('--use_bn', type=int, default=0, help='whether to use batch normalization.')
parser.add_argument('--drop_prob_lm', type=float, default=0.5, help='the dropout rate of the output layer.')
# for Relational Memory
parser.add_argument('--rm_num_slots', type=int, default=3, help='the number of memory slots.')
parser.add_argument('--rm_num_heads', type=int, default=8, help='the numebr of heads in rm.')
parser.add_argument('--rm_d_model', type=int, default=512, help='the dimension of rm.')
# Sample related
parser.add_argument('--sample_method', type=str, default='beam_search', help='the sample methods to sample a report.')
parser.add_argument('--beam_size', type=int, default=3, help='the beam size when beam searching.')
parser.add_argument('--temperature', type=float, default=1.0, help='the temperature when sampling.')
parser.add_argument('--sample_n', type=int, default=1, help='the sample number per image.')
parser.add_argument('--group_size', type=int, default=1, help='the group size.')
parser.add_argument('--output_logsoftmax', type=int, default=1, help='whether to output the probabilities.')
parser.add_argument('--decoding_constraint', type=int, default=0, help='whether decoding constraint.')
parser.add_argument('--block_trigrams', type=int, default=1, help='whether to use block trigrams.')
# Trainer settings
parser.add_argument('--n_gpu', type=int, default=1, help='the number of gpus to be used.')
parser.add_argument('--epochs', type=int, default=100, help='the number of training epochs.')
parser.add_argument('--save_dir', type=str, default='results/iu_xray', help='the patch to save the models.')
parser.add_argument('--record_dir', type=str, default='records/', help='the patch to save the results of experiments')
parser.add_argument('--save_period', type=int, default=1, help='the saving period.')
parser.add_argument('--monitor_mode', type=str, default='max', choices=['min', 'max'], help='whether to max or min the metric.')
parser.add_argument('--monitor_metric', type=str, default='BLEU_4', help='the metric to be monitored.')
parser.add_argument('--early_stop', type=int, default=50, help='the patience of training.')
# Optimization
parser.add_argument('--optim', type=str, default='Adam', help='the type of the optimizer.')
parser.add_argument('--lr_ve', type=float, default=5e-5, help='the learning rate for the visual extractor.')
parser.add_argument('--lr_ed', type=float, default=1e-4, help='the learning rate for the remaining parameters.')
parser.add_argument('--weight_decay', type=float, default=5e-5, help='the weight decay.')
parser.add_argument('--amsgrad', type=bool, default=True, help='.')
# Learning Rate Scheduler
parser.add_argument('--lr_scheduler', type=str, default='StepLR', help='the type of the learning rate scheduler.')
parser.add_argument('--step_size', type=int, default=50, help='the step size of the learning rate scheduler.')
parser.add_argument('--gamma', type=float, default=0.1, help='the gamma of the learning rate scheduler.')
# Others
parser.add_argument('--seed', type=int, default=9233, help='.')
parser.add_argument('--resume', type=str, help='whether to resume the training from existing checkpoints.')
args = parser.parse_args()
return args
def load_model():
args = get_model_args()
tokenizer = Tokenizer(args)
device = 'cuda' if torch.cuda.is_available() else 'cpu' # Determine the device dynamically
model = R2GenModel(args, tokenizer).to(device)
checkpoint_path = args.load
# Ensure the state dict is loaded onto the same device as the model
state_dict = torch.load(checkpoint_path, map_location=device)
model_state_dict = state_dict['state_dict'] if 'state_dict' in state_dict else state_dict
model.load_state_dict(model_state_dict)
model.eval()
return model, tokenizer
model, tokenizer = load_model()
def generate_report(image):
image = Image.fromarray(image).convert('RGB')
with torch.no_grad():
output = model([image], mode='sample')
reports = tokenizer.decode_batch(output.cpu().numpy())
return reports[0]
# Define Gradio interface
iface = gr.Interface(
fn=generate_report,
inputs=gr.inputs.Image(), # Define input shape as needed
outputs="text",
title="PromptNet",
description="Upload a medical image for thorax disease reporting."
)
if __name__ == "__main__":
iface.launch()