File size: 13,096 Bytes
1ec4b6d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
545374d
1ec4b6d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
from calendar import EPOCH
from geometry_utils import diffout2motion
import gradio as gr
import spaces
import torch
import os
from pathlib import Path 
import smplx
from body_renderer import get_render
import numpy as np
from download_deps import get_smpl_models, download_models, download_model_config
from download_deps import download_tmr, download_motionfix, download_motionfix_dataset
from download_deps import download_embeddings
from website import CREDITS, WEB_source, WEB_target, WEBSITE
# import cv2
# import moderngl
# ctx = moderngl.create_context(standalone=True)
# print(ctx)
# sdk_version: 5.5.0
access_token_smpl = os.environ.get('HF_SMPL_TOKEN')
os.environ["PYOPENGL_PLATFORM"] = "egl"

zero = torch.Tensor([0]).cuda()
print(zero.device) # <-- 'cuda:0' 🤗

DEFAULT_TEXT = "do it slower "


@spaces.GPU
def greet(n):
    print(zero.device) # <-- 'cuda:0' 🤗
    try:
        number = float(n)
    except ValueError:
        return "Invalid input. Please enter a number."
    return f"Hello {zero + number} Tensor"

def clear():
    return ""

def show_video(input_text, key_to_use):
    from normalization import Normalizer
    normalizer = Normalizer()
    from diffusion import create_diffusion
    from text_encoder import ClipTextEncoder
    from tmed_denoiser import TMED_denoiser
    model_ckpt = download_models()
    infeats = download_model_config()
    checkpoint = torch.load(model_ckpt)
    # motion_to_edit = download_motion_from_dataset(key_to_use)
    # ds_sample = joblib.load(motion_to_edit)
    ds_sample = MFIX_DATASET_DICT[key_to_use]
    from feature_extractor import FEAT_GET_METHODS
    data_dict_source = {f'{feat}_source': FEAT_GET_METHODS[feat](ds_sample['motion_source'])[None].to('cuda')
                        for feat in infeats}
    data_dict_target = {f'{feat}_target': FEAT_GET_METHODS[feat](ds_sample['motion_target'])[None].to('cuda')
                        for feat in infeats}
    full_batch = data_dict_source | data_dict_target
    in_batch = normalizer.norm_and_cat(full_batch, infeats)
    source_motion_norm = in_batch['source']
    target_motion_norm = in_batch['target']

    seqlen_tgt = source_motion_norm.shape[0]
    seqlen_src = target_motion_norm.shape[0]
    # import ipdb; ipdb.set_trace()
    checkpoint = {k.replace('denoiser.', ''): v for k, v in checkpoint.items()}
    tmed_denoiser = TMED_denoiser().to('cuda')
    tmed_denoiser.load_state_dict(checkpoint, strict=False)
    tmed_denoiser.eval()
    text_encoder = ClipTextEncoder()
    texts_cond = [input_text]
    
    diffusion_process = create_diffusion(timestep_respacing=None,
                                         learn_sigma=False, sigma_small=True,
                                         diffusion_steps=300,
                                         noise_schedule='squaredcos_cap_v2',
                                         predict_xstart=True)
    bsz = 1
    no_of_texts = len(texts_cond)
    texts_cond = ['']*no_of_texts + texts_cond
    texts_cond = ['']*no_of_texts + texts_cond
    text_emb, text_mask = text_encoder(texts_cond)
    
    cond_emb_motion = source_motion_norm
    cond_motion_mask = torch.ones((bsz, seqlen_src),
                                  dtype=bool, device='cuda')
    mask_target = torch.ones((bsz, seqlen_tgt),
                             dtype=bool, device='cuda')
    diff_out = tmed_denoiser._diffusion_reverse(text_emb.to(cond_emb_motion.device),
                                                text_mask.to(cond_emb_motion.device),
                                                cond_emb_motion,
                                                cond_motion_mask,
                                                mask_target,
                                                diffusion_process,
                                                init_vec=None,
                                                init_from='noise',
                                                gd_text=2.0,
                                                gd_motion=2.0,
                                                steps_num=300)
    edited_motion = diffout2motion(diff_out.permute(1,0,2), normalizer).squeeze()
    gt_source = diffout2motion(source_motion_norm.permute(1,0,2),
                               normalizer).squeeze()
    # import ipdb; ipdb.set_trace()
    # aitrenderer = get_renderer()
    # SMPL_LAYER = SMPLLayer(model_type='smplh', ext='npz', gender='neutral')
    # edited_mot_to_render = pack_to_render(rots=edited_motion[..., 3:],
    #                                       trans=edited_motion[..., :3])

    SMPL_MODELS_PATH = str(Path(get_smpl_models()))
    body_model=smplx.SMPLHLayer(f"{SMPL_MODELS_PATH}/smplh", 
                                model_type='smplh',
                                gender='neutral',ext='npz')

    # run_smpl_fwd_verticesbody_model, body_transl, body_orient, body_pose, 
    # edited_mot_to_render
    from body_renderer import get_render
    from transform3d import transform_body_pose
    # import ipdb; ipdb.set_trace()
    edited_motion_aa = transform_body_pose(edited_motion[:, 3:], 
                                           '6d->aa')
    gt_source_aa = transform_body_pose(gt_source[:, 3:], 
                                           '6d->aa')

    if os.path.exists('./output_movie.mp4'):
        os.remove('./output_movie.mp4')
    from transform3d import rotate_body_degrees
    gen_motion_trans = edited_motion[..., :3].detach().cpu()
    gen_motion_rots_aa = edited_motion_aa.detach().cpu()
    source_motion_trans = gt_source[..., :3].detach().cpu()
    source_motion_rots_aa = gt_source_aa.detach().cpu()
    
    gen_rots_rotated, gen_trans_rotated  = rotate_body_degrees(transform_body_pose(
                                            gen_motion_rots_aa, 
                                           'aa->rot'),
                                             gen_motion_trans, offset=np.pi)
    src_rots_rotated, src_trans_rotated = rotate_body_degrees(transform_body_pose(
                                            source_motion_rots_aa, 
                                           'aa->rot'),
                                             source_motion_trans, offset=np.pi)

    src_rots_rotated_aa = transform_body_pose(src_rots_rotated, 
                                             'rot->aa')
    gen_rots_rotated_aa = transform_body_pose(gen_rots_rotated, 
                                             'rot->aa')
    fname = get_render(body_model, 
               [gen_trans_rotated, src_trans_rotated],
               [gen_rots_rotated_aa[:, 0], src_rots_rotated_aa[:, 0]],
               [gen_rots_rotated_aa[:, 1:], src_rots_rotated_aa[:, 1:]],
                output_path='./output_movie.mp4',
                text='', colors=['sky blue', 'red'])

    # fname = render_motion(AIT_RENDERER, [edited_mot_to_render],
    #                       f"movie_example--{str(xx)}",
    #                       pose_repr='aa',
    #                       color=[color_map['generated']],
    #                       smpl_layer=SMPL_LAYER)
    print(fname)
    print(os.path.abspath(fname))
    return fname



MFIX_p = download_motionfix() + '/motionfix'
SOURCE_MOTS_p = download_embeddings() + '/embeddings'
MFIX_DATASET_DICT = download_motionfix_dataset() 

import gradio as gr

def clear():
    return ""

def random_source_motion(set_to_pick):
    # import ipdb;ipdb.set_trace()
    mfix_train, mfix_test = load_motionfix(MFIX_p)
    if set_to_pick == 'all':
        current_set = mfix_test | mfix_train
    elif set_to_pick == 'train':
        current_set = mfix_train
    elif set_to_pick == 'test':
        current_set = mfix_test
    import random
    random_key = random.choice(list(current_set.keys()))
    curvid = current_set[random_key]['motion_a']
    text_annot = current_set[random_key]['annotation']
    return curvid, text_annot, random_key, text_annot

def retrieve_video(retrieve_text):
    tmr_text_encoder = get_tmr_model(download_tmr())
    # import ipdb;ipdb.set_trace()
    # text_encoded = tmr_text_encoder([retrieve_text])
    motion_embeds = None
    from gen_utils import read_json
    import numpy as np

    motion_embeds = torch.load(SOURCE_MOTS_p+'/source_motions_embeddings.pt')
    motion_keyids =np.array(read_json(SOURCE_MOTS_p+'/keyids_embeddings.json'))

    mfix_train, mfix_test = load_motionfix(MFIX_p)
    all_mots = mfix_test | mfix_train
    scores = tmr_text_encoder.compute_scores(retrieve_text, embs=motion_embeds)
    sorted_idxs = np.argsort(-scores)
    best_keyids = motion_keyids[sorted_idxs]
    # best_scores = scores[sorted_idxs]

    top_mot = best_keyids[0]
    curvid = all_mots[top_mot]['motion_a']
    text_annot = all_mots[top_mot]['annotation']
    return curvid, text_annot


with gr.Blocks(css="""
    .gradio-row {
        display: flex;
        gap: 20px;
    }
    .gradio-column {
        flex: 1;
    }
    .gradio-container {
        display: flex;
        flex-direction: column;
        gap: 10px;
    }
    .gradio-button-row {
        display: flex;
        gap: 10px;
    }
    .gradio-textbox-row {
        display: flex;
        gap: 10px;
        align-items: center;
    }
    .gradio-edit-row {
        gap: 10px;
        align-items: center;
    }
    .gradio-textbox-with-button {
        display: flex;
        align-items: center;
    }
    .gradio-textbox-with-button input {
        flex-grow: 1;
    }
""") as demo:
    gr.Markdown(WEBSITE)
    random_key_state = gr.State()

    with gr.Row(elem_id="gradio-row"):
        with gr.Column(scale=5, elem_id="gradio-column"):
            gr.Markdown(WEB_source)
            with gr.Row(elem_id="gradio-button-row"):
                # iterative_button = gr.Button("Iterative")
                # retrieve_button = gr.Button("TMRetrieve")
                random_button = gr.Button("Random")

            with gr.Row(elem_id="gradio-textbox-row"):
                with gr.Column(scale=5, elem_id="gradio-textbox-with-button"):
                #     retrieve_text = gr.Textbox(placeholder="Type the text for the motion you want to Retrieve:",
                #                                show_label=True, label="Retrieval Text", 
                #                                value=DEFAULT_TEXT)
                    clear_button_retrieval = gr.Button("Clear", scale=0)

            with gr.Row(elem_id="gradio-textbox-row"):
                suggested_edit_text = gr.Textbox(placeholder="Texts likely to edit the motion:",
                                                 show_label=True, label="Suggested Edit Text", 
                                                 value='')

            xxx = 'https://motion-editing.s3.eu-central-1.amazonaws.com/collection_wo_walks_runs/rendered_pairs/011327_120_240-002682_120_240.mp4'
            set_to_pick = gr.Radio(['all', 'train', 'test'],
                                   value='all', 
                                   label="Set to pick from", 
                                   info="Motion will be picked from whole dataset or test or train data.")
            # import ipdb; ipdb.set_trace()
            retrieved_video_output = gr.Video(label="Retrieved Motion",
                                            #   value=xxx,
                                              height=360, width=480)
            

        with gr.Column(scale=5, elem_id="gradio-column"):
            gr.Markdown(WEB_target)
            with gr.Row(elem_id="gradio-edit-row"):
                clear_button_edit = gr.Button("Clear", scale=0)
                edit_button = gr.Button("Edit", scale=0)
    
            with gr.Row(elem_id="gradio-textbox-row"):
                input_text = gr.Textbox(placeholder="Type the edit text you want:",
                                        show_label=False, label="Input Text", 
                                        value=DEFAULT_TEXT)
                
            video_output = gr.Video(label="Generated Video", height=360, 
                                    width=480)

    def process_and_show_video(input_text, random_key_state):
        fname = show_video(input_text, random_key_state)
        return fname

    def process_and_retrieve_video(input_text):
        fname = retrieve_video(input_text)
        return fname

    from retrieval_loader import get_tmr_model
    from dataset_utils import load_motionfix
        
    edit_button.click(process_and_show_video, inputs=[input_text, random_key_state], outputs=video_output)
    # retrieve_button.click(process_and_retrieve_video, inputs=retrieve_text, outputs=[retrieved_video_output, suggested_edit_text])
    random_button.click(random_source_motion, inputs=set_to_pick, 
                        outputs=[retrieved_video_output,
                                 suggested_edit_text,
                                 random_key_state,
                                 input_text])
    print(random_key_state)
    clear_button_edit.click(clear, outputs=input_text)
    # clear_button_retrieval.click(clear, outputs=retrieve_text)
    gr.Markdown(CREDITS)

demo.launch(share=True)